Old people intelligent monitoring data processing method and system based on artificial intelligence

Abstract

The invention discloses an intelligent monitoring data processing method and system for the elderly based on artificial intelligence, and relates to the technical field of intelligent monitoring for the elderly, and the method comprises the steps: obtaining the behavior collection data of the elderly in a visual monitoring area, obtaining a sensing utilization load factor corresponding to a monitoring device in the visual monitoring area, carrying out the corresponding configuration control of the monitoring device, and carrying out the monitoring of the elderly. And obtaining a monitoring video stream transmission indication label, obtaining an adjustment execution parameter of the communication transmission channel, and carrying out quick response adjustment processing on the communication transmission channel through the adjustment execution parameter. The system can better adapt to different environments and behavior characteristics of old people, improves the effectiveness and accuracy of monitoring, feeds back and processes abnormal behaviors of the old people in time in a targeted manner, provides more reliable guarantee for safety management of the old people, ensures that monitoring video streams can be stably and efficiently transmitted to a general control display terminal, and improves the safety of the old people. And smooth operation of the whole monitoring system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring of the elderly, and in particular to an artificial intelligence-based intelligent monitoring data processing method and system for the elderly. Background Art

[0002] In today's society, the problem of population aging is becoming increasingly serious, and the safety of the elderly has become a vital social concern. With the rapid development of artificial intelligence technology, its application in the field of intelligent monitoring of the elderly is becoming more and more extensive. Especially for some large-scale retirement communities, due to the dense distribution of the elderly and the large area covered, there are limitations such as high management difficulty, which makes the monitoring requirements for the elderly increasingly higher.

[0003] The prior art, such as the invention patent with announcement number: CN104966381 B, is a real-time monitoring and early warning system for the condition of left-behind elderly people, including a strain gauge sensor, a signal processing circuit and a wireless transmission circuit connected in sequence, the signal transmitted by the wireless transmission circuit is received by a terminal receiving device, the output end of the terminal receiving device is connected to an alarm, and the strain gauge sensor is attached to the outer wall of the household water supply pipe. The present invention uses a strain gauge sensor to measure the vibration signal of the water flow in the household water supply pipe, obtains the household water use time and water use period, and compares it with the water use time and water use period of the left-behind elderly in normal conditions preset in the signal processing circuit. After comparison, if the water use data is abnormal, the signal processing circuit outputs a control signal, and this control signal is transmitted to the terminal receiving device through the wireless transmission circuit, and the terminal receiving device controls the alarm to alarm, so that family members or other people can judge whether the left-behind elderly have an accident that causes abnormal water use.

[0004] The prior art, such as the invention patent with announcement number: CN113706824B, is a home care system for the elderly based on Internet of Things monitoring. The environment monitoring module is used to perform an environmental analysis on the care site of the cared person to obtain the ambient shadow value. After the environment monitoring module feeds back the ambient shadow value to the server, the server sends the ambient shadow value together with the real-time body data to the physical condition monitoring module. The physical condition monitoring module receives the ambient shadow value of the care site and the real-time body data of the cared person sent by the server. The physical condition monitoring module is used to perform physical condition monitoring and analysis on the physical condition data of the cared person. The physical condition monitoring module sends a care warning signal to the server; after receiving the care warning signal, the server sends it to the user terminal.

[0005] From the above solutions, it can be seen that the current intelligent monitoring system for the elderly only obtains data from a single dimension for analysis, or only makes judgments based on environmental analysis combined with physical data. However, the actual behavior monitoring of the elderly will be affected by the diversity of their daily activities and the complexity of the environment they are in, and the accuracy of the monitoring is limited by the monitoring device. Therefore, if only a single level of data analysis is used, it may not be possible to fully and accurately capture the real-time status of the elderly, thereby affecting the effective monitoring of the elderly's safety. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an artificial intelligence-based intelligent monitoring data processing method and system for the elderly, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an intelligent monitoring data processing method for the elderly based on artificial intelligence, which is characterized by comprising the following steps: S1, counting the coverage monitoring area corresponding to the monitoring devices installed in the statistical area, recorded as the visual monitoring area, and collecting the elderly behavior data in the visual monitoring area through the monitoring device within the preset monitoring period, thereby obtaining the elderly behavior collection data in the visual monitoring area.

[0008] S2, based on the elderly behavior data collected in the visual monitoring area, and simultaneously obtain the sensor interference data of the visual monitoring area for analysis, to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby configuring and controlling the monitoring device accordingly according to the sensor utilization load factor.

[0009] S3, record the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and obtain a monitoring video stream transmission indicator label in combination with the sensor interference data analysis and processing of the visible monitoring area.

[0010] S4, lock the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, recorded as the communication transmission channel, thereby obtaining the status data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel.

[0011] S5, the communication transmission channel is adjusted and processed for rapid response by adjusting the execution parameters, and the monitoring video stream is transmitted to the master control display terminal after the adjustment process.

[0012] The second aspect of the present invention provides an intelligent monitoring data processing system for the elderly based on artificial intelligence, which is characterized by: including: an elderly behavior data collection module, which is used to count the coverage monitoring area corresponding to the monitoring devices installed in the area, recorded as the visual monitoring area, and collect the elderly behavior data of the visual monitoring area through the monitoring devices within a preset monitoring period, thereby obtaining the elderly behavior collection data in the visual monitoring area.

[0013] The monitoring device configuration control module is used to collect data based on the elderly behavior in the visual monitoring area, and simultaneously obtain the sensor interference data in the visual monitoring area for analysis, so as to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby giving corresponding configuration control to the monitoring device according to the sensor utilization load factor.

[0014] The monitoring video stream transmission indicator label analysis module is used to record the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and combining the sensor interference data analysis and processing of the visible monitoring area to obtain the monitoring video stream transmission indicator label;

[0015] The communication transmission channel status analysis module is used to lock the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, recorded as the communication transmission channel, thereby obtaining the status data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel.

[0016] The communication transmission channel response adjustment module is used to quickly respond to and adjust the communication transmission channel by adjusting the execution parameters, and transmit the monitoring video stream to the master control display terminal after the adjustment process.

[0017] Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0018] (1) The present invention provides an intelligent monitoring data processing method and system for the elderly based on artificial intelligence. In terms of data collection and analysis, the comprehensive collection and analysis of the elderly behavior data and sensor interference data in the visual monitoring area can make it better adapt to different environments and elderly behavior characteristics, thereby improving the effectiveness and accuracy of monitoring. Secondly, in the monitoring of abnormal behavior of the elderly, by analyzing and processing a variety of behavioral data of the elderly, and setting different video stream transmission indicator label levels according to different judgment indicators, the abnormal behavior of the elderly can be fed back and processed in a timely and targeted manner, providing more reliable protection for the safety of the elderly. In terms of communication transmission, by analyzing and processing the status data of the communication transmission channel, and rationally adjusting the communication transmission channel based on this, it is ensured that the monitoring video stream can be stably and efficiently transmitted to the master control display terminal, thereby ensuring the smooth operation of the entire monitoring system.

[0019] (2) The present invention controls the configuration of the monitoring device accordingly according to the load factor sensed, so that the monitoring device can better adapt to the actual environmental conditions of the visible monitoring area, accurately determine whether the monitoring device needs to be adjusted and how to adjust it, thereby improving the applicability and effectiveness of the monitoring device. Reasonable configuration control can optimize the performance of the monitoring device, more clearly capture the behavioral details of the elderly, reduce monitoring errors caused by environmental factors, and improve the quality and reliability of monitoring data. At the same time, it helps to save resources. When the environment is relatively stable and the load factor does not exceed the threshold, the monitoring device runs with the default configuration parameters, avoiding unnecessary waste of resources, while also extending the service life of the equipment and reducing maintenance costs.

[0020] (3) The present invention obtains the monitoring video stream transmission indicator label through processing, and can accurately classify the transmission demand of the video stream according to the monitoring video stream transmission demand indicator parameter of the visible monitoring area, which helps to improve the efficiency and pertinence of the entire monitoring system. Relevant personnel can quickly understand the general situation of the elderly's behavior according to the transmission indicator label, and give priority to processing the video stream with a higher degree of abnormality, so as to more effectively protect the safety and health of the elderly. Through the reasonable processing of the video stream transmission indicator label, it can also better adapt to different network environments and monitoring needs, realize the optimal allocation of resources, and improve the overall performance of the system.

[0021] (4) The present invention obtains the status data of the communication transmission channel and analyzes the monitoring video stream transmission indicator label to obtain the adjustment execution parameters of the communication transmission channel. It can accurately determine the adjustment execution parameters according to the actual status of the communication transmission channel and the needs of video stream transmission, ensure that the communication transmission channel can adapt to the requirements of the monitoring system in the best state, effectively improve the stability and reliability of communication transmission, and ensure that the monitoring video can be stably transmitted to the master control display terminal. At the same time, through the reasonable adjustment of the communication transmission channel, it can optimize resource utilization, avoid over-configuration or under-configuration, and improve the operating efficiency and performance of the entire monitoring system.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0024] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1 As shown, an embodiment of the present invention provides an intelligent monitoring data processing method for the elderly based on artificial intelligence, which is characterized by comprising the following steps: S1, counting the coverage monitoring area corresponding to the monitoring devices installed in the area, recorded as the visual monitoring area, and collecting the elderly behavior data in the visual monitoring area through the monitoring devices within a preset monitoring period, thereby obtaining the elderly behavior collection data in the visual monitoring area.

[0027] In this embodiment, the collected data on the elderly's behavior in the visual monitoring area includes the average daily flow of people in the visual monitoring area, the average daily activity density of the elderly, the average daily activity frequency of the elderly, and the average movement speed of the elderly.

[0028] It should be noted that the average daily flow of people refers to the average number of people moving every day in the visual monitoring area.

[0029] The average daily activity density of the elderly refers to the ratio of the area covered by the elderly's activities on average every day within the visible monitoring area to the total area of ​​the visible monitoring area.

[0030] The average daily activity frequency of the elderly refers to the average frequency of the elderly entering the visual monitoring area within the visual monitoring area every day.

[0031] It should also be noted that the data collected about the elderly's behavior in the visual monitoring area is obtained by collecting monitoring videos using cameras installed in the visual monitoring area and extracting them from the collected monitoring videos.

[0032] S2, based on the elderly behavior data collected in the visual monitoring area, and simultaneously obtain the sensor interference data of the visual monitoring area for analysis, to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby configuring and controlling the monitoring device accordingly according to the sensor utilization load factor.

[0033] In this embodiment, the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is specifically analyzed as follows: based on the elderly behavior collection data in the visual monitoring area and the sensor interference data of the visual monitoring area, the sensor interference data of the visual monitoring area includes the average daily temperature, average daily light intensity, average daily noise level and average daily electromagnetic interference intensity of the visual monitoring area, and the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is obtained by analysis.

[0034] It should be noted that the sensor interference data of the visual monitoring area is collected by a sensor set of the monitoring device, and the sensor set includes an average daily temperature sensor, a light intensity sensor, a noise sensor and a magnetic field intensity detector.

[0035] The sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is a numerical result of quantifying the elderly behavior collection data and the sensor interference data in the visual monitoring area, and is used to characterize the configuration control requirement degree of the monitoring device in the visual monitoring area.

[0036] In a specific embodiment, the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is obtained in the following manner:

[0037] Extract the reference elderly behavior collection data and reference sensor interference data preset in the database, wherein the reference elderly behavior collection data includes reference average daily flow of people, reference average daily activity density of the elderly, reference average daily activity frequency of the elderly and reference average movement speed of the elderly.

[0038] The reference sensor interference data includes an ideal daily average temperature, an ideal daily average light intensity, a reference daily average noise level and a reference daily average electromagnetic interference intensity.

[0039] It should be noted that the reference elderly behavior collection data and reference sensor interference data are obtained by averaging the historical data of the elderly behavior collection data and sensor interference data before establishing the database.

[0040]

[0041] in,

[0042] Among them, B is the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, D is the sensor interference index of the visual monitoring area, f is the average daily flow of people in the visual monitoring area, and f 0 is the reference daily average flow of people, g is the average daily activity density of the elderly in the visual monitoring area, and g 0 is the average daily activity density of the elderly, h is the average daily activity frequency of the elderly in the visual monitoring area, and h 0 is the average daily activity frequency of the elderly, j is the average movement speed of the elderly in the visual monitoring area, and j 0 is the average movement speed of the elderly, k is the average daily temperature of the visible monitoring area, and k 0 is the ideal daily average temperature, m is the daily average light intensity in the visible monitoring area, m 0 is the ideal daily average light intensity, n is the daily average noise level in the visible monitoring area, and n 0is the reference daily average noise level, p is the daily average electromagnetic interference intensity in the visible monitoring area, and p 0 is the reference daily average electromagnetic interference intensity, y 1 is the daily average flow weight, y 2 is the average daily activity density weight of the elderly, y 3 is the weight of the average daily activity frequency of the elderly, y 4 is the average action speed weight of the elderly, y 5 is the daily average temperature weight, y 6 is the daily average light intensity weight, y 7 is the daily average noise level weight, y 8 is the daily average electromagnetic interference intensity weight, and e is a natural constant.

[0043] It should be noted that the average daily flow weight, the average daily activity density weight of the elderly, the average daily activity frequency weight of the elderly, the average movement speed weight of the elderly, the average daily temperature weight, the average daily light intensity weight, the average daily noise level weight and the average daily electromagnetic interference intensity weight all have a value range between 0 and 1. In this embodiment, the average daily flow weight is the average daily flow weight preset in the database, which represents the numerical value of the influence of the average daily flow on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The average daily activity density weight of the elderly is the average daily activity density weight preset in the database, which represents the influence of the average daily activity density of the elderly on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The weight of the average daily activity frequency of the elderly is the weight of the average daily activity frequency of the elderly preset in the database, which indicates the numerical value of the influence of the average daily activity frequency of the elderly on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The weight of the average action rate of the elderly is the weight of the average action rate of the elderly preset in the database, which indicates the numerical value of the influence of the average action rate of the elderly on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The weight of the average daily temperature is the weight of the average daily temperature preset in the database, which indicates the numerical value of the influence of the average daily temperature on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The weight of the average daily light intensity is the weight of the average daily light intensity preset in the database. The daily average light intensity weight represents the influence of the daily average light intensity on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The daily average noise level weight is the daily average noise level weight preset in the database, which represents the influence of the daily average noise level on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. The daily average electromagnetic interference intensity weight is the daily average electromagnetic interference intensity weight preset in the database, which represents the influence of the daily average electromagnetic interference intensity on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area. When used, the preset daily average flow weight, the daily average activity density weight of the elderly, and the daily average flow of the elderly can be directly obtained from the database. The activity frequency weight, the average action speed weight of the elderly, the average daily temperature weight, the average daily light intensity weight, the average daily noise level weight and the average daily electromagnetic interference intensity weight, and their corresponding relationship can be a pre-set mapping relationship. For example, the average daily flow of people, the average daily activity density of the elderly, the average daily activity frequency of the elderly, the average action speed of the elderly, the average daily temperature, the average daily light intensity, the average daily noise level and the average daily electromagnetic interference intensity respectively form a mapping set with the preset daily flow of people weight, the average daily activity density weight, the average daily activity frequency weight, the average action speed weight, the average daily temperature weight, the average daily light intensity weight, the average daily noise level weight and the average daily electromagnetic interference intensity weight in the database.The real-time average daily flow of people, average daily activity density of the elderly, average daily activity frequency of the elderly, average movement speed of the elderly, average daily temperature, average daily light intensity, average daily noise level and average daily electromagnetic interference intensity are respectively input into the mapping set to obtain the average daily flow of people weight, average daily activity density of the elderly, average daily activity frequency of the elderly, average movement speed of the elderly, average daily temperature weight, average daily light intensity weight, average daily noise level weight and average daily electromagnetic interference intensity weight, where the mapping relationship is one-to-one correspondence. ,

[0044] It should also be noted that the present embodiment obtains the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area by analyzing the elderly behavior collection data in the visual monitoring area and the sensor interference data in the visual monitoring area, taking into account the mutual influence between these parameters. For example, the average daily flow of people will affect the activity space and mode of the elderly. A large flow of people may lead to limited activities of the elderly, thereby affecting the average daily activity density and activity frequency of the elderly. At the same time, a large flow of people may also bring more electromagnetic interference and noise. Changes in the average daily activity density and frequency of the elderly will in turn affect the space utilization of the visual monitoring area, further affecting the flow pattern of the flow of people. Changes in the average daily temperature may affect the elderly's willingness to move and physical function. For example, too high or too low average daily temperature may slow down the elderly's activity speed and change the average action rate of the elderly. At the same time, the average daily temperature will also affect the performance of the equipment, such as the working state of the light intensity sensor, the electromagnetic interference sensor, etc., thereby affecting the measurement accuracy of the average daily light intensity and the average daily electromagnetic interference intensity. The average daily light intensity will not only affect the quality of the monitoring image, but also may affect the visual perception and activity patterns of the elderly. For example, too strong or too weak light may cause the elderly to have difficulty in moving or change their activity area, thus affecting the average daily activity density and frequency of the elderly. The average daily noise level will interfere with the auditory perception of the elderly, which may make them feel irritable and affect their activity status and activity frequency. It may also cover up some sound information collected by the monitoring device. The average daily electromagnetic interference intensity will interfere with the electronic components of the monitoring device, affect the accuracy of data collection, and may cause deviations in the data collected on the elderly's behavior. It may also interfere with other electronic equipment and affect the stability of the electromagnetic environment of the entire visual monitoring area. These parameters are intertwined and affect each other, and work together to affect the monitoring environment and elderly behavior in the visual monitoring area.

[0045] The present invention can comprehensively consider the influence of various factors in the visual monitoring area on the monitoring device by analyzing the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby more accurately reflecting the actual environment and load conditions faced by the monitoring device, enabling the monitoring device to better adapt to the environment and improve the accuracy and effectiveness of monitoring. When the load factor does not exceed the threshold, the monitoring device runs with the default configuration to avoid unnecessary waste of resources. When adjustment is needed, the configuration optimization can be carried out in a targeted manner to improve the operating efficiency and economy of the entire monitoring system.

[0046] In this embodiment, the monitoring device is configured and controlled accordingly according to the sensor utilization load factor. The specific process is as follows: extracting the sensor utilization load factor threshold corresponding to the monitoring device of the visual monitoring area preset in the database.

[0047] It should be noted that the configuration control includes required configuration adjustment and no configuration adjustment.

[0048] It should be noted that the sensor utilization load factor threshold corresponding to the monitoring device in the visible monitoring area is extracted from the database and is used to reflect the critical degree of the configuration adjustment demand of the monitoring device in the visible monitoring area.

[0049] In a specific embodiment, the sensor utilization load factor threshold corresponding to the monitoring device in the visual monitoring area is pre-set in the database and can be obtained by a variety of methods. For example, a large number of relevant data samples of the visual monitoring area under different monitoring environments, different personnel flow conditions and different interference factors are collected, and these sample data are subjected to detailed statistical analysis and processing to obtain the monitoring device configuration control demand evaluation value of the visual monitoring area, and then these evaluation values ​​are weighted averaged or calculated by taking specific percentile values ​​to obtain the sensor utilization load factor threshold corresponding to the monitoring device in the visual monitoring area, or experienced experts in the field of intelligent monitoring technology can determine the limit of the monitoring device configuration adjustment demand of the visual monitoring area based on their long-term accumulated experience to set the threshold.

[0050] The sensor utilization load factor corresponding to the monitoring device in the visible monitoring area and the sensor utilization load factor threshold corresponding to the monitoring device in the visible monitoring area are subjected to difference processing to obtain the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area.

[0051] The difference processing refers to subtracting the sensor utilization load factor corresponding to the monitoring device in the visible monitoring area from the sensor utilization load factor corresponding to the monitoring device in the visible monitoring area, and the difference processing result can be greater than zero, less than zero or equal to zero.

[0052] Extract the monitoring device configuration control parameters corresponding to each sensor utilization load deviation factor interval stored in the database, and map the monitoring device configuration control parameters corresponding to the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area, and mark them as the configuration control parameters corresponding to the monitoring device in the visible monitoring area.

[0053] The monitoring device is configured and controlled accordingly based on the configuration control parameters corresponding to the monitoring device in the visible monitoring area.

[0054] It should be noted that if the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is less than or equal to zero, the monitoring device will be marked as requiring no configuration adjustment, and the monitoring operation of the monitoring device will continue with the default configuration parameters. If the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is greater than zero, the monitoring device will be marked as requiring configuration adjustment, and the monitoring device will be adjusted based on the configuration control parameters corresponding to the monitoring device in the visible monitoring area.

[0055] It should be understood that if the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is less than or equal to zero, it means that the sensor utilization load of the monitoring device caused by comprehensive factors such as the current environmental conditions of the visible monitoring area and the behavior patterns of the elderly has not reached the critical level that requires adjustment of the control parameters. At this time, the monitoring device can continue to operate with the default configuration parameters to meet the monitoring needs. If the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is greater than zero, it means that the current comprehensive situation has caused the sensor utilization load of the monitoring device to reach the critical level that requires adjustment of the control parameters. The monitoring device needs to be adjusted accordingly based on the configuration control parameters corresponding to the monitoring device in the visible monitoring area to better adapt to the environment and the behavioral characteristics of the elderly and achieve more effective monitoring.

[0056] In a specific embodiment, if the visible monitoring area is an activity hall on a certain floor of a nursing home, the average daily flow of people in this area is large, the average daily activity density of the elderly is moderate, the average daily activity frequency of the elderly is high, and the light intensity is sufficient but the noise level is slightly high. Based on this analysis and calculation, the sensor utilization load factor corresponding to the monitoring device in the visible monitoring area is 0.82, the sensor utilization load factor threshold corresponding to the monitoring device in the visible monitoring area is 0.6, and the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is 0.22.

[0057] If the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area is greater than zero, the resolution and frame rate of the corresponding monitoring device are increased according to the control parameters preset in the database.

[0058] The present invention controls the corresponding configuration of the monitoring device according to the sensor load factor, so that the monitoring device can better adapt to the actual environmental conditions of the visible monitoring area, accurately judge whether the monitoring device needs to be adjusted and how to adjust it, thereby improving the applicability and effectiveness of the monitoring device. Reasonable configuration control can optimize the performance of the monitoring device, more clearly capture the behavioral details of the elderly, reduce monitoring errors caused by environmental factors, and improve the quality and reliability of monitoring data. At the same time, it helps to save resources. When the environment is relatively stable and the load factor does not exceed the threshold, the monitoring device runs with the default configuration parameters, avoiding unnecessary waste of resources, while also extending the service life of the equipment and reducing maintenance costs.

[0059] S3, record the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and obtain a monitoring video stream transmission indicator label in combination with the sensor interference data analysis and processing of the visible monitoring area.

[0060] In this embodiment, intelligent monitoring of the elderly is performed in the visible monitoring area by using a suitable monitoring device. The specific process is as follows: within a preset monitoring period, intelligent monitoring of the elderly is performed in the visible monitoring area by using a suitable monitoring device to obtain a behavior monitoring video stream of the elderly, from which basic data of the elderly are extracted, and the basic data of the elderly include the shoulder width, body contour volume and height of the elderly.

[0061] Synchronously, based on the video stream of the elderly person's behavior monitoring, the center point of the elderly person's head is identified and located, and then the movement curve of the center point of the elderly person's head within the preset monitoring period is monitored to obtain the movement curve of the center point of the elderly person's head within the preset monitoring period, identify and locate the starting position point and the ending position point of the center point of the elderly person's head in the monitoring period, and obtain the straight-line interval distance between the starting position point and the ending position point of the center point of the elderly person's head in the monitoring period, which is recorded as the straight-line distance of the elderly person's head movement.

[0062] The time taken by the center point of the elderly's head to move from the starting position to the ending position during the monitoring period is extracted, which is recorded as the elderly's head movement time. The length of the moving curve of the center point of the elderly's head during the preset monitoring period is extracted, and the moving speed of the center point of the elderly's head during the monitoring period is calculated, which is recorded as the elderly's head movement speed.

[0063] Identify and locate the center point of the first knee joint and the second knee joint of the elderly, and then monitor the movement curve of the center point of the first knee joint and the second knee joint of the elderly within a preset monitoring period to obtain the movement curve of the center point of the first knee joint and the second knee joint of the elderly within the preset monitoring period and its corresponding length, perform difference processing on the length of the movement curve of the center point of the first knee joint of the elderly within the preset monitoring period and the length of the movement curve of the center point of the second knee joint of the elderly within the preset monitoring period to obtain the deviation value of the length of the movement curve of the center point of the knee joint of the elderly within the preset monitoring period, and record it as the deviation value of the knee joint movement distance of the elderly.

[0064] Identify and locate the center point of the ankle joint of the elderly, and then monitor the movement curve of the center point of the ankle joint of the elderly within a preset monitoring period to obtain the movement curve of the center point of the ankle joint of the elderly within the preset monitoring period and its corresponding length, thereby calculating the movement rate of the center point of the ankle joint of the elderly.

[0065] It should be understood that the movement rate of the center point of the ankle joint of the elderly is obtained by dividing the movement length of the two ankle joints of the elderly by the monitoring time, and then performing average processing to obtain the movement rate of the two ankle joints of the elderly.

[0066] The straight-line distance of the elderly's head movement, the elderly's head movement speed, the elderly's knee joint movement distance deviation value and the movement rate of the elderly's ankle joint center point are combined as monitoring video stream transmission demand determination data.

[0067] The basic data of the elderly are quantified and added to obtain the characteristic values ​​of the basic data of the elderly.

[0068] The monitoring video stream transmission demand determination reference data includes the elderly's reference moving distance, the elderly's ideal moving speed, the elderly's ideal knee joint deviation value and the elderly's ideal ankle joint center point moving speed.

[0069] It should be noted that the monitoring video stream transmission demand determination reference data is obtained by averaging historical data of the monitoring video stream transmission demand determination data before establishing the database.

[0070] In this embodiment, a monitoring video stream transmission indication label is obtained through processing, and the specific processing process is as follows: based on the monitoring video stream transmission demand determination data and the monitoring video stream transmission demand determination reference data, analysis and processing are performed to obtain a monitoring video stream transmission demand determination index, and the monitoring video stream transmission demand determination index is used to characterize the abnormal degree of the elderly's behavior in the visual monitoring area.

[0071] According to the sensor interference index of the visible monitoring area, the sensor interference correction index corresponding to each sensor interference index interval preset in the database is extracted, and the sensor interference correction index corresponding to the sensor interference index of the visible monitoring area is mapped and extracted, and marked as the sensor interference correction index of the visible monitoring area.

[0072] According to the monitoring video stream transmission demand judgment index and the sensor interference correction index of the visual monitoring area, the monitoring video stream transmission demand indicator parameters of the visual monitoring area are analyzed and processed. The monitoring video stream transmission demand indicator parameters of the visual monitoring area are used to characterize the urgency of the monitoring video stream transmission demand of the visual monitoring area under the premise of correcting the sensor interference.

[0073] In a specific embodiment, the monitoring video stream transmission demand indicator parameter of the visual monitoring area is obtained in the following manner:

[0074]

[0075] Among them, C is the parameter indicating the demand for monitoring video stream transmission in the visible monitoring area, β is the sensor interference correction index of the visible monitoring area, q is the straight-line distance of the elderly's head movement, and q 0 is the reference moving distance of the elderly, r is the moving speed of the elderly’s head, and r 0 is the ideal moving speed of the elderly, s is the deviation value of the elderly's knee joint moving distance, s 0 is the ideal knee joint deviation value for the elderly, w 0 is the moving speed of the center point of the elderly’s ankle joint, w 0 is the ideal ankle joint center moving speed for the elderly, z 1 is the moving distance weight, z 2 is the moving speed weight, z 3 is the knee joint deviation weight, z 4 is the moving speed weight of the ankle joint center point.

[0076] It should be understood that the softplus function is a built-in function in Python, softplus(x)=lg(1+e x ).

[0077] It should be noted that the moving distance weight, moving speed weight, knee joint deviation value weight and ankle joint center moving speed weight, their value ranges are all between 0 and 1. In the present embodiment, the moving distance weight is the moving distance weight preset in the database, which indicates the numerical value of the influence of the moving distance on the monitoring video stream transmission demand indicator parameter of the visual monitoring area; the moving speed weight is the moving speed weight preset in the database, which indicates the numerical value of the influence of the moving speed on the monitoring video stream transmission demand indicator parameter of the visual monitoring area; the knee joint deviation value weight is the knee joint deviation value weight preset in the database, which indicates the numerical value of the influence of the knee joint deviation value on the monitoring video stream transmission demand indicator parameter of the visual monitoring area; the moving speed weight of the ankle joint center is the moving speed weight of the ankle joint center preset in the database, which indicates the moving speed of the ankle joint center The numerical value of the influence degree of the moving rate of the point on the monitoring video stream transmission demand indicator parameter of the visual monitoring area can be directly obtained from the database when used, and the preset moving distance weight, moving speed weight, knee joint deviation value weight and ankle joint center point moving rate weight can be a pre-set mapping relationship. For example, the moving distance, moving speed, knee joint deviation value and ankle joint center point moving rate weight respectively form a mapping set with the moving distance weight, moving speed weight, knee joint deviation value weight and ankle joint center point moving rate weight preset in the database, and the real-time moving distance, moving speed, knee joint deviation value and ankle joint center point moving rate are respectively input into the mapping set to obtain the moving distance weight, moving speed weight, knee joint deviation value weight and ankle joint center point moving rate weight, and the mapping relationship is one-to-one correspondence.

[0078] It should also be noted that the present embodiment obtains the monitoring video stream transmission demand indicator parameters of the visual monitoring area by analyzing and processing the data according to the monitoring video stream transmission demand determination, taking into account the mutual imaging between these parameters. For example, the straight-line distance and the moving speed of the elderly's head movement are interrelated. Generally speaking, when the moving speed is faster, the moving distance will be longer in the same time; and a longer moving distance may also imply a relatively fast moving speed. Of course, this also needs to consider the time factor. The deviation value of the elderly's knee joint moving distance will affect their movement mode and efficiency. If the knee joint deviation value is large, it may cause abnormal walking posture, thereby affecting the moving speed and moving distance. For example, knee joint deviation may make it difficult for the elderly to walk, the stride becomes smaller, the moving speed slows down, and the final moving distance will also be reduced accordingly. If the movement rate of the center point of the ankle joint is abnormal, such as suddenly accelerating or slowing down, it may break the overall movement coordination of the body, which may cause the adjustment of the elderly's body posture, thereby affecting the movement of the head. If the movement rate of the center point of the ankle joint is abnormal, it will disrupt the normal movement rhythm of the knee joint. For example, if the ankle joint moves too fast, it may cause the knee joint to be unable to adjust in time during the flexion and extension process, overextend or flex, thereby increasing the deviation value of the knee joint moving distance.

[0079] The present invention can analyze the degree of transmission demand of the monitoring video stream based on real-time monitoring data by analyzing the transmission demand indicator parameters of the monitoring video stream in the visual monitoring area. The larger the transmission demand indicator parameters of the monitoring video stream in the visual monitoring area, the greater the possibility of abnormality in the real-time monitoring data, and the corresponding monitoring video stream needs to be transmitted more quickly, so as to timely discover potential problems or safety hazards and provide more effective protection for the elderly.

[0080] Extract the first monitoring video stream transmission requirement indicator and the second monitoring video stream transmission requirement indicator preset in the database.

[0081] It should be noted that the first monitoring video stream transmission requirement indicator is smaller than the second monitoring video stream transmission requirement indicator.

[0082] It should also be noted that the first indicator of monitoring video stream transmission demand and the second indicator of monitoring video stream transmission demand are extracted from the database and are used to reflect the minimum critical value and the maximum critical value of the abnormal degree of elderly behavior.

[0083] In a specific embodiment, the first indicator of the monitoring video stream transmission demand and the second indicator of the monitoring video stream transmission demand are pre-set in the database and can be obtained by a variety of methods. For example, a large number of behavioral data samples of elderly people with different physical conditions and different behavioral patterns are collected, and these sample data are subjected to detailed statistical analysis and processing to obtain abnormal behavior evaluation values ​​of the elderly, and then these evaluation values ​​are weighted averaged or calculated by taking specific percentile values ​​to obtain the first indicator of the monitoring video stream transmission demand and the second indicator of the monitoring video stream transmission demand, or experienced experts in the field of geriatric medicine can determine the boundaries of abnormal behavior of the elderly based on their long-term accumulated experience and thus set these indicators.

[0084] If the monitoring video stream transmission demand indicator parameter of the visual monitoring area is less than the first indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the first demand transmission level; if the monitoring video stream transmission demand indicator parameter of the visual monitoring area is greater than or equal to the first indicator of the monitoring video stream transmission demand and less than or equal to the second indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the second demand transmission level; if the monitoring video stream transmission demand indicator parameter of the visual monitoring area is greater than the second indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the third demand transmission level.

[0085] It should be noted that if the monitoring video stream transmission demand indicator parameter of the visible monitoring area is less than the first indicator of the monitoring video stream transmission demand, it means that the abnormal degree of the elderly's behavior is relatively low and has not reached a more serious level. Therefore, the monitoring video stream transmission indicator label is marked as the first demand transmission level, which is the least urgent transmission demand. If the monitoring video stream transmission demand indicator parameter of the visible monitoring area is greater than or equal to the first indicator of the monitoring video stream transmission demand and less than or equal to the second indicator of the monitoring video stream transmission demand, it means that the abnormal degree of the elderly's behavior is at a medium level, between the first judgment indicator and the second judgment indicator. At this time, the monitoring video stream transmission indicator label should be marked as the second demand transmission level. If the monitoring video stream transmission demand indicator parameter of the visible monitoring area is greater than the second indicator of the monitoring video stream transmission demand, it means that the abnormal degree of the elderly's behavior is high and has exceeded the range defined by the second judgment indicator. Therefore, the monitoring video stream transmission indicator label should be marked as the third demand transmission level, so as to distinguish different degrees of abnormal behavior and take corresponding measures.

[0086] The present invention obtains monitoring video stream transmission indicator labels through processing, and can accurately classify the transmission requirements of video streams according to the monitoring video stream transmission requirement indicator parameters of the visible monitoring area, which helps to improve the efficiency and pertinence of the entire monitoring system. Relevant personnel can quickly understand the general situation of the elderly's behavior according to the transmission indicator labels, and give priority to processing video streams with higher abnormalities, thereby more effectively ensuring the safety and health of the elderly. Through the reasonable processing of video stream transmission indicator labels, it is also possible to better adapt to different network environments and monitoring needs, achieve optimal allocation of resources, and improve the overall performance of the system.

[0087] S4, lock the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, recorded as the communication transmission channel, thereby obtaining the status data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel.

[0088] In this embodiment, the status data of the communication transmission channel includes the signal strength, signal-to-noise ratio, transmission delay duration and the total number of transmission tasks executed by the channel.

[0089] It should be noted that the status data of the communication transmission channel is collected by a sensor device of the communication transmission channel, and the sensor collection device includes a signal strength tester, a spectrum analyzer, a network traffic detector and a network management system.

[0090] In this embodiment, the adjustment execution parameters of the communication transmission channel are obtained, and the specific process is as follows:

[0091] Reference state data of the communication transmission channel is extracted, wherein the reference state data of the communication transmission channel includes a reference signal strength, a reference signal-to-noise ratio, a reference transmission delay duration, and a total number of ideal transmission tasks of the channel.

[0092] It should be noted that the reference state data of the communication transmission channel is obtained by performing mean processing on the historical state data of the communication transmission channel before establishing the database.

[0093] Based on the state data of the communication transmission channel and the reference state data of the communication transmission channel, the transmission state factor of the communication transmission channel is obtained through analysis and processing.

[0094] The transmission state factor of the communication transmission channel is used to characterize the transmission stability of the communication transmission channel.

[0095] In a specific embodiment, the transmission state factor of the communication transmission channel is obtained in the following manner:

[0096]

[0097] Among them, A is the transmission state factor of the communication transmission channel, a is the signal strength of the communication transmission channel, and a 0 is the reference signal strength of the communication transmission channel, b is the signal-to-noise ratio of the communication transmission channel, and b 0 is the reference signal-to-noise ratio of the communication transmission channel, c is the transmission delay time of the communication transmission channel, and c 0 is the reference transmission delay time of the communication transmission channel, d is the total number of transmission tasks executed by the communication transmission channel, and d 0 is the total number of ideal transmission tasks of the communication transmission channel, x 1 is the signal strength weight, x 2 is the signal-to-noise ratio weight, x 3 is the transmission delay time weight, x 4 is the weight of the total number of transmission tasks executed by the channel, which is a natural constant.

[0098] It should be understood that the sigmoid function is a built-in function in Pytnon.

[0099] It should be noted that the signal strength weight, signal-to-noise ratio weight, transmission delay time weight and the total number of transmission tasks executed by the channel all have a value range between 0 and 1. In this embodiment, the signal strength weight is a signal strength weight preset in the database, which indicates the numerical value of the influence of the signal strength on the transmission state factor of the communication transmission channel. The signal-to-noise ratio weight is a signal-to-noise ratio weight preset in the database, which indicates the numerical value of the influence of the signal-to-noise ratio on the transmission state factor of the communication transmission channel. The transmission delay time weight is a transmission delay time weight preset in the database, which indicates the numerical value of the influence of the transmission delay time on the transmission state factor of the communication transmission channel. The total number of transmission tasks executed by the channel weight is a total number of transmission tasks executed by the channel preset in the database, which indicates the total number of transmission tasks executed by the channel. The numerical value of the degree of influence on the transmission state factor of the communication transmission channel can be directly obtained from the database when used. The preset signal strength weight, signal-to-noise ratio weight, transmission delay time weight and the total weight of the channel executing transmission tasks can be a preset mapping relationship. For example, the signal strength, signal-to-noise ratio, transmission delay time and the total number of transmission tasks executed by the channel are respectively mapped with the preset signal strength weight, signal-to-noise ratio weight, transmission delay time weight and the total number of transmission tasks executed by the channel in the database to form a mapping set, and the real-time signal strength, signal-to-noise ratio, transmission delay time and the total number of transmission tasks executed by the channel are respectively input into the mapping set to obtain the signal strength weight, signal-to-noise ratio weight, transmission delay time weight and the total number of transmission tasks executed by the channel, wherein the mapping relationship is one-to-one correspondence.

[0100] It should also be noted that the present embodiment obtains the transmission state factor of the communication transmission channel by analyzing and processing the state data of the communication transmission channel, taking into account the mutual influence between these parameters. For example, the higher the signal strength, the greater the signal-to-noise ratio in the same noise environment, because the signal is relatively stronger and the influence of noise is relatively smaller. The size of the signal-to-noise ratio will also affect the transmission delay duration. A higher signal-to-noise ratio means better signal quality, and the number of errors and the need for retransmission during transmission may be reduced, thereby helping to reduce the transmission delay duration. The total number of transmission tasks executed by the channel will also affect the signal strength and signal-to-noise ratio. If the total number of transmission tasks executed is too large, the signal strength may be relatively weakened when it is assigned to each task, and more noise may also be introduced to reduce the signal-to-noise ratio. The transmission delay duration will in turn affect the total number of transmission tasks executed by the channel. A longer transmission delay may lead to task accumulation, which increases the total number of transmission tasks executed by the channel, and too many tasks may further increase the transmission delay. These parameters are interrelated and influence each other, and act together on the performance of the communication transmission channel.

[0101] By analyzing the transmission state factor of the communication transmission channel, the present invention can accurately evaluate the transmission stability of the channel and promptly determine whether the channel needs to be adjusted. When the transmission state factor exceeds the threshold, the direction and degree of adjustment can be quickly determined, and the corresponding adjustment execution parameters can be extracted to optimize the channel, thereby ensuring the stability and reliability of communication transmission, avoiding problems such as freezing and interruption in video stream transmission, and avoiding waste caused by excessive configuration of resources or insufficient configuration affecting the transmission effect through accurate grasp of the channel status, thereby improving the operating efficiency and performance of the entire monitoring system and ensuring that the monitoring video stream can be efficiently and stably transmitted to the master control display terminal.

[0102] The channel transmission state factor threshold corresponding to each transmission indication label in the database is extracted, and the channel transmission state factor threshold corresponding to the transmission indication label of the monitoring video stream is mapped and extracted, which is recorded as the transmission state factor verification threshold. The transmission state factor of the communication transmission channel and the transmission state factor verification threshold are differenced to obtain the transmission deviation value of the communication transmission channel.

[0103] It should be understood that the transmission state factor verification threshold corresponding to each transmission indication tag is extracted from a database and is used to reflect the critical state of the communication transmission channel under different transmission requirements.

[0104] In a specific embodiment, the transmission state factor verification threshold corresponding to each transmission indicator label is pre-set in the database and can be obtained by a variety of methods. For example, a large number of state parameter sample data of the communication transmission channel under different communication environments, different transmission task amounts and different signal quality conditions are collected, and these sample data are subjected to detailed statistical analysis and processing to obtain the transmission state evaluation value of the communication transmission channel, and then these evaluation values ​​are weighted averaged or calculated by taking specific percentile values ​​to obtain the transmission state factor verification threshold corresponding to each transmission indicator label, or experienced experts in the field of communication technology can judge the transmission state limit of the communication transmission channel based on their long-term accumulated experience to set the threshold.

[0105] If the transmission deviation value of the communication transmission channel is less than or equal to zero, no communication channel adjustment is performed.

[0106] If the transmission deviation value of the communication transmission channel is greater than zero, the adjustment parameters corresponding to the transmission deviation value intervals of each communication transmission channel preset in the database are extracted, and the adjustment parameters corresponding to the transmission deviation values ​​of the extracted communication transmission channels are mapped and marked as the adjustment execution parameters of the communication transmission channels.

[0107] It should be noted that if the transmission state factor of the communication transmission channel is less than or equal to the transmission state factor verification threshold, it means that the state of the current communication transmission channel meets the preset critical standard, and its transmission stability is within an acceptable range, and there is no need to perform communication channel adjustment. If the transmission state factor of the communication transmission channel is greater than the transmission state factor verification threshold, it means that the state of the current communication transmission channel exceeds the preset critical standard, and its transmission stability is insufficient. It is necessary to extract the adjustment parameters corresponding to each transmission state deviation factor interval preset in the database, and map the adjustment parameters corresponding to the transmission state deviation factor of the communication transmission channel, that is, the adjustment execution parameters of the communication transmission channel, and adjust the communication transmission channel to improve its transmission stability.

[0108] In a specific embodiment, if the transmission deviation value of the communication transmission channel is greater than zero, the adjustment parameter corresponding to the transmission deviation value interval of the communication transmission channel is extracted, and the channel bandwidth is adjusted to improve the transmission stability of the channel.

[0109] By acquiring the status data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel, the adjustment execution parameters can be accurately determined according to the actual status of the communication transmission channel and the needs of video stream transmission, ensuring that the communication transmission channel can adapt to the requirements of the monitoring system in the best state, effectively improving the stability and reliability of communication transmission, and ensuring that the monitoring video can be stably transmitted to the master control display terminal. At the same time, through the reasonable adjustment of the communication transmission channel, resource utilization can be optimized, over-configuration or under-configuration can be avoided, and the operating efficiency and performance of the entire monitoring system can be improved.

[0110] S5, the communication transmission channel is adjusted and processed for rapid response by adjusting the execution parameters, and the monitoring video stream is transmitted to the master control display terminal after the adjustment process.

[0111] See also Figure 2 As shown, an embodiment of the present invention provides an intelligent monitoring data processing system for the elderly based on artificial intelligence, which is characterized by: including: an elderly behavior data collection module, which is used to count the coverage monitoring area corresponding to the monitoring devices installed in the area, recorded as a visible monitoring area, and collect elderly behavior data for the visible monitoring area through the monitoring devices within a preset monitoring period, thereby obtaining the elderly behavior collection data in the visible monitoring area.

[0112] The monitoring device configuration control module is used to collect data based on the elderly behavior in the visual monitoring area, and simultaneously obtain the sensor interference data in the visual monitoring area for analysis, so as to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby giving corresponding configuration control to the monitoring device according to the sensor utilization load factor.

[0113] The monitoring video stream transmission indicator label analysis module is used to record the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and obtaining the monitoring video stream transmission indicator label in combination with the sensor interference data analysis and processing of the visible monitoring area.

[0114] The communication transmission channel status analysis module is used to lock the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, recorded as the communication transmission channel, thereby obtaining the status data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel.

[0115] The communication transmission channel response adjustment module is used to quickly respond to and adjust the communication transmission channel by adjusting the execution parameters, and transmit the monitoring video stream to the master control display terminal after the adjustment process.

[0116] The present invention provides an intelligent monitoring data processing method and system for the elderly based on artificial intelligence. In terms of data collection and analysis, the comprehensive collection and analysis of the elderly behavior data and sensor interference data in the visual monitoring area can make it better adapt to different environments and elderly behavior characteristics, and improve the effectiveness and accuracy of monitoring. Secondly, in the monitoring of abnormal behavior of the elderly, by analyzing and processing a variety of behavioral data of the elderly, and setting different video stream transmission indicator label levels according to different judgment indicators, the abnormal behavior of the elderly can be fed back and processed in a timely and targeted manner, providing more reliable protection for the safety of the elderly. In terms of communication transmission, by analyzing and processing the status data of the communication transmission channel, and reasonably adjusting the communication transmission channel based on this, it is ensured that the monitoring video stream can be stably and efficiently transmitted to the master control display terminal, ensuring the smooth operation of the entire monitoring system.

[0117] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0118] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the present invention, they should all belong to the protection scope of the present invention.

Claims

1. An intelligent monitoring data processing method for the elderly based on artificial intelligence, characterized by: The following steps are involved: S1, counting the covered monitoring area corresponding to the monitoring devices installed in the area, recording it as the visible monitoring area, and collecting the behavior data of the elderly in the visible monitoring area through the monitoring devices within a preset monitoring period, thereby obtaining the collected behavior data of the elderly in the visible monitoring area; S2, based on the elderly behavior data collected in the visual monitoring area, and simultaneously obtaining the sensor interference data of the visual monitoring area for analysis, to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby configuring and controlling the monitoring device accordingly according to the sensor utilization load factor; S3, recording the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and combining the sensor interference data of the visible monitoring area with analysis and processing to obtain a monitoring video stream transmission indicator label; S4, locking the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, recorded as the communication transmission channel, thereby obtaining the state data of the communication transmission channel and analyzing the monitoring video stream transmission indicator label to obtain the adjustment execution parameter of the communication transmission channel; S5, the communication transmission channel is adjusted and processed for rapid response by adjusting the execution parameters, and the monitoring video stream is transmitted to the master control display terminal after the adjustment process.

2. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 1 is characterized by: The data collected on the elderly behavior in the visual monitoring area include the average daily flow of people in the visual monitoring area, the average daily activity density of the elderly, the average daily activity frequency of the elderly and the average movement speed of the elderly.

3. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 1 is characterized by: The sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is analyzed as follows: Based on the elderly behavior collection data in the visual monitoring area and the sensor interference data in the visual monitoring area, the sensor interference data in the visual monitoring area includes the average daily temperature, average daily light intensity, average daily noise level and average daily electromagnetic interference intensity in the visual monitoring area, thereby analyzing and obtaining the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area; The sensor utilization load factor corresponding to the monitoring device in the visual monitoring area is a numerical result of quantifying the elderly behavior collection data and the sensor interference data in the visual monitoring area, and is used to characterize the configuration control requirement degree of the monitoring device in the visual monitoring area.

4. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 3 is characterized by: The monitoring device is configured and controlled accordingly based on the load factor of the sensor, and the specific process is as follows: Extracting the sensor utilization load factor threshold corresponding to the monitoring device of the visual monitoring area preset in the database; Performing difference processing on the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area and the sensor utilization load factor threshold corresponding to the monitoring device in the visual monitoring area to obtain the sensor utilization load deviation factor corresponding to the monitoring device in the visual monitoring area; Extract the monitoring device configuration control parameters corresponding to each sensor utilization load deviation factor interval stored in the database, and map and extract the monitoring device configuration control parameters corresponding to the sensor utilization load deviation factor corresponding to the monitoring device in the visible monitoring area, and mark them as the configuration control parameters corresponding to the monitoring device in the visible monitoring area; The monitoring device is configured and controlled accordingly based on the configuration control parameters corresponding to the monitoring device in the visible monitoring area.

5. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 1 is characterized by: The specific process of using the monitoring device to perform intelligent monitoring of the elderly in the visual monitoring area is as follows: During a preset monitoring period, the elderly are intelligently monitored in the visible monitoring area by using a suitable monitoring device to obtain a behavior monitoring video stream of the elderly, from which basic data of the elderly are extracted, the basic data of the elderly including the shoulder width, body contour volume and height of the elderly; Perform quantized addition processing on the basic data of the elderly to obtain the characteristic values ​​of the basic data of the elderly; Extract the reference data set of abnormal behavior of the elderly corresponding to each basic data characteristic value interval in the database, and match it to obtain the reference data for monitoring video stream transmission demand determination; Synchronously, according to the elderly behavior monitoring video stream, the center point of the elderly's head is identified and located, and then the movement curve of the center point of the elderly's head in a preset monitoring period is monitored to obtain the movement curve of the center point of the elderly's head in the preset monitoring period, identify and locate the starting position point and the ending position point of the center point of the elderly's head in the monitoring period, and obtain the straight-line interval distance between the starting position point and the ending position point of the center point of the elderly's head in the monitoring period, which is recorded as the straight-line distance of the elderly's head movement; The time taken by the center point of the elderly's head to move from the starting position to the ending position during the monitoring period is extracted, which is recorded as the elderly's head movement time, and the length of the moving curve of the center point of the elderly's head during the preset monitoring period is extracted, and the moving speed of the center point of the elderly's head during the monitoring period is calculated, which is recorded as the elderly's head movement speed; Identify and locate the center point of the first knee joint and the center point of the second knee joint of the elderly, and then monitor the movement curve of the center point of the first knee joint and the center point of the second knee joint of the elderly within a preset monitoring period, obtain the movement curve of the center point of the first knee joint and the center point of the second knee joint of the elderly within the preset monitoring period and its corresponding length, perform difference processing on the length of the movement curve of the center point of the first knee joint of the elderly within the preset monitoring period and the length of the movement curve of the center point of the second knee joint of the elderly within the preset monitoring period to obtain the deviation value of the length of the movement curve of the center point of the knee joint of the elderly within the preset monitoring period, and record it as the deviation value of the knee joint movement distance of the elderly; Identify and locate the center point of the ankle joint of the elderly, and then monitor the movement curve of the center point of the ankle joint of the elderly within a preset monitoring period, obtain the movement curve of the center point of the ankle joint of the elderly within the preset monitoring period and its corresponding length, and thereby calculate the movement rate of the center point of the ankle joint of the elderly; The straight-line distance of the elderly's head movement, the elderly's head movement speed, the elderly's knee joint movement distance deviation value and the movement rate of the elderly's ankle joint center point are combined as monitoring video stream transmission demand determination data.

6. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 5 is characterized by: The processing obtains the monitoring video stream transmission indicator label, and the specific processing process is as follows: According to the monitoring video stream transmission demand determination data and the monitoring video stream transmission demand determination reference data, analyzing and processing to obtain a monitoring video stream transmission demand determination index, wherein the monitoring video stream transmission demand determination index is used to characterize the abnormal degree of the elderly's behavior in the visual monitoring area; According to the sensor interference data of the visual monitoring area, the sensor interference factor of the visual monitoring area is obtained by analyzing and processing; Extract the monitoring device correction coefficient corresponding to each sensor interference factor interval stored in the database, and map and extract the monitoring device correction coefficient corresponding to the sensor interference factor of the visible monitoring area, and mark it as the sensor interference factor of the visible monitoring area; According to the monitoring video stream transmission demand indicator parameters of the visual monitoring area and the sensor interference factor of the visual monitoring area, the monitoring video stream transmission demand indicator parameters of the visual monitoring area are obtained by analysis and processing; Extracting a first monitoring video stream transmission requirement indicator and a second monitoring video stream transmission requirement indicator preset in a database; If the monitoring video stream transmission demand indicator parameter of the visual monitoring area is less than the first indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the first demand transmission level; if the monitoring video stream transmission demand indicator parameter of the visual monitoring area is greater than or equal to the first indicator of the monitoring video stream transmission demand and less than or equal to the second indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the second demand transmission level; if the monitoring video stream transmission demand indicator parameter of the visual monitoring area is greater than the second indicator of the monitoring video stream transmission demand, the monitoring video stream transmission indicator label of the intelligent monitoring video stream for the elderly in the visual monitoring area shall be marked as the third demand transmission level.

7. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 1 is characterized by: The status data of the communication transmission channel includes the signal strength, signal-to-noise ratio, transmission delay duration and the total number of transmission tasks executed by the channel.

8. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 7 is characterized by: The specific process of obtaining the adjustment execution parameters of the communication transmission channel is as follows: Extracting reference state data of the communication transmission channel, wherein the reference state data of the communication transmission channel includes a reference signal strength, a reference signal-to-noise ratio, a reference transmission delay duration, and a total number of ideal transmission tasks of the channel; Based on the state data of the communication transmission channel and the reference state data of the communication transmission channel, analyzing and processing to obtain the transmission state factor of the communication transmission channel; Extract the channel transmission state factor threshold corresponding to each transmission indicator label in the database, and map and extract the channel transmission state factor threshold corresponding to the monitoring video stream transmission indicator label, record it as the transmission state factor verification threshold, and perform difference processing on the transmission state factor of the communication transmission channel and the transmission state factor verification threshold to obtain the transmission deviation value of the communication transmission channel; If the transmission deviation value of the communication transmission channel is less than or equal to zero, the communication channel adjustment is not performed; If the transmission deviation value of the communication transmission channel is greater than zero, the adjustment parameters corresponding to the transmission deviation value intervals of each communication transmission channel preset in the database are extracted, and the adjustment parameters corresponding to the transmission deviation values ​​of the extracted communication transmission channels are mapped and marked as the adjustment execution parameters of the communication transmission channels.

9. The method for processing data for elderly intelligent monitoring based on artificial intelligence according to claim 8 is characterized by: The transmission state factor of the communication transmission channel is specifically obtained in the following manner: Among them, A is the transmission state factor of the communication transmission channel, a is the signal strength of the communication transmission channel, a0 is the reference signal strength of the communication transmission channel, b is the signal-to-noise ratio of the communication transmission channel, b0 is the reference signal-to-noise ratio of the communication transmission channel, c is the transmission delay duration of the communication transmission channel, c0 is the reference transmission delay duration of the communication transmission channel, d is the total number of transmission tasks executed by the communication transmission channel, d0 is the total number of ideal transmission tasks of the communication transmission channel, x1 is the signal strength weight, x2 is the signal-to-noise ratio weight, x3 is the transmission delay duration weight, x4 is the weight of the total number of transmission tasks executed by the channel, which is a natural constant.

10. The intelligent monitoring data processing system for the elderly based on artificial intelligence is characterized by: include: The elderly behavior data collection module is used to count the coverage monitoring area corresponding to the monitoring devices installed in the area, which is recorded as the visible monitoring area, and collect the elderly behavior data of the visible monitoring area through the monitoring devices within a preset monitoring period, thereby obtaining the elderly behavior collection data of the visible monitoring area; The monitoring device configuration control module is used to collect data based on the elderly behavior in the visual monitoring area, and simultaneously obtain the sensor interference data in the visual monitoring area for analysis, so as to obtain the sensor utilization load factor corresponding to the monitoring device in the visual monitoring area, thereby controlling the configuration of the monitoring device accordingly according to the sensor utilization load factor; The monitoring video stream transmission indicator label analysis module is used to record the monitoring device after configuration control as an applicable monitoring device, thereby performing intelligent monitoring of the elderly in the visible monitoring area through the applicable monitoring device, and combining the sensor interference data analysis and processing of the visible monitoring area to obtain the monitoring video stream transmission indicator label; The communication transmission channel state analysis module is used to lock the channel between the communication transmission node of the monitoring device and the corresponding communication receiving gateway node, which is recorded as the communication transmission channel, thereby obtaining the state data of the communication transmission channel and combining it with the monitoring video stream transmission indicator label analysis to obtain the adjustment execution parameters of the communication transmission channel; The communication transmission channel response adjustment module is used to quickly respond to and adjust the communication transmission channel by adjusting the execution parameters, and transmit the monitoring video stream to the master control display terminal after the adjustment process.

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