Disease prevention information pushing system and method

Abstract

The invention discloses a disease prevention information pushing system and method, and relates to the technical field of information pushing, the disease prevention information pushing system comprises a data collection module, a calculation and analysis module, an information generation module, a pushing module and a storage module, the data collection module is responsible for collecting various data, and the calculation and analysis module is responsible for sequentially outputting disease transmission risk values FC; the information generation module is responsible for drawing the adjusted disease transmission risk value TF in the recent week into a linear trend chart and adjusting and generating the push content and frequency of the push information according to the trend change, and the push module is responsible for pushing the generated push content and frequency. According to the comprehensive evaluation and dynamic adjustment system constructed by the invention, a comprehensive and accurate risk evaluation, dynamic adjustment and feedback mechanism and an accurate and personalized information pushing effect are achieved, so that optimization and innovation are realized, and more powerful support is provided for disease prevention and control.

Description

Technical Field

[0001] The present invention relates to the technical field of push information technology, and specifically relates to a disease prevention information push system and method. Background Art

[0003] Today, with the continuous development of information technology, various disease monitoring systems and information dissemination means have gradually become rich. The existing disease prevention information push methods are mainly based on simple epidemic data statistics and are released to the public through news media and announcement channels. However, these traditional methods often lack a comprehensive assessment and dynamic analysis of the disease transmission risk. Moreover, when evaluating the disease transmission risk, most traditional methods only consider a single factor, which leads to an inaccurate assessment of the disease transmission risk and cannot provide targeted and effective prevention information for the public. At the same time, the existing information push system lacks an effective feedback mechanism and cannot adjust the push content in a timely manner according to the implementation effect of actual prevention and control measures and the dynamic changes in the disease transmission situation, thus making it difficult to meet the diverse needs of the public for prevention information at different disease transmission stages. Summary of the Invention

[0004] The purpose of the present invention is to provide a disease prevention information push system, which solves the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution. A disease prevention information push system includes a data collection module, a calculation and analysis module, an information generation module, a push module, and a storage module. The calculation and analysis module includes a transmission risk prediction unit, a transmission range prediction unit, and a final risk prediction unit; Data collection module: responsible for collecting various data on the number of infected people, environment, protection measures, prevention and control measures, and transmission range in the current area, and transmitting various data to the calculation and analysis module; Calculation and analysis module: responsible for implementing the calculation logics of the transmission risk prediction unit, the transmission range prediction unit, and the final risk prediction unit, and sequentially outputting the disease transmission risk value FC, the transmission range expansion value FK, and the adjusted disease transmission risk value TF; Information generation module: based on the adjusted disease transmission risk value TF, responsible for plotting the adjusted disease transmission risk value TF of the past week into a linear trend chart, and adjusting and generating the push content and frequency of the push information according to the trend change; Push module: responsible for pushing the generated push content and frequency; Storage module: responsible for storing the information data collected, calculated, and generated by the information.

[0006] Optionally, the devices used by the data collection module include sensors, data recorders, and terminal devices; The devices used by the calculation and analysis module include servers; The devices used by the information generation module include visualization generation devices; The devices used by the push module include routers and switches; The devices used by the storage module include storage devices.

[0007] Optionally, the calculation formula of the propagation risk prediction unit is as follows: FC = (GL + HB) × (1 - F); Where: FC is the disease transmission risk value; GL is the infection change value. GL reflects the change in the number of people infected by the disease in the population. The specific calculation formula is , DR is the current number of infected people, DR last is the number of infected people in the previous cycle; If GL is positive, it means that the number of infected people is increasing and the transmission speed is accelerating; If GL is negative, it means that the number of infected people is decreasing and the transmission speed is decreasing; HB is the environmental exposure value. HB is calculated by measuring the population density and the average residence time. The specific calculation formula is HB = M × t, where M is the population density value and t is the average residence time; F is the protection effect value, and the value range of F is between 0 and 1; When the mask wearing rate approaches 100% and the environmental exposure value HB is low, F approaches 0; When the mask wearing rate is far from 100% and the environmental exposure value HB is high, F approaches 1.

[0008] Optionally, the calculation formula of the propagation range prediction unit is as follows: FK = FC × HK + CM; Where: FK is the propagation range expansion value; HK is the environmental diffusion value. The specific calculation formula of HK is HK = L × a, where L is the air flow velocity and a is the space connectivity coefficient; The value range of the space connectivity coefficient a is between 0 and 1. The space fluidity of the open space is good, and the value of a approaches 1. The space fluidity of the closed space is poor, and the value of a approaches 0; CM is the initial propagation area, and CM reflects the area value of the place where the disease initially occurred.

[0009] Optionally, the calculation formula of the final risk prediction unit is as follows: ; Where: TF is the adjusted disease transmission risk value; Q is the trend adjustment value, and the value range of Q is {0 - 2}; FJ is the measure enhancement value, and FJ reflects the intensity of the prevention and control measures taken and increased in the previous cycle, that is, the frequency of increasing the push of disease prevention information.

[0010] Optionally, based on the adjusted disease transmission risk value TF, the trend analysis of plotting the adjusted disease transmission risk value TF in the past week as a line graph is as follows: Q1: If the adjusted disease transmission risk value TF in the past week shows an upward trend on the line graph, it reflects an increase in risk; Q2: If the adjusted disease transmission risk value TF in the past week shows a downward trend on the line graph, it reflects a decrease in risk; Q3: If the adjusted disease transmission risk value TF in the past week shows a flat trend on the line graph, it reflects that the risk tends to be flat.

[0011] Optionally, when the adjusted disease transmission risk value TF is in the trend of Q1, the measure enhancement value FJ needs to be increased, and the trend adjustment value Q tends to 2; When the adjusted disease transmission risk value TF is in the trend of Q2, the measure enhancement value FJ needs to be decreased, and the trend adjustment value Q tends to 1; When the adjusted disease transmission risk value TF is in the trend of Q3, the measure enhancement value FJ needs to be maintained, and the trend adjustment value Q is less than 1.

[0012] The present invention also provides a method for pushing disease prevention information, and the method for pushing disease prevention information includes the following steps: Step 1: Use the data collection module to collect various data within the current area, including the current number of infected people DR, the number of infected people DR in the previous cycle last , population density value M, average residence time t, mask wearing rate, air circulation speed L, spatial connectivity coefficient a, initial transmission area CM, trend adjustment value Q, measure enhancement value FJ; Step 2: Use the calculation and analysis module to sequentially output the disease transmission risk value FC, the transmission range expansion value FK, and the adjusted disease transmission risk value TF; Step 3: Based on the adjusted disease transmission risk value TF, use the information generation module to draw a linear trend graph of the trend change, and adjust and generate the push content and frequency of the push information according to the trend change; Step 4: Use the push module to perform the push; Step 5: Use the storage module to store the data of steps 1 to 4.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Through the transmission risk prediction unit, the present invention comprehensively considers the infection change value GL, the environmental exposure value HB, and the protection effect value F, and can comprehensively and accurately evaluate the disease transmission risk. Specifically, in a current area with dense population and poor ventilation, that is, a large environmental exposure value HB, insufficient protection measures, that is, a small protection effect value F, and at the same time, the number of infected people shows an upward trend, that is, the infection change value GL is positive, the transmission risk prediction unit can accurately calculate a relatively high disease transmission risk value FC, and provide a reliable basis for subsequent information push.

[0014] 2. The present invention realizes dynamic adjustment and feedback by using the transmission range prediction unit and the final risk prediction unit. The transmission range prediction unit combines the disease transmission risk value FC with the environmental diffusion value HK to calculate the transmission range expansion value FK. The final risk prediction unit adjusts the risk value again according to the trend adjustment value Q and the measure enhancement value FJ to form a cyclic feedback mechanism, which means that the system can adjust the risk assessment result and the content of information push in a timely manner according to the real-time changes of disease transmission, including whether the transmission trend is accelerating or decelerating, and whether the prevention and control measures are strengthened.

[0015] In addition, based on accurate risk assessment and dynamic adjustment, the system can provide accurate and personalized disease prevention information for the public according to the trend change line graph drawn according to different risk level divisions, that is, different adjusted disease transmission risk values TF. This accurate push improves the pertinence and practicality of information, helps the public better take effective preventive measures, and thus more effectively controls the spread of diseases. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall structure of the disease prevention information push system; Figure 2 It is a method flow chart of the disease prevention information push method; Figure 3 It is a schematic diagram of the structure of the calculation and analysis module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0018] Regarding the disease prevention information push system and method, it is different from the existing disease prevention information push systems and methods. The existing disease prevention information push systems and methods have problems such as one-sided risk assessment, lack of a dynamic adjustment mechanism, and weak pertinence of information push. However, this algorithm unit achieves comprehensive and accurate risk assessment, a dynamic adjustment and feedback mechanism, and precise and personalized information push effects.

[0019] Example 1. Please refer to Figures 1 to 3 , this example provides a disease prevention information push system, including a data collection module, a calculation and analysis module, an information generation module, a push module, and a storage module. The calculation and analysis module includes a transmission risk prediction unit, a transmission range prediction unit, and a final risk prediction unit; Data collection module: Responsible for collecting various data on the number of infected people, environment, protection measures, prevention and control measures, and transmission range in the current area, and transmitting various data to the calculation and analysis module; Calculation and analysis module: Responsible for implementing the calculation logics of the transmission risk prediction unit, the transmission range prediction unit, and the final risk prediction unit, and sequentially outputting the disease transmission risk value FC, the transmission range expansion value FK, and the adjusted disease transmission risk value TF; Information generation module: Based on the adjusted disease transmission risk value TF, responsible for plotting the adjusted disease transmission risk value TF of the past week into a linear trend chart, and adjusting and generating the push content and frequency of the push information according to the trend change; Push module: Responsible for pushing the generated push content and frequency; Storage module: Responsible for storing the information data collected, calculated, and generated by the information; The devices used by the data collection module include sensors, data recorders, and terminal devices; The devices used by the calculation and analysis module include servers; The devices used by the information generation module include visualization generation devices; The devices used by the push module include routers and switches; The devices used by the storage module include storage devices.

[0020] In this embodiment, through the collaborative work of various modules, units, and the equipment used, the full - process integration from data collection, processing, analysis to information generation and push is achieved, ensuring the stable operation and efficient functioning of the system, providing strong technical support for disease prevention information push. Among them, the transmission risk prediction unit comprehensively evaluates the disease transmission risk considering multiple factors. The transmission range prediction unit estimates the spread of the transmission range by combining the risk value and environmental factors. Finally, the final risk prediction unit adjusts the risk assessment according to the transmission trend and prevention and control measures. The whole process realizes the accurate assessment of the disease transmission risk from preliminary to in - depth, from static to dynamic, comprehensively grasping the disease transmission situation. Finally, based on the accurate risk assessment results, the system can provide a scientific basis for prevention and control decisions. The calculation results at different stages help to clearly understand the degree of disease transmission risk, the possible transmission range, and the dynamic changes of the risk, so as to formulate and adjust prevention and control strategies targeted, reasonably allocate prevention and control resources, and improve prevention and control efficiency.

[0021] Please refer to Figures 1 to 3 , the calculation formula of the transmission risk prediction unit is as follows: FC=(GL + HB)×(1 - F); Where: FC is the disease transmission risk value; GL is the infection change value, and GL reflects the change in the number of people infected by the disease in the population. The specific calculation formula is , DR is the current number of infected people, and DR last is the number of infected people in the previous period; If GL is positive, it means that the number of infected people is increasing and the transmission speed is accelerating; If GL is negative, it means that the number of infected people is decreasing and the transmission speed is decreasing; HB is the environmental exposure value, and HB is calculated by measuring the population density and the average residence time. The specific calculation formula is HB = M×t, where M is the population density value and t is the average residence time; F is the protection effect value, and the value range of F is between 0 and 1; When the mask - wearing rate approaches 100% and the environmental exposure value HB is low, F approaches 0; When the mask - wearing rate is far from 100% and the environmental exposure value HB is high, F approaches 1.

[0022] In the transmission risk prediction unit of this embodiment: First, the purpose of the infection change value GL is to measure the change in the transmission speed of the disease in the population to understand the dynamic trend of disease transmission. Specifically, based on The calculation formula can obtain the relative change ratio, and through this ratio, it can be intuitively seen whether the number of infected people is increasing or decreasing, and the amplitude of increase and decrease. The infection change value GL is one of the important factors for evaluating the disease transmission risk. It directly reflects the activity degree of disease transmission within the population. A high infection change value GL means that the disease spreads rapidly among the population, which will increase the overall disease transmission risk value FC. On the contrary, it will reduce the disease transmission risk value FC; The environmental exposure value HB is used to evaluate the influence degree of external environmental conditions on disease transmission. It is calculated by using the formula HB = M × t. The greater the personnel density, the more frequent the contact between people, the longer the average residence time, and the longer the contact time. These two factors both increase the opportunity of disease transmission. Multiplying them can comprehensively measure the promoting effect of environmental factors on disease transmission. The environmental exposure value HB and the infection change value GL act together. That is, the calculation part of (GL + HB) provides a basis for disease transmission risk assessment from the environmental perspective. Even if the infection change value GL is not high, but if the environmental exposure value HB is large, the overall disease transmission risk will still increase. On the contrary, good environmental conditions will reduce the risk. The addition of the two in the calculation part of (GL + HB) is because they are independent and co - acting factors in the process of disease transmission. The infection change value GL focuses on the change of the infection situation of the population itself, and the environmental exposure value HB focuses on the promoting effect of the external environment on transmission. Adding them together can comprehensively reflect the basic level of disease transmission risk without considering protective measures; The protection effect value F is used to quantify the inhibitory effect of protective measures on disease transmission risk. It is obtained through the comprehensive evaluation of the mask wearing rate and the environmental exposure value HB, and is used to correct the basic risk value obtained from the calculation part of (GL + HB), and reasonably adjust the final disease transmission risk value FC according to the actual protection situation to make the evaluation more in line with the actual situation; This transmission risk prediction unit comprehensively considers various factors such as population dynamics, environmental factors, and protection means in the process of disease transmission by incorporating the infection change value GL, the environmental exposure value HB, and the protection effect value F into the calculation. And by synthesizing these three factors, it can accurately evaluate the disease transmission risk, avoiding misjudgment of risks caused by only focusing on a single factor. When actually evaluating the disease transmission risk of a large gathering place, if only focusing on the infection change value GL, the environmental exposure factors such as high personnel density and poor air circulation in this place, as well as the ineffective protection measures caused by the low mask wearing rate of the people in the area will be ignored. While the transmission risk prediction unit can comprehensively consider these factors and give a more accurate risk assessment.

[0023] Please refer to Figures 1 to 3 , the calculation formula of the transmission range prediction unit is as follows: FK = FC × HK + CM; Where: FK is the value for the spread range expansion; HK is the value for environmental diffusion. The specific calculation formula for HK is HK = L × a, where L is the air circulation speed and a is the space connectivity coefficient; The value range of the space connectivity coefficient a is between 0 and 1. For an open space with good air circulation, the value of a tends to 1, and for a closed space with poor air circulation, the value of a tends to 0; CM is the initial spread area, and CM reflects the area value of the place where the disease initially occurred.

[0024] In the spread range prediction unit of this embodiment, first, the disease spread risk value FC is used as the basic input value calculated by the spread range prediction unit, which reflects the internal risk degree of disease spread and provides a basis for the spread range expansion calculation. The environmental diffusion value HK is used to measure the promoting ability of environmental factors on disease diffusion, and it is calculated through the formula HK = L × a. Among them, when the air circulation speed is fast, the disease is more likely to spread in the air, and when the space connectivity is good, the path and range of disease spread are wider. The multiplication of the two can quantify the promoting effect of the environment on disease diffusion. In the FC × HK calculation part, multiplying the disease spread risk value FC by the environmental diffusion value HK can obtain the potential range factor of additional disease spread under the current disease spread risk and environmental conditions. The calculation result of this part is an important part of the spread range expansion, reflecting the potential diffusion ability of the disease under the current risk and environmental factors; The initial spread area CM serves as the basis for the spread range expansion calculation, and all expansions are carried out on this initial range. What the FC × HK calculation part can obtain is the possible additional spread range of the disease. Adding the initial spread area CM to this basis can obtain the total range that the disease may spread to under the current situation. This is based on the actual logic that disease spread starts from the starting point and continuously expands under the action of risk and environmental factors. The result obtained is the final target value of the spread range prediction unit, that is, the spread range expansion value FK, which is used to evaluate the size of the possible spread range of the disease; The spread range prediction unit highlights the important role of environmental factors in the process of disease spread. Among them, the introduction of the environmental diffusion value HK enables the calculation result to reflect the influence of environmental factors such as the air circulation speed L and the space connectivity coefficient a on the disease spread range. In different environmental scenarios, the spread range prediction unit can accurately calculate the expansion of the disease spread range in different environments according to these differences, which helps to formulate more targeted prevention and control strategies.

[0025] Please refer to Figures 1 to 3 , the calculation formula of the final risk prediction unit is as follows: ; Where: TF is the adjusted disease transmission risk value; Q is the trend adjustment value, and the value range of Q is {0 - 2}; FJ is the measure enhancement value, and FJ reflects the intensity of the prevention and control measures taken and increased in the previous cycle, that is, the frequency of increasing the push of disease prevention information.

[0026] In the transmission range prediction unit of this embodiment, first, the transmission range expansion value FK is one of the basic input values calculated by the final risk prediction unit, reflecting the expansion of the disease transmission range in the current stage, providing a basis for adjusting the disease transmission risk. The trend adjustment value Q quantifies the impact of the disease transmission trend on the transmission range and risk according to the historical data of disease transmission and the current monitoring data. Specifically, the specific value is determined by analyzing whether the disease transmission is accelerating, decelerating or stable. The trend adjustment value Q is greater than 1 under the accelerating trend, less than 1 under the decelerating trend, and tends to 1 under the stable trend. As the FK×Q calculation part of the numerator, it adjusts the impact of the transmission range on the disease transmission risk according to the transmission trend. The accelerating trend will increase the transmission risk, and the decelerating trend will reduce the transmission risk. The calculation result of this part is an important intermediate value for adjusting the disease transmission risk and lays a foundation for considering the impact of prevention and control measures in the future; The measure enhancement value FJ is used to quantify the inhibitory effect of the newly adopted prevention and control measures on the disease transmission risk. The greater the intensity of the prevention and control measures, the greater the measure enhancement value FJ. If no new prevention and control measures are taken, the measure enhancement value FJ is 0. The 1 + F calculation part is the denominator in the calculation part, which is used to reasonably adjust the transmission risk value considering the strengthening of prevention and control measures. "1" represents the basic situation without the strengthening of prevention and control measures. Adding the measure enhancement value FJ, as the intensity of prevention and control measures increases, the denominator increases. This setting is because the strengthening of prevention and control measures will reduce the transmission risk, and by increasing the denominator, the relevant values of the transmission range are reasonably reduced to obtain the finally adjusted disease transmission risk value, that is, the adjusted disease transmission risk value TF; The final risk prediction unit comprehensively considers the spread range expansion value FK, the trend adjustment value Q, and the measure enhancement value FJ. Thus, it can dynamically adjust the disease transmission risk assessment according to the real-time trend of disease transmission and the implementation effect of prevention and control measures. Among them, the trend adjustment value Q quantifies the transmission trend based on historical data and current monitoring data of disease transmission. The trend adjustment value Q is greater than 1 under an accelerating trend and less than 1 under a decelerating trend. By multiplying with the spread range expansion value FK, it can timely reflect the impact of the transmission trend on the risk. The measure enhancement value FJ is evaluated according to the intensity of newly adopted prevention and control measures. Dividing the product of the FK×Q calculation part by the 1+F calculation part reflects the inhibitory effect of prevention and control measures on the transmission risk. In this way, the final risk prediction unit can continuously adjust the disease transmission risk value according to the actual situation, making the risk assessment more in line with the reality.

[0027] Example 2, please refer to Figures 1 to 3 , based on the adjusted disease transmission risk value TF, and the trend analysis of plotting the adjusted disease transmission risk value TF in the past week into a line chart is as follows: Q1: If the adjusted disease transmission risk value TF in the past week shows an upward trend in the line chart, it reflects an increase in risk; Q2: If the adjusted disease transmission risk value TF in the past week shows a downward trend in the line chart, it reflects a decrease in risk; Q3: If the adjusted disease transmission risk value TF in the past week shows a flat trend in the line chart, it reflects that the risk tends to be flat; When the adjusted disease transmission risk value TF is in the trend of Q1, it is necessary to increase the measure enhancement value FJ, and the trend adjustment value Q tends to take a value of 2; When the adjusted disease transmission risk value TF is in the trend of Q2, it is necessary to reduce the measure enhancement value FJ, and the trend adjustment value Q tends to take a value of 1; When the adjusted disease transmission risk value TF is in the trend of Q3, it is necessary to maintain the measure enhancement value FJ, and the trend adjustment value Q takes a value less than 1.

[0028] In this embodiment, the adjusted disease transmission risk value TF calculated by the final risk prediction unit is fed back to the transmission risk prediction unit, enabling the transmission risk prediction unit to perform the next round of calculation based on the latest risk assessment result. This cyclic influence forms a dynamic risk assessment system that can continuously adapt to various changes in the disease transmission process. In practical applications, as time goes by and prevention and control measures are implemented, the disease transmission trend changes, and the measure enhancement value FJ will also change accordingly. After the final risk prediction unit incorporates these changing factors into the calculation, it feeds back the adjusted risk value to the transmission risk prediction unit. The transmission risk prediction unit can more accurately assess the disease transmission risk in the new round of calculation, avoiding risk misjudgment caused by static assessment; Through the cyclic influence of the final risk prediction unit on the transmission risk prediction unit, the system can timely adjust the risk assessment according to the actual prevention and control effect and the change of disease transmission, thereby providing a basis for the optimization of prevention and control measures. If the adjusted disease transmission risk value TF fed back by the final risk prediction unit to the transmission risk prediction unit is high, it indicates that the current prevention and control measures have not effectively controlled the disease transmission and it is necessary to further strengthen and adjust the prevention and control measures, that is, increase the measure enhancement value FJ. On the contrary, if the adjusted disease transmission risk value TF decreases, it indicates that the prevention and control measures have achieved certain results and it is possible to appropriately maintain and adjust the prevention and control intensity, that is, maintain or reduce the measure enhancement value FJ. And in this process, the trend adjustment value Q also needs to be adjusted according to the change trend of the adjusted disease transmission risk value TF. This cyclic feedback mechanism helps to continuously optimize the prevention and control measures, improve the effectiveness of prevention and control, and minimize the disease transmission risk to the greatest extent.

[0029] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it is understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A disease prevention information push system, characterized in that, It includes a data collection module, a calculation and analysis module, an information generation module, a push module, and a storage module. The calculation and analysis module includes a transmission risk prediction unit, a transmission range prediction unit, and a final risk prediction unit; Data collection module: Responsible for collecting various data on the number of infected people, environment, protection measures, prevention and control measures, and transmission range within the current area, and transmitting various data to the calculation and analysis module; Calculation and analysis module: Responsible for implementing the calculation logics of the transmission risk prediction unit, the transmission range prediction unit, and the final risk prediction unit, and sequentially outputting the disease transmission risk value FC, the transmission range expansion value FK, and the adjusted disease transmission risk value TF; Information generation module: Based on the adjusted disease transmission risk value TF, responsible for plotting the adjusted disease transmission risk value TF of the past week into a linear trend chart, and adjusting and generating the push content and frequency of the push information according to the trend change; Push module: Responsible for pushing the generated push content and frequency; Storage module: Responsible for storing the information data collected, calculated, and generated by the information; 2. A disease prevention information push system according to claim 1, wherein The devices used by the data collection module include sensors, data recorders, and terminal devices; The devices used by the calculation and analysis module include servers; The devices used by the information generation module include visualization generation devices; The devices used by the push module include routers and switches; The devices used by the storage module include storage devices.

3. The disease prevention information push system according to claim 2, wherein: The calculation formula of the transmission risk prediction unit is as follows: FC = (GL + HB) × (1 - F); Where: FC is the disease transmission risk value; GL is the infection change value, which reflects the change in the number of transmissions of the disease in the population. The specific calculation formula is , DR is the current number of infected people, and DR last is the number of infected people in the previous period; If GL is positive, it means that the number of infected people is increasing and the transmission speed is accelerating; If GL is negative, it means that the number of infected people is decreasing and the transmission speed is decreasing; HB is the environmental exposure value, and HB is calculated by measuring the personnel density and the average residence time. The specific calculation formula is HB = M × t, where M is the personnel density value and t is the average residence time; F is the protection effect value, and the value range of F is between 0 and 1; When the mask wearing rate approaches 100% and the environmental exposure value HB is low, F approaches 0; When the mask wearing rate is far from 100% and the environmental exposure value HB is high, F approaches 1.

4. The disease prevention information push system according to claim 3, characterized in that: The calculation formula of the transmission range prediction unit is as follows: FK = FC × HK + CM; Where: FK is the transmission range expansion value; HK is the environmental diffusion value, and the specific calculation formula of HK is HK = L × a, where L is the air flow velocity and a is the space connectivity coefficient; The value range of the space connectivity coefficient a is between 0 and 1. The space fluidity of the open space is good, and the value of a approaches 1. The space fluidity of the closed space is poor, and the value of a approaches 0; CM is the initial transmission area, and CM reflects the area value of the place where the disease initially occurred.

5. The disease prevention information push system according to claim 4, wherein: The calculation formula of the final risk prediction unit is as follows: ； Where: TF is the adjusted disease transmission risk value; Q is the trend adjustment value, and the value range of Q is {0 - 2}; FJ is the measure enhancement value, which reflects the intensity of the prevention and control measures taken and increased in the previous cycle, that is, the frequency of increasing the push of disease prevention information.

6. The disease prevention information push system according to claim 5, wherein: Based on the adjusted disease transmission risk value TF, the trend analysis of plotting the adjusted disease transmission risk value TF in the past week as a line graph is as follows: Q1: If the adjusted disease transmission risk value TF in the past week shows an upward trend on the line graph, it reflects an increase in risk; Q2: If the adjusted disease transmission risk value TF in the past week shows a downward trend on the line graph, it reflects a decrease in risk; Q3: If the adjusted disease transmission risk value TF in the past week shows a flat trend on the line graph, it reflects that the risk tends to be flat.

7. The disease prevention information push system according to claim 6, characterized in that: When the adjusted disease transmission risk value TF is in the trend of Q1, it is necessary to increase the measure enhancement value FJ, and the trend adjustment value Q tends to 2; When the adjusted disease transmission risk value TF is in the trend of Q2, it is necessary to reduce the measure enhancement value FJ, and the trend adjustment value Q tends to 1; When the adjusted disease transmission risk value TF is in the trend of Q3, it is necessary to maintain the measure enhancement value FJ, and the trend adjustment value Q is less than 1.

8. The disease prevention information push method of a disease prevention information push system according to claims 1-7, characterized in that: The disease prevention information push method described above includes the following steps: Step 1: Use the data collection module to collect various types of data in the current area, including the current number of infected people DR, the number of infected people DR in the previous cycle last , the personnel density value M, the average residence time t, the mask wearing rate, the air circulation speed L, the space connectivity coefficient a, the initial transmission area CM, the trend adjustment value Q, and the measure enhancement value FJ; Step 2: Use the calculation and analysis module to sequentially output the disease transmission risk value FC, the transmission range expansion value FK, and the adjusted disease transmission risk value TF; Step 3: Based on the adjusted disease transmission risk value TF, use the information generation module to draw a linear trend graph of the trend change, and adjust and generate the push content and frequency of the push information according to the trend change; Step 4: Use the push module to perform the push; Step 5: Use the storage module to store the data from Step 1 to Step 4.

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