Induction pressure data analysis processing method and pressure reduction mattress

Abstract

The invention provides an inductive pressure data analysis and processing method and a pressure reduction mattress, and belongs to the technical field of data processing.The method specifically comprises the steps that the pain association conditions of stress deviation positions and other stress deviation positions under different disease types are determined, and the pain association conditions of the stress deviation positions and other stress deviation positions under different disease types are determined according to the concurrent association conditions of the disease types and the disease types of patients; according to the method, the pain related position of the stress deviation position is determined, the pressure monitoring data and pain risk types of different pain related positions are obtained, and the adjustment processing strategy of the mattress pressure of the stress deviation position is determined in combination with the pain risk types of the stress deviation position, so that the influence of pressure adjustment on the pain position of a patient is avoided; and the sleep quality is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and particularly relates to a method for analyzing and processing sensed pressure data and a decompression mattress. Background Art

[0002] In the field of medical care, for patients in intensive care units, hemiplegic and paralyzed patients, and elderly and frail patients who are bedridden for a long time, due to maintaining a fixed body position for a long time or limited body movement, local tissues of the body are compressed for a long time, resulting in blood circulation disorders, and the incidence of pressure injuries (commonly known as bedsores) remains high. Traditional ordinary mattresses only provide basic support. Although air mattresses have a certain decompression effect, they cannot accurately adjust the pressure distribution in different body positions.

[0003] Therefore, by setting up a mattress that automatically senses pressure, the medical experience of patients can be improved. Specifically, in the invention patent application CN201510631431.2 "A Sleeping Posture Detection System Based on Human Body Pressure Distribution", the pressure data at representative pressure sensor position points and a patient's personalized sleeping posture database are analyzed through a machine learning model to output the patient's sleeping posture state, improving the patient's sleep experience. However, there are the following technical problems: When adjusting and processing the pressure of the mattress, the existing technical solutions ignore the patient's disease type. There is a certain correlation between pain positions due to differences in disease types. Therefore, how to determine the pressure adjustment strategy of the mattress at different positions based on the above correlation, and improve the comfort of the patient's mattress on the basis of reducing the impact on the patient's sleep quality has become a technical problem to be solved urgently.

[0004] To solve the above technical problems, the present application provides a method for analyzing and processing sensed pressure data and a decompression mattress. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions: Specifically, the present application provides a method for analyzing and processing sensed pressure data, which specifically includes: S1 Based on the analysis result of the patient's disease type, determine the distribution deviation of pain positions under the disease type. When it is determined that the distribution of pain positions under the disease type is discrete based on the distribution deviation, determine the force deviation position of the patient according to the pressure monitoring data of the mattress; When S2 determines that the pain risk type of the force deviation position does not belong to the target risk type based on the distribution data of the force deviation position at the pain position of patients with the disease type, it determines the pain association situation of the force deviation position and other force deviation positions under different disease types, and combines the concurrent association situation between the disease type and the patient's disease type to determine the pain association position of the force deviation position; S3 obtains the pressure monitoring data and pain risk types of different pain association positions, and combines the pain risk type of the force deviation position to determine the adjustment and treatment strategy for the mattress pressure at the force deviation position.

[0006] The beneficial effects of the present invention are as follows: By using the distribution deviation situation of the pain positions under the disease type, it determines whether the distribution of the pain positions under the disease type is discrete, fully considering the difference in the distribution dispersion degree of the pain positions caused by the differences in the disease types of patients, and avoiding the emergence of the technical problem of too high a risk of secondary injury to patients caused by adjusting the mattress pressure when the distribution is too discrete, thus ensuring the sleep quality of patients.

[0007] Based on the pressure monitoring data and pain risk types of different pain association positions and the pain risk type of the force deviation position, it determines the adjustment and treatment strategy for the mattress pressure at the force deviation position. It not only considers the difference in the risk degree of the pain positions caused by the pain risk type of the force deviation position, but also further considers the pressure monitoring data and pain risk types of the pain association positions of the force deviation position, fully considering the reference value of the monitoring data of its own pressure change for adjusting the pressure of other positions. Through differential pressure adjustment, it ensures the reliability of the adjustment and treatment of the mattress pressure, and at the same time reduces the impact on the sleep quality of patients.

[0008] A further technical solution is that the distribution deviation situation of the pain positions is determined according to the deviation quantity of the pain positions among different patients.

[0009] A further technical solution is that determining the distribution of the pain positions under the disease type is discrete specifically includes: Based on the distribution deviation situation, it determines the deviation quantity of the pain positions among different patients under the disease type; Based on the deviation quantity of the pain positions from other patients, it determines other patients whose deviation quantity is greater than the preset deviation quantity and takes them as position deviation patients; According to the number of position deviation patients, it determines whether the distribution of the pain positions under the disease type is discrete.

[0010] A further technical solution is that when the number of patients with position deviation is greater than the preset threshold of the number of patients with deviation, it is determined that the distribution of pain positions under the disease type is discrete.

[0011] A further technical solution is that when the distribution of pain positions under the disease type is discrete, no adjustment process of the mattress pressure is performed on the force deviation positions of the patients.

[0012] A further technical solution is that the method for determining the adjustment process strategy of the mattress pressure at the force deviation position is as follows: Based on the pressure monitoring data of different pain-related positions at the force deviation position, determine the force deviation position among the pain-related positions and use it as the deviation-related position; According to the pain risk types of different deviation-related positions, determine the deviation-related positions of one type of risk type and use them as the risk positions of one type; Determine the adjustment process strategy of the mattress pressure at the force deviation position through the number of the risk positions of one type and the pain risk type of the force deviation position.

[0013] A further technical solution is that determining the adjustment process strategy of the mattress pressure at the force deviation position through the number of the risk positions of one type and the pain risk type of the force deviation position specifically includes: When the pain risk type of the force deviation position is the risk type of one type, use the force deviation positions of the force deviation position belonging to the risk positions of one type as the characteristic positions, and determine whether the adjustment process of the mattress pressure at the force deviation position can be performed according to the adjustment data of the mattress pressure at the characteristic positions; When the pain risk type of the force deviation position is the risk type of two types, determine the adjustment process strategy of the mattress pressure at the force deviation position according to the number of the risk positions of one type.

[0014] In the second aspect, the present invention provides a decompression mattress, which adopts the above-mentioned method for analyzing and processing induction pressure data, and specifically includes: A deviation position recognition module, a correlation relationship recognition module, and a pressure adjustment module; Wherein the deviation position recognition module is responsible for determining the force deviation position of the patient; The correlation relationship recognition module is responsible for determining the pain-related positions of the force deviation position; The pressure adjustment module is responsible for determining the adjustment process strategy of the mattress pressure at the force deviation position and performing the adjustment of the mattress pressure.

[0015] Other features and advantages will be described in the following specification. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the specification and the accompanying drawings.

[0016] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following provides preferred embodiments in conjunction with the accompanying drawings for detailed description as follows. Brief Description of the Drawings

[0017] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0018] Figure 1 is a flowchart of a method for analyzing and processing inductive pressure data; Figure 2 is a flowchart for determining the distribution dispersion of pain locations under a disease type; Figure 3 is a flowchart of a method for determining the pain risk type at the force deviation location; Figure 4 is a flowchart of a method for determining the pain-related location at the force deviation location; Figure 5 is a framework diagram of a decompression mattress. Detailed Embodiments

[0019] To enable those skilled in the art of the present technology to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0020] In this application, according to the pain risk type at the associated force deviation location and the pain risk type at the force deviation location, fully considering the risk of the pain location and the association relationship with other force deviation locations, the conditions for adjusting and processing the mattress pressure are determined, reducing the impact of the mattress pressure adjustment on the patient's sleep quality.

[0021] Specific examples are as follows: When, under the disease type of the patient, the number of historical patients with a deviation quantity of more than 3 from the pain locations of other patients is within a preset quantity range, the distribution dispersion of the pain locations under the disease type is determined.

[0022] The force deviation location is the body location where the pressure monitoring data is not within the preset pressure range.

[0023] When the proportion of the number of patients whose force deviation positions belong to pain positions under the disease type is above 0.2, it is determined that the pain risk type of the force deviation position belongs to the target risk type.

[0024] Pain-related positions of the force deviation position: When the proportion of the number of patients whose other force deviation positions and the force deviation position both belong to pain positions is greater than 0.1, it is determined that the other force deviation positions are pain-related positions.

[0025] When the pain risk type of the force deviation position is a secondary risk type, directly perform mattress pressure adjustment processing. When the pain risk type of the force deviation position is a primary risk type, when the number of those belonging to the primary risk type among its pain-related positions is more than 2, directly perform mattress pressure adjustment processing.

[0026] Embodiment 1 As Figure 1 shown, the present application provides a method for analyzing and processing sensed pressure data, specifically including: S1 Based on the analysis result of the patient's disease type, determine the distribution deviation of pain positions under the disease type. When it is determined that the distribution of pain positions under the disease type is discrete based on the distribution deviation, determine the force deviation position of the patient according to the pressure monitoring data of the mattress; Furthermore, the distribution deviation of pain positions is determined according to the deviation quantity of pain positions among different patients.

[0027] Specifically, as Figure 2 shown, determining that the distribution of pain positions under the disease type is discrete specifically includes: Based on the distribution deviation, determine the deviation quantity of pain positions among different patients under the disease type; Based on the deviation quantity of pain positions from other patients, determine other patients whose deviation quantity is greater than the preset deviation quantity, and use them as position deviation patients; According to the number of patients belonging to position deviation patients, determine whether the distribution of pain positions under the disease type is discrete.

[0028] Furthermore, when the number of patients belonging to position deviation patients is greater than the preset deviation patient number threshold, it is determined that the distribution of pain positions under the disease type is discrete.

[0029] It can be understood that when the distribution of pain positions under the disease type is discrete, no mattress pressure adjustment processing is performed on the force deviation positions of the patient.

[0030] In another possible embodiment, determining the discrete distribution of pain positions under the disease type specifically includes: Based on the distribution deviation situation, determining the number of deviations in pain positions among different patients under the disease type; Based on the number of deviations in pain positions from other patients, determining other patients with the number of deviations greater than the preset deviation number, and taking them as position deviation patients; Based on the number of different patients who are position deviation patients, determining the distribution discrete patients among the patients, and based on the number of distribution discrete patients, determining whether the distribution of pain positions under the disease type is discrete.

[0031] Further, the distribution discrete patients are those whose number of belonging to position deviation patients is greater than the preset number threshold.

[0032] Specifically, when the number of distribution discrete patients is greater than the preset number threshold of discrete patients, it is determined that the distribution of pain positions under the disease type is discrete.

[0033] Further, the force deviation position of the patient is the body position of the patient where the sensed pressure in the mattress is less than the preset sensed pressure threshold.

[0034] S2 When determining that the pain risk type of the force deviation position does not belong to the target risk type based on the distribution data of the pain positions of the patients of the disease type at the force deviation position, determining the pain association situation between the force deviation position and other force deviation positions under different disease types, and combining the concurrent association situation between the disease type and the disease type of the patient, determining the pain associated position of the force deviation position; Specifically, as Figure 3 shown, the method for determining the pain risk type of the force deviation position is: Based on the distribution data of the pain positions of the patients of the disease type at the force deviation position, determining the number of the force deviation position belonging to the pain positions among the patients of the disease type, and taking it as the number of matching pain patients; According to the proportion of the number of matching pain patients among the patients of the disease type, determining the position pain matching value; Based on the position pain matching value, determining the pain risk type of the force deviation position.

[0035] Further, determining the pain risk type of the force deviation position based on the position pain matching value specifically includes: When the position pain matching value is greater than the preset pain matching threshold, it is determined that the pain risk type of the force deviation position is the target risk type; When the position pain matching value is not greater than the preset pain matching threshold, it is determined whether the position pain matching value is less than the pain matching preset value. If so, it is determined that the pain risk type of the force deviation position is a type II risk type. If not, it is determined that the pain risk type of the force deviation position is a type I risk type.

[0036] In addition, it should be noted that when the pain risk type of the force deviation position is the target risk type, there is no need to adjust the mattress pressure at the force deviation position.

[0037] Specifically, as Figure 4 shown, the method for determining the pain-related position of the force deviation position is as follows: Based on the pain correlation between the force deviation position and other force deviation positions under different disease types, determine the proportion of the number of patients with both the force deviation position and other force deviation positions being pain positions among the patients under different disease types, and use it as the pain correlation value under the disease type; According to the concurrent correlation between different disease types and the disease types of the patients, determine the number of patients with both the disease type and the disease types of the patients, and use it as the number of concurrent patients. The disease type with the number of concurrent patients greater than the preset patient number threshold is used as the concurrent disease type; Based on the pain correlation values under different concurrent disease types, determine whether the other force deviation position is the pain-related position.

[0038] Furthermore, when there is a concurrent disease type with a pain correlation value greater than the preset correlation threshold, it is determined that the other force deviation position is the pain-related position.

[0039] In another possible embodiment, the method for determining the pain-related position of the force deviation position is as follows: Based on the pain correlation between the force deviation position and other force deviation positions under different disease types, determine the proportion of the number of patients with both the force deviation position and other force deviation positions being pain positions among the patients under different disease types, and use it as the pain correlation value under the disease type. Use the pain correlation value to determine the associated disease type in the disease type; According to the concurrent correlation between different associated disease types and the disease types of the patients, determine the number of patients with both the associated disease type and the disease types of the patients, and use it as the number of concurrent patients; Based on the number of concurrent patients under different associated disease types, determine whether the other force deviation position is the pain-related position.

[0040] Further, the associated disease type is a disease type with a pain association value greater than a preset association threshold.

[0041] Specifically, when there is an associated disease type with the number of concurrent patients greater than the preset patient number threshold, then determine the other force deviation position as the pain association position.

[0042] S3 Obtain the pressure monitoring data and pain risk types of different pain association positions, and combine the pain risk types of the force deviation positions to determine the adjustment processing strategy for the mattress pressure at the force deviation positions.

[0043] Specifically, the method for determining the adjustment processing strategy for the mattress pressure at the force deviation positions is as follows: Use the pressure monitoring data of different pain association positions at the force deviation positions to determine the force deviation position among the pain association positions, and use it as the deviation association position; According to the pain risk types of different deviation association positions, determine the deviation association positions of one type of risk type, and use it as the one-type risk position; Determine the adjustment processing strategy for the mattress pressure at the force deviation positions through the number of the one-type risk positions and the pain risk type of the force deviation position.

[0044] Further, determining the adjustment processing strategy for the mattress pressure at the force deviation positions through the number of the one-type risk positions and the pain risk type of the force deviation position specifically includes: When the pain risk type of the force deviation position is the one-type risk type, then use the force deviation positions of the force deviation position belonging to the one-type risk position as the characteristic positions, and determine whether the adjustment of the mattress pressure at the force deviation position can be performed according to the adjustment data of the mattress pressure at the characteristic positions; When the pain risk type of the force deviation position is the two-type risk type, then determine the adjustment processing strategy for the mattress pressure at the force deviation position according to the number of the one-type risk positions.

[0045] It can be understood that determining the adjustment processing strategy for the mattress pressure at the force deviation position according to the number of the one-type risk positions specifically includes: When the number of the one-type risk positions is greater than the preset risk position number threshold, then the adjustment processing of the mattress pressure at the force deviation position needs to be performed immediately; When the number of the first type of risk positions is not greater than the preset risk position number threshold, after the mattress pressure adjustment process of the force deviation position that needs to be immediately performed and when the patient does not issue a warning signal, the mattress pressure adjustment process of the force deviation position is performed again.

[0046] Further, after the pressure of the pain position of the patient is adjusted and the patient's sleep state is affected, a warning signal is issued.

[0047] Specifically, according to the adjustment data of the mattress pressure at the characteristic positions, it is determined whether the mattress pressure adjustment process of the force deviation position can be performed, which specifically includes: When the number of the characteristic positions is not greater than the preset characteristic position number threshold, it is determined that the mattress pressure adjustment process of the force deviation position cannot be performed; When the number of the characteristic positions is greater than the preset characteristic position number threshold, after the mattress pressures of the preset number of characteristic positions are adjusted and the patient does not issue a warning signal, the mattress pressure adjustment process of the force deviation position is performed again.

[0048] It should be noted that the value of the preset number is 3.

[0049] Embodiment 2 In a second aspect, as Figure 5 shown, the present invention provides a decompression mattress, which adopts the above-mentioned method for analyzing and processing inductive pressure data, and specifically includes: a deviation position recognition module, a correlation relationship recognition module, and a pressure adjustment module; wherein the deviation position recognition module is responsible for determining the force deviation position of the patient; the correlation relationship recognition module is responsible for determining the pain correlation position of the force deviation position; the pressure adjustment module is responsible for determining the mattress pressure adjustment process strategy of the force deviation position and performing the mattress pressure adjustment.

[0050] In another possible embodiment, determining the distribution dispersion of the pain positions under the disease type specifically includes: S11 Determine the number of patients with different pain positions under the disease type. Based on the number of patients with different pain positions under the disease type and in combination with the number of patients under the disease type, determine the pain position distribution dispersion value; It should be noted that before proceeding to the next step, it is necessary to determine in sequence whether there are pain locations where the proportion of the number of patients does not meet the requirements, whether the number of pain locations where the proportion of the number of patients does not meet the requirements is too large, and whether the discrete value of the pain location distribution is too large. Among them, not meeting the requirements, being too large or being too large is determined by a preset threshold judgment condition.

[0051] It can be understood that when there are no pain locations where the proportion of the number of patients does not meet the requirements, that is, when the ratio of the number of patients existing in different pain locations under the disease type to the number of patients under the disease type is relatively large, that is, all are greater than a certain threshold, at this time, it can be directly determined that the distribution of pain locations under the disease type is not discrete.

[0052] In addition, it should be noted that if there are pain locations where the proportion of the number of patients does not meet the requirements, that is, there are pain locations where the proportion of the number of patients is relatively low. At this time, if the number of pain locations where the proportion of the number of patients does not meet the requirements is too large, that is, greater than the threshold, since there are a large number of pain locations with a relatively small number of patients, it can be determined at this time that the distribution of pain locations under the disease type is discrete.

[0053] Specifically, even when the number of pain locations where the proportion of the number of patients does not meet the requirements is not large, based on the number of patients existing in different pain locations under the disease type and in combination with the number of patients under the disease type, the pain location distribution discrete value is determined. Specifically, it can be determined by using the difference between a preset value and the average value of the proportion of the number of patients in different pain locations. When the pain location distribution discrete value is too large, that is, greater than the threshold, it can be determined that the distribution of pain locations under the disease type is discrete, and only when the pain location distribution discrete value is not too large, proceed to the next step to determine the deviation patient evaluation quantity.

[0054] S12; Based on the distribution deviation situation, determine the deviation quantity of pain locations between different patients under the disease type. Based on the deviation quantity of pain locations between different patients and other patients, determine other patients whose deviation quantity is greater than the preset deviation quantity, and regard them as location deviation patients. According to the number of different patients who belong to location deviation patients and the deviation quantity of pain locations with the corresponding patients when they belong to location deviation patients, determine the deviation patient evaluation quantity; In addition, it should be noted that in the above step S12, it is also necessary to further determine whether there are location deviation patients, determine the discrete patients among the patients based on the number of different patients who belong to location deviation patients, whether the number of discrete patients is too large, and whether the deviation patient evaluation quantity is too large. Being too large or being too large is determined by means of a threshold.

[0055] In addition, it can be understood that when there are no patients with position deviation, since the pain positions among different patients are similar, the distribution of pain positions under the disease type can be directly determined to be non-discrete. However, when there are patients with position deviation, if the number of patients with discrete distribution is relatively large or the evaluation quantity of patients with deviation is relatively large, it can be directly determined that the distribution of pain positions under the disease type is discrete, where the evaluation quantity of patients with deviation is determined based on the output result of a neural network model with the number of patients belonging to position deviation patients and the number of deviations of pain positions from the corresponding patients as input quantities.

[0056] Only when all the above conditions are met, that is, the number of patients with discrete distribution is not relatively large or the evaluation quantity of patients with deviation is not relatively large, then the next step is to determine the position discrete value.

[0057] S13 Determine the position discrete value under the disease type according to the pain position distribution discrete value and the deviation quantity of patients with deviation, and determine whether the distribution of pain positions under the disease type is discrete based on the position discrete value.

[0058] It can be understood that the value range of the position discrete value under the disease type is between 0 and 1. In one of the embodiments, the position discrete value under the disease type is determined according to the average value of the pain position distribution discrete value and the deviation quantity of patients with deviation. When the position discrete value is greater than 0.6, it is determined that the distribution of pain positions under the disease type is discrete.

[0059] Embodiment 3 In another possible embodiment, the method for determining the adjustment processing strategy of the mattress pressure at the force deviation position is as follows: S41 Use the pressure monitoring data of different pain-related positions at the force deviation position to determine the force deviation position in the pain-related positions, and take it as the deviation-related position. According to the pain risk types of different deviation-related positions, determine the deviation-related positions of one type of risk type, and take it as the first-class risk positions. Determine the comprehensive correlation factor of the force deviation position according to the number of the first-class risk positions; Specifically, before entering step S41, it is necessary to determine whether the pain risk type of the force deviation position is the second-class risk type. Only when it belongs to the second-class risk type, then step S41 is entered.

[0060] It should be further noted that before entering the next step, it is also necessary to determine whether the number of deviation-related positions is excessive, whether the number of first-class risk positions is excessive, and whether the comprehensive correlation factor of the force deviation positions reaches the target conditions. If the number of deviation-related positions is excessive, the number of first-class risk positions is excessive, or the comprehensive correlation factor of the force deviation positions reaches the target conditions, that is, when it is greater than the set threshold, it can be directly determined that the mattress pressure at the force deviation positions needs to be adjusted immediately.

[0061] Specifically, when judging whether the number of deviation-related positions is excessive and the number of first-class risk positions is excessive, it can be determined by setting thresholds.

[0062] In addition, it should be noted that if the above conditions are not met, and the number of deviation-related positions is too small and the number of first-class risk positions is too small, it is directly determined that when the force deviation position adjustment process that needs to be carried out immediately for the mattress pressure at the force deviation positions is completed and the patient does not issue a warning signal, the mattress pressure at the force deviation positions is adjusted again.

[0063] In a possible embodiment, the comprehensive correlation factor of the force deviation positions is determined according to the product of the number of first-class risk positions and a preset proportional factor.

[0064] S42 determines the correlation influence factor based on the coincidence of different first-class risk positions and the pain-related positions of different force deviation positions, and combines the pain risk types of different force deviation positions; In addition, it can be understood that in the above step S42, it is also necessary to further determine whether the number of first-class risk positions that only belong to the force deviation positions meets the requirements and whether the correlation influence factor meets the requirements. Specifically, it can be determined by setting thresholds.

[0065] In another embodiment, when the number of first-class risk positions that only belong to the force deviation positions is excessive, that is, when it meets the requirements, since the influence degree of the force deviation positions is relatively high, it can be directly determined that the mattress pressure at the force deviation positions needs to be adjusted immediately.

[0066] Even when the number of a type of risk positions that only belong to the stress deviation position does not meet the requirements, when the associated influence factor is relatively large at this time, it can be determined by means of a threshold at this time, indicating that the stress deviation position has a relatively large reference value for judging whether a type of risk position belongs to a pain position. Therefore, it can be directly determined that the mattress pressure at the stress deviation position needs to be adjusted immediately. The associated influence factor is determined by the coincidence of the pain associated positions of different types of risk positions and different stress deviation positions, and combined with the output of a neural network model with the pain risk types of different stress deviation positions as input quantities.

[0067] S43 determines the adjustment demand factor of the stress deviation position through the comprehensive correlation factor, the associated influence factor, and the pain risk type of the stress deviation position, and determines the adjustment processing strategy of the mattress pressure at the stress deviation position based on the adjustment demand factor.

[0068] In a possible embodiment, the value range of the adjustment demand factor of the stress deviation position is between 0 and 1. Among them, when the adjustment demand factor of the stress deviation position is above 0.5, the mattress pressure at the stress deviation position needs to be adjusted immediately. In other cases, when the stress deviation position that needs to be adjusted immediately has been adjusted and the patient does not send a warning signal, the mattress pressure at the stress deviation position is adjusted again.

[0069] In addition, it can be understood that in one of the embodiments, the adjustment demand factor of the stress deviation position can be based on the comprehensive correlation factor, the associated influence factor, and the pain risk type of the stress deviation position to construct an analytic hierarchy model to determine the adjustment demand factor of the stress deviation position.

[0070] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and non-volatile computer storage medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0071] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The above are only one or more embodiments of this specification and are not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A method for analyzing and processing sensed pressure data, characterized in that, Specifically include: Based on the analysis result of the patient's disease type, determine the distribution deviation of pain positions under the disease type. When determining the distribution dispersion of pain positions under the disease type based on the distribution deviation, determine the force deviation position of the patient according to the pressure monitoring data of the mattress; Based on the distribution data of the force deviation position in the pain positions of patients with the disease type, when determining that the pain risk type of the force deviation position does not belong to the target risk type, determine the pain association situation between the force deviation position and other force deviation positions under different disease types, and combine the concurrent association situation between the disease type and the patient's disease type to determine the pain association position of the force deviation position; Obtain the pressure monitoring data and pain risk types of different pain association positions, and combine the pain risk type of the force deviation position to determine the adjustment processing strategy for the mattress pressure of the force deviation position.

2. The induction pressure data analysis and processing method according to claim 1, wherein The distribution deviation of the pain positions is determined according to the deviation quantity of pain positions among different patients.

3. The induction pressure data analysis and processing method according to claim 1, wherein Determining the distribution dispersion of pain positions under the disease type specifically includes: Based on the distribution deviation, determine the deviation quantity of pain positions among different patients under the disease type; Based on the deviation quantity of pain positions from other patients, determine other patients with a deviation quantity greater than the preset deviation quantity, and use them as position deviation patients; According to the number of patients belonging to the position deviation patients, determine whether the distribution of pain positions under the disease type is discrete.

4. The induction pressure data analysis and processing method according to claim 3, characterized in that When the number of patients belonging to the position deviation patients is greater than the preset deviation patient quantity threshold, it is determined that the distribution of pain positions under the disease type is discrete.

5. The method for analyzing and processing induction pressure data according to claim 1, wherein The force deviation position of the patient is the body position of the patient where the sensed pressure in the mattress is less than the preset sensed pressure threshold.

6. The method for analyzing and processing induction pressure data according to claim 1, wherein The method for determining the pain risk type of the force deviation position is: Based on the distribution data of the force deviation position in the pain positions of patients with the disease type, determine the number of the force deviation position belonging to the pain positions among the patients with the disease type, and use it as the number of matching pain patients; According to the proportion of the number of matching pain patients among the patients with the disease type, determine the position pain matching value; Based on the position pain matching value, determine the pain risk type of the force deviation position.

7. The method for analyzing and processing induction pressure data according to claim 6, wherein Based on the position pain matching value, determining the pain risk type of the force deviation position specifically includes: When the position pain matching value is greater than the preset pain matching threshold, it is determined that the pain risk type of the force deviation position is the target risk type; When the position pain matching value is not greater than the preset pain matching threshold, determine whether the position pain matching value is less than the pain matching preset value. If so, it is determined that the pain risk type of the force deviation position is the secondary risk type. If not, it is determined that the pain risk type of the force deviation position is the primary risk type.

8. The inductive pressure data analysis and processing method according to claim 7, characterized in that, When the pain risk type of the force deviation position is the target risk type, there is no need to adjust the mattress pressure of the force deviation position.

9. The induction pressure data analysis and processing method according to claim 1, characterized in that, The method for determining the adjustment and treatment strategy of the mattress pressure at the force deviation position is as follows: Based on the pressure monitoring data of different pain-related positions at the force deviation position, determine the force deviation position among the pain-related positions and use it as the deviation-related position; According to the pain risk types of different deviation-related positions, determine the deviation-related positions of one type of risk type and use them as the risk positions of one type; Determine the adjustment and treatment strategy of the mattress pressure at the force deviation position through the number of the risk positions of one type and the pain risk type of the force deviation position.

10. A decompression mattress adopting a method for analyzing and processing sensed pressure data according to any one of claims 1-9, characterized in that Specifically, it includes: A deviation position identification module, a correlation relationship identification module, and a pressure adjustment module; Among them, the deviation position identification module is responsible for determining the force deviation position of the patient; The correlation relationship identification module is responsible for determining the pain-related positions of the force deviation position; The pressure adjustment module is responsible for determining the adjustment and treatment strategy of the mattress pressure at the force deviation position and adjusting the mattress pressure.

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