Epidemic propagation information detection method, device, equipment and medium

Abstract

The invention discloses an epidemic propagation information detection method, device and equipment and a medium. The method comprises the following steps: acquiring online drug sales data provided by a user of at least one drug corresponding to the same disease in a historical time period; sales data and spatial data are extracted from the online medicine sales data; generating a time vector according to the sales data; generating a spatial vector according to the spatial data; and according to the time vector and the space vector, determining prediction spatio-temporal information of the disease. According to the embodiment of the invention, the epidemic disease transmission condition can be accurately predicted according to the drug sales data.

Description

Technical Field

[0001] The present invention relates to the field of epidemic control technology, and in particular to a method, device, equipment and medium for detecting epidemic transmission information. Background Art

[0002] With the acceleration of globalization and the continuous development of human society, the speed and scope of epidemic spread are increasing, which brings huge challenges to public health and social economy. Therefore, it is necessary to accurately predict the spread of epidemics.

[0003] Currently, epidemic predictions rely on traditional statistical models and manual analysis to predict the future spread of diseases.

[0004] However, the above prediction methods have the disadvantages of low efficiency and low prediction accuracy. Summary of the invention

[0005] The present invention provides a method, device, equipment and medium for detecting epidemic spread information. The embodiments of the present invention can accurately predict the spread of an epidemic based on drug sales data.

[0006] In a first aspect, an embodiment of the present invention provides a method for detecting epidemic spread information, the method comprising:

[0007] Obtain online drug sales data provided by users for at least one drug corresponding to the same disease within a historical period;

[0008] Extract sales data and spatial data from online drug sales data;

[0009] Generate a time vector based on sales data;

[0010] Generate a spatial vector according to the spatial data;

[0011] According to the time vector and space vector, the predicted spatiotemporal information of the disease is determined.

[0012] In a second aspect, an embodiment of the present invention further provides an epidemic spread information detection device, the device comprising:

[0013] A data acquisition module, used to acquire online drug sales data provided by users in a historical period of time for at least one drug corresponding to the same disease;

[0014] A data extraction module, used to extract sales data and spatial data from online drug sales data;

[0015] A time vector generation module, used to generate a time vector according to sales data;

[0016] A space vector generation module is used to generate a space vector according to the space data;

[0017] The prediction module is used to determine the predicted spatiotemporal information of the disease based on the time vector and the space vector.

[0018] In a third aspect, an embodiment of the present invention further provides an epidemic spread information detection device, the epidemic spread information detection device comprising:

[0019] at least one processor; and

[0020] a memory communicatively connected to at least one processor; wherein,

[0021] The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor so that the at least one processor can execute the epidemic propagation information detection method of any embodiment of the present invention.

[0022] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable a processor to implement the epidemic transmission information detection method of any embodiment of the present invention when executed.

[0023] The technical solution of the embodiment of the present invention, by acquiring online drug sales data within a historical time period, can accumulate drug sales data through an online platform, providing a data basis for epidemic spread prediction; by extracting sales data and spatial data, it can capture drug sales information from two dimensions of time and space respectively; by generating time vectors, it can model the temporal dynamics of disease transmission, thereby revealing the cyclical changes and trends of epidemic transmission, and providing time series data support for predicting future epidemic change trends; by generating space vectors, it can model the spatial distribution of epidemic transmission, revealing the transmission differences in different regions, and providing a basis for the analysis of the geographical characteristics of epidemic transmission; by combining time vectors and space vectors, it can realize spatiotemporal prediction of epidemic transmission, and provide the epidemic transmission situation at a certain time and place in the future.

[0024] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0026] Figure 1 A flowchart of a method for detecting epidemic spread information provided by an embodiment of the present invention;

[0027] Figure 2 A flowchart of a method for detecting epidemic spread information provided by an embodiment of the present invention;

[0028] Figure 3 A schematic diagram of a space matrix provided by an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of a timing downsampling module provided by an embodiment of the present invention;

[0030] Figure 5 A schematic diagram of a temporal image encoder provided by an embodiment of the present invention;

[0031] Figure 6 A schematic diagram of a sequential DiT module provided by an embodiment of the present invention;

[0032] Figure 7 A schematic diagram of the structure of an epidemic spread information detection device provided by an embodiment of the present invention;

[0033] Figure 8 A schematic diagram of the structure of an epidemic spread information detection device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0036] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of online drug sales data, user purchase records and user purchase behaviors provided by users are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0037] Figure 1 A flowchart of a method for detecting epidemic spread information provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the case of detecting epidemic spread information, and the method can be executed by an epidemic spread information detection device, which can be implemented in the form of hardware and / or software.

[0038] See also Figure 1 The epidemic spread information detection method shown includes:

[0039] S101. Obtain online drug sales data provided by users for at least one drug corresponding to the same disease within a historical period.

[0040] Among them, the same disease can be treated by taking multiple types of drugs, for example, disease A can be treated by taking drug A, drug B and / or drug C. A single drug cannot fully represent the spread of the disease, so obtaining online drug sales data provided by users for at least one drug corresponding to the same disease in a historical period can fully understand which drugs were used to treat the disease in the historical period.

[0041] Among them, online drug sales data can refer to drug sales data collected on a daily basis through online platforms, for example, drug sales data collected on January 1, January 2, and January 3 by online platforms. Online drug sales data can include the number of clicks on the drug details page, the time the drug details page was opened, the number of orders placed, the IP address of the drug details page, and the delivery address. Online drug sales data can be used to indirectly analyze user behavior and infer disease-related drug demand and potential transmission areas.

[0042] S102. Extract sales data and spatial data from online drug sales data.

[0043] Among them, sales data may refer to data related to the use of drugs in online drug sales data. Sales data may include the number of clicks on the drug details page, the time when the drug details page was opened, and the number of orders. By analyzing sales data, users' use of drugs and purchase intentions can be reflected. Exemplary sales data may include the number of clicks on the drug details page of drug X on January 1, the time when the drug details page was opened is YY, and the number of orders is ZZ; and the number of clicks on the drug details page of drug X on January 2 is XXX, the time when the drug details page was opened is YYY, and the number of orders is ZZZ.

[0044] Among them, spatial data may refer to data related to position and location in online drug sales data. Spatial data may include IP addresses and delivery addresses for browsing drug details pages. By analyzing spatial data, the geographical transmission paths and hot spots of diseases can be reflected. Exemplarily, spatial data may be the IP addresses and delivery addresses for browsing drug details pages of drug X on January 1, where the IP addresses for browsing drug details pages include X, Y, and Z, etc., and the delivery addresses include A, B, and C, etc.; and the IP addresses and delivery addresses for browsing drug details pages of drug X on January 2, where the IP addresses for browsing drug details pages include XX, YY, and ZZ, etc., and the delivery addresses include AA, BB, and CC, etc.

[0045] S103. Generate a time vector based on the sales data.

[0046] The time vector may refer to a feature vector generated based on the timestamp introduced into the sales data. The sales data reflecting user behavior is quantified into feature values ​​in the time dimension by formula calculation and normalization processing on the sales data. Since the sales data itself does not exist in a time series, the sales data can be arranged in a specific time order to construct a time series, thereby generating a time vector.

[0047] S104. Generate a spatial vector according to the spatial data.

[0048] The spatial vector may refer to a feature vector generated based on spatial data. The spatial vector includes disease transmission information related to geographic location. The spatial data of the IP address and delivery address of the drug details page are mapped to a spatial matrix and then converted into a feature vector to describe the distribution characteristics of the disease in geographic space. The spatial vector represents the probability of disease transmission in different geographic locations and is used to predict the future transmission area of ​​the disease.

[0049] S105. Determine predicted spatiotemporal information of the disease based on the time vector and the space vector.

[0050] The predicted spatiotemporal information may refer to information about the future time, geographic location, and probability of disease spread. The predicted spatiotemporal information of a disease may be generated by fusing the time vector and the space vector and combining the prediction model.

[0051] It can be seen that in the embodiments of the present application, by acquiring online drug sales data within a historical time period, the accumulation of drug sales data can be achieved through the online platform, providing a data basis for epidemic spread prediction; by extracting sales data and spatial data, the sales information of drugs can be captured from the two dimensions of time and space respectively; by generating time vectors, the temporal dynamics of disease transmission can be modeled, thereby revealing the cyclical changes and trends of epidemic transmission, and providing time series data support for predicting future epidemic change trends; by generating space vectors, the spatial distribution of epidemic transmission can be modeled, revealing the differences in transmission in different regions, and providing a basis for the analysis of the geographical characteristics of epidemic transmission; by combining time vectors and space vectors, the spatiotemporal prediction of epidemic transmission can be achieved, providing the epidemic transmission situation at a certain time and place in the future.

[0052] The operation of epidemic transmission information detection is improved by refining "generating a time vector according to sales data" into "calculating the probability of disease attention according to multiple sales parameters and corresponding parameter values ​​included in the sales data; generating a time vector according to the probability of disease attention and the collection time of each parameter in the sales data". It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments. Figure 2 A flowchart of a method for detecting epidemic spread information provided by an embodiment of the present invention.

[0053] See also Figure 2 The epidemic spread information detection method shown includes:

[0054] S201. Obtain online drug sales data provided by users for at least one drug corresponding to the same disease within a historical period.

[0055] S202. Extract sales data and spatial data from online drug sales data.

[0056] S203. Calculate the probability of disease attention based on multiple sales parameters and corresponding parameter values ​​included in the sales data.

[0057] Among them, the sales parameters may refer to the number of clicks on the drug details page, the time when the drug details page is opened, and the number of orders in the sales data. Among them, the parameter value may refer to the specific value of the sales parameter. Exemplarily, the sales parameters may refer to the number of clicks on the drug details page of drug X on January 1st as A; the time when the drug details page of drug X was opened on January 1st as B; the number of orders placed on drug X on January 1st as C; among them, the number of clicks on the drug details page, the time when the drug details page was opened, and the number of orders placed are sales parameters, and A, B, and C are parameter values.

[0058] The attention probability may refer to the probability value of the degree of attention to a drug. The specific usage and purchase intention of the drug can be obtained by calculating the attention probability. The greater the attention probability, the higher the degree of attention to the drug. The attention probability can be calculated through multiple sales parameters and corresponding parameter values.

[0059] S204: Generate a time vector according to the probability of disease attention and the collection time of each parameter in the sales data.

[0060] The collection time may refer to the timestamp information corresponding to each parameter in the sales data. The collection time is used to describe the change pattern of each parameter value over time. Since the sales data itself does not have a time feature, a time vector can be generated based on the probability of disease attention and the collection time of each parameter in the sales data.

[0061] S205. Generate a spatial vector according to the spatial data.

[0062] S206. Determine predicted spatiotemporal information of the disease based on the time vector and the space vector.

[0063] It can be seen that in this embodiment, by integrating multiple sales parameters and their corresponding parameter values, the user's attention to and purchasing behavior of drugs can be fully characterized, avoiding information bias caused by a single indicator; by calculating the attention probability of the disease, the key characteristics of the user behavior can be extracted, and the complex original data can be converted into concise and meaningful numerical indicators; by using the calculation method of attention probability, the correlation between different parameters can be captured, thereby improving the sensitivity to the disease transmission trend and the accuracy of the prediction; by combining the attention probability and the collection time, a time vector reflecting the dynamic changes in drug demand over time can be generated, supporting the time series analysis of epidemic spread.

[0064] In some embodiments, calculating the probability of concern of a disease based on a plurality of sales parameters and corresponding parameter values ​​included in the sales data includes:

[0065] According to the weight of each sales parameter on the drug and the parameter value corresponding to each sales parameter, the probability of attention of each disease is calculated.

[0066] The weight may refer to the degree of influence of the sales parameter on the drug's attention. The larger the weight, the greater the influence of the sales parameter on the drug's attention; the smaller the weight, the smaller the influence of the sales parameter on the drug's attention. The weight may be set by empirical value.

[0067] Sales parameters include the number of clicks on the drug details page, the time the drug details page is opened, and the number of orders. Among them, the number of clicks on the drug details page is positively correlated with the probability of attention, but when the drug details page is clicked too many times, there may be malicious clicks. Therefore, the relationship between the number of clicks on the drug details page and the probability of attention is a logarithmic positive correlation, which can reduce the problem of excessive amplification of the impact caused by too many clicks on the drug details page; the time the drug details page is opened and the probability of attention also show a positive correlation trend, but when the drug details page is opened for too long, it may be caused by the user forgetting to close the details page after clicking on the drug details page. Therefore, the longer the drug details page is opened, the greater the impact on the probability of attention in the initial stage, but after exceeding a certain time threshold, the impact of the probability of attention decreases due to exponential decay; the number of orders and the probability of attention are linearly positively correlated. The greater the number of orders, the higher the probability of attention, and the fewer the number of orders, the lower the probability of attention.

[0068] In a specific example, the probability of interest for a disease is given by:

[0069]

[0070] In the formula, E(c, t, o) represents the probability of attention to a certain disease; c represents the parameter value of the number of clicks on the drug details page; t represents the parameter value of the time the drug details page is opened; o represents the parameter value of the number of orders; 0.3 represents the weight of the parameter value of the number of clicks on the drug details page; 0.2 represents the weight of the parameter value of the time the drug details page is opened; and 0.5 represents the weight of the number of orders.

[0071] It can be seen that in this embodiment, by assigning weights to sales parameters, it is possible to distinguish the importance of different sales parameters; by combining the parameter values ​​corresponding to the sales parameters, it is possible to quantify user behavior and make the numerical expression of the original data more accurate; by combining the weights and parameter values ​​of the sales parameters, it is possible to accurately estimate the probability of disease concern.

[0072] In some embodiments, generating a space vector according to the space data includes:

[0073] Generate a spatial matrix based on the spatial data, where the position of each element in the matrix corresponds to the geographical location, and the element value of each element corresponds to the usage probability;

[0074] Generate a space vector based on the space matrix.

[0075] The spatial matrix may refer to a matrix used to describe the use of drugs in a geographical space. The use probability may refer to the probability value of using each drug to treat a disease in a certain geographical location.

[0076] The spatial matrix may be input into a VAE (Variational Autoencoder) encoder to obtain a spatial vector, wherein the spatial vector is a two-dimensional vector.

[0077] In a specific example, Figure 3 A schematic diagram of a spatial matrix provided in an embodiment of the present invention can divide the predicted area into a 4*4 spatial matrix. Assuming that there is an IP address X for browsing the drug details page on January 1, the IP address X is converted to geographic coordinates (2, 1); there is a delivery address Y on January 1, and the delivery address Y is converted to geographic coordinates (3, 4). As long as there is an IP address and a delivery address for browsing the drug details page at a certain geographical location on a certain day, the initial usage probability corresponding to the geographical location is set to 1, and the assigned weights of the IP address and the delivery address for browsing the drug details page are 0.1 and 0.5 respectively, and the following is obtained: Figure 3 The corresponding usage probability in , such as 0.1 in the 2nd column of the 3rd row, indicates that the probability value of using each drug to treat a certain disease in this region is 0.1, and 0.5 in the 4th column of the 5th row indicates that the probability value of using each drug to treat a certain disease in this region is 0.5. Among them, since the delivery address is more likely to be the disease transmission area than the IP address of browsing the drug details page, the assignment weight of the delivery address can be set to five times the assignment weight of the IP address of browsing the drug details page.

[0078] It can be seen that in this embodiment, by generating a spatial matrix from spatial data, a structured representation of spatial information can be achieved; by corresponding the position of each element in the matrix to the geographical location, accurate segmentation and positioning of the geographical space can be achieved; by corresponding the value of each element in the matrix to the probability of use, a quantitative description of the intensity of drug use or the probability of disease transmission in the geographical location can be achieved; by generating a spatial vector from the spatial matrix, compression and simplification of the representation of two-dimensional spatial data can be achieved, and by generating a spatial vector, a digital representation of spatial information can be achieved.

[0079] In some embodiments, generating a spatial matrix according to the spatial data includes:

[0080] extracting at least one spatial parameter from the spatial data;

[0081] According to the weight corresponding to each spatial parameter, the probability of using the drug for the same disease in the geographical location corresponding to each spatial parameter is calculated.

[0082] The spatial parameter may refer to a parameter set according to the IP address of the drug details page and the delivery address. For example, as long as there is an IP address and / or a delivery address for browsing drug details page at a certain geographical location, the spatial parameter of the geographical location is set to 1, wherein the weights of the IP address and the delivery address for browsing drug details page are 0.1 and 0.5 respectively. When there are both an IP address and a delivery address for browsing drug details page at a certain geographical location, the weight corresponding to the spatial parameter of the geographical location is set to the weight of the delivery address.

[0083] It can be seen that in this embodiment, the extraction of spatial parameters can capture the characteristics of the geographical location and provide basic data for the generation of the spatial matrix in the subsequent steps; by assigning weights to each spatial parameter, the importance of different spatial characteristics can be quantified, and the method of calculating the probability of use based on the spatial parameters and their weights can improve the accurate description of the distribution of drug use, thereby providing more accurate input for the prediction of disease transmission.

[0084] In some embodiments, determining predicted spatiotemporal information of a disease based on a time vector and a space vector includes:

[0085] According to the time vector and the space vector, determine the purchase probability of the medicine for the disease at each geographical location in the future time period;

[0086] The predicted spatiotemporal information of the disease is determined based on the purchase probability of the disease's medicine at each geographical location in the future time period; the predicted spatiotemporal information includes distribution time, distribution address and occurrence probability.

[0087] The purchase probability at each geographical location may refer to the probability value of purchasing each drug for treating a certain disease at each geographical location. The purchase probability at each geographical location reflects the demand intensity of the drug in each geographical area and is used to predict the spread of the disease.

[0088] The distribution time refers to the predicted time range of the disease spread in the future. The distribution time can refer to the range of a certain day in the future time period.

[0089] The distribution address may refer to the geographical location range of the disease spread in the forecast, and may include specific cities and geographical coordinates.

[0090] Among them, the occurrence probability refers to the probability value of the disease spreading at each distribution time and each distribution address.

[0091] In a specific example, according to the time vector and the space vector, the purchase probability of the drug for the disease at each geographical location in the future time period can be determined by the following method:

[0092] Since sales data is one-dimensional data and spatial data is two-dimensional data, sales data needs to be converted into two-dimensional data. Sales data, attention probability and the acquisition time of each parameter in sales data are input into the time series downsampling module to obtain a multi-scale time series vector, which is then passed through the time series image encoder to obtain a time series two-dimensional vector, which can refer to a time vector.

[0093] In order to be compatible with the disease transmission cycle, the time series downsampling module inputs the sales data within a period of time and outputs the multi-scale time series vector within the period of time. The time series downsampling module can be composed of M layers of one-dimensional convolutional layers with a step size of 2, and outputs time series vectors of different scales layer by layer, wherein the time series vectors of different scales are all one-dimensional vectors. Figure 4 A schematic diagram of a time series downsampling module provided for an embodiment of the present invention, wherein the first data may refer to sales data, and the right side of the figure represents time series vectors of different scales.

[0094] The time series vectors of different scales are input into the time series image encoder, and a two-dimensional time series image vector is output. The time series vector is segmented according to a certain period, and then spliced ​​to obtain a single-channel two-dimensional vector, and then passed through a normalization layer to obtain a normalized single-channel two-dimensional vector. This two-dimensional vector is regarded as a grayscale image, and passed through a color filling network to generate a three-channel two-dimensional vector representing a color image. The three-channel two-dimensional vector is a two-dimensional time series image vector, and the two-dimensional time series image vector can refer to a time vector. Figure 5 A schematic diagram of a time series image encoder provided for an embodiment of the present invention, wherein the top row of numbers in the figure represents the various values ​​in the time series vector, indicating the value of the time series data at each time point, and the matrix in the middle of the figure may refer to the operation of segmenting and splicing time series vectors of different scales according to a period of 4 values ​​per row.

[0095] The spatial matrix generated according to the spatial data is input into the VAE encoder to obtain a spatial two-dimensional vector, which may refer to a spatial vector.

[0096] Inputting the time vector, space vector and random Gaussian noise into the time series DiT (Dynamic Information Transformer) module can determine the purchase probability of each drug for the disease at each geographical location in the future time period. Inputting random Gaussian noise into the embedding model can output the noise embedding vector, and add each vector in the multi-scale time vector set to obtain a new vector set, which passes through N layers of DiT layers, where each DiT layer can include a time attention layer, a space attention layer and a normalization layer. Figure 6A schematic diagram of a temporal DiT module provided by an embodiment of the present invention. The spatial vector vector passes through the temporal attention layer, and the spatial vector features are added to the DiT layer. The feature vector set output by the last DiT layer passes through the scale fusion layer, and the feature vector sets representing multiple scales are fused to determine the purchase probability of the drug for the disease at each geographical location in the future time period.

[0097] It can be seen that in this embodiment, by determining the purchase probability of medicines based on the time vector and the space vector, accurate prediction of the demand for medicines can be achieved; by determining the predicted spatiotemporal information of the disease based on the purchase probability of medicines, a more accurate prediction of the disease transmission trend can be achieved, providing data support for public health decision-making; by providing the distribution time, distribution address and occurrence probability of the disease, a comprehensive evaluation of the time, place and possibility of the future occurrence of the disease can be achieved.

[0098] Optionally, the predicted spatiotemporal information of the disease is determined based on the purchase probability of the drug for the disease at each geographical location in the future time period and the disease treatment relationship between the drug and the corresponding disease treatment. The disease treatment relationship between the drug and the corresponding disease treatment may refer to the relationship between which drugs can treat a certain disease.

[0099] In some embodiments, determining the purchase probability of a drug for a disease at each geographical location in a future time period based on the time vector and the space vector includes:

[0100] According to the time vector and the space vector, the purchase probability of the medicine for the disease at each geographical location in multiple future sub-time periods is determined; wherein the historical time period includes: multiple historical sub-time periods, and the historical sub-time periods correspond to the future sub-time periods one by one.

[0101] In a specific example, the historical time period may refer to January 1, 2024 to January 5, 2024, and the corresponding historical sub-time period may refer to January 1, 2024, January 2, 2024, January 3, 2024, January 4, 2024, and January 5, 2024, that is, the historical sub-time period may refer to a certain day in the historical time period. When the historical time period is January 1, 2024 to January 5, 2024, the future time period may refer to January 1, 2025 to January 5, 2025, and the future sub-time period may refer to January 1, 2025, January 2, 2025, January 3, 2025, January 4, 2025, and January 5, 2025, that is, the future sub-time period may refer to a certain day in the future time period. The one-to-one correspondence between the historical sub-time periods and the future sub-time periods may mean that the dates of the historical sub-time periods and the future sub-time periods also have a corresponding relationship. For example, when the historical sub-time period is January 1, 2024, the future sub-time period is January 1, 2025.

[0102] It can be seen that in this embodiment, by determining the purchase probability of drugs based on the time vector and the space vector, an accurate prediction of drug demand can be achieved; by subdividing the future time period into multiple sub-time periods and performing purchase probability prediction, a more detailed prediction of drug demand fluctuations can be achieved; by making a one-to-one correspondence between historical time periods and future sub-time periods, historical data can be effectively used to predict the purchase probability of future drugs, thereby improving the accuracy of the prediction.

[0103] Figure 7 A schematic diagram of the structure of an epidemic spread information detection device provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the case of epidemic spread information detection, the device can execute the epidemic spread information detection method, and the device can be implemented in the form of hardware and / or software.

[0104] See also Figure 7 The epidemic spread information detection device shown includes: a data acquisition module 701, a data extraction module 702, a time vector generation module 703, a space vector generation module 704 and a prediction module 705, wherein:

[0105] The data acquisition module 701 is used to acquire online drug sales data provided by users in a historical period of time for at least one drug corresponding to the same disease;

[0106] A data extraction module 702 is used to extract sales data and spatial data from online drug sales data;

[0107] A time vector generating module 703, used to generate a time vector according to the sales data;

[0108] A space vector generating module 704, used to generate a space vector according to the space data;

[0109] The prediction module 705 is used to determine the predicted spatiotemporal information of the disease according to the time vector and the space vector.

[0110] The technical solution of the embodiment of the present invention, by acquiring online drug sales data within a historical time period, can accumulate drug sales data through an online platform, providing a data basis for epidemic spread prediction; by extracting sales data and spatial data, it can capture drug sales information from two dimensions of time and space respectively; by generating time vectors, it can model the temporal dynamics of disease transmission, thereby revealing the cyclical changes and trends of epidemic transmission, and providing time series data support for predicting future epidemic change trends; by generating space vectors, it can model the spatial distribution of epidemic transmission, revealing the transmission differences in different regions, and providing a basis for the analysis of the geographical characteristics of epidemic transmission; by combining time vectors and space vectors, it can realize spatiotemporal prediction of epidemic transmission, and provide the epidemic transmission situation at a certain time and place in the future.

[0111] In some embodiments, in terms of generating a time vector according to sales data, the time vector generating module 703 is specifically used to:

[0112] Calculating the probability of disease attention according to a plurality of sales parameters and corresponding parameter values ​​included in the sales data;

[0113] A time vector is generated based on the probability of concern of the disease and the collection time of each parameter in the sales data.

[0114] In some embodiments, in calculating the probability of concern of a disease according to a plurality of sales parameters and corresponding parameter values ​​included in the sales data, the time vector generation module 703 is specifically used to:

[0115] According to the weight of each sales parameter on the drug and the parameter value corresponding to each sales parameter, the probability of attention of each disease is calculated.

[0116] In some embodiments, in terms of generating a space vector according to the space data, the space vector generating module 704 is specifically used to:

[0117] Generate a spatial matrix based on the spatial data, where the position of each element in the matrix corresponds to the geographical location, and the element value of each element corresponds to the usage probability;

[0118] Generate a space vector based on the space matrix.

[0119] In some embodiments, in terms of generating a spatial matrix according to spatial data, the spatial vector generating module 704 is specifically used to:

[0120] extracting at least one spatial parameter from the spatial data;

[0121] According to the weight corresponding to each spatial parameter, the probability of using the drug for the same disease in the geographical location corresponding to each spatial parameter is calculated.

[0122] In some embodiments, in determining predicted spatiotemporal information of a disease based on a time vector and a space vector, the prediction module 705 is specifically configured to:

[0123] According to the time vector and the space vector, determine the purchase probability of the medicine for the disease at each geographical location in the future time period;

[0124] The predicted spatiotemporal information of the disease is determined based on the purchase probability of the disease's medicine at each geographical location in the future time period; the predicted spatiotemporal information includes distribution time, distribution address and occurrence probability.

[0125] In some embodiments, in determining the purchase probability of a drug for a disease at each geographical location in a future time period according to the time vector and the space vector, the prediction module 705 is specifically used to:

[0126] According to the time vector and the space vector, the purchase probability of the medicine for the disease at each geographical location in multiple future sub-time periods is determined; wherein the historical time period includes: multiple historical sub-time periods, and the historical sub-time periods correspond to the future sub-time periods one by one.

[0127] The epidemic spread information detection device provided in the embodiment of the present invention can execute the epidemic spread information detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the epidemic spread information detection method.

[0128] Figure 8 A schematic diagram of the structure of an epidemic spread information detection device provided by an embodiment of the present invention.

[0129] like Figure 8 As shown, the epidemic spread information detection device 800 includes at least one processor 801, and a memory connected to the at least one processor 801, such as a read-only memory (ROM) 802, a random access memory (RAM) 803, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 801 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 802 or the computer program loaded from the storage unit 808 to the random access memory (RAM) 803. In RAM803, various programs and data required for the operation of the epidemic spread information detection device 800 can also be stored. The processor 801, ROM802 and RAM803 are connected to each other via a bus 804. An input / output (I / O) interface 808 is also connected to the bus 804.

[0130] A number of components in the epidemic spread information detection device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the epidemic spread information detection device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0131] The processor 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 801 performs the various methods and processes described above, such as an epidemic transmission information detection method.

[0132] In some embodiments, the epidemic spread information detection method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the epidemic spread information detection device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the processor 801, one or more steps of the epidemic spread information detection method described above may be performed. Alternatively, in other embodiments, the processor 801 may be configured to perform the epidemic spread information detection method in any other appropriate manner (e.g., by means of firmware).

[0133] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0134] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0135] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0136] To provide interaction with a user, the systems and techniques described herein may be implemented on an operating detection device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which a user can provide input to the epidemic spread information detection device. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0137] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0138] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0139] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0140] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting epidemic spread information, characterized in that: The method comprises: Obtain online drug sales data provided by users for at least one drug corresponding to the same disease within a historical period; extracting sales data and spatial data from the online drug sales data; Generate a time vector according to the sales data; Generate a spatial vector according to the spatial data; The predicted spatiotemporal information of the disease is determined according to the time vector and the space vector.

2. The method according to claim 1, characterized in that: The step of generating a time vector according to the sales data comprises: Calculating the probability of concern of the disease according to a plurality of sales parameters and corresponding parameter values ​​included in the sales data; A time vector is generated according to the probability of concern of the disease and the collection time of each parameter in the sales data.

3. The method according to claim 2, characterized in that Calculating the probability of concern of the disease according to the multiple sales parameters and corresponding parameter values ​​included in the sales data, including: The probability of attention of each of the diseases is calculated based on the weight of each of the sales parameters to the drug and the parameter value corresponding to each of the sales parameters.

4. The method according to claim 1, characterized in that Generating a space vector according to the space data includes: Generate a spatial matrix according to the spatial data, wherein the position of each element in the matrix corresponds to the geographical location, and the element value of each element corresponds to the usage probability; A space vector is generated according to the space matrix.

5. The method according to claim 4, characterized in that Generating a spatial matrix according to the spatial data includes: extracting at least one spatial parameter from the spatial data; According to the weights corresponding to the spatial parameters, the usage probability of the medicine for the same disease at the geographical location corresponding to the spatial parameters is calculated.

6. The method according to claim 1, characterized in that Determining predicted spatiotemporal information of the disease according to the time vector and the space vector includes: Determine, based on the time vector and the space vector, the purchase probability of the medicine for the disease at each geographical location in a future time period; The predicted spatiotemporal information of the disease is determined according to the purchase probability of the medicine for the disease at each geographical location in a future time period; the predicted spatiotemporal information includes distribution time, distribution address and occurrence probability.

7. The method according to claim 6, characterized in that Determining the purchase probability of the drug for the disease at each geographical location in a future time period according to the time vector and the space vector includes: According to the time vector and the space vector, the purchase probability of the medicine for the disease at each geographical location in multiple future sub-time periods is determined; wherein the historical time period includes: multiple historical sub-time periods, and the historical sub-time periods correspond to the future sub-time periods one by one.

8. An epidemic spread information detection device, characterized in that: include: A data acquisition module, used to acquire online drug sales data provided by users in a historical period of time for at least one drug corresponding to the same disease; A data extraction module, used to extract sales data and space data from the online drug sales data; A time vector generating module, used for generating a time vector according to the sales data; A space vector generation module, used to generate a space vector according to the space data; A prediction module is used to determine the predicted spatiotemporal information of the disease based on the time vector and the space vector.

9. An epidemic spread information detection device, characterized in that: The epidemic spread information detection device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the epidemic propagation information detection method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the epidemic spread information detection method described in any one of claims 1 to 7 when executed.

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