Attachment abnormality detection method, detection device and computer readable storage medium

Abstract

The present application provides a method for detecting an abnormality of an accessory. The monitoring device obtains monitoring data collected by at least one accessory connected to itself during a first time period, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data; for any of the at least one accessory, the monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameters; if the proportion of abnormal data in the monitoring data is greater than a preset threshold value corresponding to the accessory in the monitoring device, the monitoring device determines that the accessory is in an abnormal state, and issues a reminder corresponding to the abnormal state of the accessory. The method of the present application can monitor the working status of the accessory in real time, and avoid the undesirable phenomenon that the monitoring data does not meet the working requirements due to improper connection position of the accessory, failure of the accessory, or loose attachment or wearing of the accessory. The present application also relates to an accessory abnormality detection device and a computer-readable storage medium.

Description

Technical Field

[0001] The present application relates to the field of medical monitoring technology, and in particular to a method for detecting an abnormality of an accessory in a monitoring device, an abnormality detection device for an accessory, and a computer-readable storage medium. Background Art

[0002] Current clinical treatment methods usually use various monitoring devices to collect patients' vital information parameters. Monitoring devices are usually connected to the corresponding parts of the patient through accessories to collect the vital information parameters corresponding to the parts. The accessories connected to the monitoring device can be regarded as connection consumables corresponding to various monitoring parameters. Depending on the type of monitoring device, the accessories connected may include ECG lead wires and electrodes, blood oxygen lead wires and probes, respiratory leads, etc.

[0003] One side of the accessory is connected to the monitoring device, and the other side is connected to the patient. For the side of the accessory connected to the patient, the monitoring parameters collected may be abnormal due to the initial connection error or unreasonable wearing of the accessory, or because the accessory fails or falls off during the monitoring process.

[0004] Existing monitoring equipment usually has an alarm function for abnormal monitoring parameters to remind users that there are problems with accessory connections. However, the alarm or reminder functions of existing monitoring equipment are mostly based on short-term parameter feature judgments, or based on more common detachment alarms: such as lead detachment and sensor detachment. This type of alarm cannot cover common accessory abnormality problems in clinical practice, such as improper accessory connection position, excessive use of accessories, or loose attachment. Such accessory abnormality problems often manifest themselves as intermittent improvements in monitoring parameter monitoring. It is sometimes difficult for medical staff to detect the problem through a single-point check, and long-term statistical monitoring is required to discover it. Summary of the invention

[0005] The present application provides an attachment anomaly detection method for real-time statistical analysis of whether an attachment is in an abnormal state, as well as an attachment anomaly detection device and a computer-readable storage medium. The present application specifically includes the following solutions:

[0006] In a first aspect, the present application provides a method for detecting an abnormality in an attachment, comprising the following steps:

[0007] The monitoring device acquires monitoring data collected by at least one accessory connected to the monitoring device during a first period of time, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data;

[0008] For any one of the at least one attachment, the monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameter;

[0009] If the proportion of abnormal data in the monitoring data is greater than a preset threshold value corresponding to the accessory in the monitoring device, the monitoring device determines that the accessory is in an abnormal state and issues a reminder corresponding to the abnormal state of the accessory.

[0010] The monitoring device calculates the proportion of abnormal data in the monitoring data based on the characteristic parameters, including:

[0011] The monitoring device counts the proportion of the accumulated duration of abnormal data in the monitoring data acquired by the monitoring device within the first time period based on the characteristic parameter; and / or,

[0012] The monitoring device calculates, based on the characteristic parameters, the proportion of the number of abnormal data in the monitoring data acquired by the monitoring device in the total amount of the monitoring data.

[0013] The monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameters, and further includes:

[0014] The monitoring device counts, based on the characteristic parameters, the proportion of valid data in the monitoring data collected by the monitoring device through the accessory connected to the monitoring device;

[0015] The monitoring device counts the proportion of abnormal data in the monitoring data within the valid data in the monitoring data based on the characteristic parameter as the proportion of abnormal data in the monitoring data.

[0016] The monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameters, and further includes:

[0017] The monitoring device counts, based on the characteristic parameters, a proportion of invalid data in the monitoring data collected by the monitoring device through the accessory connected to the monitoring device as a proportion of abnormal data in the monitoring data.

[0018] Wherein, if the proportion of abnormal data in the monitoring data is greater than a threshold value preset in the monitoring device corresponding to the attachment, the monitoring device determines that the attachment is in an abnormal state, and issues a reminder corresponding to the abnormal state of the attachment, further comprising:

[0019] The monitoring device acquires the monitoring data collected by the accessory in an abnormal state within a second time period, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data;

[0020] The monitoring device counts the proportion of abnormal data in the monitoring data within the second time period based on the characteristic parameter;

[0021] If the proportion of abnormal data in the monitoring data during the second time period is lower than a preset exit threshold corresponding to the accessory, the monitoring device stops issuing reminders corresponding to the abnormal state of the accessory.

[0022] The monitoring device counts the proportion of abnormal data in the monitoring data in the second time period based on the characteristic parameter, and further includes:

[0023] The monitoring device counts the proportion of the monitoring data collected by the attachment during the second time period that is valid data based on the characteristic parameter;

[0024] The monitoring device counts the proportion of abnormal data in the monitoring data to the valid data based on the characteristic parameter as the proportion of abnormal data in the monitoring data in the second time period.

[0025] The monitoring device counts the proportion of abnormal data in the monitoring data in the second time period based on the characteristic parameter, and further includes:

[0026] The monitoring device counts, based on the characteristic parameter, the proportion of the monitoring data collected by the attachment in the second time period that is invalid data as the proportion of abnormal data in the monitoring data in the second time period.

[0027] The duration of the second time period is shorter than the duration of the first time period.

[0028] Wherein, if the proportion of abnormal data in the monitoring data is greater than a threshold value preset in the monitoring device corresponding to the attachment, the monitoring device determines that the attachment is in an abnormal state, and issues a reminder corresponding to the abnormal state of the attachment, further comprising:

[0029] Generate and display to the user an operational guide for eliminating the abnormal data.

[0030] The step of generating and displaying to the user an operation guide for eliminating the abnormal data includes:

[0031] The monitoring device searches a pre-established database for at least one accessory abnormality influencing factor that matches the type of the accessory and the characteristic parameter;

[0032] The monitoring device sorts the at least one accessory abnormality influencing factor based on priority;

[0033] The monitoring device displays to the user the operation instructions corresponding to one or more of the accessory abnormality influencing factors with the highest priority.

[0034] The types of monitoring data collected by the accessory connected to the monitoring device include one or more of electrocardiogram, blood oxygen, blood pressure and respiration.

[0035] The characteristic parameters of the monitoring data collected by the accessory connected to the monitoring device include one or more of the value, rhythm, intensity, quality and form of the monitoring data.

[0036] The monitoring device acquires monitoring data collected by at least one accessory connected to the monitoring device during the first period of time, and further includes:

[0037] The monitoring device slides the time window of the first time period based on a preset time interval to continuously obtain the monitoring data collected during the first time period through at least one of the accessories connected to the monitoring device.

[0038] In an accessory abnormality detection method provided in the first aspect of the present application, the monitoring device can extract characteristic parameters for each monitoring data based on its type by continuously acquiring the monitoring data collected by one or more accessories connected to itself within a first period of time. Then, by analyzing each characteristic parameter, the proportion of abnormal characteristic parameters can be determined. The abnormal characteristic parameter corresponds to abnormal monitoring data. Through the method of the present application, the proportion of abnormal data contained in the monitoring data collected by each accessory within the first period of time can be determined. Then, through the threshold preset in the monitoring device corresponding to the monitoring data, it can be determined whether the accessory is in an abnormal state. After reaching the abnormal state, a reminder that the accessory is in an abnormal state can be issued accordingly. This method is applicable to phenomena such as improper accessory connection position, accessory failure due to long-term use, and loose attachment and wearing. At this time, although the accessory does not fall off directly, its monitoring data no longer meets the monitoring needs, and the occurrence of such undesirable phenomena can be effectively eliminated.

[0039] In a second aspect, the present application also relates to an accessory abnormality detection device, comprising:

[0040] A parameter feature extraction module, used to obtain monitoring data collected by at least one accessory connected to the monitoring device during a first period of time, and extract characteristic parameters of the monitoring data based on the type of the monitoring data;

[0041] An attachment abnormality judgment module, used for counting the proportion of abnormal data in the monitoring data based on the characteristic parameter for any one of the at least one attachment;

[0042] The attachment reminder module is used to issue a reminder corresponding to the abnormal state of the attachment when the monitoring device determines that the attachment is in an abnormal state if the proportion of abnormal data in the monitoring data is greater than a threshold preset in the monitoring device corresponding to the attachment.

[0043] Wherein, the attachment anomaly detection device also includes an operation guide generation module for generating and displaying to the user an operation guide for eliminating the abnormal data.

[0044] Wherein, the attachment abnormality judgment module includes:

[0045] an abnormal state determiner, for determining the current state of the accessory of the monitoring device;

[0046] An accessory abnormal entry judger, used to judge whether the accessory of the monitoring device enters an abnormal state;

[0047] An accessory abnormal exit judger is used to judge whether the accessory of the monitoring device has exited an abnormal state;

[0048] The accessory status outputter is used to output the type of the accessory of the monitoring device and the current status of the accessory of the monitoring device.

[0049] Wherein, the attachment abnormality judgment module also includes a valid state judger for judging whether the monitoring data is valid based on the characteristic parameters.

[0050] In a third aspect, the present application provides a computer-readable storage medium storing executable instructions and configured to cause a processor to execute the executable instructions to implement the above-mentioned attachment anomaly detection method.

[0051] It can be seen that in the second and third aspects of the present application, the principles of application are similar to the accessory abnormality detection method in the first aspect of the present application, which is to obtain the monitoring data collected from the various accessories connected to the monitoring device, extract the characteristic parameters of the accessories according to the type of the accessories, and then statistically calculate the characteristic parameters as the proportion of abnormal data, and when the proportion of abnormal data exceeds the threshold preset in the monitoring device, issue a reminder corresponding to the abnormal state of the accessory. The accessory abnormality detection device and computer-readable storage medium of the present application are also applicable to phenomena such as improper accessory connection position, accessory failure due to long-term use, and loose attachment and wearing, and can effectively eliminate the occurrence of such undesirable phenomena. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a working schematic diagram of the connection between the monitoring device and the accessories involved in this application;

[0053] Figure 2 is a flow chart of an attachment anomaly detection method provided in an embodiment of the present application;

[0054] Figure 3 yes Figure 2 A flowchart of another embodiment of an accessory abnormality detection method is shown;

[0055] Figure 4 - Figure 7They are Figure 2 Flow charts of other embodiments of the attachment anomaly detection method shown;

[0056] Figure 8 yes Figure 7 A flow chart of sub-steps of step S70 in the accessory abnormality detection method shown;

[0057] Fig. 9 is a framework diagram of an accessory abnormality detection device provided in an embodiment of the present application;

[0058] Fig.10 yes Fig. 9 A frame structure diagram of another embodiment of an accessory abnormality detection device shown;

[0059] Fig.11 yes Fig. 9 The framework structure diagram of the accessory abnormality judgment module in the accessory abnormality detection device shown;

[0060] Fig.12 It is a framework structure diagram of the computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0062] Please see first Figure 1 The present application is shown as a monitoring device 100. The monitoring device 100 has at least one accessory 10, which is connected to various parts of the patient's body and connected to the patient by wearing, bonding, intubation, etc. That is, the accessory 10 is connected between the monitoring device 100 and the patient to collect and transmit the patient's physiological signals.

[0063] The monitoring device 100 includes, but is not limited to, ECG monitoring devices, blood pressure monitoring devices, blood oxygen monitoring devices, and respiratory monitoring devices. The accessories 10 led out of the corresponding monitoring device 100 include, but are not limited to, electrodes and electrode wires for monitoring ECG, arm sleeves and connecting wires for monitoring blood pressure, finger sleeves and connecting wires for monitoring blood oxygen, and electrodes and electrode wires for monitoring respiration. In some embodiments, when the monitoring device 100 monitors a physiological signal, it may be necessary to lead out multiple connecting wires or electrode wires at the same time, and correspondingly fix multiple electrode sheets or other forms of connecting components on the patient. At this time, multiple electrode sheets or other forms of connecting components used to monitor the same physiological signal, as well as multiple electrode wires or connecting wires, can be defined as an accessory 10. The accessory 10 corresponds to collecting a physiological signal for the monitoring device 100 to obtain.

[0064] In some embodiments, the monitoring device 100 may also be a comprehensive monitoring station, which has the ability to simultaneously monitor multiple different types of physiological signals, and correspondingly, multiple accessories 10 may be connected to the patient.

[0065] In order to further clearly describe the specific implementation schemes of various aspects of the present application, the following uses a monitoring device 100 with an ECG monitoring function to illustrate various embodiments. Figure 2 The flowchart of a method for detecting an abnormality of an attachment provided by the present application shown in the figure specifically includes the following steps:

[0066] S10, the monitoring device 100 obtains the monitoring data collected by at least one accessory 10 connected to itself during the first period T1, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data;

[0067] Specifically, the attachment 10 is fixed on the patient's body and is used to collect physiological signals of the corresponding parts of the patient. The monitoring device 100 can obtain the physiological signal through the attachment 10 connected to itself, that is, the monitoring device 100 obtains the monitoring data through the attachment 10. It can be understood that based on the difference of the attachment 10, the part where it is fixed on the patient is also different. The types of physiological signals collected by different attachments 10 are different. Based on the type of attachment 10, the monitoring device 100 also obtains different types of monitoring data.

[0068] In this embodiment, the accessory 10 connected to the monitoring device 100 with ECG monitoring function is usually an electrode sheet connected with an electrode wire, and the electrode sheet is attached to the patient. Therefore, the monitoring data obtained by the monitoring device 100 through the accessory 10 connected to it is ECG monitoring data.

[0069] The monitoring device 100 continuously obtains the monitoring data collected by the attachment 10 in the first time period T1, and extracts the characteristic parameters of the monitoring data based on the type of the monitoring data. The type of monitoring data is determined by the type of the attachment 10 and the different parts fixed to the patient, and is also determined based on the type of the monitoring device 100. The monitoring device 100 can determine the type of monitoring data in the first time period T1 through the attachment 10 connected to the corresponding interface position. For example, in this embodiment, the monitoring device 100 is a monitoring device 100 with an ECG monitoring function, and the attachment 10 connected to the corresponding interface is also in the form of an electrode wire and an electrode sheet, so the monitoring device 100 can determine that the monitoring data collected through the attachment 10 is ECG monitoring data.

[0070] Corresponding to different types of monitoring data, the monitoring device 100 extracts different characteristic parameters, including but not limited to parameter values, morphological information, rhythm information, intensity information, quality information, alarm type and alarm time information of the monitoring data, which are all characteristic parameters that the monitoring device 100 can extract based on the acquired monitoring data.

[0071] It is understandable that for different types of monitoring data, the characteristic parameters that need to be extracted may be the same, different, or may intersect with each other. The method of the present application corresponds to detecting whether the monitoring data is abnormal. Therefore, when extracting the characteristic parameters corresponding to the monitoring data, it is advisable to extract the characteristic parameters that can reflect whether the type of monitoring data is in an abnormal state. For the monitoring device 100 with ECG monitoring function, the ECG QRS wave amplitude information can be extracted as the characteristic parameter.

[0072] On the other hand, the extraction of characteristic parameters in the first time period T1 can be performed in a fixed period extraction manner. That is, in the first time period T1, a monitoring data of this type is collected at each interval of a period, so as to obtain a collection of multiple monitoring data in the first time period T1, and extract the characteristic parameters of each monitoring data in the collection. In one embodiment, 1 second can be defined as a cycle, that is, the monitoring device 100 collects a monitoring data every second through the connected accessory 10, and extracts the characteristic parameters of the monitoring data.

[0073] S20, for any one of the at least one attachment 10, the monitoring device 100 counts the proportion of abnormal data in the monitoring data based on the characteristic parameters;

[0074] Specifically, the monitoring device 100 needs to preset reference values ​​corresponding to the type of monitoring data. After extracting characteristic parameters based on the type of monitoring data, the characteristic parameters are compared with the preset reference values ​​to determine whether the characteristic parameters of the monitoring data are normal.

[0075] It is understandable that for a piece of monitoring data acquired by the monitoring device 100 through the attachment 10 at a certain moment in the first time period T1, one or more characteristic parameters can be correspondingly extracted. And for each characteristic parameter, a corresponding reference value is preset in the monitoring device 100. By comparing each characteristic parameter with its corresponding reference value, it can be determined whether the monitoring data is abnormal. It is understandable that when the monitoring device 100 extracts one characteristic parameter for the monitoring data, it is only necessary to compare the one characteristic parameter with its corresponding reference value. When the characteristic parameter is greater than or equal to the reference value, it can be determined that the monitoring data corresponding to the characteristic parameter is normal data; and when the characteristic parameter is less than the preset reference value, it can be determined that the monitoring data acquired at this moment is abnormal data.

[0076] When there are multiple characteristic parameters extracted for a monitoring data, each characteristic parameter needs to be compared with its corresponding reference value. At this time, the monitoring data can be judged as abnormal data based on only one characteristic parameter being less than its corresponding reference value; or it can be judged as abnormal data based on multiple characteristic parameters including all characteristic parameters being less than their corresponding reference values; and it can also be judged as abnormal data based on two or more associated characteristic parameters being less than their corresponding reference values.

[0077] Of course, in other embodiments, the reference value can be set to define that the monitoring data is abnormal data when the characteristic parameter is greater than the reference value; or the reference value can be defined as a range value, and the characteristic parameter is defined to be within the reference value range or outside the reference value range to determine that the monitoring data is abnormal data. The specific judgment logic can be arbitrarily set based on the type of monitoring data and actual scenario needs.

[0078] For the monitoring device 100 with ECG monitoring function, when the ECG QRS wave amplitude is lower than the ECG reference amplitude in the monitoring device 100, the ECG monitoring data acquired at the current moment is judged to be abnormal data. There are many factors that cause the ECG monitoring data to be abnormal data, such as the electrode patch position deviation, the electrode patch is not firmly attached, or the electrode patch is attached for too long, causing the impedance between the electrode and the patient's skin to increase, etc., which will cause the ECG monitoring data collected by the attachment 10 to be abnormal, and then manifested as the ECG monitoring data acquired by the monitoring device 100 through the attachment 10 The extracted QRS wave amplitude is low.

[0079] As mentioned above, in the first time period T1, the monitoring data acquired by the monitoring device 100 through the attachment 10 is in the form of a collection. Based on extracting characteristic parameters from each monitoring data in the collection and comparing the characteristic parameters with the preset reference values, the proportion of abnormal data in the collection of monitoring data acquired in the first time period T1 can be determined.

[0080] S30. If the proportion of abnormal data in the monitoring data is greater than a preset threshold corresponding to the accessory 10 in the monitoring device 100, the monitoring device 100 determines that the accessory 10 is in an abnormal state and issues a reminder of the abnormal state of the corresponding accessory 10.

[0081] Specifically, the monitoring device 100 also presets a threshold value corresponding to the attachment 10, and the threshold value is used to determine whether the attachment 10 is in an abnormal state. Based on the statistics of the proportion of abnormal data in the monitoring data in the first time period T1 in the previous step, the monitoring device 100 can compare the proportion of abnormal data with the preset threshold value. When the proportion of abnormal data is greater than the preset threshold value, it is determined that the attachment 10 is in an abnormal state, and a reminder of the abnormal state of the attachment 10 is issued accordingly.

[0082] For the monitoring device 100 with ECG monitoring function, within the first time period T1, by counting the ECG monitoring data collection collected by the attachment 10, when the QRS wave amplitude extracted from the ECG monitoring data is lower than the preset ECG reference amplitude in the collection and the proportion exceeds the preset threshold, it can be judged that the electrode sheet in the attachment 10 has poor adhesion, offset adhesion position, increased impedance between the electrode sheet and the patient's skin, and other undesirable phenomena. The monitoring device 100 with ECG monitoring function accordingly issues a reminder corresponding to the abnormal state of the attachment 10. Subsequent medical staff or patients can readjust or replace the electrode sheet based on the reminder, thereby eliminating the abnormal state of the attachment 10 and ensuring the accuracy of the ECG monitoring data subsequently acquired by the monitoring device 100.

[0083] In one embodiment, the first time period T1 corresponding to the monitoring device 100 with ECG monitoring function can be defined as 30 minutes, and the preset threshold value is 65%. That is, within 30 minutes, if the proportion of ECG monitoring data judged as abnormal data in all monitoring data exceeds 65%, it can be judged that the accessory 10 is in an abnormal state, and the monitoring device 100 with ECG monitoring function issues an abnormal state reminder corresponding to the accessory 10.

[0084] The attachment abnormality detection method of the present application continuously extracts characteristic parameters of the monitoring data collected by attachment 10 within the first time period T1, and determines whether each monitoring data is abnormal based on the comparison of the reference value corresponding to the characteristic parameter. Finally, by counting the proportion of abnormal data in the first time period T1 in the monitoring data collection, it is determined whether attachment 10 is in an abnormal state. This method can cover defects that are difficult to detect due to improper connection position, too long use time or loose adhesion and wearing, but may cause distortion of monitoring data, in addition to the defect that attachment 10 cannot collect physiological signals after falling off. The phenomenon of affecting the monitoring effect due to attachment 10 being in an abnormal state is avoided.

[0085] It can be understood that the attachment abnormality monitoring method of the present application is an effective supplement to the abnormal alarm of monitoring parameters in the prior art. Therefore, the attachment abnormality monitoring method of the present application can also be carried out simultaneously with the existing abnormal alarm function of monitoring parameters. Among them, the existing abnormal alarm function of monitoring parameters is used to monitor short-term failure phenomena such as attachment shedding, and the attachment abnormality monitoring method of the present application is used for long-term statistics and timely detection of abnormal states of attachments 10. The two complement each other and can further enhance the monitoring effect of the monitoring device 100.

[0086] The present application method does not specifically limit the reminder method issued by the monitoring device 100. The monitoring device 100 can issue visual reminders to medical staff and patients through its own display or indicator light; it can also issue auditory reminders to medical staff and patients through its own audio output device; it can even issue vibration reminders to patients through its own vibration motor, etc., as well as combinations of the above reminder methods, all of which belong to reminder methods that can be used in the present application method.

[0087] For an example, see Figure 3 In step S20 “the monitoring device 100 calculates the proportion of abnormal data in the monitoring data based on the characteristic parameters”, it may include:

[0088] S20a, the monitoring device 100 counts the proportion of the accumulated duration of abnormal data in the monitoring data acquired by the monitoring device 100 within the first time period T1 based on the characteristic parameters; and / or,

[0089] S20b: The monitoring device 100 counts the proportion of the number of abnormal data in the monitoring data acquired by the monitoring device 100 in the total amount of monitoring data based on the characteristic parameters.

[0090] Specifically, in the implementation of S20b, the monitoring device 100 includes multiple monitoring data in the collection of monitoring data acquired by the attachment 10 in the first time period T1. The corresponding statistical monitoring data is the number of abnormal data, and the ratio of the number of abnormal data to the total number of monitoring data in the collection is used as the proportion of abnormal data in the monitoring data, which belongs to an implementation method of abnormal data proportion statistics.

[0091] For the monitoring device 100 with ECG monitoring function, the first time period T1 is also defined as 30 minutes, and the monitoring data is collected based on a 1-second interval period. Therefore, the number of monitoring data within 30 minutes is 30*60=1800 (pieces). Furthermore, the preset threshold is 65%. That is, in the collection of 1800 monitoring data, when more than 1800*65%=1170 (pieces) of ECG monitoring data are judged as abnormal data, it can be judged that the attachment 10 is in an abnormal state, and the monitoring device 100 with ECG monitoring function issues an abnormal state reminder corresponding to the attachment 10.

[0092] In the implementation of S20a, because the monitoring device 100 periodically acquires monitoring data in the first time period T1, each monitoring data can be regarded as the monitoring data corresponding to the cycle duration. That is, in the implementation of S20a, the monitoring device 100 counts the total duration of the cycle that is abnormal data by counting the sum of the cycle durations corresponding to the monitoring data. Then, by counting the proportion of the total duration of the cycle that is abnormal data in the first time period T1 as the proportion of abnormal data in the monitoring data, the statistical operation of the proportion of abnormal data can also be achieved.

[0093] The monitoring device 100 with ECG monitoring function is also used, and the first time period T1 is defined as 30 minutes, and the monitoring data is collected based on a fixed time interval period. Therefore, each monitoring data within 30 minutes corresponds to the duration of an interval period. The preset threshold is 65%, that is, within the 30 minutes of the first time period T1, the cumulative duration of the period in which the ECG monitoring data is judged as abnormal data exceeds 30*65%=19.5 (minutes), then it can be judged that the attachment 10 is in an abnormal state, and the monitoring device 100 with ECG monitoring function issues an abnormal state reminder corresponding to the attachment 10.

[0094] For an example, see Figure 4 In step S20 "the monitoring device 100 calculates the proportion of abnormal data in the monitoring data based on the characteristic parameters", it may also include:

[0095] S21, the monitoring device 100 counts the proportion of valid data in the monitoring data collected by the monitoring device 100 through the accessory 10 connected to the monitoring device 100 based on the characteristic parameters;

[0096] S22. The monitoring device 100 counts the proportion of abnormal data in the monitoring data to the valid data in the monitoring data based on the characteristic parameters as the proportion of abnormal data in the monitoring data.

[0097] Specifically, in this embodiment, the monitoring device 100 also needs to first count the proportion of valid data in the monitoring data. The valid data in this embodiment is defined corresponding to invalid data. Invalid data can be understood as the inability to collect monitoring data when the attachment 10 falls off, data transmission is interrupted, etc., or it can be described as the situation that the intensity of the collected monitoring data is obviously weak, the data quality is obviously deviated, and even technical alarms are triggered. At this time, because the monitoring device 100 continues to work, the monitoring device 100 will continue to obtain data through the attachment 10. At this time, the monitoring device 100 extracts characteristic parameters from the monitoring data again, which often causes the characteristic parameters to be lower than the corresponding preset threshold. Therefore, when the above-mentioned scene appears in the attachment 10, the invalid data obtained by the monitoring device 100 will be judged as abnormal data, and then the abnormal data in this scene will be included in the statistics of whether the attachment 10 is abnormal.

[0098] By counting the proportion of valid data, it is possible to exclude the scenario where the attachment 10 falls off or the data transmission is interrupted, which may affect the abnormal state monitoring of the attachment 10. That is, the monitoring device 100 can preset an effective threshold value corresponding to the attachment 10, and for the monitoring data collected by the attachment 10, the characteristic parameters of the monitoring data are extracted and compared with the corresponding effective threshold value. When the characteristic parameter is within the corresponding effective threshold range, the monitoring data is judged to be valid data. When the characteristic parameter is outside the corresponding effective threshold range, the monitoring data is judged to be invalid data, thereby eliminating the interference that may be caused to the abnormal state judgment of the attachment 10 after the invalid data is identified as abnormal data.

[0099] Corresponding to the monitoring device 100 with ECG monitoring function, its invalid state scenarios may include the electrode wire or electrode sheet falling off, and the ECG self-learning state of the monitoring device 100. Define the first time period T1 as 30 minutes, and at the same time define that the proportion of valid data is not less than 80%, and the threshold of abnormal data is maintained at 65%. At this time, it is necessary to ensure that there is at least 30*80%=24 (minutes) in the first time period T1, and the monitoring data obtained by the monitoring device 100 through attachment 10 is valid data. Furthermore, it is also necessary to ensure that the duration of abnormal data in the monitoring data is less than 24*65%=15.6 (minutes) to judge that attachment 10 is in a normal state. When the duration of abnormal data is longer than 15.6 minutes, it is judged that attachment 10 is in an abnormal state.

[0100] Alternatively, in the collection of monitoring data acquired in the first period T1, 3600*80%=2880 monitoring data are required to be valid data. Then, the number of abnormal data cannot exceed 2880*65%=1872 (pieces) to determine that the attachment 10 is in a normal state. When the number of abnormal data exceeds 1872, it is determined that the attachment 10 is in an abnormal state.

[0101] For an example, please continue to see Figure 4 In step S20 "the monitoring device 100 calculates the proportion of abnormal data in the monitoring data based on the characteristic parameters", it may also include:

[0102] S21a. The monitoring device 100 counts the proportion of invalid data in the monitoring data collected by the monitoring device 100 through the accessory 10 connected to the monitoring device 100 based on the characteristic parameters as the proportion of abnormal data in the monitoring data.

[0103] Specifically, for reasons similar to those in the previous embodiment, when the attachment 10 falls off or data transmission is interrupted, some invalid data will appear in the monitoring data in the first time period T1. In order to avoid interference caused by invalid data on the abnormal state monitoring of the attachment 10, the previous embodiment only counts the proportion of abnormal data in the valid data to more accurately monitor the abnormal state of the attachment 10.

[0104] However, when accessory 10 is in a state of falling off or data transmission is interrupted for a long time in the first time period T1, accessory 10 has become invalid. The method of the present application can also simultaneously monitor the invalid state of accessory 10, and use the invalid state as one of the abnormal states of the accessory, and issue a corresponding abnormal state reminder of accessory 10 to medical staff or patients.

[0105] In this embodiment, the abnormal state of the attachment 10 can include both the abnormal state in the aforementioned embodiment and the invalid state of the attachment 10. At this time, the attachment abnormality monitoring method of the present application is no longer a supplement to the abnormal alarm of the monitoring parameter in the prior art, but can simultaneously monitor whether the attachment 10 is in an abnormal state and whether the attachment 10 is in an invalid state. In both scenarios, a status reminder corresponding to the attachment 10 can be issued, thereby expanding the scope of application of the method of the present application.

[0106] Corresponding to the monitoring device 100 with ECG monitoring function, the first time period T1 is also defined as 30 minutes, and the proportion of valid data is defined to be no less than 80%. At this time, it is necessary to ensure that there is at least 30*80%=24 (minutes) of time in the first time period T1, and the monitoring data obtained by the monitoring device 100 through the attachment 10 is valid data. That is, the time when the monitoring data in the first time period T1 is invalid data does not exceed 6 minutes. When the duration of the invalid data exceeds 6 minutes, it is judged that the attachment 10 is in an abnormal state, and a reminder corresponding to the attachment 10 being in an abnormal state is issued. It can be understood that the monitoring device 100 can also use the number of invalid data to determine whether the attachment 10 is in an abnormal state.

[0107] For an example, see Figure 5 , after step S30 “if the proportion of abnormal data in the monitoring data is greater than the threshold preset in the monitoring device 100 corresponding to the attachment 10, the monitoring device 100 determines that the attachment 10 is in an abnormal state, and issues a reminder of the abnormal state of the corresponding attachment 10”, it also includes:

[0108] S40, the monitoring device 100 obtains the monitoring data collected by the attachment 10 in the abnormal state within the second time period T2, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data;

[0109] S50, the monitoring device 100 counts the proportion of abnormal data in the monitoring data in the second time period T2 based on the characteristic parameters;

[0110] S60 . If the proportion of abnormal data in the monitoring data in the second time period T2 is lower than the preset exit threshold of the corresponding attachment 10 , the monitoring device 100 stops issuing reminders of the abnormal state of the corresponding attachment 10 .

[0111] Specifically, after determining that the attachment 10 is in an abnormal state and issuing a reminder, the monitoring device 100 can continue to monitor the attachment 10 and continue to obtain the monitoring data collected by the attachment 10. Furthermore, after issuing a reminder of the abnormal state, the monitoring device 100 can analyze the monitoring data collected by the attachment 10 in the second time period T2 to determine whether the attachment 10 has escaped the abnormal state, and stop issuing the reminder action corresponding to the abnormal state of the attachment 10.

[0112] Specifically, the monitoring device 100 can also preset an exit threshold corresponding to the attachment 10, and continuously obtain the monitoring data collected by the attachment 10 within the second time period T2. After extracting the characteristic parameters of the monitoring data, it is determined whether the monitoring data is abnormal data. After the second time period T2 ends, the monitoring device 100 counts the proportion of abnormal data in the monitoring data. At this time, the monitoring data counted are all the monitoring data in the collection corresponding to the second time period T2, and the abnormal data are also understood to be the abnormal data within the second time period T2. The proportion of abnormal data thus counted is the proportion of abnormal data within the second time period T2. In order to clearly describe the solution in this embodiment, the proportion of abnormal data within the second time period T2 can be defined as the proportion of exit abnormal data.

[0113] The monitoring device 100 compares the proportion of abnormal exit data in the second time period T2 with the preset exit threshold of the corresponding attachment 10. If the proportion of abnormal exit data is still higher than the exit threshold, it is determined that the attachment 10 is still in an abnormal state, and the monitoring device 100 can continue to issue reminders corresponding to the abnormal state of the attachment 10; if the proportion of abnormal exit data is lower than the exit threshold, the monitoring device 100 can determine that the attachment 10 has ended the previous abnormal state, or describe that the previous abnormal state of the attachment 10 has been resolved. At this time, the monitoring device 100 can stop issuing abnormal state reminders for the attachment 10 and resume normal monitoring.

[0114] Corresponding to the monitoring device 100 with ECG monitoring function, the second time period T2 can be defined as 2 minutes, and the exit threshold can be defined as 2%. At this time, the monitoring device 100 continuously obtains the monitoring data collected by the attachment 10 within 2 minutes, and extracts the characteristic parameters of the monitoring data, so that the proportion of abnormal exit data in the second time period T2 can be counted. There needs to be a time of no more than 2*60*2%=2.4 (seconds) in the second time period T2, and the monitoring data obtained by the monitoring device 100 through the attachment 10 is abnormal data, and the monitoring device 100 will stop the corresponding abnormal state reminder of the attachment 10. Alternatively, among the monitoring data obtained by the monitoring device 100 in the second time period T2, no more than 2.4 (i.e., 2) monitoring data need to be abnormal data, and the monitoring device 100 will stop the abnormal state reminder of the attachment 10.

[0115] In this embodiment, the time for stopping the reminder of the abnormal state, that is, the duration of the second period T2 is usually set to be shorter, and the duration of the second period T2 is usually shorter than the duration of the first period T1, so that the monitoring device 100 can stop the reminder action as soon as possible to avoid the reminder action lasting too long and affecting the normal operation of the monitoring device 100. In order to ensure the accuracy of the automatic stop reminder operation of the monitoring device 100 and avoid misjudging the attachment 10 that is still in an abnormal state as having returned to a normal state, it is also necessary to increase the standard of the exit threshold accordingly, and stop the abnormal state reminder operation of the attachment 10 after ensuring that the attachment 10 is no longer in an abnormal state within the second period T2.

[0116] For an example, see Figure 6 , for step S50 "the monitoring device 100 counts the proportion of abnormal data in the monitoring data in the second time period T2 based on the characteristic parameters", it also includes:

[0117] S51, the monitoring device 100 counts the proportion of monitoring data collected by the attachment 10 in the second time period T2 as valid data based on the characteristic parameters;

[0118] S52. The monitoring device 100 counts the proportion of abnormal data in the monitoring data in the valid data based on the characteristic parameters as the proportion of abnormal data in the monitoring data in the second time period T2.

[0119] Specifically, similar to the principles of the above-mentioned steps S21 and S22, when the monitoring device 100 counts the percentage of abnormal exit data in the second time period T2, it can also first count the percentage of valid data in the second time period T2, and then count the percentage of abnormal data in the valid data as the percentage of abnormal exit data. Such a setting can also avoid the influence of invalid data in the second time period T2 on the percentage of abnormal exit data, and improve the accuracy of the monitoring device 100 in judging the status of the attachment 10.

[0120] Corresponding to the monitoring device 100 with ECG monitoring function, the second time period T2 is further defined as 2 minutes, the exit threshold is 2%, and the valid data ratio is defined as not less than 83%. At this time, it is necessary to ensure that there is at least 2*60*83%=99.6 (seconds) in the second time period T2, and the monitoring data obtained by the monitoring device 100 through the attachment 10 is valid data. Furthermore, it is also necessary to ensure that the duration of abnormal data in the monitoring data is less than 99.6*2%=1.992 (seconds) before it can be judged that the attachment 10 has ended the abnormal state and returned to the normal state. That is, when the monitoring data obtained by the monitoring device 100 is valid data for a duration of 99.6 seconds, the monitoring device 100 judges that the duration of abnormal data in the obtained valid data is less than 1.992 seconds, and it can be judged that the attachment 10 has returned to the normal state, and the monitoring device 100 then stops issuing abnormal state reminders of the attachment 10.

[0121] Please continue to see Figure 6 , for step S50 "the monitoring device 100 counts the proportion of abnormal data in the monitoring data in the second time period T2 based on the characteristic parameters", it also includes:

[0122] S51a. The monitoring device 100 counts the proportion of invalid data in the monitoring data collected by the attachment 10 in the second time period T2 based on the characteristic parameters as the proportion of abnormal data in the monitoring data in the second time period T2.

[0123] Based on a principle similar to step S21a above, when continuously monitoring accessory 10 in the second time period T2, the method of the present application can also simultaneously monitor the invalid state of accessory 10, and use the invalid state as one of the abnormal states of the accessory, and issue a corresponding abnormal state reminder of accessory 10 to medical staff or patients.

[0124] Corresponding to the monitoring device 100 with ECG monitoring function, the second time period T2 is also defined as 2 minutes, and the proportion of valid data is defined to be no less than 80%. At this time, it is necessary to ensure that there is at least 2*60*80%=96 (seconds) in the second time period T2, and the monitoring data obtained by the monitoring device 100 through the attachment 10 is valid data. That is, the time when the monitoring data in the second time period T2 is invalid data does not exceed 2*60-96=24 seconds. When the duration of the invalid data exceeds 24 seconds, it is judged that the attachment 10 is in an abnormal state. The reminder action corresponding to the abnormal state of the attachment 10 will not be stopped. It can be understood that the monitoring device 100 can also use the number of invalid data to determine whether the attachment 10 is in an abnormal state, and this application will not be further elaborated here.

[0125] For an example, see Figure 7 , after step S30 “if the proportion of abnormal data in the monitoring data is greater than the threshold preset in the monitoring device 100 corresponding to the attachment 10, the monitoring device 100 determines that the attachment 10 is in an abnormal state, and issues a reminder of the abnormal state of the corresponding attachment 10”, it also includes:

[0126] S70: Generate and display to the user an operation guide for eliminating abnormal data.

[0127] Specifically, based on the above analysis of the abnormal state of the attachment 10, after determining that the attachment 10 is in an abnormal state, the attachment abnormality detection method of the present application can also generate operation instructions for the user based on the reasons why the attachment 10 may cause the abnormal state, so as to guide the user to operate according to the operation instructions to eliminate the abnormal state of the attachment 10. Generally, the attachment 10 is mostly a component with a simple structure, and the causes of its abnormal state are mostly simple reasons such as poor connection and poor transmission.

[0128] In some embodiments, the operation of eliminating the abnormal state may include replacing the accessory 10, adjusting the wearing or sticking position of the accessory 10, changing the parameter configuration, etc. Some operations can be completed by simple operations even for the patient, thereby eliminating the need for medical staff to intervene. By generating and displaying the operation guide for eliminating abnormal input data to the user by the monitoring device 100, the work pressure of medical staff can also be appropriately reduced, and the abnormal state can be immediately eliminated.

[0129] It is understandable that after determining that a certain accessory 10 is in an abnormal state, the monitoring device 100 can use its own display to play a video or dynamic image of instructions on how to eliminate the abnormal state of the accessory 10 to the user, or use the display to output text instructions to the user. When the monitoring device 100 is provided with an audio output device, it can also output operation instructions to the user in the form of voice to guide the user to operate according to the operation instructions and eliminate the abnormal state of the accessory 10.

[0130] Corresponding to the monitoring device 100 with ECG monitoring function, after the reminder of the accessory 10 entering an abnormal state appears, it can be judged that the electrode sheet may be not firmly attached, the attachment position is offset, the impedance between the electrode sheet and the patient's skin increases, etc. At this time, the monitoring device 100 outputs the corresponding operation instructions for correcting the above defects to the patient, guiding the patient to eliminate them in sequence based on the above operations, so that the monitoring device 100 can resume normal working state.

[0131] For an example, see Figure 8 , step S70 "generating and displaying to the user an operation guide for eliminating abnormal data" may also include the following sub-steps:

[0132] S71, the monitoring device 100 searches a pre-established database for at least one accessory abnormality influencing factor that matches the type and characteristic parameters of the accessory 10;

[0133] S72, the monitoring device 100 sorts at least one accessory abnormality influencing factor based on priority;

[0134] S73. The monitoring device 100 displays to the user the operation instructions corresponding to one or more accessory abnormality influencing factors with the highest priority.

[0135] Specifically, after the monitoring device 100 extracts characteristic parameters from the monitoring data collected from the attachment 10, it can determine the possible influencing factors that cause the abnormal state of the attachment 10 based on the type of monitoring data and the analysis of the characteristic parameters. A database can be preset in the monitoring device 100 through neural network learning, historical data statistics, etc. In the database, a classification set of attachment abnormality influencing factors is respectively provided for each type of attachment 10 connected to the monitoring device 100. In each classification set, multiple attachment abnormality influencing factors are preset for the type of attachment 10, and each attachment abnormality influencing factor corresponds to a different characteristic parameter expression form. That is, a database is preset in the monitoring device 100, and at least one attachment abnormality influencing factor is preset for different types of attachments 10 in the database. And the characteristic parameters extracted from the monitoring data corresponding to each attachment abnormality influencing factor are different.

[0136] Therefore, after extracting characteristic parameters based on the monitoring data collected from the attachment 10 and determining that the attachment 10 is in an abnormal state, the monitoring device 100 can also match one or more attachment abnormality influencing factors in a preset database based on the performance of the characteristic parameters. Then, the monitoring device 100 can further analyze and match based on the characteristic parameters, prioritize multiple attachment abnormality influencing factors, and identify one or more attachment abnormality influencing factors that are most likely to cause the attachment 10 to be in an abnormal state.

[0137] Then, based on the result of the priority sorting, the monitoring device 100 displays the operation instructions corresponding to one or more accessory abnormality influencing factors with the highest priority to the user when displaying the operation instructions to the user. In this embodiment, when there are many situations where the accessory 10 may cause an abnormal state, the most likely cause of the current abnormal state can be further searched in a preset database based on the analysis of the monitoring data, and then the operation instructions are displayed to the user specifically for the more likely cause, so that the user can more quickly resolve the current accessory abnormal state according to the operation instructions.

[0138] For the monitoring device 100 with ECG monitoring function, the abnormal state of the attachment 10 may be caused by the electrode sheet not being firmly attached, the position of the attachment being offset, the impedance between the electrode sheet and the patient's skin being increased, etc. In the case where the electrode sheet is not firmly attached, when the patient changes his posture in the first time period T1, the electrode sheet will change its state of attachment to the patient's body as the patient's posture changes, that is, the electrode sheet will be relatively tightly attached in some time periods and relatively separated in other time periods. This is reflected in the monitoring data collected by the electrode sheet, and the amplitude of the monitoring data fluctuates with different time periods. The amplitude is relatively high in some time periods, and relatively low in other time periods.

[0139] As for the case of the offset of the fitting position, because the fitting of the electrode sheet is relatively firm, the monitoring data collected therefrom tends to be expressed in the characteristic parameters in the form of: the amplitude of the monitoring data is generally low.

[0140] When the impedance between the electrode and the patient's skin gradually increases, the monitoring data collected by the electrode tends to be expressed in the characteristic parameters in the form of: the amplitude of the monitoring data gradually decreases over time.

[0141] Therefore, in the database preset by the monitoring device 100 with ECG monitoring function, different amplitude change states of monitoring data of the electrode sheet can be corresponded, and different accessory abnormality influencing factors can be formed accordingly. After determining that the electrode sheet is in an abnormal state, the monitoring device 100 with ECG monitoring function also searches for accessory abnormality influencing factors that better match the law in the database based on the amplitude change law of the monitoring data.

[0142] It is understandable that, through the analysis of the amplitude of the monitoring data, when the monitoring device 100 determines that the amplitude of the monitoring data fluctuates greatly in different time periods, but is relatively balanced in the same time period, through matching search in the database, it is determined that the abnormal state of the electrode sheet may be caused by poor adhesion, which is the most likely accessory abnormality factor causing the abnormal monitoring data. At this time, the operating instructions displayed by the monitoring device 100 need to include instructions to remind the user to re-attach the electrode sheet and instructions to remind the user to replace the electrode sheet.

[0143] Furthermore, when the monitoring device 100 simultaneously searches for two or more accessory abnormality influencing factors that have matching characteristic parameter performances through analysis of characteristic parameters of monitoring data, it can also display operation instructions corresponding to each accessory abnormality influencing factor to the user in sequence, thereby ensuring that the user eliminates the abnormal state of the current accessory 10 based on each operation instruction. It is understandable that the monitoring device 100 can also adjust the order of each accessory abnormality influencing factor based on the matching similarity between the characteristic parameters and each accessory abnormality influencing factor, thereby preferentially displaying the operation instructions corresponding to the accessory abnormality influencing factors with higher similarity to the user, so that the user can preferentially eliminate the abnormal state with higher probability, thereby improving the efficiency of eliminating the accessory abnormal state.

[0144] For an example, please see Figure 3 , for step 10 “the monitoring device 100 acquires the monitoring data collected by at least one accessory 10 connected to itself during the first time period T1”, it also includes:

[0145] S10a: The monitoring device 100 slides the time window of the first time period T1 based on a preset time interval to continuously obtain monitoring data collected during the first time period T1 through at least one accessory 10 connected to the monitoring device 100.

[0146] Specifically, in this embodiment, the monitoring device 100 works continuously, so it is also necessary to continuously obtain monitoring data through the attachment 10 to achieve the function of continuously monitoring the patient's physiological signals. That is, the monitoring device 100 monitors the attachment 10 in the first time period T1, and it is necessary to continue to advance as the monitoring device 100 works. By sliding the time window of the first time period T1 at a preset time interval, the monitoring device 100 can continuously monitor the attachment 10 in the first time period T1 during the working process, thereby achieving the effect of ensuring that the attachment 10 is in a normal working state throughout the working process of the monitoring device 100.

[0147] Fig. 9 The present invention illustrates an accessory abnormality detection device 200, which specifically includes:

[0148] The parameter feature extraction module 201 is used to obtain the monitoring data collected by at least one accessory 10 connected to the monitoring device 100 during the first period T1, and extract the characteristic parameters of the monitoring data based on the type of the monitoring data;

[0149] The attachment abnormality judgment module 202 is used to count the proportion of abnormal data in the monitoring data based on the characteristic parameters for any attachment 10 of the at least one attachment 10;

[0150] The attachment reminder module 203 is used to issue a reminder of the abnormal state of the corresponding attachment 10 when the monitoring device 100 determines that the attachment 10 is in an abnormal state if the proportion of abnormal data in the monitoring data is greater than a preset threshold of the corresponding attachment 10 in the monitoring device 100.

[0151] The attachment abnormality monitoring device 200 of the present application can execute the above-mentioned attachment abnormality detection method through the setting and mutual cooperation of the parameter feature extraction module 201, the attachment abnormality judgment module 202 and the attachment reminder module 203, and then achieve the effect of state monitoring of the attachment 10 connected to the monitoring device 100 during the operation of the monitoring device 100, similar to the attachment abnormality detection method of the present application. This avoids the defect that the monitoring data obtained by the monitoring device 100 cannot accurately reflect the patient's physiological parameters because the attachment 10 is in an abnormal state.

[0152] It can be understood that the attachment abnormality monitoring device 200 of the present application can be set inside the monitoring device 100, or communicate with the monitoring device 100, so as to achieve the above-mentioned attachment abnormality monitoring method and perform status monitoring on the attachment 10 connected to the monitoring device 100. When the attachment abnormality monitoring device 200 of the present application is set outside the monitoring device 100 and communicates with the monitoring device 100, the attachment abnormality monitoring device 200 of the present application can also communicate with multiple monitoring devices 100 at the same time, and then simultaneously monitor the attachments 10 connected to each of the multiple monitoring devices 100, to ensure that the multiple monitoring devices 100 connected to it are in a normal working state.

[0153] For an example, see Fig.10 , the attachment abnormality detection device 200 may also include an operation guide generation module 204. The operation guide generation module 204 is used to generate and display to the user an operation guide for eliminating abnormal data. It is understandable that the operation guide generation module 204 may also have a built-in database for analyzing the characteristic parameters of the detection data, searching for attachment abnormality influencing factors corresponding to the characteristic data, and then optimizing the display action of the operation guide.

[0154] For an example, see Fig.11 , the attachment abnormality judgment module 202 may also include:

[0155] The abnormal state determiner 2021 is used to determine the current state of the accessory 10 of the monitoring device 100;

[0156] The accessory abnormal entry determiner 2022 is used to determine whether the accessory 10 of the monitoring device 100 enters an abnormal state;

[0157] The accessory abnormal exit judgement device 2023 is used to judge whether the accessory 10 of the monitoring device 100 exits an abnormal state;

[0158] The accessory status outputter 2024 is used to output the type of the accessory 10 of the monitoring device 100 and the current status of the accessory 10 of the monitoring device 100 .

[0159] Specifically, the abnormal state judger 2021 may correspond to the execution of the above method, based on the proportion of abnormal data in the monitoring data in the first time period T1, to determine whether the attachment 10 is in an abnormal state; the attachment abnormal entry judger 2022 may correspond to the execution of the action that when the proportion of abnormal data exceeds a preset threshold, the monitoring device 100 determines that the attachment 10 is in an abnormal state; the attachment abnormal exit judger 2023 may correspond to the execution of the action of determining whether the attachment 10 has released the current abnormal state based on the proportion of abnormal data in the monitoring data in the second time period T2; the attachment state outputter 2024 may correspond to the execution of the above method, in which the monitoring device 100 issues a reminder that the attachment 10 is in an abnormal state, or stops issuing reminders. Furthermore, the attachment state outputter 2024 may also execute the action of showing the user that the current attachment 10 is in a normal working state.

[0160] It is understandable that the state of the attachment 10 output by the attachment state outputter 2024 needs to be outputted correspondingly based on the judgment results of the abnormal state judger 2021, the attachment abnormal entry judger 2022, and the attachment abnormal exit judger 2023. Furthermore, the attachment state outputter 2024 can also further display the specific value of the current monitoring data, the duration of monitoring, the proportion of abnormal data in the monitoring data, the value of abnormal data in the monitoring data, and other information to the user based on the real-time statistical results, thereby more comprehensively displaying the monitoring state of the monitoring device 100.

[0161] In one embodiment, the attachment abnormality judgment module 202 further includes a valid state judger 2025. The valid state judger 2025 can judge whether the monitoring data is valid based on the characteristic parameters, and correspondingly perform the above-mentioned statistical abnormal data ratio in the valid data, statistical invalid data ratio in the monitoring data and other actions, so as to further improve the accuracy of the attachment abnormality detection device 200 of the present application.

[0162] See also Fig.12 The third aspect of the present application provides a computer-readable storage medium 300. The computer-readable storage medium 300 stores executable instructions and is configured to cause the processor 301 to execute the executable instructions to implement the above-mentioned attachment abnormality detection method.

[0163] Specifically, in one embodiment, the processor 301 calls the program instructions stored in the storage device 302 to perform the following operations:

[0164] Control the monitoring device 100 to obtain the monitoring data collected by at least one accessory 10 connected to the monitoring device 100 during the first period T1, and extract characteristic parameters of the monitoring data based on the type of the monitoring data;

[0165] For any one of the at least one attachment 10, the monitoring device 100 counts the proportion of abnormal data in the monitoring data based on the characteristic parameters;

[0166] If the proportion of abnormal data in the monitoring data is greater than the preset threshold value of the corresponding accessory 10 in the monitoring device 100, it is determined that the accessory 10 is in an abnormal state, and the monitoring device 100 is controlled to issue a reminder of the abnormal state of the corresponding accessory 10.

[0167] The storage device 302 may include a volatile memory device (volatile memory), such as a random-access memory device (RAM); the storage device 302 may also include a non-volatile memory device (non-volatile memory), such as a flash memory device (flash memory), a solid-state drive (SSD), etc.; the storage device 302 may also include a combination of the above-mentioned types of storage devices.

[0168] The processor 301 may be a central processing unit (CPU). The processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (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 the processor may also be any conventional processor, etc.

[0169] It should be pointed out that in the second and third aspects of the present application, because the principles applied are similar to those of the method for detecting an abnormality of an accessory in the first aspect of the present application, the data of the accessory 10 connected to the monitoring device 100 is obtained, and the characteristic parameters are extracted and compared with the preset threshold value, so as to determine whether the accessory 10 is in an abnormal state, thereby achieving the effect of monitoring and identifying the abnormal state of the accessory 10. Therefore, the development of each embodiment in the above two aspects can also be carried out based on the various embodiments of the method for detecting an abnormality of an accessory in the first aspect of the present application, and this specification will not be repeated here.

[0170] The above-described implementation methods do not constitute a limitation on the protection scope of the technical solution. Any modification, equivalent replacement and improvement made within the spirit and principle of the above-described implementation methods shall be included in the protection scope of the technical solution.

Claims

1. A method for detecting an abnormality in an attachment, characterized in that: The steps include: The monitoring device acquires monitoring data collected by at least one accessory connected to the monitoring device during a first period of time, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data; For any one of the at least one attachment, the monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameter; If the proportion of abnormal data in the monitoring data is greater than a preset threshold value corresponding to the accessory in the monitoring device, the monitoring device determines that the accessory is in an abnormal state and issues a reminder corresponding to the abnormal state of the accessory.

2. The method for detecting an abnormality of an attachment according to claim 1, wherein: The monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameter, including: The monitoring device counts the proportion of the accumulated duration of abnormal data in the monitoring data acquired by the monitoring device within the first time period based on the characteristic parameter; and / or, The monitoring device calculates, based on the characteristic parameters, the proportion of the number of abnormal data in the monitoring data acquired by the monitoring device in the total amount of the monitoring data.

3. The method for detecting an abnormality of an attachment according to claim 2, wherein: The monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameter, and further includes: The monitoring device counts, based on the characteristic parameters, the proportion of valid data in the monitoring data collected by the monitoring device through the accessory connected to the monitoring device; The monitoring device counts the proportion of abnormal data in the monitoring data within the valid data in the monitoring data based on the characteristic parameter as the proportion of abnormal data in the monitoring data.

4. The method for detecting an abnormality of an attachment according to claim 2, wherein: The monitoring device counts the proportion of abnormal data in the monitoring data based on the characteristic parameter, and further includes: The monitoring device counts, based on the characteristic parameters, a proportion of invalid data in the monitoring data collected by the monitoring device through the accessory connected to the monitoring device as a proportion of abnormal data in the monitoring data.

5. The attachment abnormality detection method according to claim 1, characterized in that: If the proportion of abnormal data in the monitoring data is greater than a threshold value preset in the monitoring device corresponding to the attachment, the monitoring device determines that the attachment is in an abnormal state and issues a reminder corresponding to the abnormal state of the attachment, the method further includes: The monitoring device acquires the monitoring data collected by the accessory in an abnormal state within a second time period, and extracts characteristic parameters of the monitoring data based on the type of the monitoring data; The monitoring device counts the proportion of abnormal data in the monitoring data within the second time period based on the characteristic parameter; If the proportion of abnormal data in the monitoring data during the second time period is lower than a preset exit threshold corresponding to the accessory, the monitoring device stops issuing reminders corresponding to the abnormal state of the accessory.

6. The method for detecting an abnormality of an attachment according to claim 5, wherein: The monitoring device counts the proportion of abnormal data in the monitoring data in the second time period based on the characteristic parameter, and further includes: The monitoring device counts the proportion of the monitoring data collected by the attachment during the second time period that is valid data based on the characteristic parameter; The monitoring device counts the proportion of abnormal data in the monitoring data to the valid data based on the characteristic parameter as the proportion of abnormal data in the monitoring data in the second time period.

7. The method for detecting an abnormality of an attachment according to claim 5, wherein: The monitoring device counts the proportion of abnormal data in the monitoring data in the second time period based on the characteristic parameter, and further includes: The monitoring device counts, based on the characteristic parameter, the proportion of the monitoring data collected by the attachment in the second time period that is invalid data as the proportion of abnormal data in the monitoring data in the second time period.

8. The method for detecting an abnormality of an attachment according to claim 5, wherein: The duration of the second period is shorter than the duration of the first period.

9. The method for detecting an abnormality of an accessory according to any one of claims 1 to 8, characterized in that: If the proportion of abnormal data in the monitoring data is greater than a threshold value preset in the monitoring device corresponding to the attachment, the monitoring device determines that the attachment is in an abnormal state and issues a reminder corresponding to the abnormal state of the attachment, the method further includes: Generate and display to the user an operational guide for eliminating the abnormal data.

10. The method for detecting an abnormality of an attachment according to claim 9, wherein: The generating and displaying to the user an operation guide for eliminating the abnormal data includes: The monitoring device searches a pre-established database for at least one accessory abnormality influencing factor that matches the type of the accessory and the characteristic parameter; The monitoring device sorts the at least one accessory abnormality influencing factor based on priority; The monitoring device displays to the user the operation instructions corresponding to one or more of the accessory abnormality influencing factors with the highest priority.

11. The method for detecting an abnormality of an attachment according to any one of claims 1 to 8, characterized in that: The types of monitoring data collected by the accessory connected to the monitoring device include one or more of electrocardiogram, blood oxygen, blood pressure and respiration.

12. The method for detecting an abnormality of an attachment according to any one of claims 1 to 8, characterized in that: The characteristic parameters of the monitoring data collected by the accessory connected to the monitoring device include one or more of the value, rhythm, intensity, quality and form of the monitoring data.

13. The method for detecting an abnormality of an accessory according to any one of claims 1 to 8, characterized in that: The monitoring device acquires monitoring data collected by at least one accessory connected to the monitoring device during a first period of time, and further includes: The monitoring device slides the time window of the first time period based on a preset time interval to continuously obtain the monitoring data collected during the first time period through at least one of the accessories connected to the monitoring device.

14. An accessory abnormality detection device, characterized in that: include: A parameter feature extraction module, used to obtain monitoring data collected by at least one accessory connected to the monitoring device during a first period of time, and extract characteristic parameters of the monitoring data based on the type of the monitoring data; An attachment abnormality judgment module, used for counting the proportion of abnormal data in the monitoring data based on the characteristic parameter for any one of the at least one attachment; The attachment reminder module is used to issue a reminder corresponding to the abnormal state of the attachment when the monitoring device determines that the attachment is in an abnormal state if the proportion of abnormal data in the monitoring data is greater than a threshold preset in the monitoring device corresponding to the attachment.

15. The accessory abnormality detection device according to claim 14, characterized in that: The attachment anomaly detection device also includes an operation guide generation module for generating and presenting to a user an operation guide for eliminating the abnormal data.

16. The accessory abnormality detection device according to claim 14 or 15, characterized in that: The attachment abnormality judgment module includes: An accessory abnormal entry judger, used to judge whether an accessory of the monitoring device enters an abnormal state; An accessory abnormal exit judger is used to judge whether the accessory of the monitoring device has exited an abnormal state; The accessory status outputter is used to output the type of the accessory of the monitoring device and the current status of the accessory of the monitoring device.

17. The accessory abnormality detection device according to claim 16, characterized in that: The attachment abnormality judgment module also includes a valid state judger for judging whether the monitoring data is valid based on the characteristic parameters.

18. A computer-readable storage medium, characterized in that: Executable instructions are stored and configured to cause a processor to execute the executable instructions to implement the accessory anomaly detection method described in any one of claims 1 to 13.

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