CN118213064B - Intelligent health state detection system and method based on sensor technology - Google Patents
Intelligent health state detection system and method based on sensor technology Download PDFInfo
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Abstract
The invention relates to the technical field of health state detection, in particular to an intelligent health state detection system and method based on a sensor technology, wherein the system comprises a detection result dynamic feedback management module, and the detection result dynamic feedback management module acquires position information in each urine volume detection information in an alarm feedback set and generates an abnormal processing planning route; and selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to the mobile terminal worn by the region manager to be detected from the cloud through a wireless network. According to the invention, in the process of detecting the urinary incontinence of the user, the alarm threshold value of the abnormal detection state can be dynamically adjusted according to the actual demand; and the service route of the manager is dynamically planned in combination with the actual demand, so that the unordered service behavior of the user to be serviced by a caretaker is avoided, and the intelligent detection and treatment of the health state of the urinary incontinence old are realized.
Description
Technical Field
The invention relates to the technical field of health state detection, in particular to an intelligent health state detection system and method based on a sensor technology.
Background
Urinary incontinence is a common problem for the elderly, and can seriously affect the quality of life of the patient, causing serious psychological burden to the patient, and at the same time, urinary incontinence can affect the comfort of the elderly, and even cause certain diseases. And further, aiming at the situation that the aged suffers from incontinence and the aged cannot send out signals autonomously, how to timely detect the aged incontinence is a great difficulty in the current field.
The existing mode for detecting incontinence of the old is often checked by a caretaker in a manual observation mode, the caretaker is often required to frequently touch the diaper, and the mode has a large defect, so that on one hand, the mode of manual observation is often incapable of timely detecting the incontinence behavior and the corresponding incontinence time of the old; on the other hand, in a nursing home, the act of a caretaker touching the diaper often is time consuming and laborious.
With the rapid development of sensor technology in recent years, the real-time monitoring of urinary incontinence of aged people in the nursing home by using a sensor gradually becomes a trend, but the existing monitoring of urinary incontinence by using sensor technology still has a certain degree of defects: on the one hand, the alarm threshold value of the abnormal detection state in the prior art for detecting the urinary incontinence is always fixed and cannot be dynamically adjusted according to actual demands, so that the situation that a plurality of users to be serviced correspond to one time point in actual use and caregivers cannot service the users is often caused; on the other hand, when a plurality of users to be served are corresponding to the same time point, the service route of the nursing staff is dynamically planned without combining with the actual demands in the prior art, so that the nursing staff has a disordered condition on the service behaviors of the users to be served, and the actual service effect on the urinary incontinence old is affected.
Disclosure of Invention
The invention aims to provide an intelligent health state detection system and method based on a sensor technology, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a method for intelligent detection of health status based on sensor technology, the method comprising the steps of:
S100, acquiring urine volume detection information corresponding to paper urine pads of all users in a region to be detected in real time through a humidity sensor, and transmitting the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
S200, the radio frequency signal receiving sensor transmits the received urine volume detection information to the cloud end, and the cloud end predicts the alarm threshold value of the current abnormal detection state based on the busy influence value of the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
S300, optimizing and updating the current abnormal detection state alarm threshold based on the route interference complexity of the time zone to which the current time belongs by combining the current abnormal detection state alarm threshold, judging each piece of urine detection information received by the cloud terminal at the current time according to the optimized and updated abnormal detection state alarm threshold, and summarizing each piece of urine detection information with abnormal detection state in real time to generate an alarm feedback set;
S400, acquiring position information in each urine volume detection information in the alarm feedback set, and generating an exception handling planning route; and selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to the mobile terminal worn by the region manager to be detected from the cloud through a wireless network.
Further, one or more moisture sensors are bound on the paper urine pad to which the user belongs, each moisture sensor is bound with a radio frequency signal transmitting sensor, and radio frequency signals transmitted by the radio frequency signal transmitting sensors are urine volume detection information acquired by the bound moisture sensors;
the urine volume detection information comprises a user number, a user bed position and a humidity sensor monitoring value;
Each user in the area to be measured corresponds to a unique user number, and each user in the area to be measured corresponds to a unique user bed position;
The setting position of the radio frequency signal receiving sensor is fixed.
The position of the radio frequency signal receiving sensor is set, so that the whole coverage of a radio frequency signal receiving area in a to-be-detected area is realized, and the situation that corresponding radio frequency signals cannot be transmitted to a cloud end due to the loss of radio frequency signals transmitted by the radio frequency signal transmitting sensor is avoided.
Further, the time sections are prefabricated in a database, and each day is uniformly divided into n time sections with equal duration, wherein n is more than or equal to 2; numbering each time zone;
When the predicted current abnormal detection state alarm threshold value is based on the busy influence value of the time zone to which the current time belongs, acquiring urine volume detection information in the time zone to which the current time belongs, each time zone in the historical data and urine volume detection information when a user changes paper diapers, marking the predicted result of the current abnormal detection state alarm threshold value based on the busy influence value of the time zone to which the current time belongs as YD,
，
Wherein DS represents a time zone to which the current time belongs; QNW denotes a set of urine volume detection information in the nth time zone on the W-th day in the history data; BNW represents the average value of the alarm threshold values of the abnormal detection states corresponding to different times in the N time section of the W day in the historical data; n is more than or equal to 1 and less than or equal to N; w1 represents the total number of days corresponding to the historical data;
GD （DS,W） represents the number of urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW being greater than or equal to the alarm threshold value of the current abnormal detection state when N is DS;
GL （DS,W） shows that when N is DS, the number of the urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW is larger than or equal to BNW;
When the current abnormality detection state alarm threshold is calculated based on the route interference complexity of the time section to which the current time belongs, the route interference complexity of the current abnormality detection state alarm threshold based on the time section to which the current time belongs is marked as E,
。
The invention predicts the busy influence value of the current abnormality detection state alarm threshold value based on the time zone to which the current time belongs, and aims to dynamically adjust the current abnormality detection state alarm threshold value in the subsequent process so as to ensure that the number of objects to be served is kept in the range which can be processed by an administrator.
Further, in S300, the step of optimally updating the alarm threshold of the current abnormal detection state is as follows:
S301, acquiring a current abnormality detection state alarm threshold value based on the route interference complexity E of a time section to which the current time belongs, and judging whether the current abnormality detection state alarm threshold value needs to be optimally updated;
When the |E| is greater than or equal to the first threshold, judging that the current abnormal detection state alarm threshold needs to be optimally updated, and jumping to S302; the first threshold is a constant preset in a database;
When the I E I is smaller than the first threshold value, judging that the current abnormal detection state alarm threshold value does not need to be optimally updated;
S302, acquiring a set formed by corresponding abnormal detection state alarm thresholds in a time section to which the current time of the historical data belongs, and arranging elements in the acquired set in the order of from small to large in value to generate an alarm threshold sequence;
S303, determining a reference value of the alarm threshold of the current abnormal detection state,
When E is less than 0, taking the minimum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
when E is more than 0, taking the maximum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
S304, taking the determined reference value of the current abnormality detection state alarm threshold as a new current abnormality detection state alarm threshold, calculating the new current abnormality detection state alarm threshold, and jumping to S305 based on the route interference complexity of the time section to which the current time belongs;
s305 comparing the obtained route disturbance complexity with a first threshold value,
When the absolute value of the obtained route interference complexity is greater than or equal to a first threshold value, jumping to S306;
When the absolute value of the obtained route interference complexity is smaller than a first threshold value, judging that the abnormality detection state alarm threshold value corresponding to the obtained route interference complexity is an optimized updating result of the current abnormality detection state alarm threshold value;
S306, when the product of the obtained route interference complexity and E is a negative number, marking the average value of the alarm thresholds of the abnormality detection states corresponding to the obtained route interference complexity and E respectively as a new reference value of the alarm threshold of the current abnormality detection state, and jumping to S304;
when the product of the obtained route interference complexity and E is a positive number, taking the abnormality detection state alarm threshold corresponding to the obtained route interference complexity as an optimization updating result of the current abnormality detection state alarm threshold;
And when judging each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold in the S300, judging that the detection states of all pieces of urine volume detection information corresponding to the optimized and updated abnormal detection state alarm threshold are abnormal, and judging that the detection states of all pieces of urine volume detection information corresponding to the humidity sensor monitoring values are larger than the optimized and updated abnormal detection state alarm threshold are normal.
In the process of optimizing and updating the current abnormal detection state alarm threshold, the optimized and updated result is the result influenced by various data (the current abnormal detection state alarm threshold is based on the route interference complexity and the historical data of the time zone to which the current time belongs); the obtained optimized updating result of the alarm threshold value of the current abnormal detection state is used for obtaining the detection information of each urine volume in abnormal detection state, generating an alarm feedback set and providing data support for screening the optimal abnormal processing planning route in the subsequent step.
Further, when generating the abnormality processing planned route in S400, obtaining the user numbers corresponding to the urine volume detection information in the alarm feedback set, arranging and combining the obtained sequences of the user numbers to generate different combined results, wherein each combined result corresponds to an abnormality processing planned route,
And acquiring a navigation section from the current position of the regional manager to be detected to the position of the user bed corresponding to the first user number in the corresponding combined result, respectively acquiring the navigation section between the positions of the user beds corresponding to any two adjacent user numbers in the corresponding combined result, and wherein the abnormal processing planning route is a splicing result of each navigation section corresponding to the same combined result.
Further, the step of selecting the optimal exception handling planned route in S400 is as follows:
s401, acquiring each generated exception handling planning route, and marking the generated ith exception handling planning route as Xi;
S402, acquiring characteristic information of an administrator in a region to be detected, wherein the characteristic information comprises average duration T of helping a user to replace a paper urine pad by a corresponding administrator in historical data and average moving speed V of the corresponding administrator in the historical data during the process of going to the user to be replaced;
s403, calculating an execution scheduling influence value of the ith exception handling planned route, marking as Hi,
，
Wherein TRij represents a predicted value of the time length from the current time to the time length when the corresponding administrator helps the j element in the ith exception handling planning route to replace the paper diaper by the corresponding user;
The said ，
Ji represents the total number of elements contained in the ith exception handling planned route;
TLij represents a period from the time when the j-th element in the ith exception handling planned route appears to the current time when the detected state exception is helpful for an administrator;
CD （i,k） represents the length of the navigation section from the bed position corresponding to the kth-1 element to the bed position corresponding to the kth element in the ith exception handling planned route; when k=1, CD （i,k） represents the length of the navigation section between the current position and the bed position corresponding to the 1 st element in the i-th abnormality processing planned route by the corresponding administrator;
gij represents the replacement interference coefficient of the jth element corresponding to the user in the ith exception handling planned route,
The replacement interference coefficient is the ratio of the sum of the interval duration of each replacement of the paper diaper in a preset time period before the current time and the duration corresponding to the preset time period in the database; the interval time of each replacement of the paper urine pad is the interval time between the time point when the detection state corresponding to the corresponding user is abnormal and the time point when the corresponding administrator helps the corresponding user to complete the replacement of the corresponding paper urine pad;
S404, taking the exception handling planned route with the smallest corresponding execution scheduling influence value as the optimal exception handling planned route.
A sensor technology-based health status intelligent detection system, the system comprising the following modules:
The sensor data acquisition module acquires urine volume detection information corresponding to paper urine pads of all users in an area to be detected in real time through a humidity sensor, and sends the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
The cloud abnormal detection data analysis module transmits the received urine volume detection information to the cloud through the radio frequency signal receiving sensor, and the cloud predicts the busy influence value of the current abnormal detection state alarm threshold value based on the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
The threshold optimization and feedback data extraction module is combined with the current abnormal detection state alarm threshold, and based on the route interference complexity of the time zone to which the current time belongs, the current abnormal detection state alarm threshold is optimally updated, and each piece of urine detection information received by the cloud terminal at the current time is judged according to the optimized and updated abnormal detection state alarm threshold, and each piece of urine detection information with abnormal detection state is summarized in real time to generate an alarm feedback set;
The detection result dynamic feedback management module acquires position information in each urine volume detection information in the alarm feedback set and generates an abnormal processing planning route; and selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to the mobile terminal worn by the region manager to be detected from the cloud through a wireless network.
Further, the threshold optimization and feedback data extraction module comprises a threshold optimization updating unit and a dynamic detection feedback unit,
The threshold value optimizing and updating unit is combined with the current abnormality detection state alarm threshold value to optimally update the current abnormality detection state alarm threshold value based on the route interference complexity of the time section to which the current time belongs;
And the dynamic detection feedback unit judges each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold value, gathers each piece of urine volume detection information with abnormal detection state in real time, and generates an alarm feedback set.
Compared with the prior art, the invention has the following beneficial effects: according to the invention, in the process of detecting the urinary incontinence of the user, the alarm threshold value of the abnormal detection state can be dynamically adjusted according to the actual demand, so that the situation that the manager cannot serve due to the fact that more users to be served exist at the same time point in actual use is avoided; and the service route of the manager is dynamically planned in combination with the actual demand, so that the unordered service behavior of the user to be serviced by a caretaker is avoided, and the intelligent detection and treatment of the health state of the urinary incontinence old are realized.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for intelligent detection of health status based on sensor technology according to the present invention;
fig. 2 is a schematic structural diagram of an intelligent health state detection system based on sensor technology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: a method for intelligent detection of health status based on sensor technology, the method comprising the steps of:
S100, acquiring urine volume detection information corresponding to paper urine pads of all users in a region to be detected in real time through a humidity sensor, and transmitting the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
binding one or more moisture sensors on the paper urine pad to which the user belongs, wherein each moisture sensor is bound with a radio frequency signal transmitting sensor, and the radio frequency signal transmitted by the radio frequency signal transmitting sensor is urine volume detection information acquired by the bound moisture sensor;
the urine volume detection information comprises a user number, a user bed position and a humidity sensor monitoring value;
Each user in the area to be measured corresponds to a unique user number, and each user in the area to be measured corresponds to a unique user bed position;
The setting position of the radio frequency signal receiving sensor is fixed.
S200, the radio frequency signal receiving sensor transmits the received urine volume detection information to the cloud end, and the cloud end predicts the alarm threshold value of the current abnormal detection state based on the busy influence value of the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
The time sections are prefabricated in a database, and each day is uniformly divided into n time sections with equal duration, wherein n is more than or equal to 2; numbering each time zone;
When the predicted current abnormal detection state alarm threshold value is based on the busy influence value of the time zone to which the current time belongs, acquiring urine volume detection information in the time zone to which the current time belongs, each time zone in the historical data and urine volume detection information when a user changes paper diapers, marking the predicted result of the current abnormal detection state alarm threshold value based on the busy influence value of the time zone to which the current time belongs as YD,
，
Wherein DS represents a time zone to which the current time belongs; QNW denotes a set of urine volume detection information in the nth time zone on the W-th day in the history data; BNW represents the average value of the alarm threshold values of the abnormal detection states corresponding to different times in the N time section of the W day in the historical data; n is more than or equal to 1 and less than or equal to N; w1 represents the total number of days corresponding to the historical data;
GD （DS,W） represents the number of urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW being greater than or equal to the alarm threshold value of the current abnormal detection state when N is DS;
GL （DS,W） shows that when N is DS, the number of the urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW is larger than or equal to BNW;
When the current abnormality detection state alarm threshold is calculated based on the route interference complexity of the time section to which the current time belongs, the route interference complexity of the current abnormality detection state alarm threshold based on the time section to which the current time belongs is marked as E,
。
S300, optimizing and updating the current abnormal detection state alarm threshold based on the route interference complexity of the time zone to which the current time belongs by combining the current abnormal detection state alarm threshold, judging each piece of urine detection information received by the cloud terminal at the current time according to the optimized and updated abnormal detection state alarm threshold, and summarizing each piece of urine detection information with abnormal detection state in real time to generate an alarm feedback set;
in S300, the step of optimizing and updating the alarm threshold of the current abnormal detection state is as follows:
S301, acquiring a current abnormality detection state alarm threshold value based on the route interference complexity E of a time section to which the current time belongs, and judging whether the current abnormality detection state alarm threshold value needs to be optimally updated;
When the |E| is greater than or equal to the first threshold, judging that the current abnormal detection state alarm threshold needs to be optimally updated, and jumping to S302; the first threshold is a constant preset in a database;
When the I E I is smaller than the first threshold value, judging that the current abnormal detection state alarm threshold value does not need to be optimally updated;
S302, acquiring a set formed by corresponding abnormal detection state alarm thresholds in a time section to which the current time of the historical data belongs, and arranging elements in the acquired set in the order of from small to large in value to generate an alarm threshold sequence;
S303, determining a reference value of the alarm threshold of the current abnormal detection state,
When E is less than 0, taking the minimum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
when E is more than 0, taking the maximum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
S304, taking the determined reference value of the current abnormality detection state alarm threshold as a new current abnormality detection state alarm threshold, calculating the new current abnormality detection state alarm threshold, and jumping to S305 based on the route interference complexity of the time section to which the current time belongs;
s305 comparing the obtained route disturbance complexity with a first threshold value,
When the absolute value of the obtained route interference complexity is greater than or equal to a first threshold value, jumping to S306;
When the absolute value of the obtained route interference complexity is smaller than a first threshold value, judging that the abnormality detection state alarm threshold value corresponding to the obtained route interference complexity is an optimized updating result of the current abnormality detection state alarm threshold value;
S306, when the product of the obtained route interference complexity and E is a negative number, marking the average value of the alarm thresholds of the abnormality detection states corresponding to the obtained route interference complexity and E respectively as a new reference value of the alarm threshold of the current abnormality detection state, and jumping to S304;
when the product of the obtained route interference complexity and E is a positive number, taking the abnormality detection state alarm threshold corresponding to the obtained route interference complexity as an optimization updating result of the current abnormality detection state alarm threshold;
And when judging each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold in the S300, judging that the detection states of all pieces of urine volume detection information corresponding to the optimized and updated abnormal detection state alarm threshold are abnormal, and judging that the detection states of all pieces of urine volume detection information corresponding to the humidity sensor monitoring values are larger than the optimized and updated abnormal detection state alarm threshold are normal.
S400, acquiring position information in each urine volume detection information in the alarm feedback set, and generating an exception handling planning route; and selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to the mobile terminal worn by the region manager to be detected from the cloud through a wireless network.
When generating an abnormality processing planning route in the S400, obtaining user numbers corresponding to the urine volume detection information in the alarm feedback set respectively, arranging and combining the sequences of the obtained user numbers to generate different combined results, wherein each combined result corresponds to one abnormality processing planning route,
And acquiring a navigation section from the current position of the regional manager to be detected to the position of the user bed corresponding to the first user number in the corresponding combined result, respectively acquiring the navigation section between the positions of the user beds corresponding to any two adjacent user numbers in the corresponding combined result, and wherein the abnormal processing planning route is a splicing result of each navigation section corresponding to the same combined result.
In this embodiment, if the current abnormality detection state alarm threshold after optimization and update is adopted, the generated alarm feedback set is denoted as U, if the U contains 4 elements, denoted as UY1, UY2, UY3 and UY4 respectively,
The combined result generated contains 24 kinds of products, respectively ：UY1→UY2→UY3→UY4、UY1→UY2→UY4→UY3、UY1→UY3→UY2→UY4、UY1→UY3→UY4→UY2、UY1→UY4→UY2→UY3、UY1→UY4→UY3→UY2、UY2→UY1→UY3→UY4、UY2→UY1→UY4→UY3、UY2→UY3→UY1→UY4、UY2→UY3→UY4→UY1、UY2→UY4→UY1→UY3、UY2→UY4→UY3→UY1、UY3→UY1→UY2→UY4、UY3→UY1→UY4→UY2、UY3→UY2→UY1→UY4、UY3→UY2→UY4→UY1、UY3→UY4→UY1→UY2、UY3→UY4→UY2→UY1、UY4→UY1→UY2→UY3、UY4→UY1→UY3→UY2、UY4→UY2→UY1→UY3、UY4→UY2→UY3→UY1、UY4→UY3→UY1→UY2、UY4→UY3→UY2→UY1.
The step of selecting the optimal exception handling planned route in S400 is as follows:
s401, acquiring each generated exception handling planning route, and marking the generated ith exception handling planning route as Xi;
S402, acquiring characteristic information of an administrator in a region to be detected, wherein the characteristic information comprises average duration T of helping a user to replace a paper urine pad by a corresponding administrator in historical data and average moving speed V of the corresponding administrator in the historical data during the process of going to the user to be replaced;
s403, calculating an execution scheduling influence value of the ith exception handling planned route, marking as Hi,
，
Wherein TRij represents a predicted value of the time length from the current time to the time length when the corresponding administrator helps the j element in the ith exception handling planning route to replace the paper diaper by the corresponding user;
The said ，
Ji represents the total number of elements contained in the ith exception handling planned route;
TLij represents a period from the time when the j-th element in the ith exception handling planned route appears to the current time when the detected state exception is helpful for an administrator;
CD （i,k） represents the length of the navigation section from the bed position corresponding to the kth-1 element to the bed position corresponding to the kth element in the ith exception handling planned route; when k=1, CD （i,k） represents the length of the navigation section between the current position and the bed position corresponding to the 1 st element in the i-th abnormality processing planned route by the corresponding administrator;
gij represents the replacement interference coefficient of the jth element corresponding to the user in the ith exception handling planned route,
The replacement interference coefficient is the ratio of the sum of the interval duration of each replacement of the paper diaper in a preset time period before the current time and the duration corresponding to the preset time period in the database; the interval time of each replacement of the paper urine pad is the interval time between the time point when the detection state corresponding to the corresponding user is abnormal and the time point when the corresponding administrator helps the corresponding user to complete the replacement of the corresponding paper urine pad;
S404, taking the exception handling planned route with the smallest corresponding execution scheduling influence value as the optimal exception handling planned route.
In this embodiment, there are three main parts, namely a data acquisition end (including a humidity sensor, a radio frequency signal transmitting sensor and a radio frequency signal receiving sensor), a cloud end and a mobile end,
The data acquisition end is mainly used for acquiring the detection result of the urinary incontinence behavior and the urinary incontinence degree of the user;
The cloud is mainly responsible for analyzing the data uploaded by the data acquisition end and generating an optimal exception handling planning route;
The mobile terminal receives feedback results transmitted by the cloud through the wireless network and presents the feedback results to a corresponding administrator.
As shown in fig. 2, a sensor technology-based intelligent health status detection system comprises the following modules:
The sensor data acquisition module acquires urine volume detection information corresponding to paper urine pads of all users in an area to be detected in real time through a humidity sensor, and sends the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
The cloud abnormal detection data analysis module transmits the received urine volume detection information to the cloud through the radio frequency signal receiving sensor, and the cloud predicts the busy influence value of the current abnormal detection state alarm threshold value based on the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
The threshold optimization and feedback data extraction module is combined with the current abnormal detection state alarm threshold, and based on the route interference complexity of the time zone to which the current time belongs, the current abnormal detection state alarm threshold is optimally updated, and each piece of urine detection information received by the cloud terminal at the current time is judged according to the optimized and updated abnormal detection state alarm threshold, and each piece of urine detection information with abnormal detection state is summarized in real time to generate an alarm feedback set;
The detection result dynamic feedback management module acquires position information in each urine volume detection information in the alarm feedback set and generates an abnormal processing planning route; and selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to the mobile terminal worn by the region manager to be detected from the cloud through a wireless network.
The threshold optimization and feedback data extraction module comprises a threshold optimization updating unit and a dynamic detection feedback unit,
The threshold value optimizing and updating unit is combined with the current abnormality detection state alarm threshold value to optimally update the current abnormality detection state alarm threshold value based on the route interference complexity of the time section to which the current time belongs;
And the dynamic detection feedback unit judges each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold value, gathers each piece of urine volume detection information with abnormal detection state in real time, and generates an alarm feedback set.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. The intelligent detection method for the health state based on the sensor technology is characterized by comprising the following steps of:
S100, acquiring urine volume detection information corresponding to paper urine pads of all users in a region to be detected in real time through a humidity sensor, and transmitting the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
S200, the radio frequency signal receiving sensor transmits the received urine volume detection information to the cloud end, and the cloud end predicts the alarm threshold value of the current abnormal detection state based on the busy influence value of the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
S300, optimizing and updating the current abnormal detection state alarm threshold based on the route interference complexity of the time zone to which the current time belongs by combining the current abnormal detection state alarm threshold, judging each piece of urine detection information received by the cloud terminal at the current time according to the optimized and updated abnormal detection state alarm threshold, and summarizing each piece of urine detection information with abnormal detection state in real time to generate an alarm feedback set;
S400, acquiring position information in each urine volume detection information in the alarm feedback set, and generating an exception handling planning route; selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to a mobile terminal worn by the region manager to be detected from a cloud through a wireless network;
binding one or more moisture sensors on the paper urine pad to which the user belongs, wherein each moisture sensor is bound with a radio frequency signal transmitting sensor, and the radio frequency signal transmitted by the radio frequency signal transmitting sensor is urine volume detection information acquired by the bound moisture sensor;
the urine volume detection information comprises a user number, a user bed position and a humidity sensor monitoring value;
Each user in the area to be measured corresponds to a unique user number, and each user in the area to be measured corresponds to a unique user bed position;
the setting position of the radio frequency signal receiving sensor is fixed;
The time sections are prefabricated in a database, and each day is uniformly divided into n time sections with equal duration, wherein n is more than or equal to 2; numbering each time zone;
When the predicted current abnormal detection state alarm threshold value is based on the busy influence value of the time zone to which the current time belongs, acquiring urine volume detection information in the time zone to which the current time belongs, each time zone in the historical data and urine volume detection information when a user changes paper diapers, marking the predicted result of the current abnormal detection state alarm threshold value based on the busy influence value of the time zone to which the current time belongs as YD,
，
Wherein DS represents a time zone to which the current time belongs; QNW denotes a set of urine volume detection information in the nth time zone on the W-th day in the history data; BNW represents the average value of the alarm threshold values of the abnormal detection states corresponding to different times in the N time section of the W day in the historical data; n is more than or equal to 1 and less than or equal to N; w1 represents the total number of days corresponding to the historical data;
GD （DS,W） represents the number of urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW being greater than or equal to the alarm threshold value of the current abnormal detection state when N is DS;
GL （DS,W） shows that when N is DS, the number of the urine volume detection information with the monitoring value of the corresponding moisture sensor in the set corresponding to QNW is larger than or equal to BNW;
When the current abnormality detection state alarm threshold is calculated based on the route interference complexity of the time section to which the current time belongs, the route interference complexity of the current abnormality detection state alarm threshold based on the time section to which the current time belongs is marked as E,
。
2. The intelligent detection method for health status based on sensor technology according to claim 1, wherein: in S300, the step of optimizing and updating the alarm threshold of the current abnormal detection state is as follows:
S301, acquiring a current abnormality detection state alarm threshold value based on the route interference complexity E of a time section to which the current time belongs, and judging whether the current abnormality detection state alarm threshold value needs to be optimally updated;
When the |E| is greater than or equal to the first threshold, judging that the current abnormal detection state alarm threshold needs to be optimally updated, and jumping to S302; the first threshold is a constant preset in a database;
When the I E I is smaller than the first threshold value, judging that the current abnormal detection state alarm threshold value does not need to be optimally updated;
S302, acquiring a set formed by corresponding abnormal detection state alarm thresholds in a time section to which the current time of the historical data belongs, and arranging elements in the acquired set in the order of from small to large in value to generate an alarm threshold sequence;
S303, determining a reference value of the alarm threshold of the current abnormal detection state,
When E is less than 0, taking the minimum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
when E is more than 0, taking the maximum value in the generated alarm threshold sequence as the reference value of the alarm threshold of the current abnormal detection state;
S304, taking the determined reference value of the current abnormality detection state alarm threshold as a new current abnormality detection state alarm threshold, calculating the new current abnormality detection state alarm threshold, and jumping to S305 based on the route interference complexity of the time section to which the current time belongs;
s305 comparing the obtained route disturbance complexity with a first threshold value,
When the absolute value of the obtained route interference complexity is greater than or equal to a first threshold value, jumping to S306;
When the absolute value of the obtained route interference complexity is smaller than a first threshold value, judging that the abnormality detection state alarm threshold value corresponding to the obtained route interference complexity is an optimized updating result of the current abnormality detection state alarm threshold value;
S306, when the product of the obtained route interference complexity and E is a negative number, marking the average value of the alarm thresholds of the abnormality detection states corresponding to the obtained route interference complexity and E respectively as a new reference value of the alarm threshold of the current abnormality detection state, and jumping to S304;
when the product of the obtained route interference complexity and E is a positive number, taking the abnormality detection state alarm threshold corresponding to the obtained route interference complexity as an optimization updating result of the current abnormality detection state alarm threshold;
And when judging each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold in the S300, judging that the detection states of all pieces of urine volume detection information corresponding to the optimized and updated abnormal detection state alarm threshold are abnormal, and judging that the detection states of all pieces of urine volume detection information corresponding to the humidity sensor monitoring values are larger than the optimized and updated abnormal detection state alarm threshold are normal.
3. The intelligent detection method for health status based on sensor technology according to claim 2, wherein: when generating an abnormality processing planning route in the S400, obtaining user numbers corresponding to the urine volume detection information in the alarm feedback set respectively, arranging and combining the sequences of the obtained user numbers to generate different combined results, wherein each combined result corresponds to one abnormality processing planning route,
And acquiring a navigation section from the current position of the regional manager to be detected to the position of the user bed corresponding to the first user number in the corresponding combined result, respectively acquiring the navigation section between the positions of the user beds corresponding to any two adjacent user numbers in the corresponding combined result, and wherein the abnormal processing planning route is a splicing result of each navigation section corresponding to the same combined result.
4. A method for intelligent detection of health status based on sensor technology according to claim 3, characterized in that: the step of selecting the optimal exception handling planned route in S400 is as follows:
s401, acquiring each generated exception handling planning route, and marking the generated ith exception handling planning route as Xi;
S402, acquiring characteristic information of an administrator in a region to be detected, wherein the characteristic information comprises average duration T of helping a user to replace a paper urine pad by a corresponding administrator in historical data and average moving speed V of the corresponding administrator in the historical data during the process of going to the user to be replaced;
s403, calculating an execution scheduling influence value of the ith exception handling planned route, marking as Hi,
，
Wherein TRij represents a predicted value of the time length from the current time to the time length when the corresponding administrator helps the j element in the ith exception handling planning route to replace the paper diaper by the corresponding user;
The said ，
Ji represents the total number of elements contained in the ith exception handling planned route;
TLij represents a period from the time when the j-th element in the ith exception handling planned route appears to the current time when the detected state exception is helpful for an administrator;
CD （i,k） represents the length of the navigation section from the bed position corresponding to the kth-1 element to the bed position corresponding to the kth element in the ith exception handling planned route; when k=1, CD （i,k） represents the length of the navigation section between the current position and the bed position corresponding to the 1 st element in the i-th abnormality processing planned route by the corresponding administrator;
gij represents the replacement interference coefficient of the jth element corresponding to the user in the ith exception handling planned route,
The replacement interference coefficient is the ratio of the sum of the interval duration of each replacement of the paper diaper in a preset time period before the current time and the duration corresponding to the preset time period in the database; the interval time of each replacement of the paper urine pad is the interval time between the time point when the detection state corresponding to the corresponding user is abnormal and the time point when the corresponding administrator helps the corresponding user to complete the replacement of the corresponding paper urine pad;
S404, taking the exception handling planned route with the smallest corresponding execution scheduling influence value as the optimal exception handling planned route.
5. A sensor technology-based health status intelligent detection system, characterized in that the system comprises the following modules:
The sensor data acquisition module acquires urine volume detection information corresponding to paper urine pads of all users in an area to be detected in real time through a humidity sensor, and sends the acquired urine volume detection information to a peripheral radio frequency signal receiving sensor in real time through a radio frequency signal transmitting sensor;
The cloud abnormal detection data analysis module transmits the received urine volume detection information to the cloud through the radio frequency signal receiving sensor, and the cloud predicts the busy influence value of the current abnormal detection state alarm threshold value based on the time zone of the current time by combining the time zone of the current time, the urine volume detection information in each time zone in the historical data and the urine volume detection information when the user changes paper diapers; calculating the route interference complexity of the current abnormality detection state alarm threshold value based on the time section to which the current time belongs;
The threshold optimization and feedback data extraction module is combined with the current abnormal detection state alarm threshold, and based on the route interference complexity of the time zone to which the current time belongs, the current abnormal detection state alarm threshold is optimally updated, and each piece of urine detection information received by the cloud terminal at the current time is judged according to the optimized and updated abnormal detection state alarm threshold, and each piece of urine detection information with abnormal detection state is summarized in real time to generate an alarm feedback set;
The detection result dynamic feedback management module acquires position information in each urine volume detection information in the alarm feedback set and generates an abnormal processing planning route; selecting an optimal abnormal processing planning route by combining the characteristic information of the region manager to be detected, and feeding back the optimal abnormal processing planning route to a mobile terminal worn by the region manager to be detected from a cloud through a wireless network;
The threshold optimization and feedback data extraction module comprises a threshold optimization updating unit and a dynamic detection feedback unit,
The threshold value optimizing and updating unit is combined with the current abnormality detection state alarm threshold value to optimally update the current abnormality detection state alarm threshold value based on the route interference complexity of the time section to which the current time belongs;
And the dynamic detection feedback unit judges each piece of urine volume detection information received at the current time of the cloud according to the optimized and updated abnormal detection state alarm threshold value, gathers each piece of urine volume detection information with abnormal detection state in real time, and generates an alarm feedback set.
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