Medical health early warning system and method based on 4G communication mode

Abstract

The invention relates to the technical field of medical health early warning, in particular to a medical health early warning system and method based on a 4G communication mode. A data feature analysis module in the system calls a preset collection frequency in a database when a capacitance fluctuation value of a capacitance sensor in a paper diaper of a user in the recent unit time is larger than a fluctuation threshold value; keeping the called acquisition frequency unchanged until the medical staff finishes processing; fitting the relation function of the capacitance data acquisition result of the capacitance sensor in the paper diaper of the user along with the change of the acquisition time, and extracting capacitance change characteristics in the fitting relation; and user demand type identification is carried out based on the obtained capacitance change characteristics. In the process of monitoring the incontinence behavior of the elderly through the built-in sensor, the change relation of the capacitance data acquisition result of the capacitance sensor in the paper diaper of the user along with the acquisition time is fitted, the fitting result is subjected to capacitance change characteristics, and the defecation behavior and the urination behavior of the elderly are accurately distinguished based on the obtained capacitance change characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of medical and health warning, and specifically to a medical and health warning system and method based on a 4G communication mode. Background Art

[0002] Incontinence is a common problem among the elderly, which will affect the comfort of the elderly and even cause certain diseases; for the elderly who are unable to send signals independently, how to detect the incontinence of the elderly in a timely manner and manage it is a major problem in the current field.

[0003] With the rapid development of sensor technology in recent years, it has gradually become a trend to use sensors to monitor the urinary incontinence of the elderly in nursing homes in real time. There are adult diapers on the existing market, which often monitor the incontinence behavior of the elderly through built-in sensors, but they cannot effectively distinguish the defecation behavior and urination behavior of the elderly; at the same time, in special scenarios (such as nursing homes), there is often a situation where one caregiver serves multiple elderly people at the same time (usually it takes a certain amount of time to change the adult diaper for the elderly each time), and the prior art cannot dynamically update the service execution route and user status according to the actual environment. Therefore, there are major defects in the prior art. Summary of the Invention

[0004] The purpose of the present invention is to provide a medical and health warning system and method based on a 4G communication mode to solve the problems raised in the above background art.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: A medical and health warning method based on a 4G communication mode, including: Step S100: Collect capacitance data of a user wearing an adult diaper with an in-built capacitive sensor, upload the capacitance data collection result to the cloud through a 4G communication mode, and dynamically adjust the capacitance data collection frequency according to the user's historical monitoring data and the current diaper wearing status of the user; Step S200: When the capacitance fluctuation value of the capacitive sensor in the user's adult diaper is greater than the fluctuation threshold within the most recent unit time, retrieve the preset collection frequency in the database and keep the retrieved collection frequency unchanged until the medical staff finishes handling; fit the relationship function between the capacitance data collection result of the capacitive sensor in the user's adult diaper and the collection time, extract the capacitance change characteristics in the fitting relationship; and identify the type of user needs based on the obtained capacitance change characteristics; Step S300: Combine the type of user needs identification result and the capacitance data collection result to calculate the emergency demand coefficient of the user; Step S400: Obtain the emergency demand coefficient corresponding to each user received from the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generate the best execution planning route for emergency demand and feed it back to the medical staff, and change the diapers for the users in need in turn according to the best execution planning route for emergency demand, and dynamically update the wearing status of the corresponding diapers after the change.

[0006] Furthermore, the step S100 includes: The capacitance data acquisition result includes the capacitance monitoring value and the corresponding acquisition time; In the process of dynamically adjusting the capacitance data acquisition frequency according to the user's historical monitoring data and the user's diaper wearing status at the current time, the diaper wearing status of the user at the current time includes the usage time of the corresponding user's diaper and the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time; the usage time of the corresponding user's diaper indicates the interval between the initial replacement time of the corresponding user's diaper and the current time; the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time is equal to the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value of the capacitance sensor in the user's diaper within the most recent unit time; Calculate the acquisition frequency adjustment coefficient of the built-in capacitive sensor of the user's diaper at the current time. The calculation formula is as follows: ; Among them, τ represents the acquisition frequency adjustment coefficient of the built-in capacitive sensor in the user's diaper at the current time; T represents the usage time of the user's diaper; CA represents the capacitance fluctuation value of the capacitive sensor in the user's diaper in the most recent unit time; P1 {T,CA} P2 represents the ratio of the frequency statistics of the corresponding user's diaper changing times in the historical database when the corresponding T value is less than or equal to the current user's diaper usage time when the corresponding CA is equal to the fluctuation threshold before each diaper change; {T,CA} It represents the ratio of the frequency statistics of the T value corresponding to the corresponding CA equal to the fluctuation threshold before each diaper change to the number of diaper changes of all users in the historical database in the historical monitoring data of all users; β represents a preset constant; The current time, the acquisition frequency of the built-in capacitive sensor of the user's diaper is adjusted to be equal to the initial acquisition frequency preset in the database and e τ The product of , e is a natural constant.

[0007] The present invention combines the user's diaper usage time, the user's corresponding capacitance data collection and historical monitoring data for comprehensive analysis, and realizes the adjustment management of the data collection frequency of the capacitance sensor built into the user's diaper. The larger the collection frequency adjustment coefficient of the capacitance sensor built into the user's diaper at the current time, the higher the adjusted collection frequency. It can effectively reduce the user's data processing volume under normal conditions and save cloud computing resources.

[0008] Furthermore, the step S200 includes: Step S201, retrieve the capacitance data collection result corresponding to the collection frequency of the built-in capacitance sensor of the user's diaper when it is the collection frequency preset in the database; construct a capacitance monitoring data analysis pair corresponding to each collection time, wherein the first parameter of the capacitance monitoring data analysis pair is the collection time, and the second parameter is the capacitance monitoring value corresponding to the corresponding collection time; Step S202: y=a / (1+e -x )+b is a function model to fit the constructed capacitance monitoring data analysis pair, and obtain the relationship function fitting result of the capacitance data collection result of the capacitance sensor in the user's diaper with the collection time, wherein a and b are constants, y is the capacitance monitoring value, and x is the collection time; Step S203, obtaining a derivative function of a fitting result of a relationship function between a capacitance data collection result of a capacitance sensor in a user's diaper and a collection time, and recording the result as a feature extraction function of the corresponding user; generating a capacitance change feature in the fitting relationship, wherein the capacitance change feature includes a maximum function value in the feature extraction function of the corresponding user, an interval length of a time interval in which a corresponding function value is greater than a preset function value, and a quotient of an integral value of the feature extraction function of the corresponding user in a time interval in which a corresponding function value is greater than a preset function value divided by a corresponding interval length; Step S204: Calculate the user demand category evaluation coefficient according to the obtained capacitance change characteristics, and the calculation formula is as follows: ; Wherein, W represents the user demand category evaluation coefficient corresponding to the obtained capacitance change feature; YD represents the maximum function value in the feature extraction function of the corresponding user in the capacitance change feature; LD represents the interval length of the time interval in which the corresponding function value in the capacitance change feature is greater than the preset function value; YDP represents the quotient of the integral value of the feature extraction function of the corresponding user in the capacitance change feature in the time interval in which the corresponding function value is greater than the preset function value divided by the corresponding interval length; Respectively represent the preset first evaluation conversion factor and the second evaluation conversion factor; Step S205: Identify the user demand type corresponding to the obtained capacitance change feature. When the user demand type evaluation coefficient W is greater than or equal to the preset evaluation value, it is determined that the user demand type corresponding to the obtained capacitance change characteristic is the first demand type (the type with only urination behavior); otherwise, it is determined that the user demand type corresponding to the obtained capacitance change characteristic is the second demand type (the type with both urination behavior and defecation behavior).

[0009] Further, the formula for calculating the emergency demand coefficient of the user in step S300 is as follows: ; where G represents the emergency demand coefficient of the user; CAC represents the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value in the capacitance data acquisition result of the capacitance sensor in the user's diaper; TC represents the time interval between the time point corresponding to the maximum function value in the feature extraction function of the corresponding user and the current time; r represents the normalization weight coefficient.

[0010] Further, in step S400, each user with an emergency demand coefficient greater than the preset demand coefficient is used as an emergency treatment object, and the order of the obtained emergency treatment objects is randomly combined. The splicing result of the shortest planned path segments between the positions of any two adjacent users in the random combination scheme is used as an emergency demand execution planning route. The shortest planned path segment between the positions of the two adjacent users is obtained by querying the preset form in the database; according to the sorting result of the emergency treatment objects in descending order of the emergency demand coefficient, a first emergency analysis sequence is obtained; Calculate the scenario adaptation value of the emergency demand execution planning route, and use the emergency demand execution planning route with the largest scenario adaptation value as the best emergency demand execution planning route. The involved calculation formula is as follows: ; where SP k represents the scenario adaptation value of the kth emergency demand execution planning route; ik represents the number of emergency treatment objects in the kth emergency demand execution planning route; G （k，i） represents the emergency demand coefficient of the ith emergency treatment object passed by the kth emergency demand execution planning route; F （k，i） represents the influence factor corresponding to the serial number of the ith emergency treatment object passed by the kth emergency demand execution planning route in the first emergency analysis sequence. The influence factor of each serial number in the first emergency analysis sequence is obtained by querying the preset form in the database; τ （k，i，j） represents the acquisition frequency adjustment coefficient of the built-in capacitance sensor of the diaper obtained most recently by the jth user who does not belong to the emergency treatment object in the planned area where the ith emergency treatment object passed by the kth emergency demand execution planning route is located; the planned area where each user is located is preset; J （k，i）It represents the number of users who do not belong to the emergency treatment objects within the planned area where the \(i\)-th emergency treatment object passed by in the planned route of the \(k\)-th emergency requirement execution; \(g\) represents the weight constant.

[0011] Further, during the process of executing the optimal execution planned route of the emergency requirement in step S400, the unexecuted route segments in the optimal execution planned route of the emergency requirement are extracted in real time; all emergency treatment objects whose shortest distance between the newly generated position during the execution process and the unexecuted route segments in the optimal execution planned route of the emergency requirement is less than the preset distance are marked, and the emergency treatment object closest to the unexecuted route segments in the optimal execution planned route of the emergency requirement among the marked emergency treatment objects is retrieved and recorded as the reference object corresponding to the corresponding mark; and the corresponding marked emergency treatment object is inserted into the next execution object of the corresponding marked reference object, and the route segment between the corresponding marked emergency treatment object and the corresponding marked reference object is obtained by querying the database. In the updated optimal execution planned route of the emergency requirement, the sequence order among the various emergency treatment objects within the unexecuted route segments in the optimal execution planned route of the emergency requirement before the update remains unchanged; each time the optimal execution planned route of the emergency requirement is updated, it is fed back to the medical staff. After changing the diaper for the user in need, update the usage duration of the corresponding user's diaper in the diaper wearing status to 0, and when the user's diaper replacement time is less than the unit time, record the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time as 0.

[0012] In the process of feeding back the optimal execution planned route of the emergency requirement to the medical staff in the present invention, the various emergency treatment objects passed by in the optimal execution planned route of the emergency requirement and the recognition results of the user demand types corresponding to each emergency treatment object are also fed back together.

[0013] A medical and health early warning system based on the 4G communication mode, the system includes a data collection and communication transmission module, a data feature analysis module, a user demand analysis module and a data early warning management module; The data collection and communication transmission module collects capacitance data of users wearing built-in capacitive sensing diapers, uploads the capacitance data collection results to the cloud through the 4G communication mode, and dynamically adjusts the capacitance data collection frequency according to the user's historical monitoring data and the current diaper wearing status of the user. When the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time is greater than the fluctuation threshold, the data feature analysis module retrieves the preset acquisition frequency in the database and keeps the retrieved acquisition frequency unchanged until the medical staff finishes the processing; fits the relationship function between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, extracts the capacitance change features in the fitting relationship; and identifies the types of user needs based on the obtained capacitance change features. The user need analysis module calculates the emergency need coefficient of the user in combination with the identification result of the user need type and the capacitance data acquisition result. The data warning management module obtains the emergency need coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generates the optimal execution planning route for the emergency need and feeds it back to the medical staff, and sequentially changes the diapers for the users in need according to the optimal execution planning route for the emergency need, and dynamically updates the wearing status of the corresponding diapers after the change.

[0014] Further, the data feature analysis module includes a frequency modulation management unit, a capacitance change feature extraction unit, and a user need analysis unit. When the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time is greater than the fluctuation threshold, the frequency modulation management unit retrieves the preset acquisition frequency in the database and keeps the retrieved acquisition frequency unchanged until the medical staff finishes the processing. The capacitance change feature extraction unit fits the relationship function between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, and extracts the capacitance change features in the fitting relationship. The user need analysis unit identifies the types of user needs based on the obtained capacitance change features.

[0015] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: (1) The present invention comprehensively analyzes multiple factors such as the usage duration of the user's diaper, the capacitance data acquisition corresponding to the user, and the historical monitoring data, realizes the adjustment and management of the data acquisition frequency of the built-in capacitance sensor of the user's diaper, and can effectively reduce the data processing volume of the user in the normal state and save the computing power resources of the cloud. (2) When the present invention monitors the incontinence behavior of the elderly through the built-in sensor, it fits the relationship between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, performs capacitance change features on the fitting result, and accurately distinguishes the defecation behavior and urination behavior of the elderly based on the obtained capacitance change features. (3) By analyzing the emergency demand coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, the present invention realizes the dynamic screening of the optimal execution planning route for emergency demands and gives real-time warnings to medical staff, facilitating the medical staff to accurately understand the user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a schematic structural diagram of a medical and health warning system based on the 4G communication mode of the present invention; Figure 2 is a schematic flow diagram of a medical and health warning method based on the 4G communication mode of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0018] The present invention provides a technical solution: As Figure 1 shown, in this embodiment, a medical and health warning system based on the 4G communication mode, the system includes a data collection and communication transmission module, a data feature analysis module, a user demand analysis module, and a data warning management module; The data collection and communication transmission module collects capacitance data of users wearing built-in capacitive sensing diapers, uploads the capacitance data collection results to the cloud through the 4G communication mode, and dynamically adjusts the capacitance data collection frequency according to the historical monitoring data of the users and the diaper wearing status of the users at the current time; The data feature analysis module includes a frequency modulation management unit, a capacitance change feature extraction unit, and a user demand analysis unit, When the capacitance fluctuation value of the capacitance sensor in the user's diaper is greater than the fluctuation threshold within the most recent unit time, the frequency modulation management unit retrieves the preset collection frequency in the database and keeps the retrieved collection frequency unchanged until the medical staff finishes processing; The capacitance change feature extraction unit fits the relationship function between the capacitance data collection results of the capacitance sensor in the user's diaper and the collection time, and extracts the capacitance change features in the fitting relationship; The user demand analysis unit identifies the types of user demands based on the obtained capacitance change features; The user demand analysis module combines the user demand type recognition result and the capacitance data acquisition result to calculate the emergency demand coefficient of the user; The data warning management module obtains the emergency demand coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generates the optimal execution planning route for the emergency demand and feeds it back to the medical staff, and sequentially changes diapers for the users with demands according to the optimal execution planning route for the emergency demand, and dynamically updates the wearing status of the corresponding diapers after the change.

[0019] Such as Figure 2 shown, the medical health warning method based on the 4G communication mode in this embodiment includes: Step S100: Collect capacitance data of users wearing diapers with built-in capacitance sensors, upload the capacitance data acquisition result to the cloud through the 4G communication mode, and dynamically adjust the capacitance data acquisition frequency according to the historical monitoring data of the users and the wearing status of the users' diapers at the current time; The step S100 includes: The capacitance data acquisition result includes the capacitance monitoring value and the corresponding acquisition time; In the process of dynamically adjusting the capacitance data acquisition frequency according to the historical monitoring data of the users and the wearing status of the users' diapers at the current time, the wearing status of the users' diapers at the current time includes the usage duration of the corresponding users' diapers and the capacitance fluctuation value of the capacitance sensor in the users' diapers in the most recent unit time; the usage duration of the corresponding users' diapers represents the interval duration from the initial replacement time of the corresponding users' diapers to the current time; the capacitance fluctuation value of the capacitance sensor in the users' diapers in the most recent unit time is equal to the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value of the capacitance sensor in the users' diapers in the most recent unit time; Calculate the acquisition frequency adjustment coefficient of the capacitance sensor built in the users' diapers at the current time, and the calculation formula is as follows: ; where τ represents the acquisition frequency adjustment coefficient of the capacitance sensor built in the users' diapers at the current time; T represents the usage duration of the users' diapers; CA represents the capacitance fluctuation value of the capacitance sensor in the users' diapers in the most recent unit time; P1 {T,CA} represents the ratio of the statistical result of the frequency that the corresponding T value is less than or equal to the usage duration of the current users' diapers when the corresponding CA is equal to the fluctuation threshold before each diaper change in the historical monitoring data of the corresponding users to the number of diaper changes of the corresponding users in the historical database; P2 {T,CA}It represents the ratio of the frequency statistical result that the T value corresponding to each time the corresponding CA is equal to the fluctuation threshold before changing the diaper is less than or equal to the diaper usage duration of the current user in the historical monitoring data of all users to the number of diaper changes of all users in the historical database; β represents a preset constant; The acquisition frequency of the capacitance sensor inside the diaper of the user at the current time after adjustment is equal to the product of the initial acquisition frequency preset in the database and e τ , where e is the natural constant.

[0020] Step S200: When the capacitance fluctuation value of the capacitance sensor in the user's diaper is greater than the fluctuation threshold in the most recent unit time, retrieve the acquisition frequency preset in the database and keep the retrieved acquisition frequency unchanged until the medical staff finishes the processing; fit the relationship function between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, and extract the capacitance change characteristics in the fitting relationship; and identify the types of user needs based on the obtained capacitance change characteristics; The said step S200 includes: Step S201: Retrieve the acquisition result of the capacitance data when the acquisition frequency of the capacitance sensor inside the user's diaper is the acquisition frequency preset in the database; construct a capacitance monitoring data analysis pair corresponding to each acquisition time, where the first parameter in the capacitance monitoring data analysis pair is the acquisition time and the second parameter is the capacitance monitoring value corresponding to the corresponding acquisition time; Step S202: Fit the constructed capacitance monitoring data analysis pair with the function model of y = a / (1 + e -x ) + b to obtain the fitting result of the relationship function between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, where a and b both represent constants, y represents the capacitance monitoring value, and x represents the acquisition time; Step S203: Obtain the derivative function of the fitting result of the relationship function between the acquisition result of the capacitance data of the capacitance sensor in the user's diaper and the acquisition time, denoted as the feature extraction function of the corresponding user; generate the capacitance change characteristics in the fitting relationship, and the capacitance change characteristics include the maximum function value in the feature extraction function of the corresponding user, the interval length of the time interval where the corresponding function value is greater than the preset function value, and the quotient of the integral value of the feature extraction function of the corresponding user in the time interval where the corresponding function value is greater than the preset function value divided by the corresponding interval length; Step S204: Calculate the user need type evaluation coefficient according to the obtained capacitance change characteristics, and the calculation formula is as follows: ; Wherein, W represents the evaluation coefficient of the type of user requirements corresponding to the obtained capacitance change characteristics; YD represents the maximum function value in the feature extraction function of the corresponding user in the capacitance change characteristics; LD represents the interval length of the time interval in the capacitance change characteristics where the corresponding function value is greater than the preset function value; YDP represents the quotient obtained by dividing the integral value of the feature extraction function of the corresponding user in the capacitance change characteristics within the time interval where the corresponding function value is greater than the preset function value by the corresponding interval length; respectively represent the preset first evaluation conversion factor and the second evaluation conversion factor; Step S205, identify the type of user requirements corresponding to the obtained capacitance change characteristics, When the user requirement type evaluation coefficient W is greater than or equal to the preset evaluation value, it is determined that the type of user requirements corresponding to the obtained capacitance change characteristics is the first type of requirements (the type with only urination behavior); otherwise, it is determined that the type of user requirements corresponding to the obtained capacitance change characteristics is the second type of requirements (the type with both urination behavior and defecation behavior doped).

[0021] Step S300, combine the user requirement type identification result and the capacitance data acquisition result to calculate the emergency requirement coefficient of the user; The formula for calculating the emergency requirement coefficient of the user in the step S300 is as follows: ; Wherein, G represents the emergency requirement coefficient of the user; CAC represents the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value in the capacitance data acquisition result of the capacitance sensor in the user's diaper; TC represents the time interval between the time point corresponding to the maximum function value in the feature extraction function of the corresponding user and the current time; r represents the normalized weight coefficient.

[0022] Step S400, obtain the emergency requirement coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generate the best execution planning route for the emergency requirements and feedback it to the medical staff, and sequentially change the diapers for the users in need according to the best execution planning route for the emergency requirements, and dynamically update the wearing status of the corresponding diapers after the change; In the step S400, each user with a corresponding emergency requirement coefficient greater than the preset requirement coefficient is used as an emergency treatment object, and the order of the obtained emergency treatment objects is randomly combined. The splicing result of the shortest planning path segments between the locations of any two adjacent users in the random combination scheme is used as an execution planning route for the emergency requirements. The shortest planning path segments between the locations of the adjacent two users are obtained by querying the preset form in the database; according to the sorting result of the emergency treatment objects in the order of the emergency requirement coefficient from large to small, the first emergency analysis sequence is obtained; Calculate the scenario adaptation value of the execution planning route for emergency requirements, and take the execution planning route for emergency requirements with the largest scenario adaptation value as the best execution planning route for emergency requirements. The involved calculation formula is as follows: ; Among them, SP k represents the scenario adaptation value of the k-th execution planning route for emergency requirements; ik represents the number of emergency handling objects in the k-th execution planning route for emergency requirements; G （k，i） represents the emergency requirement coefficient of the i-th emergency handling object passed through in the k-th execution planning route for emergency requirements; F （k，i） represents the influence factor corresponding to the serial number where the i-th emergency handling object passed through in the k-th execution planning route for emergency requirements is located in the first emergency analysis sequence. The influence factor of each serial number in the first emergency analysis sequence is obtained by querying the preset form in the database; τ （k，i，j） represents the acquisition frequency adjustment coefficient of the built-in capacitance sensor of the diaper of the j-th user who is not an emergency handling object in the planning area where the i-th emergency handling object passed through in the k-th execution planning route for emergency requirements is located; the planning area where each user is located is preset; J （k，i） represents the number of users who are not emergency handling objects in the planning area where the i-th emergency handling object passed through in the k-th execution planning route for emergency requirements is located; g represents the weight constant.

[0023] During the process of executing the best execution planning route for emergency requirements in step S400, continuously extract the unexecuted route segments in the best execution planning route for emergency requirements; mark all emergency handling objects whose newly generated shortest distance during the execution process and located between the unexecuted route segments in the best execution planning route for emergency requirements is less than the preset distance, retrieve the emergency handling object closest to the unexecuted route segment in the best execution planning route for emergency requirements among the marked emergency handling objects, and record it as the reference object corresponding to the corresponding mark; and insert the corresponding marked emergency handling object after the next execution object of the corresponding marked reference object. The route segment between the corresponding marked emergency handling object and the corresponding marked reference object is obtained by querying the database. In the updated best execution planning route for emergency requirements, the sequence order among the various emergency handling objects within the unexecuted route segments in the best execution planning route for emergency requirements before the update remains unchanged; each time the best execution planning route for emergency requirements is updated, it is fed back to the medical staff; After changing the diaper for the demand user, update the usage duration of the corresponding user's diaper in the diaper wearing status to 0, and when the user's diaper change time is less than the unit time, record the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time as 0.

[0024] In this embodiment, the warning information fed back by the cloud to medical staff includes the best execution planning route for emergency requirements, each emergency treatment object passed through on the best execution planning route for emergency requirements, and the recognition result of the type of user requirements corresponding to each emergency treatment object.

[0025] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0026] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A medical health warning method based on the 4G communication mode, characterized in that Including: Step S100: Collect capacitance data of users wearing diapers with built-in capacitive sensors, upload the capacitance data collection results to the cloud through the 4G communication mode, and dynamically adjust the capacitance data collection frequency according to the historical monitoring data of the users and the diaper wearing status of the users at the current time; Step S200: When the capacitance fluctuation value of the capacitive sensor in the user's diaper is greater than the fluctuation threshold within the most recent unit time, retrieve the preset collection frequency in the database and keep the retrieved collection frequency unchanged until the medical staff finishes the treatment; Fit the relationship function between the capacitance data collection results of the capacitive sensor in the user's diaper and the collection time, and extract the capacitance change characteristics in the fitting relationship; And identify the types of user needs based on the obtained capacitance change characteristics; Step S300: Combine the user need type identification result and the capacitance data collection result to calculate the emergency need coefficient of the user; Step S400: Obtain the emergency need coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generate the best execution planning route for the emergency needs and feedback it to the medical staff, and sequentially change the diapers for the users with needs according to the best execution planning route for the emergency needs, and dynamically update the wearing status of the corresponding diapers after the change.

2. The medical and health warning method based on the 4G communication mode according to claim 1, wherein: The said step S100 includes: The capacitance data collection results include capacitance monitoring values and corresponding collection times; During the process of dynamically adjusting the capacitance data collection frequency according to the historical monitoring data of the users and the diaper wearing status of the users at the current time, the diaper wearing status of the users at the current time includes the usage duration of the corresponding user's diaper and the capacitance fluctuation value of the capacitive sensor in the user's diaper within the most recent unit time; the usage duration of the corresponding user's diaper represents the interval duration from the initial replacement time of the corresponding user's diaper to the current time; the capacitance fluctuation value of the capacitive sensor in the user's diaper within the most recent unit time is equal to the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value of the capacitive sensor in the user's diaper within the most recent unit time; Calculate the collection frequency adjustment coefficient of the built-in capacitive sensor in the user's diaper at the current time, and the calculation formula is as follows: ； Among them, τ represents the acquisition frequency adjustment coefficient of the built-in capacitance sensor of the user's diaper at the current time; T represents the usage duration of the user's diaper; CA represents the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time; P1 {T,CA} represents the ratio of the frequency statistics result that the T value corresponding to CA equal to the fluctuation threshold before each diaper change in the historical monitoring data of the corresponding user is less than or equal to the usage duration of the current user's diaper to the number of diaper changes of the corresponding user in the historical database; P2 {T,CA} represents the ratio of the frequency statistics result that the T value corresponding to CA equal to the fluctuation threshold before each diaper change in the historical monitoring data of all users is less than or equal to the usage duration of the current user's diaper to the number of diaper changes of all users in the historical database; β represents a preset constant; The acquisition frequency of the built-in capacitive sensor of the user's diaper after adjustment is equal to the product of the initial acquisition frequency preset in the database and e τ , where e is the natural constant.

3. The medical health warning method based on the 4G communication mode according to claim 1, wherein: The said step S200 includes: Step S201: Retrieve the capacitance data collection results corresponding to the collection frequency of the built-in capacitive sensor in the user's diaper being the preset collection frequency in the database; construct a capacitance monitoring data analysis pair corresponding to each collection time, where the first parameter in the capacitance monitoring data analysis pair is the collection time, and the second parameter is the capacitance monitoring value corresponding to the corresponding collection time; Step S202: Use the function model y = a / (1 + e -x ) + b to fit the constructed capacitance monitoring data pair, and obtain the fitting result of the relationship function between the capacitance data acquisition result of the capacitance sensor in the user's diaper and the acquisition time. Here, both a and b represent constants, y represents the capacitance monitoring value, and x represents the acquisition time; Step S203: Obtain the derivative function of the fitting result of the relationship function between the capacitance data acquisition result of the capacitance sensor in the user's diaper and the acquisition time, denoted as the feature extraction function of the corresponding user; generate the capacitance change features in the fitting relationship, where the capacitance change features include the maximum function value in the feature extraction function of the corresponding user, the interval length of the time interval corresponding to the function value greater than the preset function value, and the quotient of the integral value of the feature extraction function of the corresponding user within the time interval corresponding to the function value greater than the preset function value divided by the corresponding interval length; Step S204: Calculate the user demand type evaluation coefficient according to the obtained capacitance change features, and the calculation formula is as follows: ； Among them, W represents the evaluation coefficient of the type of user requirements corresponding to the obtained capacitance change characteristics; YD represents the maximum function value in the feature extraction function of the corresponding user in the capacitance change characteristics; LD represents the interval length of the time interval in the capacitance change characteristics where the corresponding function value is greater than the preset function value; YDP represents the quotient obtained by dividing the integral value of the feature extraction function of the corresponding user in the capacitance change characteristics within the time interval where the corresponding function value is greater than the preset function value by the corresponding interval length; respectively represent the preset first evaluation conversion factor and the second evaluation conversion factor; Step S205: Identify the user demand type corresponding to the obtained capacitance change features. When the user demand type evaluation coefficient W is greater than or equal to the preset evaluation value, it is determined that the user demand type corresponding to the obtained capacitance change features is the first demand type; otherwise, it is determined that the user demand type corresponding to the obtained capacitance change features is the second demand type.

4. The medical health warning method based on the 4G communication mode according to claim 3, wherein: The formula for calculating the emergency demand coefficient of the user in step S300 is as follows: ； Among them, G represents the emergency demand coefficient of the user; CAC represents the difference between the maximum capacitance monitoring value and the minimum capacitance monitoring value in the capacitance data acquisition result of the capacitance sensor in the user's diaper; TC represents the time interval between the time point corresponding to the maximum function value in the feature extraction function of the corresponding user and the current time; r represents the normalized weight coefficient.

5. The medical and health warning method based on the 4G communication mode according to claim 2, characterized in that: In step S400, each user with an emergency demand coefficient greater than the preset demand coefficient is used as an emergency processing object, and the order of the obtained emergency processing objects is randomly combined. The splicing result of the shortest planned path segment between the positions of any two adjacent users in the random combination plan is used as an emergency demand execution plan route, and the shortest planned path segment between the positions of the two adjacent users is obtained by querying the preset form in the database; according to the sorting result of the emergency processing objects in descending order of the emergency demand coefficient, the first emergency analysis sequence is obtained; Calculate the scene adaptation value of the emergency demand execution plan route, and use the emergency demand execution plan route with the largest scene adaptation value as the best emergency demand execution plan route. The relevant calculation formula is as follows: ； Among them, SP k represents the scenario adaptation value of the k-th emergency demand execution planning route; ik represents the number of emergency handling objects in the k-th emergency demand execution planning route; G （k，i） represents the emergency demand coefficient of the i-th emergency handling object passed through in the k-th emergency demand execution planning route; F （k，i） represents the influence factor corresponding to the serial number of the i-th emergency handling object passed by the k-th emergency demand execution planning route in the first emergency analysis sequence, and the influence factor of each serial number in the first emergency analysis sequence is obtained by querying the preset form in the database; τ （k，i，j） represents the acquisition frequency adjustment coefficient of the diaper built-in capacitance sensor obtained most recently by the j-th user who does not belong to the emergency handling object in the planning area where the i-th emergency handling object passed by the k-th emergency demand execution planning route is located; the planning area where each user is located is preset; J （k，i） represents the number of users who do not belong to the emergency handling object in the planning area where the i-th emergency handling object passed by the k-th emergency demand execution planning route is located; g represents the weight constant.

6. The medical health warning method based on the 4G communication mode according to claim 5, wherein: During the process of executing the optimal execution planning route for emergency requirements in step S400, the unexecuted route segments in the optimal execution planning route for emergency requirements are extracted in real time; all emergency treatment objects newly generated during the execution process and with the shortest distance between their positions and the unexecuted route segments in the optimal execution planning route for emergency requirements less than the preset distance are marked, and the emergency treatment object closest to the marked emergency treatment object in the unexecuted route segment of the optimal execution planning route for emergency requirements is retrieved and recorded as the reference object for the corresponding mark; and the corresponding marked emergency treatment object is inserted into the next execution object of the corresponding marked reference object, and the route segment between the corresponding marked emergency treatment object and the corresponding marked reference object is obtained by querying the database. In the updated optimal execution planning route for emergency requirements, the order among the various emergency treatment objects within the unexecuted route segment of the optimal execution planning route for emergency requirements before the update remains unchanged; each time the optimal execution planning route for emergency requirements is updated, it is fed back to the medical staff; After changing the diaper for the demand user, update the usage duration of the corresponding user's diaper in the diaper wearing status to 0, and when the user's diaper change time is less than the unit time, record the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time as 0.

7. A medical and health warning system based on the 4G communication mode, applying the medical and health warning method based on the 4G communication mode described in any one of claims 1-6, characterized in that: The system includes a data acquisition and communication transmission module, a data feature analysis module, a user demand analysis module, and a data warning management module; The data acquisition and communication transmission module collects capacitance data of users wearing diapers with built-in capacitance sensors, uploads the capacitance data acquisition results to the cloud through the 4G communication mode, and dynamically adjusts the capacitance data acquisition frequency according to the historical monitoring data of the users and the diaper wearing status of the users at the current time; When the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time is greater than the fluctuation threshold, the data feature analysis module retrieves the preset acquisition frequency in the database and keeps the retrieved acquisition frequency unchanged until the medical staff finishes the treatment; Fit the relationship function between the capacitance data acquisition results of the capacitance sensor in the user's diaper and the acquisition time, and extract the capacitance change characteristics in the fitting relationship; And based on the obtained capacitance change characteristics, identify the types of user requirements; The user demand analysis module calculates the emergency demand coefficient of the user in combination with the user demand type identification result and the capacitance data acquisition result; The data warning management module obtains the emergency demand coefficients corresponding to each user received by the cloud at the current time, the location of each user, and the monitoring data of each user in the historical data, generates an optimal execution planning route for emergency requirements and feeds it back to the medical staff, and sequentially changes the diapers for the demand users according to the optimal execution planning route for emergency requirements, and dynamically updates the wearing status of the corresponding diapers after the change.

8. The medical health warning system based on the 4G communication mode according to claim 7, characterized in that: The data feature analysis module includes a frequency modulation management unit, a capacitance change feature extraction unit, and a user demand analysis unit, When the capacitance fluctuation value of the capacitance sensor in the user's diaper within the most recent unit time is greater than the fluctuation threshold, the frequency modulation management unit retrieves the preset acquisition frequency in the database and keeps the retrieved acquisition frequency unchanged until the medical staff finishes the processing; The capacitance change feature extraction unit fits the relationship function of the capacitance data acquisition result of the capacitance sensor in the user's diaper changing with the acquisition time, and extracts the capacitance change features in the fitting relationship; The user demand analysis unit identifies the types of user demands based on the obtained capacitance change features.

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