Gastric lavage equipment safety detection method and system

Abstract

The invention provides a safety detection method and system for gastric lavage equipment, and relates to the technical field of safety detection. The method comprises the following steps: in a current detection period, acquiring a gastric lavage equipment image through image acquisition equipment; determining an appearance score according to the gastric lavage equipment image; measuring working data of the gastric lavage equipment through a detection instrument; determining a work score according to the work data; acquiring storage environment data of the gastric lavage equipment at multiple moments in the current detection period; inputting the storage environment data into a trained stomach tube bacteria prediction model to obtain predicted stomach tube bacteria data at multiple moments in the current detection period; averaging the predicted gastric tube bacteria data at multiple moments in the current detection period to obtain a bacteria score; and determining a safety score of the gastric lavage equipment according to the appearance score, the work score and the bacteria score. According to the invention, the safety of the gastric lavage equipment can be comprehensively detected according to the appearance condition, the working state and the bacterial condition of the gastric lavage equipment.

Description

Technical Field

[0001] This invention relates to the field of safety testing technology, and in particular to a method and system for safety testing of gastric lavage equipment. Background Technology

[0002] In related technologies, CN112604058A discloses a safe gastric lavage machine, including a liquid tank base mechanism, an integrated electronic control mechanism, a gastric fluid collection mechanism, a solvent filtration mechanism, and a solution preparation mechanism. The liquid tank base mechanism serves as a support device for the tank, providing support for the entire machine. The integrated electronic control mechanism is a control structure with a human-machine interface and operating interface for ease of use. The gastric fluid collection mechanism collects gastric contents and gastric fluid, automatically recovering the gastric contents during operation and collecting them into the tank. The solvent filtration mechanism filters the prepared gastric lavage solvent, ensuring the safety of the gastric lavage process and reducing harm to the patient. The solution preparation mechanism is a mechanism for preparing the gastric lavage solvent as a whole; multiple gastric lavage agents can be prepared according to specifications, suitable for patients in different situations. This solution enables safe gastric lavage for patients, automatically collecting gastric contents and residues, and automatically preparing the gastric lavage solution, improving the safety and efficiency of gastric lavage.

[0003] CN116370738A relates to a timed circulation monitoring device for a gastric lavage machine, characterized by comprising a main unit, a touch screen, a drain port, a gastric outlet, a gastric inlet, a liquid inlet, a working switch, a power socket, a pneumatically controlled robotic arm, and monitoring components. The touch screen, drain port, gastric outlet, gastric inlet, liquid inlet, and working switch are respectively installed on the front of the main unit. A power socket is installed on the side of the main unit. A pneumatically controlled robotic arm is installed on the top of the main unit. The monitoring components are installed inside the main unit. This solution also relates to a monitoring method that, by monitoring the pressure and operating status in the gastric lavage machine's piping, can promptly detect malfunctions in the gastric lavage machine, reducing the failure rate of gastric lavage machines in clinical practice and improving the safety and reliability of the gastric lavage machine during use.

[0004] Therefore, although the relevant technologies can improve the safety and reliability of gastric lavage machines during use, they do not consider the impact of the appearance, working status, and bacterial status of the gastric lavage equipment on its safety. In other words, it is impossible to comprehensively test the safety of the gastric lavage equipment based on its appearance, working status, and bacterial status.

[0005] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] This invention provides a method and system for safety testing of gastric lavage equipment, which can solve the technical problem that related technologies cannot comprehensively test the safety of gastric lavage equipment based on its appearance, working status, and bacterial status.

[0007] According to a first aspect of the present invention, a method for safety testing of a gastric lavage device is provided, comprising: During the current testing cycle, images of the gastric lavage equipment are acquired using image acquisition devices; An appearance score is determined based on the image of the gastric lavage equipment; During the current testing cycle, the operating data of the gastric lavage equipment is measured using testing instruments. The operating data includes pump casing temperature, operating noise, oral cannula flow rate, and nasal cannula flow rate. Based on the work data, determine the work score; Acquire storage environment data of the gastric lavage device at multiple moments during the current testing cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; The storage environment data is input into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle. The bacterial score is obtained by averaging the predicted gastric tube bacterial data at multiple points in the current testing cycle. The safety score of the gastric lavage device is determined based on the appearance score, the operational score, and the bacterial score.

[0008] Further, based on the images from the gastric lavage device, an appearance score is determined, including: In the image of the gastric lavage device, an image detection model is used to identify whether there are cracks in the outer shell of the gastric lavage device; If a crack exists, determine the length of the crack in the outer casing; In the image of the gastric lavage device, an image detection model is used to identify whether there is damage to the instruments in the gastric lavage device, and the instrument identification result is determined. In the image of the gastric lavage device, the image detection model is used to identify whether the LCD screen of the gastric lavage device is clear, and the LCD screen recognition result is determined. An appearance score is determined based on the crack length, the appliance identification result, and the LCD screen identification result.

[0009] Further, based on the crack length, the appliance identification result, and the LCD screen identification result, an appearance score is determined, including: According to the formula

[0010] Determine appearance score ,in, The length of the crack. To preset the crack length threshold, For the device identification results, The results of the LCD screen recognition. , , and For preset weights, if is a conditional function, and , .

[0011] Furthermore, based on the work data, a work score is determined, including: Based on clinical needs, working data thresholds are set, including pump housing temperature threshold, working noise threshold, oral cannula flow rate threshold, and nasal cannula flow rate threshold. A work score is determined based on the work data threshold and the work data.

[0012] Further, based on the work data threshold and the work data, a work score is determined, including: According to the formula

[0013] Determine job performance rating ,in, For the i-th working data, Let be the threshold for the i-th working data, i≤4, and i is a positive integer, and if is a conditional function.

[0014] Furthermore, the training steps of the gastric tube bacterial prediction model include: The historical storage environment data of the gastric lavage device at multiple times during multiple historical testing cycles are obtained, wherein the historical storage environment data includes historical temperature data, historical humidity data, and historical environmental bacteria data; By using a gastric tube bacteria prediction model, the historical storage environment data is processed to obtain historical predicted gastric tube bacteria data at multiple times in multiple historical detection cycles. Acquire historical gastric tube bacterial data from multiple time points across multiple historical testing cycles using the gastric lavage device; Based on the historical storage environment data, the historical predicted gastric tube bacteria data, and the historical gastric tube bacteria data, the loss function of the gastric tube bacteria prediction model is determined; The gastric tube bacteria prediction model is trained based on the loss function of the gastric tube bacteria prediction model to obtain the trained gastric tube bacteria prediction model.

[0015] Further, based on the historical storage environment data, the historical predicted gastric tube bacteria data, and the historical gastric tube bacteria data, the loss function of the gastric tube bacteria prediction model is determined, including: According to the formula

[0016] Determine the loss function of the gastric tube bacterial prediction model. ,in, This refers to the historical gastric tube bacterial data at time k in the h-th historical detection cycle. This is the historical predicted gastric tube bacterial data at time k in the h-th historical detection cycle. This refers to the historical temperature data at time k in the h-th historical detection cycle. For standard temperature data, This refers to the historical humidity data at time k in the h-th historical detection cycle. Standard humidity data, This represents the historical environmental bacterial data at time k in the h-th historical detection cycle. Let M be the threshold for environmental bacteria data, M be the number of time points in the detection cycle, H be the number of historical detection cycles, k ≤ M, h ≤ H, and k, h, M and H are all positive integers.

[0017] According to a second aspect of the present invention, a safety detection system for gastric lavage equipment is provided, comprising: The image module for the gastric lavage equipment is used to acquire images of the gastric lavage equipment through an image acquisition device during the current detection cycle. The appearance rating module is used to determine the appearance rating based on the image of the gastric lavage device; The working data module is used to measure the working data of the gastric lavage equipment using a detection instrument during the current detection cycle. The working data includes pump casing temperature, operating noise, oral cannula flow rate, and nasal cannula flow rate. The job scoring module is used to determine the job score based on the job data; The storage environment data module is used to acquire the storage environment data of the gastric lavage device at multiple moments in the current testing cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; The gastric tube bacteria data prediction module is used to input the stored environment data into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle. The bacterial scoring module is used to average the predicted gastric tube bacterial data at multiple points in the current testing cycle to obtain a bacterial score. The gastric lavage equipment safety scoring module is used to determine the safety score of the gastric lavage equipment based on the appearance score, the operational score, and the bacterial score.

[0018] Technical Effects: According to this invention, data acquisition using modern technologies such as image acquisition equipment and detection instruments improves the accuracy and efficiency of detection. By using a trained gastric tube bacterial prediction model combined with storage environment data, the predicted gastric tube bacterial data of the gastric lavage device at different times can be predicted relatively accurately. This allows for the timely detection and handling of potential hygiene problems, reducing the probability of bacterial contamination. A comprehensive safety inspection of the gastric lavage device can be conducted based on its appearance, operating status, and bacterial status, improving the comprehensiveness, objectivity, and accuracy of the safety inspection results. When determining the appearance score, it can be based on crack length, instrument identification results, and LCD screen identification results. Considering the three structures of the gastric lavage device—its shell, instruments, and LCD screen—comprehensively reflects multiple important features of the device's appearance, allowing for the timely detection of external problems and the implementation of corresponding maintenance measures, thus improving the accuracy and comprehensiveness of the appearance score. When determining the operational score, it can be based on operational data thresholds and operational data. By judging whether the operational data reaches or exceeds the corresponding operational data thresholds, the degree of operational abnormality of the gastric lavage device is determined, ensuring that the operational score accurately describes the operational abnormalities of the tested gastric lavage device. When determining the loss function of the gastric tube bacteria prediction model, the influence of temperature, humidity, and environmental bacteria on the number of bacteria in the gastric tube can be used to determine the impact of these factors on the error of historical predicted gastric tube bacteria data. Based on this influence and the relative difference between historical gastric tube bacteria data and historical predicted gastric tube bacteria data, and by setting weights based on the characteristic that the shorter the time interval from the start time, the higher the accuracy, the loss function is obtained by weighted summing of the errors output by the gastric tube bacteria prediction model at each time point in multiple historical detection cycles. This improves the design accuracy and objectivity of the loss function, thereby improving training efficiency and increasing the accuracy of the gastric tube bacteria prediction model during training.

[0019] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort. Figure 1 A schematic flowchart of a gastric lavage device safety testing method according to an embodiment of the present invention is shown as an example; Figure 2A block diagram of a gastric lavage device safety detection system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0023] Figure 1 An exemplary flowchart illustrates a safety testing method for a gastric lavage device according to an embodiment of the present invention, the method comprising: Step S101: In the current detection cycle, acquire an image of the gastric lavage device using an image acquisition device; Step S102: Determine the appearance score based on the image of the gastric lavage device; Step S103: In the current detection cycle, the working data of the gastric lavage equipment is measured by the detection instrument, wherein the working data includes pump casing temperature, working noise, oral cannula flow rate and nasal cannula flow rate; Step S104: Determine the work score based on the work data; Step S105: Obtain the storage environment data of the gastric lavage device at multiple moments in the current detection cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; Step S106: Input the storage environment data into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle; Step S107: Average the predicted gastric tube bacteria data at multiple times in the current detection cycle to obtain a bacterial score; Step S108: Determine the safety score of the gastric lavage device based on the appearance score, the operational score, and the bacterial score.

[0024] The gastric lavage device safety testing method according to embodiments of the present invention utilizes modern technological means such as image acquisition devices and testing instruments for data collection, thereby improving the accuracy and efficiency of testing. By employing a trained gastric tube bacterial prediction model, combined with storage environment data, the method can accurately predict the predicted gastric tube bacterial data at different times, enabling timely detection and handling of potential hygiene problems, reducing the probability of bacterial contamination. Furthermore, it allows for comprehensive safety testing of the gastric lavage device based on its appearance, operating status, and bacterial composition, improving the comprehensiveness, objectivity, and accuracy of the safety testing results.

[0025] According to one embodiment of the present invention, in step S101, each detection cycle can be set to 12 hours, 24 hours, etc., and the present invention does not limit this. A high-definition camera can be used to photograph the gastric lavage equipment, capturing images of the equipment and clearly showing the condition of its casing, instruments, and LCD screen.

[0026] According to one embodiment of the present invention, in step S102, an appearance score is determined based on the image of the gastric lavage device.

[0027] According to an embodiment of the present invention, step S102 includes: identifying whether there is a crack in the outer shell of the gastric lavage device in the image of the gastric lavage device using an image detection model; if there is a crack, determining the length of the crack in the outer shell; identifying whether there is damage to the instruments in the gastric lavage device in the image of the gastric lavage device using an image detection model, and determining the instrument identification result; identifying whether the LCD screen of the gastric lavage device is clear in the image of the gastric lavage device using an image detection model, and determining the LCD screen identification result; and determining an appearance score based on the crack length, the instrument identification result, and the LCD screen identification result.

[0028] According to one embodiment of the present invention, the image detection model is a type of deep learning model. The model is trained using historical data to identify whether there are cracks in the outer casing of the gastric lavage device. If cracks are found, image analysis techniques (e.g., edge detection, contour extraction) are used to determine the crack length, which can be the sum of the lengths of all detected cracks. The image detection model also identifies whether the instruments of the gastric lavage device are damaged, such as deformed or broken instruments. These instruments include gastric lavage tubes, denture clips, etc. If the instruments are undamaged, the instrument identification result is 1; if they are damaged, the result is 0. The image detection model also identifies whether the LCD screen of the gastric lavage device is clear. If the LCD screen is clear, the identification result is 1; if it is unclear, the result is 0. An appearance score is determined based on the crack length, instrument identification result, and LCD screen identification result.

[0029] According to one embodiment of the present invention, determining an appearance score based on the crack length, the appliance identification result, and the liquid crystal screen identification result includes: determining the appearance score according to formula (1). , , in, The length of the crack. To preset the crack length threshold, For the device identification results, The results of the LCD screen recognition. , , and For preset weights, if is a conditional function, and , .

[0030] According to an embodiment of the present invention, in formula (1), The condition function value is 1 when the crack length is less than or equal to the preset crack length threshold, indicating that the damage to the outer shell of the gastric lavage device is within an acceptable range; otherwise, the damage to the outer shell of the gastric lavage device may affect the safe use of the gastric lavage device, and the condition function value is 0. The result of the instrument identification indicates that the gastric lavage equipment is intact when there is no damage to the instrument. A value of 1 indicates that the gastric lavage equipment is unusable; conversely, a value of 0 indicates that the equipment is not usable. The value is 0. The LCD screen's recognition result indicates that the screen of the gastric lavage device is functioning normally when it is clear. A value of 1 indicates that the LCD screen of the gastric lavage equipment is affecting normal use; conversely, a value of 0 indicates that the LCD screen of the gastric lavage equipment is malfunctioning. The value is 0. , and The appearance score is obtained by weighted summation. The higher the appearance score, the better the condition of the external structure and related components of the gastric lavage equipment, and the safer the gastric lavage equipment. The lower the appearance score, the worse the condition of the external structure and related components of the gastric lavage equipment, and the less safe the gastric lavage equipment.

[0031] In this way, the appearance score can be determined based on the crack length, instrument recognition results, and LCD screen recognition results. By considering the three structures of the gastric lavage equipment—the shell, instruments, and LCD screen—multiple important features of the appearance of the gastric lavage equipment can be comprehensively reflected. Problems on the exterior of the gastric lavage equipment can be detected in a timely manner, and corresponding maintenance measures can be taken, thus improving the accuracy and comprehensiveness of the appearance score.

[0032] According to one embodiment of the present invention, in step S103, the temperature value of the pump casing under working conditions is measured by an infrared thermometer to determine whether there is overheating or other abnormalities. The operating noise of the gastric lavage equipment is measured by a sound level meter. The magnitude of the operating noise reflects the operating status of the internal mechanical components of the gastric lavage equipment and whether there is a potential risk of failure. The flow rate of the oral cannula and the flow rate of the nasal cannula are measured by a flow sensor or a flow meter. The measured flow rates of the oral cannula and the nasal cannula reflect the efficiency of the gastric lavage equipment during the cleaning process and whether it can meet clinical needs.

[0033] According to one embodiment of the present invention, in step S104, a work score is determined based on the work data.

[0034] According to an embodiment of the present invention, step S104 includes: setting working data thresholds according to clinical usage requirements, wherein the working data thresholds include a pump housing temperature threshold, a working noise threshold, an oral cannula flow rate threshold, and a nasal cannula flow rate threshold; and determining a working score based on the working data thresholds and the working data.

[0035] According to one embodiment of the present invention, working data thresholds can be set according to clinical needs. For example, the temperature rise of the pump casing of the gastric lavage device should not exceed 60 degrees Celsius, the working noise of the gastric lavage device should not exceed 65 dB A-weighted sound level, the flow rate should not be less than 2.0 L / min when using an oral cannula, and the flow rate should not be less than 1.0 L / min when using a nasal cannula.

[0036] According to one embodiment of the present invention, determining a work score based on the work data threshold and the work data includes: determining the work score according to formula (2). , , in, For the i-th working data, Let be the threshold for the i-th working data, i≤4, and i is a positive integer, and if is a conditional function.

[0037] According to an embodiment of the present invention, in formula (2), The condition function is set to 1 if the i-th working data point is greater than or equal to the i-th working data threshold, and 0 otherwise. For example, if the pump casing temperature is greater than or equal to the pump casing temperature threshold, it indicates that the gastric lavage equipment is malfunctioning. The average value of the conditional functions corresponding to the four working data points represents the degree of abnormality in the operation of the gastric lavage equipment. Subtracting the average value of the conditional functions corresponding to the four working data points from 1 yields the working score. The higher the working score, the lower the degree of abnormality in the operation of the gastric lavage equipment and the safer the equipment is. Conversely, the lower the working score, the higher the degree of abnormality in the operation of the gastric lavage equipment and the less safe it is.

[0038] In this way, a work score can be determined based on the work data threshold and the work data. By judging whether the work data reaches or exceeds the corresponding work data threshold, the degree of abnormality of the gastric lavage equipment can be determined, so that the work score can accurately describe the abnormality of the gastric lavage equipment under test.

[0039] According to one embodiment of the present invention, in step S105, the interval between adjacent moments can be set to 1 hour, 2 hours, etc., and the present invention does not limit this. Temperature data, humidity data, and environmental bacterial data of the storage environment of the gastric lavage equipment are collected using detection instruments, such as thermometers, hygrometers, and bacterial detectors.

[0040] According to an embodiment of the present invention, in step S106, the stored environmental data is input into the trained gastric tube bacteria prediction model. The gastric tube bacteria prediction model can be a neural network model, which can be trained based on a large amount of sample data through machine learning or deep learning technology to obtain predicted gastric tube bacteria data.

[0041] According to an embodiment of the present invention, the training steps of the gastric tube bacterial prediction model include: acquiring historical storage environment data of the gastric lavage device at multiple times during multiple historical detection cycles, wherein the historical storage environment data includes historical temperature data, historical humidity data, and historical environmental bacterial data; processing the historical storage environment data using the gastric tube bacterial prediction model to obtain historical predicted gastric tube bacterial data at multiple times during multiple historical detection cycles; acquiring historical gastric tube bacterial data of the gastric lavage device at multiple times during multiple historical detection cycles; determining the loss function of the gastric tube bacterial prediction model based on the historical storage environment data, the historical predicted gastric tube bacterial data, and the historical gastric tube bacterial data; and training the gastric tube bacterial prediction model based on the loss function of the gastric tube bacterial prediction model to obtain the trained gastric tube bacterial prediction model.

[0042] According to one embodiment of the present invention, bacteria in the storage environment of the gastric lavage device are sampled and detected to obtain environmental bacterial data, which represents the degree of contamination in the storage environment. The temperature, humidity, and bacterial count of the storage environment affect the bacterial count in the gastric tube of the gastric lavage device. For example, higher storage temperature and humidity lead to faster bacterial growth; similarly, a higher bacterial count in the storage environment contaminates the gastric tube, resulting in an increased bacterial count. A gastric tube bacterial prediction model can predict historical predicted gastric tube bacterial data at multiple times across multiple historical detection periods based on the aforementioned relationship between temperature, humidity, and gastric tube bacteria, and on historical storage environment data. A loss function is determined based on the relative difference between the historical predicted gastric tube bacterial data and the historical gastric tube bacterial data. The trained gastric tube bacterial prediction model is obtained by adjusting the loss function through feedback.

[0043] According to one embodiment of the present invention, determining the loss function of the gastric tube bacteria prediction model based on the historical storage environment data, the historical predicted gastric tube bacteria data, and the historical gastric tube bacteria data includes: determining the loss function of the gastric tube bacteria prediction model according to formula (3). , , in, This refers to the historical gastric tube bacterial data at time k in the h-th historical detection cycle. This is the historical predicted gastric tube bacterial data at time k in the h-th historical detection cycle. This refers to the historical temperature data at time k in the h-th historical detection cycle. For standard temperature data, This refers to the historical humidity data at time k in the h-th historical detection cycle. Standard humidity data, This represents the historical environmental bacterial data at time k in the h-th historical detection cycle. Let M be the threshold for environmental bacteria data, M be the number of time points in the detection cycle, H be the number of historical detection cycles, k ≤ M, h ≤ H, and k, h, M and H are all positive integers.

[0044] According to one embodiment of the present invention, in formula (3), The relative difference between the historical gastric tube bacterial data at time k in the h-th historical detection period and the historical predicted gastric tube bacterial data at time k in the h-th historical detection period. This is the ratio of the historical temperature data at time k in the h-th historical detection cycle to the standard temperature data. The larger this ratio, the larger the historical temperature data. This is the ratio of the historical humidity data at time k in the h-th historical detection cycle to the standard humidity data. The larger this ratio, the larger the historical humidity data. This is the ratio of historical environmental bacteria data at time k in the h-th historical detection period to the environmental bacteria data threshold. The larger this ratio, the larger the historical environmental bacteria data. This indicates a positive correlation between historical temperature data, historical humidity data, and historical environmental bacterial data and gastric tube bacterial data. For example, higher historical temperature data, historical humidity data, and historical environmental bacterial data indicate that a hot, humid environment with severe bacterial contamination is conducive to the proliferation of gastric tube bacteria. Therefore, the higher the historical temperature data, historical humidity data, and historical environmental bacterial data relative to their respective standard values ​​and thresholds, the better. , and The larger the value, the greater the impact on the error of historical predictions of gastric tube bacteria data. The weight at time k is used to reasonably weight the relative errors at different times in the loss function. The accuracy of the historical predicted gastric bacteria data at time k output by the gastric tube bacteria prediction model is usually higher than that at time k+1. That is, the longer the time interval between the historical predicted gastric bacteria data at a certain time output by the gastric tube bacteria prediction model and the start time, the less accurate the prediction result is. To improve training efficiency, the weight is set higher. Conversely, the more accurate the prediction result is, the lower the weight is.

[0045] According to one embodiment of the present invention, utilizing and The relative errors of historical predicted gastric tube bacteria data at multiple time points across multiple historical detection periods are weighted and averaged to obtain the training loss function. During the training of the gastric tube bacteria prediction model, some parameters within the model are adjusted by backpropagating the loss function to reduce its value, thereby improving the accuracy of the gastric tube bacteria prediction model and obtaining the trained gastric tube bacteria prediction model.

[0046] In this way, the influence of temperature, humidity, and environmental bacteria on the number of bacteria in the gastric tube can be determined to identify the impact of these factors on the error of historical predictions of gastric tube bacteria. Based on this influence and the relative difference between historical and predicted gastric tube bacteria data, and by setting weights according to the principle that shorter time intervals from the start time result in higher accuracy, the errors output by the gastric tube bacteria prediction model at each time point across multiple historical detection cycles are weighted and summed to obtain a loss function. This improves the design accuracy and objectivity of the loss function, thereby increasing training efficiency and improving the accuracy of the gastric tube bacteria prediction model.

[0047] According to one embodiment of the present invention, in step S107, the gastric tube bacteria prediction model, trained at multiple times during the current detection cycle, is used to predict the gastric tube bacteria, obtaining predicted gastric tube bacteria data, representing the number or density of bacteria in the gastric tube. The predicted gastric tube bacteria data at multiple times during the current detection cycle are averaged to obtain a bacterial score. The higher the bacterial score, the more bacteria are present in the gastric tube, the more susceptible the patient is to infection using the gastric lavage device, and the less safe the gastric lavage device is.

[0048] According to one embodiment of the present invention, in step S108, the difference between the appearance score, the working score and 1 minus the bacterial score is weighted and summed to obtain the gastric lavage equipment safety score. The higher the gastric lavage equipment safety score, the safer the gastric lavage equipment, and vice versa.

[0049] The gastric lavage device safety testing method according to embodiments of the present invention utilizes modern technological means such as image acquisition devices and testing instruments for data collection, improving the accuracy and efficiency of testing. By employing a trained gastric tube bacterial prediction model, combined with storage environment data, the method can accurately predict the predicted gastric tube bacterial data at different times, enabling timely detection and handling of potential hygiene problems, reducing the probability of bacterial contamination. It allows for comprehensive safety testing of the gastric lavage device based on its appearance, operating status, and bacterial condition, improving the comprehensiveness, objectivity, and accuracy of the safety testing results. When determining the appearance score, it can be based on crack length, instrument identification results, and LCD screen identification results. By considering the three structures of the gastric lavage device—its shell, instruments, and LCD screen—it comprehensively reflects multiple important features of the device's appearance, allowing for timely detection of external problems and the implementation of corresponding maintenance measures, thus improving the accuracy and comprehensiveness of the appearance score. When determining the operating score, it can be based on operating data thresholds and operating data. By judging whether the operating data reaches or exceeds the corresponding operating data thresholds, the degree of operational abnormality of the gastric lavage device is determined, ensuring that the operating score accurately describes the abnormal operating conditions of the tested gastric lavage device. When determining the loss function of the gastric tube bacteria prediction model, the influence of temperature, humidity, and environmental bacteria on the number of bacteria in the gastric tube can be used to determine the impact of these factors on the error of historical predicted gastric tube bacteria data. Based on this influence and the relative difference between historical gastric tube bacteria data and historical predicted gastric tube bacteria data, and by setting weights based on the characteristic that the shorter the time interval from the start time, the higher the accuracy, the loss function is obtained by weighted summing of the errors output by the gastric tube bacteria prediction model at each time point in multiple historical detection cycles. This improves the design accuracy and objectivity of the loss function, thereby improving training efficiency and increasing the accuracy of the gastric tube bacteria prediction model during training.

[0050] Figure 2 An exemplary block diagram of a gastric lavage device safety detection system according to an embodiment of the present invention is shown, the system comprising: The image module for the gastric lavage equipment is used to acquire images of the gastric lavage equipment through an image acquisition device during the current detection cycle. The appearance rating module is used to determine the appearance rating based on the image of the gastric lavage device; The working data module is used to measure the working data of the gastric lavage equipment using a detection instrument during the current detection cycle. The working data includes pump casing temperature, operating noise, oral cannula flow rate, and nasal cannula flow rate. The job scoring module is used to determine the job score based on the job data; The storage environment data module is used to acquire the storage environment data of the gastric lavage device at multiple moments in the current testing cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; The gastric tube bacteria data prediction module is used to input the stored environment data into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle. The bacterial scoring module is used to average the predicted gastric tube bacterial data at multiple points in the current testing cycle to obtain a bacterial score. The gastric lavage equipment safety scoring module is used to determine the safety score of the gastric lavage equipment based on the appearance score, the operational score, and the bacterial score.

[0051] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0052] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1. A method for safety testing of gastric lavage equipment, characterized in that, include: During the current testing cycle, images of the gastric lavage equipment are acquired using image acquisition devices; An appearance score is determined based on the image of the gastric lavage equipment; During the current testing cycle, the operating data of the gastric lavage equipment is measured using testing instruments. The operating data includes pump casing temperature, operating noise, oral cannula flow rate, and nasal cannula flow rate. Based on the work data, determine the work score; Acquire storage environment data of the gastric lavage device at multiple moments during the current testing cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; The storage environment data is input into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle. The bacterial score is obtained by averaging the predicted gastric tube bacterial data at multiple points in the current testing cycle. The safety score of the gastric lavage device is determined based on the appearance score, the operational score, and the bacterial score.

2. The safety testing method for gastric lavage equipment according to claim 1, characterized in that, Based on the images of the gastric lavage equipment, an appearance score is determined, including: In the image of the gastric lavage device, an image detection model is used to identify whether there are cracks in the outer shell of the gastric lavage device; If a crack exists, determine the length of the crack in the outer casing; In the image of the gastric lavage device, an image detection model is used to identify whether there is damage to the instruments in the gastric lavage device, and the instrument identification result is determined. In the image of the gastric lavage device, the image detection model is used to identify whether the LCD screen of the gastric lavage device is clear, and the LCD screen recognition result is determined. An appearance score is determined based on the crack length, the appliance identification result, and the LCD screen identification result.

3. The safety testing method for gastric lavage equipment according to claim 2, characterized in that, Based on the crack length, the appliance identification result, and the LCD screen identification result, an appearance score is determined, including: According to the formula Determine appearance score ,in, The length of the crack. To preset the crack length threshold, For the device identification results, The results of the LCD screen recognition. , , and For preset weights, if is a conditional function, and , .

4. The safety testing method for gastric lavage equipment according to claim 1, characterized in that, Based on the work data, a work score is determined, including: Based on clinical needs, working data thresholds are set, including pump housing temperature threshold, working noise threshold, oral cannula flow rate threshold, and nasal cannula flow rate threshold. A work score is determined based on the work data threshold and the work data.

5. The safety testing method for gastric lavage equipment according to claim 4, characterized in that, Based on the work data threshold and the work data, a work score is determined, including: According to the formula Determine job performance rating ,in, For the i-th working data, Let be the threshold for the i-th working data, i≤4, and i is a positive integer, and if is a conditional function.

6. The safety testing method for gastric lavage equipment according to claim 1, characterized in that, The training steps for the gastric tube bacterial prediction model include: The historical storage environment data of the gastric lavage device at multiple times during multiple historical testing cycles are obtained, wherein the historical storage environment data includes historical temperature data, historical humidity data, and historical environmental bacteria data; By using a gastric tube bacteria prediction model, the historical storage environment data is processed to obtain historical predicted gastric tube bacteria data at multiple times in multiple historical detection cycles. Acquire historical gastric tube bacterial data from multiple time points across multiple historical testing cycles using the gastric lavage device; Based on the historical storage environment data, the historical predicted gastric tube bacteria data, and the historical gastric tube bacteria data, the loss function of the gastric tube bacteria prediction model is determined; The gastric tube bacteria prediction model is trained based on the loss function of the gastric tube bacteria prediction model to obtain the trained gastric tube bacteria prediction model.

7. The safety testing method for gastric lavage equipment according to claim 6, characterized in that, Based on the historical storage environment data, the historical predicted gastric tube bacteria data, and the historical gastric tube bacteria data, the loss function of the gastric tube bacteria prediction model is determined, including: According to the formula Determine the loss function of the gastric tube bacterial prediction model. ,in, This refers to the historical gastric tube bacterial data at time k in the h-th historical detection cycle. This is the historical predicted gastric tube bacterial data at time k in the h-th historical detection cycle. This refers to the historical temperature data at time k in the h-th historical detection cycle. For standard temperature data, This refers to the historical humidity data at time k in the h-th historical detection cycle. Standard humidity data, This represents the historical environmental bacterial data at time k in the h-th historical detection cycle. Let M be the threshold for environmental bacteria data, M be the number of time points in the detection cycle, H be the number of historical detection cycles, k ≤ M, h ≤ H, and k, h, M and H are all positive integers.

8. A gastric lavage equipment safety testing system for performing the gastric lavage equipment safety testing method as described in any one of claims 1-7, characterized in that, include: The image module for the gastric lavage equipment is used to acquire images of the gastric lavage equipment through an image acquisition device during the current detection cycle. The appearance rating module is used to determine the appearance rating based on the image of the gastric lavage device; The working data module is used to measure the working data of the gastric lavage equipment using a detection instrument during the current detection cycle. The working data includes pump casing temperature, operating noise, oral cannula flow rate, and nasal cannula flow rate. The job scoring module is used to determine the job score based on the job data; The storage environment data module is used to acquire the storage environment data of the gastric lavage device at multiple moments in the current testing cycle, wherein the storage environment data includes temperature data, humidity data, and environmental bacteria data; The gastric tube bacteria data prediction module is used to input the stored environment data into the trained gastric tube bacteria prediction model to obtain predicted gastric tube bacteria data at multiple times in the current detection cycle. The bacterial scoring module is used to average the predicted gastric tube bacterial data at multiple points in the current testing cycle to obtain a bacterial score. The gastric lavage equipment safety scoring module is used to determine the safety score of the gastric lavage equipment based on the appearance score, the operational score, and the bacterial score.

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