Pension information monitoring method, device, equipment, medium and product

Abstract

The invention provides a pension information monitoring method, device and equipment, a medium and a product, and belongs to the field of Internet of Things, and the method comprises the steps: obtaining the body data of a target person collected by Internet of Things pension equipment and the equipment information of the Internet of Things pension equipment; selecting a health analysis model corresponding to the equipment information from a plurality of health analysis models corresponding to the basic disease information as a target health analysis model; inputting the measured value of the body index required by the target health analysis model into the target health analysis model to obtain a health analysis result output by the target health analysis model; and feeding back the health analysis result to the old-age nursing personnel of the target personnel. The measurement values of the multiple body indexes of the target person are collected, and health analysis is performed by using the health analysis model corresponding to the Internet-of-Things old-age care equipment, so that the monitoring accuracy is effectively improved, and the problems of poor user experience and poor nursing efficiency caused by false alarm are solved.

Description

Technical Field

[0001] This application relates to the field of the Internet of Things, and particularly to a method, device, equipment, medium and product for monitoring elderly care information. Background Art

[0002] With the development of Internet of Things technology, many devices for monitoring various physical indicators of the elderly have emerged on the market, including weight, blood pressure, blood sugar, body temperature, blood oxygen, and blood lipid, etc., and a unified warning value is set for each physical indicator. When a certain physical indicator reaches the corresponding warning value, an alarm message is generated, and according to the alarm content, the generated alarm message is responded to in a timely manner through manual or preset computer programs in advance. The response actions include calling an emergency phone number, notifying an emergency contact, providing first aid guidance, etc.

[0003] However, it is not accurate to evaluate the physical condition of users through single physical indicators. The various physical indicators of the elderly will be affected due to their multiple underlying diseases. There may be a situation where a certain indicator is abnormal, but the overall physical condition is good. If the alarm is often triggered due to this physical indicator, the user experience will become worse. On the other hand, in scenarios with a large number of elderly people such as elderly care institutions and senior communities, if the elderly care attendants are often called to handle the alarm due to a single physical indicator, the work pressure of the elderly care attendants will be greatly increased, the labor cost will be extremely increased, and there may be a situation where the elderly who really need timely care and treatment do not receive the corresponding care and treatment, resulting in a decline in the care efficiency and care effect of management institutions such as elderly care institutions and senior communities. Summary of the Invention

[0004] This application provides a method, device, equipment, medium and product for monitoring elderly care information, which solves the problems of poor user experience and poor care efficiency caused by false alarms.

[0005] This application provides a method for monitoring elderly care information, including: Obtaining the physical data of a target person collected by an Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the underlying disease information of the target person, at least one physical indicator, and the measurement values of each physical indicator; Selecting the health analysis model corresponding to the device information from multiple health analysis models corresponding to the underlying disease information as the target health analysis model; Inputting the measurement values of the physical indicators required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; Feeding back the health analysis result to the elderly care attendants of the target person.

[0006] As an embodiment, the device information includes the types of the IoT elderly care devices, and any one of the health analysis models includes a storage unit and an analysis unit; The storage unit is used to store a comparison sequence and a reference sequence, and the types of physical indicators included in the comparison sequence and the reference sequence are determined based on the device type; The analysis unit is used to evaluate the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result.

[0007] As an embodiment, the evaluating the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result includes: Constructing an analysis matrix according to the comparison sequence and the measured values of the input physical indicators; Determining the maximum difference and the minimum difference of each physical indicator according to the analysis matrix and the reference sequence; Constructing a correlation coefficient matrix according to the maximum difference, the minimum difference of each physical indicator and a preset correlation coefficient; Determining the evaluation value of each matrix element in the analysis matrix according to the correlation coefficient matrix and a preset weight matrix of each physical indicator; Taking the ranking of the evaluation value corresponding to each physical indicator among the evaluation values of each matrix element in the analysis matrix as the health analysis result.

[0008] As an embodiment, if the types of physical indicators collected by the IoT elderly care device are higher than a preset value, at least one physical indicator with the highest correlation with the basic disease information is selected as the physical indicator included in the comparison sequence and the reference sequence.

[0009] As an embodiment, the feedback of the health analysis result to the elderly care personnel of the target person includes: Determining the feedback priority value of the health analysis result according to the weight of the basic disease information, the verification value of the target health analysis model and the health analysis result; Sequentially feedbacking the health analysis result to the elderly care personnel of the target person according to the feedback priority value.

[0010] As an embodiment, the weight of the basic disease information is determined based on the severity at the time of onset and the length of the best rescue time, and the verification value of the target health analysis model is determined based on the percentage value of the health analysis result.

[0011] This application also provides an elderly care information monitoring device, including: An acquisition module, configured to acquire the physical data of a target person collected by an Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measurement values of each physical index; A determination module, configured to select, from multiple health analysis models corresponding to the basic disease information, the health analysis model corresponding to the device information as the target health analysis model; An analysis module, configured to input the measurement values of the physical indexes required by the target health analysis model into the target health analysis model to obtain a health analysis result output by the target health analysis model; A feedback module, configured to feedback the health analysis result to the elderly care caregiver of the target person.

[0012] This application also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the above-mentioned elderly care information monitoring method is implemented.

[0013] This application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned elderly care information monitoring method is implemented.

[0014] This application also provides a computer program product, including a computer program. When the computer program is executed by a processor, the above-mentioned elderly care information monitoring method is implemented.

[0015] The elderly care information monitoring method, device, equipment, medium, and product provided by this application collect the measurement values of multiple physical indexes of a target person through an Internet of Things elderly care device, and use the health analysis model corresponding to the Internet of Things elderly care device for analysis, improving the accuracy of the health analysis result, and solving the problems of poor user experience and poor caregiver efficiency caused by false alarms. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in this application or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 is one of the flow diagrams of the elderly care information monitoring method provided by this application.

[0018] Figure 2 is the second flow diagram of the elderly care information monitoring method provided by this application.

[0019] Figure 3 It is a schematic structural diagram of the elderly care information monitoring device provided by this application.

[0020] Figure 4 It is a schematic structural diagram of the electronic device provided by this application. Specific embodiments

[0021] To make the objectives, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below with reference to the accompanying drawings in this application. Obviously, the described embodiments are some but not all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without making creative efforts shall fall within the protection scope of this application.

[0022] It should be noted that all actions of obtaining signals, information or data in this application are carried out on the premise of complying with the corresponding data protection regulations and policies of the location and with the authorization given by the owner of the corresponding device.

[0023] Figure 1 It is one of the flow schematic diagrams of the elderly care information monitoring method provided by this application. As Figure 1 shown, this application provides an elderly care information monitoring method, including step S100-step S400.

[0024] Step S100, obtain the physical data of the target person collected by the Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index and the measurement values of each physical index.

[0025] The Internet of Things elderly care device includes a variety of Internet of Things elderly care devices, such as sphygmomanometers, glucometers, oximeters, heart rate monitors, thermometers, cameras, etc., which are used to collect multiple physical indexes of users. The physical indexes include blood pressure, blood sugar, blood oxygen, heart rate, body temperature, photos, etc. This application can also be connected to the HIS system interface of hospitals or elderly care institutions to collect information such as the medical treatment data and past medical history of users.

[0026] The Internet of Things elderly care device can be applied to scenarios such as families, hospitals or elderly care institutions. Correspondingly, the target persons it collects are family users, patients or institutional users. For professional elderly care places such as community hospitals and elderly care institutions, they are equipped with more detection devices, more items of physical indexes are collected, and the measurement values are relatively accurate. However, the types and models of the Internet of Things elderly care devices configured in each family are different, resulting in certain differences in the physical index items corresponding to each user.

[0027] Based on this, the present application pre-constructs different health analysis models for each underlying disease and various IoT elderly care devices. There are also certain differences in the input data of each health analysis model because different types of IoT elderly care devices are available in different user families, elderly care institutions, and other places, resulting in different types of physical indicators of users being collected. Therefore, several health analysis models are set for the same underlying disease. For example, for users with relatively affluent finances, they use more types of IoT elderly care devices and can collect more types of physical indicators related to the underlying disease; while for users with ordinary conditions, they use relatively fewer types of IoT elderly care devices and can collect fewer types of physical indicators related to the underlying disease. If these two types of users are input into the same health analysis model, the analysis of the users' physical indicators will be inaccurate. Therefore, the present application sets multiple health analysis models for the same underlying disease.

[0028] The present application makes each underlying disease correspond to several health analysis models, and there are certain differences in the input data of the health analysis models corresponding to the same underlying disease, solving the problem that different types of IoT elderly care devices available in different user families, elderly care institutions, and other places result in different types of physical indicators of users being collected and the inability to perform data processing through the same health analysis model. Therefore, when the present application obtains the physical data of the target person, it will simultaneously obtain the device information of the IoT elderly care device that collected the physical data, and the device information is used to reflect the type and model of the IoT elderly care device.

[0029] For household users, the IoT elderly care device can be connected to the smart gateway. After the elderly person uses the IoT elderly care device to measure physical indicators, it is uploaded to the cloud platform through the smart gateway; data collection functions can also be developed based on smart speakers or mobile phone APPs, and the IoT elderly care device is connected through the smart speaker or smartphone to collect the measured values of each physical indicator.

[0030] Step S200, select the health analysis model corresponding to the device information from the multiple health analysis models corresponding to the underlying disease information as the target health analysis model.

[0031] Based on the differences in the physical data collected by the IoT elderly care device, the present application constructs multiple health analysis models for each underlying disease. There is a mapping relationship among the device information, underlying disease information, and health analysis model of the IoT elderly care device. After determining the underlying disease suffered by the target person according to the underlying disease information and determining the type and model of the IoT elderly care device according to the device information, the health analysis model corresponding to the device information can be selected from the multiple health analysis models as the target health analysis model through the mapping relationship.

[0032] Step S300: Input the measured values of the physical indicators required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model.

[0033] The input data required for each health analysis model is different. When determining the target health analysis model, the input data required by the model can be determined. Filter out the required data from all the physical data uploaded by the IoT elderly care device and input it into the target health analysis model. The target health analysis model is used to determine whether the target person has physical abnormalities based on the input measured values of the physical indicators.

[0034] Step S400: Feed back the health analysis result to the elderly care provider of the target person.

[0035] If the target health analysis model determines that the target person has physical abnormalities, the health analysis result will be fed back to the elderly care provider of the target person. If the IoT elderly care device is applied to a home scenario, the elderly care provider refers to the relatives or nannies of the target person. If the IoT elderly care device is applied to scenarios such as hospitals or elderly care institutions, the elderly care provider refers to nurses or caregivers. In other embodiments, even if the target health analysis model determines that the target person does not have physical abnormalities, the health analysis result can also be fed back to the elderly care provider of the target person. The ways of feeding back the health analysis result include emails, text messages, the display screens in hospitals or elderly care institutions, and voice broadcasts, etc.

[0036] It can be understood that in this application, the measured values of multiple physical indicators of the target person are collected through the IoT elderly care device, and analyzed using the corresponding health analysis model of the IoT elderly care device, which improves the accuracy of the health analysis result and solves the problems of poor user experience and low care efficiency caused by false alarms.

[0037] Based on the above embodiments, as an optional embodiment, the device information includes the device type of the IoT elderly care device, and any one of the health analysis models includes a storage unit and an analysis unit.

[0038] The storage unit is used to store the comparison sequence and the reference sequence, and the types of physical indicators included in the comparison sequence and the reference sequence are determined based on the device type.

[0039] The analysis unit is used to evaluate the input measured values of the physical indicators according to the comparison sequence and the reference sequence to obtain the health analysis result.

[0040] The construction processes and principles of all health analysis models are the same. Only the inputs of each health analysis model are different, which are applicable to the analysis of various underlying diseases. The advantage is that the amount of code development for R & D personnel is small. The specific construction process of the health analysis model includes determining the types of physical indicators included in the comparison sequence and the reference sequence according to the type numbers of the Internet of Things elderly care devices to obtain the storage unit, and then determining the working process of the analysis unit to obtain the analysis unit.

[0041] The target health analysis model also pre-stores a comparison sequence and a reference sequence, and evaluates the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result.

[0042] Optionally, if the types of physical indicators collected by the Internet of Things elderly care devices are higher than the preset value, at least one with the highest correlation with the underlying disease information is selected as the physical indicators included in the comparison sequence and the reference sequence.

[0043] For the same underlying disease, there are strongly correlated indicators and weakly correlated indicators among the physical indicators affected by it. The distinction between strongly correlated indicators and weakly correlated indicators is obtained through big data analysis and expert experience guidance; for users with a large number of physical indicator types, strongly correlated indicators can be preferentially selected from them and input into the corresponding health analysis model, with a high degree of selectivity; while for those with a small number of physical indicator types, only try to obtain the indicators related to the underlying disease, which may be strongly correlated or weakly correlated, with a low degree of selectivity.

[0044] It can be understood that this application sets multiple health analysis models for the same underlying disease, expanding the applicable population, ensuring the accuracy of the health analysis results of users using different types of Internet of Things elderly care devices, being able to adapt to existing Internet of Things elderly care devices, and reducing the economic cost of users.

[0045] Based on the above embodiments, as an optional embodiment, the evaluating the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result includes: Constructing an analysis matrix according to the comparison sequence and the measured values of the input physical indicators; Determining the maximum difference and the minimum difference of each physical indicator according to the analysis matrix and the reference sequence; Constructing a correlation coefficient matrix according to the maximum difference, the minimum difference of each physical indicator and the preset correlation coefficient; Determining the evaluation value of each matrix element in the analysis matrix according to the correlation coefficient matrix and the preset weight matrix of each physical indicator; Taking the ranking of the evaluation value corresponding to each physical indicator among the evaluation values of each matrix element in the analysis matrix as the health analysis result.

[0046] All health analysis models have the same structural framework. The differences lie in the different physical indicators of the pre-stored comparison sequences and reference sequences, and the different physical indicators required as input. However, the processing flow for the measured values of physical indicators is the same. The above steps are the processes for all health analysis models to achieve health analysis, which will be described in detail below in combination with embodiments.

[0047] Let the measured values of the input physical indicators, i.e., the input data, be S = [s1, s2, …, sn] T , where n is a positive integer and n is greater than or equal to 3; Si is the measured value of the i-th physical indicator. Before inputting the input data into the health analysis model, it is also necessary to perform standardization processing on each physical indicator in the input data. The standardization processing includes dimensionless processing and positive normalization processing; obtain the processed standard input matrix A = [a1, a2, …, an] T , where n is a positive integer and n is greater than or equal to 3.

[0048] There are significant differences in the models of Internet of Things elderly care devices, and there are inconsistent data formats. Therefore, it is necessary to process the measured values of the physical indicators of each elderly person into a unified data format; in addition, the accuracy of Internet of Things elderly care devices is also different. Therefore, it is necessary to calibrate their measured values according to the models of Internet of Things elderly care devices.

[0049] Unifying the data formats of the measured values of the physical indicators of each user and performing data calibration can make the results of subsequent analysis and judgment more accurate. The method of data calibration can be to use multiple Internet of Things elderly care devices to measure and obtain test results for the same user within the same test period; through a certain amount of data, calibrate the measured data of each Internet of Things elderly care device. It is also possible to calibrate the Internet of Things elderly care devices with professional medical measurement devices in hospitals, so that professional doctors can analyze the physical indicators of users, making the physical indicators collected by Internet of Things elderly care devices more professionally valuable as a reference.

[0050] In some other solutions, it is also possible to calibrate the Internet of Things elderly care devices on the market with the devices of the corresponding target institutions, so as to facilitate the analysis and judgment of the management personnel of the target management institutions. Among them, the target management institutions include community management institutions, nursing homes, health care institutions, hospitals, etc. Each target management institution is set with a corresponding management scope. Each target management institution manages and monitors the physical indicators of the users within its management scope to ensure that the longest time for the target management institution to reach each user does not exceed the preset optimal treatment time. The preset optimal treatment time can judge the high-incidence diseases of the users according to their underlying diseases, and then determine the preset optimal treatment time of the users according to the high-incidence diseases. For example, the best rescue time for acute myocardial infarction is two hours; while the best rescue time for cardiac arrest is only four minutes. In institutions such as nursing homes and hospitals, the living location of the users can be arranged according to their corresponding preset optimal treatment time. During the data calibration process, it is also necessary to delete the obviously incorrect physical indicators to avoid the influence of the obviously incorrect data on the results of the analysis and judgment. And when the same physical indicator of the same user has obvious errors exceeding the set number of times, the user is reminded to replace or repair the corresponding Internet of Things elderly care device to avoid the failure to detect the abnormal physical condition of the user in time due to the inaccurate measurement caused by the damage of the Internet of Things elderly care device.

[0051] The comparison sequence B and the reference sequence Z are pre-stored in the physical analysis model; among them, the number of data in the comparison sequence exceeds 50 groups of data; in the embodiment of the present application, the comparison sequence has 99 groups of comparison data, B = [B1, B2, B3, … B99]; B1 = [b11, b21, …, bn1] T ; Z = [z1, z2, …, zn] T 。

[0052] The comparison sequence and the standard input matrix form an analysis matrix D = [B1, B2, B3, … B99, A] with n rows and 100 columns, and the analysis matrix has n * 100 elements.

[0053] Obtain the minimum difference and the maximum difference corresponding to each physical indicator according to the analysis matrix and the reference sequence. The formula is as follows: Among them, dij is the element in the i-th row and j-th column of the analysis matrix D, and zi is the element in the i-th row of the reference sequence Z, that is, the reference value corresponding to this physical indicator.

[0054] Calculate the correlation coefficient corresponding to each element in the analysis matrix according to the correlation coefficient formula to obtain the correlation coefficient matrix G. The correlation coefficient formula is as follows: Among them, gij is the correlation coefficient of the element in the i-th row and j-th column of the analysis matrix D, Cmini is the minimum difference of the i-th physical index, Cmaxi is the maximum difference of the i-th physical index; ρ is the resolution, and ρ takes 0.6 in the embodiments of the present application; dij is the element in the i-th row and j-th column of the analysis matrix D; zi is the i-th element in the reference sequence Z; 1 ≤ i ≤ n; 1 ≤ j ≤ 100.

[0055] Obtain the evaluation values of the standard input data and each comparison data according to the correlation coefficient matrix G and the weight matrix Q, where G = [G1, G2,..., G100], G1 = [g11, g21,..., gn1] T ; Q = (q1, q2,..., qn); qn is the weight of the n-th physical index; PJj = Q * Gj Among them, PJj is the evaluation value corresponding to the j-th group of data, Gj is the correlation coefficient column matrix corresponding to the j-th group of data, 1 ≤ j ≤ 100, and Q is the weight matrix.

[0056] According to the evaluation values of 99 items of data in the standard input matrix and the comparison matrix, obtain the ranking of the evaluation value corresponding to the standard data matrix as the analysis result. The higher the ranking, the better the physical indicators of the user are relative to this underlying disease.

[0057] In other embodiments, when the physical indicators of multiple users are applicable to the same health analysis model, the standard input matrices corresponding to multiple users can be input into the health analysis model together, thereby reducing the calculation amount of the health analysis model and improving the analysis speed of the health analysis model.

[0058] It can be understood that the present application uses the comparison sequence and the reference sequence, combines the correlation coefficient and the weight matrix to evaluate the measured values of the input physical indicators. Compared with the monitoring method of setting a unified warning value, it can greatly improve the accuracy of physical indicator monitoring.

[0059] Figure 2 It is the second flow chart of the elderly care information monitoring method provided by the present application. As Figure 2 shown, on the basis of the above embodiments, as an optional embodiment, the step of feeding back the health analysis result to the elderly care provider of the target person includes steps S410 - S420.

[0060] Step S410, determine the feedback priority value of the health analysis result according to the weight of the underlying disease information, the verification value of the target health analysis model, and the health analysis result.

[0061] Optionally, the weight of the underlying disease information is determined based on the severity at the onset of the disease and the length of the optimal rescue time, and the verification value of the target health analysis model is determined based on the percentage value of the health analysis result.

[0062] Step S420: According to the feedback priority value, sequentially feedback the health analysis results to the elderly care providers of the target person.

[0063] In step S410, in the embodiment of the present application, a corresponding weight value WL is set for each underlying disease. The specific setting of the weight value WL can be confirmed according to the severity at the onset of the underlying disease and the length of the optimal rescue time. The more severe the underlying disease, the greater the corresponding weight value WL.

[0064] For multiple health analysis models of the same underlying disease, corresponding verification values WQ are also set; since the number and types of physical indicators of multiple health analysis models of the same underlying disease are different, before comparing the analysis results output by different health analysis models corresponding to the same underlying disease, it is necessary to verify through the verification value corresponding to each health analysis model, so that the analysis results output by different health analysis models of the same underlying disease are comparable. Since the analysis results output by the health analysis model are represented by rankings, it is necessary to preprocess the analysis results. The preprocessing includes determining the ranking percentage as the analysis value according to the ranking; for example, the ranking of a user's analysis result in a health analysis model is 30; there are a total of 100 groups of data, then the ranking percentage is 30%; the larger the analysis value, the worse the user's health condition.

[0065] The calculation formula of the feedback priority value is as follows: Among them, YX is the feedback priority value; WL K is the weight corresponding to the user's k-th underlying disease; WQ K is the verification value of the health analysis model corresponding to the user's K-th underlying disease; FX K is the health analysis value corresponding to the K-th underlying disease.

[0066] In step S420, the embodiment of the present application takes the feedback method for the monitoring large screen as an example for illustration.

[0067] The monitoring large screen is provided with a first page and a second page; both the first page and the second page are provided with a number of information columns; each information column is used to display the user's physical indicators; the first page is used to display the physical indicators of key monitored users; the second page is used to alternately and scrollingly display the physical indicators of non-key monitored users.

[0068] Among them, the display conditions include: Users with a display priority value greater than or equal to the first priority value are regarded as key inspection users; users with a display priority value less than the first preset value are regarded as non-key inspection users. For the first page, determine whether the number of key monitored users is greater than the number of columns in the information bar that can be fully displayed on the first page. If not, place the physical indicators of all key inspection users in each information bar on the first page and display them in descending order of the display priority value. At this time, each information bar is fixed. If so, place the physical indicators of the top 10 users in terms of display priority value in each information bar and display them in descending order of the display priority value. The information bars where the physical indicators of the top 10 users are placed are fixed for display; while the physical indicators of the users ranked after that are placed behind the information bars on the first page and are displayed in a rotating and scrolling manner on the first page.

[0069] In other solutions, when the number of key monitored users is greater than the preset number, create a third page and use the third page as the secondary page of the first page. For the secondary page of the first page, it needs to be placed on another screen different from the display screen where the first page is located, so as to facilitate arranging multiple elderly care guardians for inspection; for the second page, it can share a display screen with the first page or the third page, and switch between the first page and the second page, or the second page and the third page by using a preset switching action on the display screen.

[0070] In a hospital or elderly care institution, inspectors check the physical indicators of each user during the inspection period. For non-key inspection users, they can conduct a rough inspection; a virtual confirmation button is set in the information bar corresponding to the non-key inspection users; after the inspectors check the physical indicators of the user and confirm that there are no problems, they click the virtual confirmation button with the mouse to save the physical indicators of the user and delete them in the corresponding information bar on the second page. An avatar display frame can be set in each information bar to display the avatar of the user, and when the mouse hovers over the avatar, the corresponding identity information and basic disease information of the user are displayed.

[0071] For key inspection users, especially those fixedly displayed on the first page, a virtual confirmation button and a virtual feedback button are set in the corresponding information bar; inspectors need to take corresponding disposal measures for the users on the first page, and the disposal measures include information reminder, voice reminder, treatment, etc.; the inspectors click the virtual feedback button with the mouse or finger to obtain an upload interface to upload the disposal result, and then click the virtual confirmation button to send the user's physical indicators and disposal result to the superior inspector for confirmation. After the superior inspector confirms that it is correct, save the physical indicator data of the user and delete it in the corresponding information bar on the first page.

[0072] It should be noted that each user on the first page or the third page needs to be processed in sequence during the current inspection period, and the sequence is determined by the order of the display priority values from high to low.

[0073] This application can help institutions such as elderly care institutions, hospitals, and community health centers centrally monitor the physical health conditions of users within their management scope, and is particularly suitable for institutions with a large number of chronic disease patients such as those with underlying diseases. The information display page and the inspection and processing method proposed in this application can provide a reference for elderly care institutions, etc. to quickly inspect the physical indicators of users, and process them in a timely manner, and can quickly obtain users who need key care, improving the efficiency of monitoring and nursing.

[0074] In summary, this application is applicable to nursing homes, hospital inpatient departments, and community medical centers, providing a new monitoring mode for elderly care information. It can rely on existing IoT elderly care devices deployed in families and various elderly care places, and by developing and deploying health analysis models in the cloud or backend, etc., it can monitor elderly care information such as the physical indicators of multiple elderly people, without spending a large amount of manpower and material resources to promote and deploy IoT elderly care devices separately, and can provide elderly care monitoring services for more elderly people.

[0075] The following describes the elderly care information monitoring device provided by this application. The elderly care information monitoring device described below can be mutually referred to with the elderly care information monitoring method described above.

[0076] Figure 3 is a schematic structural diagram of the elderly care information monitoring device provided by this application, as Figure 3 shown, this application also provides an elderly care information monitoring device, including: An acquisition module 310, configured to acquire the physical data of the target person collected by the IoT elderly care device and the device information of the IoT elderly care device, where the physical data includes the underlying disease information of the target person, at least one physical indicator, and the measurement values of each physical indicator; A determination module 320, configured to select the health analysis model corresponding to the device information from the multiple health analysis models corresponding to the underlying disease information as the target health analysis model; An analysis module 330, configured to input the measurement values of the physical indicators required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; A feedback module 340, configured to feedback the health analysis result to the elderly care personnel of the target person.

[0077] As an embodiment, the device information includes the device type of the IoT elderly care device, and any one of the health analysis models includes a storage unit and an analysis unit; The storage unit is used to store a comparison sequence and a reference sequence, and the types of physical indicators included in the comparison sequence and the reference sequence are determined based on the type of the device; The analysis unit is used to evaluate the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result.

[0078] As an embodiment, the evaluating the measured values of the input physical indicators according to the comparison sequence and the reference sequence to obtain a health analysis result includes: Constructing an analysis matrix according to the comparison sequence and the measured values of the input physical indicators; Determining the maximum difference and the minimum difference of each physical indicator according to the analysis matrix and the reference sequence; Constructing a correlation coefficient matrix according to the maximum difference, the minimum difference of each physical indicator and a preset correlation coefficient; Determining the evaluation value of each matrix element in the analysis matrix according to the correlation coefficient matrix and a preset weight matrix of each physical indicator; Taking the ranking of the evaluation value corresponding to each physical indicator among the evaluation values of each matrix element in the analysis matrix as the health analysis result.

[0079] As an embodiment, if the types of physical indicators collected by the Internet of Things elderly care device are higher than a preset value, at least one of the physical indicators with the highest correlation with the basic disease information is selected as the physical indicators included in the comparison sequence and the reference sequence.

[0080] As an embodiment, the feedback module 340 is further used for: Determining the feedback priority value of the health analysis result according to the weight of the basic disease information, the verification value of the target health analysis model and the health analysis result; Feedbacking the health analysis result to the elderly care personnel of the target person in sequence according to the feedback priority value.

[0081] As an embodiment, the weight of the basic disease information is determined based on the severity at the time of onset and the length of the best rescue time, and the verification value of the target health analysis model is determined based on the percentage value of the health analysis result.

[0082] Figure 4 Illustrated is a schematic physical structure diagram of an electronic device, such as Figure 4As shown in the figure, the electronic device may include: a processor 410, a communications interface 420, a memory 430, and a communication bus 440. Among them, the processor 410, the communications interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logical instructions in the memory 430 to execute the old-age information monitoring method, which includes: obtaining the physical data of the target person collected by the Internet of Things old-age care device and the device information of the Internet of Things old-age care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measurement values of each physical index; selecting the health analysis model corresponding to the device information from the multiple health analysis models corresponding to the basic disease information as the target health analysis model; inputting the measurement values of the physical indexes required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; and feeding back the health analysis result to the old-age care personnel of the target person.

[0083] In addition, when the logical instructions in the above-mentioned memory 430 are implemented in the form of software functional units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, may be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0084] On the other hand, the present application also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the elderly care information monitoring method provided by each of the above methods. The method includes: obtaining the physical data of a target person collected by an Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measurement values of each physical index; selecting the health analysis model corresponding to the device information from multiple health analysis models corresponding to the basic disease information as the target health analysis model; inputting the measurement values of the physical indexes required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; and feeding back the health analysis result to the elderly care caregiver of the target person.

[0085] On another aspect, the present application also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the elderly care information monitoring method provided by each of the above methods. The method includes: obtaining the physical data of a target person collected by an Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measurement values of each physical index; selecting the health analysis model corresponding to the device information from multiple health analysis models corresponding to the basic disease information as the target health analysis model; inputting the measurement values of the physical indexes required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; and feeding back the health analysis result to the elderly care caregiver of the target person.

[0086] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative labor.

[0087] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring pension information, characterized in that, Including: Obtain the physical data of the target person collected by the Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measured values of each physical index; Select the health analysis model corresponding to the device information from the multiple health analysis models corresponding to the basic disease information as the target health analysis model; Input the measured values of the physical indexes required by the target health analysis model into the target health analysis model to obtain the health analysis result output by the target health analysis model; Feed back the health analysis result to the elderly care personnel of the target person.

2. The elderly care information monitoring method according to claim 1, wherein The device information includes the device type of the Internet of Things elderly care device, and any one of the health analysis models includes a storage unit and an analysis unit; The storage unit is used to store a comparison sequence and a reference sequence, and the types of physical indexes included in the comparison sequence and the reference sequence are determined based on the device type; The analysis unit is used to evaluate the measured values of the input physical indexes according to the comparison sequence and the reference sequence to obtain a health analysis result.

3. The elderly care information monitoring method according to claim 2, wherein The evaluating the measured values of the input physical indexes according to the comparison sequence and the reference sequence to obtain a health analysis result includes: Construct an analysis matrix according to the comparison sequence and the measured values of the input physical indexes; Determine the maximum difference and the minimum difference of each physical index according to the analysis matrix and the reference sequence; Construct a correlation coefficient matrix according to the maximum difference, the minimum difference of each physical index, and a preset correlation coefficient; Determine the evaluation value of each matrix element in the analysis matrix according to the correlation coefficient matrix and the preset weight matrix of each physical index; Take the ranking of the evaluation values corresponding to each physical index among the evaluation values of each matrix element in the analysis matrix as the health analysis result.

4. The elderly care information monitoring method according to claim 2, characterized in that If the type of physical indexes collected by the Internet of Things elderly care device is higher than the preset value, select at least one with the highest correlation with the basic disease information as the physical indexes included in the comparison sequence and the reference sequence.

5. The elderly care information monitoring method according to any one of claims 1-4, characterized in that The feeding back the health analysis result to the elderly care personnel of the target person includes: Determine the feedback priority value of the health analysis result according to the weight of the basic disease information, the verification value of the target health analysis model, and the health analysis result; Feed back the health analysis result to the elderly care personnel of the target person in sequence according to the feedback priority value.

6. The elderly care information monitoring method according to claim 5, characterized in that The weight of the basic disease information is determined based on the severity at the time of onset and the length of the best rescue time, and the verification value of the target health analysis model is determined based on the percentage value of the health analysis result.

7. An old-age information monitoring device, characterized in that, Including; An acquisition module, configured to obtain the physical data of the target person collected by the Internet of Things elderly care device and the device information of the Internet of Things elderly care device, where the physical data includes the basic disease information of the target person, at least one physical index, and the measured values of each physical index; A determination module, configured to select the health analysis model corresponding to the device information from the multiple health analysis models corresponding to the basic disease information as the target health analysis model; An analysis module, configured to input measurement values of physical indicators required by the target health analysis model into the target health analysis model, and obtain a health analysis result output by the target health analysis model; A feedback module, configured to feedback the health analysis result to the elderly care provider of the target person.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and running on the processor, characterized in that, When the processor executes the computer program, the elderly care information monitoring method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the elderly care information monitoring method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the elderly care information monitoring method according to any one of claims 1 to 6 is implemented.

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