A big data health medical management system

Abstract

The application is suitable for the technical field of big data health management, and particularly relates to a big data health medical management system. The system comprises a monitoring point position determination module, which is used for determining monitoring point positions corresponding to public cameras in record in a regional map, and determining a correction coefficient of the monitoring point positions based on boundary lines and regional markers; a video acquisition and identification module, which is used for receiving videos collected by the public cameras, identifying the videos, and calculating a motion amount in a preset time period; and a health degree calculation module, which is used for calculating a total motion amount of each sub-region according to the correction coefficient and the motion amount, and calculating a sub-region health degree according to the total motion amount and a number of people in the sub-region. The application hardly involves human cost, and construction personnel do not need to invest too much energy, and the application enriches the data acquisition process and is extremely convenient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of big data health management, and particularly relates to a big data health medical management system. BACKGROUND

[0002] With the gradual improvement of people's material living standards, there are great progress in eating, wearing, living and traveling, which improves the sense of happiness while bringing some small troubles, that is, some groups lack exercise and are in a state of sub-health. In order to solve this problem, some health management measures need to be provided according to the health status of residents, such as building parks and other outdoor sports places or hospitals and other medical service places. The existing construction scheme is completed by manual work, including the data collection process in the early stage. The more perfect the data collection is, the better the construction scheme obtained is. Therefore, how to assist the construction personnel to perform the data collection work in the early stage to obtain a better construction scheme is a technical problem to be solved by the technical scheme of the present application. SUMMARY

[0003] The present application aims to provide a big data health medical management system to solve the problems in the background art.

[0004] To achieve the above object, the present application provides the following technical scheme.

[0005] A big data health medical management system, the system comprises:

[0006] A region map establishing module is configured to obtain region information based on a preset permission, and establish a region map containing a virtual boundary according to the region information; the virtual boundary is configured to divide the region map into sub-regions;

[0007] A monitoring point position determining module is configured to determine a monitoring point position corresponding to a public camera in the region map, and determine a correction coefficient of the monitoring point position based on a boundary line and a region marker;

[0008] A video acquisition and identification module is configured to receive a video collected by the public camera, identify the video, and calculate a motion amount in a preset time period;

[0009] A health degree calculating module is configured to calculate a total motion amount of each sub-region according to the correction coefficient and the motion amount, and calculate a sub-region health degree according to the total motion amount and a number of people in the sub-region;

[0010] A reference scheme determining module is configured to determine a reference scheme of a health facility according to the sub-region health degree, and send the reference scheme to a planning end; the reference scheme contains positions of health facilities of different types.

[0011] Further, the region map establishing module comprises:

[0012] A reference map construction unit is configured to read the boundary of the region and construct a reference map according to the boundary;

[0013] A region segmentation unit is configured to query land use planning information of the region, segment the reference map according to the land use planning information, and obtain a sub-region;

[0014] A marker query unit is configured to query a region marker of each sub-region and establish a region map; the region marker includes commercial land, comprehensive land, industrial land, residential land and green land;

[0015] A region map updating unit is configured to generate a collection instruction at a regular time, randomly obtain a preset number of overhead images containing coordinates, and update the region map based on the overhead images;

[0016] The overhead image acquisition subject includes a drone and a remote sensing satellite.

[0017] Further, the monitoring point position determination module includes:

[0018] A position query unit is configured to query the installation position of the public camera in the record library based on the pre-acquired permission;

[0019] A point position query unit is configured to query the monitoring point position corresponding to the installation position in the region map according to the same scale;

[0020] An influence value calculation unit is configured to traverse all monitoring point positions, calculate the distance between the monitoring point position and each sub-region, and query the influence value corresponding to the region marker of each sub-region;

[0021] An influence value statistical unit is configured to calculate the influence value based on the distance and determine the correction coefficient of each monitoring point position.

[0022] Further, the video acquisition and identification module includes:

[0023] A video identification unit is configured to receive the video collected by the public camera, identify the video, determine the dynamic contour, the motion speed and the motion distance;

[0024] A first calculation unit is configured to determine the contour identity according to the dynamic contour and the motion speed, and query the unit motion amount according to the contour identity;

[0025] A second calculation unit is configured to calculate the video motion amount with the time span of the video as a label according to the unit motion amount and the motion distance;

[0026] A motion amount statistical unit is configured to receive a time period input by the management party, calculate the video motion amount corresponding to the public camera based on the time period, and obtain the motion amount within the time period.

[0027] Further, the health degree calculation module comprises:

[0028] The first query unit is configured to query the motion amount in the time period corresponding to each public camera;

[0029] The second query unit is configured to query the correction coefficient of the monitoring point corresponding to each public camera;

[0030] The accumulation unit is configured to accumulate the motion amount according to the correction coefficient to obtain the total motion amount;

[0031] The third calculation unit is configured to query the number of people in the sub-region, and calculate the sub-region health degree according to the total motion amount and the number of people in the sub-region.

[0032] Further, the reference scheme determination module comprises:

[0033] The third query unit is configured to query the facility type and the number of facilities of the health facility;

[0034] The alternative block determination unit is configured to determine the alternative block of each facility type in the regional map;

[0035] The alternative block selection unit is configured to select the number of alternative blocks of each facility type in the alternative block of each facility type;

[0036] The scheme generation unit is configured to count the selected results of all facility types to obtain a reference scheme;

[0037] The scheme evaluation unit is configured to evaluate the reference scheme based on the sub-region health degree of the sub-region, and determine a final scheme;

[0038] The scheme sending unit is configured to send the final scheme to the planning end.

[0039] Further, the process of evaluating the reference scheme based on the sub-region health degree of the sub-region to determine the final scheme comprises:

[0040] Iterate through each block in the reference scheme, query the facility type of the block, and query the service health degree corresponding to the facility type;

[0041] Query the sub-region health degree of the sub-region within a preset range centered on the block;

[0042] Obtain the mean value of the sub-region health degree, calculate the difference between the service health degree and the mean value, and determine the evaluation score of each block according to the inverse ratio of the absolute value of the difference;

[0043] Accumulate the evaluation scores of all blocks, select the reference scheme with the largest evaluation score as the final scheme.

[0044] Further, the process of selecting the number of selected alternative plots in each facility type includes:

[0045] Inquiring the traffic convenience of each alternative plot; the traffic convenience is determined by road condition information and station information, and is a predetermined value in the plot planning process;

[0046] Inquiring the distance of each alternative plot from all selected alternative plots, and selecting the minimum distance;

[0047] Determining the selection utility value of each alternative plot according to the traffic convenience and the minimum distance;

[0048] Selecting the number of alternative plots in descending order of the selection utility value.

[0049] Further, the working content of the system also includes:

[0050] Inquiring and counting the number of motion words in a preset time period in a social App in real time based on the pre-acquired permission;

[0051] Constructing a training set and a test set according to the number of motion words and the sub-area health degree, training a neural network model based on the training set, and calculating the accuracy of the neural network model based on the test set;

[0052] Updating the test set based on the newly acquired number of motion words and the sub-area health degree at a regular time, and extracting the data in the test set to the training set; wherein the test set is a queue structure, and the data storage amount is a preset value;

[0053] When the accuracy reaches a preset accuracy threshold, calculating the sub-area health degree based on the trained neural network model and the collected number of motion words.

[0054] Compared with the prior art, the beneficial effects of the present application are:

[0055] The present application acquires the amount of motion based on the camera in the region, adjusts the amount of motion in combination with the position of the camera, acquires the adjusted amount of motion of all cameras in each sub-area, determines the health degree of each sub-area, and uses the health degree as the reference data of the construction scheme, which almost does not involve human cost, and the construction personnel does not need to invest too much effort, and the data acquisition process is enriched based on the existing facilities, and the convenience is extremely high. BRIEF DESCRIPTION OF DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application.

[0057] Figure 1The large data health medical management system provided by the embodiment of the present application has a component structure block diagram. DETAILED DESCRIPTION

[0058] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0059] Figure 1 The component structure block diagram of the large data health medical management system is shown, and in the embodiment of the present application, a large data health medical management system comprises:

[0060] A region map establishing module 11 is configured to acquire region information based on a preset permission, and establish a region map containing a virtual boundary according to the region information; the virtual boundary is used to divide the region map into sub-regions.

[0061] The technical solution of the present application is used to manage a region, and different plots are present in the region, and the types and ranges of the different plots are different, which are collectively referred to as region information. The region information is acquired based on a preset permission, and the region information is analyzed to be converted into a map form to obtain a map containing a virtual boundary, which is referred to as a region map. The virtual boundary is the contour of each plot, and is used to divide the region map into sub-regions. Specifically, the types of the plots include commercial land, comprehensive land, residential land, industrial land and other land.

[0062] A monitoring point determining module 12 is configured to determine a monitoring point corresponding to a public camera in the region map, and determine a correction coefficient of the monitoring point based on a boundary line and a region marker.

[0063] A plurality of public cameras are arranged in the region, and are generally arranged on roads. If the permission is sufficient, internal monitoring information of many public regions can be acquired. It should be noted that the acquired monitoring image must be a public image, otherwise the subsequent process cannot be performed.

[0064] The point corresponding to the public camera in the established region map is referred to as a monitoring point. Based on the position of the monitoring point, the influence of each sub-region on the monitoring point can be calculated. In addition, the influence range of each sub-region marker is different, and the influence of each sub-region on each monitoring point is counted, so that the correction coefficient of each monitoring point can be obtained.

[0065] Further, regarding the physical meaning of the above-mentioned correction coefficient, the purpose of the technical scheme of the present application is to calculate the motion amount of the entire region according to the motion amount near each monitoring point, which is a summation process; if the monitoring point is close to the industrial land, the proportion of its motion amount to the health degree will be low (even possibly negative), and accordingly, the motion amount calculated by the monitoring point needs to be multiplied by a smaller coefficient in the summation process of the health degree, and the coefficient is the above-mentioned correction coefficient.

[0066] From the above, it can be seen that the correction coefficient is related to the surrounding area, and the process of area affecting the correction coefficient is based on two parameters, one is distance, and the other is area label, different area labels have different influence amplitudes on the monitoring point, and according to the influence amplitude ratio or the value determined by the distance, the correction coefficient inversely proportional to the distance can be determined.

[0067] The video acquisition and identification module 13 is configured to receive the video collected by the public camera, identify the video, and calculate the motion amount in a preset time period;

[0068] A connection channel with the public camera is established to receive the video collected by the public camera, and an existing identification algorithm is used to locate the motion subject in the video, determine the identity of the motion subject, the identity includes pedestrians and cyclists, obtain the motion distance of the motion subject, and thus the motion amount can be counted. The motion amount involved in the present application is the motion amount of pedestrians and cyclists, and the motion mode includes walking, running and cycling.

[0069] The health degree calculation module 14 is configured to calculate the total motion amount of each sub-region according to the correction coefficient and the motion amount, and calculate the sub-region health degree according to the total motion amount and the number of people in the sub-region.

[0070] Each monitoring video is analyzed to obtain the corresponding motion amount of each sub-region, and the sub-region health degree of the residents in the sub-region can be calculated by combining the corresponding correction coefficient.

[0071] The reference scheme determination module 15 is configured to determine the reference scheme of the health facility according to the sub-region health degree, and send the reference scheme to the planning end; the reference scheme contains the positions of different types of health facilities.

[0072] After the sub-region health degree is calculated, the health degree of the personnel in different sub-regions in the entire region has been quantified, which can be used as a reference for the installation of health facilities and sent to the planning end. In the above content, the present application will generate some plans according to the requirements on the basis of the feedback of the health degree, and provide more rich reference content.

[0073] As a preferred embodiment of the technical scheme of the present application, the working idea of the technical scheme of the present application includes:

[0074] According to the state of each region, the environment state of each monitoring point is determined, a mover is positioned according to the monitoring video, and the movement process of the mover is recognized to calculate the movement amount; the movement amount is adjusted in combination with the environment state (the movement amount is greater in the region with poor environment, the growth speed of the health degree is lower, and even a negative value), the movement amount obtained by all monitoring videos in the sub-region is accumulated to obtain the total movement amount of the sub-region, and the total movement amount is adjusted to obtain the health degree of the sub-region; wherein the relationship between the total movement amount and the health degree of the sub-region is determined by the staff in advance, which is a functional relationship.

[0075] As a preferred embodiment of the technical scheme of the application, the region map establishing module 11 comprises:

[0076] A reference map construction unit is configured to read the boundary of the region and construct a reference map according to the boundary;

[0077] A region segmentation unit is configured to query land use planning information of the region, segment the reference map according to the land use planning information, and obtain a sub-region;

[0078] A marker query unit is configured to query the region marker of each sub-region and establish a region map; the region marker comprises commercial land, comprehensive land, industrial land, residential land and green land;

[0079] A region map updating unit is configured to generate a collection instruction at a regular time, randomly obtain a preset number of overhead images containing coordinates, and update the region map based on the overhead images.

[0080] The overhead image acquisition subject comprises a drone and a remote sensing satellite.

[0081] The above content limits the construction process of the region map. First, the region boundary is read, and a range can be determined at a preset scale, which is called a reference map. Then, the land use planning information of the region is queried (with the help of a polygon map or a map service, which has the default permission), the reference map is segmented, a plurality of sub-regions can be obtained, and the region marker of each sub-region can be determined. The region marker is used to represent the purpose of each sub-region, and the region map can be obtained. Further, the application also provides a sub-region map updating interface, so that the staff can adjust the generated sub-region map.

[0082] As a preferred embodiment of the technical scheme of the application, the monitoring point determination module 12 comprises:

[0083] A position query unit is configured to query the installation position of the public camera in the record library based on the pre-acquired permission;

[0084] A point query unit is configured to query the monitoring point corresponding to the installation position in the region map according to the same scale.

[0085] An influence value calculation unit is configured to traverse all monitoring points, calculate the distance between the monitoring points and each sub-region, and query the influence value corresponding to the region marker of each sub-region;

[0086] An influence value statistics unit is configured to determine the correction coefficient of each monitoring point based on the distance and the influence value.

[0087] The core process of the correction coefficient calculation process is to superimpose the influence of each sub-region on it together. The influence value of each sub-region is determined. The farther the distance, the smaller the influence on the monitoring point. According to the distance, the influence values of all regions are superimposed together to obtain the total influence value of each monitoring point. The total influence value of each monitoring point is summed up, and then the ratio of each total influence value is calculated, that is, the correction coefficient of each monitoring point is obtained.

[0088] Specifically, the mathematical form of the correction coefficient calculation process is as follows:

[0089] The correction coefficient calculation process is as follows:

[0090] In the formula, α i is the correction coefficient of the i-th monitoring point, B i is the comprehensive influence value of the i-th monitoring point, N is the total number of monitoring points, Y j is the influence value corresponding to the j-th sub-region, dis(q j ,w i is the distance between the center point of the j-th sub-region and the i-th monitoring point, q j is the center point of the j-th sub-region, w i is the i-th monitoring point, and M is the total number of sub-regions

[0091] As a preferred embodiment of the technical scheme of the present application, the video acquisition and identification module 13 comprises:

[0092] A video identification unit is configured to receive the video collected by the public camera, identify the video, determine the dynamic contour, the motion speed, and the motion distance;

[0093] A first calculation unit is configured to determine the contour identity according to the dynamic contour and the motion speed, and query the unit motion amount according to the contour identity;

[0094] A second calculation unit is configured to calculate the video motion amount with the time span of the video as the label according to the unit motion amount and the motion distance.

[0095] The motion amount statistical unit is configured to receive a time period input by the manager, and to calculate the motion amount corresponding to the public camera based on the time period.

[0096] The connection channel with the public camera is established, and the video acquired by the public camera is received.

[0097] According to the motion amount corresponding to the unit distance, the motion distance corresponding to each dynamic contour is read, and the motion distance is multiplied by the motion amount corresponding to the unit distance, so that the motion amount corresponding to each video is obtained.

[0098] The time period is input by the staff, for example, one day.

[0099] As a preferred embodiment of the technical scheme of the present application, the health degree calculation module 14 comprises:

[0100] The first query unit is configured to query the motion amount in the time period corresponding to each public camera.

[0101] The second query unit is configured to query the correction coefficient of the monitoring point corresponding to each public camera.

[0102] The accumulation unit is configured to accumulate the motion amount according to the correction coefficient to obtain the total motion amount.

[0103] The third calculation unit is configured to query the number of people in the sub-region, and to calculate the health degree of the sub-region according to the total motion amount and the number of people in the sub-region.

[0104] The motion amount in the time period corresponding to each public camera is queried, the correction coefficient of the monitoring point corresponding to each public camera is queried, the correction coefficient is multiplied by the motion amount and accumulated, and the total motion amount corresponding to each public camera is obtained. The monitoring point in the sub-region is queried, the motion amount of each monitoring point is queried, the total motion amount of the sub-region is calculated, and the health degree of the sub-region is calculated in combination with the number of people in the sub-region. The calculation process of the health degree of the sub-region can be:

[0105] The K is a sub-area health degree, and β is a preset adjustment constant, S i is the motion amount corresponding to the i th monitoring point, and R is the number of people in the sub-area.

[0106] The above content is to calculate the ratio of the total motion amount to the number of people in the sub-area, and it should be noted that, since the unit of the total motion amount and the unit of the number of people are different, when calculating the ratio, the result has a unit, and for this, a unit for balancing (the reciprocal of the unit of the result) is introduced in β, so that the sub-area health degree becomes a dimensionless data.

[0107] It should be noted that the technical scheme of the present application actually analyzes the entire area first to determine the correction coefficient of different monitoring points, and the correction coefficient is proportional to the influence value, which represents the comprehensive influence, and is also related to the area mark of each sub-area. The healthier the area mark is, the greater the influence value is. For example, the influence value of green land is greater than that of industrial land, and therefore, the correction coefficient of the monitoring point closer to the green land is greater.

[0108] Further, the correction coefficient actually reflects the effectiveness. For example, a certain monitoring point monitors a large amount of motion, but it is close to industrial land, and the corresponding correction coefficient is small. The technical scheme of the present application considers that the effectiveness of the motion in improving the health degree is small, and accordingly, the total motion amount calculated according to the correction coefficient and the motion amount is small. Finally, the total motion amount of all monitoring points in each sub-area is accumulated to calculate the sub-area health degree.

[0109] As a preferred embodiment of the technical scheme of the present application, the reference scheme determination module 15 comprises:

[0110] A third query unit is configured to query the facility type and the number of facilities of the health facility.

[0111] An alternative plot determination unit is configured to determine the alternative plots of each facility type in the area map.

[0112] An alternative plot selection unit is configured to select the number of alternative plots of each facility type from the alternative plots of each facility type.

[0113] A scheme generation unit is configured to count the selection results of all facility types to obtain a reference scheme.

[0114] A scheme evaluation unit is configured to evaluate the reference scheme based on the sub-area health degree of the sub-area to determine a final scheme.

[0115] A scheme sending unit is configured to send the final scheme to the planning end.

[0116] In an example of the technical scheme of the present application, a specific reference scheme generation process is provided, the facility types of the health facilities are inquired, and the number of facilities of each facility type is determined, the facility types of the health facilities include different levels of hospitals, service units and fitness facilities, etc., which are specifically determined by the staff according to the budget, and the number of facilities of each facility type is also preset.

[0117] For the selection process of the reference scheme, the candidate plots of each facility type are determined in the regional map, and each facility type can be installed on which plots, which are determined by the regional markers of the plots, a number of candidate plots of each facility type are selected from the candidate plots, the selection results of all facility types are counted to obtain a reference scheme, the reference scheme is evaluated based on the sub-area health degrees of the sub-areas, the final scheme is determined, and the final scheme is sent to the planning end; this process is actually a process of enumeration and optimization selection, and a better scheme can be quickly obtained.

[0118] Further, the process of evaluating the reference scheme based on the sub-area health degrees of the sub-areas and determining the final scheme includes:

[0119] Each plot in the reference scheme is traversed, the facility type of the plot is inquired, and the service health degree corresponding to the facility type is inquired;

[0120] The sub-area health degrees of the sub-areas in a preset range centered on the plot are inquired;

[0121] The mean value of the sub-area health degrees is obtained, the difference between the service health degree and the mean value is calculated, and the evaluation score of each plot is determined according to the inverse ratio of the absolute value of the difference;

[0122] The evaluation scores of all plots are accumulated, the reference scheme with the largest evaluation score is selected as the final scheme.

[0123] For the evaluation process, each plot in the reference scheme is traversed, the facility type of the plot is inquired, and the service health degree corresponding to the facility type is inquired, the relationship between the facility type and the service health degree is a preset value, which represents how much health degree it can provide, the service capabilities of different facility types are different, and the service health degrees are also different; the sub-area health degrees of the sub-areas in a preset range centered on the plot are inquired, the mean value is calculated, the preset range corresponds to the facility type, the difference between the service health degree and the mean value is calculated, the smaller the absolute value of the difference, the more suitable the installation of the facility, and the service resources can be serviced without waste, accordingly, the higher the evaluation score, a reference scheme has multiple plots on which facilities are installed, which corresponds to multiple evaluation scores, the evaluation scores of all plots are accumulated, and the reference scheme with the largest evaluation score is selected as the final scheme.

[0124] As a preferred embodiment of the technical scheme of the present application, the process of selecting a number of candidate plots of each facility type from the candidate plots includes:

[0125] query the traffic convenience of each alternative plot, wherein the traffic convenience is determined by road condition information and station information, and is a predetermined value in the plot planning process;

[0126] query the distance between each alternative plot and all selected alternative plots, and select the minimum distance;

[0127] determine the selection utility value of each alternative plot according to the traffic convenience and the minimum distance;

[0128] select a predetermined number of alternative plots in descending order of the selection utility value.

[0129] In the selection process of the alternative plots, the traffic convenience of each alternative plot is also considered, wherein the traffic convenience is a predetermined value in the plot planning process, the distance between each alternative plot and all selected alternative plots is queried, the minimum distance is selected, the selection utility value of each alternative plot is determined according to the traffic convenience and the minimum distance, the selection utility value is proportional to the traffic convenience and inversely proportional to the minimum distance, and then a predetermined number of alternative plots are selected in descending order of the selection utility value, which can make the alternative plots with more convenient traffic and farther distance from the selected alternative plots more likely to be selected.

[0130] As a preferred embodiment of the technical scheme of the present application, the working content of the system 10 further includes:

[0131] query and count the number of motion words in a preset time period in a social App in real time based on the pre-acquired permission;

[0132] construct a training set and a test set according to the number of motion words and the sub-area health degree, train a neural network model based on the training set, and calculate the accuracy of the neural network model based on the test set;

[0133] update the test set based on the newly acquired number of motion words and the sub-area health degree at a regular time, and extract the data in the test set to the training set; wherein the test set is a queue structure, and the data storage amount is a preset value;

[0134] when the accuracy reaches a preset accuracy threshold, calculate the sub-area health degree based on the trained neural network model and the collected number of motion words.

[0135] In the technical scheme of the present application, a quick calculation scheme of the sub-area health degree is also provided to reduce the execution frequency of steps S100 to S400. The principle is that, on the basis of the calculated sub-area health degree, the number of motion-related entries in the social App is queried. These data are statistical data, and certain permission is required to obtain them. If the permission is low, the number of entries related to the preset keyword is retrieved by means of the search box of the social App as the number of motion entries. This way, the registration account can be completed after registration.

[0136] According to the obtained number of motion entries and the calculated sub-area health degree, the neural network model can be trained to obtain a mapping relationship between the number of motion entries and the sub-area health degree. When the accuracy of the neural network model is higher and higher, the execution frequency of steps S100 to S400 can be gradually reduced, and finally a complementary effect can be achieved. That is, steps S100 to S400 are a time-consuming but more accurate determination scheme, and the neural network model is a time-consuming but slightly less accurate determination scheme. The accuracy of the latter is constantly calculated by the former, which can improve the determination speed and ensure the accuracy of the determination.

[0137] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent flow transformation based on the content of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A big data health and medical management system, characterized in that, The system includes: The regional map creation module is used to obtain regional information based on preset permissions and create a regional map containing virtual boundaries based on the regional information; the virtual boundaries are used to divide the regional map into sub-regions. The monitoring point determination module is used to identify the monitoring points corresponding to the registered public cameras on the area map, and to determine the correction coefficients for the monitoring points based on boundary lines and area markers. Specifically, the mathematical form of the correction coefficient calculation process is as follows: The calculation process for the correction factor is as follows: In the formula, , Let be the correction coefficient for the i-th monitoring point. Let be the comprehensive impact value of the i-th monitoring point, and N be the total number of monitoring points. The influence value corresponding to the j-th sub-region is... Let be the distance between the center point of the j-th sub-region and the i-th monitoring point. Let j be the center point of the j-th sub-region. Let M be the i-th monitoring point, and M be the total number of sub-regions; The video acquisition and recognition module is used to receive videos captured by public cameras, recognize the videos, and calculate the amount of movement within a preset time period. The health calculation module is used to calculate the total amount of exercise in each sub-region based on the correction coefficient and the amount of exercise, and to calculate the health of the sub-region based on the total amount of exercise and the number of people in the sub-region. The reference scheme determination module is used to determine reference schemes for health facilities based on the health status of the sub-area and send them to the planning end; the reference schemes contain the locations of different types of health facilities. The health calculation module includes: The first query unit is used to query the amount of movement within the time period corresponding to each public camera; The second query unit is used to query the correction coefficient of the monitoring point corresponding to each public camera; The accumulation unit is used to accumulate the amount of exercise according to the correction coefficient to obtain the total amount of exercise; The third calculation unit is used to query the number of people in a sub-region and calculate the health status of the sub-region based on the total amount of exercise and the number of people in the sub-region.

2. The big data health and medical management system according to claim 1, characterized in that, The regional map creation module includes: The baseline map construction unit is used to read the boundaries of the region and construct a baseline map based on the boundaries. The regional segmentation unit is used to query the land use planning information of a region, and to segment the base map according to the land use planning information to obtain sub-regions; The tag query unit is used to query the area tags of each sub-region and establish an area map; the area tags include commercial and service land, comprehensive land, industrial land, residential land and green space; The regional map update unit is used to generate collection instructions at regular intervals, randomly acquire a preset number of top-view images containing coordinates, and update the regional map based on the top-view images; The aerial images were acquired primarily by drones and remote sensing satellites.

3. The big data health and medical management system according to claim 1, characterized in that, The monitoring point determination module includes: The location query unit is used to query the installation location of public cameras in the filing database based on pre-acquired permissions; The location query unit is used to query the monitoring points corresponding to the installation location on the regional map according to the same scale. The impact value calculation unit is used to traverse all monitoring points, calculate the distance between the monitoring points and each sub-region, and query the impact value corresponding to the area marker of each sub-region. The influence value statistics unit is used to calculate the influence value based on the distance and determine the correction coefficient for each monitoring point.

4. The big data health and medical management system according to claim 1, characterized in that, The video acquisition and recognition module includes: The video recognition unit is used to receive videos captured by public cameras, recognize the videos, and determine the dynamic contours, movement speed, and movement distance. The first calculation unit is used to determine the contour identity based on the dynamic contour and its movement speed, and to query the unit movement amount based on the contour identity. The second calculation unit is used to calculate the video motion amount labeled with the time span of the video based on the unit motion amount and the motion distance; The motion statistics unit is used to receive the time period input by the administrator, and to calculate the motion volume of the video corresponding to the public camera based on the time period to obtain the motion volume within the time period.

5. The big data health and medical management system according to claim 1, characterized in that, The reference scheme determination module includes: The third query unit is used to query the type and quantity of health facilities; The alternative site selection unit is used to identify alternative sites for each facility type on the regional map; The alternative site selection unit is used to select a number of alternative sites for each type of facility. The scheme generation unit is used to statistically analyze the selection results of all facility types and obtain a reference scheme. The scheme evaluation unit is used to evaluate the reference scheme based on the sub-region health status of the sub-region and determine the final scheme; The scheme sending unit is used to send the final scheme to the planning end.

6. The big data health and medical management system according to claim 5, characterized in that, The process of evaluating the reference scheme based on the sub-region health status to determine the final scheme includes: Iterate through each plot in the reference plan, query the facility type of the plot, and query the service key of the facility type; Query the health status of sub-regions within a preset range centered on the land parcel; Obtain the average health score of the sub-area, calculate the difference between the service health score corresponding to the facility type and the average health score of the sub-area, and determine the evaluation score of each plot based on the inverse ratio of the absolute value of the difference; The evaluation scores of all plots are accumulated, and the reference scheme with the highest evaluation score is selected as the final scheme.

7. The big data health and medical management system according to claim 5, characterized in that, The process of selecting a number of candidate sites for each type of facility includes: The transportation convenience of each candidate plot is queried; the transportation convenience is determined by road condition information and station information, which are values ​​predetermined during the plot planning process; Query the distance between each candidate plot and all selected candidate plots, and select the minimum distance; The selection utility value of each candidate site is determined based on the aforementioned accessibility and minimum distance. The number of candidate sites is selected based on the descending order of the selected utility value.

8. The big data health and medical management system according to claim 1, characterized in that, The system's operations also include: Based on pre-acquired permissions, the system queries and counts the number of sports-related terms within a preset time period in social apps in real time. A training set and a test set are constructed based on the number of exercise terms and the health status of sub-regions. A neural network model is trained based on the training set, and the accuracy of the neural network model is calculated based on the test set. The test set is updated periodically based on the number of newly acquired sports terms and the health status of sub-regions, and the data in the test set is extracted into the training set; wherein, the test set is a queue structure and its data storage size is a preset value; When the accuracy reaches a preset accuracy threshold, the health of the sub-region is calculated based on the trained neural network model and the number of collected motion words.

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