Intelligent tourniquet softening control system and method based on temperature-sensitive monitoring

Abstract

The invention discloses an intelligent tourniquet softening control system and method based on temperature-sensitive monitoring, and relates to the technical field of medical equipment, and the system comprises an area state classification module, a matrix analysis and instruction generation module, a power regulation and control and execution module, and a state verification and decision module. A target part is divided in a gridding mode, pressure and temperature data are collected in real time, softening control areas and non-softening control areas are dynamically classified and marked, a pressure-temperature matrix is constructed, a softening instruction matrix is generated, and heating power of all the areas is precisely regulated and controlled in combination with a preset threshold value and a safety coefficient to conduct softening control. Meanwhile, whether secondary softening control is needed or not is judged by checking the area state after softening control, and closed-loop control is achieved; the problems that a traditional tourniquet cannot dynamically respond to pressure and temperature changes and is low in control precision are solved, vein visibility and use safety are remarkably improved, and the risk of skin injury is reduced.

Description

Technical Field

[0001] This invention belongs to the field of medical device technology, specifically a temperature-sensitive pulse cuff intelligent softening control system and method. Background Technology

[0002] Traditional tourniquets are widely used in medical settings (such as blood draws and venipuncture) to improve vein visibility by applying pressure to block blood flow. However, existing technologies have the following significant problems:

[0003] Static adjustment is disconnected from dynamic needs: Traditional tourniquets rely on preset fixed pressure or manual adjustment, which cannot respond in real time to changes in pressure distribution and skin surface temperature fluctuations at the user's target site. For example, during prolonged use or in patients of different body types, uneven pressure distribution may lead to local overpressure, causing skin damage or insufficient blood flow obstruction, thus affecting operational efficiency.

[0004] Lack of ability to identify local abnormal areas: Traditional methods use a global unified adjustment strategy, which cannot accurately identify local areas with abnormal pressure or temperature in the target site (such as sub-regions with excessive pressure), which can easily cause patient discomfort or difficulty in vein localization.

[0005] Based on this, a temperature-sensitive monitoring-based intelligent softening control system and method for pressure pulse bands are proposed. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent softening control system and method for pressure pulse bands based on temperature-sensitive monitoring.

[0007] The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring includes: a regional state classification module, a matrix analysis and command generation module, a power regulation and execution module, and a state verification and decision-making module, wherein:

[0008] The regional status classification module is used to monitor and analyze the regional status data of each region of the target body after the bandage is adjusted based on the preset thickness parameters of the target body part. Based on the analysis results, the regional status of each region of the target body part is dynamically classified and marked.

[0009] The regional status classification labels for each area of ​​the target location include softened control area type labels and non-softened control area type labels;

[0010] The matrix analysis and instruction generation module is used to filter and analyze the regional state data based on the type label of the softening control area corresponding to the target part, and construct a pressure-temperature matrix based on the analysis results. It also generates a softening instruction matrix for the softening control area based on the pressure-temperature matrix of the corresponding softening control area.

[0011] The power regulation and execution module includes a heating control power range determination unit and a softening control unit. The heating control power range determination unit is used to determine the heating control power range and the actual heating control power in the softening command matrix corresponding to each softening control area and update the softening command matrix. The softening control unit is used to perform softening control on each softening control area based on the updated softening command matrix.

[0012] The status verification and decision-making module is used to verify the overall regional status of the target area of ​​the pressure pulse after softening control based on the softening command matrix; and to further determine whether to perform secondary softening control based on the regional status judgment result.

[0013] As a further aspect of the present invention: Adjusting the tourniquet based on preset thickness parameters of the user's target body part includes: adjusting the tourniquet according to the standard circumference dimensions of the user's target body part from a preset physiological characteristic size database.

[0014] As a further aspect of the present invention: monitoring and processing the regional state data of each area of ​​the target site after the initial adjustment, and performing data analysis, including: collecting the initial pressure value of each sub-region of the target site through the built-in pressure sensor of the intelligent pressure belt, forming an initial pressure matrix P. initial =[P ij Each sub-region is defined by dividing the target area into M*N uniform grid sub-regions using a gridding method; where M is the number of horizontal grids and N is the number of vertical grids, and each sub-region is uniquely identified by coordinates (i,j); where i represents the horizontal row coordinate of the grid sub-region of the target area, and i = 1, 2, ..., M; j represents the vertical column coordinate of the grid sub-region of the target area, and j = 1, 2, ..., N;

[0015] The initial skin surface temperature of each sub-region of the target area is collected by the built-in temperature sensor of the smart pressure band, forming an initial skin surface temperature matrix T. initial =[t ij ];

[0016] Based on the regional state data corresponding to each sub-region of the target location, i.e., the initial pressure value P ij and initial skin surface temperature t ij Calculate the corresponding pressure deviation value D. p,ij and skin surface temperature deviation value D t,ij According to the formula:

[0017] Pressure deviation Skin surface temperature deviation Where max(p) i ) represents the maximum pressure value in the same horizontal row, p ijis the initial pressure value of the current sub-region, t ij is the initial skin surface temperature value of the current sub-region, t avg is the average skin surface temperature of the current horizontal row sub-region;

[0018] Based on the pressure deviation value D corresponding to each sub-region of the target site p,ij and the skin surface temperature deviation value D t,ij , the first state value Q is calculated and obtained ij , according to the formula:

[0019] Q ij = α1·D p,ij +(1 - α1)·D t,ij ; where, α1 is the weight coefficient of the pressure deviation value D p,ij , (1 - α1) is the preset weight coefficient of the skin surface temperature deviation value D t,ij ;

[0020] And, the second state value C corresponding to each sub-region of the target site is calculated through the formula ; in the formula, α2, α3, α4 are respectively the preset weight coefficients of the initial pressure value P ij the initial skin surface temperature t ij and ;

[0021] As a further solution of the present invention: Calculate according to the first state value and the second state value to obtain the regional state evaluation value of each sub-region of the target site In the formula, ∈ is the preset safety factor, S th is the preset threshold of state evaluation;

[0022] [[ID=, the regional state recognition model expression is established as:

[0023] When ZC = 1, it indicates that the pressure-temperature state abnormality degree of the sub-region corresponding to the target site is high, and this sub-region is marked as the softening control region type mark; and enter the softening control module; when ZC = 0, it indicates that the pressure-temperature state of the sub-region corresponding to the target site is balanced, and this sub-region is marked as the non-softening control region type mark and maintains the current control state.

[0024] As a further solution of the present invention: The specific implementation steps of the matrix analysis and instruction generation module include: Count the softening control regions RH in the target site k , where k is the softening control region index and k = 1, 2,..., e; e is the number of softening control regions and e < M * N; and from the initial pressure matrix P​​​​initial and the initial skin surface temperature matrix T initial Extract the regional state data corresponding to all softening control areas, including initial pressure values ​​and initial skin surface temperature values. Normalize the corresponding initial pressure values ​​and skin surface temperature values ​​to eliminate dimensional differences and retain the p-values ​​that pass the screening. ’ ij and t ’ ij And respectively marked as selected pressure value p ’ ij and selected skin surface temperature value t ’ ij ;

[0025] For each softening control region, its selected pressure value and selected skin surface temperature value are mapped to the physical location of the softening control region according to the original coordinates, and a corresponding pressure-temperature matrix is ​​constructed, with the coordinates of the corresponding pressure-temperature matrix being consistent with the original mesh (i,j);

[0026] Based on the pressure-temperature matrix corresponding to each softening control zone of the target area, the original heating power required for each softening zone is calculated. In the formula, T safe To preset a safe skin temperature threshold, p max The preset pressure sensing threshold is β1 and β2, which are the preset weight coefficients of the corresponding formula terms.

[0027] Based on the calculated original heating power required for each softened area of ​​the target location. Forming a softening instruction matrix And soften the position of elements in the instruction matrix One-to-one correspondence between domains.

[0028] As a further aspect of the present invention: the process of determining the heating control power range and the actual heating control power in the softening command matrix corresponding to each softening control region, and updating the softening command matrix, is as follows: for each softening control region RH k Calculate its allowable heating power range: the lower limit of heating power is The upper limit of heating power is Wherein, γ1 and γ2 represent the preset lower limit proportional coefficient and upper limit proportional coefficient, respectively. This represents the original heating power required for the corresponding softening control area, H. device-max Set the upper limit of the pressure band device temperature;

[0029] For each softening control area RH k According to the determined correspondence Scope, will Mapped to actual heating power:

[0030] By determining the corresponding softening control area The range determines the actual heating power and updates the corresponding softening command matrix to form the updated softening command matrix.

[0031] As a further aspect of the present invention: the softening control unit performs softening control on each softening control region based on the updated softening command matrix, the process of which includes: the softening control includes performing independent softening control on each softening control region based on the updated softening command matrix or performing collaborative softening control on each softening control region based on the updated softening command matrix; specifically: calculating the global pressure-temperature Pearson correlation coefficient ρ among all softening control regions in the target area and the variance of the global softening region state evaluation value corresponding to all softening control regions. Combining the global pressure-temperature Pearson correlation coefficient ρ and the variance of the global softened region state assessment value Its corresponding preset threshold ρ th and Establish softening control trigger conditions;

[0032] When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... It is not possible to simultaneously satisfy the condition of being greater than or equal to its corresponding preset threshold ρ th and Then an independent softening control command is generated;

[0033] The independent softening control command is to use the built-in heating element of the pressure pulse band to perform local softening control on each softening control area according to the softening command matrix corresponding to each softening control area in the target area;

[0034] When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... Simultaneously satisfying the condition of being greater than or equal to its corresponding preset threshold ρ th and Then, a collaborative softening control command is generated.

[0035] As a further aspect of the present invention: when generating a collaborative softening control command, grouping is performed based on the correlation between all softening control regions in the target area, including: calculating the pressure-temperature correlation between each pair of all softening control regions to obtain an e×e correlation matrix R, where R(a,b) is the Pearson correlation coefficient between region a and region b.

[0036] Each softening control region is treated as a node in a graph using a graph clustering algorithm, and the correlation coefficient between each softening control region is correlated with its corresponding threshold ρ.th Compare; if |R(a,b)|≥ρ th Then add an edge between nodes a and b; find the connected components in the graph, each connected component constitutes a set of regions that need to be updated collaboratively, thus obtaining multiple sets of regions that need to be updated collaboratively.

[0037] When a collaborative softening control command is generated, the heating element built into the pressure pulse belt is used to perform collaborative softening control on the set of regions that need collaborative softening control according to the softening command matrix corresponding to the set of regions in the target area that need collaborative softening control.

[0038] As a further aspect of the present invention: after the softening control execution module performs softening control based on the softening control trigger condition, it recalculates and obtains a new state evaluation value corresponding to the target part. If the state evaluation value of a sub-region is still less than or equal to its corresponding preset state evaluation threshold, a second softening control is triggered, that is, the module returns to the initial adjustment and data acquisition module. If the state evaluation value of a sub-region is not less than or equal to its corresponding preset threshold, the softening control ends.

[0039] As a further aspect of the present invention: the method of the intelligent softening control system for pressure band based on temperature-sensitive monitoring includes: Step 1: After adjusting the pressure band based on the preset thickness parameters of the target body part, the regional state data of each area of ​​the target body part after adjustment is monitored, processed, and analyzed, and the regional state of each area of ​​the target body part is dynamically classified and marked according to the analysis results.

[0040] Step 2: Filter and analyze the regional status data based on the type label of the softening control area corresponding to the target part, construct a pressure-temperature matrix based on the analysis results, and generate a softening command matrix for the softening control area based on the pressure-temperature matrix of the corresponding softening control area.

[0041] Step 3: Determine the heating control power range in the softening command matrix corresponding to each softening control region, and perform softening control on each softening control region based on the determined heating control power range;

[0042] Step 4: After verifying the softening control based on the softening command matrix, determine the overall regional state of the target area of ​​the pressure pulse zone; based on the regional state judgment result, further determine whether to perform secondary softening control.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] (1) This invention uses a regional state classification module and a matrix analysis module to grid the target area and analyze pressure and temperature data in real time, dynamically marking the area that needs to be softened, and generating a personalized softening instruction matrix by combining the pressure-temperature matrix. Compared with the traditional single adjustment method, this invention can accurately identify abnormal areas and control the heating power differently, avoiding local overpressure or overheating, and significantly improving adjustment accuracy and safety.

[0045] (2) In this invention, the state verification and decision-making module re-evaluates the regional state after softening control, triggering secondary adjustment or ending control to form a closed-loop feedback. The power regulation module further introduces a collaborative control mechanism, which performs collaborative heating on highly correlated regions based on the correlation between regions to ensure overall pressure-temperature balance. Compared with the open-loop control of the prior art, this invention enhances the stability and adaptability of the system through dynamic verification and collaborative strategies, effectively reducing operational repetition and improving user experience. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the system framework structure of the present invention;

[0047] Figure 2 This is a schematic diagram of the module interaction logic of the present invention;

[0048] Figure 3 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] In this embodiment, the intelligent pressure band is a mechanical structure with a built-in temperature sensor, pressure sensor, and heating element.

[0051] Example 1: Please refer to Figures 1 to 2 This application provides a temperature-sensitive monitoring-based intelligent softening control system for pressure pulses, including: a regional state classification module, a matrix analysis and command generation module, a power regulation and execution module, and a state verification and decision-making module;

[0052] The area status classification module is used to monitor and analyze the area status data of each area of ​​the target body after adjusting the pressure band based on preset thickness parameters of the target body part. Based on the analysis results, the area status of each area of ​​the target body part is dynamically classified and marked. The area status classification and marking of each area of ​​the target body part includes softening control area type marking and non-softening control area type marking.

[0053] Specifically, the target area can be the upper arm, forearm, leg, etc. During blood drawing, a tourniquet is used to apply pressure to block blood flow, thereby increasing the visibility of veins and reducing the risk of bleeding. This step involves initially adjusting the tightness of the tourniquet based on preset thickness parameters for the target area during the application of the smart tourniquet to the target area on the user's body. The preset thickness parameters for the target area can be specific circumference standard dimensions of the target area that the user has pre-measured and stored, such as the circumference of the arm or leg.

[0054] Among them, a preset physiological characteristic size database can be established, which includes multiple variables such as body type, gender, age and weight, covering the standard circumference size data of different target parts of various populations;

[0055] Using a gridding method, the target area is divided into multiple sub-regions, namely M*N uniform grid sub-regions; where M is the number of horizontal grids and N is the number of vertical grids, and each sub-region is uniquely identified by coordinates (i,j); where i represents the horizontal row coordinate of the grid sub-region of the target area, and i=1,2,...,M; j represents the vertical column coordinate of the grid sub-region of the target area, and j=1,2,...,N;

[0056] The regional status data of each area of ​​the target location after the initial adjustment are monitored, processed, and analyzed. Based on the analysis results, the regional status of each area of ​​the target location is dynamically classified and labeled, including:

[0057] The initial pressure value of each sub-region of the target area is collected by the built-in pressure sensor of the intelligent pressure belt, forming an initial pressure matrix P. initial =[P ij ];

[0058] The initial skin surface temperature of each sub-region of the target area is collected by the built-in temperature sensor of the smart pressure band, forming an initial skin surface temperature matrix T. initial =[t ij ];

[0059] Based on the regional state data corresponding to each sub-region of the target location, i.e., the initial pressure value P ij and initial skin surface temperature t ij Calculate the corresponding pressure deviation value D. p,ijand skin surface temperature deviation value D t,ij According to the formula:

[0060] Pressure deviation Skin surface temperature deviation Where max(p) i ) represents the maximum pressure value in the same horizontal row, p ij t represents the initial pressure value for the current sub-region. ij t represents the initial skin surface temperature value of the current sub-region. avg This represents the average skin surface temperature of the current horizontal row sub-region;

[0061] Based on the pressure deviation value D corresponding to each sub-region of the target location p,ij and skin surface temperature deviation value D t,ij The first state value Q is calculated. ij According to the formula:

[0062] Q ij =α1·D p,ij +(1-α1)·D t,ij ; where α1 is the weighting coefficient, and its specific value is set by researchers based on experience;

[0063] And, through the formula The temperature and pressure coefficients corresponding to each sub-region of the target area are calculated and marked as the second state values; where α2, α3, and α4 are the initial pressure values ​​P, respectively. ij Initial skin surface temperature t ij and The preset weighting coefficients;

[0064] The regional state assessment value of each sub-region of the target location is obtained by calculating based on the first and second state values. In the formula, ∈ represents the preset safety factor, and S th Preset thresholds for state assessment;

[0065] Based on the status assessment value S corresponding to each sub-region of the target location ij The expression for establishing the regional state recognition model is as follows:

[0066] When ZC=1, it indicates that the pressure-temperature state of the sub-region corresponding to the target location is abnormally high, and the region is marked as a softening control region type; and then the softening control module is entered.

[0067] When ZC = 0, it indicates that the pressure-temperature state of the sub-region corresponding to the target location is in balance, and the region is marked as a non-softening control region type and the current control state is maintained.

[0068] The matrix analysis and instruction generation module is used to screen and analyze the regional status data according to the softening control area type corresponding to the target part, construct a pressure-temperature matrix based on the analysis results, and generate a softening instruction matrix for the softening control area according to the pressure-temperature matrix corresponding to the softening control area type;

[0069] The softening instruction is the heating control power;

[0070] Statistical RH of each softening control area in the target part k , where k is the softening control area index and k = 1, 2,..., e; e is the number of softening control areas and e < M * N; and extract the regional status data corresponding to all softening control areas from the initial pressure matrix P initial and the initial skin surface temperature matrix T initial , including the initial pressure value and the initial skin surface temperature value, normalize their corresponding initial pressure value and initial skin surface temperature value, such as Min-Max or Z-Score, eliminate the dimension difference, and retain the screened p ’ ij and t ’ ij , and mark them as the selected pressure value p ’ ij and the selected skin surface temperature value t ’ ij ; For each softening control area, map its selected pressure value and selected skin surface temperature value to the physical position of the softening control area according to the original coordinates, construct the corresponding pressure-temperature matrix, and the coordinates of the corresponding pressure-temperature matrix are consistent with the original grid (i, j);

[0071] Calculate the original required heating power of each softening area based on the pressure-temperature matrix corresponding to each softening control area of the target part In the formula, T safe is the preset skin temperature safety threshold, p max is the preset pressure perception threshold, and β1, β2 are the corresponding weight coefficients, and their specific values are set by relevant personnel;

[0072] According to the calculated original required heating power corresponding to each softening area of the target part Form a softening instruction matrix and the element positions in the softening instruction matrix correspond one by one;

[0073] In this embodiment, by statistically analyzing and filtering the regional state data corresponding to each softening control zone, a pressure-temperature matrix is ​​constructed to generate a softening command matrix corresponding to each softening control zone. This not only ensures the reliability and interpretability of subsequent softening control execution data, but also calculates the heating power H of each softening zone based on the selected data. ij This forms a softening command matrix, which directly guides the dynamic heating control of the pressure pulse band, enabling precise local adjustment.

[0074] The power regulation and execution module includes a heating control power range determination unit and a softening control unit. The heating control power range determination unit is used to determine the heating control power range and actual heating control power in the softening command matrix corresponding to each softening control region and update the softening command matrix. The softening control unit is used to perform softening control on each softening control region based on the updated softening command matrix, including:

[0075] The heating control power range determination unit is used to determine the heating control power range in the softening command matrix corresponding to each softening control region, and to determine the corresponding actual heating power, including: for each softening control region RH k Calculate its allowable heating power range: the lower limit of heating power is The upper limit of heating power is Wherein, γ1 and γ2 represent the preset lower limit proportional coefficient and upper limit proportional coefficient, respectively. This represents the original heating power required for the corresponding softening control area, H. device-max Set the upper limit of the pressure band device temperature;

[0076] For each softening control area RH k According to the determined correspondence Range, H ij Mapped to actual heating power:

[0077] when At that time, the corresponding softening control region RH k The actual heating power is

[0078] when At that time, the corresponding softening control region RH k The actual heating power is

[0079] when At that time, the corresponding softening control region RH k The actual heating power is

[0080] The softening control unit is used to perform softening control on each softening control zone based on a defined range of heating control power. Specifically, it calculates the global pressure-temperature Pearson correlation coefficient ρ among all softening control zones in the target area and the variance of the global softening zone state evaluation value corresponding to all softening control zones. Combining the global pressure-temperature Pearson correlation coefficient ρ and the variance of the global softened region state assessment value Its corresponding preset threshold ρ th and Establish softening control trigger conditions;

[0081] When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... It is not possible to simultaneously satisfy the condition of being greater than or equal to its corresponding preset threshold ρ th and Then an independent softening control command is generated;

[0082] The independent softening control command is to use the built-in heating element of the pressure pulse band to perform local softening control on each softening control area according to the softening command matrix corresponding to each softening control area in the target area;

[0083] When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... Simultaneously satisfying the condition of being greater than or equal to its corresponding preset threshold ρ th and Then a collaborative softening control command is generated;

[0084] Furthermore, when generating the collaborative softening control command, the grouping is based on the correlation between all softening control areas in the target area, including: calculating the pressure-temperature correlation of each pair of all softening control areas to obtain an e×e correlation matrix R, where R(a,b) is the Pearson correlation coefficient between area a and area b.

[0085] The correlation coefficients between each softening control region and their corresponding threshold ρ are compared. th Compare; if |R(a,b)|≥ρ th Regions a and b are considered highly correlated; a graph clustering algorithm is then used.

[0086] Treating each softening control region as a node in the graph, if |R(a,b)| ≥ ρ th Then add an edge between nodes a and b; find the connected components in the graph, each connected component constitutes a set of regions that need to be updated collaboratively, thus obtaining multiple sets of regions that need to be updated collaboratively.

[0087] When a collaborative softening control command is generated, the heating element built into the pressure pulse belt is used to perform collaborative softening control on the set of regions that need collaborative softening control according to the softening command matrix corresponding to the set of regions in the target area that need collaborative softening control.

[0088] The status verification and decision module is used to verify the overall regional status of the pressure pulse target area after softening control based on the softening command matrix; based on the regional status judgment result, it further determines whether to perform secondary softening control; if yes, it returns to the initial adjustment and data acquisition module; otherwise, it ends the softening control of the pressure pulse.

[0089] Specifically, the determination of the overall regional state of the target area of ​​the pressure band involves the following steps: after the softening control execution module performs softening control based on the softening control trigger condition, it recalculates and obtains a new state evaluation value corresponding to the target area. If the state evaluation value of a sub-region is still less than or equal to its corresponding preset threshold, a second softening control is triggered, i.e., the system returns to the initial adjustment and data acquisition module. If the state evaluation value of no sub-region is less than or equal to its corresponding preset threshold, the softening control ends, indicating that the control state is stable.

[0090] In this example, the intelligent softening control system for pressure bandages based on temperature-sensitive monitoring includes a region status classification module, a matrix analysis and command generation module, a power regulation and execution module, and a status verification and decision-making module. By dividing the target area into grids and collecting pressure and temperature data in real time, the system dynamically classifies and marks softening control areas and non-softening control areas, constructs a pressure-temperature matrix, and generates a softening command matrix. Combined with preset thresholds and safety factors, it precisely regulates the heating power of each area. Simultaneously, by verifying the region status after softening control, it determines whether secondary softening control is needed, achieving closed-loop control. This invention solves the problems of traditional pressure bandages' inability to dynamically respond to pressure and temperature changes and low control accuracy, significantly improving vein visibility and usage safety, and reducing the risk of skin damage.

[0091] Example 2: Refer to Figure 3 The intelligent softening control method for pressure bands based on temperature-sensitive monitoring includes the following steps: Step 1: After adjusting the pressure band based on preset thickness parameters of the target body part, monitor and analyze the regional status data of each area of ​​the target body part after adjustment, and dynamically classify and mark the regional status of each area of ​​the target body part according to the analysis results.

[0092] Step 2: Filter and analyze the regional status data based on the type label of the softening control area corresponding to the target part, construct a pressure-temperature matrix based on the analysis results, and generate a softening command matrix for the softening control area based on the pressure-temperature matrix of the corresponding softening control area.

[0093] Step 3: Determine the heating control power range in the softening command matrix corresponding to each softening control region, and perform softening control on each softening control region based on the determined heating control power range;

[0094] Step 4: After verifying the softening control based on the softening command matrix, determine the overall regional state of the target area of ​​the pressure pulse zone; based on the regional state judgment result, further determine whether to perform secondary softening control.

[0095] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0096] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A pressure pulse intelligent softening control system based on temperature-sensitive monitoring, characterized in that, include: The module comprises a regional state classification module, a matrix analysis and instruction generation module, a power regulation and execution module, and a state verification and decision-making module, among which: The regional status classification module is used to monitor and analyze the regional status data of each region of the target body after the bandage is adjusted based on the preset thickness parameters of the target body part. Based on the analysis results, the regional status of each region of the target body part is dynamically classified and marked. The regional status classification labels for each area of ​​the target location include softened control area type labels and non-softened control area type labels; The matrix analysis and instruction generation module is used to filter and analyze the regional state data based on the type label of the softening control area corresponding to the target part, and construct a pressure-temperature matrix based on the analysis results. It also generates a softening instruction matrix for the softening control area based on the pressure-temperature matrix of the corresponding softening control area. The power regulation and execution module includes a heating control power range determination unit and a softening control unit. The heating control power range determination unit is used to determine the heating control power range and the actual heating control power in the softening command matrix corresponding to each softening control area and update the softening command matrix. The softening control unit is used to perform softening control on each softening control area based on the updated softening command matrix. The status verification and decision-making module is used to verify the overall regional status of the target area of ​​the pressure pulse after softening control based on the softening command matrix; and to further determine whether to perform secondary softening control based on the regional status judgment result.

2. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, Adjusting the pressure band based on preset thickness parameters of the user's target body part includes: adjusting the pressure band according to the standard circumference size data of the user's target body part in a preset physiological characteristic size database.

3. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, The system monitors and analyzes the regional status data of each area of ​​the target site after the initial adjustment, including: collecting the initial pressure values ​​of each sub-region of the target site through the built-in pressure sensor of the smart pressure belt to form an initial pressure matrix P. initial =[P ij Each sub-region is defined by dividing the target area into M*N uniform grid sub-regions using a gridding method; where M is the number of horizontal grids and N is the number of vertical grids, and each sub-region is uniquely identified by coordinates (i,j); where i represents the horizontal row coordinate of the grid sub-region of the target area, and i = 1, 2, ..., M; j represents the vertical column coordinate of the grid sub-region of the target area, and j = 1, 2, ..., N; The initial skin surface temperature of each sub-region of the target area is collected by the built-in temperature sensor of the smart pressure band, forming an initial skin surface temperature matrix T. initial =[t ij ]; Based on the regional state data corresponding to each sub-region of the target location, i.e., the initial pressure value P ij and initial skin surface temperature t ij Calculate the corresponding pressure deviation value D. p,ij and skin surface temperature deviation value D t,ij According to the formula: Pressure deviation Skin surface temperature deviation Where max(p) i ) represents the maximum pressure value in the same horizontal row, p ij t represents the initial pressure value for the current sub-region. ij t represents the initial skin surface temperature value of the current sub-region. avg This represents the average skin surface temperature of the current horizontal row sub-region; Based on the pressure deviation value D corresponding to each sub-region of the target location p,ij and skin surface temperature deviation value D t,ij The first state value Q is calculated. ij According to the formula: Q ij =α1·D p,ij +(1-α1)·D t,ij Where α1 is the pressure deviation value D p,ij The weighting coefficient, (1-α1) is the skin surface temperature deviation value D. t,ij The preset weighting coefficients; And, through the formula The second state value C corresponding to each sub-region of the target part is calculated. ij In the formula, α2, α3, and α4 represent the initial pressure values ​​P. ij Initial skin surface temperature t ij and The preset weighting coefficients.

4. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 3, characterized in that, Based on the first and second state values, the regional state assessment value of each sub-region of the target area is obtained. In the formula, ∈ represents the preset safety factor, and S th Preset thresholds for state assessment; Based on the status assessment value S corresponding to each sub-region of the target location ij The expression for establishing the regional state recognition model is as follows: When ZC=1, it indicates that the sub-region corresponding to the target location has a high degree of pressure-temperature abnormality, and the sub-region is marked as a softening control area type. And then enter the softening control module; When ZC=0, it indicates that the pressure-temperature state of the sub-region corresponding to the target location is in balance, and the sub-region is marked as a non-softening control region type and the current control state is maintained.

5. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, The specific implementation steps of the matrix analysis and instruction generation module include: counting each softening control region RH in the target part k , where k is the softening control region index and k = 1, 2,..., e; e is the number of softening control regions and e < M * N; and extracting the region state data corresponding to all softening control regions from the initial pressure matrix P initial and the initial skin surface temperature matrix T initial , including the initial pressure value and the initial skin surface temperature value, normalizing their corresponding initial pressure value and initial skin surface temperature value, eliminating the dimension difference and retaining the filtered p ’ ij and t ’ ij , and respectively marking them as the selected pressure value p ’ ij and the selected skin surface temperature value t ’ ij ; For each softening control region, its selected pressure value and selected skin surface temperature value are mapped to the physical location of the softening control region according to the original coordinates, and a corresponding pressure-temperature matrix is ​​constructed, with the coordinates of the corresponding pressure-temperature matrix being consistent with the original mesh (i,j); Based on the pressure-temperature matrix corresponding to each softening control zone of the target area, the original heating power required for each softening zone is calculated. In the formula, T safe To preset a safe skin temperature threshold, p max The preset pressure sensing threshold is β1 and β2, which are the preset weight coefficients of the corresponding formula terms. Based on the calculated original heating power required for each softened area of ​​the target location. Forming a softening instruction matrix And soften the position of elements in the instruction matrix One-to-one correspondence between domains.

6. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, The process of determining the heating control power range in the softening command matrix corresponding to each softening control region and the actual heating control power, and updating the softening command matrix, is as follows: For each softening control region RH k Calculate its allowable heating power range: the lower limit of heating power is The upper limit of heating power is Wherein, γ1 and γ2 represent the preset lower limit proportional coefficient and upper limit proportional coefficient, respectively. This represents the original heating power required for the corresponding softening control area, H. device-max Set the upper limit of the pressure band device temperature; For each softening control area RH k According to the determined correspondence Scope, will Mapped to actual heating power: By determining the corresponding softening control area The range determines the actual heating power and updates the corresponding softening command matrix to form the updated softening command matrix.

7. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, The softening control unit performs softening control on each softening control region based on the updated softening command matrix. The softening control includes either independent softening control of each softening control region based on the updated softening command matrix or coordinated softening control of each softening control region based on the updated softening command matrix. Specifically, it calculates the global pressure-temperature Pearson correlation coefficient ρ among all softening control regions in the target area and the variance of the global softening region state evaluation value corresponding to all softening control regions. Combining the global pressure-temperature Pearson correlation coefficient ρ and the variance of the global softened region state assessment value Its corresponding preset threshold ρ th and Establish softening control trigger conditions; When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... It is not possible to simultaneously satisfy the condition of being greater than or equal to its corresponding preset threshold ρ th and Then an independent softening control command is generated; The independent softening control command is to use the built-in heating element of the pressure pulse band to perform local softening control on each softening control area according to the softening command matrix corresponding to each softening control area in the target area; When the global pressure-temperature Pearson correlation coefficient ρ of the target location and the variance of the global softened area state assessment value... Simultaneously satisfying the condition of being greater than or equal to its corresponding preset threshold ρ th and Then, a collaborative softening control command is generated.

8. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 7, characterized in that, When generating the collaborative softening control command, the grouping is based on the correlation between all softening control areas in the target area, including: calculating the pressure-temperature correlation of each pair of softening control areas to obtain the e×e correlation matrix R, where R(a,b) is the Pearson correlation coefficient between area a and area b. Each softening control region is treated as a node in a graph using a graph clustering algorithm, and the correlation coefficient between each softening control region is correlated with its corresponding threshold ρ. th Compare; if |R(a,b)|≥ρ th Then add an edge between nodes a and b; find the connected components in the graph, each connected component constitutes a set of regions that need to be updated collaboratively, thus obtaining multiple sets of regions that need to be updated collaboratively. When a collaborative softening control command is generated, the heating element built into the pressure pulse belt is used to perform collaborative softening control on the set of regions that need collaborative softening control according to the softening command matrix corresponding to the set of regions in the target area that need collaborative softening control.

9. The intelligent softening control system for pressure pulses based on temperature-sensitive monitoring according to claim 1, characterized in that, The status verification and decision-making module includes: after the softening control execution module performs softening control based on the softening control trigger condition, it recalculates and obtains the new status evaluation value corresponding to the target part. If the status evaluation value of the sub-region is still less than or equal to its corresponding preset status evaluation threshold, it triggers secondary softening control, that is, returns to the initial adjustment and data acquisition module; if the status evaluation value of the non-sub-region is less than or equal to its corresponding preset threshold, it ends the softening control.

10. A method for implementing the intelligent softening control system for pressure pulse bands based on temperature-sensitive monitoring as described in any one of claims 1-9, characterized in that, include: Step 1: After adjusting the pressure band based on the preset thickness parameters of the target body part, monitor and analyze the regional status data of each area of ​​the target body part after adjustment, and dynamically classify and mark the regional status of each area of ​​the target body part according to the analysis results. Step 2: Filter and analyze the regional status data based on the type label of the softening control area corresponding to the target part, construct a pressure-temperature matrix based on the analysis results, and generate a softening command matrix for the softening control area based on the pressure-temperature matrix of the corresponding softening control area. Step 3: Determine the heating control power range in the softening command matrix corresponding to each softening control region, and perform softening control on each softening control region based on the determined heating control power range; Step 4: After verifying the softening control based on the softening command matrix, determine the overall regional state of the target area of ​​the pressure pulse zone; based on the regional state judgment result, further determine whether to perform secondary softening control.

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