A medical smart park environment monitoring system based on the Internet of Things

Abstract

The present invention provides a medical smart park environment monitoring system based on the Internet of Things, comprising a data acquisition module, a data processing module, and a display module; the data acquisition module is used to collect current environmental information of each target point in the medical smart park; the data processing module is used to process and analyze the current environmental information of each target point in the medical smart park collected by the data acquisition module, and generate comprehensive environmental information in the medical smart park; the display module is used to display the current environmental information of each target point and the comprehensive environmental information, so as to better monitor the entire medical smart park.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring technology, and in particular to a medical smart park environmental monitoring system based on the Internet of Things. Background Art

[0002] At present, with the rapid development of market economy and science and technology, the medical level has also improved. As a result, a medical smart park has emerged, which can use the integrated application of new generation information technologies such as the Internet of Things, cloud computing, and mobile Internet to provide patients with a safe, comfortable, convenient, modern, and intelligent living environment. Environmental factors are a key factor in medical smart parks. Medical smart parks have office areas, drug warehouses, living areas, etc. Each area has very high requirements for environmental factors. The existing environmental monitoring system has a small monitoring range, a short communication distance, and can only obtain the environmental conditions of the measurement point, but not the overall environmental conditions in a park, which cannot meet the standards to be achieved by modern medical smart parks. Summary of the invention

[0003] The present invention provides a medical smart park environment monitoring system based on the Internet of Things, which is used to monitor the environment in the park, and processes the current environmental information of each monitored target point to obtain comprehensive environmental information in the park, which is convenient for the management of the overall environment of the park.

[0004] To achieve the above objectives, the embodiment of the present invention proposes a medical smart park environment monitoring system based on the Internet of Things, including:

[0005] Data collection module, used to collect current environmental information of each target point in the medical smart park;

[0006] A data processing module, used to process and analyze the current environmental information of each target point in the medical smart park collected by the data collection module, and generate comprehensive environmental information in the medical smart park;

[0007] The display module is used to display the current environment information of each target point and the comprehensive environment information.

[0008] Preferably, in the medical smart park environmental monitoring system based on the Internet of Things, the current environmental information includes wind speed, temperature, humidity and PM2.5 of the corresponding target point in the medical smart park.

[0009] Preferably, in the medical smart park environment monitoring system based on the Internet of Things, the data processing module includes:

[0010] Establish a unit for building a simulation model of a smart medical park;

[0011] An input unit is used to input current environmental information and simulate the information of each target point in the park;

[0012] The output unit calculates and simulates the environmental information of the medical smart park based on the information of each target point and outputs the result.

[0013] Preferably, the medical smart park environment monitoring system based on the Internet of Things further includes:

[0014] A video acquisition module, composed of a plurality of cameras, is arranged in the medical smart park and is used to acquire the internal video of the medical smart park in real time;

[0015] An information extraction module, connected to the video acquisition module, for calibrating the person in the internal video according to the person's characteristics to obtain a first calibrated person, and intercepting the video information of the first calibrated person in a fixed time period, and sending it to the intelligent analysis module;

[0016] An intelligent analysis module, used for analyzing the video information sent by the information extraction module;

[0017] Intelligent analysis module, including:

[0018] A facial recognition submodule, used for extracting facial information of people in the video information, and searching for corresponding information of people based on a cloud database, and sending a first work instruction to the control module if the person is not an internal staff of the medical smart park;

[0019] a state analysis submodule, for obtaining the motion status of the first calibrated person according to the video information, and extracting the facial information of the first calibrated person according to the facial recognition submodule, obtaining the facial expression of the person, and judging whether the first calibrated person is in a dangerous state according to the motion status and the facial expression, and if so, sending a second working instruction to the control module;

[0020] A control module, configured to remind the staff to conduct a check on the outsider upon receiving the first work instruction;

[0021] The control module is used to locate the position information of the first calibrated person when he is in a dangerous state of illness based on the positioning submodule when receiving the second work instruction, and plan the fastest emergency route according to the current park environment corresponding to the position information. At the same time, it issues emergency instructions to emergency personnel and analyzes the illness condition of the first calibrated person based on the facial expression of the person and the current park environment, so as to send rescue advice to the medical staff of the medical staff.

[0022] Preferably, in the medical smart park environment monitoring system based on the Internet of Things, the Internet cloud database is used to store internal personnel information of the medical smart park, current environmental information of each target point in the medical smart park collected by the data acquisition module, comprehensive environmental information generated by the data processing module, and internal video taken by the video acquisition module.

[0023] Preferably, the medical smart park environment monitoring system based on the Internet of Things is characterized by further comprising:

[0024] Thermal sensing module, used to detect the body temperature of people in the medical smart park and generate infrared thermal images of the human body;

[0025] A data analysis module is used to compare the body temperature of each person in the measured medical smart park with a preset body temperature threshold. If the body temperature of the person in the measured medical smart park is greater than the preset body temperature threshold, the measured person is calibrated to obtain a second calibrated person, and the historical body temperature information of the second calibrated person is searched in the cloud database to form a body temperature change curve of the second calibrated person;

[0026] The data analysis module is further used to establish the medical smart park simulation model according to the human infrared imaging image generated by the thermal sensing module, calculate the proportion of the second calibrated person in the total number of people in the medical smart park simulation model, calculate the risk coefficient, and compare the risk coefficient with a preset risk coefficient threshold;

[0027] If the risk factor is greater than the preset risk factor threshold, a third working instruction is sent to the central control module, and a fourth working instruction is sent to the confirmation module;

[0028] A confirmation module, configured to receive the fourth work instruction, obtain the movement route map of the second calibrated person, integrate the movement route maps of all the second calibrated persons, determine the sensitive area, obtain the distribution of the second calibrated persons in the sensitive area, and classify the danger level of the sensitive area according to the distribution density of the second calibrated persons, the temperature change of the second calibrated persons, and the regional environmental information of the sensitive area;

[0029] The central control module is used to receive the third work instruction sent by the data analysis module, issue an isolation instruction, remind unmarked people in the medical smart park to avoid sensitive areas, generate corresponding processing plans based on the danger level classification, issue the fifth work instruction to the user terminal of the staff, and control the alarm module to sound an alarm.

[0030] Preferably, in the medical smart park environment monitoring system based on the Internet of Things, the thermal sensing module includes:

[0031] Infrared detection unit, used to detect infrared rays emitted by the human body;

[0032] A signal conversion unit, used to convert the infrared rays detected by the infrared detection unit into electrical signals;

[0033] The signal processing unit is used to process the electrical signal, obtain the body temperature of the measured person according to the processing result, and generate an infrared thermal imaging image of the human body.

[0034] Preferably, the medical smart park environment monitoring system based on the Internet of Things further includes: a data detection module, a calculation module, and a feedback module;

[0035] The data measurement module is used to detect the final expected number of transmissions of the video encoding packet of the corresponding video being received and confirmed by the cloud database during the process of uploading the video captured by the video acquisition module to the cloud database;

[0036] The calculation module is used to calculate the coding gain of the video captured by the video acquisition module during uploading to the cloud database, including:

[0037] According to the following formula, the consumption function C(Z) in the process of uploading the video captured by the video acquisition module to the cloud database is calculated:

[0038]

[0039] Wherein, Z is the video coding packet; NT(Z) is the final expected number of transmissions of the video coding packet received and confirmed by the cloud database; log() is a logarithmic function; β1 is the strength of the effective signal in the internal video; β2 is the strength of the noise signal in the internal video; P is the loss coefficient of the internal video during transmission, and its value range is [0.8, 0.9]; X is the total number of transmission nodes that the internal video passes through during transmission to the cloud database; K i is the current data flow of the internal video transmitted by the i-th node; K is the average data flow of the internal video transmitted by all nodes; Δ() represents the fine-tuning function of the number of transmissions corresponding to the data flow transmitted by different transmission nodes, and when When it is greater than 0, the corresponding Δ() value is 1. When it is equal to or less than 0, the corresponding Δ() value is 0;

[0040] According to the obtained consumption function C(Z), the coding gain G(Z) of uploading the captured video to the cloud database by the video acquisition module is calculated:

[0041]

[0042] Wherein, η is the coding weight of the video coding packet; is the average importance of the video coding packet, and its value range is [0.9, 1.1]; is the image distortion factor during the transmission of the captured video, and its value range is [0.8, 0.9];

[0043] The calculation module is further used to determine whether the obtained coding gain G(Z) is greater than the preset coding gain. If not, the feedback module is controlled to feed back the calculation result to the client.

[0044] Preferably, the medical smart park environment monitoring system based on the Internet of Things, the data analysis module further includes:

[0045] The risk factor calculation unit is used to calculate the risk factor according to the following formula:

[0046]

[0047] Among them, S d S is the area containing the second calibration person in the medical smart park simulation model; q is the total area of ​​the medical smart park simulation model; k d k is the number of second-calibrated people in the medical smart park simulation model; z is the total number of people in the medical smart park simulation model; α is the error coefficient of the medical smart park simulation model. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0049] Figure 1 This is a flow chart of a medical smart park environment monitoring system based on the Internet of Things according to an embodiment of the present invention;

[0050] Figure 2 A flowchart of a medical smart park environment monitoring system based on the Internet of Things according to another embodiment of the present invention;

[0051] Figure 3 A flowchart of a medical smart park environment monitoring system based on the Internet of Things according to another embodiment of the present invention;

[0052] Figure 4 This is a flow chart of a medical smart park environment monitoring system based on the Internet of Things according to another embodiment of the present invention.

[0053] In the figure: 1. Data acquisition module; 2. Data processing module; 201. Establishment unit; 202. Input unit; 203. Output unit; 3. Display module; 4. Video acquisition module; 5. Information extraction module; 6. Intelligent analysis module; 7. Control module; 701. Positioning submodule; 8. Thermal sensing module; 801. Infrared detection unit; 802. Signal conversion unit; 803. Signal processing unit; 9. Data analysis module; 901. Risk factor calculation unit; 10. Central control module; 11. Confirmation module; 12. Alarm module; 13. Data detection module; 14. Calculation module; 15. Feedback module. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0055] Reference below Figures 1 to 4 To describe a medical smart park environment monitoring system based on the Internet of Things proposed in an embodiment of the present invention.

[0056] Embodiment 1:

[0057] The present invention provides a medical smart park environment monitoring system based on the Internet of Things, such as Figure 1 As shown, including:

[0058] Data collection module 1, used to collect current environmental information of each target point in the medical smart park;

[0059] Data processing module 2, used to process and analyze the current environmental information of each target point in the medical smart park collected by the data collection module, and generate comprehensive environmental information in the medical smart park;

[0060] The display module 3 is used to display the current environment information of each target point and the comprehensive environment information.

[0061] In this embodiment, the target point is a pre-selected data collection point; the current environmental information is the data information collected at each target point; and the comprehensive environmental information is the overall environmental information within the medical smart park analyzed based on the current environmental information.

[0062] The working principle of the above scheme is as follows: the data acquisition module 1 collects the current environmental information of each target point in the medical smart park, the data processing module 2 processes and analyzes the current environmental information of each target point in the medical smart park collected by the data acquisition module, generates comprehensive environmental information within the medical smart park, and the display module 3 displays the current environmental information of each target point and the comprehensive environmental information.

[0063] The beneficial effects of the above technical solution are: monitoring the environment within the park, processing the current environmental information of each monitored target point to obtain comprehensive environmental information within the park, and facilitating the adjustment and management of the overall environment of the park.

[0064] Embodiment 2:

[0065] Based on Example 1, the current environmental information includes wind speed, temperature, humidity and PM2.5 of the corresponding target point in the medical smart park.

[0066] The beneficial effect of the above technical solution is that it can provide a more detailed understanding of the various environmental data in the park, making the analysis more comprehensive and providing basic parameters for the subsequent determination of the overall environment of the park.

[0067] Embodiment 3:

[0068] Based on Example 1, the data processing module includes:

[0069] Establishing unit 201, for establishing a medical smart park simulation model;

[0070] An input unit 202 is used to input current environmental information and simulate information of each target point in the park;

[0071] The output unit 203 calculates the simulated medical smart park environment information based on the information of each target point and outputs the result.

[0072] In this embodiment, the simulation model is a model that simulates the change patterns of the system structure and functions in the park over time; the information of each target point in the simulated park is to use the current environmental information as a parameter to simulate the operation of the park.

[0073] The beneficial effects of the above scheme are as follows: the present invention establishes a simulation model, inputs the collected information into the simulation model, simulates the park environment, outputs the overall environment in the park, and facilitates the overall management of the park.

[0074] Embodiment 4:

[0075] Based on Example 1, Figure 2 As shown, it also includes:

[0076] The video acquisition module 4 is composed of a plurality of cameras and is arranged in the medical smart park, and is used to acquire the internal video of the medical smart park in real time;

[0077] The information extraction module 5 is connected to the video acquisition module 4 and is used to calibrate the person in the internal video according to the person's characteristics to obtain a first calibrated person, and intercept the video information of the first calibrated person in a fixed time period and send it to the intelligent analysis module 6;

[0078] Intelligent analysis module 6, used for analyzing the video information sent by the information extraction module;

[0079] Intelligent analysis module, including:

[0080] The facial recognition submodule 601 is used to extract facial information of people in the video information, and search for corresponding information of people based on the cloud database. If the person is not an internal staff of the medical smart park, a first work instruction is sent to the control module;

[0081] The state analysis submodule 602 is used to obtain the movement of the first calibrated person according to the video information, and extract the facial information of the first calibrated person according to the facial recognition submodule to obtain the facial expression of the person, and determine whether the first calibrated person is in a dangerous state according to the movement and facial expression, and if so, send a second working instruction to the control module;

[0082] A control module 7, configured to remind the staff to conduct a check on the outsider upon receiving the first work instruction;

[0083] The control module 7 is used to locate the position information of the first calibrated person when he is in a dangerous state of illness based on the positioning submodule 701 when receiving the second work instruction, and plan the fastest emergency route according to the current park environment corresponding to the position information. At the same time, it issues emergency instructions to emergency personnel and analyzes the illness condition of the first calibrated person based on the facial expression of the person and the current park environment, so as to send rescue advice to the medical staff of the medical staff.

[0084] In this embodiment, character features are information that can express human body features, such as a person's nose, eyes, mouth, eyebrows and other features; the first calibrated character is a character whose overall facial contour is extracted based on the character features; the fixed time period is a preset video capture duration, or video capture is performed at a certain time point; the movement status is the movement route and movement of the first calibrated character in the video; the facial information is the feature information of specific parts of the face of the first calibrated character and the overall feature information; the first work instruction is to remind the staff to check for outsiders; the second work instruction is to control the positioning submodule to start working and issue a work instruction to the control module; the disease condition is to predict the cause of the disease based on the case of the person in the cloud database and the state at the time of the disease.

[0085] In this embodiment, the current park information is obtained based on the data acquisition module to obtain information corresponding to the current location, so as to facilitate further effective route planning and analysis of disease conditions based on environmental information, thereby improving the accuracy of planning and analysis.

[0086] The beneficial effects of the above technical solution: the present invention can shoot the internal video of the medical smart park according to the video acquisition module, extract the character information therein, and perform intervention when the extracted character information is an outsider, which can effectively reduce safety hazards, and judge that the person is in a dangerous state of illness according to the movement and facial expression of the person, and arrange staff for rescue in time, and plan reasonable rescue routes and rescue plans to enhance rescue efficiency, and facilitate further effective route planning and analysis of illness conditions based on environmental information, improve the accuracy of planning and analysis, and provide convenience for effective management of the park.

[0087] Embodiment 5:

[0088] Based on the fourth embodiment, the invention further includes:

[0089] The Internet cloud database is used to store internal personnel information of the medical smart park, current environmental information of each target point in the medical smart park collected by the data acquisition module, comprehensive environmental information generated by the data processing module, and internal videos shot by the video acquisition module.

[0090] The beneficial effects of the above technical solution are as follows: the cloud database can store the internal personnel information of the medical smart park in real time, which is convenient for reading the calibrated person information at any time, and can store the current environmental information of each target point in the medical smart park collected by the data acquisition module, the comprehensive environmental information generated by the data processing module, and the internal video taken by the video acquisition module, which is convenient for viewing when problems arise.

[0091] Embodiment 6:

[0092] Based on Example 1, Figure 3 As shown, it also includes:

[0093] Thermal sensing module 8, used to detect the body temperature of people in the medical smart park and generate infrared thermal imaging images of the human body;

[0094] The data analysis module 9 is used to compare the body temperature of each person in the measured medical smart park with a preset body temperature threshold. If the body temperature of the person in the measured medical smart park is greater than the preset body temperature threshold, the measured person is calibrated to obtain a second calibrated person, and the historical body temperature information of the second calibrated person is searched in the cloud database to form a body temperature change curve of the second calibrated person;

[0095] The data analysis module 9 is further used to establish the medical smart park simulation model according to the human infrared imaging image generated by the thermal sensing module, calculate the proportion of the second calibrated person in the total number of people in the medical smart park simulation model, calculate the risk coefficient, and compare the risk coefficient with a preset risk coefficient threshold;

[0096] If the risk factor is greater than the preset risk factor threshold, a third work instruction is sent to the central control module 10, and a fourth work instruction is sent to the confirmation module 11;

[0097] The confirmation module 11 is used to receive the fourth work instruction, obtain the movement route map of the second calibrated person, integrate the movement route maps of all the second calibrated persons, determine the sensitive area, obtain the distribution of the second calibrated persons in the sensitive area, and classify the danger level of the sensitive area according to the distribution density of the second calibrated persons, the temperature change of the second calibrated persons, and the regional environmental information of the sensitive area;

[0098] The central control module 10 is used to receive the third work instruction sent by the data analysis module, issue an isolation instruction, remind the unmarked people in the medical smart park to avoid sensitive areas, generate corresponding processing plans based on the risk level classification, issue a fifth work instruction to the user terminal of the staff, and control the alarm module 12 to sound an alarm;

[0099] In this embodiment, the second calibrated person is a person whose body temperature is greater than a preset temperature threshold; the danger coefficient represents the degree of danger, and the larger the danger coefficient, the higher the corresponding degree of danger; the sensitive area is the movement area of ​​people with excessive body temperature; the danger level of the sensitive area is the division of the area according to the density of people with excessive body temperature in the area; the isolation instruction is to isolate the second calibrated person; the third work instruction is to send an isolation instruction to the second calibrated person; and the fourth work instruction is to control the confirmation module to start working.

[0100] In this embodiment, the regional environmental information of the sensitive area may affect the level of the sensitive area, such as excessively high PM2.5 concentration.

[0101] The beneficial effects of the above scheme are as follows: the present invention can detect the body temperature of people in the park according to the thermal sensing module. When it is detected that the body temperature of a person exceeds the standard, he / she will be isolated. According to the historical body temperature of the person exceeding the standard and the movement route, the sensitive area is determined, and the people in the park are reminded to avoid the sensitive area, which can effectively prevent the spread of the virus. The sensitive areas are divided based on the regional environmental information, and corresponding processing solutions are given to improve the processing efficiency and further realize the effective management of the park.

[0102] Embodiment 7:

[0103] Based on the sixth embodiment, the thermal sensing module 8 includes:

[0104] Infrared detection unit 801, used to detect infrared rays emitted by the human body;

[0105] A signal conversion unit 802, used to convert the infrared rays detected by the infrared detection unit into electrical signals;

[0106] The signal processing unit 803 is used to process the electrical signal, obtain the body temperature of the measured person according to the processing result, and generate an infrared thermal imaging image of the human body;

[0107] In this embodiment, infrared rays are heat rays generated by the human body itself; and infrared thermal imaging images are images that can reflect the temperature field of the surface of an object.

[0108] The beneficial effects of the above technical solution are: it can detect the body temperature of people in the park in real time and generate thermal images, which is convenient for establishing simulation models and facilitating the management of the park.

[0109] Embodiment 8:

[0110] Based on Example 6, the data analysis module further includes:

[0111] The risk factor calculation unit 901 is used to calculate the risk factor according to the following formula:

[0112]

[0113] Among them, S d S is the area containing the second calibration person in the medical smart park simulation model; q is the total area of ​​the medical smart park simulation model; k d k is the number of second-calibrated people in the medical smart park simulation model; z is the total number of people in the medical smart park simulation model; α is the error coefficient of the medical smart park simulation model.

[0114] The beneficial effects of the above scheme are as follows: the risk coefficient can be calculated in real time according to the proportion of the second calibrated person in the total number of people in the medical smart park simulation model, and instructions can be issued according to the risk coefficient.

[0115] Embodiment 9:

[0116] Based on Example 4, Figure 4 As shown, it also includes: a data measurement module 13, a calculation module 14, and a feedback module 15;

[0117] The data measurement module 13 is used to measure the final expected number of transmissions of the video encoding packet of the corresponding video being received and confirmed by the cloud database during the process of uploading the video captured by the video acquisition module 4 to the cloud database;

[0118] The calculation module 14 is used to calculate the coding gain of the video captured by the video acquisition module 4 during uploading to the cloud database, including:

[0119] According to the following formula, the consumption function C(Z) in the process of uploading the video captured by the video acquisition module 4 to the cloud database is calculated:

[0120]

[0121] Wherein, Z is the video coding packet; NT(Z) is the final expected number of transmissions of the video coding packet received and confirmed by the cloud database; log() is a logarithmic function; β1 is the strength of the effective signal in the internal video; β2 is the strength of the noise signal in the internal video; P is the loss coefficient of the internal video during transmission, and its value range is [0.8, 0.9]; X is the total number of transmission nodes that the internal video passes through during transmission to the cloud database; K i is the current data flow of the internal video transmitted by the i-th node; K is the average data flow of the internal video transmitted by all nodes; Δ() represents the fine-tuning function of the number of transmissions corresponding to the data flow transmitted by different transmission nodes, and when When it is greater than 0, the corresponding Δ() value is 1. When it is equal to or less than 0, the corresponding Δ() value is 0;

[0122] According to the obtained consumption function C(Z), the coding gain G(Z) of uploading the captured video to the cloud database by the video acquisition module is calculated:

[0123]

[0124] Wherein, η is the coding weight of the video coding packet; δ is the average importance of the video coding packet, and its value range is [0.9, 1.1]; is the image distortion factor during the transmission of the captured video, and its value range is [0.8, 0.9];

[0125] The calculation module is further used to calculate whether the obtained coding gain G(Z) is greater than the preset coding gain. If not, the feedback module is controlled to feed back the calculation result to the client.

[0126] The working principle of the above scheme is as follows: the data measurement module 13 measures the final expected number of transmissions of the video encoding packet of the corresponding video being received and confirmed by the cloud database during the process of uploading the video captured by the video acquisition module 4 to the cloud database; the calculation module 14 calculates the consumption function C(Z) of the video captured by the video acquisition module 4 during the process of uploading the video to the cloud database according to the formula; and calculates the coding gain G(Z) of the video captured by the video acquisition module when uploading the video to the cloud database according to the consumption function C(Z); and determines whether the obtained coding gain G(Z) is greater than the preset coding gain. If not, the control feedback module feeds back the calculation result to the client;

[0127] In the above embodiment, the final expected number of transmissions refers to the number of times the video encoding packets of the captured video are received by the cloud database during the transmission process.

[0128] In the above embodiment, after the control feedback module feeds back the calculation result to the client, the client performs relevant adjustment operations according to the feedback result.

[0129] The beneficial effects of the above scheme are as follows: the present invention can measure in real time the final expected number of transmissions of the video coding package of the corresponding captured video being received and confirmed by the cloud database during the process of uploading the video captured by the video acquisition module 4 to the cloud database, and calculate the coding gain based on the measured data. When the obtained coding gain is less than the preset coding gain, feedback is given to the client to remind the staff that the video transmission effect is poor and timely maintenance can be carried out to avoid the impact caused by the poor video quality in the cloud database, facilitate and effectively obtain monitoring videos, and improve the rationality of monitoring and management of the park.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A medical smart park environment monitoring system based on the Internet of Things, characterized in that: include: A data collection module (1) is used to collect current environmental information of each target point in the medical smart park; A data processing module (2) is used to process and analyze the current environmental information of each target point in the medical smart park collected by the data collection module to generate comprehensive environmental information in the medical smart park; A display module (3), used to display the current environmental information of each target point and the comprehensive environmental information; The data processing module comprises: an establishment unit (201) for establishing a medical smart park simulation model; an input unit (202) for inputting current environmental information to simulate information of each target point in the park; an output unit (203) for calculating comprehensive environmental information of the simulated medical smart park based on the information of each target point and outputting the result; A video acquisition module (4), composed of a plurality of cameras, is arranged in the medical smart park and is used to acquire the internal video of the medical smart park in real time; An information extraction module (5) is connected to the video acquisition module (4) and is used to calibrate the person in the internal video according to the person's characteristics to obtain a first calibrated person, and to intercept the video information of the first calibrated person in a fixed time period and send it to the intelligent analysis module (6); An intelligent analysis module (6), used for analyzing the video information sent by the information extraction module; The intelligent analysis module comprises: a facial recognition submodule (601), which is used to extract facial information of a person in the video information, and search for corresponding information of the person based on a cloud database, and if the person is not an internal staff of the medical smart park, send a first work instruction to the control module; a state analysis submodule (602), which is used to obtain the movement of the first calibrated person according to the video information, and extract the facial information of the first calibrated person according to the facial recognition submodule, obtain the facial expression of the person, and judge whether the first calibrated person is in a dangerous state according to the movement and facial expression, and if so, send a second work instruction to the control module; A control module (7) is used to remind the staff to conduct a check on the outsider when receiving the first work instruction; The control module (7) is used to locate the position information of the first calibrated person when the person is in a dangerous state of illness based on the positioning submodule (701) when the second work instruction is received, and plan the fastest emergency route according to the current park environment corresponding to the position information, and at the same time, issue emergency instructions to emergency personnel, and analyze the illness condition of the first calibrated person according to the facial expression of the person and the current park environment, so as to send rescue advice to the medical staff's medical end; Data measurement module (13), calculation module (14), feedback module (15); The data measurement module (13) is used to measure the final expected number of transmissions of the video encoding packets corresponding to the captured video being received and confirmed by the cloud database during the process of uploading the video captured by the video acquisition module (4) to the cloud database; The calculation module (14) is used to calculate the coding gain of the video captured by the video acquisition module (4) during uploading to the cloud database, and includes: According to the following formula, the consumption function of the video captured by the video acquisition module (4) in the process of uploading to the cloud database is calculated: : Wherein, Z is a video coding package; The final expected number of transmissions of the video encoding packet received and confirmed by the cloud database; is a logarithmic function; is the strength of the effective signal in the internal video; is the intensity of the noise signal in the internal video; is the loss coefficient of the internal video during transmission, and its value range is [0.8, 0.9]; The total number of transmission nodes that the internal video passes through during transmission to the cloud database; For the The current data stream of internal video transmission by each node; The average data flow for all nodes to transmit internal videos; represents the fine-tuning function of the number of transmissions corresponding to the data streams transmitted based on different transmission nodes, and when When greater than 0, the corresponding The value of is 1, when When it is equal to or less than 0, the corresponding The value is 0; According to the consumption function , calculate the encoding gain of the video acquisition module uploading the captured video to the cloud database : in, is the encoding weight of the video encoding package; is the average importance of the video coding packet, and its value range is [0.9, 1.1]; is the image distortion factor during the transmission of the captured video, and its value range is [0.8, 0.9]; The calculation module is also used to determine the obtained coding gain. Whether it is greater than the preset coding gain, if not, the control feedback module feeds back the calculation result to the client.

2. The medical smart park environment monitoring system based on the Internet of Things as described in claim 1 is characterized in that: The current environmental information includes wind speed, temperature, humidity and PM2.5 of the corresponding target point in the medical smart park.

3. The medical smart park environment monitoring system based on the Internet of Things as claimed in claim 1, characterized in that: The cloud database is used to store internal personnel information of the medical smart park, current environmental information of each target point in the medical smart park collected by the data acquisition module, comprehensive environmental information generated by the data processing module, and internal videos taken by the video acquisition module.

4. The medical smart park environment monitoring system based on the Internet of Things as claimed in claim 1, characterized in that: Also includes: A thermal sensing module (8) is used to detect the body temperature of people in the medical smart park and generate infrared thermal imaging images of the human body; The data analysis module (9) is used to compare the body temperature of each person in the measured medical smart park with a preset body temperature threshold value. If the body temperature of the person in the measured medical smart park is greater than the preset body temperature threshold value, the measured person is calibrated to obtain a second calibrated person, and the historical body temperature information of the second calibrated person is searched in the cloud database to form a body temperature change curve diagram of the second calibrated person; The data analysis module (9) is further used to establish the medical smart park simulation model based on the human infrared imaging image generated by the thermal sensing module, calculate the proportion of the second calibrated person in the total number of people in the medical smart park simulation model, calculate the risk coefficient, and compare the risk coefficient with a preset risk coefficient threshold; if the risk coefficient is greater than the preset risk coefficient threshold, send a third work instruction to the central control module (10) and send a fourth work instruction to the confirmation module (11); A confirmation module (11) is used to receive the fourth work instruction, obtain the movement route map of the second calibrated person, integrate the movement route maps of all the second calibrated persons, determine the sensitive area, obtain the distribution of the second calibrated persons in the sensitive area, and classify the danger level of the sensitive area according to the distribution density of the second calibrated persons, the temperature change of the second calibrated persons, and the regional environmental information of the sensitive area; The central control module (10) is used to receive the third work instruction sent by the data analysis module, issue an isolation instruction, remind unmarked people in the medical smart park to avoid sensitive areas, generate corresponding processing plans based on the danger level classification, issue a fifth work instruction to the user terminal of the staff, and control the alarm module (12) to sound an alarm.

5. The medical smart park environment monitoring system based on the Internet of Things as described in claim 4 is characterized in that: The thermal sensing module (8) comprises: An infrared detection unit (801) is used to detect infrared rays emitted by a human body; A signal conversion unit (802), used for converting the infrared rays detected by the infrared detection unit into an electrical signal; The signal processing unit (803) is used to process the electrical signal, obtain the body temperature of the measured person according to the processing result, and generate an infrared thermal imaging image of the human body.

6. The medical smart park environment monitoring system based on the Internet of Things as claimed in claim 4, characterized in that: The data analysis module further includes: The risk factor calculation unit (901) is used to calculate the risk factor according to the following formula: in, The area containing the second calibration person in the medical smart park simulation model; The total area of ​​the simulation model for the medical smart park; The number of second-level calibrated characters in the medical smart park simulation model; The total number of people in the medical smart park simulation model; It is the error coefficient of the medical smart park simulation model.

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