Intelligent scheduling and resource management system for microbiological testing laboratories

Abstract

The present invention discloses an intelligent scheduling and resource management system for a microbial testing laboratory, which relates to the field of intelligent management technology. The system comprises a resource allocation module, which uses a quantum computing adaptation modeling method to perform spatiotemporal resource allocation with biosafety constraints, generate a task scheduling optimization problem, and use a quantum annealing algorithm to search for the optimal solution to obtain a benchmark resource scheduling plan; a scheduling module, which uses a preset public health risk monitoring threshold for analysis, uses a dynamic resource reorganization algorithm to adjust resource usage and allocation, and obtains resource scheduling instructions; a collaboration module, which uses a metabolomics data analysis method to predict laboratory reagent consumption trends, and uses a GAN network to construct a cross-laboratory emergency resource sharing path and output an emergency resource sharing plan; the present invention uses quantum computing adaptation modeling to convert biosafety isolation rules into quantum bit coupling relationships, thereby improving the resource scheduling efficiency and instrument utilization of high-level experiments.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, in particular to an intelligent scheduling and resource management system for a microbiological detection laboratory. Background Art

[0002] Resource scheduling and safety management technologies for microbiology testing laboratories have been gradually evolving toward intelligent systems in recent years. Traditional methods primarily rely on manual scheduling based on static rules (such as CLSI standards). However, existing technologies have incorporated graph network models to detect spatiotemporal conflicts and employ heuristic algorithms to optimize resource allocation. The application of quantum computing to combinatorial optimization problems offers new insights into high-dimensional resource scheduling. For example, the D-Wave system has attempted to solve simple laboratory instrument scheduling problems.

[0003] Current microbiology laboratory management systems struggle to dynamically couple biosafety levels with resource scheduling. Traditional approaches typically employ fixed permission allocation mechanisms, which are unable to dynamically adjust instrument and personnel access permissions based on real-time testing task risks (such as those associated with BSL-3 pathogen manipulation). This leads to conflicts between isolation measures for high-level experiments and resource scheduling requirements. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an intelligent scheduling and resource management system for microbiological testing laboratories to solve the problem of difficulty in achieving dynamic coupling between biosafety levels and resource scheduling.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an intelligent scheduling and resource management system for a microbiological detection laboratory, which includes:

[0008] The data acquisition module collects multi-dimensional resource data of the microbial testing laboratory and performs preprocessing; the isolation module divides resource usage permissions through the dynamic empowerment method of biosafety levels to obtain a resource permission constraint table, and uses the space-time graph network model to perform security isolation analysis and generate a security isolation task queue; the resource allocation module uses the quantum computing adaptation modeling method to perform space-time resource allocation with biosafety constraints, generate a task scheduling optimization problem, and uses the quantum annealing algorithm to search for the optimal solution to obtain a benchmark resource scheduling plan; the scheduling module analyzes through preset public health risk monitoring thresholds, uses a dynamic resource reorganization algorithm to adjust resource usage and allocation, and obtains resource scheduling instructions; the collaboration module uses metabolomics data analysis methods to predict laboratory reagent consumption trends, and constructs cross-laboratory emergency resource sharing paths through the GAN network to output emergency resource sharing plans; the optimization module combines the emergency resource sharing plan with the benchmark resource scheduling plan, performs iterative optimization through a multi-objective reinforcement learning method, and generates the optimal microbial testing laboratory operation strategy.

[0009] As a preferred solution of the intelligent scheduling and resource management system of the microbiological testing laboratory of the present invention, the following is provided:

[0010] The multi-dimensional resource data of the microbiological testing laboratory includes laboratory instrument status data, personnel flow data, environmental parameter data, reagent and consumables inventory data, sample processing progress data and biosafety level data;

[0011] The preprocessing includes data cleaning, format standardization, key parameter dimensionality reduction and time series alignment.

[0012] As a preferred solution of the intelligent scheduling and resource management system of the microbiological detection laboratory of the present invention, wherein: the resource use rights are divided by the dynamic weighting method of biosafety level to obtain a resource authority constraint table, the specific steps are as follows:

[0013] Based on the pre-processed multi-dimensional resource data, a multi-factor weighted fusion is performed through the fuzzy comprehensive evaluation algorithm to generate a safety level scoring matrix;

[0014] The security level scoring matrix and the pre-processed multi-dimensional resource data are analyzed in time and space to obtain the laboratory operation permission conflict matrix. The rule reasoning engine is then used to match the multi-dimensional constraints to generate a preliminary permission mapping table.

[0015] The preliminary permission mapping table is combined with the preprocessed multi-dimensional resource data, and multi-dimensional conflict detection is performed through the graph coloring algorithm to obtain a prioritized permission conflict list. Combined with historical violation record data, the permission weights are iteratively optimized through the reinforcement learning algorithm to output the resource permission constraint table.

[0016] As a preferred solution of the intelligent scheduling and resource management system of the microbiological detection laboratory of the present invention, the specific steps of generating a safe isolation task queue are as follows:

[0017] Based on the resource permission constraint table, the rule matching engine identifies high-risk operations and isolates them to obtain an isolation combination list;

[0018] Based on the historical isolation combination list, the spatiotemporal graph network model is trained through the spatiotemporal graph attention mechanism method, and the overlapping resource conflict relationship graph with time window is output;

[0019] The overlapping resource conflict relationship graph is analyzed through a heuristic scheduling algorithm to resolve resource conflicts and generate a security isolation strategy. The task urgency is evaluated using a multi-objective optimization algorithm to obtain a security isolation task queue with priority markings.

[0020] As a preferred solution for the intelligent scheduling and resource management system of the microbial detection laboratory described in the present invention, the quantum computing adaptive modeling method is used to perform spatiotemporal resource allocation under biosafety constraints and generate a task scheduling optimization problem. The specific steps are as follows:

[0021] Based on a secure isolated task queue, a quantum bit mapping algorithm is used to convert high-risk operations into binary decision variable groups, and output conflicting operation vectors with weight encoding.

[0022] Through the biosafety constraint embedding method, the isolation combination list is converted into chain coupling strength, generating a Hamiltonian containing spatiotemporal isolation constraints, and then converted into a task scheduling optimization problem through the QUBO-to-Ising conversion rule.

[0023] As a preferred solution of the intelligent scheduling and resource management system of the microbial detection laboratory of the present invention, the quantum annealing algorithm is used to search for the optimal solution and obtain the benchmark resource scheduling solution. The specific steps are as follows:

[0024] Through the D-Wave quantum processor interface, the optimization problem elements in the task scheduling optimization problem are converted into quantum bit coupling parameters and local magnetic field parameters to obtain a binary input stream;

[0025] The optimal original bit state solution set is obtained by performing a ground state search on the D-Wave quantum processor using the quantum annealing algorithm;

[0026] The optimal original bit state solution set is decoded and verified through classical post-processing methods, invalid original bit state solutions that do not meet the constraints are eliminated, and feasible scheduling solutions are generated;

[0027] Through the greedy matching algorithm, the feasibility scheduling solution is mapped to the three elements of laboratory spatiotemporal resource allocation, and the benchmark resource scheduling plan is output.

[0028] As a preferred solution of the intelligent scheduling and resource management system of the microbiological testing laboratory of the present invention, the following specific steps are taken:

[0029] Based on the baseline resource scheduling plan, the system analyzes the preset public health risk monitoring thresholds and outputs a set of risk operation markers that exceed the preset public health risk monitoring thresholds;

[0030] Through adaptive genetic algorithms, the resource usage and allocation of the three elements of laboratory time and space resource allocation are dynamically adjusted to generate resource scheduling instructions.

[0031] As a preferred solution of the intelligent scheduling and resource management system of the microbial detection laboratory of the present invention, wherein: the metabolomics data analysis method is used to predict the consumption trend of laboratory reagents. The specific steps are as follows:

[0032] Based on resource scheduling instructions, the experimental record parsing engine extracts historical laboratory reagent consumption data, performs time alignment and outlier removal, and outputs a timestamped laboratory reagent usage sequence.

[0033] Based on the laboratory reagent usage sequence, the LC-MS metabolomics peak area integration method is used to extract the concentration and time curve characteristics of laboratory reagent metabolites, and nonlinear regression fitting is performed to output the metabolic kinetic parameters of laboratory reagent consumption;

[0034] By combining the reverse analysis of experimental procedures with real-time laboratory reagent inventory, the metabolic kinetic parameters are mapped and analyzed for laboratory reagent consumption and trend extrapolation, and the predicted consumption trend of laboratory reagents is output.

[0035] As a preferred solution for the intelligent scheduling and resource management system of the microbiological testing laboratory of the present invention, the following specific steps are used to construct a cross-laboratory emergency resource sharing path through the GAN network and output the emergency resource sharing plan:

[0036] Based on the predicted consumption trend of laboratory reagents, the resource demand feature vector is quantized and encoded through the generator of the GAN network, and a standardized resource demand tensor is output;

[0037] The multi-laboratory resource matching analysis is performed through the discriminator of the GAN network, and the optimal allocation path is generated by combining the graph network shortest path algorithm to obtain an emergency resource sharing plan.

[0038] As a preferred solution for the intelligent scheduling and resource management system of the microbiological testing laboratory of the present invention, the emergency resource sharing solution and the benchmark resource scheduling solution are combined, and iterative optimization is performed through a multi-objective reinforcement learning method to generate the optimal microbiological testing laboratory operation strategy. The specific steps are as follows:

[0039] Based on the emergency resource sharing scheme and the benchmark resource scheduling scheme, the spatiotemporal resource conflicts are resolved and integrated through a multi-constraint fusion algorithm, and the optimization objective function is output;

[0040] Through multi-objective reinforcement learning, Monte Carlo simulation iteration of resource scheduling strategy is performed to output the optimal microbial testing laboratory operation strategy.

[0041] The beneficial effects of this invention are as follows: through quantum computing adaptive modeling, biosafety isolation rules are converted into quantum bit coupling relationships, improving the resource scheduling efficiency and instrument utilization of high-level experiments; through the collaborative steps of metabolomics and GAN networks, based on reagent metabolic feature analysis and cross-laboratory resource matching modeling, the accuracy of reagent demand prediction and the efficiency of emergency resource sharing are improved. Through dynamic parameter coupling to form a closed-loop system, the cross-laboratory collaboration mechanism is automatically triggered when insufficient resources are detected, which not only improves the overall resource utilization of the laboratory, but also achieves an improvement in the level of biosafety management, achieving the goal of coordinated optimization of safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a schematic diagram of the intelligent scheduling and resource management system for the microbiological testing laboratory in China;

[0044] Figure 2 Schematic diagram generated for the resource permission constraint table;

[0045] Figure 3 Schematic diagram generated for the benchmark resource scheduling scheme;

[0046] Figure 4 Schematic diagram generated for the optimal operating strategy. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides an intelligent scheduling and resource management system for a microbiological testing laboratory, including the following steps:

[0051] The data acquisition module collects multi-dimensional resource data of the microbiological testing laboratory and performs preprocessing.

[0052] The multi-dimensional resource data of microbial testing laboratories include laboratory instrument status data, personnel flow data, environmental parameter data, reagent and consumables inventory data, sample processing progress data, and biosafety level data.

[0053] Specifically, the multi-dimensional resource data of the microbial testing laboratory collects the real-time operating parameters of the laboratory instrument status data through sensor networks, Internet of Things terminals, and log recording devices; collects the trajectory coordinates of personnel flow data through access control tracking devices and positioning tags; collects environmental parameter data such as temperature, humidity, and pressure difference values ​​through environmental monitoring probes; collects reagent and consumable inventory data, balance and expiration information through barcode scanning equipment and inventory management terminals; collects sample processing progress data stage identification and time nodes through sample tracking devices and process management terminals; and collects biosafety level data through security audit records.

[0054] Preprocessing includes data cleaning, format standardization, key parameter dimensionality reduction and time series alignment.

[0055] Specifically, the multi-dimensional resource data preprocessing operation of the microbiological testing laboratory performs data cleaning steps, uses the Z-score anomaly detection algorithm to calculate the mean and standard deviation of numerical fields, deletes examples in the experimental instrument status data where the voltage value deviates from the mean by more than 3σ, and eliminates records in the environmental parameter data where the continuous sampling of temperature and humidity exceeds the range of ±3σ;

[0056] Perform the format standardization step and apply the ISO 8601 time parser to convert the timestamps of the sample processing progress data into a standardized timestamp format. Use the forced type conversion method to convert the coordinate units of the personnel flow data from feet to meters, for example, 1 foot = 0.3048 meters. Use the unit mapping table method to convert the Fahrenheit of the environmental parameter data to Celsius, for example, ℉→℃ formula conversion. Implement the date formatting function to reconstruct the expiration date of the reagent and consumable inventory data into the YYYY-MM-DD format.

[0057] Execute the key parameter dimensionality reduction step, use the principal component analysis method to extract the environmental parameter data, retain the dimensions with a cumulative contribution rate greater than 85% of the principal components, use the variance threshold feature selection method to screen the experimental instrument status data, and use the trajectory key point extraction algorithm to retain the coordinates of the trajectory inflection points of the personnel flow data with a stay time of more than 5 minutes. Apply rule-driven feature screening to focus on the balance / expiry date / storage condition warning dimensions of the reagent and consumables inventory data; execute the time series alignment step, use the linear interpolation compensation method to fill in the timestamp missing values ​​of the environmental parameter data and the sample processing progress data, and compensate the missing time periods by the forward filling method.

[0058] The isolation module divides resource usage permissions through a dynamic authorization method based on biosafety levels to obtain a resource permission constraint table.

[0059] Based on the pre-processed multi-dimensional resource data, a multi-factor weighted fusion is performed through the fuzzy comprehensive evaluation algorithm to generate a safety level scoring matrix.

[0060] Specifically, a fuzzy comprehensive evaluation algorithm is used to construct membership functions for each dimension of data. Fuzzy processing is performed on laboratory instrument status data, personnel flow data, environmental parameter data, reagent and consumables inventory data, sample processing progress data, and biosafety level data, generating membership matrices for each dimension. Fuzzy matrix multiplication is then performed to combine the factor weight vectors generated by the analytic hierarchy process with the membership matrix of the preprocessed multidimensional resource data (e.g., a membership of 0.98 for a pressure difference of 4.9 Pa in environmental parameter data and a membership of 0.12 for an abnormal voltage in laboratory instrument status data). Each resource's safety dimension score is calculated by multiplying the corresponding weights with the membership values ​​and then accumulating them to generate a safety level score matrix. Each element in the safety level score matrix represents the comprehensive score of a resource in a specific safety dimension. For example, the instrument risk score of a biosafety cabinet is 0.92, which is equal to the instrument risk weight (0.3) × the membership value (0.92) + the personnel qualification weight (0.2) × the membership value (0.85) + ... + the sample risk weight (0.1) × the membership value (0.78) = 0.92. The final output is the safety level score matrix.

[0061] The security level scoring matrix and the pre-processed multi-dimensional resource data are subjected to spatiotemporal correlation analysis to obtain the laboratory operation permission conflict matrix, and a rule reasoning engine is used to perform multi-dimensional constraint matching to generate a preliminary permission mapping table.

[0062] Specifically, the security level scoring matrix is ​​subjected to spatiotemporal correlation analysis with the pre-processed multi-dimensional resource data. The resource security score in the security level scoring matrix is ​​subjected to spatial overlay analysis with the real-time location coordinates in the personnel flow data to identify spatial conflicts between security level requirements and the actual distribution of personnel. The time dimension score in the security level scoring matrix is ​​compared with the operation time nodes in the sample processing progress data to detect conflicts between security time requirements and actual operation timing.

[0063] The laboratory biosafety management specification clauses are loaded through the rule reasoning engine, and the identified spatial conflicts and temporal conflicts are matched with the specification clauses one by one; the condition matching in the rule reasoning engine calls the corresponding constraint conditions according to the conflict type, spatial conflicts match the spatial isolation specification clauses, and temporal conflicts match the operation timing specification clauses, for example, the sterilization time is reserved before and after BSL-3 level sample processing; the successfully matched constraint conditions generate permission control instructions, spatial conflict instructions restrict personnel from entering high-risk areas, and temporal conflict instructions adjust the instrument usage time period; the output of the rule reasoning engine organizes the matching results into permission mapping relationships, spatial permission mapping records the access control relationship between resources, personnel, and areas, and temporal permission mapping records the use control relationship between instruments and time periods, and generates a preliminary permission mapping table containing resource ID, personnel ID, area permission, and time period permission fields.

[0064] The preliminary permission mapping table is combined with the preprocessed multi-dimensional resource data, and multi-dimensional conflict detection is performed through the graph coloring algorithm to obtain a prioritized permission conflict list. Combined with historical violation record data, the permission weights are iteratively optimized through the reinforcement learning algorithm to output the resource permission constraint table.

[0065] Specifically, the preliminary permission mapping table is matched with the experimental instrument status data, personnel flow data, environmental parameter data, reagent and consumables inventory data, sample processing progress data, and biosafety level data fields in the preprocessed multi-dimensional resource data to perform permission-resource association matching; the permission conflict detection problem is transformed into a resource-personnel permission graph structure through a graph coloring algorithm: nodes represent resource-personnel permission pairs in the preliminary permission mapping table, and edges represent mutually exclusive relationships between different permission pairs;

[0066] A greedy algorithm is used to assign colors to the graph nodes of resource-personnel permissions, with each color corresponding to a permission conflict type, and a permission conflict list containing conflict type code, conflict resource ID, conflict personnel ID, and conflict priority fields is generated; the conflict priority is set according to the frequency of similar conflicts in historical violation record data; a permission weight optimization environment is constructed through a reinforcement learning algorithm: the state space is the conflict type and priority in the permission conflict list, the action space is the permission weight adjustment operation in the preliminary permission mapping table, and the reward function is calculated based on the proportion of violation rate reduction in historical violation record data; Q-learning is performed to iteratively update the permission weight parameters, and when the number of high-priority conflicts in the permission conflict list drops to 50% of the average number of similar conflict events in the laboratory safety audit report in the past 30 days, the conflict number threshold is set as 20 times, and the optimization is terminated; the resource permission constraint table is output.

[0067] The spatiotemporal graph network model is used to perform security isolation analysis and generate a security isolation task queue.

[0068] Based on the resource permission constraint table, high-risk operations are identified through the rule matching engine and isolated to obtain an isolation combination list.

[0069] Specifically, the resource ID field in the resource permission constraint table is encoded as a resource identification matching condition, for example, resource ID = EQ001; the permission type field is encoded as an operation type matching condition, for example, permission type = centrifuge operation; the optimization weight value field is encoded as a weight threshold matching condition, for example, optimization weight value = 0.4; and the conflict priority field is encoded as a priority determination condition, for example, conflict priority = 0.9. Each rule in the matching engine rule base is defined using a "when-then" structure. The example rule is defined as "When the permission type is centrifuge operation and the optimization weight value is less than the example value of 0.5, it is marked as a high-risk operation." When the rule matching engine is executed, it first loads all records in the resource permission constraint table and matches them one by one with the conditions in the matching engine rule base. During the matching process, the Rete algorithm is used to optimize search efficiency, and the pattern matching network is used to quickly locate data records that meet the conditions. Successfully matched high-risk operation records are processed according to the conflict priority field value: Records with a priority greater than 0.8 (example value), are immediately terminated and an isolation instruction is generated (example isolation period = 08:00-10:00). Records with a priority less than or equal to 0.8 (example value), are allowed to complete the current operation step before an isolation instruction is generated (example isolation area range = Laboratory Area 3). An isolation instruction includes three essential parameters: the operation resource ID (example EQ001), the isolation period (example 14:00-16:00), and the isolation area range (example Biosafety Cabinet No. 2). All generated isolation instructions are then compiled into an isolation combination list.

[0070] Based on the historical isolation combination list, the spatiotemporal graph network model is trained through the spatiotemporal graph attention mechanism method, and the overlapping resource conflict relationship graph with time window is output.

[0071] Specifically, the isolated resource ID, associated operator ID, isolation start time, and expected release time fields in the historical isolation combination list are input into the spatiotemporal graph attention mechanism method. The node feature is encoded as a combined vector of resource type and personnel qualification, and the edge feature is encoded as the time window of the isolation period, such as 14:00-15:00, and the conflict type code. The spatiotemporal graph attention mechanism method calculates the conflict association weights of different time windows through a multi-head attention layer, such as the conflict weight of 0.92 for the 14:00-14:30 period. The graph convolution layer aggregates the spatiotemporal features of adjacent nodes to generate a node embedding vector.

[0072] The mean square error loss function is used in the training phase. The optimization goal is to predict the matching degree between the conflict relationship and the real records in the historical isolation combination list. The Adam optimizer is used to update the network parameters. The training is terminated when the number of rounds reaches a preset value, such as 100 rounds. The trained spatiotemporal graph network model performs forward reasoning on the isolated resource ID, associated operator ID, isolation start time, and expected release time fields in the input isolation combination list, and outputs an overlapping resource conflict relationship graph with a time window.

[0073] The overlapping resource conflict relationship graph is analyzed through a heuristic scheduling algorithm to resolve resource conflicts and generate a security isolation strategy. The task urgency is evaluated using a multi-objective optimization algorithm to obtain a security isolation task queue with priority markings.

[0074] Specifically, the conflict time window, resource ID, personnel ID, and emergency index fields in the overlapping resource conflict relationship diagram with time windows are input into the heuristic scheduling algorithm. Based on the overlap degree of the conflict time windows and the emergency index value, the earliest schedulable time window priority strategy is applied. For example, tasks with an emergency index greater than 0.8 are prioritized for scheduling, and the time window of the conflicting resources is shifted. For example, the original time window of 14:00-14:30 is adjusted to 14:30-15:00. The generated security isolation strategy contains the resource ID, the adjusted time window, and the isolation operator ID fields.

[0075] The isolation reason code fields in the security isolation strategy and the historical isolation combination list are input into the multi-objective optimization algorithm. The optimization goals are set to minimize the total isolation time, maximize resource utilization, and balance the distribution of the emergency index. The NSGA-II algorithm is used to calculate the Pareto optimal solution set example to generate three groups of candidate solutions; the laboratory task priority judgment standard example is an example in which the solution that meets both the isolation time of less than 2 hours and the emergency index of more than 0.7 is marked as high priority, the candidate solutions are prioritized, and a security isolation task queue with priority tags is output.

[0076] The resource allocation module uses quantum computing adaptation modeling methods to perform spatiotemporal resource allocation under biosafety constraints and generate task scheduling optimization problems.

[0077] Based on the secure isolation task queue, high-risk operations are converted into binary decision variable groups through the quantum bit mapping algorithm, and conflict operation vectors with weight encoding are output.

[0078] Specifically, based on the resource ID, adjusted time window, executor ID, and priority code fields in the security isolation task queue, the quantum bit mapping algorithm encodes the high-risk operation status of the security isolation task team into a binary decision variable: the priority codes P1-P3 are mapped to weight codes, for example, P1 corresponds to 0.9, P2 corresponds to 0.7, and P3 corresponds to 0.5. The resource ID is combined with the adjusted time window to generate a unique operation identifier, for example, centrifuge_2023-10-05T14:30-15:00; the high-risk operation status is assigned according to the execution result of the security isolation policy, for example, the isolated operation is 1 and the non-isolated operation is 0, the weight code is combined with the binary state to form a conflict operation vector, for example, resource ID = centrifuge, binary state = 1, weight code = 0.9; the conflict operation vector with weight coding is output.

[0079] Through the biosafety constraint embedding method, the isolation combination list is converted into chain coupling strength, generating a Hamiltonian containing spatiotemporal isolation constraints, and then converted into a task scheduling optimization problem through the QUBO-to-Ising conversion rule.

[0080] It should be noted that the expression for generating the Hamiltonian including the space-time isolation constraint is:

[0081] ;

[0082] in, is the Hamiltonian, is the isolation combination list, is the set of all isolated operations, is the operation pair number such as = centrifugal operation, = Sterilization operation, It is a centrifugal operation and sterilization operations The coupling strength between It is a centrifugal operation The quantum bit (value -1 or +1), It is a sterilization operation The quantum bit (value -1 or +1), It is a centrifugal operation The local magnetic field strength;

[0083] Specifically, based on the resource ID, time window, weight code, and binary status field in the security isolation task queue and the conflict operation vector with weight coding, the biosafety constraint embedding method converts the isolation operation pairs in the isolation combination list, for example, centrifuge operation and sterilization operation, into chain coupling strength: according to the degree of overlap of the time window of the resource ID, the example centrifuge operation time window 14:30-15:00 and the sterilization operation time window 14:45-15:15 have a 15-minute overlap, and the coupling strength value example is set. =0.8, based on the difference in weight encoding, for example, centrifuge weight 0.9 and sterilization weight 0.7, adjust the local magnetic field strength example =0.9-0.7=0.2; when generating a Hamiltonian containing space-time isolation constraints, the binary state (0 or 1) in the conflicting operation vector of the binary decision variable group is converted into the quantum bit spin state (-1 or +1) through the QUBO-to-Ising conversion rule. The mapping relationship is that binary state 0 corresponds to spin -1, and binary state 1 corresponds to spin +1; the task scheduling optimization problem is transformed into solving the ground state configuration of the Hamiltonian H, and the task scheduling plan that matches the quantum bit spin state with the coupling strength is output.

[0084] The quantum annealing algorithm is used to search for the optimal solution and obtain the benchmark resource scheduling solution.

[0085] Through the D-Wave quantum processor interface, the optimization problem elements in the task scheduling optimization problem are converted into quantum bit coupling parameters and local magnetic field parameters to obtain a binary input stream.

[0086] Specifically, the chain coupling strength and local magnetic field parameters in the task scheduling optimization problem are mapped to quantum bit physical parameters through the D-Wave quantum processor interface: the chain coupling strength example, the coupling strength of the centrifuge operation and the sterilization operation, matches the quantum bit number combination of the connectable quantum bit pair example in the physical topology of the D-Wave quantum processor, and the chain coupling strength value is directly assigned to the coupling parameter field of the corresponding quantum bit pair; the logical quantum bit example that requires a chain structure spans a long chain of multiple physical quantum bits, and the D-Wave chain coupling configuration method is used to map the same logical quantum bit to multiple physical quantum bits, and the quantum bit coupling parameters are assigned a fixed high-intensity value to ensure logical consistency;

[0087] The local magnetic field example maps the magnetic field strength of the centrifuge operation to the magnetic field parameter field of the corresponding qubit number example qubit identifier; when a logical qubit is mapped to multiple physical qubits, the local magnetic field value is evenly distributed to each physical qubit example allocation rule. The standard data encapsulation function of the D-Wave quantum processor interface is called to encode the qubit number, qubit coupling parameters, and local magnetic field parameters into a binary input stream using the QMASM protocol format.

[0088] The quantum annealing algorithm is used to perform a ground state search on the D-Wave quantum processor to obtain the optimal original bit state solution set.

[0089] Specifically, the quantum annealing algorithm is executed through the D-Wave quantum processor to load the quantum bit number, quantum bit number and local magnetic field parameters in the binary input stream into the physical quantum bit of the D-Wave quantum processor to initialize the quantum bit spin state; the quantum annealing algorithm constructs an energy landscape based on the coupling parameters and magnetic field parameters, and evolves the quantum tunneling effect intensity from high temperature to low temperature to make the quantum bit spin state converge to the lowest energy ground state configuration; after the annealing process, the reading circuit of the D-Wave quantum processor measures the quantum bit spin state (-1 or +1), and after multiple annealing cycles for 1000 times, the spin state combination with the highest probability of occurrence is statistically analyzed as the optimal original bit state solution set; the quantum bit spin state is converted into a binary state (-1 corresponds to 0, +1 corresponds to 1), and it is verified whether the ground state energy of the optimal original bit state solution set meets the Hamiltonian definition; the optimal original bit state solution set is output.

[0090] The optimal original bit state solution set is decoded and verified through classical post-processing methods, invalid original bit state solutions that do not meet the constraints are eliminated, and a feasible scheduling solution is generated.

[0091] Specifically, the resource ID, time window, and binary status field in the optimal original bit state solution set are decoded through classical post-processing methods: the binary status field is mapped to an actual operation instruction example, binary state 1 corresponds to "operation allowed", and 0 corresponds to "operation prohibited"; it is verified whether the decoded operation instruction meets the spatiotemporal isolation constraints in the isolation combination list; invalid solutions that violate the provisions of the biosafety operation specifications are eliminated, such as the number of people using high-risk instruments exceeding the limit in the same period; the resource utilization of the remaining valid original bit state solutions is evaluated to obtain the total isolation time and instrument idle rate, and the original bit state solutions that meet both the minimum isolation time and the maximum resource utilization are retained; and a feasible scheduling solution is generated.

[0092] Through the greedy matching algorithm, the feasibility scheduling solution is mapped to the three elements of laboratory spatiotemporal resource allocation, and the benchmark resource scheduling plan is output.

[0093] Specifically, the resource ID, adjusted time window, and operation status fields in the feasibility scheduling solution are processed by the greedy matching algorithm, and the feasibility tasks in the feasibility scheduling solution are sorted from high to low. For example, the centrifuge operation with an urgency index of 0.9 takes precedence over the sterilization operation with an urgency index of 0.7; the sorted feasibility task list is traversed, and the resource ID of the feasibility task is matched with the three elements of laboratory spatiotemporal resource allocation, which come from the equipment registration table, personnel schedule, and experimental task reservation table in the laboratory resource management database (the three elements of laboratory spatiotemporal resource allocation include resource ID, allocation time window, and execution personnel ID). For example, the matching biological safety cabinet is available during the period of 09:00-10:00; for example, priority is given to continuous time windows when allocating resources. For example, the centrifuge operation is allocated to the entire period of 14:30-15:00; conflict detection uses a time window boundary alignment strategy. For example, the sterilization operation time window is adjusted to 15:00-15:30 to avoid overlapping with the centrifuge operation; the allocation results are recorded in the benchmark resource scheduling plan, and the fields include resource ID, allocation time window, and executor ID; before output, it is verified whether the allocation results meet the priority tag in the safety isolation task queue. For example, the P1 task must be assigned to the designated safety area.

[0094] The scheduling module analyzes the preset public health risk monitoring thresholds, uses the dynamic resource reorganization algorithm to adjust resource usage and allocation, and obtains resource scheduling instructions.

[0095] Based on the benchmark resource scheduling plan, analysis is performed through the preset public health risk monitoring threshold, and a set of risk operation markers that exceed the preset public health risk monitoring threshold is output.

[0096] Specifically, the public health risk monitoring threshold is set, the single concurrent operation threshold of high-risk instruments is extracted, and the maximum number of centrifuge operations in a single time window is 2 times; the restriction clauses on the personnel carrying capacity of the physical area of ​​the laboratory in the same regulation are analyzed, and the personnel density threshold is extracted. For example, the maximum number of people in the experimental area at the same time is 5 people; the violation scenarios described in the clauses are directly mapped with the preset risk type codes. For example, the "instrument overload operation" in the clause corresponds to the instrument overload risk code, and the "overcrowding operation" corresponds to the personnel density risk code. The public health risk monitoring threshold is strictly associated with the number of operations and the upper limit of the number of people defined in the original text of the regulations. The risk code is bound one by one to the description of the violation of the clause. Finally, the public health risk monitoring threshold is set by citing the public content of the standard clauses.

[0097] Based on the resource ID, allocation time window, and executor ID fields in the benchmark resource scheduling plan, and the preset public health risk monitoring threshold, the number of concurrent operations of high-risk instruments in the same time window in the benchmark resource scheduling plan is analyzed. For example, the centrifuge was assigned 3 operations during the 14:30-15:00 period, exceeding the operation threshold by 2 times. The number of operators in the same physical area was counted. For example, the number of operators in the BSL-3 experimental area during the same period exceeded the personnel density threshold of 5 people; the statistical values ​​were compared with the public health risk monitoring threshold item by item to identify operation records that exceeded the public health risk monitoring threshold. For example, the number of centrifuge operations = 3 > operation threshold 2, the number of personnel = 6 > personnel density threshold 5; risk type labels were added to operation records that exceeded the public health risk monitoring threshold. For example, instrument overload risk code R001, personnel density risk code R002; and a risk operation tag set was generated.

[0098] Through adaptive genetic algorithms, the resource usage and allocation of the three elements of laboratory time and space resource allocation are dynamically adjusted to generate resource scheduling instructions.

[0099] Specifically, the resource ID and risk time window fields in the risk operation tag set are used as input parameters; the initialization population contains multiple candidate adjustment plans, each plan consists of adjustment actions of the three elements of laboratory spatiotemporal resource allocation, which include resource ID, allocation time window, and executor ID. The adjustment action is to reallocate the usage period of the instrument corresponding to the resource ID or replace the executor ID; the evaluation method is to count the total number of risk type codes in the risk operation tag set, count the number of resource conflict events after adjustment, and combine the total number of risk type codes and the number of resource conflicts according to the weight ratio, for example, the total number of risk type codes has a weight of 0.6 and the number of resource conflicts has a weight of 0.4 to generate a fitness score. The lower the fitness score value, the better the comprehensive risk and conflict level of the candidate adjustment plan.

[0100] Perform a selection operation to retain the top 30% candidate solutions in terms of fitness; perform a crossover operation on the selected solutions, randomly swapping the time window segments of the same resource ID in the two solutions, for example, swapping the centrifuge allocation between 14:30-15:00 in solution A and 15:30-16:00 in solution B; perform a mutation operation to randomly replace the executor ID with a probability of 5% or reset the time window in the instrument's available time pool; iteratively perform the fitness evaluation, selection, crossover, and mutation steps until the termination condition is reached, for example, 100 iterations or 10 consecutive times without fitness change; output the candidate adjustment solution with the highest fitness as the resource scheduling instruction.

[0101] The collaborative module uses metabolomics data analysis methods to predict laboratory reagent consumption trends.

[0102] Based on resource scheduling instructions, the experimental record parsing engine extracts historical laboratory reagent consumption data, performs time alignment and outlier removal, and outputs a timestamped laboratory reagent usage sequence.

[0103] Specifically, the experimental record parsing engine reads the resource ID, executor ID, and adjusted time window fields in the resource scheduling instruction; associates the historical laboratory reagent consumption data storage table according to the resource ID, and filters the corresponding operation records according to the combination of resource ID and executor ID; aligns the timestamps of the filtered reagent consumption data with the adjusted time window in the resource scheduling instruction, and intercepts the records whose consumption data timestamps fall within the adjusted time window. For example, the adjusted time window is 14:30-15:00, and only the reagent consumption data with 14:30≤t<15:00 is retained; the reagent consumption data outside the time window is eliminated; the Laida criterion is used to identify outliers. If the recorded reagent consumption exceeds the historical mean ±3 times the standard deviation range (for example, the historical mean reagent consumption of the centrifuge in the same period is 20ml and the standard deviation is 5ml), then the records with consumption <5ml or >35ml are eliminated, marked as outliers and removed; the processed reagent consumption is combined and counted according to the adjusted time window to generate a laboratory reagent usage sequence with a timestamp.

[0104] Based on the laboratory reagent usage sequence, the LC-MS metabolomics peak area integration method was used to extract the concentration and time curve characteristics of laboratory reagent metabolites, and nonlinear regression fitting was performed to output the metabolic kinetic parameters of laboratory reagent consumption.

[0105] Specifically, the reagent consumption field in the timestamp laboratory reagent usage sequence is associated with the original metabolite mass spectrometry data detected by LC-MS, and the data are aligned and matched according to the timestamp; the mass spectrometry data collected at each time point are integrated to identify the characteristic ion peak of the target metabolite (for example, the peak of trypsin metabolite m / z=567.3), the peak area value is generated by integration and converted into the metabolite concentration (for example, peak area=12000 corresponds to concentration=25μM); the laboratory reagent metabolite concentration values ​​of all time points are merged to generate a concentration-time curve; the nonlinear regression fitting method is used to input the concentration-time curve generated by the laboratory reagent metabolite concentration value into the kinetic equation (for example, Michaelis-Menten equation or first-order kinetic equation), and the equation parameters are iteratively optimized by the least squares method (for example, the maximum number of iterations is 1000 times and the convergence standard is 1e-6), and the metabolic kinetic parameters (for example, half-life t1 / 2=4.2h and maximum reaction rate Vmax=58μM / h) are fitted; the residual sum of squares of the fitting result is verified to meet the preset standard (for example, the residual sum of squares ≤0.05 is considered valid), and the verified metabolic kinetic parameters are output.

[0106] By combining the reverse analysis of experimental procedures with real-time laboratory reagent inventory, the metabolic kinetic parameters are mapped and analyzed for laboratory reagent consumption and trend extrapolation, and the predicted consumption trend of laboratory reagents is output.

[0107] Specifically, the half-life and maximum reaction rate fields in the metabolic kinetic parameters of laboratory reagent consumption are associated with the current inventory and procurement cycle fields of the real-time laboratory reagent inventory; the relationship between the timestamp and consumption in the historical laboratory reagent usage sequence is parsed, and the reagent consumption rate relationship is generated based on the half-life and reaction rate of the metabolic kinetic parameters, and the rate relationship is trend-fitted with the historical consumption sequence; the current inventory of the real-time inventory and the procurement cycle example are 50ml, and the procurement cycle is 7 days. The future consumption is extrapolated according to the time window. For example, the predicted consumption for the next 7 days = the average daily consumption 3.5ml×7=24.5ml; when the predicted consumption exceeds the preset standard of the real-time inventory example, predicted consumption>50ml-safety stock 10ml=40ml, the time node that needs to be replenished is marked; the output laboratory reagent predicted consumption trend includes the time window, predicted consumption, and inventory warning status fields. For example, time window = 5th day, predicted consumption = 42ml, inventory warning status = needs to be replenished.

[0108] A cross-laboratory emergency resource sharing path is constructed through the GAN network, and an emergency resource sharing plan is output.

[0109] Based on the predicted consumption trend of laboratory reagents, the resource demand feature vector is quantized and encoded through the generator of the GAN network, and a standardized resource demand tensor is output.

[0110] Specifically, the time window, predicted consumption, and inventory warning status fields in the laboratory reagent consumption forecast trend are converted into multidimensional numerical vectors. For example, the time window is encoded as the starting hour. For example, 14 represents 14:00, the predicted consumption retains the original value, and the inventory warning status is encoded in binary, with replenishment required = 1 and no warning = 0. The multidimensional numerical vector is input into the generator input layer of the GAN network. The generator of the GAN network contains a fully connected layer and a convolutional layer. The fully connected layer maps the input vector to the hidden feature space. For example, the input dimension 3 is mapped to a 128-dimensional latent vector. The convolutional layer performs feature enhancement on the latent vector to generate a high-dimensional tensor.

[0111] Perform normalization on the tensor output by the generator of the GAN network, and use the mean square error normalization method defined in the laboratory resource encoding standard to scale the values ​​of each channel of the tensor to the interval [-1, 1]. Verify that the dimension of the generated tensor is consistent with the input format required by the laboratory resource scheduling interface. The output tensor shape is the example batch size, 64, 64, 3, corresponding to a 64×64 gridded resource distribution map and 3 feature channels, and outputs a standardized resource demand tensor.

[0112] The multi-laboratory resource matching analysis is performed through the discriminator of the GAN network, and the optimal allocation path is generated by combining the graph network shortest path algorithm to obtain an emergency resource sharing plan.

[0113] Specifically, the standardized resource demand tensor is input into the discriminator of the GAN network. The discriminator structure consists of a convolutional layer and a fully connected layer. The convolutional layer extracts the instrument usage density, personnel distribution density, and reagent consumption density features in the tensor; the fully connected layer compares the features with the available reagent inventory and instrument idle time fields in the real-time resource list of multiple laboratories, and outputs a matching score. For example, the matching degree between the resource list of laboratory A and the demand tensor is 0.92.

[0114] Traverse all laboratory resource lists and screen candidate laboratories whose matching scores meet the preset validity standards. For example, Laboratory A has a matching degree of 0.92 and Laboratory B has a matching degree of 0.85. Construct a graph network structure for inter-laboratory transfer paths, with nodes representing laboratory location coordinates and edge weights representing inter-laboratory transportation time, transportation cost, and path reliability parameters. For example, the transportation time from Laboratory A to Laboratory B is 2 hours and the cost is 500 yuan. Run the Dijkstra shortest path algorithm in the graph network, with minimizing the weighted sum of transportation time and cost as the optimization goal, to generate the optimal transfer path from the demand-side laboratory to the candidate laboratory. The example path is demand-side → Laboratory C → Laboratory A. Bind the optimal transfer path to the resource allocation amount and time window fields of the matching laboratory to generate an emergency resource sharing plan. The example plan includes the transfer path "demand-side → Laboratory A", allocation amount = 42 ml, and time window = 14:30-16:00.

[0115] The optimization module combines the emergency resource sharing scheme and the benchmark resource scheduling scheme, performs iterative optimization through a multi-objective reinforcement learning method, and generates the optimal microbial testing laboratory operation strategy.

[0116] Based on the emergency resource sharing scheme and the benchmark resource scheduling scheme, the spatiotemporal resource conflicts are resolved and integrated through a multi-constraint fusion algorithm, and the optimization objective function is output.

[0117] Specifically, the allocation path, allocation amount, and time window fields in the emergency resource sharing plan are extracted from the resource ID, allocation time window, and executor ID fields in the benchmark resource scheduling plan; according to the conflict type examples defined in the laboratory resource scheduling conflict detection rules, the resource IDs in the emergency resource sharing plan and the benchmark resource scheduling plan are compared item by item to see whether the instrument usage periods overlap, and the density of personnel areas exceeds the limit. For example, the centrifuge resource ID = EQ001 is occupied by both plans at the same time from 14:30 to 15:00, and whether the executor ID is in the same physical area, which exceeds the carrying limit. For example, the number of personnel in the BSL-3 experimental area = 7> the upper limit 5;

[0118] Apply conflict resolution rules to the detected conflict types. For example, the emergency resource sharing plan covers the baseline resource scheduling plan. The centrifuge time period occupied by the allocation path is given priority, and the conflict period in the baseline plan is automatically shifted back. The resource allocation data after conflict resolution is input into the multi-constraint fusion algorithm. The multi-constraint fusion algorithm is based on the preset operation delay time weight of 0.4 and resource utilization weight of 0.6, and performs a weighted fusion of resource utilization, operation delay time, and conflict resolution times. For example, resource utilization × 0.6 + (1-operation delay coefficient) × 0.4; output the optimization objective function.

[0119] Through multi-objective reinforcement learning, Monte Carlo simulation iteration of resource scheduling strategy is performed to output the optimal microbial testing laboratory operation strategy.

[0120] Specifically, the comprehensive evaluation value, resource utilization, and operation delay coefficient fields in the optimization objective function are mapped to the state space; the policy parameters are initialized, and the action is defined as adjusting the instrument usage period, staffing ratio, and reagent allocation path fields. Example action = "Adjust the usage period of centrifuge resource ID = EQ001 from 14:30-15:00 to 15:00-15:30";

[0121] Perform Monte Carlo simulation iterations, running 1,000 samples to meet the standard example. Each simulation selects an action based on the current strategy, updates the laboratory resource allocation status, and calculates the immediate reward values ​​for reducing the number of biosafety violations, reducing costs, and increasing time delay penalties. For example, the reward for reducing the number of biosafety violations is +5, the reward for reducing costs is +3, and the penalty for increasing time delay is -2. Update the strategy parameters through temporal difference error. The error calculation method uses the weighted average of the long-term rewards of state-action pairs in historical simulation data. Screen for non-inferior solutions that simultaneously meet biosafety requirements, cost constraints, and time efficiency standards. The screening method is Pareto frontier analysis. For example, retain the strategy combination with the number of biosafety violations ≤ 1, cost ≤ 800 yuan, and delay ≤ 60 minutes.

[0122] Output the optimal microbial testing laboratory operation strategy, including an instrument usage period adjustment table, a staffing plan, and a reagent allocation priority list. For example, for centrifuge resource ID = EQ001, the time period is adjusted to 15:00-15:30, the BSL-3 laboratory area has a maximum staffing limit of 5 people, and the trypsin allocation path priority = demander → laboratory A.

[0123] In summary, the present invention improves the resource scheduling efficiency and instrument utilization of high-level experiments by: quantum computing adaptive modeling, converting biosafety isolation rules into quantum bit coupling relationships; through the collaborative steps of metabolomics and GAN networks, based on reagent metabolic feature analysis and cross-laboratory resource matching modeling, the reagent demand prediction accuracy and emergency resource sharing efficiency are improved. Through dynamic parameter coupling to form a closed-loop system, the cross-laboratory collaborative mechanism is automatically triggered when insufficient resources are detected, which not only improves the overall resource utilization of the laboratory, but also realizes the improvement of the level of biosafety management, achieving the collaborative optimization goal of safety and efficiency.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Intelligent scheduling and resource management system for microbiological testing laboratories, characterized by: include, Data acquisition module, which collects multi-dimensional resource data of the microbiological testing laboratory and performs pre-processing; The isolation module uses a dynamic biosafety level weighting method to divide resource usage permissions to obtain a resource permission constraint table, and uses a spatiotemporal graph network model to perform security isolation analysis and generate a security isolation task queue; Isolation module, The following steps are included: Based on the pre-processed multi-dimensional resource data, a multi-factor weighted fusion is performed through the fuzzy comprehensive evaluation algorithm to generate a safety level scoring matrix; The security level scoring matrix and the pre-processed multi-dimensional resource data are analyzed in time and space to obtain the laboratory operation permission conflict matrix. The rule reasoning engine is then used to match the multi-dimensional constraints to generate a preliminary permission mapping table. The preliminary permission mapping table is combined with the pre-processed multi-dimensional resource data, and multi-dimensional conflict detection is performed using a graph coloring algorithm to obtain a prioritized permission conflict list. Combined with historical violation record data, the permission weights are iteratively optimized using a reinforcement learning algorithm to output a resource permission constraint table. Based on the resource permission constraint table, the rule matching engine identifies high-risk operations and isolates them to obtain an isolation combination list; Based on the historical isolation combination list, the spatiotemporal graph network model is trained through the spatiotemporal graph attention mechanism method, and the overlapping resource conflict relationship graph with time window is output; Through the heuristic scheduling algorithm, the overlapping resource conflict relationship graph is analyzed to resolve resource conflicts and generate a security isolation strategy. The multi-objective optimization algorithm is used to evaluate the urgency of tasks and obtain a security isolation task queue with priority tags. The resource allocation module uses quantum computing adaptive modeling methods to perform spatiotemporal resource allocation under biosafety constraints, generates task scheduling optimization problems, and uses quantum annealing algorithms to search for the optimal solution to obtain a benchmark resource scheduling solution. The scheduling module analyzes the preset public health risk monitoring thresholds, uses a dynamic resource reorganization algorithm to adjust resource usage and allocation, and obtains resource scheduling instructions; The collaborative module uses metabolomics data analysis methods to predict laboratory reagent consumption trends, builds cross-laboratory emergency resource sharing paths through the GAN network, and outputs emergency resource sharing plans; The optimization module combines the emergency resource sharing scheme and the benchmark resource scheduling scheme, performs iterative optimization through a multi-objective reinforcement learning method, and generates the optimal microbial testing laboratory operation strategy.

2. The intelligent scheduling and resource management system for microbiological testing laboratories according to claim 1 is characterized by: The multi-dimensional resource data of the microbiological testing laboratory includes laboratory instrument status data, personnel flow data, environmental parameter data, reagent and consumables inventory data, sample processing progress data and biosafety level data; The preprocessing includes data cleaning, format standardization, key parameter dimensionality reduction and time series alignment.

3. The intelligent scheduling and resource management system for microbiological testing laboratories according to claim 1 is characterized by: The quantum computing adaptive modeling method is used to perform spatiotemporal resource allocation under biosafety constraints and generate task scheduling optimization problems. The specific steps are as follows: Based on a secure isolated task queue, a quantum bit mapping algorithm is used to convert high-risk operations into binary decision variable groups, and output conflicting operation vectors with weight encoding. Through the biosafety constraint embedding method, the isolation combination list is converted into chain coupling strength, generating a Hamiltonian containing spatiotemporal isolation constraints, and then converted into a task scheduling optimization problem through the QUBO-to-Ising conversion rule.

4. The intelligent scheduling and resource management system for a microbiological testing laboratory according to claim 1, characterized in that: The specific steps of obtaining the benchmark resource scheduling solution are as follows: Through the D-Wave quantum processor interface, the optimization problem elements in the task scheduling optimization problem are converted into quantum bit coupling parameters and local magnetic field parameters to obtain a binary input stream; The optimal original bit state solution set is obtained by performing a ground state search on the D-Wave quantum processor using the quantum annealing algorithm; The optimal original bit state solution set is decoded and verified through classical post-processing methods, invalid original bit state solutions that do not meet the constraints are eliminated, and feasible scheduling solutions are generated; Through the greedy matching algorithm, the feasibility scheduling solution is mapped to the three elements of laboratory spatiotemporal resource allocation, and the benchmark resource scheduling plan is output.

5. The intelligent scheduling and resource management system for microbiological testing laboratories according to claim 1 is characterized by: The above mentioned steps are as follows: Based on the baseline resource scheduling plan, the system analyzes the preset public health risk monitoring thresholds and outputs a set of risk operation markers that exceed the preset public health risk monitoring thresholds; Through adaptive genetic algorithms, the resource usage and allocation of the three elements of laboratory time and space resource allocation are dynamically adjusted to generate resource scheduling instructions.

6. The intelligent scheduling and resource management system for microbiological testing laboratories according to claim 1 is characterized by: The metabolomics data analysis method is used to predict the consumption trend of laboratory reagents. The specific steps are as follows: Based on resource scheduling instructions, the experimental record parsing engine extracts historical laboratory reagent consumption data, performs time alignment and outlier removal, and outputs a timestamped laboratory reagent usage sequence. Based on the laboratory reagent usage sequence, the LC-MS metabolomics peak area integration method is used to extract the concentration and time curve characteristics of laboratory reagent metabolites, and nonlinear regression fitting is performed to output the metabolic kinetic parameters of laboratory reagent consumption; By combining the reverse analysis of experimental procedures with real-time laboratory reagent inventory, the metabolic kinetic parameters are mapped and analyzed for laboratory reagent consumption and trend extrapolation, and the predicted consumption trend of laboratory reagents is output.

7. The intelligent scheduling and resource management system for microbiological testing laboratories according to claim 1, characterized in that: The GAN network is used to construct a cross-laboratory emergency resource sharing path and output an emergency resource sharing plan. The specific steps are as follows: Based on the predicted consumption trend of laboratory reagents, the resource demand feature vector is quantized and encoded through the generator of the GAN network, and a standardized resource demand tensor is output; The multi-laboratory resource matching analysis is performed through the discriminator of the GAN network, and the optimal allocation path is generated by combining the graph network shortest path algorithm to obtain an emergency resource sharing plan.

8. The intelligent scheduling and resource management system for a microbiological testing laboratory according to claim 1, characterized in that: The emergency resource sharing scheme and the benchmark resource scheduling scheme are combined and iteratively optimized through a multi-objective reinforcement learning method to generate the optimal microbial testing laboratory operation strategy. The specific steps are as follows: Based on the emergency resource sharing scheme and the benchmark resource scheduling scheme, the spatiotemporal resource conflicts are resolved and integrated through a multi-constraint fusion algorithm, and the optimization objective function is output; Through multi-objective reinforcement learning, Monte Carlo simulation iteration of resource scheduling strategy is performed to output the optimal microbial testing laboratory operation strategy.

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