Monitoring and testing method and system for microscopic apical surgery saliva suction tube

Abstract

The invention relates to the technical field of medical equipment, in particular to a monitoring and testing method and system for a saliva suction tube in microscopic root tip surgery, and the method comprises the steps: obtaining the related information of an initial saliva suction tube, obtaining a historical reference data set through the related information, building an optimized fault tree model based on the historical reference data set, and obtaining the optimized fault tree model. And obtaining a predicted life based on the optimized fault tree model, executing a flow detection operation on the initial saliva suction pipe by using a flow detection unit to obtain a detection flow time sequence, calculating an ideal flow based on the initial negative pressure intensity and a flow conversion relational expression, and determining that the detection flow time sequence is a target flow time sequence. Obtaining a comprehensive flow evaluation value based on the target flow time sequence and the ideal flow, obtaining the reference life of the initial saliva suction tube, obtaining a comprehensive use ratio based on the comprehensive flow evaluation value, the predicted life and the reference life, and testing the initial saliva suction tube based on the comprehensive use ratio. The intelligent degree and accuracy of saliva suction tube testing can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a monitoring and testing method, system, electronic equipment and computer-readable storage medium for a saliva suction tube used in micro-apical surgery. Background Art

[0002] In microapical surgery, a saliva suction tube is an indispensable tool. It is used to remove the fluid in the patient's mouth to keep the surgical area clean. In actual use, the saliva suction tube often has problems such as unstable suction, easy blockage, and leakage. These problems may cause oral fluid to infect the surgical site or even cause infection. Therefore, accurate and comprehensive testing of the saliva suction tube before use is of great significance to ensure its reliable application.

[0003] At present, saliva suction tubes are usually tested by pressure testing, air tightness testing, etc. before leaving the factory. Although these testing methods can test the saliva suction tubes, they cannot characterize the stability and performance of the saliva suction tubes after being used for a period of time. Therefore, the intelligence and accuracy of the testing of saliva suction tubes need to be improved. Summary of the invention

[0004] The present invention provides a monitoring and testing method for a saliva suction tube in micro-apical surgery and a computer-readable storage medium, the main purpose of which is to improve the intelligence and accuracy of the saliva suction tube test.

[0005] To achieve the above-mentioned purpose, the present invention provides a monitoring and testing method for a saliva suction tube for micro-apical surgery, comprising:

[0006] receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow test mechanism;

[0007] Obtaining relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, using the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and constructing an initial fault tree model based on the historical reference data set;

[0008] Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model;

[0009] Based on the flow detection unit, a flow detection operation is performed on the initial saliva suction tube to obtain a detection flow timing sequence, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset;

[0010] Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship;

[0011] After confirming that the detected flow timing is a preset target flow timing, obtaining a comprehensive flow evaluation value based on the target flow timing and the ideal flow;

[0012] The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

[0013] Optionally, constructing an initial fault tree model based on the historical reference data set includes:

[0014] Obtaining a fault cause set for classifying the saliva suction tube, wherein the fault cause set includes four different fault causes, and the four different fault causes are: an operation fault cause, a life fault cause, a product quality fault cause, and other fault causes;

[0015] Extracting historical reference data from the historical reference data set in sequence and performing the following operations on the extracted historical reference data:

[0016] Obtain a text recognition model set for recognizing text, wherein there are five text recognition models in the text recognition model set, and obtain a target fault cause based on the text recognition model set and the extracted historical reference data, wherein the target fault cause is composed of an operation fault cause, a life fault cause, a product quality fault cause, or other fault causes;

[0017] Using the target fault cause, performing an identification operation on the extracted historical reference data to obtain identification reference data, and using the identification reference data, respectively aggregating the identification reference data for the fault causes in the fault cause set to obtain four different fault data sets, wherein the four different fault data sets are: an operation fault data set, a life fault data set, a product quality fault data set, and other fault data sets;

[0018] The initial fault tree model was constructed with the saliva suction tube fault as the root node and the four different fault causes in the fault cause set as child nodes.

[0019] Optionally, the obtaining a target fault cause based on the text recognition model set and the extracted historical reference data includes:

[0020] Randomly extracting two text recognition models from the text recognition model set, and using the two extracted text recognition models to perform text recognition operations on the fault cause texts of the historical reference data, respectively, to obtain a first fault cause and a second fault cause, wherein the first fault cause and the second fault cause are both composed of an operation fault cause, a life fault cause, a product quality fault cause or other fault causes;

[0021] If the first fault cause is the same as the second fault cause, confirming that the first fault cause or the second fault cause is the target fault cause;

[0022] If the first fault cause is different from the second fault cause, the remaining three text recognition models in the text recognition model set are used to perform recognition operations on the fault cause text of the historical reference data to obtain the third fault cause, the fourth fault cause and the fifth fault cause;

[0023] A target fault cause is obtained based on the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, wherein the target fault cause is the fault cause corresponding to the majority of the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, and the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause are all composed of operation fault causes, life fault causes, product quality fault causes or other fault causes.

[0024] Optionally, optimizing the initial fault tree model to obtain an optimized fault tree model includes:

[0025] Acquire an initial analysis data set in the initial fault tree model, wherein the subnode corresponding to the initial analysis data set in the initial fault tree model is the life failure cause;

[0026] Acquire an initial life average value based on the initial analysis data set, wherein the initial life average value is an average value of the usage time corresponding to a plurality of historical reference data in the initial analysis data set;

[0027] Extract historical reference data from the initial analysis data set in sequence, and perform the following operations on the extracted historical reference data:

[0028] Obtaining an absolute life difference based on the extracted historical reference data and the initial life mean, wherein the absolute life difference is the absolute difference between the usage time corresponding to the extracted historical reference data and the initial life mean;

[0029] Summarizing the absolute lifespan differences to obtain an absolute lifespan difference set, and performing a sorting operation on the absolute lifespan differences in the absolute lifespan difference set in descending order to obtain an absolute lifespan difference sequence;

[0030] The absolute life difference values ​​are sequentially extracted from the absolute life difference value sequence and the following operations are performed on the extracted absolute life difference values:

[0031] comparing the extracted absolute life span difference with a preset absolute life span threshold;

[0032] If the absolute life difference is greater than the absolute life threshold, the historical reference data corresponding to the absolute life difference is eliminated in the initial analysis data set, and the number of eliminated absolute life differences is recorded to obtain the elimination number, and the time is recorded with the time when the absolute life difference is eliminated as the time starting point to obtain the elimination time, until the elimination number reaches the preset elimination threshold or the elimination time reaches the preset time threshold, confirming the receipt of the data update instruction from the data update unit, obtaining the updated reference data based on the data update instruction, supplementing the updated reference data to the initial analysis data set after eliminating the historical reference data corresponding to the absolute life difference greater than the absolute life threshold, to obtain an updated reference data set, and taking the updated reference data set as the historical reference data set, returning to the step of constructing the initial fault tree model based on the historical reference data set until the absolute life difference is less than or equal to the absolute life threshold to obtain the optimized fault tree model.

[0033] Optionally, obtaining the predicted life based on the optimized fault tree model includes:

[0034] Using the optimized fault tree model to obtain a target analysis data set, wherein the sub-node corresponding to the target analysis data set in the optimized fault tree model is a life failure cause;

[0035] The predicted life span is calculated based on the target analysis data set and the pre-constructed life span assessment relationship, wherein the life span assessment relationship is as follows:

[0036]

[0037] Where H represents the predicted life span, w i Indicates the preset i-th coefficient, h i represents the i-th target reference data in the target analysis data set, where the target reference data includes historical reference data and updated reference data, and n represents that there are n target reference data in the target analysis data set.

[0038] Optionally, performing a flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain a detection flow time sequence includes:

[0039] The negative pressure generating mechanism is connected to the initial saliva suction tube to obtain a target detection mechanism, the initial negative pressure is input into the negative pressure generating mechanism in the flow detection unit, and the target detection mechanism after the initial negative pressure is input absorbs the pre-acquired initial liquid to obtain the target liquid containing the flow;

[0040] Starting the flow testing mechanism, and setting the frequency used for detection by the flow testing mechanism as the sampling frequency, and performing a detection operation on the target liquid based on the set flow testing mechanism to obtain a detection flow;

[0041] The detection flows are summarized to obtain a detection flow set, the time corresponding to the detection flows is obtained, and the detection flows in the detection flow set are sorted in order from earliest to latest time to obtain a detection flow time sequence.

[0042] Optionally, the flow conversion relationship is as follows:

[0043]

[0044] Among them, q represents the ideal flow rate, f represents the flow correction coefficient, d0 represents the equivalent diameter of the initial saliva suction tube, and the equivalent diameter is related to the size parameter, P1 represents the atmospheric pressure, P0 represents the initial negative pressure, and ρ represents the density of the initial liquid.

[0045] Optionally, the confirming that the detected flow timing is a preset target flow timing includes:

[0046] Extract the detection traffic from the detection traffic time sequence in sequence and perform the following operations on the extracted detection traffic:

[0047] Based on the extracted detection flow, the initial flow is confirmed in the detection flow time series, and the stability evaluation value is calculated based on the extracted detection flow, the initial flow and the pre-built flow stability evaluation relationship, wherein the flow stability evaluation relationship is as follows:

[0048] W=|L j -L j+1 |

[0049] Where W represents the stability evaluation value, L j represents the detection flow extracted from the detection flow time series, L j+1 represents the initial flow rate, wherein 1≤j≤m-1, wherein m represents that there are m detection flows in the detection flow sequence, and j is only used for counting;

[0050] comparing the stability assessment value with a preset stability threshold;

[0051] If the stability evaluation values ​​are all less than or equal to the stability threshold, confirming that the detected flow timing is the target flow timing;

[0052] Otherwise, it indicates that the initial saliva suction tube is abnormal.

[0053] Optionally, obtaining a comprehensive flow evaluation value based on the target flow time sequence and the ideal flow includes:

[0054] The target flow variance is obtained by using the target flow time series, wherein the target flow variance is the variance of multiple detection flows in the target flow time series;

[0055] The comprehensive flow evaluation value is calculated based on the target flow variance and the ideal flow, and the calculation formula is as follows:

[0056]

[0057] Wherein, E represents the comprehensive flow evaluation value, F represents the target flow variance, β and γ are both preset coefficients, and a k Represents the kth detected flow in the target flow time series.

[0058] To achieve the above object, the present invention also provides a monitoring and testing system for a saliva suction tube in micro-apical surgery, comprising:

[0059] a saliva suction tube test environment confirmation module, used for receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism;

[0060] A saliva suction tube test preparation module is used to obtain relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and execution standards, and use the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and an initial fault tree model is constructed based on the historical reference data set;

[0061] Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model;

[0062] A saliva suction tube testing module, used for performing a flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain a detection flow timing, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset;

[0063] Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship;

[0064] A saliva suction tube test evaluation module, used to obtain a comprehensive flow evaluation value based on the target flow timing and the ideal flow after confirming that the detected flow timing is a preset target flow timing;

[0065] The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

[0066] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0067] a memory storing at least one instruction; and

[0068] The processor executes the instructions stored in the memory to implement the above-mentioned monitoring and testing method for the saliva suction tube in micro-apical surgery.

[0069] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned monitoring and testing method for the saliva suction tube for micro-apical surgery.

[0070] The present invention is to solve the problem described in the background technology. The present invention receives a saliva suction tube test instruction, and confirms a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism. It can be seen that the saliva suction tube test environment confirmed by the embodiment of the present invention is not only used for a simple test of the saliva suction tube, but also lays a foundation for subsequent combination with actual conditions, thereby improving the accuracy and intelligence of the test of the saliva suction tube. The present invention obtains relevant information of an initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, and uses the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and an initial fault tree model is constructed based on the historical reference data set, and the initial fault tree model is optimized to obtain an optimized fault tree model, and a predicted life is obtained based on the optimized fault tree model. It can be seen that the present invention not only considers the possible failures of the saliva suction tube in actual use, and constructs the initial fault tree model based on this, but also realizes the dynamic update of the initial fault tree model, thereby improving the accuracy of obtaining the predicted life by using the optimized fault tree model, and further improving the accuracy of evaluating the saliva suction tube. The present invention performs flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain the detection flow sequence, calculates the ideal flow based on the initial negative pressure and the pre-constructed flow conversion relationship, confirms that the detection flow sequence is the preset target flow sequence, obtains the comprehensive flow evaluation value based on the target flow sequence and the ideal flow, obtains the reference life of the initial saliva suction tube, obtains the comprehensive use ratio based on the comprehensive flow evaluation value, the predicted life and the reference life, and implements the test of the initial saliva suction tube based on the comprehensive use ratio. It can be seen that when the embodiment of the present invention tests the saliva suction tube, the difference between the detection flow and the ideal flow of the saliva suction tube during the test is considered, and the comprehensive use ratio is calculated in combination with the reference life of the saliva suction tube, the ratio of the saliva suction tube in the optimized fault tree model and the target flow sequence. Furthermore, the evaluation of the saliva suction tube through the comprehensive use ratio can improve the intelligence and accuracy of the saliva suction tube test. Therefore, the present invention can improve the intelligence and accuracy of the saliva suction tube test. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic flow chart of a method for monitoring and testing a saliva suction tube for micro-apical surgery provided by an embodiment of the present invention;

[0072] Figure 2 A functional module diagram of a monitoring and testing system for a saliva suction tube for micro-apical surgery provided by an embodiment of the present invention;

[0073] Figure 3 A schematic diagram of the structure of an electronic device for implementing the monitoring and testing method of a saliva suction tube for micro-apical surgery provided in one embodiment of the present invention.

[0074] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0075] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0076] The embodiment of the present application provides a monitoring and testing method for a saliva suction tube for micro-apical perioperative surgery. The execution subject of the monitoring and testing method for a saliva suction tube for micro-apical perioperative surgery includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided in the embodiment of the present application. In other words, the monitoring and testing method for a saliva suction tube for micro-apical perioperative surgery can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0077] Reference Figure 1 FIG. 1 is a flow chart of a monitoring and testing method for a saliva suction tube for micro-apical surgery provided by an embodiment of the present invention. In this embodiment, the monitoring and testing method for a saliva suction tube for micro-apical surgery includes:

[0078] S1. Receive a saliva suction tube test instruction, and confirm a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism.

[0079] It should be explained that the saliva suction tube is mainly used to clean the liquid in the patient's mouth during micro-apical surgery, so as to ensure that the medical staff can clearly observe the current surgical situation when performing micro-apical surgery on the patient, and avoid infection of the patient's wound and discomfort caused by the reflux of oral fluid. Therefore, the embodiment of the present invention is mainly to realize the monitoring and testing of the reusable saliva suction tube.

[0080] Furthermore, the saliva suction tube test instruction is an instruction issued by medical staff before using the saliva suction tube to test whether the saliva suction tube to be used can be used normally. The saliva suction tube test environment refers to the necessary environment for testing the saliva suction tube, and the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism. The initial saliva suction tube refers to the saliva suction tube to be used. The saliva suction tube test system refers to the software or APP that realizes the automated testing of the saliva suction tube, and the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism. Please refer to the subsequent embodiments for the application of specific units.

[0081] For example, Xiao Zhang, as a medical staff in a hospital, before performing microscopic periapical surgery on a patient, in order to test whether the saliva suction tube to be used in the surgery can work properly, Xiao Zhang issues the saliva suction tube test instruction and confirms the saliva suction tube test environment.

[0082] S2. Obtain relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, and use the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data consists of usage time and fault cause text, and construct an initial fault tree model based on the historical reference data set.

[0083] It should be understood that historical reference data refers to data recorded by medical staff during the actual use of the saliva suction tube, and the preparation materials, implementation standards and size parameters corresponding to the historical reference data in the historical reference data set are the same as the relevant information of the initial saliva suction tube. For example, medical staff in multiple hospitals are now using saliva suction tubes with the same preparation materials, size parameters and implementation standards. When the saliva suction tube fails, the medical staff records the information of the saliva suction tube to obtain historical reference data.

[0084] Furthermore, constructing an initial fault tree model based on the historical reference data set includes:

[0085] Obtaining a fault cause set for classifying the saliva suction tube, wherein the fault cause set includes four different fault causes, and the four different fault causes are: an operation fault cause, a life fault cause, a product quality fault cause, and other fault causes;

[0086] Extracting historical reference data from the historical reference data set in sequence and performing the following operations on the extracted historical reference data:

[0087] Obtain a text recognition model set for recognizing text, wherein there are five text recognition models in the text recognition model set, and obtain a target fault cause based on the text recognition model set and the extracted historical reference data, wherein the target fault cause is composed of an operation fault cause, a life fault cause, a product quality fault cause, or other fault causes;

[0088] Using the target fault cause, performing an identification operation on the extracted historical reference data to obtain identification reference data, and using the identification reference data, respectively aggregating the identification reference data for the fault causes in the fault cause set to obtain four different fault data sets, wherein the four different fault data sets are: an operation fault data set, a life fault data set, a product quality fault data set, and other fault data sets;

[0089] The initial fault tree model was constructed with the saliva suction tube fault as the root node and the four different fault causes in the fault cause set as child nodes.

[0090] It should be explained that the operational failure cause refers to the failure cause of the saliva suction tube not working properly due to improper operation during actual use. For example: when the saliva suction tube is disinfected, the corrosiveness of the disinfectant used is too strong, resulting in the saliva suction tube being unable to be used normally. The life failure cause refers to the failure cause of the saliva suction tube not being able to be used normally due to the use time of the saliva suction tube reaching a certain number of years. The product quality failure cause refers to the failure cause of the saliva suction tube not being able to be used normally due to the imperfection of the process flow during production. For example: in actual production, the actual service life of a saliva suction tube is far less than the normal service life due to the failure of the production machine. Other failure causes refer to the failure causes of the saliva suction tube due to other factors that cannot be divided into: operational failure causes, life failure causes and product quality failure causes, that is, other failure causes are the complement of operational failure causes, life failure causes and product quality failure causes. For example: during transportation, the saliva suction tube cannot be used normally due to extrusion deformation. The text recognition model refers to a model that can recognize text. Optionally, a pre-trained neural network is used as the text recognition model. The technology of constructing a tree structure using root nodes and child nodes is an existing technology and will not be described in detail here.

[0091] It should be understood that the step of obtaining the target fault cause based on the text recognition model set and the extracted historical reference data includes:

[0092] Randomly extracting two text recognition models from the text recognition model set, and using the two extracted text recognition models to perform text recognition operations on the fault cause texts of the historical reference data, respectively, to obtain a first fault cause and a second fault cause, wherein the first fault cause and the second fault cause are both composed of an operation fault cause, a life fault cause, a product quality fault cause or other fault causes;

[0093] If the first fault cause is the same as the second fault cause, confirming that the first fault cause or the second fault cause is the target fault cause;

[0094] If the first fault cause is different from the second fault cause, the remaining three text recognition models in the text recognition model set are used to perform recognition operations on the fault cause text of the historical reference data to obtain the third fault cause, the fourth fault cause and the fifth fault cause;

[0095] A target fault cause is obtained based on the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, wherein the target fault cause is the fault cause corresponding to the majority of the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, and the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause are all composed of operation fault causes, life fault causes, product quality fault causes or other fault causes.

[0096] It should be explained that different text recognition models in the text recognition model set have different structures and compositions, which will lead to different results of text recognition for the same historical fault data using different text recognition models. For example: the historical fault data is: in the third month of using the saliva suction tube, increasing the negative pressure of the saliva suction tube caused the saliva suction tube to leak. Using different text recognition models may identify the fault cause text as: operational fault cause, life fault cause, product quality fault cause or other fault cause. The third fault cause, the fourth fault cause and the fifth fault cause are defined the same as the first fault cause, and will not be repeated here.

[0097] It should be understood that when five text recognition models are used to perform text recognition on the historical fault data, there must be at least two text recognition models that have the same results after recognizing the historical fault data. Therefore, the use of five text recognition models can certainly obtain the target fault cause. In the embodiment of the present invention, the fault cause corresponding to the mode refers to the fault cause with the largest number of operation fault causes, life fault causes, product quality fault causes or other fault causes among the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause. For example, the operation fault cause appears twice, the life fault cause, the product quality fault cause and other fault causes appear once, so the fault cause corresponding to the mode refers to the operation fault cause.

[0098] Further, in the embodiment of the present invention, when using the text recognition model, multiple time thresholds are first formulated, and different ratios are formulated in combination with the multiple time thresholds and the use time in the historical reference data to improve the accuracy of identifying the fault cause text. For example, first obtain multiple life expectancies corresponding to the initial saliva suction tube provided by the saliva suction tube manufacturer, wherein the multiple life expectancies are: 6 months, 6 months, 8 months, 3 months, 10 months and 9 months, then use the multiple life expectancies to calculate the mean and standard deviation respectively, wherein the mean is: 7, the standard deviation is about: 2.3, then the given time thresholds are: 2.4 months and 11.6 months, when the use time is less than or equal to 2.4 months, the proposed use time ratio is 0.6, when the use time is greater than 2.4 and less than 11.6, the proposed use time ratio is 0.5, when the use time is greater than or equal to 11.6, the proposed use time ratio is 0.3, and other technologies can achieve the same effect, which will not be repeated here. The purpose of establishing the ratio of usage time is to improve the accuracy of the text recognition model in recognizing the historical reference data, thereby improving the accuracy of testing the saliva suction tube.

[0099] S3. Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model.

[0100] It should be explained that the optimizing the initial fault tree model to obtain the optimized fault tree model includes:

[0101] Acquire an initial analysis data set in the initial fault tree model, wherein the subnode corresponding to the initial analysis data set in the initial fault tree model is the life failure cause;

[0102] Acquire an initial life average value based on the initial analysis data set, wherein the initial life average value is an average value of the usage time corresponding to a plurality of historical reference data in the initial analysis data set;

[0103] Extract historical reference data from the initial analysis data set in sequence, and perform the following operations on the extracted historical reference data:

[0104] Obtaining an absolute life difference based on the extracted historical reference data and the initial life mean, wherein the absolute life difference is the absolute difference between the usage time corresponding to the extracted historical reference data and the initial life mean;

[0105] Summarizing the absolute lifespan differences to obtain an absolute lifespan difference set, and performing a sorting operation on the absolute lifespan differences in the absolute lifespan difference set in descending order to obtain an absolute lifespan difference sequence;

[0106] The absolute life difference values ​​are sequentially extracted from the absolute life difference value sequence and the following operations are performed on the extracted absolute life difference values:

[0107] comparing the extracted absolute life span difference with a preset absolute life span threshold;

[0108] If the absolute life difference is greater than the absolute life threshold, the historical reference data corresponding to the absolute life difference is eliminated in the initial analysis data set, and the number of eliminated absolute life differences is recorded to obtain the elimination number, and the time is recorded with the time when the absolute life difference is eliminated as the time starting point to obtain the elimination time, until the elimination number reaches the preset elimination threshold or the elimination time reaches the preset time threshold, confirming the receipt of the data update instruction from the data update unit, obtaining the updated reference data based on the data update instruction, supplementing the updated reference data to the initial analysis data set after eliminating the historical reference data corresponding to the absolute life difference greater than the absolute life threshold, to obtain an updated reference data set, and taking the updated reference data set as the historical reference data set, returning to the step of constructing the initial fault tree model based on the historical reference data set until the absolute life difference is less than or equal to the absolute life threshold to obtain the optimized fault tree model.

[0109] It should be understood that the initial analysis data set refers to a collection of historical reference data whose subnodes are life failure causes. In order to predict the life of the saliva suction tube, it is necessary to analyze the historical reference data whose subnodes are life failure causes in the initial fault tree model, and when analyzing, it is necessary to consider the actual use of the saliva suction tube. Therefore, in the embodiment of the present invention, the initial analysis data set is used to predict the life of the saliva suction tube, and the historical reference data in the initial analysis data set are filtered so that the filtered historical reference data in the initial analysis data set can characterize the actual use of the saliva suction tube.

[0110] Furthermore, as time changes and the historical reference data in the initial analysis data set decreases, the use of the same initial life average to screen the initial analysis data set will be inaccurate. Therefore, it is necessary to update the data in the initial analysis data set in combination with the elimination quantity and the time threshold, and the data used to update the initial analysis data set is the updated reference data. Optionally, the method for obtaining the updated reference data is the same as the method for obtaining the historical reference data, which will not be repeated here.

[0111] For example, for ease of understanding, only quantity is used as an example. There are 10 historical reference data in the initial analysis data set, and these 10 historical reference data are: 10, 11, 9, 7, 12, 15, 12, 13, 16 and 11, then the initial life mean corresponding to the initial analysis data set is: 11.8, and the absolute life difference values ​​are: 1.8, 0.8, 2.8, 4.8, 0.2, 3.2, 0.2, 1.2, 4.2 and 0.8, respectively. The absolute life difference sequence obtained using the absolute life difference values ​​is: 4.8, 4.2, 3.2, 2.8, 1.8, 1.2, 0.8, 0.8, 0.2 and 0.2. The preset absolute life threshold is 3, the elimination number is 2, and after eliminating the data of 4.8 and 4.2 from the absolute life difference sequence, if the elimination number reaches the preset elimination threshold, the updated reference data is obtained. If there is no updated reference data, the updated reference data set is: 10, 11, 9, 12, 15, 12, 13 and 11, and the updated reference data set is used as the historical reference data set, and the step of constructing the initial fault tree model based on the historical reference data set is returned. The effects that can be achieved by the elimination time and time threshold are the same as those that can be achieved by the elimination number and the elimination threshold, and will not be repeated here.

[0112] It is understandable that the embodiment of the present invention not only considers the impact of quantity but also the impact of time when screening data, thereby achieving dynamic updating of historical reference data in the initial analysis data set, thereby improving the accuracy of obtaining predicted life using the optimized fault tree model.

[0113] It should be explained that the obtaining of predicted life based on the optimized fault tree model includes:

[0114] Using the optimized fault tree model to obtain a target analysis data set, wherein the sub-node corresponding to the target analysis data set in the optimized fault tree model is a life failure cause;

[0115] The predicted life span is calculated based on the target analysis data set and the pre-constructed life span assessment relationship, wherein the life span assessment relationship is as follows:

[0116]

[0117] Where H represents the predicted life span, w i Indicates the preset i-th coefficient, h i represents the i-th target reference data in the target analysis data set, where the target reference data includes historical reference data and updated reference data, and n represents that there are n target reference data in the target analysis data set.

[0118] It should be understood that, with the dynamic update of the optimized fault tree model, the target reference data in the target analysis data set may also change over time, thereby achieving an update and iteration of the predicted life, and further improving the accuracy of the test on the saliva suction tube.

[0119] S4. Based on the flow detection unit, a flow detection operation is performed on the initial saliva suction tube to obtain a detection flow timing sequence, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset.

[0120] It should be explained that the flow detection unit performs a flow detection operation on the initial saliva suction tube to obtain a detection flow timing sequence, including:

[0121] The negative pressure generating mechanism is connected to the initial saliva suction tube to obtain a target detection mechanism, the initial negative pressure is input into the negative pressure generating mechanism in the flow detection unit, and the target detection mechanism after the initial negative pressure is input absorbs the pre-acquired initial liquid to obtain the target liquid containing the flow;

[0122] Starting the flow testing mechanism, and setting the frequency used for detection by the flow testing mechanism as the sampling frequency, and performing a detection operation on the target liquid based on the set flow testing mechanism to obtain a detection flow;

[0123] The detection flows are summarized to obtain a detection flow set, the time corresponding to the detection flows is obtained, and the detection flows in the detection flow set are sorted in order from earliest to latest time to obtain a detection flow time sequence.

[0124] Furthermore, the initial negative pressure refers to the pressure that can be generated by the negative pressure generating mechanism, and the pressure is less than the atmospheric pressure. The sampling frequency refers to the frequency used by the flow testing mechanism when testing the flow rate. For example: once per second.

[0125] Optionally, a suction device is used as the negative pressure generating mechanism, and other technologies can achieve the same effect, which will not be repeated here. Optionally, a volumetric flow meter is used as the flow testing mechanism, and other technologies can achieve the same effect, which will not be repeated here.

[0126] Exemplarily, one end of the saliva suction tube is connected to an aspirator and a negative pressure that the aspirator can generate is set, and the negative pressure is the initial negative pressure. The other end of the saliva suction tube is placed in static water to absorb water, and the absorbed water is the target liquid containing the flow. The volumetric flowmeter is started to measure the target liquid containing the flow at a sampling frequency to obtain a detection flow, and the obtained detection flows are arranged in chronological order from first to last to obtain the detection flow sequence.

[0127] S5. Calculate the ideal flow rate based on the initial negative pressure and the pre-constructed flow conversion relationship.

[0128] It should be explained that the flow conversion relationship is as follows:

[0129]

[0130] Among them, q represents the ideal flow rate, f represents the flow correction coefficient, d0 represents the equivalent diameter of the initial saliva suction tube, and the equivalent diameter is related to the size parameter, P1 represents the atmospheric pressure, P0 represents the initial negative pressure, and ρ represents the density of the initial liquid.

[0131] It should be understood that, in general, the initial saliva suction pipe is not a regular cylinder. For the convenience of calculation, the equivalent diameter is used to represent the diameter of the initial saliva suction pipe. The equivalent diameter refers to the diameter of a circular pipe with the same hydraulic characteristics as the initial saliva suction pipe. Optionally, the flow correction coefficient is obtained by experiment.

[0132] S6. After confirming that the detected flow timing is the preset target flow timing, a comprehensive flow evaluation value is obtained based on the target flow timing and the ideal flow.

[0133] It should be explained that the step of confirming that the detected flow timing is a preset target flow timing includes:

[0134] Extract the detection traffic from the detection traffic time sequence in sequence and perform the following operations on the extracted detection traffic:

[0135] Based on the extracted detection flow, the initial flow is confirmed in the detection flow time series, and the stability evaluation value is calculated based on the extracted detection flow, the initial flow and the pre-built flow stability evaluation relationship, wherein the flow stability evaluation relationship is as follows:

[0136] W=|L j -L j+1 |

[0137] Where W represents the stability evaluation value, L j represents the detection flow extracted from the detection flow time series, L j+1represents the initial flow rate, wherein 1≤j≤m-1, wherein m represents that there are m detection flows in the detection flow sequence, and j is only used for counting;

[0138] comparing the stability assessment value with a preset stability threshold;

[0139] If the stability evaluation values ​​are all less than or equal to the stability threshold, confirming that the detected flow timing is the target flow timing;

[0140] Otherwise, it indicates that the initial saliva suction tube is abnormal.

[0141] Furthermore, the initial flow refers to the detection flow that is located after the extracted detection flow in the detection flow sequence. For example, there are 5 detection flows in the detection flow sequence, and these 5 detection flows are: 1, 2, 3, 4 and 5, then when the extracted detection flow is 1, the initial flow is 2, when the extracted detection flow is 2, the initial flow is 3, and so on. When the stability evaluation value is greater than the stability threshold, it means that the flow that can be generated by the initial saliva suction tube is unstable. When in use, it may lead to untimely cleaning of accumulated fluid or harm to the patient, etc., thus indicating that the initial saliva suction tube is abnormal. The initial saliva suction tube abnormality means that the initial saliva suction tube can no longer meet normal work.

[0142] It should be understood that the step of obtaining a comprehensive flow evaluation value based on the target flow timing and the ideal flow includes:

[0143] The target flow variance is obtained by using the target flow time series, wherein the target flow variance is the variance of multiple detection flows in the target flow time series;

[0144] The comprehensive flow evaluation value is calculated based on the target flow variance and the ideal flow, and the calculation formula is as follows:

[0145]

[0146] Wherein, E represents the comprehensive flow evaluation value, F represents the target flow variance, β and γ are both preset coefficients, and a k Represents the kth detected flow in the target flow time series.

[0147] Furthermore, when the comprehensive flow evaluation value is smaller, it means that the performance of the initial saliva suction tube is better, and the smaller the variance is, the more stable the initial saliva suction tube is. The smaller it is, the closer the detected flow rate of the initial saliva suction tube is to the ideal flow rate.

[0148] S7. Obtain a reference life of the initial saliva suction tube, obtain a comprehensive use ratio based on the comprehensive flow evaluation value, the predicted life and the reference life, and test the initial saliva suction tube based on the comprehensive use ratio.

[0149] It will be appreciated that the reference lifespan refers to the time that the initial saliva suction tube has been in use.

[0150] It should be explained that the comprehensive use ratio obtained based on the comprehensive flow evaluation value, predicted life and reference life includes:

[0151] The comprehensive utilization ratio is calculated based on the comprehensive flow evaluation value, predicted life and reference life. The calculation formula is as follows:

[0152]

[0153] Among them, B represents the comprehensive usage ratio, ε, θ, All of them are preset coefficients, s1 represents reference life, s2 represents predicted life, G2 represents the number of target reference data contained in the target analysis data set, and G1 represents the number of fault history data in the reference analysis data set, where the reference analysis data set refers to the child node in the optimized fault tree model that is the cause of product quality failure.

[0154] It is understandable that the definition of the reference analysis data set is similar to that of the target analysis data set, which will not be repeated here. In combination with the data in the optimized fault tree model, the coefficients are formulated, and the numerical range of the comprehensive use ratio under normal circumstances can be limited to between 0 and 1. The closer the comprehensive use ratio is to 1, the more likely the initial saliva suction tube is to fail. Conversely, the closer the comprehensive use ratio is to 0, the more stable the initial saliva suction tube is and the better the performance. Optionally, standardization is used to achieve the setting of the coefficients so that the range of the comprehensive use ratio is between 0 and 1. Other technologies can achieve the same effect, which will not be repeated here.

[0155] The present invention is to solve the problem described in the background technology. The present invention receives a saliva suction tube test instruction, and confirms a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism. It can be seen that the saliva suction tube test environment confirmed by the embodiment of the present invention is not only used for a simple test of the saliva suction tube, but also lays a foundation for subsequent combination with actual conditions, thereby improving the accuracy and intelligence of the test of the saliva suction tube. The present invention obtains relevant information of an initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, and uses the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and an initial fault tree model is constructed based on the historical reference data set, and the initial fault tree model is optimized to obtain an optimized fault tree model, and a predicted life is obtained based on the optimized fault tree model. It can be seen that the present invention not only considers the possible failures of the saliva suction tube in actual use, and constructs the initial fault tree model based on this, but also realizes the dynamic update of the initial fault tree model, thereby improving the accuracy of obtaining the predicted life by using the optimized fault tree model, and further improving the accuracy of evaluating the saliva suction tube. The present invention performs flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain the detection flow sequence, calculates the ideal flow based on the initial negative pressure and the pre-constructed flow conversion relationship, confirms that the detection flow sequence is the preset target flow sequence, obtains the comprehensive flow evaluation value based on the target flow sequence and the ideal flow, obtains the reference life of the initial saliva suction tube, obtains the comprehensive use ratio based on the comprehensive flow evaluation value, the predicted life and the reference life, and implements the test of the initial saliva suction tube based on the comprehensive use ratio. It can be seen that when the embodiment of the present invention tests the saliva suction tube, the difference between the detection flow and the ideal flow of the saliva suction tube during the test is considered, and the comprehensive use ratio is calculated in combination with the reference life of the saliva suction tube, the ratio of the saliva suction tube in the optimized fault tree model and the target flow sequence. Furthermore, the evaluation of the saliva suction tube through the comprehensive use ratio can improve the intelligence and accuracy of the saliva suction tube test. Therefore, the present invention can improve the intelligence and accuracy of the saliva suction tube test.

[0156] like Figure 2 1 is a functional module diagram of a monitoring and testing system for a saliva suction tube in micro-apical surgery provided by an embodiment of the present invention.

[0157] The monitoring and testing system 100 for micro-apical surgery saliva suction tube of the present invention can be installed in an electronic device. According to the functions to be implemented, the monitoring and testing system 100 for micro-apical surgery saliva suction tube can include a saliva suction tube test environment confirmation module 101, a saliva suction tube test preparation module 102, a saliva suction tube test module 103 and a saliva suction tube test evaluation module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0158] The saliva suction tube test environment confirmation module 101 is used to receive a saliva suction tube test instruction, and confirm a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism;

[0159] The saliva suction tube test preparation module 102 is used to obtain relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and execution standards, and use the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and an initial fault tree model is constructed based on the historical reference data set;

[0160] Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model;

[0161] The saliva suction tube test module 103 is used to perform a flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain a detection flow timing, wherein the sampling frequency of the flow test mechanism and the initial negative pressure of the negative pressure generating mechanism are preset;

[0162] Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship;

[0163] The saliva suction tube test evaluation module 104 is used to obtain a comprehensive flow evaluation value based on the target flow timing and the ideal flow after confirming that the detected flow timing is a preset target flow timing;

[0164] The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

[0165] In detail, the modules in the monitoring and testing system 100 for micro-apical surgery saliva suction tube in the embodiment of the present invention are used in the same manner as described above. Figure 1 The monitoring and testing methods for the saliva suction tube in micro-apical surgery are the same as those described in the text and can produce the same technical effects, so they will not be repeated here.

[0166] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing a monitoring and testing method for a saliva suction tube for micro-apical surgery provided by an embodiment of the present invention.

[0167] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a monitoring and testing method program for a saliva suction tube for micro-apical surgery.

[0168] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the monitoring and testing method program for the micro-apical surgery saliva suction tube, but also can be used to temporarily store data that has been output or is to be output.

[0169] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as monitoring and testing method programs for micro-apical surgery saliva suction tubes, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0170] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0171] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0172] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, etc. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0173] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0174] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0175] The monitoring and testing method program for the micro-apical surgery saliva suction tube stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0176] receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow test mechanism;

[0177] Obtaining relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, using the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and constructing an initial fault tree model based on the historical reference data set;

[0178] Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model;

[0179] Based on the flow detection unit, a flow detection operation is performed on the initial saliva suction tube to obtain a detection flow timing sequence, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset;

[0180] Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship;

[0181] After confirming that the detected flow timing is a preset target flow timing, obtaining a comprehensive flow evaluation value based on the target flow timing and the ideal flow;

[0182] The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

[0183] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0184] Furthermore, if the module / unit integrated in the electronic device 1 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. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0185] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0186] receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow test mechanism;

[0187] Obtaining relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, using the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and constructing an initial fault tree model based on the historical reference data set;

[0188] Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model;

[0189] Based on the flow detection unit, a flow detection operation is performed on the initial saliva suction tube to obtain a detection flow timing sequence, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset;

[0190] Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship;

[0191] After confirming that the detected flow timing is a preset target flow timing, obtaining a comprehensive flow evaluation value based on the target flow timing and the ideal flow;

[0192] The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

[0193] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0194] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0196] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A monitoring and testing method for a saliva suction tube for micro-apical surgery, characterized in that: The method comprises: receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow test mechanism; Obtaining relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and implementation standards, using the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and constructing an initial fault tree model based on the historical reference data set; Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model; Based on the flow detection unit, a flow detection operation is performed on the initial saliva suction tube to obtain a detection flow timing sequence, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset; Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship; After confirming that the detected flow timing is a preset target flow timing, obtaining a comprehensive flow evaluation value based on the target flow timing and the ideal flow; The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

2. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 1, characterized in that: The constructing of an initial fault tree model based on the historical reference data set comprises: Obtaining a fault cause set for classifying the saliva suction tube, wherein the fault cause set includes four different fault causes, and the four different fault causes are: an operation fault cause, a life fault cause, a product quality fault cause, and other fault causes; Extracting historical reference data from the historical reference data set in sequence and performing the following operations on the extracted historical reference data: Obtain a text recognition model set for recognizing text, wherein there are five text recognition models in the text recognition model set, and obtain a target fault cause based on the text recognition model set and the extracted historical reference data, wherein the target fault cause is composed of an operation fault cause, a life fault cause, a product quality fault cause, or other fault causes; Using the target fault cause, performing an identification operation on the extracted historical reference data to obtain identification reference data, and using the identification reference data, respectively aggregating the identification reference data for the fault causes in the fault cause set to obtain four different fault data sets, wherein the four different fault data sets are: an operation fault data set, a life fault data set, a product quality fault data set, and other fault data sets; The initial fault tree model was constructed with the saliva suction tube fault as the root node and the four different fault causes in the fault cause set as child nodes.

3. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 2, characterized in that: The obtaining of the target fault cause based on the text recognition model set and the extracted historical reference data includes: Randomly extracting two text recognition models from the text recognition model set, and using the two extracted text recognition models to perform text recognition operations on the fault cause texts of the historical reference data, respectively, to obtain a first fault cause and a second fault cause, wherein the first fault cause and the second fault cause are both composed of an operation fault cause, a life fault cause, a product quality fault cause or other fault causes; If the first fault cause is the same as the second fault cause, confirming that the first fault cause or the second fault cause is the target fault cause; If the first fault cause is different from the second fault cause, the remaining three text recognition models in the text recognition model set are used to perform recognition operations on the fault cause text of the historical reference data to obtain the third fault cause, the fourth fault cause and the fifth fault cause; A target fault cause is obtained based on the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, wherein the target fault cause is the fault cause corresponding to the majority of the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause, and the first fault cause, the second fault cause, the third fault cause, the fourth fault cause and the fifth fault cause are all composed of operation fault causes, life fault causes, product quality fault causes or other fault causes.

4. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 3, characterized in that: The step of optimizing the initial fault tree model to obtain an optimized fault tree model includes: Acquire an initial analysis data set in the initial fault tree model, wherein the subnode corresponding to the initial analysis data set in the initial fault tree model is the life failure cause; Acquire an initial life average value based on the initial analysis data set, wherein the initial life average value is an average of the usage times corresponding to a plurality of historical reference data in the initial analysis data set; Extract historical reference data from the initial analysis data set in sequence, and perform the following operations on the extracted historical reference data: Obtaining an absolute life difference based on the extracted historical reference data and the initial life mean, wherein the absolute life difference is the absolute difference between the usage time corresponding to the extracted historical reference data and the initial life mean; Summarizing the absolute lifespan differences to obtain an absolute lifespan difference set, and performing a sorting operation on the absolute lifespan differences in the absolute lifespan difference set in descending order to obtain an absolute lifespan difference sequence; The absolute life difference values ​​are sequentially extracted from the absolute life difference value sequence and the following operations are performed on the extracted absolute life difference values: comparing the extracted absolute life span difference with a preset absolute life span threshold; If the absolute life difference is greater than the absolute life threshold, the historical reference data corresponding to the absolute life difference is eliminated in the initial analysis data set, and the number of eliminated absolute life differences is recorded to obtain the elimination number, and the time is recorded with the time when the absolute life difference is eliminated as the time starting point to obtain the elimination time, until the elimination number reaches the preset elimination threshold or the elimination time reaches the preset time threshold, confirming the receipt of the data update instruction from the data update unit, obtaining the updated reference data based on the data update instruction, supplementing the updated reference data to the initial analysis data set after eliminating the historical reference data corresponding to the absolute life difference greater than the absolute life threshold, to obtain an updated reference data set, and taking the updated reference data set as the historical reference data set, returning to the step of constructing the initial fault tree model based on the historical reference data set until the absolute life difference is less than or equal to the absolute life threshold to obtain the optimized fault tree model.

5. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 4, characterized in that: The obtaining of predicted life based on the optimized fault tree model comprises: Using the optimized fault tree model to obtain a target analysis data set, wherein the sub-node corresponding to the target analysis data set in the optimized fault tree model is a life failure cause; The predicted life span is calculated based on the target analysis data set and the pre-constructed life span assessment relationship, wherein the life span assessment relationship is as follows: Where H represents the predicted life span, w i Indicates the preset i-th coefficient, h i represents the i-th target reference data in the target analysis data set, where the target reference data includes historical reference data and updated reference data, and n represents that there are n target reference data in the target analysis data set.

6. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 5, characterized in that: The flow detection unit performs a flow detection operation on the initial saliva suction tube to obtain a detection flow time sequence, including: The negative pressure generating mechanism is connected to the initial saliva suction tube to obtain a target detection mechanism, the initial negative pressure is input into the negative pressure generating mechanism in the flow detection unit, and the target detection mechanism after the initial negative pressure is input absorbs the pre-acquired initial liquid to obtain the target liquid containing the flow; Starting the flow testing mechanism, and setting the frequency used for detection by the flow testing mechanism as the sampling frequency, and performing a detection operation on the target liquid based on the set flow testing mechanism to obtain a detection flow; The detection flows are summarized to obtain a detection flow set, the time corresponding to the detection flows is obtained, and the detection flows in the detection flow set are sorted in order from earliest to latest time to obtain a detection flow time sequence.

7. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 6, characterized in that: The flow conversion relationship is as follows: Among them, q represents the ideal flow rate, f represents the flow correction coefficient, d0 represents the equivalent diameter of the initial saliva suction tube, and the equivalent diameter is related to the size parameter, P1 represents the atmospheric pressure, P0 represents the initial negative pressure, and ρ represents the density of the initial liquid.

8. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 7, characterized in that: The step of confirming that the detected flow timing is a preset target flow timing includes: Extract the detection traffic from the detection traffic time sequence in sequence and perform the following operations on the extracted detection traffic: Based on the extracted detection flow, the initial flow is confirmed in the detection flow time series, and the stability evaluation value is calculated based on the extracted detection flow, the initial flow and the pre-built flow stability evaluation relationship, wherein the flow stability evaluation relationship is as follows: W=|L j -L j+1 | Where W represents the stability evaluation value, L j represents the detection flow extracted from the detection flow time series, L j+1 represents the initial flow rate, wherein 1≤j≤m-1, wherein m represents that there are m detection flows in the detection flow sequence, and j is only used for counting; comparing the stability assessment value with a preset stability threshold; If the stability evaluation values ​​are all less than or equal to the stability threshold, confirming that the detected flow timing is the target flow timing; Otherwise, it indicates that the initial saliva suction tube is abnormal.

9. The monitoring and testing method for the saliva suction tube for micro-apical surgery according to claim 8, characterized in that: The obtaining of a comprehensive flow evaluation value based on the target flow time sequence and the ideal flow includes: The target flow variance is obtained by using the target flow time series, wherein the target flow variance is the variance of multiple detection flows in the target flow time series; The comprehensive flow evaluation value is calculated based on the target flow variance and the ideal flow, and the calculation formula is as follows: Wherein, E represents the comprehensive flow evaluation value, F represents the target flow variance, β and γ are both preset coefficients, and a k Represents the kth detected flow in the target flow time series.

10. A monitoring and testing system for a saliva suction tube in micro-apical surgery, characterized in that: The system comprises: a saliva suction tube test environment confirmation module, used for receiving a saliva suction tube test instruction, and confirming a saliva suction tube test environment based on the saliva suction tube test instruction, wherein the saliva suction tube test environment includes: an initial saliva suction tube and a saliva suction tube test system, wherein the saliva suction tube test system includes: a flow detection unit and a data update unit, wherein the flow detection unit is composed of a negative pressure generating mechanism and a flow testing mechanism; A saliva suction tube test preparation module is used to obtain relevant information of the initial saliva suction tube, wherein the relevant information includes: preparation materials, size parameters and execution standards, and use the relevant information to obtain a historical reference data set, wherein the historical reference data set includes: historical reference data of multiple reference saliva suction tubes, wherein the historical reference data is composed of usage time and fault cause text, and an initial fault tree model is constructed based on the historical reference data set; Optimizing the initial fault tree model to obtain an optimized fault tree model, and obtaining a predicted life based on the optimized fault tree model; A saliva suction tube testing module, used for performing a flow detection operation on the initial saliva suction tube based on the flow detection unit to obtain a detection flow timing, wherein a sampling frequency of the flow test mechanism and an initial negative pressure intensity of the negative pressure generating mechanism are preset; Calculating an ideal flow rate based on the initial negative pressure and a pre-constructed flow rate conversion relationship; A saliva suction tube test evaluation module, used to obtain a comprehensive flow evaluation value based on the target flow timing and the ideal flow after confirming that the detected flow timing is a preset target flow timing; The reference life of the initial saliva suction tube is obtained, the comprehensive use ratio is obtained based on the comprehensive flow evaluation value, the predicted life and the reference life, and the initial saliva suction tube is tested based on the comprehensive use ratio.

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