A testing method, server, medium and program product for body temperature probe

Abstract

The present application provides a test method, server, medium and program product for temperature probes, which relate to the field of medical equipment. In this method, a qualified probe is first determined as a reference object, and then multiple probes to be tested and reference objects are placed in a constant temperature environmental box at the same time, and the real-time temperature of each probe is wirelessly read to determine the temperature difference between each probe and the reference object. Those that meet the threshold requirements enter the next round of testing; the temperature of the environmental box is increased to a preset value, and the temperature data of each probe is obtained to determine its temperature change curve, and the test result is determined by comparing the curve with the reference curve. High-throughput and high-precision temperature probe testing is achieved. The implementation of this method can realize automated testing of temperature probes, reduce manual intervention, improve test efficiency and accuracy, and comprehensively and accurately evaluate the performance of temperature probes.

Description

Technical Field

[0001] The present application relates to the field of medical equipment, and in particular to a testing method, server, medium and program product for a temperature probe. Background Art

[0002] In the medical field, accurate measurement of body temperature is crucial for the diagnosis, treatment and prevention of diseases. As an important tool for measuring body temperature, the accuracy and reliability of temperature probes will greatly affect medical decisions and the health status of patients.

[0003] The traditional method of testing a temperature probe usually involves preparing a test container, adjusting the temperature in the container to a certain value, and then placing the temperature probe to be tested in the container for testing. During the test, the temperature probe is left in the container for a period of time, which is pre-set based on experience. After the preset time, the manager records the temperature value displayed by the temperature probe. After that, the tester will repeat the above test process multiple times to obtain the temperature data of the temperature probe under different temperature conditions, and manually judge whether the temperature probe is qualified or not based on this data.

[0004] However, the existing methods are not only inefficient, but also prone to human errors. Therefore, it is often difficult to test body temperature probes in large quantities and efficiently. Summary of the invention

[0005] The present application provides a testing method, server, medium and program product for a body temperature probe, which is used to realize automated testing of body temperature probes when it is difficult to achieve high-efficiency and unified testing of body temperature probes in batches with the existing technology, thereby reducing manual intervention and improving testing efficiency and accuracy.

[0006] In a first aspect, the present application provides a testing method for a body temperature probe, which is applied to a server, and the method includes: after receiving an instruction from a management terminal to determine a qualified body temperature probe, determining the qualified body temperature probe as a reference object; after receiving an instruction to start the test from the management terminal, placing multiple body temperature probes to be tested and the reference object in an environmental box with a first temperature and heat insulation performance by controlling a robotic arm; simultaneously receiving the real-time measured temperature of each of the body temperature probes to be tested through a wireless reading device; determining the temperature difference between the real-time measured temperature corresponding to each of the body temperature probes to be tested and the real-time measured temperature corresponding to the reference object; and when the temperature difference is within a preset threshold When the temperature of the environmental box is within the range, the corresponding body temperature probe to be tested is marked as the body temperature probe to be tested for the second time; the temperature of the environmental box is steadily and uniformly increased by controlling the intelligent temperature control device until it reaches a preset second temperature, and the second temperature is kept stable for at least a set time; in the process of temperature rising, the temperature data of all the body temperature probes to be tested for the second time are received at a set sampling frequency, and a plurality of corresponding temperature change curves are determined according to the temperature data; the plurality of temperature change curves are compared with a reference curve through a curve comparison algorithm, and the reference curve refers to the temperature change curve of the reference object; and the test result corresponding to the body temperature probe to be tested for the second time is determined according to the comparison result.

[0007] By adopting the above technical solution, it is possible to realize the automated testing of the temperature probe, reduce manual intervention, and improve the testing efficiency and accuracy. The application of the intelligent temperature control device and the curve comparison algorithm can comprehensively and accurately evaluate the performance of the temperature probe, thereby ensuring the measurement accuracy and reliability of the temperature probe.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the test result corresponding to the body temperature probe to be tested a second time based on the comparison result, it also includes: after determining the unique number of each body temperature probe to be tested a second time, the unique number is associated with the corresponding temperature change curve and the comparison result and stored.

[0009] By adopting the above technical solution, the unique number is associated with the temperature change curve and the comparison result and stored, which is convenient for subsequent query and tracing of the test data. When a problem occurs, the specific temperature probe and its test data can be quickly located, which is helpful for analyzing the cause of the problem and improving product quality.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after determining the unique number of each temperature probe to be tested a second time, after the step of associating and storing the unique number with the corresponding temperature change curve and comparison result, it also includes: analyzing the test results to determine the temperature difference characteristics of each temperature probe to be tested and the reference object; determining a severely unqualified temperature probe based on the temperature probe to be tested and the temperature probe to be tested a second time; determining a conventional unqualified temperature probe based on the temperature probe to be tested a second time and the temperature difference characteristics; determining the unqualified type of the conventional unqualified temperature probe based on the temperature difference characteristics, and associating the unqualified type with the corresponding unique number; sending the severely unqualified temperature probe, the conventional unqualified temperature probe, the unqualified type and the unique number to the management end.

[0011] By adopting the above technical solution, the analysis of test results and classification of unqualified probes can timely identify problems with temperature probes and take corresponding measures. Identifying seriously unqualified and conventional unqualified temperature probes, as well as the association between unqualified types and unique numbers, helps to accurately manage unqualified products. Sending relevant information to the management end can promptly notify relevant personnel for processing, improve production efficiency and product quality, and reduce the risk of unqualified products entering the market.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the severely unqualified temperature probe, the conventional unqualified temperature probe, the unqualified type and the unique number to the management end, it also includes: obtaining production batch information of unqualified temperature probes, the unqualified temperature probes including the severely unqualified temperature probes and the conventional unqualified temperature probes; querying the corresponding historical unqualified records according to the production batch information, and determining that the probe test unqualified rate of the target batch is higher than the set threshold; and sending prompt information of batches with excessively high unqualified rates to the management end.

[0013] By adopting the above technical solution, the production batch information of unqualified temperature probes can be obtained, and combined with historical unqualified records for analysis, batch problems can be discovered in a timely manner.

[0014] In combination with some embodiments of the first aspect, in some embodiments, before the step of comparing the multiple temperature change curves with the reference curve through a curve comparison algorithm, it also includes: obtaining the historical temperature change curve data of the reference object; determining a reference curve model based on the historical temperature change curve data combined with a machine learning algorithm; and determining the temperature curve generated by the reference curve model as the reference curve.

[0015] By adopting the above technical solution, the temperature curve generated by the reference curve model is determined as the reference curve, which can improve the accuracy and fairness of the curve comparison algorithm, more accurately evaluate the performance of the body temperature probe to be tested, and provide a more scientific basis for product quality judgment.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the step of placing multiple temperature probes to be measured and reference objects in the environmental chamber at the same time by controlling the robotic arm specifically includes: controlling the robotic arm to evenly distribute the temperature probes to be measured and the reference objects in the environmental chamber; determining the spatial coordinate information of each temperature probe to be measured by an image acquisition device; and optimizing the placement layout of each temperature probe to be measured based on the spatial coordinate information.

[0017] By adopting the above technical solution, it can ensure that each probe is subjected to uniform temperature during the test process, improving the accuracy and reliability of the test results. At the same time, optimizing the layout helps to improve test efficiency and reduce test time and cost.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of comparing the multiple temperature change curves with the reference curve through a curve comparison algorithm, it also includes: determining the temperature measurement accuracy level corresponding to the temperature probe to be tested for the second time based on the comparison results combined with the existing temperature probe accuracy rating model, the temperature probe accuracy rating model is a model that collects historical test data, specification parameters and corresponding temperature change curves of multiple temperature probes with known temperature measurement accuracy levels in advance, and is established through a machine learning algorithm based on the historical test data, the specification parameters and the temperature change curves.

[0019] By adopting the above technical solution, the temperature measurement accuracy level can be determined based on the comparison results combined with the existing temperature probe accuracy rating model, which can more comprehensively evaluate the performance of the temperature probe. The model is established based on historical test data, specification parameters and temperature change curves, and can accurately determine the accuracy level of the temperature probe through analysis by machine learning algorithms. This helps manufacturers to manage products in a graded manner, meet the needs of different users, and improve the market competitiveness of products.

[0020] In a second aspect, the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a server, causes the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0024] 1. Due to the use of technical means such as robotic arm control placement, wireless reading equipment to obtain temperature data in real time, intelligent temperature control device and curve comparison algorithm, the technical problems of low efficiency, easy errors and unstable constant temperature environment in the existing technology of manual operation are effectively solved, thereby realizing the automated testing of the temperature probe, improving the test efficiency and accuracy, and ensuring the technical effect of the temperature probe measurement accuracy and reliability.

[0025] 2. Due to the technical means of obtaining the production batch information of unqualified temperature probes and analyzing it in combination with historical unqualified records, the technical problem of difficulty in timely discovering batch problems in the existing technology is effectively solved, thereby achieving the technical effect of timely discovering batch problems, prompting manufacturers to adjust production processes and quality control measures, improve product quality stability, and reduce losses caused by batch problems.

[0026] 3. By controlling the robotic arm to evenly distribute the probes, the image acquisition device to determine the spatial coordinate information, and the optimized placement layout, the technical problem of the uneven test environment affecting the accuracy of the test results in the prior art is effectively solved, thereby ensuring that each probe is affected by a uniform temperature, improving the accuracy and reliability of the test results, and at the same time improving the test efficiency, reducing the test time and cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a testing method for a body temperature probe in an embodiment of the present application;

[0028] Figure 2 is another flow chart of a method for testing a temperature probe in an embodiment of the present application;

[0029] Figure 3 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0031] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0032] For ease of understanding, an application scenario of the relevant method is introduced below.

[0033] A medical equipment factory produced a batch of temperature probes that needed to be tested before leaving the factory. According to the traditional method, Xiao Ming, the tester, first adjusted the temperature to 37°C in a large constant temperature box, then took out a temperature probe and put it into the box for testing. He used a stopwatch to record the time, took out the probe reading 2 minutes after the probe was put in, and recorded that the displayed temperature of the probe was 36.8°C. Xiao Ming then repeated this process to test dozens of other probes. It took more than 2 minutes to test each probe. When all the tests were completed, several hours had passed. Xiao Ming calculated the temperature readings of all the probes and judged based on experience that the readings of two temperature probes were too deviated and unqualified. The next day, Xiao Ming's colleague Xiao Li repeated the test and found that a temperature probe that Xiao Ming judged to be qualified the day before had a large difference in today's test readings. Xiao Li was experienced and found that the probe might have a fault, but due to the lack of the original readings from the previous day, it was impossible to determine whether the probe reading had a problem when it was tested yesterday. From this scenario, it can be seen that the existing manual recording test method is inefficient and it takes a long time to test a probe. At the same time, since the temperature is read manually, there may be errors in the test results of different people, making it difficult to ensure the accuracy of the judgment.

[0034] The embodiment of the present application adopts a series of technical solutions such as receiving instructions from the management end to determine the reference object, controlling the robotic arm to place the probe, wirelessly reading the temperature, calculating the temperature difference, controlling the temperature rise, obtaining the temperature curve, and the curve comparison algorithm. By realizing efficient and automated testing of multiple temperature probes at the same time, an accurate temperature change curve is generated, and accurate qualification judgment is made, high-throughput and high-precision temperature probe testing is achieved, which not only greatly improves the test efficiency, but also avoids the errors caused by manual operation. The following introduces the scenarios in which the test method for temperature probes in this application is used.

[0035] First, select a body temperature probe with known good accuracy as a reference; place the reference and the probe to be tested in a constant temperature environmental chamber at the same time through a robotic arm; then read the temperature value of each probe in real time through a wireless device, and calculate the temperature difference with the reference. If the temperature difference is within an acceptable range, mark the probe as a qualified product for the next round of testing; otherwise, mark it as a failed product. In the next round of testing, increase the temperature of the environmental chamber to a preset value, let the probe change with the temperature, and collect the temperature data of each probe at a certain frequency, and generate a corresponding temperature change curve. Finally, compare the temperature curve of each probe with the temperature curve of the reference to determine the test results. In this way, the test method in the present application realizes efficient and automated testing of multiple body temperature probes at the same time, greatly shortens the test time, and avoids the errors caused by manual reading. It can accurately generate temperature change curves and make accurate qualification judgments, thereby ensuring the measurement performance of the body temperature probe.

[0036] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , is a flow chart of a testing method for a body temperature probe in an embodiment of the present application.

[0037] S101, after receiving an instruction from the management terminal to determine a qualified body temperature probe, determine the qualified body temperature probe as a reference object;

[0038] In this step, the server needs to receive an instruction from the management end to determine the qualified body temperature probe. The management end can be the quality management department of the body temperature probe manufacturer or a third-party testing agency.

[0039] After the batch production and factory inspection of temperature probes are completed, the management staff can select one or several qualified temperature probes with excellent indicators and high accuracy based on all test data, determine them as reference objects, and send the specific model information, factory number and other information of the reference object to the server. After receiving the instruction, the server will query the basic information of all temperature probes to be tested stored in the database. Using the factory number or model information as an index, match the qualified probe specified by the management end and mark it as a "reference object". The test data of the reference object will be recorded, including the temperature sampling value during the factory test, as well as the empirically proven accuracy level or verified measurement accuracy error range, as the basis for generating a reference curve in subsequent tests.

[0040] The benefit of using a qualified probe as a reference is that its measurement accuracy has been verified and it can provide accurate and reliable temperature change data for comparison with the probe to be tested, avoiding the influence of individual differences of a single probe. If a probe to be tested is randomly selected as a reference each time a test is performed, it may have certain errors, which will reduce the fairness and accuracy of the test. In this step, using a verified high-precision qualified probe as a reference can greatly improve the reliability of subsequent tests.

[0041] When the server has completed the determination of the reference object, it will send a confirmation message to the management end, informing that the reference object has been set and the status of the reference object in the temperature probe library to be tested has been marked as "reference object". At this point, the server has completed the determination of the reference object according to the instruction. In subsequent tests, the reference object will be placed in the environmental chamber for testing together, and its temperature change data will be used as a reference standard.

[0042] S102, after receiving the test start instruction sent by the management end, placing a plurality of body temperature probes to be tested and a reference object in an environmental box with a first temperature and heat insulation performance at the same time by controlling the mechanical arm;

[0043] In this step, the server will first receive an instruction to start the test from the management end. The instruction will specify the number, type, number and other information of the body temperature probes to be tested involved in this test. The server will retrieve the relevant information of this batch of probes to be tested from the database according to the instruction, including the number, specification model, production batch and other parameters of each probe. The server needs to calculate the number of probes for this test, and optimize the placement layout of the probes to be tested by using an algorithm based on the size and space characteristics of the environmental chamber, and calculate the specific placement position coordinates of each probe. Specifically, the server will calculate the number of probes to be tested and the size of the probes for this test, and use the spatial distribution algorithm to calculate the coordinate position of each probe to be tested in the environmental chamber in combination with the three-dimensional space of the environmental chamber. The algorithm will optimize the calculation so that each probe to be tested can be evenly dispersed in the environmental chamber, with high space utilization in the box, while avoiding contact between probes when the distance between them is too close. The server will control the robotic arm to grab each probe to be tested one by one according to the calculated coordinates and place them in the corresponding spatial position. After the placement is completed, the server will control the optical camera and other image acquisition devices to scan and recognize the probes to be tested in the environmental chamber, and obtain the two-dimensional or three-dimensional coordinate information of each probe, that is, the actual spatial position of each probe in the chamber. And compare it with the theoretically calculated placement coordinates to verify the accuracy of the placement.

[0044] For example, the error between the actual coordinates and the expected coordinates of each probe after placement can be compared to see if it is within the allowable range. If the placement coordinates of individual probes are found to have too large a deviation, the server can determine that the placement has failed and needs to control the robotic arm to re-place them. By comparing the actual spatial coordinates of image recognition with the theoretically calculated coordinates, the server can optimize and adjust the placement layout. If it is found that the distance between two probes is too small or less than the set distance threshold, there may be a risk of contact, and it can be determined that the layout needs to be optimized. Then the coordinates of the two probes are recalculated, and the robotic arm is controlled to swap the placement positions of the two probes until the distance between the probes meets the requirements to avoid contact and stacking, thereby optimizing the test layout and improving the accuracy of the test results.

[0045] S103, simultaneously receiving the real-time measured temperature of each body temperature probe to be measured through a wireless reading device;

[0046] In this step, the server will create the database table corresponding to this test in advance according to the management instructions, detect the working status of the wireless reading device, and determine the communication connection with the wireless reading device. The server will control the wireless reading device to monitor the output of all body temperature probes to be tested, including the reference object, in real time. The wireless reading device contains multiple radio frequency modules, which will establish wireless communication connections with each body temperature probe to be tested. Each body temperature probe has a built-in digital temperature sensor that can detect the temperature of the probe end in real time. This temperature will be converted into a digital signal by the sensor and then sent out through the wireless communication module in the body temperature probe. The server will parse the digital signal and extract the data of each probe, including the digital temperature value and the acquisition timestamp.

[0047] The server needs to continuously and stably collect temperature data. On the one hand, the data will be continuously verified, such as checking the regularity of time intervals, checking the correctness of data formats, checking the reasonable value range of data, etc., to filter out invalid discrete points. On the other hand, the server also needs to smooth the readings through algorithms to remove high-frequency random noise during the collection process. The valid temperature readings that have been verified and smoothed will be associated with the corresponding unique number of the body temperature probe and the collection timestamp, summarized as time series data, and stored in sequence in the test database. This historical temperature data is the raw material for generating the temperature change curve. The server will continue to collect temperature series data until the end of the test.

[0048] Through the use of wireless reading devices, the server can automatically and real-timely collect data from each probe, avoiding the inefficiency of manual timed reading and the errors caused by manual entry. The collected accurate time series data can support the subsequent generation of high-precision temperature change curves.

[0049] S104, determining the temperature difference between the real-time measured temperature corresponding to each of the body temperature probes to be measured and the real-time measured temperature corresponding to the reference object;

[0050] In this step, the server will compare and analyze the temperature time series data of the body temperature probe to be tested and the reference object stored in the database. The temperature data at the same time in the two series data are read point by point, the temperature difference between the body temperature probe to be tested and the reference object at each time is calculated, and then organized into new temperature difference time series data and stored in the database. For example, assuming that the temperature of the reference object is 36.5°C at time t1, and the temperature of the probe A to be tested is 36.3°C at time t1, then the temperature difference of the probe A to be tested at time t1 is -0.2°C. Calculate the temperature difference at each moment in turn to obtain the temperature difference sequence between the probe A to be tested and the reference object during the entire test period. Afterwards, perform similar calculations on all probes to be tested in turn to obtain the time series data of their temperature differences with the reference object.

[0051] The temperature difference sequence data reflects the temperature determination deviation between each body temperature probe to be tested and a reference object with known accuracy under the same conditions, and can be used to evaluate the temperature measurement accuracy of the probe to be tested and determine whether the test is qualified or not.

[0052] S105, when the temperature difference is within a preset threshold range, marking the corresponding body temperature probe to be tested as a body temperature probe to be tested again;

[0053] In this step, the server will read and analyze the temperature difference time series data of each probe. The server needs to set the allowable threshold range of the temperature difference in advance, such as ±0.3°C, as the judgment standard. The server will judge the temperature difference at each moment. If the temperature difference between the body temperature probe to be tested and the reference object remains within the set threshold range throughout the initial test phase, the probe to be tested will be marked as a probe to be tested again. For example, if the absolute value of the temperature difference time series of probe A to be tested in the initial test phase is less than 0.3°C, the server will mark the status of probe A to be tested as "to be tested again". If the temperature difference of probe B to be tested exceeds the threshold range during the period, it will be marked as "unqualified".

[0054] Through automatic pre-judgment of the temperature difference, probes marked as to be tested again indicate that their temperature measurement accuracy is acceptable and meet the conditions for entering the temperature increase stage test, while temperature probes that exceed the threshold will be directly judged as unqualified. This can improve test efficiency while reducing the unqualified rate.

[0055] S106, controlling the intelligent temperature control device to steadily and evenly increase the temperature of the environmental box until it reaches a preset second temperature, and keeping the second temperature stable for at least a preset time;

[0056] In this step, the server will send a heating instruction to the intelligent temperature control device after the set time, specifying the target heating temperature value (second temperature value). The intelligent temperature control device will calculate the heating curve and control the heating of the heating component in the environmental chamber. During the heating process, the server will collect the real-time temperature in the environmental chamber collected by the temperature sensor. If it detects that the temperature rise is uneven or fluctuates too much, it will immediately feedback to the intelligent temperature control device. After receiving the feedback, the intelligent temperature control device will adjust the heating method and stabilize the temperature rise rate so that the temperature in the environmental chamber rises steadily to the target second temperature value. When the set second temperature value is reached, the intelligent temperature control device will accurately control the temperature of the environmental chamber to remain at the second temperature for a time that is not less than the stabilization time specified by the server, so as to ensure the controllability and stability of the test environment temperature.

[0057] S107, in the process of temperature rising, receiving the temperature data of all the temperature probes to be tested for the second time at a set sampling frequency, and determining a plurality of corresponding temperature change curves according to the temperature data;

[0058] During the entire process of heating up the environmental chamber, the server will continue to control the wireless reading device to collect the temperature value of each body temperature probe to be tested twice in real time at a predetermined fixed sampling frequency (such as collecting temperature data once every 10 seconds). At the same time, the server will also collect the real-time temperature data of the reference object. The collected data will be filtered and smoothed to generate a temperature time series data set. Then the server will draw a smooth temperature change curve based on the temperature time series data set of each probe by curve fitting or interpolation. For example, the server can use the cubic spline interpolation algorithm to fit the temperature change curve of the probe A to be tested, the temperature change curve of the probe B to be tested, and the temperature change curve of the reference object according to the temperature sampling points. These temperature change curves will be saved in the database as a basis for curve comparison in subsequent test stages.

[0059] Through point-by-point data collection and curve fitting, an accurate and smooth temperature change curve can be obtained for each probe under test during the heating process, and the temperature jitter caused by possible airflow disturbance can be filtered out to perform a more accurate and fair curve comparison and determine the thermal response characteristics of the probe.

[0060] S108, comparing the plurality of temperature change curves with a reference curve using a curve comparison algorithm, where the reference curve refers to the temperature change curve of the reference object;

[0061] In this step, the server will implement comparative analysis of the temperature change curve. The server will extract the temperature change curve data of each probe to be tested and the temperature change curve data of the reference object stored in the database. The server will call the optimized curve comparison algorithm and load the reference curve and the probe curve to be tested. The algorithm will calculate the numerical difference between the two curves at the same temperature point point by point, and calculate the comprehensive indicators of the difference, such as the mean square error, maximum deviation, etc. For example, the square of the difference between the probe curve value and the reference curve value at each temperature point can be calculated, and the mean square error can be obtained by averaging; the absolute value of the maximum temperature point difference value is found as the maximum deviation. After algorithm calculation, quantitative indicators can be obtained to judge the fit between the curves.

[0062] The server can also extract characteristic parameters of the curve through algorithms, such as the rising slope of the curve, the position of the inflection point, etc., for comparative analysis. For example, it can be determined whether the slopes of the two curves are basically consistent within the error range to evaluate the thermal response characteristics. The server automatically completes the entire process using algorithms, avoiding errors caused by manual interpretation of the curve. By using reference curves and multiple indicators for comparison, a comprehensive curve analysis can be performed to accurately determine the deviation between the probe curve to be tested and the reference curve of qualified products, providing a basis for the final test judgment.

[0063] In some embodiments, the server can also obtain a more accurate and reliable reference curve through the following steps: the server needs to collect and store the temperature change curve data of the reference object in multiple tests. During each test, the server will extract the temperature time series of the reference object and fit it to generate a smooth temperature change curve. The server will record the reference object temperature curve data collected in each test to accumulate a relatively rich historical temperature curve data set. The server inputs the collected historical temperature change curve data of the reference object into the machine learning algorithm for training to obtain a reference curve model. The model comprehensively learns the characteristic parameters of the reference object curve, such as the rate of rise, the shape of the curve and other information. Through machine learning training, the model can extract the essential characteristics of the reference object curve, which helps to generate a more representative reference curve. In actual testing, the server can use the reference curve model obtained by training, and according to the environmental parameters of the current test, let the model generate the temperature data sequence of the reference curve. That is, the reference curve is generated by using the model prediction. In this way, a reference curve reflecting the true thermal response characteristics of the reference object can be obtained, and it is not affected by the accidental environment of the current test.

[0064] In some embodiments, after the curve comparison, the server can further determine the temperature measurement accuracy level of the probe to be tested based on the comparison results combined with the existing temperature probe accuracy rating model. Among them, the temperature probe accuracy rating model is a machine learning model established in advance by the server to evaluate the temperature measurement accuracy of the probe. The basic data sources for establishing the model are as follows: collect historical test data of temperature probes with multiple known accuracy levels. The accuracy levels of these probes can be verified by professional verification agencies. The server will obtain test curve data, specification parameters and other information of probes with different accuracy levels. Obtain product specification parameters of multiple known accuracy level probes. For example, probe model, production batch number, sensor parameters, etc. These parameters reflect the impact of product design and manufacturing process on accuracy. Obtain temperature change curves corresponding to probes with different accuracy levels. The test curve reflects the actual thermal response characteristics of the probe. The above three types of data are summarized and sorted as model training samples, in which the accuracy level is used as a sample label. Then, the server will call the machine learning algorithm to train the temperature probe accuracy rating model with historical data. In actual testing, the server first performs curve comparison, and then inputs the specification parameters of the probe to be tested and its temperature change curve into the rating model, and the model can predict the accuracy level of the probe. For example, after a probe to be tested is input into the model, the model output results determine that its accuracy level is "medium accuracy".

[0065] In this way, the accuracy level of each probe to be tested can be automatically determined through the machine learning model, achieving a more comprehensive and accurate quality assessment.

[0066] S109: Determine the test result corresponding to the temperature probe to be tested a second time according to the comparison result.

[0067] In this step, the server will draw the final test conclusion of the temperature probe to be tested again based on the results of the curve comparison analysis. For example, if the mean square error and maximum deviation of the probe curve to be tested and the reference curve are both within the allowable error range, it is judged to be qualified; if any indicator exceeds the threshold, it is judged to be unqualified. For unqualified temperature probes, the server can further analyze the reasons for the failure based on the characteristics of the curve. For example, a slow rise in the curve may be due to the slow response speed of the probe, and large fluctuations in the curve may be due to poor stability of the probe. These reasons will generate corresponding unqualified types, which will be stored in association with the unique number of each temperature probe, so that quality analysts can conduct root cause analysis for different problems later.

[0068] The server will eventually generate a test report with information such as the test results and the type of failure, and send it to the quality management system and production monitoring system for statistical analysis of the quantity, proportion, trend, etc. of different types of failures, as well as comparison with historical data to identify batches with increased probe test failure rates, etc., to provide a basis for decision-making to improve product quality and production processes.

[0069] By adopting the technical means such as the robotic arm controlled placement, the wireless reading device to obtain temperature data in real time, the intelligent temperature control device and the curve comparison algorithm in the embodiments of the present application, high-throughput and high-precision temperature probe testing is achieved by realizing automated testing of the temperature probe, generating accurate temperature change curves and making accurate qualification judgments. This not only greatly improves the test efficiency, but also avoids the errors caused by manual operation, and can comprehensively and accurately evaluate the performance of the temperature probe.

[0070] In some embodiments, although there are many types of reasons for the unqualified temperature probes, in actual life, the management personnel will subsequently adjust the unqualified temperature probes to make them meet the qualified standards. If the type of unqualified temperature probe can be directly determined during the test process, it will be convenient for subsequent management personnel to make unified adjustments.

[0071] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the testing method for the temperature probe in the embodiment of the present application.

[0072] S201, after determining the unique number of each temperature probe to be tested for the second time, the unique number is associated with the corresponding temperature change curve and comparison result and stored;

[0073] The server will assign a unique number to each temperature probe to be tested in advance. The unique number can include basic information of the temperature probe and the number of tests, etc., to distinguish different test records. For example, the unique number can be set to "XX Company-Y Model-20190815-002", indicating that this is the Y model temperature probe produced by XX Company and the second test was conducted on August 15, 2019.

[0074] The server associates the unique number with the temperature change curve data of the temperature probe and the result of the curve comparison and stores it in a relational database. A record in the database may include: probe unique number (primary key), probe production batch number, temperature change curve data table, curve comparison result (error range and other indicators), test result (qualified / unqualified). In this way, the temperature curve data, comparison results and final test conclusions of each test can be uniquely numbered and identified, which is convenient for subsequent query and statistical analysis. For example, the detailed data of multiple tests of a probe can be queried according to the number to analyze the consistency of the test results; the qualified rate of different batches of products can also be counted.

[0075] S202, analyzing the test results to determine the temperature difference characteristics between each of the body temperature probes to be tested and the reference object;

[0076] In this step, the server will analyze and process the results of all the temperature probes to be tested that have completed the test and are stored in the database. It mainly analyzes the difference characteristics between the temperature curve of each temperature probe and the reference object, such as the maximum temperature difference, the difference in curve slope, the difference in fluctuation degree, etc. The server can call the intelligent analysis algorithm to automatically extract the curve difference characteristics and obtain clear feature descriptions such as "maximum positive difference 3 degrees" and "slow slope". These temperature difference characteristics are then associated with the unique number of each temperature probe to be tested and stored in the database. In this way, when the management personnel inquire about the test results of a certain number, they can directly see the specific difference between the temperature probe and the reference curve, such as excessive positive deviation resulting in high measurement, etc., which is convenient for quickly determining the specific cause of the test failure.

[0077] S203, determining a seriously unqualified temperature probe according to the temperature probe to be tested and the temperature probe to be tested again;

[0078] In this step, the server needs to distinguish between the temperature probe to be tested and the temperature probe to be tested for the second time. Among them, the temperature probe to be tested whose temperature deviation exceeds the allowable range in the preliminary test stage will be directly judged as unqualified and marked as a seriously unqualified temperature probe. For the temperature probe to be tested for the second time whose temperature deviation is within the allowable range, it will enter the temperature increase stage for further testing, and the qualified or unqualified can be determined based on the final test result. The server can directly judge the seriously unqualified temperature probe based on the temperature deviation range of the preliminary test in the first stage. For example, if the allowable range of temperature deviation is set to ±0.3°C, all probes to be tested with a deviation exceeding ±0.3°C will be judged as seriously unqualified. The unique number of this type of seriously unqualified probe will be marked by the server and recorded in the abnormal data table for subsequent data statistical analysis. If it is detected that the proportion of seriously unqualified products in a single batch of products is too high, the server can actively prompt that the relevant production process needs to be checked and optimized.

[0079] In some embodiments, multiple tests may be performed and a test number threshold may be set. A temperature probe that fails any test within the test number threshold is determined as a seriously unqualified temperature probe, which is not limited here.

[0080] S204, determining a conventional unqualified temperature probe according to the temperature probe to be tested for a second time and the temperature difference feature;

[0081] In this step, the server will judge the body temperature probes that have completed the secondary test and are waiting for the secondary test, and determine the body temperature probes whose final test results are generally unqualified based on the difference characteristics between their temperature change curves and the reference curve. The server will read the temperature difference characteristics stored in the database, such as "maximum positive difference 2 degrees", "slow curve slope", etc. And according to the preset judgment rules, it is judged as generally unqualified based on the temperature difference characteristics. For example, if the maximum positive difference exceeds 1.5 degrees, it is judged as unqualified; if the curve slope differs from the reference curve by more than 10%, it can also be judged as generally unqualified.

[0082] The unique numbers of these routinely unqualified temperature probes will also be recorded for subsequent quality analysis.

[0083] S205, determining the unqualified type of the conventional unqualified body temperature probe according to the temperature difference feature, and associating the unqualified type with the corresponding unique number;

[0084] In this step, the server needs to determine the specific unqualified type of the conventional unqualified body temperature probe based on the temperature difference characteristics. The server can determine that different temperature difference characteristics correspond to different unqualified types according to preset rules. For example: if the maximum positive difference of the temperature curve is between 1.5-2 degrees, the unqualified type is "high temperature measurement"; if the temperature curve rise rate is more than 10% slower than the reference curve, the unqualified type is "slow response speed"; if the temperature curve has multiple peak-to-valley changes with fluctuations exceeding 1 degree, the unqualified type is "unstable temperature measurement". The server can also comprehensively judge the unqualified type based on multiple temperature difference characteristics. For example, if the maximum positive difference of a probe is 1.8 degrees and the curve rise rate is slow, the unqualified type of the probe can be determined as "high temperature measurement, slow response speed". After determining the unqualified type, the server needs to associate it with the unique number of the corresponding body temperature probe for storage. For example, if the probe number is "X001", the storage association relationship is "X001-high temperature measurement, slow response speed".

[0085] In this way, managers can directly see the type of unqualified temperature probe by querying a certain number, quickly determine the cause of the problem, and make targeted adjustments to different problems later to improve production efficiency.

[0086] S206, sending the seriously unqualified body temperature probe, the conventional unqualified body temperature probe, the unqualified type and the unique number to the management end;

[0087] In this step, the server will send all the unqualified data generated by the test to the management end. Including the number of seriously unqualified temperature probes; the number of conventional unqualified temperature probes; the unqualified type corresponding to the conventional unqualified temperature probes; the unique number of all temperature probes. For example, it can be organized into a table to list the temperature probes with different numbers and their unqualified categories and other information.

[0088] The server can send these sorted unqualified data to the management end such as the quality management system or the production monitoring system. The sending method can adopt standard database synchronization or data interface push, etc., to ensure that the management end can receive the unqualified information generated by the test.

[0089] S207, obtaining production batch information of unqualified temperature probes, where the unqualified temperature probes include the seriously unqualified temperature probes and the conventional unqualified temperature probes;

[0090] In this step, the server needs to obtain the production batch information of the identified unqualified temperature probes. There are two types of unqualified temperature probes: (1) Severely unqualified temperature probes: temperature probes that are directly judged as unqualified in the preliminary testing stage. (2) Conventional unqualified temperature probes: temperature probes that are finally judged as unqualified after the second test. The server can find the corresponding production batch information by querying the unique numbers of these two types of unqualified temperature probes. For example, if a temperature probe with a certain number is "XX Company-Y Model-20190312-053", then its production batch is "20190312". The server associates the production batch information with the unique number of the unqualified temperature probe and stores it in the relevant table of the database. The number of unqualified probes in the same batch can be counted to support subsequent analysis of the problem batch.

[0091] By obtaining the production batch information of unqualified temperature probes, we can find out which batches the quality problems are concentrated in and determine whether the quality problems have batch clustering characteristics, so as to improve the product quality of the problem batches found, or check whether there are specific links in the production process that cause batch problems.

[0092] S208, querying the corresponding historical unqualified records according to the production batch information, and determining that the probe test unqualified rate of the target batch is higher than the set threshold;

[0093] In this step, the server will query the historical unqualified records of the batch stored in the database based on the production batch information of the unqualified temperature probe obtained. The server needs to establish and continuously update a batch quality database, which contains the unqualified rate data of the temperature probe test of each production batch. The database can regularly synchronize the unqualified results of each batch of products generated by the detection. For the production batch of the unqualified probe currently obtained, the server will search for its historical unqualified rate data in the batch quality database. For example, if the batch of the current unqualified probe is "20190312", the historical unqualified rate record of the batch "20190312" is queried. The server will read the historical unqualified rate of the batch and compare it with the preset unqualified rate threshold. The threshold can be determined based on the enterprise quality standards and historical batch data analysis, for example, set to 5%. If it is found that the historical unqualified rate of a batch exceeds 5%, it is determined that the unqualified rate of the batch is too high.

[0094] S209: Send prompt information of batches with too high unqualified rate to the management end.

[0095] After determining that there is a problem batch with a high unqualified rate, the server needs to actively send a prompt message to the management end to notify relevant personnel. The prompt message may include: the problem batch number, such as "20190312"; the most recent unqualified rate of the batch, such as "8%"; the historical unqualified rate of the batch, such as "exceeding 5% for 3 consecutive times". The server can send the prompt message to the quality management department by email, SMS or message push. After receiving the prompt message, the quality department can check the various control parameters of the batch in the production process to find the reasons for the batch failure, such as: whether there is a problem with the raw material batch, whether there is a problem with the equipment commissioning, whether the process control parameters need to be adjusted, etc., and guide the production department to make improvements accordingly to reduce the unqualified rate of the problem batch. At the same time, the quality department can also increase the sampling ratio of the batch of products, strengthen the inspection efforts, and avoid too many unqualified products from entering the market.

[0096] By automatically analyzing and prompting problematic batches through the server, companies can promptly discover batch clustering problems in product quality, take targeted measures to improve them, and ensure product stability. This is more efficient than manual statistical analysis, shortens the company's response speed, and reduces losses caused by batch problems.

[0097] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0098] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0099] like Figure 3 As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0100] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0101] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0102] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0103] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0104] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the test method for the body temperature probe provided in the above embodiment is implemented.

[0105] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the test method for the body temperature probe provided in the above embodiment.

[0106] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0107] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0108] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A testing method for a temperature probe, characterized in that: Applied to a server, the method comprises: After receiving the instruction of determining the qualified temperature probe from the management end, determining the qualified temperature probe as a reference object; After receiving the test start instruction sent by the management end, the plurality of body temperature probes to be tested and the reference objects are placed simultaneously in an environmental box with a first temperature and heat insulation performance by controlling the mechanical arm; Simultaneously receiving the real-time measured temperature of each of the body temperature probes to be measured through a wireless reading device; Determine the temperature difference between the real-time measured temperature corresponding to each of the body temperature probes to be measured and the real-time measured temperature corresponding to the reference object; When the temperature difference is within a preset threshold range, the corresponding body temperature probe to be tested is marked as a body temperature probe to be tested again; By controlling the intelligent temperature control device, the temperature of the environmental box is raised steadily and uniformly until it reaches a preset second temperature, and the second temperature is kept stable for at least a set time; In the process of temperature rising, the temperature data of all the body temperature probes to be tested for the second time are received at a set sampling frequency, and a plurality of corresponding temperature change curves are determined according to the temperature data; Comparing the plurality of temperature change curves with a reference curve using a curve comparison algorithm, wherein the reference curve refers to the temperature change curve of the reference object; The test result corresponding to the body temperature probe to be tested a second time is determined according to the comparison result.

2. The method according to claim 1, characterized in that After the step of determining the test result corresponding to the temperature probe to be tested for the second time according to the comparison result, the method further includes: After determining the unique number of each of the body temperature probes to be tested a second time, the unique number is associated with the corresponding temperature change curve and comparison result and stored.

3. The method according to claim 2, characterized in that After determining the unique number of each of the body temperature probes to be tested for the second time, after the step of associating and storing the unique number with the corresponding temperature change curve and comparison result, the method further includes: Analyze the test results to determine the temperature difference characteristics between each of the body temperature probes to be tested and the reference object; Determine a seriously unqualified temperature probe according to the temperature probe to be tested and the temperature probe to be tested again; Determine a conventional unqualified temperature probe according to the temperature probe to be tested for a second time and the temperature difference characteristic; Determine the unqualified type of the conventional unqualified body temperature probe according to the temperature difference feature, and associate the unqualified type with the corresponding unique number; The seriously unqualified body temperature probe, the conventional unqualified body temperature probe, the unqualified type and the unique number are sent to the management end.

4. The method according to claim 3, characterized in that After the step of sending the seriously unqualified body temperature probe, the conventional unqualified body temperature probe, the unqualified type and the unique number to the management end, the method further includes: Acquire production batch information of unqualified temperature probes, wherein the unqualified temperature probes include the seriously unqualified temperature probes and the conventional unqualified temperature probes; According to the production batch information, the corresponding historical unqualified records are searched to determine that the probe test unqualified rate of the target batch is higher than the set threshold; Send reminder information about batches with too high a failure rate to the management end.

5. The method according to claim 1, characterized in that Before the step of comparing the plurality of temperature change curves with the reference curve by using a curve comparison algorithm, the method further includes: Acquiring historical temperature change curve data of the reference object; Determine a reference curve model based on the historical temperature change curve data combined with a machine learning algorithm; The temperature curve generated by the reference curve model is determined as the reference curve.

6. The method according to claim 1, characterized in that The step of placing a plurality of temperature probes to be measured and a reference object in the environmental box at the same time by controlling the robotic arm specifically includes: Controlling the robotic arm to evenly distribute the temperature probes to be measured and the reference objects in the environmental chamber; Determine the spatial coordinate information of each of the body temperature probes to be measured by an image acquisition device; According to the spatial coordinate information, the placement layout of each of the body temperature probes to be measured is optimized.

7. The method according to claim 1, characterized in that After the step of comparing the plurality of temperature change curves with the reference curve by using a curve comparison algorithm, the method further includes: The temperature measurement accuracy level corresponding to the temperature probe to be tested for the second time is determined based on the comparison results and the existing temperature probe accuracy rating model. The temperature probe accuracy rating model is established by collecting historical test data, specification parameters and corresponding temperature change curves of multiple temperature probes with known temperature measurement accuracy levels in advance, and based on the historical test data, the specification parameters and the temperature change curves, through a machine learning algorithm.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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