A multi-pathogen microfluidic detection and contact signal fusion monitoring system and method

Abstract

The application discloses a kind of multi-pathogen microfluidic detection and contact signal fusion monitoring system and method, including microfluidic chip module, contact interactive signal acquisition module, local control center module and internet of things communication module, biological detection is carried out to the sample to be measured by microfluidic chip, and the close contact interactive signal data of sample to be measured is synchronously acquired in detection process;The detection result and contact signal are time-aligned and structuredly associated, to generate associated detection data unit;Further based on the associated detection data unit, construct dynamic weight multi-pathogen fusion propagation network of fusion contact intensity, contact duration and time attenuation factor, carry out propagation risk assessment and key network node identification to network node, to realize the early identification of high-risk individual and the tracking analysis of propagation chain, can improve the timeliness and risk identification precision of infectious disease monitoring, suitable for public health monitoring and epidemic prevention and control scene.

Description

Technical Field

[0001] This invention relates to the fields of biological detection technology, public health monitoring technology, and Internet of Things information processing technology, specifically to a monitoring system and method for multi-pathogen microfluidic detection and contact signal fusion. Background Technology

[0002] In recent years, a number of infectious diseases, including Severe Acute Respiratory Syndrome (SARS), H1N1 influenza, Middle East Respiratory Syndrome (MERS), Ebola virus disease, Zika virus disease, and novel coronavirus infection, have broken out globally, significantly impacting public health systems and social operations. Rapid testing and transmission chain tracing are crucial technical means for epidemic monitoring and risk control in infectious disease prevention and control. Currently, infectious disease testing mainly relies on centralized laboratory testing, such as PCR-based nucleic acid testing. While these methods are highly accurate, their reliance on large equipment, professional personnel, and centralized sample transportation results in long testing cycles, making it difficult to meet the needs of rapid screening for large populations. To address this issue, point-of-care testing (POCT) technology based on microfluidic chips has demonstrated high application value in primary healthcare and on-site testing scenarios due to its fast testing speed, small device size, and simplified operation.

[0003] However, in practical applications, existing technologies still have the following significant drawbacks:

[0004] First, the detection data is fragmented and lacks information correlation mechanisms. Existing microfluidic detection devices are mostly deployed in a decentralized manner as independent terminals, with each device only outputting a single detection result. The data is usually stored locally or on a single cloud platform. Although some systems have achieved remote transmission, the lack of effective collaborative analysis capabilities between different terminals results in the detection network nodes being independent of each other at the system level, making it difficult to reflect the potential transmission relationships among the population as a whole.

[0005] Second, there is a disconnect between biometric testing results and contact tracing data. Although some existing systems can record contact information via Bluetooth or location technology, there is a lack of structured correlation between these contact relationships and actual biometric testing results. This makes it difficult for epidemic prevention personnel to effectively screen contact relationships based on confirmed cases, and consequently, to accurately determine the transmission route.

[0006] Third, there is insufficient modeling capability in scenarios involving the coexistence of multiple pathogens. In real-world scenarios, there are often complex situations where multiple infectious diseases with similar symptoms are circulating simultaneously. Because different pathogens (such as COVID-19 and influenza) differ in their transmissibility, incubation period, and transmission routes, existing systems typically analyze only a single pathogen, lacking the ability to uniformly perceive and integrate multi-pathogen detection signals and contact behavior characteristics. Furthermore, existing technologies lack mechanisms for selecting differentiated transmission rules based on different pathogen types, making it difficult to achieve accurate identification and collaborative analysis of transmission risks under conditions of parallel transmission of multiple pathogens.

[0007] In view of this, the present invention proposes a monitoring system and method for multi-pathogen microfluidic detection and contact signal fusion. Summary of the Invention

[0008] The purpose of this invention is to provide a monitoring system and method for multi-pathogen microfluidic detection and contact signal fusion, which aims to solve the technical problem that the multi-pathogen detection results and individual contact relationships in the existing infectious disease monitoring system are disconnected, making it difficult to build an association model that reflects complex transmission paths in the scenario of multiple pathogens coexisting, and thus causing insufficient accuracy and timeliness in the identification of transmission risks and the tracing of transmission chains.

[0009] In a first aspect, the present invention provides a monitoring system for multi-pathogen microfluidic detection and contact signal fusion, comprising a terminal carrier, a chip carrier structure disposed on the terminal carrier, an embedded cavity disposed within the chip carrier structure, a microfluidic chip disposed within the embedded cavity, a microfluidic driving device connected to the microfluidic chip, and a protective shell disposed outside the terminal carrier. The microfluidic chip is provided with at least two independent detection chambers for parallel detection of at least two target pathogens in the same sample, and outputs multi-pathogen detection results and detection timestamps.

[0010] The contact interaction signal acquisition module is used to acquire close-range contact interaction signal data of the sample under test within a preset backtracking time window. The contact interaction signal data includes at least contact intensity information, contact duration information, and contact time information.

[0011] The local control center module is used to perform time alignment processing on the multi-pathogen detection results and the contact interaction signal data based on the detection time information, and to establish an identification association between the sample to be tested and the contact object to generate an associated detection data unit. The associated detection data unit includes multi-pathogen detection results, detection time information and corresponding contact record information.

[0012] The IoT communication module is used to encapsulate the associated detection data unit and send it to the cloud platform.

[0013] As a preferred embodiment of the first aspect of the present invention, the microfluidic chip includes a reaction chamber and a serpentine mixing channel;

[0014] The microfluidic drive device drives the washing buffer to form a reciprocating flow between the reaction chamber and the serpentine mixing channel to achieve nucleic acid washing and purification; the serpentine mixing channel is also used to mix the amplification reaction reagents and deliver them to the amplification chamber, and the products of the amplification chamber enter different detection chambers through the splitting structure.

[0015] As a preferred embodiment of the first aspect of the present invention, the detection chamber includes a first detection chamber and a second detection chamber, wherein the first detection chamber and the second detection chamber are respectively provided with specific recognition systems for different target pathogens;

[0016] The specific recognition system includes recognition molecules and signal output components that match the target nucleic acid sequence, so that the amplification products entering different detection chambers will undergo recognition reactions in the corresponding detection chambers and output distinguishable detection signals to form multi-pathogen detection signals for subsequent transmission analysis.

[0017] As a preferred embodiment of the first aspect of the present invention, a protective shell disposed on the outside of the terminal carrier is used to encapsulate and protect the microfluidic chip and the microfluidic driving device.

[0018] As a preferred embodiment of the first aspect of the present invention, the associated detection data unit includes:

[0019] The multi-pathogen detection result field is used to record the detection results of multiple pathogens corresponding to different detection chambers;

[0020] The detection time field is used to record the detection time information corresponding to the multi-pathogen detection results;

[0021] The contact interaction signal field is used to record multiple contact records corresponding to the detection time; wherein each contact record includes at least contact object identification data, contact intensity data, contact duration data, and contact timestamp data.

[0022] Secondly, a monitoring method for multi-pathogen microfluidic detection and contact signal fusion, based on the implementation of the first aspect, includes the following steps:

[0023] The associated detection data unit is acquired, and the corresponding detection records and contact records are extracted according to the pathogen type. Multiple single pathogen transmission sub-networks are constructed. The terminal carrier corresponding to the detection object is used as the network node. The connection relationship between the network nodes is established based on the contact interaction signal data between the detection objects within the corresponding pathogen transmission time window. At the same time, the dynamic edge weight of the connection relationship in the single pathogen transmission sub-network is determined based on the contact intensity, contact duration, contact frequency and time decay factors.

[0024] Based on the transmission time window of the corresponding pathogen, the basic transmission probability parameters, the symptom matching parameters, and the transmission path screening conditions, the transmission risk assessment is performed on the network nodes in each single pathogen transmission sub-network to obtain the transmission risk value of the tested object for different pathogens.

[0025] For each single pathogen transmission subnetwork, a transmission calculation rule corresponding to its pathogen type is matched. The transmission calculation rule includes transmission characteristic parameters set for different pathogen types. Based on the transmission calculation rule, the transmission risk is calculated for the network nodes to obtain the transmission risk value of the same detection object for different pathogens.

[0026] When the same detected object has a transmission association in multiple single pathogen transmission sub-networks and the symptom information corresponds to multiple pathogens, the detected object is marked as a multi-pathogen attribution conflict node. The probability of attribution of different pathogens is compared by combining the multi-pathogen detection results, symptom matching values, upstream candidate network node detection results and dynamic edge weights. Based on the same network node identifier and overlapping contact relationship, each single pathogen transmission sub-network is connected to form a multi-pathogen fusion transmission network.

[0027] Network nodes that generate detection signals, have risk assessment needs, or are judged to be high-risk are identified as target network nodes. Starting from the target network node, a bidirectional propagation chain recursive tracing process is performed. The upstream propagation chain is restored by reverse tracing, and suspected infected groups are identified by forward screening. The propagation risk assessment results and early warning information are then output.

[0028] As a preferred embodiment of the second aspect of the present invention, the dynamic edge weights of the connection relationship... Determine according to the following formula:

[0029] ;

[0030] in, Represents network nodes With network nodes In pathogens The propagation of related edge rights, Indicates the contact strength coefficient. Indicates the contact duration coefficient. This represents the contact frequency coefficient. This represents the time decay coefficient.

[0031] As a preferred embodiment of the second aspect of the present invention, the transmission calculation rule includes transmission characteristic parameters set separately for different pathogens to adapt to the heterogeneity of different pathogens in terms of infectivity. The transmission characteristic parameters specifically include:

[0032] Based on the incubation period of the corresponding pathogen, a time span threshold is set to limit the recursive tracking depth of the network;

[0033] The basic probability constant is set based on the biological infectivity of the corresponding pathogen;

[0034] A probabilistic feature value that measures the consistency between the symptom characteristics of a network node and the typical clinical characteristics of the corresponding pathogen; the symptom characteristics include at least one of symptom type, time of occurrence, and severity;

[0035] The risk threshold for pruning low-weight edges during the propagation path search process;

[0036] Based on the pathogen type corresponding to the target network node, the matching transmission feature parameters are retrieved from the preset rule base and called to assign differentiated values ​​to the edge weights or network node attributes corresponding to each single pathogen transmission subnetwork in the multi-pathogen fusion transmission network.

[0037] As a preferred embodiment of the second aspect of the present invention, the combined processing includes:

[0038] Based on the dynamic edge weights, the basic propagation probability parameters, the symptom matching parameters, and the detection confidence level, the risk value of network nodes to pathogen transmission is calculated. The calculation formula is:

[0039] ;

[0040] in: Represents network nodes For pathogens The comprehensive risk of transmission; Indicates pathogen The basic propagation probability parameters; Represents network nodes Dynamic edge weights; Represents network nodes Symptom match value; Represents network nodes The detection confidence level corresponding to the detection result; Represents network nodes Detection confidence level when acting as an upstream propagator;

[0041] After obtaining the transmission risk values ​​of multiple pathogens for the target network nodes, a risk assessment is performed:

[0042] The maximum value among multiple pathogen transmission risk values ​​is selected as the final transmission risk assessment result of the network node, so as to achieve high-risk priority early warning;

[0043] The propagation risk values ​​are weighted and synthesized according to preset weights to obtain the comprehensive propagation risk value of the target network node;

[0044] When the transmission risk values ​​corresponding to at least two pathogens both meet the preset threshold conditions, the network node is marked as a composite risk node or a node with a conflicting affiliation.

[0045] As a preferred embodiment of the second aspect of the present invention, the logic of the bidirectional propagation chain recursive tracing is as follows:

[0046] Reverse tracing: Starting from the target network node, recursively search for upstream network nodes along the multi-pathogen fusion propagation network within a preset time window, and update the edge weight data of the corresponding connection relationship based on the contact intensity, contact duration and time decay results to determine candidate upstream propagation network nodes;

[0047] Forward investigation: Starting from the target network node, downstream network nodes are recursively searched along the multi-pathogen fusion transmission network within a preset time window. The risk value data of the corresponding network node is calculated based on the contact intensity, contact frequency and time decay results to identify network nodes suspected of being affected by transmission.

[0048] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0049] This invention introduces a contact interaction signal acquisition mechanism into the terminal carrier 1 and aligns and structurally associates the detection results with the contact signals in the time dimension to form an associated detection data unit containing the detection results and contact records. Based on this, a dynamic weighted multi-pathogen fusion transmission network integrating contact intensity, contact duration, and time decay factors is further constructed. Transmission risk assessment and key network node identification are performed on network nodes, ensuring that the detection results are no longer isolated but can be embedded in the transmission relationship for analysis. This enables early identification of high-risk individuals and effective tracking of the transmission chain. Therefore, compared to solutions relying solely on a single detection result or simple contact tracing, this application can improve the timeliness and positioning accuracy of infectious disease risk assessment while ensuring detection accuracy, thereby achieving precise intervention and effective control of the infectious disease transmission process. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a schematic diagram of the infectious disease monitoring system of the present invention;

[0052] Figure 2 This is a schematic diagram of the terminal carrier 1 of the present invention;

[0053] Figure 3 This is a schematic diagram of the internal structure of the microfluidic chip of the present invention;

[0054] Figure 4 This is a schematic diagram of the infectious disease monitoring system of the present invention;

[0055] In the diagram: 1. Terminal carrier; 2. Chip carrier structure; 3. Embedded cavity; 4. Microfluidic chip; 401. Reaction chamber; 402. First storage chamber; 403. Second storage chamber; 404. Third storage chamber; 405. Serpentine mixing channel; 406. Waste liquid chamber; 407. First detection chamber; 408. Second detection chamber; 409. Amplification chamber; 5. Microfluidic driving device; 6. Protective shell. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings.

[0057] Throughout the accompanying drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions. The described embodiments are only a part of the embodiments of this application, not all of them. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0058] Example 1

[0059] like Figure 1-2As shown, this embodiment provides a monitoring system for multi-pathogen microfluidic detection and contact signal fusion, including a terminal carrier 1 and a microfluidic chip 4 fixed on the terminal carrier 1, a contact interaction signal acquisition module, a local control center module, and an Internet of Things (IoT) communication module, which connects to a cloud platform based on the IoT communication module; wherein:

[0060] The chip carrier structure 2 is disposed on the terminal carrier 1 and is used to install the microfluidic chip 4 and define its detection position; the embedded cavity 3 is disposed in the chip carrier structure 2 and is used to accommodate the microfluidic chip 4; the microfluidic driving device 5 is connected to the microfluidic chip 4 and is used to drive the sample to be tested and the reaction reagent to flow in the microfluidic chip 4 according to a preset flow path; the protective shell 6 is disposed on the outside of the terminal carrier 1 and is used to encapsulate and protect the microfluidic chip 4 and the microfluidic driving device 5.

[0061] Furthermore, the microfluidic chip 4 can be configured as a nucleic acid detection chip, an immunoassay chip, or other biosensor chip suitable for infectious disease detection, depending on application requirements. Its detection results are output in the form of electrical signals, optical signals, fluorescence signals, or digital signals. The microfluidic chip 4 is installed within the embedding cavity 3 and completes sample processing and detection reactions under the action of the microfluidic driving device 5. Figure 2 As shown, taking a smartphone as an example, the microfluidic driving device 5 and the microfluidic chip 4 form an assembly relationship. By applying mechanical extrusion, pneumatic force, centrifugal force or other driving action to a predetermined area in the microfluidic chip 4, it achieves precise control of the microfluidic fluid, so that the sample to be tested flows in the microchannel and each functional chamber according to a predetermined path, completing the entire process of sample processing, reaction and detection.

[0062] The chip carrier structure 2 also fixes the embedded cavity 3 and the microfluidic driving device 5 to a predetermined position on the terminal carrier 1 to ensure the relative stability between the chip position, driving position, and signal acquisition position during the detection process. The terminal carrier 1 reads the detection signal generated by the microfluidic chip 4 and converts it into detection result data through its built-in camera module, optical components, or electrical interface circuit.

[0063] like Figure 3 As shown, the microfluidic chip 4 integrates a reaction chamber 401, a first storage chamber 402, a second storage chamber 403, a third storage chamber 404, a serpentine mixing channel 405, a waste liquid chamber 406, an amplification chamber 409, and independent first and second detection chambers 407 and 408. The chambers are interconnected via microfluidic channels, and the directional transport and reaction control of different reagents between the functional chambers are achieved through a preset fluid drive method. The specific detection process is as follows:

[0064] The sample to be tested is first introduced into the reaction chamber 401, which is pre-set with a solid-phase carrier or lysis medium for nucleic acid extraction. After the sample enters, the cells or viruses are lysed and the target nucleic acid molecules are released. At the same time, the nucleic acid is fixed or adsorbed on the surface of the solid-phase carrier, achieving preliminary enrichment and stable preservation.

[0065] Subsequently, the pre-stored washing buffer in the third storage chamber 404 enters the reaction chamber 401 under the action of the microfluidic drive device 5, and forms a reciprocating flow in the serpentine mixing channel 405 connected to it, thoroughly washing and purifying the nucleic acids on the surface of the solid-phase carrier, removing unbound proteins, impurities, and reaction residues. The washed waste liquid is introduced into the waste liquid chamber 406 for collection along the microfluidic channel.

[0066] After washing and purification, the amplification reaction-related reagents pre-stored in the first storage chamber 402 and the second storage chamber 403 are released sequentially, mixed in the connecting channel, and then transported to the reaction chamber 401 or the independent amplification chamber 409, where they come into contact with the fixed nucleic acid template and undergo an exponential amplification reaction. The amplification reaction can employ techniques such as polymerase chain reaction (PCR) or loop-mediated isothermal amplification (LAMP), with the appropriate temperature control method selected according to actual needs.

[0067] After amplification, the amplification products are transported to the detection area along a microfluidic channel under fluid-driven action, and then enter the first detection chamber 407 and the second detection chamber 408 through a quantitative distribution structure. The first detection chamber 407 contains a pre-set specific detection system for the first target pathogen (including recognition molecules and signal reporter components matching the target nucleic acid sequence), while the second detection chamber 408 contains a pre-set specific detection system for the second target pathogen. The amplification products undergo specific recognition reactions in their respective detection chambers, generating detectable signals, thereby enabling parallel differentiation and detection of different pathogens.

[0068] The first and second target pathogens are preferably respiratory pathogens with similar clinical manifestations, such as novel coronavirus and influenza A / B virus, respiratory syncytial virus and influenza virus, etc. By setting up multiple independent detection chambers and configuring different specific detection systems within the same chip, parallel detection or typing detection of multiple pathogens can be effectively achieved.

[0069] Furthermore, the serpentine mixing channel 405 extends the fluid path and enhances fluid turbulence, thereby achieving thorough mixing between different reagents and improving reaction efficiency. Fluid limiting structures or quantitative distribution structures can also be set in the microfluidic channel to ensure that the liquid volume entering each detection chamber is consistent, thereby improving the repeatability and accuracy of detection results.

[0070] Throughout the detection process, the terminal carrier 1, which controls the connectivity and fluid transport paths between chambers, is pre-defined by the microfluidic channel structure design. This allows sample input, nucleic acid extraction, washing and purification, amplification reaction, and multi-target detection to be automatically completed within the same chip in a predetermined sequence, achieving a fully integrated detection process from sample to multi-pathogen detection result output. The terminal carrier 1 can be a dedicated detection terminal, a smartphone, a tablet computer, or other electronic devices with signal acquisition and processing capabilities. The terminal carrier 1 also includes:

[0071] The contact interaction signal acquisition module is used to acquire near-field contact interaction signal data of the detected object within a preset backtracking time window. The contact interaction signal data includes the contact object identifier, contact intensity, contact duration, contact frequency, and contact timestamp. The contact intensity can be determined by Bluetooth signal strength, near-field communication signal strength, or other near-field sensing signals.

[0072] The local control center module controls the microfluidic detection process and performs time alignment processing on multi-pathogen detection results and contact interaction signal data based on the detection timestamp. This establishes an identification association between the detected object and the contact object, generating associated detection data units. Each associated detection data unit includes the detected object identifier, multi-pathogen detection results, detection time information, contact object identifier, contact intensity, contact duration, contact frequency, contact timestamp, and symptom information.

[0073] The symptom information includes at least one of the following: symptom type, symptom onset time, symptom duration, symptom severity, and number of symptoms. This symptom information can be input by the subject through terminal carrier 1 or recorded by the associated management terminal. Since different pathogens may correspond to the same or similar symptoms, such as fever, cough, fatigue, sore throat, difficulty breathing, or muscle aches, symptom information serves as a probabilistic auxiliary feature in subsequent transmission attribution analysis.

[0074] The IoT communication module is used to upload associated detection data units to the cloud platform. The cloud platform is used to receive associated detection data units uploaded by multiple microfluidic detection terminals, and construct multiple single pathogen transmission sub-networks based on multi-pathogen detection results, contact interaction signal data, and symptom information, and perform transmission risk assessment, key network node identification, reverse tracing, and forward investigation.

[0075] With the above structure, this embodiment enables the microfluidic detection terminal to not only output multi-pathogen detection results, but also to form data units associated with contact relationships in the same detection process, thereby avoiding the separation of detection data and contact data.

[0076] Example 2

[0077] Based on Example 1, this embodiment provides a monitoring method for multi-pathogen microfluidic detection and contact signal fusion, including the following steps:

[0078] Before the test begins, the test subject adds the sample to be tested into the sample inlet of the microfluidic chip 4, and then assembles the microfluidic chip 4 into the chip carrier structure 2. The embedding cavity 3 positions and fixes the microfluidic chip 4. After the test subject triggers the test process through the terminal carrier 1, the local control center module controls the microfluidic drive device 5 to start.

[0079] During the detection execution phase, the microfluidic drive device 5 drives the sample to be tested sequentially through the reaction chamber 401, the serpentine mixing channel 405, and the amplification chamber 409, completing sample lysis, nucleic acid extraction, washing and purification, and amplification reactions. After amplification, the amplification products enter the first detection chamber 407 and the second detection chamber 408, respectively, and react with the specific recognition system of the corresponding pathogens, outputting detection signals corresponding to multiple pathogens.

[0080] The local control center module interprets the detection signals and generates multi-pathogen detection results. These results include the pathogen type, detection result, detection confidence level, and detection timestamp for each detection chamber.

[0081] During the data generation phase, the local control center module uses the detection timestamp as a reference to extract contact interaction signal data within a preset retrospective time window from the contact interaction signal acquisition module, and aligns the multi-pathogen detection results with the contact interaction signal data in time. For any contact record, the local control center module records the contact object identifier, contact intensity, contact duration, contact frequency, and contact timestamp, and encapsulates the above data together with the detection object identifier, multi-pathogen detection results, and symptom information into a correlated detection data unit.

[0082] During the data upload phase, the IoT communication module uploads the associated detection data units to the cloud platform. The cloud platform parses, deduplicates, and sorts the associated detection data units from multiple detection terminals by time, forming a dataset for propagation analysis.

[0083] Example 3

[0084] This embodiment, based on Embodiments 1 and 2, describes the process of multi-pathogen transmission risk analysis performed by a cloud platform using associated detection data units. This process addresses the problem that when multiple infectious diseases with similar symptoms are prevalent simultaneously, a single transmission network may struggle to distinguish the source of the pathogen, the transmission route, and overlapping exposure relationships.

[0085] After receiving the associated detection data units, the cloud platform constructs multiple single-pathogen transmission sub-networks according to pathogen type. Each single-pathogen transmission sub-network corresponds to a target pathogen. The network nodes in the single-pathogen transmission sub-network are the detection objects, and the edges represent the contact relationships formed between the detection objects within a preset transmission time window. The state of the network nodes is determined by the detection results, symptom information, and risk level, while the edge weights are determined by the contact intensity, contact duration, contact frequency, and time decay results.

[0086] For any two detected objects, if they have contact records within the transmission time window corresponding to a certain pathogen, the cloud platform establishes an edge in the single pathogen transmission sub-network corresponding to that pathogen. The dynamic edge weight is determined as follows:

[0087] ;

[0088] in, Represents network nodes With network nodes In pathogens The propagation of related edge rights, This represents the contact strength coefficient, if the original contact strength comes from the distance. The closer the distance, the higher the risk. , This is the distance attenuation parameter; Indicates the contact duration coefficient. ,but: This refers to the cumulative duration of contact. For time scale parameters; This represents the contact frequency coefficient. ,but: To determine the number of contacts within a preset transmission time window, For frequency scale parameters; Indicates the time decay coefficient. ,but: This is the time difference between the contact time and the detection or analysis time. pathogen The corresponding time decay parameter.

[0089] Contact strength coefficient Determined based on close-range contact signal strength; contact duration coefficient. Determined based on the time interval to which the contact duration belongs; contact frequency coefficient Determined based on the number of contacts within the same propagation time window; time decay coefficient. Determined based on the time difference between contact time and detection time or current analysis time. The closer the contact time is to the detection time or symptom onset time, the higher the time decay coefficient. The larger the coefficient, the further the time between the contact time and the detection time or the onset of symptoms, the greater the time decay coefficient. The smaller.

[0090] The cloud platform configures different transmission calculation rules for different pathogens. These rules include a transmission time window, basic transmission probability parameters, symptom matching parameters, and transmission path screening conditions. For pathogens with short incubation periods, a shorter transmission time window is configured; for pathogens with long incubation periods, a longer transmission time window is configured. The cloud platform calls the corresponding transmission calculation rule based on the pathogen type in the detection results, enabling the same contact record to obtain different transmission weights in different single-pathogen transmission sub-networks.

[0091] The cloud platform further calculates symptom matching values ​​based on symptom information. For a target network node, the cloud platform compares its symptom type, onset time, duration, and severity with the symptom feature sets corresponding to each pathogen to obtain the symptom matching value of the target network node relative to different pathogens. When symptom information is missing, ambiguous, or incomplete, the cloud platform uses default probabilities, historical statistical features, or symptom information from adjacent network nodes for compensation to avoid interruption of transmission attribution analysis.

[0092] After multiple single-pathogen transmission sub-networks are constructed, the cloud platform connects these sub-networks based on shared network nodes, similar symptom presentations, and overlapping contact relationships, forming a multi-pathogen fusion transmission network. Shared network nodes represent the cross-status of the same detected object across multiple pathogen transmission sub-networks; similar symptom presentations indicate the possibility of pathogen attribution conflicts between different pathogens; and overlapping contact relationships indicate that the same contact behavior may participate in multiple pathogen transmission pathways.

[0093] When a target network node has propagation associations in multiple single-pathogen transmission sub-networks, and its symptom information matches multiple pathogens simultaneously, the cloud platform marks the target network node as a multi-pathogen attribution conflict node. For multi-pathogen attribution conflict nodes, the cloud platform combines microfluidic detection results, symptom matching values, propagation time windows, upstream candidate network node detection results, and dynamic edge weights to compare the probability of attribution to different pathogens.

[0094] For the target network node In pathogens The risk of transmission is denoted as follows: Let its upstream candidate set be... Then, calculate the corresponding transmission risk value for each of the multiple pathogens. :

[0095] ;

[0096] in: Represents network nodes For pathogens The comprehensive risk of transmission; Indicates pathogen The basic propagation probability parameters; Represents network nodes Dynamic edge weights; Represents network nodes Symptom match value; Represents network nodes The detection confidence level corresponding to the detection result; Represents network nodes Detection confidence level when acting as an upstream propagator.

[0097] When a target network node corresponds to multiple pathogen transmission risk values, the cloud platform calculates the transmission risk value for each pathogen separately and determines the candidate pathogen type based on the magnitude of the risk value. If multiple pathogen transmission risk values ​​all meet the preset risk level conditions, the target network node is marked as a composite risk node, and the multi-pathogen risk assessment result is output.

[0098] In a preferred embodiment, the cloud platform can perform multi-pathogen combination processing on multiple pathogen candidate results corresponding to the same target network node. The multi-pathogen combination processing includes at least one of the following methods:

[0099] a) Calculate the transmission risk value of each pathogen corresponding to the target network node, and output each transmission risk value separately to form a parallel risk result for multiple pathogens;

[0100] b. The transmission risk values ​​corresponding to multiple pathogens are weighted and synthesized according to preset weights to obtain the comprehensive transmission risk value of the target network node. The preset weights can be determined based on pathogen transmission capacity, detection confidence, symptom matching degree, historical epidemic intensity or preset management strategy.

[0101] c. Select the maximum value from the transmission risk values ​​corresponding to multiple pathogens as the final transmission risk value of the target network node, so as to achieve high-risk priority early warning;

[0102] d. Compare the transmission risk values ​​corresponding to multiple pathogens with the corresponding risk thresholds. When the transmission risk values ​​corresponding to at least two pathogens meet the preset conditions, mark the target network node as a composite risk node, a multi-pathogen associated network node, or a multi-pathogen belonging conflict node.

[0103] e. Group multiple pathogens based on their transmission routes, symptom similarity, test result categories, or transmission time windows, and perform risk comparisons within groups and weighted synthesis or priority determination between groups.

[0104] In a preferred embodiment, when a target network node corresponds to multiple pathogen transmission risk values, the cloud platform performs combined processing on the transmission risk values ​​corresponding to the multiple pathogens. The combined processing may include at least one of the following:

[0105] 1. Weighted composition:

[0106] ;

[0107] in, For the target network node to the first The risk value of transmission of each pathogen For the corresponding weights;

[0108] 2. Maximum value selection:

[0109] .

[0110] In this way, the cloud platform can incorporate the detection results of different pathogens into the corresponding single pathogen transmission sub-networks in scenarios where multiple pathogens coexist, and form a multi-pathogen fusion transmission network through common network nodes, similar symptoms and overlapping contact relationships, thereby improving the completeness of transmission risk analysis.

[0111] Example 4

[0112] This embodiment, based on embodiment 3, explains the reverse tracing and forward investigation processes.

[0113] During the reverse tracing process, the cloud platform uses the target network node as the starting point and searches for candidate parent network nodes in the corresponding single pathogen transmission sub-network. A candidate parent network node must simultaneously meet the following conditions: there is a contact record between the candidate parent network node and the target network node; the contact time is earlier than the target network node's detection time or symptom onset time; the time difference between the contact time and the target network node's detection time or symptom onset time falls within the corresponding pathogen transmission time window; and the candidate parent network node's detection results, symptom matching values, or risk level meet the transmission source screening criteria.

[0114] The cloud platform ranks candidate parent network nodes based on dynamic edge weights, symptom matching values, test result confidence levels, and transmission time differences. The candidate parent network nodes with higher rankings are identified as more likely upstream sources of transmission and used as the tracking network nodes for the next round of reverse attribution. If a target network node corresponds to multiple pathogen candidate attributions, the cloud platform performs reverse attribution in multiple single-pathogen transmission sub-networks, comparing the paths of candidate parent network nodes for different pathogens to determine the more likely pathogen source and transmission chain.

[0115] During the forward screening process, the cloud platform uses identified high-risk target network nodes as parent or key network nodes, and searches for candidate sub-network nodes in the corresponding single-pathogen transmission sub-network or multi-pathogen fusion transmission network. Candidate sub-network nodes must simultaneously meet the following conditions: there is a contact record between the candidate sub-network node and the target network node; the contact time is later than the symptom onset time, detection time, or high-risk determination time of the target network node; the contact time is within the corresponding pathogen transmission time window; and the candidate sub-network node's symptom matching value, contact edge weight, or detection result meets the risk stratification conditions.

[0116] The cloud platform categorizes candidate sub-network nodes into different risk levels based on their transmission risk values ​​and generates corresponding treatment recommendations. These recommendations include retesting, isolation, reducing contact, confirmatory testing, or continuous observation. When a candidate sub-network node is associated with transmission in multiple single-pathogen transmission sub-networks, the cloud platform combines the multi-pathogen detection results and symptom information of that candidate sub-network node to correct its pathogen attribution and adjust its risk level.

[0117] If a newly discovered network node is identified as a high-risk network node during reverse tracing or forward investigation, the cloud platform adds the newly discovered network node to the tracking queue and continues recursive tracking. Simultaneously, it updates the network node status, edge weights, and risk levels in the multi-pathogen fusion transmission network. Through this recursive update process, the system can gradually reconstruct the potential transmission chain and provide tiered alerts for high-risk individuals.

[0118] Example 5

[0119] This embodiment, based on Embodiments 3 and 4, illustrates the application process of multi-pathogen fusion transmission networks in actual public health monitoring scenarios.

[0120] In scenarios where multiple respiratory infectious diseases coexist, subject A completes sample testing via a microfluidic detection terminal. Microfluidic chip 4 detects the first target pathogen in the first detection chamber 407 and the second target pathogen in the second detection chamber 408, generating corresponding multi-pathogen detection results. Subject A simultaneously records symptom information, including fever, cough, and sore throat, via terminal carrier 1, and the system records the time of symptom onset.

[0121] The local control center module uses the detection time as a benchmark to extract contact records between detection object A and detection objects B, C, and D within a preset backtracking time window, and generates associated detection data units. The IoT communication module uploads these associated detection data units to the cloud platform.

[0122] Based on the multi-pathogen detection results of subject A, the cloud platform maps subject A to the single-pathogen transmission sub-network corresponding to the first pathogen and the single-pathogen transmission sub-network corresponding to the second pathogen. If there is a high contact intensity and a long contact duration between subject A and subject B, and the contact time falls within the first pathogen transmission time window, the cloud platform establishes an edge between A and B in the first pathogen transmission sub-network and calculates the dynamic edge weight. If the contact time between subject A and subject C both fall within the second pathogen transmission time window, the cloud platform establishes a corresponding edge in the second pathogen transmission sub-network.

[0123] When the symptom information of test subject A matches both the first pathogen and the second pathogen, the cloud platform marks test subject A as a multi-pathogen conflict node. Combining microfluidic detection results, symptom matching values, candidate parent network node detection results, and dynamic edge weights, the platform calculates the transmission risk value of test subject A for different pathogens. If the transmission risk value of the first pathogen is higher than that of the second pathogen, the cloud platform considers the first pathogen as the primary candidate pathogen type for test subject A; if both risk values ​​meet the risk level criteria, test subject A is marked as a composite risk node.

[0124] Subsequently, the cloud platform performs reverse tracing with the test subject A as the target network node, searching for candidate parent network nodes that had contact with the test subject A before the onset of symptoms and that meet the transmission time window; at the same time, it performs forward screening with the test subject A as the key network node, searching for candidate child network nodes that had contact with the test subject A after the high-risk assessment, and provides tiered prompts for the candidate child network nodes based on the transmission risk value.

[0125] Through the above application process, the system can integrate multi-pathogen detection results, contact interaction signals, and symptom information into the transmission risk analysis. It can not only identify single pathogen transmission chains, but also identify cross-contact, overlapping exposure, and pathogen attribution conflict nodes in the case of multi-pathogen coexistence, thereby improving the completeness of transmission chain tracing and the accuracy of transmission risk assessment.

[0126] This embodiment integrates multi-pathogen microfluidic detection, contact interaction signal acquisition, and multi-pathogen transmission risk analysis into a single monitoring system. This allows for a structured correlation between the multi-pathogen detection results of the detected object and the contact intensity, duration, frequency, and timestamp within the corresponding time window. Based on this, the cloud platform constructs single-pathogen transmission sub-networks and forms a multi-pathogen fusion transmission network through identical network nodes, similar symptom presentations, and overlapping contact relationships. This enables joint judgment of the transmission paths of different pathogens within the same analytical framework. Furthermore, the transmission risk value is calculated using dynamic edge weights, symptom matching values, transmission time windows, and detection confidence levels, achieving parent network node screening, sub-network node expansion, pathogen attribution correction, and risk stratification alerts. Therefore, compared to solutions relying solely on a single detection result or a single contact transmission network, this application improves the pre-emptiveness of high-risk individual identification, the completeness of transmission chain tracing, and the accuracy of transmission risk assessment in multi-pathogen coexistence scenarios.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A monitoring system for multi-pathogen microfluidic detection and contact signal fusion, characterized in that, The device includes a terminal carrier (1), a chip carrier structure (2) disposed on the terminal carrier (1), an embedded cavity (3) disposed in the chip carrier structure (2), a microfluidic chip (4) disposed in the embedded cavity (3), a microfluidic driving device (5) connected to the microfluidic chip (4), and a protective shell (6) disposed on the outside of the terminal carrier (1). The microfluidic chip (4) is provided with at least two independent detection chambers for parallel detection of at least two target pathogens in the same sample, and outputs multi-pathogen detection results and detection timestamps. The contact interaction signal acquisition module is used to acquire close-range contact interaction signal data of the sample under test within a preset backtracking time window. The contact interaction signal data includes at least contact intensity information, contact duration information, and contact time information. The local control center module is used to perform time alignment processing on the multi-pathogen detection results and the contact interaction signal data based on the detection time information, and to establish an identification association between the sample to be tested and the contact object to generate an associated detection data unit. The associated detection data unit includes multi-pathogen detection results, detection time information and corresponding contact record information. The IoT communication module is used to encapsulate the associated detection data unit and send it to the cloud platform.

2. The monitoring system for multi-pathogen microfluidic detection and contact signal fusion according to claim 1, characterized in that, The microfluidic chip (4) includes a reaction chamber (401) and a serpentine mixing channel (405). The microfluidic driving device (5) drives the washing buffer to form a reciprocating flow between the reaction chamber (401) and the serpentine mixing channel (405) to achieve nucleic acid washing and purification; The serpentine mixing channel (405) is also used to mix the amplification reaction reagents and deliver them to the amplification chamber (409), and the products of the amplification chamber (409) enter different detection chambers through the splitting structure.

3. The monitoring system for multi-pathogen microfluidic detection and contact signal fusion according to claim 2, characterized in that, The detection chamber includes a first detection chamber (407) and a second detection chamber (408), and the first detection chamber (407) and the second detection chamber (408) are respectively equipped with specific recognition systems for different target pathogens; The specific recognition system includes recognition molecules and signal output components that match the target nucleic acid sequence, so that the amplification products entering different detection chambers will undergo recognition reactions in the corresponding detection chambers and output distinguishable detection signals to form multi-pathogen detection signals for subsequent transmission analysis.

4. The monitoring system for multi-pathogen microfluidic detection and contact signal fusion according to claim 1, characterized in that, The protective shell (6) disposed on the outside of the terminal carrier (1) is used to encapsulate and protect the microfluidic chip (4) and the microfluidic driving device (5).

5. The monitoring system for multi-pathogen microfluidic detection and contact signal fusion according to claim 1, characterized in that, The associated detection data unit includes: The multi-pathogen detection result field is used to record the detection results of multiple pathogens corresponding to different detection chambers; The detection time field is used to record the detection time information corresponding to the multi-pathogen detection results; The contact interaction signal field is used to record multiple contact records corresponding to the detection time; wherein each contact record includes at least contact object identification data, contact intensity data, contact duration data, and contact timestamp data.

6. A monitoring method for multi-pathogen microfluidic detection and contact signal fusion, based on the implementation of the monitoring system for multi-pathogen microfluidic detection and contact signal fusion as described in any one of claims 1-5, characterized in that, Data analysis of the associated detection data units in the cloud platform includes the following steps: Acquire associated detection data units, extract corresponding detection records and contact records according to pathogen types, and construct multiple single pathogen transmission sub-networks. Among them, the terminal carrier (1) corresponding to the detection object is used as the network node, and the connection relationship between the network nodes is established based on the contact interaction signal data between the detection objects within the corresponding pathogen transmission time window. At the same time, the dynamic edge weight of the connection relationship in the single pathogen transmission sub-network is determined based on the contact intensity, contact duration, contact frequency and time decay factors. Based on the transmission time window of the corresponding pathogen, the basic transmission probability parameters, the symptom matching parameters, and the transmission path screening conditions, the transmission risk assessment is performed on the network nodes in each single pathogen transmission sub-network to obtain the transmission risk value of the tested object for different pathogens. For each single pathogen transmission subnetwork, a transmission calculation rule corresponding to its pathogen type is matched. The transmission calculation rule includes transmission characteristic parameters set for different pathogen types. Based on the transmission calculation rule, the transmission risk is calculated for the network nodes to obtain the transmission risk value of the same detection object for different pathogens. When the same detected object has a transmission association in multiple single pathogen transmission sub-networks and the symptom information corresponds to multiple pathogens, the detected object is marked as a multi-pathogen attribution conflict node. The probability of attribution of different pathogens is compared by combining the multi-pathogen detection results, symptom matching values, upstream candidate network node detection results and dynamic edge weights. Based on the same network node identifier and overlapping contact relationship, each single pathogen transmission sub-network is connected to form a multi-pathogen fusion transmission network. Network nodes that generate detection signals, have risk assessment needs, or are judged to be high-risk are identified as target network nodes. Starting from the target network node, a bidirectional propagation chain recursive tracing process is performed. The upstream propagation chain is restored by reverse tracing, and suspected infected groups are identified by forward screening. The propagation risk assessment results and early warning information are then output.

7. The monitoring method for multi-pathogen microfluidic detection and contact signal fusion according to claim 6, characterized in that, The dynamic edge weights of the connection relationship Determine according to the following formula: ； in, Represents network nodes With network nodes In pathogens The propagation of related edge rights, Indicates the contact strength coefficient. Indicates the contact duration coefficient. This represents the contact frequency coefficient. This represents the time decay coefficient.

8. The monitoring method for multi-pathogen microfluidic detection and contact signal fusion according to claim 7, characterized in that, In the single pathogen transmission subnetwork, the corresponding transmission calculation rule is selected based on the pathogen detection results corresponding to the network node to adapt to the transmission characteristic parameters of different pathogens. The transmission characteristic parameters specifically include: Based on the incubation period of the corresponding pathogen, a time span threshold is set to limit the depth of recursive tracking in the network; The basic probability constant is set based on the biological infectivity of the corresponding pathogen; A probabilistic feature value that measures the consistency between the symptom characteristics of a network node and the typical clinical characteristics of the corresponding pathogen; the symptom characteristics include at least one of symptom type, time of occurrence, and severity; The risk threshold for performing pruning on low-weight edges during the propagation path search process; Based on the pathogen type corresponding to the target network node, the matching transmission feature parameters are retrieved from the preset rule base and called to assign differentiated values ​​to the edge weights or network node attributes corresponding to each single pathogen transmission subnetwork in the multi-pathogen fusion transmission network.

9. The monitoring method for multi-pathogen microfluidic detection and contact signal fusion according to claim 8, characterized in that, The combined processing includes: Based on the dynamic edge weights, basic transmission probability parameters, symptom matching parameters, and detection confidence scores in the single pathogen transmission subnetwork, the transmission risk value of network nodes to the pathogen is calculated. The calculation formula is: ； in: Represents network nodes For pathogens The comprehensive risk of transmission; Indicates pathogen The basic propagation probability parameters; Represents network nodes Dynamic edge weights; Represents network nodes Symptom match value; Represents network nodes The detection confidence level corresponding to the detection result; Represents network nodes Detection confidence level when acting as an upstream propagator; After obtaining the transmission risk values ​​of multiple pathogens for the target network nodes, a risk assessment is performed: The maximum value among multiple pathogen transmission risk values ​​is selected as the final transmission risk assessment result of the network node, so as to achieve high-risk priority early warning; The propagation risk values ​​are weighted and synthesized according to preset weights to obtain the comprehensive propagation risk value of the target network node; When the transmission risk values ​​corresponding to at least two pathogens both meet the preset threshold conditions, the network node is marked as a composite risk node or a node with a conflicting affiliation.

10. The monitoring method for multi-pathogen microfluidic detection and contact signal fusion according to claim 6, characterized in that, The logic for the recursive tracing of the bidirectional propagation chain is as follows: Reverse tracing: Starting from the target network node, recursively search for upstream network nodes along the multi-pathogen fusion propagation network within a preset time window, and update the edge weight data of the corresponding connection relationship based on the contact intensity, contact duration and time decay results to determine candidate upstream propagation network nodes; Forward investigation: Starting from the target network node, downstream network nodes are recursively searched along the multi-pathogen fusion transmission network within a preset time window. The risk value data of the corresponding network node is calculated based on the contact intensity, contact frequency and time decay results to identify network nodes suspected of being affected by transmission.

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