A dynamic density risk control method and system for local medical treatment data

Abstract

The present invention relates to the technical field of medical data analysis, and specifically discloses a dynamic density risk control method and system for local medical data, the method comprising querying registered medical service points in a region, determining the theoretical flow of the medical service points according to a map service; creating a flow detection terminal at the medical service point, obtaining the actual flow according to the flow detection terminal; analyzing the actual flow and theoretical flow to determine the risk of each medical service point; and calculating the risk of each medical service point to determine the risk level. The present invention determines the theoretical flow of each medical service point through a map service, compares the theoretical flow with the actual flow collected by each medical service point, and can judge the risk level of each medical service point, and then determine whether a large area of ​​similar patients appears in the nearby area, thereby conducting a rapid and timely health status assessment of the entire area.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data analysis, and in particular to a dynamic density risk control method and system for local medical data. Background Art

[0002] With the increase and popularization of medical resources, medical activities are widely present in people's daily lives. Each medical service point will collect and retain its own medical data. The purpose of these retained data is only to provide a reference for the next medical treatment. The degree of development of use is very low. In fact, these medical data can reflect the health status of an area. How to evaluate the health status of the entire area based on these medical data is a technical problem that the technical solution of the present invention wants to solve. Summary of the invention

[0003] The purpose of the present invention is to provide a dynamic density risk control method and system for local medical data to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A dynamic density risk control method for local medical treatment data, the method comprising:

[0006] Search for registered medical service points in the region and determine the theoretical flow of medical service points based on map services;

[0007] A flow detection terminal is created at the medical service point, and the actual flow is obtained according to the flow detection terminal; wherein the flow detection terminal has the authority to read the data of the access control terminal and the authority to read the data of the consultation terminal;

[0008] Analyze actual and theoretical traffic to determine the risk level of each medical service point;

[0009] Count the risk of each medical service point and determine the risk level;

[0010] The units of the theoretical flow and the actual flow are both the number of people per unit time.

[0011] As a further solution of the present invention: the step of searching for registered medical service points in the region and determining the theoretical flow of the medical service points according to the map service includes:

[0012] Search for registered medical service points within a pre-defined area;

[0013] Taking the medical service point as the center, a number of circular test areas are constructed; the radii of the number of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values;

[0014] For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index;

[0015] For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

[0016] As a further solution of the present invention: for any circular test area, the known position in the circular test area is queried, navigation information is queried with the known position as the starting point and the medical service point as the end point, the convenience of passage to the medical service point is calculated according to the navigation information, and the radius of the circular test area is read as an index, and the steps include:

[0017] For any circular test area, query the map service for the named locations within the circular test area as known locations;

[0018] For any known location, the travel time of different traffic modes during peak hours in the morning and evening is queried with the known location as the starting point and the medical service point as the end point, and the theoretical travel time of the known location is calculated based on the travel time of different traffic modes;

[0019] The theoretical travel time of all known locations is accumulated to obtain the total travel time value, and the travel convenience is determined by the total travel time value;

[0020] The calculation process of the theoretical travel time is as follows:

[0021]

[0022] The calculation process of traffic convenience is as follows:

[0023]

[0024] Where, T j is the theoretical travel time of the jth known location; α i is the preset weight of the ith traffic mode at the jth known location, t i is the travel time of the i-th traffic mode at the j-th known location during peak hours in the morning and evening, N is the total number of traffic modes; B is the travel convenience of a circular test area, M is the total number of known points, and β is the preset magnification.

[0025] As a further solution of the present invention: the step of creating a flow detection terminal at the medical service point and obtaining the actual flow according to the flow detection terminal includes:

[0026] Send a traffic detection request to the medical service point, and when receiving the permission granted by the medical service point, create a traffic detection terminal at the medical service point;

[0027] Establish a connection channel between the flow detection terminal and the access control terminal, and establish a connection channel between the flow detection terminal and the consultation and calling system;

[0028] The access control terminal detects the flow of people passing through, and the consultation and calling system detects the flow of patients;

[0029] The actual flow is obtained by combining the pedestrian flow and patient flow.

[0030] As a further solution of the present invention: the step of analyzing the actual flow rate and the theoretical flow rate to determine the risk level of each medical service point includes:

[0031] Read the theoretical flow of each medical service point and calculate the theoretical flow ratio of each medical service point;

[0032] Read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio;

[0033] For each medical service point, the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio is calculated, and the benchmark risk level is determined according to the difference rate;

[0034] For each medical service point, the ratio of the flow of pedestrians to the flow of patients is calculated, and the baseline risk is corrected according to the ratio, and the corrected baseline risk is used as the final risk;

[0035] Among them, the correction process is a multiplication process, the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio.

[0036] As a further solution of the present invention: the step of counting the risk of each medical service point and determining the risk level includes:

[0037] For any medical service point, its risk level in a time period is counted to obtain a risk level array; the element number in the risk level array corresponds to the time, and the element value is the risk level;

[0038] The risk level is determined based on the mean and minimum values ​​of the risk array.

[0039] The technical solution of the present invention also provides a dynamic density risk control system for local medical treatment data, the system comprising:

[0040] Theoretical flow prediction module, used to query the registered medical service points in the area and determine the theoretical flow of the medical service points based on the map service;

[0041] The actual flow detection module is used to create a flow detection terminal at the medical service point and obtain the actual flow according to the flow detection terminal; wherein the flow detection terminal has the authority to read the data of the access control terminal and the authority to read the data of the consultation terminal;

[0042] The risk calculation module is used to analyze the actual flow and theoretical flow to determine the risk level of each medical service point;

[0043] The risk application module is used to calculate the risk of each medical service point and determine the risk level;

[0044] The units of the theoretical flow and the actual flow are both the number of people per unit time.

[0045] As a further solution of the present invention: the theoretical flow prediction module includes:

[0046] A service point query unit is used to query the registered medical service points in a pre-defined area;

[0047] The test area construction unit is used to construct a plurality of circular test areas with the medical service point as the center; the radii of the plurality of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values;

[0048] For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index;

[0049] For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

[0050] As a further solution of the present invention: the actual flow detection module includes:

[0051] A detection terminal creation unit, used to send a flow detection request to the medical service point, and when receiving permission granted by the medical service point, create a flow detection terminal at the medical service point;

[0052] A channel establishment unit is used to establish a connection channel between the flow detection terminal and the access control terminal, and to establish a connection channel between the flow detection terminal and the medical consultation and calling system;

[0053] The detection execution unit is used to detect the flow of people passing through the access control terminal and the flow of patients through the consultation and calling system;

[0054] The data statistics unit is used to combine the pedestrian flow and the patient flow to obtain the actual flow.

[0055] As a further solution of the present invention: the risk calculation module includes:

[0056] The first proportion calculation unit is used to read the theoretical flow of each medical service point and calculate the theoretical flow proportion of each medical service point;

[0057] The second ratio calculation unit is used to read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio;

[0058] A difference calculation unit, used to calculate the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio for each medical service point, and determine the benchmark risk degree according to the difference rate;

[0059] A correction unit is used to calculate the ratio of the flow of pedestrians to the flow of patients at each medical service point, correct the baseline risk according to the ratio, and use the corrected baseline risk as the final risk;

[0060] Among them, the correction process is a multiplication process, the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio.

[0061] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention determines the theoretical flow of each medical service point through map services, and compares the theoretical flow with the actual flow collected by each medical service point, so as to judge the risk level of each medical service point, and then determine whether a large area of ​​similar patient conditions occurs in the nearby area, thereby conducting a rapid and timely health status assessment of the entire area. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0063] Figure 1 This is a flowchart of the dynamic density risk control method for local medical treatment data.

[0064] Figure 2 This is the structural diagram of the dynamic density risk control system for local medical data. DETAILED DESCRIPTION

[0065] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] Figure 1 The flowchart of the dynamic density risk control method of local medical data is as follows. In an embodiment of the present invention, a dynamic density risk control method of local medical data includes:

[0067] Step S100: Query the registered medical service points in the area, and determine the theoretical flow of the medical service points according to the map service;

[0068] There are many medical service points in a region, including hospitals, pharmacies, and physical examination institutions, etc. These units will be registered. The region is the area that needs to be analyzed, such as a district or county. By querying the registered medical service points in the region and using the existing map services, the theoretical flow of each medical service point can be roughly predicted. The theoretical flow is the number of people entering the medical service point per unit time under theoretical conditions.

[0069] It should be noted that the map service may be a conventional map service, including Amap and Baidu Map. If the authority is high enough, a more sophisticated map may be used.

[0070] Step S200: creating a flow detection terminal at the medical service point, and obtaining actual flow according to the flow detection terminal; wherein the flow detection terminal has the authority to read the data of the access control terminal and the authority to read the data of the consultation terminal;

[0071] Send a permission request to each medical service point. If the medical service point allows it, create a flow detection terminal. The flow detection terminal will be connected to the access control terminal and consultation terminal of the medical service point. The access control terminal is a device installed at the medical service point for monitoring the flow of people in public areas, and the consultation terminal is a system installed at the medical service point for calling numbers. Both are existing infrastructure of the medical service point.

[0072] The technical solution of the present invention is based on the fact that the medical service point has been allowed to perform real-time monitoring of the number of people in the medical service point by the access control terminal and the consultation terminal, so as to obtain the actual flow.

[0073] Step S300: Analyze the actual flow rate and theoretical flow rate to determine the risk level of each medical service point;

[0074] Analyze the actual flow and theoretical flow. If the actual flow exceeds the theoretical flow by a large margin, it means that there are more patients near the medical service point. At this time, the risk is higher.

[0075] Step S400: Count the risk of each medical service point and determine the risk level;

[0076] Since the actual flow and theoretical flow in this application are both the number of people per unit time, this means that the actual flow and theoretical flow at different times are different, and the calculated risk level is also related to time. For each medical service point, the risk level at different times is counted and analyzed to obtain the overall risk level. Generally, the higher the risk mean, the higher the risk level, and the higher the minimum risk value, the higher the risk level; among them, a high risk level corresponds to a high degree of danger.

[0077] In some examples of the technical solution of the present invention, the step of querying the registered medical service points in the area and determining the theoretical flow of the medical service points according to the map service includes:

[0078] Search for registered medical service points within a pre-defined area;

[0079] Taking the medical service point as the center, a number of circular test areas are constructed; the radii of the number of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values;

[0080] For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index;

[0081] For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

[0082] Search for registered medical service points in a pre-defined area, and construct multiple concentric circles with the medical service point as the center. The radius of each concentric circle represents the spatial distance. For each circular test area, the convenience of travel is calculated once based on the navigation information, that is, one medical service point corresponds to the convenience of travel of multiple different radii; combined with the scale parameters of the medical service point, the theoretical flow can be calculated.

[0083] Generally speaking, the higher the ease of travel, the more convenient the transportation, the larger the scale, and the stronger the medical service point, all of which will have a positive impact on the theoretical flow; that is, the theoretical flow is positively proportional to the ease of travel and the scale parameter; among them, the scale parameter is generally related to the staff of the medical service point. For example, the total working years of the staff can be used as the scale parameter. The greater the total working years, the stronger the medical service point is considered to be and the larger its scale is.

[0084] Specifically, for any circular test area, the known position in the circular test area is queried, navigation information is queried with the known position as the starting point and the medical service point as the end point, the convenience of passage to the medical service point is calculated according to the navigation information, and the radius of the circular test area is read as an index, and the steps include:

[0085] For any circular test area, query the map service for the named locations within the circular test area as known locations;

[0086] For any known location, the travel time of different traffic modes during peak hours in the morning and evening is queried with the known location as the starting point and the medical service point as the end point, and the theoretical travel time of the known location is calculated based on the travel time of different traffic modes;

[0087] The theoretical travel time of all known locations is accumulated to obtain the total travel time value, and the travel convenience is determined by the total travel time value;

[0088] The calculation process of the theoretical travel time is as follows:

[0089]

[0090] The calculation process of traffic convenience is as follows:

[0091]

[0092] Where, T j is the theoretical travel time of the jth known location; α i is the preset weight of the ith traffic mode at the jth known location, t i is the travel time of the i-th traffic mode at the j-th known location during peak hours in the morning and evening, N is the total number of traffic modes; B is the travel convenience of a circular test area, M is the total number of known points, and β is the preset magnification.

[0093] The above content provides a solution for calculating the convenience of travel. With the help of map services, the time taken to travel between two known locations can be obtained. The time taken is inversely proportional to the convenience of travel. In addition, since the travel mode is not unique, it is necessary to combine multiple methods to calculate a comprehensive time taken, which is called the theoretical notification time.

[0094] It should be noted that this application calculates the travel time during peak hours in the morning and evening. As the travel time, the flow of people during peak hours in the morning and evening is very large. When the flow of people is large, the calculated travel convenience is more accurate and the ability to distinguish medical service points is stronger.

[0095] The theoretical travel time of all known locations is accumulated to obtain the total travel time, and an inverse function is compounded on the total travel time to obtain the travel convenience.

[0096] It should be noted that each circular test area corresponds to a degree of traffic convenience. The scheme for determining the theoretical flow rate based on the traffic convenience and scale parameters can be obtained by adding multiple power functions containing product constant terms. Each traffic convenience and scale parameter corresponds to a power function, and the product constant term is pre-set by the staff.

[0097] In some examples of the technical solution of the present invention, the step of creating a flow detection terminal at a medical service point and obtaining actual flow according to the flow detection terminal includes:

[0098] Send a traffic detection request to the medical service point, and when receiving the permission granted by the medical service point, create a traffic detection terminal at the medical service point;

[0099] Establish a connection channel between the flow detection terminal and the access control terminal, and establish a connection channel between the flow detection terminal and the consultation and calling system;

[0100] The access control terminal detects the flow of people passing through, and the consultation and calling system detects the flow of patients;

[0101] The actual flow is obtained by combining the pedestrian flow and patient flow.

[0102] The above content gives a detailed description of the actual flow detection process. The actual flow detection process is very simple. Under the premise of having the authority, you can directly read the records of the access control end and the consultation end. It should be noted that the actual flow obtained by this application is divided into two parts, one is the passerby flow and the patient flow, which is different from the traditional technical solution.

[0103] In some examples of the technical solution of the present invention, the step of analyzing the actual flow rate and the theoretical flow rate to determine the risk level of each medical service point includes:

[0104] Read the theoretical flow of each medical service point and calculate the theoretical flow ratio of each medical service point;

[0105] Read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio;

[0106] For each medical service point, the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio is calculated, and the benchmark risk level is determined according to the difference rate;

[0107] For each medical service point, the ratio of the flow of pedestrians to the flow of patients is calculated, and the baseline risk is corrected according to the ratio, and the corrected baseline risk is used as the final risk;

[0108] Among them, the correction process is a multiplication process, the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio.

[0109] The principle of the risk calculation process is to compare each medical service point, read the theoretical flow of each medical service point, get the theoretical flow ratio, read the pedestrian flow in the actual flow of each medical service point, and calculate the actual flow point ratio; the advantage of calculating the flow ratio is that there is no need to consider the specific flow value, and the data format is the same, so the comparison process is extremely easy.

[0110] For each medical service point, the difference rate between the theoretical flow ratio and the actual flow point ratio is calculated. The difference rate is the difference between the theoretical flow ratio and the actual flow point ratio divided by the theoretical flow ratio. The larger the difference rate, the higher the risk.

[0111] It should be noted that the risk degree is composed of two parts. One is the baseline risk degree determined according to the difference rate, and the other is the correction part determined according to the ratio of the pedestrian flow and the patient flow. If the ratio of the pedestrian flow and the patient flow is larger, it means that there are more people accompanying them. At this time, it means that the patients are more seriously ill, and most of them have some serious diseases. If the ratio is smaller, it means that most patients have the ability to move, but they are dealing with sick conditions, such as colds. In the technical solution of the present invention, the latter is considered to be more serious. Therefore, the ratio is inversely proportional to the correction coefficient. The smaller the ratio, the larger the correction coefficient, and the greater the corrected risk.

[0112] The actual scenario of the above content is: if the actual flow of a medical service point is significantly different from the theoretical flow, it means that a gathering phenomenon has occurred. At this time, if most of the patients in the gathering phenomenon are able to move, it means that there are infectious characteristics near the place. At this time, the risk is very high.

[0113] In some examples of the technical solution of the present invention, the step of calculating the risk of each medical service point and determining the risk level includes:

[0114] For any medical service point, its risk level in a time period is counted to obtain a risk level array; the element number in the risk level array corresponds to the time, and the element value is the risk level;

[0115] The risk level is determined based on the mean and minimum values ​​of the risk array.

[0116] For any medical service point, its risk level within a time period, such as a week, is counted, and multiple risk levels are obtained. The average is calculated, and then the risk level can be determined by combining the minimum value. Under normal circumstances, the risk level is proportional to the average, and the risk level is also proportional to the minimum value.

[0117] Figure 2 1 is a structural diagram of a dynamic density risk control system for local medical treatment data. In an embodiment of the present invention, a dynamic density risk control system for local medical treatment data, the system 10 includes:

[0118] Theoretical flow prediction module 11 is used to query the registered medical service points in the area and determine the theoretical flow of the medical service points according to the map service;

[0119] The actual flow detection module 12 is used to create a flow detection terminal at the medical service point and obtain the actual flow according to the flow detection terminal; wherein the flow detection terminal has the authority to read the access control terminal data and the authority to read the consultation terminal data;

[0120] The risk calculation module 13 is used to analyze the actual flow and theoretical flow to determine the risk of each medical service point;

[0121] The risk application module 14 is used to calculate the risk of each medical service point and determine the risk level;

[0122] The units of the theoretical flow and the actual flow are both the number of people per unit time.

[0123] Furthermore, the theoretical flow prediction module 11 includes:

[0124] A service point query unit is used to query the registered medical service points in a pre-defined area;

[0125] The test area construction unit is used to construct a plurality of circular test areas with the medical service point as the center; the radii of the plurality of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values;

[0126] For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index;

[0127] For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

[0128] Specifically, the actual flow detection module 12 includes:

[0129] A detection terminal creation unit, used to send a flow detection request to the medical service point, and when receiving permission granted by the medical service point, create a flow detection terminal at the medical service point;

[0130] A channel establishment unit is used to establish a connection channel between the flow detection terminal and the access control terminal, and to establish a connection channel between the flow detection terminal and the medical consultation and calling system;

[0131] The detection execution unit is used to detect the flow of people passing through the access control terminal and the flow of patients through the consultation and calling system;

[0132] The data statistics unit is used to combine the pedestrian flow and the patient flow to obtain the actual flow.

[0133] Furthermore, the risk calculation module 13 includes:

[0134] The first proportion calculation unit is used to read the theoretical flow of each medical service point and calculate the theoretical flow proportion of each medical service point;

[0135] The second ratio calculation unit is used to read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio;

[0136] A difference calculation unit, used to calculate the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio for each medical service point, and determine the benchmark risk degree according to the difference rate;

[0137] A correction unit is used to calculate the ratio of the flow of pedestrians to the flow of patients at each medical service point, correct the baseline risk according to the ratio, and use the corrected baseline risk as the final risk;

[0138] Among them, the correction process is a multiplication process, the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio.

[0139] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A dynamic density risk control method for local medical treatment data, characterized in that: The method comprises: Search for registered medical service points in the region and determine the theoretical flow of medical service points based on map services; A flow detection terminal is created at the medical service point, and the actual flow is obtained according to the flow detection terminal; wherein the flow detection terminal has the authority to read the data of the access control terminal and the authority to read the data of the consultation terminal; Analyze actual and theoretical traffic to determine the risk level of each medical service point; Count the risk of each medical service point and determine the risk level; The units of the theoretical flow and actual flow are both the number of people per unit time; The steps of analyzing the actual flow rate and the theoretical flow rate to determine the risk level of each medical service point include: Read the theoretical flow of each medical service point and calculate the theoretical flow ratio of each medical service point; Read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio; For each medical service point, the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio is calculated, and the benchmark risk level is determined according to the difference rate; For each medical service point, the ratio of the flow of pedestrians to the flow of patients is calculated, and the baseline risk is corrected according to the ratio, and the corrected baseline risk is used as the final risk; The correction process is a multiplication process, wherein the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio; The difference rate is the difference between the theoretical flow rate ratio and the actual flow point ratio divided by the theoretical flow rate ratio. The larger the difference rate, the higher the risk. The risk consists of two parts: one is the baseline risk determined according to the difference rate, and the other is the correction part determined according to the ratio of the passing traffic flow and the patient traffic flow. The ratio is inversely proportional to the correction coefficient. The smaller the ratio, the larger the correction coefficient and the greater the risk after correction.

2. The dynamic density risk control method for local medical treatment data according to claim 1 is characterized in that: The step of searching for registered medical service points in the region and determining the theoretical flow of the medical service points according to the map service includes: Search for registered medical service points within a pre-defined area; Taking the medical service point as the center, a number of circular test areas are constructed; the radii of the number of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values; For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index; For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

3. The dynamic density risk control method for local medical treatment data according to claim 2 is characterized in that: The steps of querying a known position in any circular test area, querying navigation information with the known position as the starting point and the medical service point as the end point, calculating the convenience of passage to the medical service point according to the navigation information, and reading the radius of the circular test area as an index include: For any circular test area, query the map service for the named locations within the circular test area as known locations; For any known location, the travel time of different traffic modes during peak hours in the morning and evening is queried with the known location as the starting point and the medical service point as the end point, and the theoretical travel time of the known location is calculated based on the travel time of different traffic modes; The theoretical travel time of all known locations is accumulated to obtain the total travel time value, and the travel convenience is determined by the total travel time value; The calculation process of the theoretical travel time is as follows: ； The calculation process of traffic convenience is as follows: ； In the formula, is the theoretical travel time to the jth known location; is the preset weight of the ith traffic mode at the jth known location, is the travel time of the i-th traffic mode at the j-th known location during the morning and evening peak hours, N is the total number of traffic modes; B is the travel convenience of a circular test area, M is the total number of known points, is the preset magnification.

4. The method for dynamic density risk control of local medical treatment data according to claim 1, characterized in that: The steps of creating a flow detection terminal at the medical service point and obtaining actual flow according to the flow detection terminal include: Send a traffic detection request to the medical service point, and when receiving the permission granted by the medical service point, create a traffic detection terminal at the medical service point; Establish a connection channel between the flow detection terminal and the access control terminal, and establish a connection channel between the flow detection terminal and the consultation and calling system; The access control terminal detects the flow of people passing through, and the consultation and calling system detects the flow of patients; The actual flow is obtained by combining the pedestrian flow and patient flow.

5. The method for dynamic density risk control of local medical treatment data according to claim 1, characterized in that: The steps of calculating the risk degree of each medical service point and determining the risk level include: For any medical service point, its risk level in a time period is counted to obtain a risk level array; the element number in the risk level array corresponds to the time, and the element value is the risk level; The risk level is determined based on the mean and minimum values ​​of the risk array.

6. A dynamic density risk control system for local medical treatment data, characterized in that: The system comprises: Theoretical flow prediction module, used to query the registered medical service points in the area and determine the theoretical flow of the medical service points based on the map service; The actual flow detection module is used to create a flow detection terminal at the medical service point and obtain the actual flow according to the flow detection terminal; wherein the flow detection terminal has the authority to read the data of the access control terminal and the authority to read the data of the consultation terminal; The risk calculation module is used to analyze the actual flow and theoretical flow to determine the risk level of each medical service point; The risk application module is used to calculate the risk of each medical service point and determine the risk level; The units of the theoretical flow and actual flow are both the number of people per unit time; The risk calculation module includes: The first proportion calculation unit is used to read the theoretical flow of each medical service point and calculate the theoretical flow proportion of each medical service point; The second ratio calculation unit is used to read the pedestrian flow in the actual flow of each medical service point and calculate the actual flow point ratio; A difference calculation unit, used to calculate the difference rate between the theoretical flow rate ratio and the actual flow rate point ratio for each medical service point, and determine the benchmark risk degree according to the difference rate; A correction unit is used to calculate the ratio of the flow of pedestrians to the flow of patients at each medical service point, correct the baseline risk according to the ratio, and use the corrected baseline risk as the final risk; The correction process is a multiplication process, in which the coefficient is determined by the ratio, and the coefficient is multiplied by the benchmark risk; the coefficient is inversely proportional to the ratio; The difference rate is the difference between the theoretical flow rate ratio and the actual flow point ratio divided by the theoretical flow rate ratio. The larger the difference rate, the higher the risk. The risk consists of two parts: one is the baseline risk determined according to the difference rate, and the other is the correction part determined according to the ratio of the passing traffic flow and the patient traffic flow. The ratio is inversely proportional to the correction coefficient. The smaller the ratio, the larger the correction coefficient and the greater the risk after correction.

7. The dynamic density risk control system for local medical treatment data according to claim 6, characterized in that: The theoretical flow prediction module includes: A service point query unit is used to query the registered medical service points in a pre-defined area; The test area construction unit is used to construct a plurality of circular test areas with the medical service point as the center; the radii of the plurality of circular test areas constitute an arithmetic progression, and the first term, the last term and the tolerance of the arithmetic progression are all preset values; For any circular test area, query the known locations within the circular test area, query navigation information with the known location as the starting point and the medical service point as the end point, calculate the access convenience of the medical service point based on the navigation information, and read the radius of the circular test area as the index; For any medical service point, the theoretical flow is calculated based on the convenience of access and scale parameters.

8. The dynamic density risk control system for local medical treatment data according to claim 6, characterized in that: The actual flow detection module comprises: A detection terminal creation unit, used to send a flow detection request to the medical service point, and when receiving permission granted by the medical service point, create a flow detection terminal at the medical service point; A channel establishment unit is used to establish a connection channel between the flow detection terminal and the access control terminal, and to establish a connection channel between the flow detection terminal and the medical consultation and calling system; The detection execution unit is used to detect the flow of people passing through the access control terminal and the flow of patients through the consultation and calling system; The data statistics unit is used to combine the pedestrian flow and the patient flow to obtain the actual flow.

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