A data processing method and system suitable for pharmaceutical big data cloud service platform

Abstract

The present invention discloses a data processing method and system suitable for a pharmaceutical big data cloud service platform, and belongs to the technical field of processing medical big data. The present invention calculates the population density in an area according to a population density calculation strategy; calculates the influenza transmission value according to an influenza transmission value calculation strategy; calculates the weather impact value on influenza transmission according to a weather impact value calculation strategy; calculates the influenza transmission comprehensive risk value according to an influenza transmission comprehensive risk value calculation strategy; obtains information on drugs for treating influenza, predicts the drug shortage probability according to a drug shortage probability prediction model, compares the predicted drug shortage probability with a preset drug shortage probability threshold, and predicts whether the drug is in short supply according to the comparison result. The present invention comprehensively predicts the influenza transmission risk by using the influenza transmission value and the weather impact value, and can more accurately predict potential shortages of drugs, thereby providing a basis for drug inventory management.

Description

Technical Field

[0001] The present invention belongs to the technical field of processing medical big data, and specifically relates to a data processing method and system suitable for a medical big data cloud service platform. Background Art

[0002] Most of the existing technologies predict the risk of drug shortages in the next cycle based on historical drug circulation data, but lack the combination of population density, influenza transmission characteristics and weather conditions to comprehensively consider the transmissibility of influenza, and then predict drug shortages based on the transmissibility of influenza. For example, in the Chinese patent application publication number CN114664422A, a drug shortage risk monitoring and early warning method and system are disclosed. The invention first obtains the circulation data of the monitored drug N months before the prediction time point, and then calculates the supply average value and demand average value of the monitored drug based on the circulation data, and uses the supply average value and demand average value as input, respectively, and uses the Poisson distribution formula to predict the supply forecast value and demand forecast value of the monitored drug one month after the prediction time point. Finally, the drug shortage index of the monitored drug one month after the prediction time point is calculated based on the supply forecast value and the demand forecast value, and the risk is graded based on the drug shortage index to determine the risk level of the monitored drug one month after the prediction time point, so that the big data resources of medical circulation can be used to find the market regulation law formed to maintain the balance of supply and demand under normal conditions, which can provide means for early discovery of drug shortage risks and move the threshold of drug shortage warning forward.

[0003] In order to solve the above problems, the present invention designs a data processing method and system suitable for a pharmaceutical big data cloud service platform. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a data processing method and system suitable for a pharmaceutical big data cloud service platform. The present invention calculates the population density in an area according to a population density calculation strategy; calculates the influenza transmission value according to an influenza transmission value calculation strategy; calculates the impact value of weather on influenza transmission according to a weather impact value calculation strategy; calculates the comprehensive risk value of influenza transmission according to an influenza transmission comprehensive risk value calculation strategy; obtains information on drugs for treating influenza, predicts the probability of drug shortage according to a drug shortage probability prediction model, compares the predicted drug shortage probability with a preset drug shortage probability threshold, and predicts whether a drug shortage occurs according to the comparison result. The present invention comprehensively predicts the influenza transmission risk by using the influenza transmission value and the weather impact value, so as to more accurately predict potential shortages of drugs and provide a basis for drug inventory management.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A data processing method applicable to a pharmaceutical big data cloud service platform comprises the following specific steps:

[0007] Step 1: Collect pictures of various places in the designated area, and calculate the crowd density in the area according to the crowd density calculation strategy;

[0008] Step 2: Collect influenza transmission characteristic data, and import the population density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value;

[0009] Step 3: Collect weather information of the designated area and calculate the impact value of weather on influenza transmission according to the weather impact value calculation strategy;

[0010] Step 4: Import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value;

[0011] Step 5. Obtain information on drugs for treating influenza, import the calculated comprehensive risk value of influenza transmission into the drug shortage probability prediction model to predict the drug shortage probability, compare the predicted drug shortage probability with the preset drug shortage probability threshold, and predict whether the drug is in short supply based on the comparison results.

[0012] Specifically, the step 1 includes the following specific steps:

[0013] S11, collecting pictures of various places in the designated area, and pre-processing the collected pictures;

[0014] S12, importing the pre-processed image into a crowd density calculation strategy to calculate the crowd density in the area, the crowd density calculation strategy includes an individual mark calculation formula, and the individual mark calculation formula is: ,in, is the total number of target labeled individuals in the image, is an image with N labeled individuals, is the coordinate of any pixel in the image, Mark the coordinates of individual pixels of the nth target in the image, Unit impulse function for target labeled individuals;

[0015] S13. The crowd density calculation strategy also includes a crowd density calculation formula, which is: ,in, is the distance between the nth target marked individual and its nearest mth marked individual, is the number of labeled individuals closest to the nth target labeled individual, is the geometry-adaptive Gaussian kernel.

[0016] Specifically, the step 2 includes the following specific steps:

[0017] S21, collecting influenza transmission characteristic data, wherein the influenza transmission characteristic data includes transmission distance and transmission speed;

[0018] S22, importing the population density and influenza transmission characteristic data into an influenza transmission value calculation strategy to calculate the influenza transmission value, wherein the influenza transmission value calculation strategy includes an influenza transmission value calculation formula, and the influenza transmission value calculation formula is: ,in, is the preset safe crowd density threshold, is the propagation probability under standard distance, is the collected infection distance, is the propagation speed of the acquisition, is the preset safe propagation speed threshold.

[0019] Specifically, the step three includes the following specific steps:

[0020] S31, collecting weather information of the designated area, wherein the weather information includes temperature, humidity and wind speed;

[0021] S32, importing the collected temperature, humidity and wind speed into a weather impact value calculation strategy to calculate the impact value of weather on influenza transmission, wherein the weather impact value calculation strategy includes a weather impact value calculation formula, and the weather impact value calculation formula is: ,in, The duration of collecting weather information. is the temperature collected at time r, is the preset minimum safe temperature threshold. hour, The value is 0. is the humidity collected at time r, is the preset maximum safe humidity threshold. hour, The value is 0. is the wind speed collected at time r, is the preset maximum safe wind speed threshold. hour, The value is 0. is the temperature ratio, is the humidity ratio, is the wind speed ratio, .

[0022] Specifically, the step 4 includes the following specific steps:

[0023] The calculated influenza transmission value and the impact value of weather on influenza transmission are introduced into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value. The influenza transmission comprehensive risk value calculation strategy includes a influenza transmission comprehensive risk value calculation formula, and the influenza transmission comprehensive risk value calculation formula is: .

[0024] Specifically, the step five includes the following specific steps:

[0025] S51, obtaining drug information for treating influenza, wherein the drug information includes drug inventory;

[0026] S52, importing the calculated influenza transmission comprehensive risk value into a drug shortage probability prediction model to predict the drug shortage probability, wherein the drug shortage probability calculation formula in the drug shortage probability prediction model is: ,in, is an exponential function with base e, The amount of medicine purchased for each patient, is the drug inventory;

[0027] S53. Compare the predicted drug shortage probability with the preset drug shortage probability threshold. If the predicted drug shortage probability is less than the preset drug shortage probability threshold, it is predicted that there will be no drug shortage. If the predicted drug shortage probability is greater than or equal to the preset drug shortage probability threshold, it is predicted that there will be a drug shortage and a drug shortage warning message will be automatically sent.

[0028] A data processing system applicable to a pharmaceutical big data cloud service platform, which is implemented based on the above-mentioned data processing method applicable to a pharmaceutical big data cloud service platform, and includes a control module, an image acquisition module, a crowd density calculation module, a data acquisition module, an influenza transmission value calculation module, a weather impact value calculation module, a comprehensive risk value calculation module, a drug shortage probability calculation module, and a comparative prediction module;

[0029] Specifically, the picture acquisition module is used to collect pictures of various places in the designated area, the crowd density calculation module is used to import the collected pictures into the crowd density calculation strategy to calculate the crowd density in the area, the data acquisition module is used to collect influenza transmission characteristic data and weather information of the designated area, the influenza transmission value calculation module is used to import the crowd density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value, and the weather impact value calculation module is used to import the collected weather information into the weather impact value calculation strategy to calculate the impact value of weather on influenza transmission;

[0030] Specifically, the comprehensive risk value calculation module is used to import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value; the drug shortage probability calculation module is used to import the calculated influenza transmission comprehensive risk value into the drug shortage probability prediction model to predict the drug shortage probability; the comparison prediction module is used to compare the predicted drug shortage probability with a preset drug shortage probability threshold, and predict whether the drug is in short supply according to the comparison result;

[0031] Specifically, the control module is used to control the operation of the image acquisition module, the crowd density calculation module, the data acquisition module, the influenza transmission value calculation module, the weather impact value calculation module, the comprehensive risk value calculation module, the drug shortage probability calculation module and the comparative prediction module.

[0032] The beneficial effects of the present invention are:

[0033] The present invention calculates the population density in the area according to the population density calculation strategy; calculates the influenza transmission value according to the influenza transmission value calculation strategy; calculates the weather impact value on influenza transmission according to the weather impact value calculation strategy; calculates the influenza transmission comprehensive risk value according to the influenza transmission comprehensive risk value calculation strategy; obtains drug information for treating influenza, predicts the drug shortage probability according to the drug shortage probability prediction model, compares the predicted drug shortage probability with a preset drug shortage probability threshold, and predicts whether the drug is in short supply according to the comparison result;

[0034] The present invention predicts the demand for drugs and potential shortages in advance, providing a basis for the adjustment of drug supply chains and inventory management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 This is a flow chart of a data processing method applicable to a pharmaceutical big data cloud service platform according to the present invention;

[0037] Figure 2 The figure is a schematic diagram of the overall framework of the data processing system applicable to the pharmaceutical big data cloud service platform of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1 , an embodiment provided by the present invention: a data processing method applicable to a pharmaceutical big data cloud service platform, comprising the following specific steps:

[0041] Step 1: Collect pictures of various places in the designated area, and calculate the crowd density in the area according to the crowd density calculation strategy;

[0042] In this embodiment, step one includes the following specific steps:

[0043] S11, collecting pictures of various places in the designated area, and pre-processing the collected pictures;

[0044] It should be noted here that the specific steps for preprocessing the image are as follows:

[0045] Image denoising: Noise will affect the accuracy of crowd detection, so the image needs to be denoised. The denoising methods include mean filtering, median filtering, Gaussian filtering, etc.

[0046] Image enhancement: In order to improve the contrast and clarity of the image, the image can be enhanced using methods such as histogram equalization and contrast enhancement;

[0047] Image segmentation: Divide the image into multiple regions to facilitate subsequent crowd detection. Image segmentation can be performed using methods such as threshold segmentation, region growing, and edge detection.

[0048] Color space conversion: images can be converted from the original color space (such as RGB) to other color spaces (such as HSV) as needed to facilitate subsequent analysis;

[0049] Feature extraction: Extract key features from the image, such as edges, corners, textures, etc., to facilitate subsequent crowd detection.

[0050] S12, importing the pre-processed image into a crowd density calculation strategy to calculate the crowd density in the area, the crowd density calculation strategy includes an individual tag calculation formula, and the individual tag calculation formula is: ,in, is the total number of target labeled individuals in the image, is an image with N labeled individuals, is the coordinate of any pixel in the image, Mark the coordinates of individual pixels of the nth target in the image, Unit impulse function for target labeled individuals;

[0051] S13. The crowd density calculation strategy also includes a crowd density calculation formula, which is: ,in, is the distance between the nth target marked individual and its nearest mth marked individual, is the number of labeled individuals closest to the nth target labeled individual, is the geometry-adaptive Gaussian kernel.

[0052] Step 2: Collect influenza transmission characteristic data, and import the population density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value;

[0053] In this embodiment, step 2 includes the following specific steps:

[0054] S21. Collect influenza transmission characteristic data, including transmission distance and transmission speed;

[0055] It should be noted that the purpose of collecting influenza transmission characteristics data is to:

[0056] The transmission distance refers to the physical distance that a virus or pathogen travels from one infected person to another uninfected person during the spread of infectious diseases such as influenza. This distance can be the distance of direct contact between two infected people, or the distance of indirect transmission through media such as air and surfaces. The definition of the transmission distance means that in the study and control of infectious diseases such as influenza, it is necessary to pay attention to the transmission methods of viruses or pathogens in different environments and their diffusion paths in the population. By accurately recording the transmission distance, researchers can better understand the transmission dynamics of the virus, assess the risk of influenza, and develop effective prevention and control measures.

[0057] The transmission rate refers to the speed at which the influenza virus spreads among the population. The collected data include the incubation period, infectious period, and outbreak period of the influenza virus. The transmission rate is obtained by collecting time series data of influenza cases, including onset date, contact history, etc., and using these data to estimate the time intervals between influenza cases, thereby calculating the transmission rate; predicting the development trend of influenza transmission. For example, if the influenza virus spreads quickly, then early control measures need to be quick and effective to slow the spread of the virus.

[0058] S22. Importing the population density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value. The influenza transmission value calculation strategy includes an influenza transmission value calculation formula. The influenza transmission value calculation formula is: ,in, is the preset safe crowd density threshold, is the propagation probability under standard distance, is the collected infection distance, is the propagation speed of the acquisition, is the preset safe propagation speed threshold;

[0059] It should be noted here that the transmission probability at the standard distance refers to the probability of a pathogen being transmitted from one infected person to another uninfected person at a specific, artificially set contact distance;

[0060] Method for obtaining the propagation probability under standard distance:

[0061] Epidemiological survey: Through retrospective survey, data on contact between contacts and susceptible persons at a specific distance, as well as subsequent infection, are collected to estimate the probability of transmission;

[0062] The role of obtaining the propagation probability under standard distance:

[0063] Precise control: At a standard distance, researchers can precisely control contact conditions, eliminate the effects of other variables on transmission probability, and obtain a more accurate estimate of transmission probability;

[0064] Help understand transmission mechanisms: Understanding the probability of transmission at a specific distance can help researchers better understand the transmission mechanisms of pathogens, including droplet transmission, contact transmission, etc.

[0065] Support public health decision-making: Transmission probability data at standard distances can help evaluate and formulate effective prevention measures, such as social distancing and mask use.

[0066] Step 3: Collect weather information of the designated area and calculate the impact value of weather on influenza transmission according to the weather impact value calculation strategy;

[0067] In this embodiment, step three includes the following specific steps:

[0068] S31, collecting weather information of the designated area, the weather information including temperature, humidity and wind speed;

[0069] It should be noted that the steps for collecting temperature are: installing temperature sensors at monitoring points. The sensors can be electronic thermometers, infrared thermometers or other types of temperature detection devices, and using data acquisition systems or weather stations to monitor and record temperature data in real time; storing the collected temperature data in a database and analyzing it using statistical software to identify any abnormal trends or patterns;

[0070] Impact of temperature on influenza transmission: Influenza viruses have different survival rates at different temperatures, and lower temperatures may help the virus survive and spread; changes in temperature may affect the seasonal pattern of influenza and help predict the peak of influenza.

[0071] The steps for collecting humidity are: installing humidity sensors at monitoring points. The sensors can be humidity and temperature composite sensors or devices specifically designed to measure humidity; using a data acquisition system or weather station to monitor and record humidity data in real time; storing the collected humidity data in a database and analyzing it using statistical software to identify any abnormal trends or patterns;

[0072] Impact of humidity on influenza transmission: Humidity affects the activity of influenza viruses, and higher humidity may help reduce the spread of the virus; changes in humidity may affect the speed and scope of influenza transmission.

[0073] The steps for collecting wind speed are as follows: install anemometers at monitoring points, which can be mechanical, electronic or ultrasonic; use data acquisition systems or weather stations to monitor and record wind speed data in real time; store the collected wind speed data in a database and analyze it using statistical software to identify any abnormal trends or patterns;

[0074] Impact of wind speed on influenza transmission: Wind speed can affect the distance that influenza virus travels in the air. Higher wind speeds may accelerate the spread of the virus. Changes in wind speed may affect the speed and range of influenza spread.

[0075] S32, importing the collected temperature, humidity and wind speed into the weather impact value calculation strategy to calculate the impact value of weather on influenza transmission, the weather impact value calculation strategy includes a weather impact value calculation formula, and the weather impact value calculation formula is: ,in, The duration of collecting weather information. is the temperature collected at time r, is the preset minimum safe temperature threshold. When The value is 0. is the humidity collected at time r, is the preset maximum safe humidity threshold. hour, The value is 0. is the wind speed collected at time r, is the preset maximum safe wind speed threshold. hour, The value is 0. is the temperature ratio, is the humidity ratio, is the wind speed ratio, .

[0076] Step 4: Import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value;

[0077] In this embodiment, step 4 includes the following specific steps:

[0078] The calculated influenza transmission value and the impact value of weather on influenza transmission are introduced into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value. The influenza transmission comprehensive risk value calculation strategy includes a influenza transmission comprehensive risk value calculation formula, which is: .

[0079] Step 5. Obtain information on drugs for treating influenza, import the calculated comprehensive risk value of influenza transmission into the drug shortage probability prediction model to predict the drug shortage probability, compare the predicted drug shortage probability with the preset drug shortage probability threshold, and predict whether the drug is in short supply based on the comparison results.

[0080] In this embodiment, step five includes the following specific steps:

[0081] S51. Obtaining drug information for treating influenza, where the drug information includes drug inventory;

[0082] S52, importing the calculated influenza transmission comprehensive risk value into a drug shortage probability prediction model to predict the drug shortage probability. The drug shortage probability calculation formula in the drug shortage probability prediction model is: ,in, is an exponential function with base e, The amount of medicine purchased for each patient, is the drug inventory;

[0083] S53. Compare the predicted drug shortage probability with the preset drug shortage probability threshold. If the predicted drug shortage probability is less than the preset drug shortage probability threshold, it is predicted that there will be no drug shortage. If the predicted drug shortage probability is greater than or equal to the preset drug shortage probability threshold, it is predicted that there will be a drug shortage and a drug shortage warning message will be automatically sent.

[0084] It should be noted here that the temperature ratio , humidity ratio , wind speed ratio , the preset safe crowd density threshold, the preset safe transmission speed threshold, the preset minimum safe temperature threshold, the preset maximum safe humidity threshold, the preset maximum safe wind speed threshold and the preset drug shortage probability threshold are determined as follows: select 50 historical records of influenza, obtain pictures of various places before the spread of influenza, obtain influenza transmission characteristic data, obtain historical weather information of various places, obtain historical inventory records of drugs for treating influenza, import the pictures into the crowd density calculation strategy to calculate the crowd density in the area; import the crowd density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value; import weather information into the weather impact value calculation strategy to calculate the impact value of weather on influenza transmission; import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the comprehensive risk value of influenza transmission; import the calculated comprehensive risk value of influenza transmission into the drug shortage probability prediction model to predict the probability of drug shortage, and import the prediction results and the historical inventory records of drugs into the fitting software to output a set of temperature proportion coefficients with the highest accuracy , humidity ratio , wind speed ratio , the values ​​of the preset safe crowd density threshold, the preset safe transmission speed threshold, the preset minimum safe temperature threshold, the preset maximum safe humidity threshold, the preset maximum safe wind speed threshold and the preset drug shortage probability threshold.

[0085] Example 2

[0086] See also Figure 2 , a data processing system applicable to a pharmaceutical big data cloud service platform, which is implemented based on the above-mentioned data processing method applicable to a pharmaceutical big data cloud service platform, and includes a control module, an image acquisition module, a crowd density calculation module, a data acquisition module, an influenza transmission value calculation module, a weather impact value calculation module, a comprehensive risk value calculation module, a drug shortage probability calculation module and a comparative prediction module;

[0087] In this embodiment, the picture acquisition module is used to collect pictures of various places in the designated area, the crowd density calculation module is used to import the collected pictures into the crowd density calculation strategy to calculate the crowd density in the area, the data acquisition module is used to collect influenza transmission characteristic data and weather information of the designated area, the influenza transmission value calculation module is used to import the crowd density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value, and the weather impact value calculation module is used to import the collected weather information into the weather impact value calculation strategy to calculate the impact value of weather on influenza transmission;

[0088] In this embodiment, the comprehensive risk value calculation module is used to import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value; the drug shortage probability calculation module is used to import the calculated influenza transmission comprehensive risk value into the drug shortage probability prediction model to predict the drug shortage probability; the comparison prediction module is used to compare the predicted drug shortage probability with the preset drug shortage probability threshold, and predict whether the drug shortage occurs according to the comparison result;

[0089] In this embodiment, the control module is used to control the operation of the image acquisition module, the crowd density calculation module, the data acquisition module, the influenza transmission value calculation module, the weather impact value calculation module, the comprehensive risk value calculation module, the drug shortage probability calculation module and the comparative prediction module.

[0090] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0091] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0092] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only one way. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0093] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

Claims

1. A data processing method suitable for a pharmaceutical big data cloud service platform, characterized in that: The specific steps include: Step 1: Collect pictures of various places in the designated area, and calculate the crowd density in the area according to the crowd density calculation strategy; The step 1 includes the following specific steps: S11, collecting pictures of various places in the designated area, and pre-processing the collected pictures; S12, importing the pre-processed image into a crowd density calculation strategy to calculate the crowd density in the area, the crowd density calculation strategy includes an individual mark calculation formula, and the individual mark calculation formula is: ,in, is the total number of target labeled individuals in the image, is an image with N labeled individuals, is the coordinate of any pixel in the image, Mark the coordinates of individual pixels of the nth target in the image, Unit impulse function for target labeled individuals; S13. The crowd density calculation strategy also includes a crowd density calculation formula, which is: ,in, is the distance between the nth target marked individual and its nearest mth marked individual, is the number of labeled individuals closest to the nth target labeled individual, is the geometrically adaptive Gaussian kernel; Step 2: Collect influenza transmission characteristic data, and import the population density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value; The step 2 includes the following specific steps: S21, collecting influenza transmission characteristic data, wherein the influenza transmission characteristic data includes transmission distance and transmission speed; S22, importing the population density and influenza transmission characteristic data into an influenza transmission value calculation strategy to calculate the influenza transmission value, wherein the influenza transmission value calculation strategy includes an influenza transmission value calculation formula, and the influenza transmission value calculation formula is: ,in, is the preset safe crowd density threshold, is the propagation probability under standard distance, is the collected infection distance, is the propagation speed of the acquisition, is the preset safe propagation speed threshold; Step 3: Collect weather information of the designated area and calculate the impact value of weather on influenza transmission according to the weather impact value calculation strategy; The step three includes the following specific steps: S31, collecting weather information of the designated area, wherein the weather information includes temperature, humidity and wind speed; S32, importing the collected temperature, humidity and wind speed into a weather impact value calculation strategy to calculate the impact value of weather on influenza transmission, wherein the weather impact value calculation strategy includes a weather impact value calculation formula, and the weather impact value calculation formula is: ,in, is the duration of collecting weather information, is the temperature collected at time r, is the preset minimum safe temperature threshold. hour, The value is 0. is the humidity collected at time r, is the preset maximum safe humidity threshold. hour, The value is 0. is the wind speed collected at time r, is the preset maximum safe wind speed threshold. hour, The value is 0. is the temperature ratio, is the humidity ratio, is the wind speed ratio, ; Step 4: Import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value; The step 4 includes the following specific steps: The calculated influenza transmission value and the impact value of weather on influenza transmission are introduced into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value. The influenza transmission comprehensive risk value calculation strategy includes a influenza transmission comprehensive risk value calculation formula, and the influenza transmission comprehensive risk value calculation formula is: ; Step 5: Obtain information on drugs for treating influenza, import the calculated comprehensive risk value of influenza transmission into a drug shortage probability prediction model to predict the drug shortage probability, compare the predicted drug shortage probability with a preset drug shortage probability threshold, and predict whether a drug shortage occurs based on the comparison result; The step five includes the following specific steps: S51, obtaining drug information for treating influenza, wherein the drug information includes drug inventory; S52, importing the calculated influenza transmission comprehensive risk value into a drug shortage probability prediction model to predict the drug shortage probability, wherein the drug shortage probability calculation formula in the drug shortage probability prediction model is: ,in, is an exponential function with base e, The amount of medicine purchased for each patient, is the drug inventory; S53. Compare the predicted drug shortage probability with the preset drug shortage probability threshold. If the predicted drug shortage probability is less than the preset drug shortage probability threshold, it is predicted that there will be no drug shortage. If the predicted drug shortage probability is greater than or equal to the preset drug shortage probability threshold, it is predicted that there will be a drug shortage and a drug shortage warning message will be automatically sent.

2. A data processing system applicable to a pharmaceutical big data cloud service platform, implemented by a data processing method applicable to a pharmaceutical big data cloud service platform as claimed in claim 1, characterized in that: It includes a control module, a picture acquisition module, a crowd density calculation module, a data acquisition module, a flu transmission value calculation module, a weather impact value calculation module, a comprehensive risk value calculation module, a drug shortage probability calculation module and a comparative prediction module. The control module is used to control the operation of the picture acquisition module, the crowd density calculation module, the data acquisition module, the flu transmission value calculation module, the weather impact value calculation module, the comprehensive risk value calculation module, the drug shortage probability calculation module and the comparative prediction module.

3. A data processing system suitable for a pharmaceutical big data cloud service platform as claimed in claim 2, characterized in that: The image acquisition module is used to collect images of various places in the designated area, the crowd density calculation module is used to import the collected images into the crowd density calculation strategy to calculate the crowd density in the area, the data acquisition module is used to collect influenza transmission characteristic data and weather information of the designated area, the influenza transmission value calculation module is used to import the crowd density and influenza transmission characteristic data into the influenza transmission value calculation strategy to calculate the influenza transmission value, and the weather impact value calculation module is used to import the collected weather information into the weather impact value calculation strategy to calculate the impact value of weather on influenza transmission.

4. A data processing system suitable for a pharmaceutical big data cloud service platform as claimed in claim 3, characterized in that: The comprehensive risk value calculation module is used to import the calculated influenza transmission value and the impact value of weather on influenza transmission into the influenza transmission comprehensive risk value calculation strategy to calculate the influenza transmission comprehensive risk value; the drug shortage probability calculation module is used to import the calculated influenza transmission comprehensive risk value into the drug shortage probability prediction model to predict the drug shortage probability; the comparison prediction module is used to compare the predicted drug shortage probability with a preset drug shortage probability threshold, and predict whether a drug shortage occurs based on the comparison result.

Images