Cardiopulmonary resuscitation parameter detection method, device, equipment and medium

Abstract

The invention relates to the technical field of medical detection, in particular to a cardiopulmonary resuscitation parameter detection method, device and equipment and a medium, when a CPR device is started, system self-inspection is started, if the system self-inspection is passed, real-time sampling data during cardiopulmonary resuscitation operation on a target object is obtained, whether the real-time sampling data meet preset conditions is judged, and if yes, the cardiopulmonary resuscitation parameter detection is completed. If the real-time sampling data meets the preset conditions, the pressing depth, the pressing frequency and the pressing release ratio are calculated according to the real-time sampling data, and then a cardio-pulmonary resuscitation parameter detection result corresponding to the target object is generated according to the pressing frequency, the pressing frequency and the pressing release ratio. It can be seen that by judging whether the real-time sampling data meet the preset conditions or not, calculation of the pressing depth, the pressing frequency and the pressing release ratio is achieved, then the cardio-pulmonary resuscitation parameter detection result is generated, and the problem that in the prior art, detection parameters are single, and consequently the accuracy rate of CPR equipment detection is low is solved.

Description

Technical Field

[0001] The present invention relates to the field of medical detection technology, and in particular to a cardiopulmonary resuscitation parameter detection method, device, equipment and medium. Background Art

[0002] Cardiopulmonary Resuscitation (CPR) is an important part of emergency treatment for cardiac and respiratory arrest. CPR has certain requirements for the frequency and depth of chest compressions. Too little pressure will not promote blood circulation, while too much pressure will cause fractures in patients, leading to more serious secondary injuries. Therefore, high CPR quality requirements are required in training, and the quality of CPR feedback from the assistive device is very important.

[0003] Currently, the CPR devices on the market directly detect the compression depth through a sensor, without using the time statistics function or the number of compressions function. In addition, due to its single detection parameter, it cannot accurately match springs of other lengths, resulting in low accuracy of CPR device detection. Summary of the invention

[0004] Based on this, it is necessary to address the above technical problems. The embodiments of the present invention provide a cardiopulmonary resuscitation parameter detection method, device, equipment and medium to solve the problem that the existing technology has a single detection parameter, resulting in low accuracy of CPR equipment detection.

[0005] A first aspect of an embodiment of the present application provides a cardiopulmonary resuscitation parameter detection method, the cardiopulmonary resuscitation parameter detection method comprising: When turning on the CPR device, enable system self-test; If the system self-check passes, real-time sampling data when performing cardiopulmonary resuscitation on the target object is obtained, wherein the real-time sampling data includes N sampling data; Determining whether the real-time sampling data meets preset conditions; If the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression-release ratio are calculated according to the real-time sampling data; A cardiopulmonary resuscitation parameter detection result corresponding to the target object is generated according to the compression depth, the compression frequency and the compression-release ratio.

[0006] A second aspect of an embodiment of the present application provides a cardiopulmonary resuscitation parameter detection device, the cardiopulmonary resuscitation parameter detection device comprising: A self-check module, used to enable system self-check when turning on the CPR device; An acquisition module, used for acquiring real-time sampling data when performing cardiopulmonary resuscitation on a target object if the system self-check passes, wherein the real-time sampling data includes N sampling data; A judgment module, used to judge whether the real-time sampling data meets the preset conditions; A calculation module, configured to calculate a compression depth, a compression frequency and a compression-to-release ratio according to the real-time sampling data if the real-time sampling data meets a preset condition; A generating module is used to generate a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio.

[0007] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the cardiopulmonary resuscitation parameter detection method as described in the first aspect is implemented.

[0008] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the cardiopulmonary resuscitation parameter detection method as described in the first aspect is implemented.

[0009] In summary, the present invention provides a cardiopulmonary resuscitation parameter detection method, device, equipment and medium. When the CPR device is turned on, the system self-check is enabled. If the system self-check passes, the real-time sampling data when the cardiopulmonary resuscitation operation is performed on the target object is obtained, and it is determined whether the real-time sampling data meets the preset conditions. If the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression release ratio are calculated according to the real-time sampling data, and then the cardiopulmonary resuscitation parameter detection result corresponding to the target object is generated according to the compression frequency, compression frequency and compression release ratio. It can be seen that the present application realizes the calculation of the compression depth, compression frequency and compression release ratio by judging whether the real-time sampling data meets the preset conditions, and then generates the cardiopulmonary resuscitation parameter detection result, so as to solve the problem that the detection parameters of the prior art are single, resulting in low accuracy of CPR equipment detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0011] Figure 1 It is a flow chart of a cardiopulmonary resuscitation parameter detection method provided by one embodiment of the present invention; Figure 2 It is a structural schematic diagram of a cardiopulmonary resuscitation parameter detection device provided by one embodiment of the present invention; Figure 3 It is a structural schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in the present specification and the appended claims, the term “if” may be interpreted as “when” or “uponce” or “in response to determining”, depending on the context. Similarly, the phrases “if it is determined” or “if matched to [described condition or event]” may be interpreted as meaning “upon determination” or “in response to determination” or “uponce matched to [described condition or event]” or “in response to matching to [described condition or event]”, depending on the context.

[0016] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0019] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0020] See also Figure 1 , is a flow chart of a cardiopulmonary resuscitation parameter detection method provided by an embodiment of the present invention, such as Figure 1 As shown, the cardiopulmonary resuscitation parameter detection method can be implemented through the following steps.

[0021] S201: When turning on the CPR device, enable system self-check.

[0022] In step S201, the CPR device system includes an interface display system, a parameter monitoring system, a spring system, a time statistics system, a data processing system and a control system. Here, the interface display system can be used to display the operating status of the CPR device, compression time, tooling depth, compression frequency, compression release ratio, number of compressions, real-time infrared data, etc. through the display screen. The parameter monitoring system can be used to monitor the real-time sampling data collected by the infrared sensor, etc. The spring system can be used to provide stable compression pressure and compression depth. The time statistics system can be used to count the cumulative compression time of the system and store and manage the time. The data processing system can be used to collect, process and analyze data from the parameter monitoring system and the time statistics system. The control system can be used to control the CPR device. System self-check can be the CPR device system checking the availability of sensors and storage chips. Here, the storage chip can be a chip that can store system data. During the self-test process, the interface display system of the CPR device will display the progress of the self-test, such as "Checking power connection", "Checking sensor connection", etc. If the self-test results show that everything is normal, the CPR device will prompt the user to proceed to the next step, that is, execute step S202 to obtain real-time sampling data when performing cardiopulmonary resuscitation on the target object, and then perform data processing later. If the self-test results show that there is a fault or abnormality, the user needs to follow the instructions in the CPR device manual to troubleshoot and handle it. For example, if the interface display system prompts "Sensor connection abnormality", the user needs to check whether the sensor cable is plugged in tightly and whether it is damaged.

[0023] In the embodiment of the present application, when the CPR device is turned on, a self-check procedure can be used to promptly detect and handle possible faults or abnormalities in the CPR device, ensuring that the CPR device can work normally at critical moments, thereby improving the stability and reliability of the CPR device.

[0024] S202: If the system self-check passes, real-time sampling data when performing cardiopulmonary resuscitation on the target object is obtained, wherein the real-time sampling data includes N sampling data.

[0025] In step S202, when the system self-check passes, when the CPR device performs cardiopulmonary resuscitation on the target object, the infrared sensor is used to collect real-time sampling data for medical staff to refer to and adjust the resuscitation operation. Among them, the target object usually refers to a patient or individual who needs cardiopulmonary resuscitation to maintain life. These patients may have severe respiratory and / or cardiac function damage due to cardiac arrest, drowning, suffocation or other reasons, and need immediate emergency measures to restore their vital signs. The real-time sampling data is the sensor data output by the infrared sensor in the CPR device, such as heart rate, blood pressure, blood oxygen saturation, electrocardiogram waveform, etc. The real-time sampling data includes N sampling data. For example, N can be equal to 5, or N can be equal to 7. The specific setting can be made according to the actual situation, and this application does not impose any limitation on this.

[0026] In one embodiment of the invention, after obtaining real-time sampling data when performing cardiopulmonary resuscitation on a target object, the method includes: Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the highest depth of the spring when it is stationary.

[0027] Specifically, a database or mapping table is pre-established, which records the relationship between different real-time sampling data (such as pressure, displacement, etc.) and the corresponding spring height. This is usually obtained through experimental measurement, that is, under different conditions (such as different pressures or displacements), the actual height of the spring is measured and recorded. This application collects and organizes the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, and uses regression analysis, machine learning algorithms and other methods to establish a mapping model based on the collected data. This model should be able to accurately predict the actual height of the spring based on the real-time sampling data. During the cardiopulmonary resuscitation operation, the data related to the spring is collected in real time by the sensor, and the real-time collected data is input into the mapping model to obtain the corresponding spring height. In this process, it may be necessary to consider data preprocessing and filtering to reduce the influence of noise and error, and then determine the highest depth of the spring at rest, that is, the first actual spring height, according to the output result of the mapping model, so that the compression depth can be quickly and accurately calculated by the first actual spring height in the future, thereby improving the efficiency and accuracy of cardiopulmonary resuscitation parameter detection.

[0028] It should be noted that the mapping relationship between the real-time sampling data and the actual spring height is established in a nonlinear manner (for example, using a machine learning model) and can also be obtained in other ways. This application does not impose any limitations on this.

[0029] In this embodiment, after the system self-check passes, real-time sampling data during the cardiopulmonary resuscitation operation can be obtained, and the operation technique and strength can be adjusted according to these real-time sampling data, thereby improving the resuscitation success rate. It can also be used for subsequent analysis and summary, providing strong support for improving cardiopulmonary resuscitation technology.

[0030] S203: Determine whether the real-time sampling data meets preset conditions.

[0031] In step S203, since the relevant data of patients with different symptoms will change accordingly during the cardiopulmonary resuscitation operation, and not necessarily all of them are fixed parameters, and the real-time sampling data may be affected by various factors, such as sensor error, environmental interference, etc., which may lead to errors in the detection, by judging whether the data meets the preset conditions, abnormal or erroneous data can be screened out to ensure the accuracy of subsequent analysis. For example, during the cardiopulmonary resuscitation process, when the real-time sampling data shows that the chest compression frequency is 90 times / minute, the preset condition is in the range of 100~120 times / minute, and its 90 times / minute is lower than the preset range of 100~120 times / minute, and does not meet the preset conditions. At this time, it is determined that the compression frequency is insufficient, and the preset conditions are met by speeding up the compression speed. It can be seen that if the real-time sampling data meets the preset conditions, step S104 is executed to calculate the compression depth, compression frequency and compression release ratio according to the real-time sampling data. If the real-time sampling data does not meet the preset conditions, the subsequent steps of the cardiopulmonary resuscitation parameter detection method are no longer executed, and the real-time sampling data needs to be re-acquired.

[0032] In one embodiment of the invention, determining whether the real-time sampling data meets a preset condition includes: Calculating a data change amount between the first sampling data and the second sampling data; Determining whether the data change exceeds a preset change range; If the data change exceeds the preset change range, determining whether the data change is a continuous change; If the data change amount is a continuous change, it is determined that the real-time sampling data meets the preset conditions; If the data variation is not a continuous variation, it is determined that the real-time sampling data does not meet the preset condition, and the real-time sampling data when performing the cardiopulmonary resuscitation operation on the target object is reacquired.

[0033] Specifically, the sampling data refers to the data obtained by sampling the compression signal. When performing cardiopulmonary resuscitation on the target object, the compression signal is sampled by the sensor, that is, within a certain time interval, the analog signal is sampled at the same time interval, so as to better restore the real situation of the analog signal, so as to convert the analog signal into a digital signal, thereby obtaining at least one set of two adjacent sampling data, namely the first sampling data and the second sampling data, the second sampling data is the previous sampling data before the first sampling data, the first sampling data and the second sampling data are any two adjacent sampling data of N sampling data, wherein, in the sampling process, a suitable sampling rate is used to ensure that the complete information of the analog signal can be captured, and by sampling the compression signal, the acquisition of adjacent sampling values ​​is realized, thereby realizing the determination of the current sampling value and the previous sampling value. First, the specific values ​​of the two sampling data are determined, and then the difference between the first sampling data and the second sampling data is calculated. This difference represents the data change amount between the two sampling data, and the data change amount (denoted as ΔX) can be calculated by the following formula: ΔX= X2-X1, wherein X2 represents the second sampling data and X1 represents the first sampling data. Then, a preset change range (for example, ±ΔX_threshold) is set, and the calculated data change ΔX is compared with the preset change range. If |ΔX|>ΔX_threshold, the data change exceeds the preset range. To further determine whether the data change is a continuous change, a display window can be set, including multiple continuous sampling points, to observe whether the data change in the window remains at a similar level or trend. If the data change continues to remain at a level exceeding the preset range in the observation window, it can be determined to be a continuous change. If the data change is a continuous change, it is determined that the real-time sampling data meets the preset conditions (for example, in cardiopulmonary resuscitation, it may mean that a certain physiological parameter is changing continuously and corresponding resuscitation measures need to be taken). If the data change is not a continuous change, it is determined that the real-time sampling data does not meet the preset conditions and needs to be re-acquired for further data analysis.

[0034] For example, during CPR, we monitor the depth of chest compressions, and the preset depth variation range (ΔX_threshold) is ±2cm. The following are sampled data at two different time points: The first sampled data (X1): the chest compression depth is 5cm; the second sampled data (X2): the next sample, the chest compression depth is 8cm, the data change: ΔX = X2 - X1 = 8cm - 5cm = 3cm, judge whether the data change exceeds the preset range, that is, |ΔX| = 3cm>ΔX_threshold = 2cm, therefore, it is determined that the data change exceeds the preset range, and then we set a display window, including the next 5 sampling data (assuming the sampling frequency is once per second): Sample 3: 8cm (ΔX2 = 8cm - 8cm = 0cm, same as X2 because it is the next sample) Sample 4: 7.5 cm (ΔX3 = 7.5 cm - 8 cm = -0.5 cm) Sample 5: 8 cm (ΔX4 = 8 cm - 7.5 cm = 0.5 cm) Sample 6: 7.8 cm (ΔX5 = 7.8 cm - 8 cm = -0.2 cm) Sample 7: 7.7 cm (ΔX6 = 7.7 cm - 7.8 cm = -0.1 cm) In the display window, although the data fluctuates, the overall depth of chest compression remains between 7.5cm and 8cm, and has not returned to the initial level of 5cm. The fluctuation range is relatively small (the maximum change is 0.5cm), which can be considered as a continuous change state (although there are slight fluctuations, the overall trend remains consistent). Since the data change amount is continuously changing (the chest compression depth continues to remain at a relatively stable level, but exceeds the preset depth change range), we can determine that the real-time sampling data meets the preset conditions. In this case, it may mean that the strength or frequency of chest compression needs to be adjusted to ensure the effectiveness of the resuscitation operation. However, if the data change amount in the display window is not continuously changing (for example, the data suddenly returns to the initial level or the fluctuation range is very large), it will be determined that the real-time sampling data does not meet the preset conditions, and the real-time sampling data needs to be re-acquired for further analysis and judgment.

[0035] It should be noted that the preset conditions and preset variation ranges can be set specifically according to actual conditions, and this application does not impose any limitation on this.

[0036] In this embodiment, determining whether the real-time sampling data meets the preset conditions is an important step to ensure data accuracy and reliability, which reduces the interference of sampling errors, helps support subsequent analysis and decision-making, and thus improves the accuracy of subsequent cardiopulmonary resuscitation parameter detection.

[0037] S204: If the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression-release ratio are calculated according to the real-time sampling data.

[0038] In step S204, if the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression release ratio are calculated based on the real-time sampling data. Among them, the compression depth refers to the depth of the sternum sinking during chest compression, which directly affects the pressure in the chest cavity, and then affects the compression effect of the heart and the recovery of blood circulation; the compression frequency refers to the number of chest compressions per unit time, which directly affects the effect of the resuscitation operation and the survival rate of the patient; the compression release ratio refers to the ratio of the duration of continuous compression action and release action during chest compression, and this ratio is mainly to ensure that the chest has enough time to recover after compression.

[0039] In one embodiment of the invention, the compression depth is calculated according to the real-time sampling data, including: Acquire N sampling data, and select a first maximum sampling data from the N sampling data, wherein the first maximum sampling data is a maximum value among the N sampling data; Repeat the above steps until a predetermined number M of first maximum sampling data are obtained, and then select the second maximum sampling data from the M first maximum sampling data, wherein M is less than N, and the second maximum sampling data is the maximum value of the M first maximum sampling data; Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, and the second maximum sampling data, a second actual spring height is obtained; Based on the second actual spring height and the first actual spring height, the pressing depth is calculated.

[0040] Specifically, since the smaller the real-time sampling data collected by infrared is, the larger the actual spring height is, the larger the real-time sampling data collected by infrared is, the smaller the actual spring height is. Therefore, after the compression begins, N sampling data are obtained, and the N sampling data may be real-time measurement values ​​of the spring compression amount. The maximum value, i.e., the first maximum sampling data, is found from the N sampling data. The first maximum sampling data is stored, and this step is repeated until a predetermined number M of the first maximum sampling data are obtained, and a new sampling data set is formed from them. The maximum value, i.e., the second maximum sampling data, is found from this new sampling data set, and then based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, the second maximum sampling data is converted into the corresponding spring height, i.e., the second actual spring height. This mapping relationship may be obtained through experiments or calibration, for example, by measuring the spring heights corresponding to different sampling data to establish it, and then the compression depth is quickly and accurately calculated according to the first actual spring height (the highest height of the spring when it is stationary) and the second actual spring height, i.e., compression depth = first actual spring height - second actual spring height, so that subsequent rescuers or therapists can obtain real-time feedback, thereby adjusting the pressing intensity and improving the overall treatment effect.

[0041] For example, if 20 sampling data are collected, namely [5, 12, 8, 15, 3, 10, 7, 14, 2, 11, 1, 4, 6, 9, 11, 13, 16, 20, 31, 18], and the maximum value selected is 31, repeat this step and continue to collect 20 sampling data for maximum value selection. Since the predetermined number is 10, the 10 first maximum sampling data are [10, 15, 8, 12, 18, 16, 20, 31, 5, 13], then these 10 values ​​constitute the first maximum sampling data set: {10, 15, 8, 12, 18, 16, 20, 31, 5, 13}, and find the largest value from the set {10, 15, 8, 12, 18, 16, 20, 31, 5, 13}, which is 31. Assuming that the spring height corresponding to the second maximum sampling data 31 is H2 (obtained through a mapping relationship), and the highest height of the spring when it is stationary is H1, the pressing depth = H1 - H2.

[0042] In one embodiment of the invention, the compression frequency and the compression-release ratio are calculated based on the real-time sampling data, including: Selecting a third maximum sampling data and a minimum sampling data from the N sampling data, wherein the third maximum sampling data is a maximum value among the N sampling data, and the minimum sampling data is a minimum value among the N sampling data; Determine whether the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold; If the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold, calculating a median according to the third maximum sampling data and the minimum sampling data; If the real-time sampling data is greater than the median value, it is determined that the spring is in a descending state when pressed; If the real-time sampling data is less than the median value, it is determined that the spring is in an ascending state when pressed; Obtaining the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state through a preset timing program; The pressing frequency and the pressing-releasing ratio are calculated according to the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state.

[0043] Specifically, after the compression begins, N sampling data are obtained, and the N sampling data may be real-time measurement values ​​of the spring compression amount. The third maximum sampling data and the minimum sampling data are selected from the N sampling data, wherein the third maximum sampling data is the maximum value among the N sampling data, and the minimum sampling data is the minimum value among the N sampling data. The absolute difference between the third maximum sampling data and the minimum sampling data is calculated, and it is determined whether the absolute difference is greater than a preset threshold value. If the absolute difference is not greater than the threshold value, the subsequent steps of the cardiopulmonary resuscitation parameter detection method are no longer executed. If the absolute difference is greater than the threshold value, the median of the third maximum sampling data and the minimum sampling data is calculated. The real-time sampling data is compared with the median. If the real-time sampling data is greater than the median, it is determined that the spring is in a descending state when pressed. If the real-time sampling data is less than the median, it is determined that the spring is in an ascending state when pressed. Each time the data is greater than the median, the number of presses increases once. By initializing a ms-level timer, counting begins after the pressing begins, and then the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state are recorded. According to the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state, the pressing frequency and the pressing-release ratio are quickly and accurately calculated, thereby helping to improve the overall effect of cardiopulmonary resuscitation. Compression frequency = total number of compressions / total time (the number of compressions completed per unit time), and compression-release ratio = pressing time / (pressing time + releasing time). The pressing time and releasing time here refer to the pressing time corresponding to the ascending state and the descending state, respectively.

[0044] For example, if there are 50 sample data: [5, 12, 8, 15, 3, 10, 7, 14, 2, 11, 1, 4, 6, 9, 11, 13, 16, 20, 32, 17, 41, 54, 68, 199, 100, 66, 77, 61, 24, 28, 35, 46, 18, 121, 29, 38, 132, 57, 62, 33, 72, 84, 99, 81, 39, 91, 88, 44, 59, 76], select the minimum sample data: 1, the third largest sample data: 199, absolute difference = |199 - 1| = 198, if the preset threshold is 150, then 198>150, meet the conditions, median = (199+ 1) / 2 =100. If the real-time sampling data is 130, then 130>100, and the spring is in a descending state. If the real-time sampling data is 31, then 31<100, and the spring is in an ascending state. If the timing program records that the pressing time corresponding to the ascending state is T1, and the pressing time corresponding to the descending state is T2, and the total number of presses is Z times, then the pressing frequency = Z / (T1 + T2) (the total time is the sum of T1 and T2), and the pressing-release ratio = T2 / (T1 + T2) (T1 is the pressing time, and T2 is the release time).

[0045] In this embodiment, when the real-time sampling data meets the preset conditions, by quickly and accurately calculating the compression depth, compression frequency and compression-release ratio based on the real-time sampling data, we can more accurately understand the actual situation during cardiopulmonary resuscitation and adjust the resuscitation operation in time to ensure the best effect.

[0046] S205: Generate a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency, and the compression-release ratio.

[0047] In step S205, the cardiopulmonary resuscitation parameter detection result refers to the detection result of various parameters when the cardiopulmonary resuscitation operation is performed on the target object, which can solve the problem of single detection parameters. Specifically, after the compression depth, compression frequency and compression release ratio are obtained, the CPR parameter detection result corresponding to the target object is generated according to the compression depth, compression frequency and compression release ratio, that is, the preset standard depth range, the preset standard frequency range and the preset standard release ratio range can be obtained, and the compression depth and the preset standard depth range can be compared to determine whether the compression depth is within the preset standard depth range, the compression frequency and the preset standard frequency range can be compared to determine whether the compression frequency is within the preset standard frequency range, and the compression release ratio and the preset standard release ratio range can be compared to determine whether the compression release ratio is within the preset standard release ratio range, and then, the compression quality is evaluated through the depth comparison result, the frequency comparison result and the release ratio comparison result, and the parameters such as the compression depth, the compression frequency, the compression release ratio, the compression pressure and the compression quality are determined as the cardiopulmonary resuscitation parameter detection result.

[0048] In one embodiment of the invention, before generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression frequency, the compression frequency and the compression-release ratio, the method includes: When the CPR device is in a compression state, obtaining the compression time when performing a cardiopulmonary resuscitation operation on the target object; Determine whether the duration of the pressing time meets a preset duration threshold; If the duration of the compression time meets the preset duration threshold, the step of generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio is performed.

[0049] Specifically, in order to ensure that the CPR device accurately records the compression time, it is equipped with a time statistics system, which can be a timer or a sensor, and this application does not impose any limitation on this. After obtaining the compression pressure of the cardiopulmonary resuscitation operation on the target object, a preset pressure threshold is obtained from a database or a client, and the preset pressure threshold is compared with the compression pressure collected during the cardiopulmonary resuscitation operation to obtain a pressure comparison result. When the pressure comparison result indicates that the compression pressure is greater than or equal to the preset pressure threshold, it is determined that the CPR device is in a compression state. When the pressure comparison result indicates that the compression pressure is less than the preset pressure threshold, it is determined that the CPR device is not in a compression state. When the CPR device is in a compression state, the total time from the start to the end of the compression is automatically recorded to obtain the compression time. A compression time threshold is pre-set (for example, the duration of CPR is 30 minutes), and it is determined whether the duration of the compression time meets the preset duration threshold. If the duration of the compression time meets the preset duration threshold, the step of generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, compression frequency and compression release ratio is continued. If the duration of the compression time does not meet the preset duration threshold, the subsequent steps of the cardiopulmonary resuscitation parameter detection method are no longer performed. For example, if the preset duration threshold is 5 minutes, the first aid personnel continued to press for 6 minutes, so the duration of the pressing time meets the preset duration threshold. Through the above steps, the detection of whether it is in the pressing state and whether the preset duration threshold is met are realized, ensuring that the subsequent parameter detection is performed in the pressing state and meets the preset duration threshold.

[0050] It should be noted that the preset pressure threshold and the preset time threshold can be set according to actual conditions, and this application does not impose any limitations on this.

[0051] In this embodiment, the compression frequency, the compression frequency and the compression-release ratio are used to generate cardiopulmonary resuscitation parameter detection results, reduce errors caused by improper operation or misjudgment, and thus improve the efficiency and accuracy of cardiopulmonary resuscitation parameter detection.

[0052] In summary, the present invention provides a cardiopulmonary resuscitation parameter detection method, device, equipment and medium. When the CPR device is turned on, the system self-check is enabled. If the system self-check passes, the real-time sampling data when the cardiopulmonary resuscitation operation is performed on the target object is obtained, and it is determined whether the real-time sampling data meets the preset conditions. If the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression release ratio are calculated according to the real-time sampling data, and then the cardiopulmonary resuscitation parameter detection result corresponding to the target object is generated according to the compression frequency, compression frequency and compression release ratio. It can be seen that the present application realizes the calculation of the compression depth, compression frequency and compression release ratio by judging whether the real-time sampling data meets the preset conditions, and then generates the cardiopulmonary resuscitation parameter detection result, so as to solve the problem that the detection parameters of the prior art are single, resulting in low accuracy of CPR equipment detection.

[0053] See also Figure 2 , Figure 2 Schematic diagram of the structure of the cardiopulmonary resuscitation parameter detection device provided by the embodiment of the present invention. In this embodiment, the terminal includes various units for executing Figure 1 For details, please refer to the steps in the corresponding embodiment. Figure 1 as well as Figure 1 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 2 The cardiopulmonary resuscitation parameter detection device 20 includes: a self-checking module 21, an acquisition module 22, a judgment module 23, a calculation module 24, and a generation module 25.

[0054] A self-check module 21, used to enable system self-check when the CPR device is turned on; An acquisition module 22, for acquiring real-time sampling data when performing cardiopulmonary resuscitation on a target object if the system self-check passes, wherein the real-time sampling data includes N sampling data; A judging module 23 is used to judge whether the real-time sampling data meets a preset condition; A calculation module 24, configured to calculate a compression depth, a compression frequency and a compression-release ratio according to the real-time sampling data if the real-time sampling data meets a preset condition; The generating module 25 is used to generate a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio.

[0055] Optionally, the acquisition module 22 is specifically used for: Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the highest depth of the spring when it is stationary.

[0056] Optionally, the real-time sampling data includes first sampling data and second sampling data, the second sampling data is the previous sampling data located before the first sampling data, the first sampling data and the second sampling data are any two adjacent sampling data of the N sampling data, and the above-mentioned judgment module 23 is specifically used for: Calculating a data change amount between the first sampling data and the second sampling data; Determining whether the data change exceeds a preset change range; If the data change exceeds the preset change range, determining whether the data change is a continuous change; If the data change amount is a continuous change, it is determined that the real-time sampling data meets the preset conditions; If the data variation is not a continuous variation, it is determined that the real-time sampling data does not meet the preset condition, and the real-time sampling data when performing the cardiopulmonary resuscitation operation on the target object is reacquired.

[0057] Optionally, the calculation module 24 is specifically used for: Acquire N sampling data, and select a first maximum sampling data from the N sampling data, wherein the first maximum sampling data is a maximum value among the N sampling data; Repeat the above steps until a predetermined number M of first maximum sampling data are obtained, and then select the second maximum sampling data from the M first maximum sampling data, wherein M is less than N, and the second maximum sampling data is the maximum value of the M first maximum sampling data; Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, and the second maximum sampling data, a second actual spring height is obtained; Based on the second actual spring height and the first actual spring height, the pressing depth is calculated.

[0058] Optionally, the calculation module 24 is further used for: Selecting a third maximum sampling data and a minimum sampling data from the N sampling data, wherein the third maximum sampling data is a maximum value among the N sampling data, and the minimum sampling data is a minimum value among the N sampling data; Determine whether the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold; If the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold, calculating a median according to the third maximum sampling data and the minimum sampling data; If the real-time sampling data is greater than the median value, it is determined that the spring is in a descending state when pressed; If the real-time sampling data is less than the median value, it is determined that the spring is in an ascending state when pressed; Obtaining the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state through a preset timing program; The pressing frequency and the pressing-releasing ratio are calculated according to the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state.

[0059] Optionally, the generating module 25 is specifically used for: When the CPR device is in a compression state, obtaining the compression time when performing a cardiopulmonary resuscitation operation on the target object; Determine whether the duration of the pressing time meets a preset duration threshold; If the duration of the compression time meets the preset duration threshold, the step of generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio is performed.

[0060] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0061] Figure 3 Schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 3 As shown, the computer device of this embodiment includes: at least one processor ( Figure 3 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned cardiopulmonary resuscitation parameter detection method embodiments are implemented.

[0062] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 3 These are merely examples of computer devices and do not constitute limitations on the computer devices. The computer devices may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, they may also include a network interface, a display screen, and an input system.

[0063] In one embodiment, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor in a computer device, the computer device can perform the steps of any embodiment of a cardiopulmonary resuscitation parameter detection method disclosed in the present invention, which will not be repeated here. The computer-readable storage medium can be non-volatile or volatile.

[0064] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0065] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments, it may also be an external storage device of the computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the memory may also include both an internal storage unit of the computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of a computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0066] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0067] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0068] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A cardiopulmonary resuscitation parameter detection method, characterized in that: include: When turning on the CPR device, enable system self-test; If the system self-check passes, real-time sampling data when performing cardiopulmonary resuscitation on the target object is obtained, wherein the real-time sampling data includes N sampling data; Determining whether the real-time sampling data meets preset conditions; If the real-time sampling data meets the preset conditions, the compression depth, compression frequency and compression-release ratio are calculated according to the real-time sampling data; A cardiopulmonary resuscitation parameter detection result corresponding to the target object is generated according to the compression depth, the compression frequency and the compression-release ratio.

2. The cardiopulmonary resuscitation parameter detection method according to claim 1, characterized in that: After obtaining the real-time sampling data when performing the cardiopulmonary resuscitation operation on the target object, the method includes: Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, a first actual spring height is obtained, wherein the first actual spring height represents the highest depth of the spring when it is stationary.

3. The cardiopulmonary resuscitation parameter detection method according to claim 1, characterized in that: The real-time sampling data includes first sampling data and second sampling data, the second sampling data is the previous sampling data located before the first sampling data, the first sampling data and the second sampling data are any two adjacent sampling data of the N sampling data, and the judging whether the real-time sampling data meets the preset condition includes: Calculating a data change amount between the first sampling data and the second sampling data; Determining whether the data change exceeds a preset change range; If the data change exceeds the preset change range, determining whether the data change is a continuous change; If the data change amount is a continuous change, it is determined that the real-time sampling data meets the preset conditions; If the data variation is not a continuous variation, it is determined that the real-time sampling data does not meet the preset condition, and the real-time sampling data when performing the cardiopulmonary resuscitation operation on the target object is reacquired.

4. The cardiopulmonary resuscitation parameter detection method according to claim 1, characterized in that: The calculating the compression depth according to the real-time sampling data comprises: Acquire N sampling data, and select a first maximum sampling data from the N sampling data, wherein the first maximum sampling data is a maximum value among the N sampling data; Repeat the above steps until a predetermined number M of first maximum sampling data are obtained, and then select the second maximum sampling data from the M first maximum sampling data, wherein M is less than N, and the second maximum sampling data is the maximum value of the M first maximum sampling data; Based on the mapping relationship between the pre-calibrated real-time sampling data and the actual spring height, and the second maximum sampling data, a second actual spring height is obtained; Based on the second actual spring height and the first actual spring height, the pressing depth is calculated.

5. The cardiopulmonary resuscitation parameter detection method according to claim 1, characterized in that: The calculating the compression frequency and the compression-release ratio according to the real-time sampling data includes: Selecting a third maximum sampling data and a minimum sampling data from the N sampling data, wherein the third maximum sampling data is a maximum value among the N sampling data, and the minimum sampling data is a minimum value among the N sampling data; Determine whether the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold; If the absolute difference between the third maximum sampling data and the minimum sampling data is greater than a preset threshold, calculating a median according to the third maximum sampling data and the minimum sampling data; If the real-time sampling data is greater than the median value, it is determined that the spring is in a descending state when pressed; If the real-time sampling data is less than the median value, it is determined that the spring is in an ascending state when pressed; Obtaining the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state through a preset timing program; The pressing frequency and the pressing-releasing ratio are calculated according to the pressing time corresponding to the ascending state and the pressing time corresponding to the descending state.

6. The cardiopulmonary resuscitation parameter detection method according to claim 1, characterized in that: Before generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio, the method includes: When the CPR device is in a compression state, obtaining the compression time when performing a cardiopulmonary resuscitation operation on the target object; Determine whether the duration of the pressing time meets a preset duration threshold; If the duration of the compression time meets the preset duration threshold, the step of generating a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio is performed.

7. A cardiopulmonary resuscitation parameter detection device, characterized in that: include: A self-check module, used to enable system self-check when turning on the CPR device; An acquisition module, used for acquiring real-time sampling data when performing cardiopulmonary resuscitation on a target object if the system self-check passes, wherein the real-time sampling data includes N sampling data; A judgment module, used to judge whether the real-time sampling data meets the preset conditions; A calculation module, configured to calculate a compression depth, a compression frequency and a compression-to-release ratio according to the real-time sampling data if the real-time sampling data meets a preset condition; A generating module is used to generate a cardiopulmonary resuscitation parameter detection result corresponding to the target object according to the compression depth, the compression frequency and the compression-release ratio.

8. The cardiopulmonary resuscitation parameter detection device according to claim 1, characterized in that: The judging module further includes: Calculating a data change amount between the first sampling data and the second sampling data; Determining whether the data change exceeds a preset change range; If the data change exceeds the preset change range, determining whether the data change is a continuous change; If the data change amount is a continuous change, it is determined that the real-time sampling data meets the preset conditions; If the data variation is not a continuous variation, it is determined that the real-time sampling data does not meet the preset condition, and the real-time sampling data when performing the cardiopulmonary resuscitation operation on the target object is reacquired.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the cardiopulmonary resuscitation parameter detection method according to any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the cardiopulmonary resuscitation parameter detection method according to any one of claims 1 to 6 is implemented.

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