A multi-parameter cooperative control method for pet pathogen detection process

Abstract

The application discloses a kind of pet pathogen detection process multi-parameter collaborative control method, it is related to detection process control technical field, for solving the problem of detection liquid consumption mismatch and detection efficiency decline, by sample volume identification system receives the effective extractable volume of pet sample to be measured, accesses detection database to obtain liquid suction peak negative pressure data and light transmission turbidity data, and based on liquid suction peak negative pressure data analysis suction blockage characteristics, combined with light transmission turbidity data to evaluate sample processing load level, collect sample source type, according to processing load level determine whether to trigger path switching mechanism, by path configuration library to call candidate detection path, collect detection liquid consumption and calculate liquid consumption adaptation coefficient, obtain target pathogen quantity, comprehensive liquid consumption adaptation coefficient and target pathogen quantity to evaluate path adaptation characteristics, after candidate detection path screening mark, execute target pathogen detection processing, realize detection path adaptive screening, improve detection stability and efficiency.

Description

Technical Field

[0001] This invention relates to the field of detection process control technology, and more specifically, to a multi-parameter collaborative control method for a pet pathogen detection process. Background Technology

[0002] With the rapid development of the pet medical industry, the demand for testing for infectious diseases in pets is constantly increasing. In the clinical diagnosis and treatment of pets, it is often necessary to perform pathogen testing on various types of samples, such as pet blood, feces, nasal secretions, saliva, and tissue fluid, in order to achieve rapid identification of pathogens such as bacteria, viruses, or parasites. Currently, common pet pathogen testing procedures usually rely on standardized sample processing paths, that is, after the sample enters the testing equipment, it is followed by a preset fixed process to complete steps such as aspiration, lysis, extraction, and amplification or detection.

[0003] The existing technology has the following shortcomings: Currently, existing technologies typically process samples from different sources uniformly based on a pre-set fixed detection process, lacking a comprehensive identification and collaborative analysis mechanism for multi-parameter information. They cannot dynamically adjust the detection path and achieve adaptive control of the detection process according to the sample processing load and detection task requirements. This leads to problems such as unstable liquid aspiration, mismatch in detection liquid consumption, and decreased detection efficiency when the sample viscosity is high, the impurity content is high, or the sample volume is limited. This affects the stability of the pet pathogen detection process and the overall detection efficiency. Therefore, a multi-parameter collaborative control method for the pet pathogen detection process is proposed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a multi-parameter collaborative control method for pet pathogen detection processes. By employing a multi-parameter collaborative analysis mechanism using liquid aspiration peak negative pressure data and light transmittance turbidity data, the method evaluates the sample aspiration resistance characteristics and processing load level, and triggers detection path switching based on sample source type. Simultaneously, it adapts and screens candidate detection paths based on detection liquid consumption and target pathogen quantity to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-parameter collaborative control method for a pet pathogen detection process, comprising the following steps: Step S1: When the effective absorbable volume of the pet sample to be tested is received through the sample volume recognition system, the peak negative pressure data of the aspiration peak and the turbidity data of the pet sample to be tested are read from the detection database, and the aspiration resistance characteristics are analyzed using the peak negative pressure data of the aspiration peak. Step S2: Evaluate the processing load level of the pet sample to be tested by combining the transmittance turbidity data and absorption retardation characteristics, collect the sample source type of the pet sample to be tested, and determine whether to trigger the path switching mechanism based on the processing load level. Step S3: When the path switching mechanism is triggered, the candidate detection path is retrieved through the path configuration library, the detection liquid consumption corresponding to each candidate detection path is collected and the liquid consumption adaptation coefficient is calculated, and the number of target pathogens in the pet samples to be tested in the current detection task is retrieved. Step S4: Evaluate the path adaptation features by combining the fluid consumption adaptation coefficient and the number of target pathogens. After screening and marking the candidate detection paths using the path adaptation features, perform target pathogen detection processing on the pet samples to be tested based on the marked candidate detection paths.

[0007] In a preferred embodiment, in step S1, the sample volume identification system completes the sample aspiration action through a negative pressure aspiration device to receive the effective absorbable volume of the pet sample to be tested. The peak negative pressure data for liquid aspiration includes the instantaneous peak negative pressure during liquid aspiration and the negative pressure establishment response time; Access the detection database to read the negative pressure monitoring data sequence corresponding to the pet sample to be tested, the corresponding acquisition time, and the start time of the liquid aspiration drive mechanism; Traverse the negative pressure monitoring data sequence and select the pressure value with the largest absolute value in the negative pressure monitoring data sequence as the instantaneous peak negative pressure of liquid absorption; Subtract the acquisition time corresponding to the instantaneous peak negative pressure of liquid aspiration from the start time of the liquid aspiration drive mechanism to obtain the negative pressure establishment response time.

[0008] In a preferred embodiment, in step S1, the light transmittance turbidity data includes the sample transmitted light intensity, and the sample transmitted light intensity of the pet sample to be tested is read by accessing the detection database. The instantaneous peak negative pressure and negative pressure establishment response time of liquid absorption are standardized to obtain the negative pressure factor and time factor. The absorption retardation characteristic value of the pet sample under test was calculated by combining the negative pressure factor and the time factor; If the absorption retardation feature value is greater than or equal to the preset absorption retardation feature threshold, the absorption retardation feature of the pet sample to be tested is determined to be a high-retardation absorption state. Conversely, if the absorption retardation characteristics of the pet sample to be tested are determined to be low-resistance absorption.

[0009] In a preferred embodiment, in step S2, the transmitted light intensity of the pet sample to be tested is compared with a preset transmitted light intensity threshold, and a judgment is made in conjunction with the absorption retardation characteristics of the pet sample to be tested. If the sample transmitted light intensity is less than or equal to the preset sample transmitted light intensity threshold, and the absorption hindrance characteristic is a high hindrance absorption state, then the processing load level is determined to be a high processing load level. If the sample transmitted light intensity is less than or equal to the preset sample transmitted light intensity threshold, and the absorption hindrance characteristic is a low hindrance absorption state, then the processing load level is determined to be a medium processing load level. If the intensity of the transmitted light from the sample is greater than the preset threshold for the intensity of the transmitted light from the sample, and the absorption hindrance characteristic is a high hindrance absorption state, then the processing load level is determined to be a medium processing load level. If the intensity of the transmitted light from the sample is greater than the preset threshold for the intensity of the transmitted light from the sample, and the absorption hindrance characteristic is in a low hindrance absorption state, then the processing load level is determined to be a low processing load level.

[0010] In a preferred embodiment, in step S2, the sample number of the pet sample to be tested is matched with the sample information database to obtain the sample source type of the pet sample to be tested; The determination is made based on the sample source type and processing load level of the pet samples to be tested: If the processing load level is high, then the path switching mechanism will be triggered. If the processing load level is medium and the sample source type is fecal sample or nasal swab sample, then the path switching mechanism is triggered. Otherwise, it is determined that the path switching mechanism will not be triggered.

[0011] In a preferred embodiment, in step S3, the sample number of the pet sample to be tested is matched with the path configuration library to obtain a candidate detection path; Read the detection liquid consumption corresponding to each candidate detection path through the path configuration library; The liquid consumption volume and the effective absorbable volume of the pet sample to be tested corresponding to each candidate detection path are standardized to obtain the liquid consumption factor and the absorbability factor. The liquid consumption matching coefficient is obtained by combining the liquid consumption factor and the absorption factor.

[0012] In a preferred embodiment, in step S3, the detection database is accessed to read the detection task information corresponding to the pet sample to be tested; Based on the detection task information corresponding to the pet sample to be tested, the target pathogen field is read through the field parsing unit, and the read target pathogen identifiers are integrated into a target pathogen list; The number of target pathogens in the pet samples to be tested is obtained by statistically analyzing the target pathogen identifiers in the target pathogen list.

[0013] In a preferred embodiment, in step S4, the liquid consumption compatibility coefficient is compared with a preset liquid consumption compatibility coefficient threshold for determination: If the liquid consumption adaptation coefficient is greater than or equal to the preset liquid consumption adaptation coefficient threshold, the candidate detection path is determined to meet the sample liquid consumption condition. If the liquid consumption adaptation coefficient is less than the preset liquid consumption adaptation coefficient threshold, the candidate detection path is determined not to meet the sample liquid consumption condition.

[0014] In a preferred embodiment, in step S4, the number of target pathogens in the pet sample to be tested is compared with a preset pathogen number threshold, and a judgment is made in conjunction with the sample fluid consumption conditions: If a candidate detection path meets the sample consumption condition and the number of target pathogens is less than or equal to the preset pathogen number threshold, then the candidate detection path is marked. If a candidate detection path does not meet the sample liquid consumption condition, then the candidate detection path will not be marked. If the number of target pathogens exceeds the preset pathogen number threshold, the candidate detection path will not be marked. The target pathogen detection process is performed on the pet samples to be tested based on the marked candidate detection paths.

[0015] The technical effects and advantages of this invention are as follows: This invention receives the effective absorbable volume of a pet sample through a sample volume identification system, accesses a detection database to read peak aspiration pressure data and transmittance turbidity data, and analyzes aspiration resistance characteristics using the peak aspiration pressure data. It then comprehensively assesses the processing load level of the pet sample by combining transmittance turbidity data and aspiration resistance characteristics. Simultaneously, it collects the sample source type and determines whether to trigger a path switching mechanism based on the processing load level. When the path switching mechanism is triggered, it retrieves candidate detection paths from a path configuration library, collects the detection liquid consumption of each candidate detection path and calculates the liquid consumption adaptation coefficient, and simultaneously retrieves the target pathogen quantity. It comprehensively assesses the path adaptation characteristics by combining the liquid consumption adaptation coefficient and the target pathogen quantity. After screening and marking the candidate detection paths, it performs target pathogen detection processing on the pet sample. By jointly analyzing the sample state using peak aspiration pressure data and transmittance turbidity data, and combining the processing load level and sample source type to determine the path, it achieves adaptive screening of detection paths, improving the stability and efficiency of target pathogen detection processing. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the implementation of a multi-parameter collaborative control method for pet pathogen detection according to the present invention.

[0017] Figure 2 This is a schematic diagram illustrating the steps of a multi-parameter collaborative control method for pet pathogen detection according to the present invention. Detailed Implementation

[0018] 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.

[0019] Example 1, as Figures 1 to 2 As shown, a multi-parameter collaborative control method for a pet pathogen detection process includes the following steps: Step S1: When the effective absorbable volume of the pet sample to be tested is received through the sample volume recognition system, the peak negative pressure data of the aspiration peak and the turbidity data of the pet sample to be tested are read from the detection database, and the aspiration resistance characteristics are analyzed using the peak negative pressure data of the aspiration peak. Step S2: Evaluate the processing load level of the pet sample to be tested by combining the transmittance turbidity data and absorption retardation characteristics, collect the sample source type of the pet sample to be tested, and determine whether to trigger the path switching mechanism based on the processing load level. Step S3: When the path switching mechanism is triggered, the candidate detection path is retrieved through the path configuration library, the detection liquid consumption corresponding to each candidate detection path is collected and the liquid consumption adaptation coefficient is calculated, and the number of target pathogens in the pet samples to be tested in the current detection task is retrieved. Step S4: Evaluate the path adaptation features by combining the fluid consumption adaptation coefficient and the number of target pathogens. After screening and marking the candidate detection paths using the path adaptation features, perform target pathogen detection processing on the pet samples to be tested based on the marked candidate detection paths.

[0020] The specific implementation is as follows: In step S1, sample aspiration is the foundation of subsequent detection operations in the pet pathogen detection process. Its smoothness directly affects the detection efficiency and the reliability of the results. Different pet samples exhibit different flow characteristics during automatic liquid aspiration due to differences in source type, matrix composition, and other factors. Some samples may experience increased liquid aspiration resistance due to high impurity content or excessive viscosity, which in turn affects the accuracy and consistency of the aspiration volume and interferes with the stability of the detection process. While the sample volume recognition system receives the effective aspirable volume of the pet sample to be tested, it needs to access the liquid aspiration peak negative pressure data and light transmittance turbidity data recorded in the detection database. By extracting and analyzing the negative pressure change characteristics during the liquid aspiration process, the degree of resistance of the sample during aspiration can be identified, providing a basis for the evaluation of the subsequent processing load level and the adaptive adjustment of the detection path. The sample volume recognition system completes the sample aspiration action through a negative pressure liquid aspiration device, and receives the effective aspirable volume of the pet sample to be tested. The peak negative pressure data refers to the set of negative pressure characteristic data obtained by the negative pressure sensor set in the aspiration channel continuously monitoring the pressure change in the channel during the aspiration process of the automated aspiration device. It is used to characterize the establishment of negative pressure and the change of negative pressure peak during the aspiration process, thereby reflecting the flow resistance state of the sample during the aspiration process, including the instantaneous peak negative pressure and the negative pressure establishment response time. Access the detection database to read the negative pressure monitoring data sequence corresponding to the pet sample to be tested, the corresponding acquisition time, and the start time of the liquid aspiration drive mechanism; Traverse the negative pressure monitoring data sequence and select the pressure value with the largest absolute value in the negative pressure monitoring data sequence as the instantaneous peak negative pressure of liquid absorption; Subtract the acquisition time corresponding to the instantaneous peak negative pressure of liquid aspiration from the start time of the liquid aspiration drive mechanism to obtain the negative pressure establishment response time; It should be noted that a negative pressure aspiration device refers to a component of an automated sample processing system. Its main function is to extract liquid samples from a sample container using the principle of negative pressure for subsequent testing. The effective aspirable volume refers to the actual volume of sample that can be stably aspirated and used for subsequent testing during the sample aspiration process performed by the automated aspiration device, under the combined effect of the aspiration needle, negative pressure aspiration capacity, and sample flow state. The start time of the aspiration drive mechanism refers to the instant when the control system sends a start command to the aspiration drive mechanism and the mechanism begins to operate, as recorded by the system.

[0021] Transmitted turbidity data refers to the optical characteristic data obtained when the pet sample to be tested is in the optical detection area. The optical detection unit emits a detection beam to the sample through the light source emitting device, and the photoelectric receiver detects the intensity of the transmitted light after passing through the sample. The transmitted light intensity of the sample is as follows: the lower the transmitted light intensity of the sample, the higher the turbidity of the sample, that is, the more suspended particles or impurities are in the sample. Access the detection database to read the transmitted light intensity of the pet sample to be tested; The instantaneous peak negative pressure and negative pressure establishment response time of liquid absorption are standardized to obtain the negative pressure factor and time factor. The absorption retardation characteristic value of the pet sample to be tested is calculated by combining the negative pressure factor and the time factor. The calculation formula is as follows: ,in, It is a negative pressure factor. As a time factor, The absorption retardation characteristic value of the pet sample to be tested; The absorption retardation characteristic value reflects the overall resistance level of the pet sample during the aspiration process, which is the sum of the instantaneous peak negative pressure and the negative pressure establishment response time, and its deviation from the preset retardation threshold. The larger the absorption retardation characteristic value, the more significant the flow resistance of the sample in the aspiration channel, and the higher the degree of absorption retardation. The smaller the absorption retardation characteristic value, the less the flow resistance of the sample in the aspiration channel, and the lower the degree of absorption retardation. The absorption retardation characteristic value of the pet sample to be tested is compared with the preset absorption retardation characteristic threshold for judgment: If the absorption retardation characteristic value of the pet sample to be tested is greater than or equal to the preset absorption retardation characteristic threshold, then the absorption retardation characteristic of the pet sample to be tested is determined to be a high-retardation absorption state. If the absorption retardation characteristic value of the pet sample to be tested is less than the preset absorption retardation characteristic threshold, then the absorption retardation characteristic of the pet sample to be tested is determined to be a low-resistance absorption state.

[0022] It should be explained that the detection database refers to the collection used to store the raw monitoring data generated during the testing of pet samples, including negative pressure monitoring data sequences, collection time, start time of the aspiration drive mechanism, sample transmitted light intensity, and corresponding detection task information. Standardization methods include, but are not limited to, standard linear transformation based on interval scaling, statistical Z-Score standardization, or normalization based on nonlinear mapping functions. The specific methods of standardization will not be elaborated upon here. The preset aspiration resistance characteristic threshold can be set based on the statistical distribution of historical test data, the physical properties of the aspiration channel, or empirical values ​​for different sample types. High-resistance aspiration state refers to the sample flow resistance state when the aspiration resistance characteristic value of the pet sample is greater than or equal to the preset aspiration resistance characteristic threshold. This state indicates that the sample exhibits high flow resistance and poor aspiration smoothness during aspiration. Low-resistance aspiration state refers to the sample flow resistance state when the aspiration resistance characteristic value of the pet sample is less than the preset aspiration resistance characteristic threshold. This state indicates that the sample exhibits low flow resistance and good aspiration smoothness during aspiration.

[0023] By obtaining the effective absorbable volume of the pet sample to be tested and combining the peak negative pressure data of aspiration and the transmittance turbidity data to analyze the aspiration resistance characteristics, the sample aspiration state and turbidity can be identified before the test, thereby providing basic data for subsequent sample processing and evaluation and improving the accuracy of the test preparation stage.

[0024] In step S2, before conducting the detection process, it is necessary to comprehensively assess the light transmittance and turbidity of the sample and the absorption retardation state to evaluate the processing load of the pet sample to be tested, and determine whether the current detection path is suitable based on the sample source type, thereby providing a basis for whether to activate the detection path switching mechanism. The transmitted light intensity of the pet sample to be tested is compared with a preset transmitted light intensity threshold, and the judgment is made in combination with the absorption retardation characteristics of the pet sample to be tested: If the transmitted light intensity of the pet sample to be tested is less than or equal to the preset transmitted light intensity threshold, and the absorption retardation characteristic of the pet sample to be tested is a high retardation absorption state, then the processing load level of the pet sample to be tested is determined to be a high processing load level. If the transmitted light intensity of the pet sample to be tested is less than or equal to the preset transmitted light intensity threshold, and the absorption retardation characteristic of the pet sample to be tested is a low retardation absorption state, then the processing load level of the pet sample to be tested is determined to be a medium processing load level. If the transmitted light intensity of the pet sample to be tested is greater than the preset transmitted light intensity threshold, and the absorption retardation characteristic of the pet sample to be tested is a high retardation absorption state, then the processing load level of the pet sample to be tested is determined to be a medium processing load level. If the transmitted light intensity of the pet sample to be tested is greater than the preset transmitted light intensity threshold, and the absorption retardation characteristic of the pet sample to be tested is a low retardation absorption state, then the processing load level of the pet sample to be tested is determined to be a low processing load level. It should be noted that the preset sample transmitted light intensity threshold can be set according to the optical path parameters of the optical detection unit, the baseline value of transmitted light intensity of the blank control sample, or the turbidity grading standard for different sample types; the high processing load level refers to one of the processing difficulty levels of the pet sample to be tested. This level indicates that the sample exhibits a high resistance to aspiration during liquid aspiration, and the sample transmitted light intensity is lower than or equal to the preset threshold. That is, the sample has the characteristics of high flow resistance and poor light transmittance, requiring the triggering of a path switching mechanism to adapt to its processing complexity; the medium processing load level indicates that the sample exhibits one of the following two situations during liquid aspiration: one is that the sample... The first level indicates that the sample has a low-resistance absorption state, where the transmitted light intensity is below or equal to the preset threshold but the absorption resistance is high. The second level indicates that the sample has a high-resistance absorption state, where the transmitted light intensity is above the preset threshold but the absorption resistance is high. This level indicates that the sample has processing difficulties only in terms of flowability or light transmittance, and it is necessary to determine whether to trigger the path switching mechanism based on the sample source type. The low processing load level indicates that the sample has a transmitted light intensity greater than the preset sample transmitted light intensity threshold during liquid aspiration, and the absorption resistance is low. That is, the sample has the characteristics of good light transmittance and low flow resistance, and the processing difficulty is low, so there is no need to trigger the path switching mechanism.

[0025] The sample number of the pet sample to be tested is matched with the sample information database to obtain the sample source type of the pet sample to be tested; The judgment is made based on the sample source type and processing load level of the pet samples to be tested: If the processing load level of the pet sample to be tested is high, it is determined that the current testing process may have excessive sample processing complexity, triggering the path switching mechanism. If the processing load level of the pet sample to be tested is medium processing load level, and the sample source type is fecal sample or nasal swab sample, then it is determined that the pet sample to be tested contains a lot of impurities or microbial particles, triggering the path switching mechanism. Otherwise, it is determined that the path switching mechanism will not be triggered.

[0026] It needs to be explained that the sample number is a unique identification code for the pet sample to be tested, used to match the sample with records in the sample information database to obtain related data such as the sample's source type and testing task information; the sample information database is a collection used to store the attribute information and related data of the pet samples to be tested. This database uses the sample number as an index and records the sample source type of each sample, such as fecal samples, nasal swab samples, and other sample attribute information, used to match and call relevant characteristic data of the sample in the testing process; the sample source type refers to the attribute identifier of the collection site or sample category of the pet sample to be tested, used to characterize the matrix characteristics of the sample; the path switching mechanism is a detection path adjustment strategy triggered when the processing load level of the pet sample to be tested is high, or although it is at a medium processing load level but the sample source type is a fecal sample or a nasal swab sample, it is used to switch the current testing process to a candidate detection path that is more suitable for high-complexity samples, so as to ensure the smoothness of the testing process and the reliability of the results.

[0027] By comprehensively assessing the processing load level of the pet sample under test using transmittance turbidity data and absorption retardation characteristics, and combining this with the sample source type to determine whether to trigger a path switching mechanism, the detection system can dynamically determine the actual state of the sample, thereby improving the adaptability of the detection process.

[0028] In step S3, in a pet pathogen detection system, multiple detection paths are typically preset to adapt to different sample processing needs. Different detection paths differ in terms of reagent usage, detection process complexity, and the number of pathogens they can support. When the system determines that a path switch is required, it needs to retrieve multiple candidate detection paths from the path configuration library and obtain the reagent consumption of each candidate detection path during the detection process to analyze the adaptability of different paths to sample aspiration volume and reagent usage. At the same time, the number of target pathogens involved in different detection tasks will also affect the selection of the detection process. During the path evaluation process, it is necessary to simultaneously obtain the number of target pathogens in the pet samples to be tested in the current detection task to provide basic data for subsequent adaptability analysis and screening of candidate detection paths. The sample number of the pet sample to be tested is matched with the path configuration library to obtain candidate detection paths; Read the detection liquid consumption corresponding to each candidate detection path through the path configuration library; The liquid consumption volume and the effective absorbable volume of the pet sample to be tested corresponding to each candidate detection path are standardized to obtain the liquid consumption factor and the absorbability factor. The liquid consumption compatibility coefficient is calculated by combining the liquid consumption factor and the absorption factor. The calculation formula is as follows: ,in, To absorb factors, As a liquid-consuming factor, This refers to the liquid consumption compatibility coefficient. The liquid consumption fit coefficient reflects the degree of matching between the liquid consumption of a candidate detection path and the effective absorbable volume of the pet sample to be tested. The larger the liquid consumption fit coefficient, the more reasonable the proportion of the liquid consumption of the path to the available sample volume, and the better the fit between its liquid consumption requirement and sample supply. The smaller the liquid consumption fit coefficient, the higher the liquid consumption of the path relative to the available sample volume, and the worse the fit between its liquid consumption requirement and sample supply. Access the testing database to retrieve the testing task information corresponding to the pet sample to be tested; Based on the detection task information corresponding to the pet sample to be tested, the target pathogen field is read through the field parsing unit, and the read target pathogen identifiers are integrated into a target pathogen list; The number of target pathogens in the pet samples to be tested is obtained by statistically analyzing the target pathogen identifiers in the target pathogen list.

[0029] It should be explained that the path configuration library refers to a collection that stores candidate detection paths and their corresponding parameters. This library uses the sample number as an index and records configuration information such as the detection liquid consumption of each candidate detection path. It is used to retrieve and match a suitable detection path for the current sample when the path switching mechanism is triggered. The detection task information refers to the relevant data set of the detection items that need to be performed on the pet sample to be tested. This information is stored in the detection database and includes parameters such as the target pathogen field. It is used to read and count the number of target pathogens through the field parsing unit. The field parsing unit is a unit that reads specific data fields from the detection task information. This unit parses the target pathogen field, extracts the identifiers of each target pathogen, and integrates them into a target pathogen list, which is used to count the number of target pathogens in the pet sample to be tested.

[0030] By retrieving candidate detection paths and obtaining the corresponding detection fluid consumption for each path, and combining this with the analysis of the number of target pathogens, a data foundation can be provided for evaluating the compatibility between different detection paths, thereby improving the rationality of detection path selection.

[0031] In step S4, the candidate detection paths are comprehensively evaluated. By analyzing the degree of matching between the reagent usage and pathogen detection requirements of the detection paths, the detection paths that are more suitable for the current detection task are identified, and the candidate detection paths that meet the conditions are screened and marked, so as to provide a reasonable path selection basis for the subsequent target pathogen detection treatment of the pet samples to be tested. The liquid consumption adaptation coefficient is compared with the preset liquid consumption adaptation coefficient threshold for judgment: If the liquid consumption adaptation coefficient is greater than or equal to the preset liquid consumption adaptation coefficient threshold, then the candidate detection path is determined to meet the sample liquid consumption condition. If the liquid consumption adaptation coefficient is less than the preset liquid consumption adaptation coefficient threshold, the candidate detection path is determined not to meet the sample liquid consumption condition. The number of target pathogens in the pet sample to be tested is compared with a preset pathogen quantity threshold, and the judgment is made in combination with the sample fluid consumption conditions: If a candidate detection path meets the sample consumption condition and the number of target pathogens in the pet sample to be tested is less than or equal to the preset pathogen number threshold, then the candidate detection path is determined to meet the detection requirements of the current detection task and is marked. If a candidate detection path does not meet the sample consumption condition, it is determined that the candidate detection path does not meet the detection requirements of the current detection task and is not marked. If the number of target pathogens in the pet sample to be tested is greater than the preset pathogen number threshold, then the candidate detection path is determined not to meet the detection requirements of the current detection task and is not marked. The target pathogen detection process is performed on the pet samples to be tested based on the marked candidate detection paths.

[0032] It should be explained that the preset liquid consumption adaptation coefficient threshold can be set according to the pipetting accuracy range of the detection equipment, the historical liquid consumption statistics of different detection paths, or the safety redundancy requirements of sample volume; the preset pathogen quantity threshold can be set according to the throughput limit of the detection reaction unit, the statistical distribution of pathogen quantity in common detection tasks, or the multiple detection capability of the detection reagent; the target pathogen detection processing refers to the complete operation process of performing target pathogen detection on the pet sample to be tested. This process includes reading the marked candidate detection path through the detection control unit and loading it into the process execution unit, controlling the automatic pipetting mechanism to distribute the sample according to the path parameters, adding the detection reagent through the reagent loading unit, mixing it through the reaction control unit, controlling the reaction according to the set conditions by the temperature control unit, and finally collecting the detection signal by the signal detection unit and transmitting it to the result analysis unit for analysis to obtain the target pathogen detection result.

[0033] By comprehensively evaluating the path adaptation characteristics based on the liquid consumption compatibility coefficient and the number of target pathogens, and by screening and marking candidate detection paths, the detection system can select a detection path that is more suitable for the current detection task, thereby reducing reagent consumption and improving the stability of target pathogen detection and processing.

[0034] Finally, it should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0035] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0036] In this document, the singular forms “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that terms such as “comprising / including” or “having” specify the presence of the stated features, integrals, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, integrals, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0037] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0038] The above description of the disclosed embodiments will enable those skilled in the art to make or use various modifications to these embodiments. It will be readily apparent to those skilled in the art that the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-parameter collaborative control method for a pet pathogen detection process, characterized in that: Includes the following steps: Step S1: When the effective absorbable volume of the pet sample to be tested is received through the sample volume recognition system, the peak negative pressure data of the aspiration peak and the turbidity data of the pet sample to be tested are read from the detection database, and the aspiration resistance characteristics are analyzed using the peak negative pressure data of the aspiration peak. Step S2: Evaluate the processing load level of the pet sample to be tested by combining the transmittance turbidity data and absorption retardation characteristics, collect the sample source type of the pet sample to be tested, and determine whether to trigger the path switching mechanism based on the processing load level. Step S3: When the path switching mechanism is triggered, the candidate detection path is retrieved through the path configuration library, the detection liquid consumption corresponding to each candidate detection path is collected and the liquid consumption adaptation coefficient is calculated, and the number of target pathogens in the pet samples to be tested in the current detection task is retrieved. Step S4: Evaluate the path adaptation features by combining the fluid consumption adaptation coefficient and the number of target pathogens. After screening and marking the candidate detection paths using the path adaptation features, perform target pathogen detection processing on the pet samples to be tested based on the marked candidate detection paths.

2. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 1, characterized in that: In step S1, the sample volume recognition system completes the sample aspiration action through the negative pressure aspiration device and receives the effective absorbable volume of the pet sample to be tested. The peak negative pressure data for liquid aspiration includes the instantaneous peak negative pressure during liquid aspiration and the negative pressure establishment response time; Access the detection database to read the negative pressure monitoring data sequence corresponding to the pet sample to be tested, the corresponding acquisition time, and the start time of the liquid aspiration drive mechanism; Traverse the negative pressure monitoring data sequence and select the pressure value with the largest absolute value in the negative pressure monitoring data sequence as the instantaneous peak negative pressure of liquid absorption; Subtract the acquisition time corresponding to the instantaneous peak negative pressure of liquid aspiration from the start time of the liquid aspiration drive mechanism to obtain the negative pressure establishment response time.

3. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 2, characterized in that: In step S1, the light transmittance turbidity data includes the sample transmitted light intensity, and the sample transmitted light intensity of the pet sample to be tested is read by accessing the detection database. The instantaneous peak negative pressure and negative pressure establishment response time of liquid absorption are standardized to obtain the negative pressure factor and time factor. The absorption retardation characteristic value of the pet sample under test was calculated by combining the negative pressure factor and the time factor; If the absorption retardation feature value is greater than or equal to the preset absorption retardation feature threshold, the absorption retardation feature of the pet sample to be tested is determined to be a high-retardation absorption state. Conversely, if the absorption retardation characteristics of the pet sample to be tested are determined to be low-resistance absorption.

4. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 3, characterized in that: In step S2, the transmitted light intensity of the pet sample to be tested is compared with a preset transmitted light intensity threshold, and a judgment is made in conjunction with the absorption retardation characteristics of the pet sample to be tested: If the sample transmitted light intensity is less than or equal to the preset sample transmitted light intensity threshold, and the absorption hindrance characteristic is a high hindrance absorption state, then the processing load level is determined to be a high processing load level. If the sample transmitted light intensity is less than or equal to the preset sample transmitted light intensity threshold, and the absorption hindrance characteristic is a low hindrance absorption state, then the processing load level is determined to be a medium processing load level. If the intensity of the transmitted light from the sample is greater than the preset threshold for the intensity of the transmitted light from the sample, and the absorption hindrance characteristic is a high hindrance absorption state, then the processing load level is determined to be a medium processing load level. If the intensity of the transmitted light from the sample is greater than the preset threshold for the intensity of the transmitted light from the sample, and the absorption hindrance characteristic is in a low hindrance absorption state, then the processing load level is determined to be a low processing load level.

5. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 4, characterized in that: In step S2, the sample number of the pet sample to be tested is matched with the sample information database to obtain the sample source type of the pet sample to be tested; The determination is made based on the sample source type and processing load level of the pet samples to be tested: If the processing load level is high, then the path switching mechanism will be triggered. If the processing load level is medium and the sample source type is fecal sample or nasal swab sample, then the path switching mechanism is triggered. Otherwise, it is determined that the path switching mechanism will not be triggered.

6. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 1, characterized in that: In step S3, the sample number of the pet sample to be tested is matched with the path configuration library to obtain candidate detection paths; Read the detection liquid consumption corresponding to each candidate detection path through the path configuration library; The liquid consumption volume and the effective absorbable volume of the pet sample to be tested corresponding to each candidate detection path are standardized to obtain the liquid consumption factor and the absorbability factor. The liquid consumption matching coefficient is obtained by combining the liquid consumption factor and the absorption factor.

7. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 1, characterized in that: In step S3, the detection database is accessed to read the detection task information corresponding to the pet sample to be tested; Based on the detection task information corresponding to the pet sample to be tested, the target pathogen field is read through the field parsing unit, and the read target pathogen identifiers are integrated into a target pathogen list; The number of target pathogens in the pet samples to be tested is obtained by statistically analyzing the target pathogen identifiers in the target pathogen list.

8. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 1, characterized in that: In step S4, the liquid consumption adaptation coefficient is compared with a preset liquid consumption adaptation coefficient threshold for determination: If the liquid consumption adaptation coefficient is greater than or equal to the preset liquid consumption adaptation coefficient threshold, the candidate detection path is determined to meet the sample liquid consumption condition. If the liquid consumption adaptation coefficient is less than the preset liquid consumption adaptation coefficient threshold, the candidate detection path is determined not to meet the sample liquid consumption condition.

9. The multi-parameter collaborative control method for a pet pathogen detection process according to claim 8, characterized in that: In step S4, the number of target pathogens in the pet sample to be tested is compared with a preset pathogen quantity threshold, and a judgment is made in conjunction with the sample consumption conditions: If a candidate detection path meets the sample consumption condition and the number of target pathogens is less than or equal to the preset pathogen number threshold, then the candidate detection path is marked. If a candidate detection path does not meet the sample liquid consumption condition, then the candidate detection path will not be marked. If the number of target pathogens exceeds the preset pathogen number threshold, the candidate detection path will not be marked. The target pathogen detection process is performed on the pet samples to be tested based on the marked candidate detection paths.

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