Strain sampling and transporting management and control system based on 5G technology

Abstract

The invention discloses a 5G technology-based strain and strain sampling and transportation management and control system. The 5G technology-based strain and strain sampling and transportation management and control system is characterized in that sample sampling points of strain and strains and state data of a transportation vehicle are acquired based on a 5G communication technology; according to the obtained state data, a multi-target optimization model is constructed with the transportation cost and the transportation workload as targets, and the multi-target optimization model serves as a target constraint; constructing a time window constraint according to the service time range of each sampling point in the state data; taking the target constraint and the time window constraint as constraint conditions, solving an initial feasible solution, and updating the new pheromone concentration; and inputting the time window constraint and the updated new pheromone concentration into a preset improved ant colony algorithm, carrying out iterative solution, outputting a planned path, and controlling the transport vehicle to sequentially collect strain strains of each sample sampling point according to the planned path. According to the method, path planning can be accurately carried out, and the working efficiency of the strain sampling and transporting process is improved.

Description

Technical Field

[0001] The present invention relates to the field of emergency health management technology, and in particular to a bacteria strain sampling and transportation control system based on 5G technology. Background Art

[0002] With the continuous growth of the number of samples collected each year in the sample library of bacteria and virus strains tested in the field of emergency health, the problem of efficient and accurate management of bacteria and virus strain specimens has become increasingly prominent, becoming a key problem that needs to be solved in the control of bacteria and virus strains. How to further improve the management efficiency of test samples and bacteria and virus strains, and optimize the workflow of sampling-transportation personnel and equipment have become important issues that need to be solved urgently.

[0003] The data collection of the bacterial strain operation process is extremely inconvenient, especially in the management and scheduling of courier personnel and the optimal recovery and transportation route planning. It is impossible to carry out the most scientific and accurate scheduling and transportation route planning according to the courier's transportation situation and specimen situation in a timely manner, and the degree of intelligence is low. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a bacterial strain sampling and transportation control system based on 5G technology, which can accurately plan routes and improve the work efficiency of biological sample sampling and transportation.

[0005] An embodiment of the present invention provides a bacterial strain sampling and transportation control system based on 5G technology, the system comprising a controller, the controller being configured to perform the following steps:

[0006] Based on 5G communication technology, the status data of each sample sampling point of bacterial strains and transport vehicles are obtained;

[0007] Based on the acquired status data, a multi-objective optimization model is constructed with transportation cost and transportation workload as the target constraints;

[0008] Constructing a time window constraint according to the service time range of each sampling point in the state data;

[0009] Taking the target constraint and the time window constraint as constraint conditions, solving the initial feasible solution, and updating the new pheromone concentration;

[0010] Input the time window constraint and the updated new pheromone concentration into a preset improved ant colony algorithm, perform iterative solution, and output a planned path;

[0011] The transport vehicle is controlled to collect the bacterial species and strains at each sample sampling point in sequence along the planned route.

[0012] Preferably, the controller is used to perform the following steps:

[0013] Step 1, initializing the variable parameters of the improved ant colony algorithm;

[0014] Step 2: Calculate the transition probability based on the new pheromone concentration and time window constraints to determine the next node;

[0015] Step 3, determining whether the next node to be calculated is a special node that meets the preset conditions;

[0016] If yes, go to step 4;

[0017] If not, return to step 2;

[0018] Step 4, calculate the time to reach the next node;

[0019] Step 5: Determine whether the time to reach the next node meets the time window constraint.

[0020] If yes, go to step 6;

[0021] If not, increase the penalty cost according to the preset penalty model and execute step 6;

[0022] Step 6, calculate the feasible solution according to the constraints;

[0023] Step 7, update the global pheromone concentration;

[0024] Step 8, determining whether the preset iteration requirements are met;

[0025] If yes, go to step 9;

[0026] If not, return to step 2;

[0027] Step 9: Determine the planned path based on the nodes determined by iteration.

[0028] As a preferred solution, the controller is used to perform the following steps:

[0029] Locate the sampling points and transport vehicles through Beidou satellite signals, and obtain status data uploaded by the sampling points and transport vehicles through 5G public network signals; or

[0030] The sampling points and transport vehicles are located through Beidou satellite signals, and the status data uploaded by the sampling points and transport vehicles are obtained through 5G self-organizing network signals.

[0031] As a preferred solution, the status data includes the sampling point location, sampling point sampling status, sample real-time dynamic data, sampling point service time window, transport vehicle location and transport vehicle operation status.

[0032] Preferably, the multi-objective optimization model is:

[0033] Among them, X ijk is the ergodic decision variable of the transport vehicle, C ij is the transportation cost between the i-th sampling point and the j-th sampling point, m is the number of transport vehicles, n is the number of sampling points, W i is the penalty for the i-th sampling point, S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time that the sampling point receives, and ε is the penalty factor.

[0034] Preferably, the controller is further configured to perform the following steps:

[0035] Calculate the waiting time of each sampling point according to the time window constraint of each sampling point;

[0036] Calculate the transition probability based on the calculated waiting time;

[0037] The transfer rules of sampling points are determined according to the transfer probability.

[0038] Preferably, the waiting time of the i-th sampling point of the i-th node is

[0039] The transfer probability of vehicle k as the kth ant from the i-th node to the j-th node

[0040] The kth ant moves from the i-th sampling point to the next sampling point according to the transfer rule.

[0041] Among them, S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time accepted by the sampling point, the i-th node selects the service time window span Th i =LT i -ET i , represents the set of nodes to be visited by the kth ant on the i-th node, τ ijis the pheromone concentration between the i-th node and the j-th node, and the expected degree of transfer from the i-th node to the j-th node d ij is the distance between the i-th node and the j-th node, q is a random variable that satisfies uniform distribution on [0,1], τ il is the pheromone concentration between the ith node and the lth node, n il is the expected degree of transfer from the i-th node to the l-th node, T l is the waiting time of the lth sampling point, Th l Select the service time window span for the lth node, q0 is the preset threshold parameter, and a, b, δ are the control variables corresponding to pheromone concentration, expectation level and waiting time respectively.

[0042] Preferably, the controller is further configured to perform the following steps:

[0043] Calculate the information increment during different ant iterations;

[0044] Calculate the total information increment based on the information increment during different ant iterations;

[0045] Update the global pheromone concentration according to the total information increment;

[0046] Preferably, the pheromone concentration τ from the i-th node to the j-th node at time t+n after updating ij (t+n)=(1-)×τ ij (t)+Δτ ij (t);

[0047] The pheromone increment when the kth ant iterates from the i-th node to the j-th node at time t

[0048] The total amount of information increment when iterating from the i-th node to the j-th node at time t

[0049] Among them, Q is the total amount of pheromone released by the ant after completing a complete path search, d ij is the distance between the i-th node and the j-th node, m is the number of ants, ρ is the pheromone volatility coefficient, and (1-ρ) is the pheromone residual factor.

[0050] Preferably, the preset conditions of the special node include: the node has not been visited and meets the capacity limit constraint of the transport vehicle.

[0051] The present invention provides a bacterial strain sampling and transportation control system based on 5G technology, which obtains the status data of the sample sampling points of bacterial strains and the transportation vehicles based on 5G communication technology; constructs a multi-objective optimization model based on the acquired status data with the transportation cost and transportation workload as the target constraint; constructs a time window constraint based on the service time range of each sampling point in the status data; uses the target constraint and the time window constraint as constraint conditions to solve the initial feasible solution and update the new pheromone concentration; inputs the time window constraint and the updated new pheromone concentration into the preset improved ant colony algorithm, performs iterative solution, outputs the planned path, and controls the transportation vehicle to collect the bacterial strains of each sample sampling point in turn according to the planned path. The present application can accurately plan the path, which improves the work efficiency of the bacterial strain sampling and transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of the work performed by a controller of a bacteria strain sampling and transportation control system based on 5G technology provided by an embodiment of the present invention;

[0053] Figure 2 It is a schematic diagram of the iterative process of the improved ant colony algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The embodiment of the present invention provides a bacterial strain sampling and transportation control system based on 5G technology, the system includes a controller, see Figure 1 , is a flowchart of the work performed by a controller of a bacteria strain sampling and transportation control system based on 5G technology provided by an embodiment of the present invention, which specifically includes the following steps;

[0056] Step S1, obtaining the status data of each sample sampling point of the bacterial strain and the transport vehicle based on 5G communication technology;

[0057] Step S2, constructing a multi-objective optimization model based on the acquired status data with transportation cost and transportation workload as the target constraints;

[0058] Step S3, constructing a time window constraint according to the service time range of each sampling point in the state data;

[0059] Step S4 uses the target constraint and the time window constraint as constraint conditions, solves the initial feasible solution, and updates the new pheromone concentration;

[0060] Step S5, inputting the time window constraint and the updated new pheromone concentration into a preset improved ant colony algorithm, performing iterative solution, and outputting a planned path;

[0061] Step S6, controlling the transport vehicle to collect the bacterial strains from each sample sampling point in sequence along the planned route.

[0062] In the specific implementation of this embodiment, when the strain specimen sampling is started, first create a unique electronic identification for the strains collected and sent by each sampling point. After the strain specimen collection project is created, add a sample collection plan, including the project, collection department, collection time and location, age range, gender, sample type, sampling site, etc. Then, by establishing a unique identification of the strain, such as the main index, identity card, etc., connect the resource metadata database, and establish the association between experimental data, resources, and metadata. The sampling personnel print and stick it on the sampling tube, and collect the specimens of the personnel: Finally, by adopting advanced 5G technology and Beidou technology, in conjunction with the intelligent strain transfer box, the entire process of "collection-transportation" of the strains and strains is realized. The sampling data collection, sampling point sampling completion status, transportation route tracking and other information data mining functions are realized, laying the foundation for subsequent work data transmission, transportation route planning, etc.

[0063] Through the interaction of sampling points, transport vehicles, etc., data feedback of transport vehicles and different samplings is completed.

[0064] Based on the acquired status data of sampling points and transport vehicle positions, a multi-objective optimization model is constructed from the two perspectives of reducing operating costs and balancing sample sampling-transportation workload as target constraints;

[0065] Considering the time window in which each sampling point can accept service, by inputting the improved ant colony algorithm, the sampling points with shorter service time windows are given priority during the transfer of ants to construct the specimen recovery-transportation path.

[0066] During the iterative operation of the improved ant colony algorithm, the initial feasible solution is solved according to the constraints and the new pheromone concentration is updated.

[0067] The time window constraint and the updated new pheromone concentration are input into a preset improved ant colony algorithm for iterative solution, and the improved ant colony algorithm is used to effectively solve the problem, and finally the planned path for the recovery vehicle is output.

[0068] According to the obtained planned path, the transport vehicle is controlled to arrive at each sample sampling point in sequence according to the planned path to complete the collection of bacterial strains at each sample sampling point. The present application scheme takes into account issues such as bacterial strain sampling-recovery personnel / vehicle path planning, and uses advanced information technology to achieve precise guidance for bacterial strain recovery. The present application can effectively optimize the sampling material distribution, sampling and recovery processes of bacterial strains by constructing a bacterial strain sampling-recovery path model with a time window for multiple vehicles without duplication. Furthermore, the transfer probability formula of the ant colony algorithm is improved so that the algorithm can find the most optimized path for problems with time windows. This innovation not only avoids repeated conflicts in the bacterial strain recovery vehicle path, but also significantly improves the overall efficiency of the sampling-recovery transportation operation, thereby reducing the labor cost of sampling-transportation personnel and balancing the workload.

[0069] In another embodiment of the present invention, the controller is further configured to perform the following steps:

[0070] The above step S5 specifically includes the following steps:

[0071] Step 1, initializing the variable parameters of the improved ant colony algorithm;

[0072] Step 2: Calculate the transition probability based on the new pheromone concentration and time window constraints to determine the next node;

[0073] Step 3, determining whether the next node to be calculated is a special node that meets the preset conditions;

[0074] If yes, go to step 4;

[0075] If not, return to step 3;

[0076] Step 4, calculate the time to reach the next node;

[0077] Step 5: Determine whether the time to reach the next node meets the time window constraint.

[0078] If yes, go to step 6;

[0079] If not, increase the penalty cost according to the preset penalty model and execute step 6;

[0080] Step 6, calculate the feasible solution according to the constraints;

[0081] Step 7, update the global pheromone concentration;

[0082] Step 8, determining whether the preset iteration requirements are met;

[0083] If yes, go to step 9;

[0084] If not, return to step 2;

[0085] Step 9: Determine the planned path based on the iteration results.

[0086] When this embodiment is implemented, see Figure 2 , is a schematic diagram of an iterative process of an improved ant colony algorithm provided by an embodiment of the present invention, specifically:

[0087] Step 1: variable initialization, that is, all parameters need to be initialized, including setting the current number of iterations, the maximum number of iterations, the number of ants, and the total amount of pheromone released Q, etc.

[0088] Step 2: Calculate the transition probability and determine the next node.

[0089] That is, the transfer probability is calculated based on the new pheromone concentration and time window constraints to determine the next node

[0090] Step 3, determine whether it is a special node;

[0091] If not, it indicates that the next node does not meet the constraint requirements, and returns to step 2 to recalculate the next node;

[0092] If so, that is, when the determined next node is a special node that meets the preset conditions, execute step 4.

[0093] Step 4, calculate the arrival time at the node, that is, calculate the time to reach the next node.

[0094] Step 5: Determine the arrival time, that is, determine whether the time to reach the next node meets the time window constraint.

[0095] If yes, go to step 6.

[0096] If not, increase the penalty for the project, that is, increase the penalty cost according to the preset penalty model.

[0097] Step 6, calculating and saving a feasible solution, that is, solving a feasible solution based on the target constraint and the time window constraint as constraint conditions.

[0098] Step 7: Globally update pheromones, that is, update the global pheromone concentration.

[0099] Step 8, determining whether the maximum number of iterations is met, that is, determining whether the preset iteration requirements are met.

[0100] If not, return to step 2;

[0101] If yes, go to step 9.

[0102] Step 9: Output the result, that is, determine the planned path according to the nodes determined by iteration.

[0103] By initializing all parameters, including setting the current number of iterations, the maximum number of iterations, the number of ants s, and the total amount of pheromone released Q, etc. Then, each ant will select the next node to visit according to the pre-set transition probability rule. During the selection process, the ants will carefully check whether each potential node meets the constraints and whether it meets the time window requirements of the point. Through this series of screening and judgment, the ants can gradually build a solution space for the optimization problem. Therefore, the practical application problem of establishing multi-vehicle conflict-free collaboration with time windows can be effectively solved by the improved ant colony algorithm, thereby obtaining the optimized path for the operation of the sample recovery emergency vehicle.

[0104] In another embodiment provided by the present invention, the above step S1 specifically includes:

[0105] Locate the sampling points and transport vehicles through Beidou satellite signals, and obtain status data uploaded by the sampling points and transport vehicles through 5G public network signals; or

[0106] The sampling points and transport vehicles are located through Beidou satellite signals, and the status data uploaded by the sampling points and transport vehicles are obtained through 5G self-organizing network signals;

[0107] Among them, the status data includes the location of the sampling point, the sampling status of the sampling point, the real-time dynamic data of the sample, the service time window of the sampling point, the location of the transport vehicle and the operating status of the transport vehicle.

[0108] In the specific implementation of this embodiment, by using 5G mobile communication and Beidou satellite short message signals to exchange information such as sampling points and transport vehicles, seamless coverage of the communication network under various geographical conditions can be guaranteed. By building a hybrid wireless communication network module based on 5G / self-organizing network, it can achieve:

[0109] In the area covered by the public network signal, the sample sampling points, transport vehicles and the rear command center use normal public network signals to transmit information. The sampling points and transport vehicles are located through Beidou satellite signals, and the status data uploaded by the sampling points and transport vehicles are obtained through 5G public network signals.

[0110] In areas without public network coverage: the sampling points and transport vehicles are located through Beidou satellite signals, and the status data uploaded by the sampling points and transport vehicles are obtained through 5G self-organizing network signals.

[0111] Sample sampling points and transport vehicles can realize the function of transmitting data to the command backend through 5G ad hoc network and drone base station technology. Taking advantage of the advantages of 5G and ad hoc network technology, real-time, two-way symmetrical information transmission between sample sampling points, transport vehicle operation sites and back-end command centers can be realized. The 5G / ad hoc network hybrid wireless communication network module consists of a 5G ad hoc network base station and its supporting ground station, an environmental information collection drone, a single-soldier Beidou handheld terminal, and a Beidou positioning module (the Beidou positioning module is designed to establish a Beidou short message cloud base station, integrate network communication and Beidou short message communication, and open up the communication link between the data transmission network and the Beidou short message).

[0112] In areas without public network signal coverage, the drone platform and its supporting ground station, individual Beidou handheld terminal, and information receiving terminal form a 5G self-organizing network for emergency communication.

[0113] Selection of 5G ad hoc network base station installation location: installed on the flat ground at the sample sampling point, or installed on a drone platform. By controlling the position of the drone, the ad hoc network signal coverage area can be flexibly changed. Among them, the 5G ad hoc network base station is in an area without public network signal coverage, forming a self-organizing network with the base station as the center, covering a certain range. After registration, the 5G+ Beidou positioning mobile phone can conduct ad hoc network communication in the ad hoc network signal coverage area.

[0114] When the sample sampling points and transport vehicles are operating in areas without public network signal coverage, the sample sampling points or transport vehicle operating terminals are controlled through the 5G self-organizing network and the information flow in the area is realized through the 5G self-organizing network (including real-time dynamic data of bacterial strain samples, such as sample quantity, sampling completion status, etc.). The sample sampling points or transport vehicle operating terminals receive Beidou satellite signals through the 5G self-organizing network + Beidou communication module, complete the information positioning of the sample sampling points or transport vehicle operating terminals, and realize information interconnection and intercommunication with the rear emergency information receiving terminal through the self-organizing network. And by establishing a Beidou short message cloud base station, integrating network communication and Beidou short message communication, opening up the communication link between the network and Beidou short message, and realizing the use of self-organizing networks for two-way information transmission in areas without public network signal coverage.

[0115] In the area covered by the public network signal, the sample sampling point or transport vehicle operation terminal first receives the Beidou satellite signal through the Beidou communication module to complete the information positioning of the emergency operation terminal, and transmits information with the rear command platform through the normal 5G public network signal (including real-time dynamic data of bacterial strain samples, such as sample quantity, sampling completion status, etc.).

[0116] The status data includes the location of the sampling point, the sampling status of the sampling point, the real-time dynamic data of the sample, the service time window of the sampling point, the location of the transport vehicle and the operating status of the transport vehicle.

[0117] This case uses the 5G+Beidou application service model to solve the communication problems under network and non-network conditions. Combined with technologies such as self-organizing networks, it completes the fusion and exchange of sample data such as bacterial strain sampling and recovery between different operating departments such as sample sampling points or transport vehicles and the background command center. In addition, based on the previously established whole genome sequencing method of strain specimens based on nanopore technology (MinION), high-throughput sequencing analysis and data transmission algorithms are developed: First, high-throughput sequencing data is transmitted in real time and at high throughput in a 5G environment, and the high-throughput sequencing data analysis process is used to further realize sequence alignment, splicing and analysis of sequence data; thereby obtaining the full genome sequence of the virus and its variation characteristics, and connecting with the epidemic emergency response system to complete real-time feedback of the analysis results, providing real-time multi-dimensional data information for bacterial strain control and early warning, and effectively improving the level of laboratory sample control.

[0118] In another embodiment provided by the present invention, the multi-objective optimization model is:

[0119] Among them, X ijk is the ergodic decision variable of the transport vehicle, C ij is the transportation cost between the i-th sampling point and the j-th sampling point, m is the number of transport vehicles, n is the number of sampling points, W i is the penalty for the i-th sampling point, S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time that the sampling point receives, and ε is the penalty factor.

[0120] In the specific implementation of this embodiment, the design decision variables are designed, and the traversal decision variables of the design transport vehicle are X ijk ,

[0121] The design decision variable is Y ijk ,

[0122] From the perspectives of reducing operating costs and balancing sample sampling-transportation workload, considering the problem of uncertain demand and time window, the multi-objective optimization model is designed as follows:

[0123]

[0124] Among them, C ijis the transportation cost between the i-th sampling point and the j-th sampling point, m is the number of transportation vehicles, and n is the number of sampling points;

[0125] W i is the penalty for the i-th sampling point,

[0126] S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time that the sampling point receives, and ε is the penalty factor.

[0127] This application focuses on optimizing the operating procedures of multiple emergency vehicles for the recovery of bacterial strains. The transport vehicles depart from the central inspection agency and perform efficient operations according to the assigned tasks. During the operation, the time window constraints of each strain specimen sampling-recovery point are fully considered to ensure that the vehicle can complete the sampling and recovery tasks within the specified time. In order to achieve the goal of optimizing the path of emergency vehicles, an improved ant colony algorithm is adopted, which can accurately plan the driving path of each vehicle and ensure non-conflicting and non-overlapping collaborative operations between multiple emergency vehicles. This innovative strategy not only improves the efficiency and accuracy of health emergency operations, but also greatly enhances the ability to respond to public health emergencies.

[0128] Assumption 1: The number of specimens set at each sampling point must be strictly controlled within the maximum capacity limit of the specimen storage box of the emergency vehicle to ensure that the vehicle can complete the sampling task smoothly and effectively.

[0129] Assumption 2: The time window of the sampling point is a soft time window requirement, which means that although the sampling point has a specified service time range, providing services beyond this time range will be subject to certain penalties, but it is not absolutely impossible. This assumption increases the flexibility and adaptability of the algorithm.

[0130] Assumption 3: Assume that the time windows of each sampling point do not overlap with each other, which means that each sampling point has its own independent service time interval, and there is no time conflict and overlap, which helps to optimize the path planning of emergency vehicles.

[0131] Hypothesis 4: Adopt a transportation mode of multiple vehicles and multiple locations with the same starting point, that is, multiple emergency vehicles depart from the same location and go to different sampling points for sampling. This mode can significantly improve sampling efficiency and shorten the overall operation time.

[0132] In view of the above assumptions, the constraints of the time window are set as soft constraints because they are more relaxed than hard constraints. The optimization goal is to provide path guidance and optimization for the recovery emergency vehicle, which means that each sampling point must be traversed and samples must be recovered. If a hard time window is used, if the recovery emergency vehicle that is traversing is not in the time window of its operation when passing through this point, the recovery vehicle will traverse the point but not operate, resulting in reduced operation efficiency, increased working time and increased operating costs. The soft time window is different, that is, when the transport vehicle does not work at the specified time, it will still operate at the point because its working time will be artificially set to infinity. In order to improve work satisfaction, if the strain transport vehicle fails to arrive at the sampling point within the specified time, the penalty is set to In the calculation process, the time constraint can be converted into a path constraint by adding the penalty value to the path cost.

[0133] In another embodiment provided by the present invention, the step 2 comprises:

[0134] Calculate the waiting time of each sampling point according to the time window constraint of each sampling point;

[0135] Calculate the transition probability based on the calculated waiting time;

[0136] Determine the transfer rule of the sampling points according to the transfer probability;

[0137] Among them, as the waiting time of the ith sampling point of the ith node S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time accepted by the sampling point is the transfer probability of vehicle k of the kth ant from the i-th node to the j-th node. The i-th node selects the service time window span Th i =LT i -ET i , represents the set of nodes to be visited by the kth ant on the i-th node, τ ij is the pheromone concentration between the i-th node and the j-th node, and the expected degree of transfer from the i-th node to the j-th node d ij is the distance between the i-th node and the j-th node, q is a random variable that satisfies uniform distribution on [0,1], τ il is the pheromone concentration between the ith node and the lth node, n ilis the expected degree of transfer from the i-th node to the l-th node, T l is the waiting time of the lth sampling point, Th l Select the service time window span for the lth node, and the kth ant transfers from the ith sampling point to the next sampling point according to the transfer rule q0 is the preset threshold parameter, and a, b, and δ are the control variables corresponding to pheromone concentration, expectation level, and waiting time, respectively.

[0138] In the specific implementation of this embodiment, the waiting time of each sampling point is calculated according to the time window constraint of each sampling point, and is used as the waiting time of the i-th sampling point of the i-th node.

[0139] T i =0, the value needs to be a small positive number to prevent the denominator in the formula from being 0; when the waiting time T of two different sampling points i The same and not outdated service, the service time window span Th is preferred i =LT i -ET i Small sampling point.

[0140] Waiting time T i This factor is taken into account in calculating the transition probability;

[0141] The transfer probability of vehicle k as the kth ant from the i-th node to the j-th node

[0142] τ ij is the pheromone concentration between the i-th node and the j-th node, and the expected degree of transfer from the i-th node to the j-th node d ij is the distance between the i-th node and the j-th node, q is a random variable that satisfies uniform distribution on [0,1], τ il is the pheromone concentration between the ith node and the lth node, n il is the expected degree of transfer from the i-th node to the l-th node, T l is the waiting time of the lth sampling point, Th l Select the service time window span for the lth node, where a, b, and δ are the control variables corresponding to pheromone concentration, expectation level, and waiting time, respectively.

[0143] The transfer rule of the sampling point is determined according to the transfer probability. The transfer rule of ant k from sampling point i to sampling point j is:

[0144]

[0145] q0 is the preset threshold parameter.

[0146] By locating the transport vehicles and sampling points through GPS, the MCU of the vehicle-mounted equipment or the sampling personnel's equipment terminal collects the location information of each transport vehicle and the adoption status of the sampling points every 15 seconds, uploads it to the server, and converts the collected data to obtain the coordinates of each sampling point and transport vehicle. Combined with the above information, a multi-objective optimization model is constructed to optimize the target values ​​from two perspectives: the fixed cost of recovering emergency vehicles and the balanced sample sampling-transportation workload.

[0147] In another embodiment provided by the present invention, the step 6 comprises:

[0148] Calculate the information increment during different ant iterations;

[0149] Calculate the total information increment based on the information increment during different ant iterations;

[0150] Update the global pheromone concentration according to the total information increment;

[0151] Among them, the pheromone increment when the kth ant iterates from the i-th node to the j-th node at time t is Q is the total amount of pheromone released by the ant after completing a complete path search, d ij is the distance between the i-th node and the j-th node, and the total information increment when iterating from the i-th node to the j-th node at time t m is the number of ants, and the pheromone concentration τ from the i-th node to the j-th node at time t+n after update ij (t+n)=(1-)×τ ij (t)+Δτ ij (t), ρ is the pheromone volatility coefficient, and 1-ρ is the pheromone residual factor.

[0152] In the specific implementation of this embodiment, the information increment during different ant iterations is calculated;

[0153] Ant s is located at node i at time t and at node j at time t+n. Represents the increment of pheromone at each iteration; represents the increment of pheromone when the sth ant performs iteration. In general, The value of

[0154] Calculate the total information increment based on the information increment during different ant iterations;

[0155] Update the global pheromone concentration according to the total information increment;

[0156] After one iteration of the ant colony algorithm, the algorithm can solve the initial feasible solution according to strict constraints and update the concentration of new pheromones based on these solutions. ij The update rules are as follows:

[0157]

[0158]

[0159] Among them, ρ represents the pheromone volatility coefficient; 1-ρ represents the pheromone residual factor.

[0160] If the sth ant is roughly regarded as a bacterial strain sampling and transport vehicle that collects samples from the specimen collection point and returns to node i to j, d ij is the distance between nodes i and j, and Q can be regarded as the total amount of pheromone released after the ant completes a complete path search. This application uses the Ant-Cycle model to complete the pheromone update process using ants to address the legacy pheromone processing problem.

[0161] In the ant colony algorithm, as the number of iterations gradually increases, the concentration of newly generated pheromones will gradually increase, which may cover the previously leftover pheromones. At the same time, considering the passage of time and the dynamic changes of the environment, some pheromones will evaporate naturally. Therefore, in order to more accurately guide the search direction of ants, this application not only considers the distribution cost target, but also considers the increment of pheromones based on distance, and innovatively adopts the global pheromone update rule. This rule not only ensures the continued effectiveness of the algorithm, but also improves the efficiency of solving and the quality of the solution.

[0162] In another embodiment provided by the present invention, the preset conditions of the special node include: the node has not been visited and meets the capacity limit constraint conditions of the transport vehicle.

[0163] When this embodiment is implemented, the ants will carefully check whether each potential node meets the following conditions: whether the node has been visited and whether it meets the capacity limit constraint of the transport vehicle.

[0164] Only when a node has not been visited and meets the maximum capacity limit of the specimen storage box of the specimen transport vehicle, will it be used as the next node for path planning.

[0165] This application comprehensively considers the situation of bacteria strain sampling-recovery personnel / vehicle paths and operational conflicts, uses information technology to accurately guide bacteria strain recovery, constructs a multi-vehicle non-repetitive bacteria strain sampling-recovery path model with time windows, and obtains the optimal path with time window problems by improving the transition probability formula of the ant colony algorithm. First, we need to initialize all the parameters of the algorithm. Subsequently, each ant follows a specific transition probability rule to select the next node to visit. During the selection process, the ant will carefully judge whether the selected node meets a series of key constraints: whether the node has been visited, whether it exceeds the maximum capacity limit of the vehicle specimen storage box, and whether it meets the time window requirements of the point. Through this series of rigorous screening and decision-making, the ants jointly construct a solution space for the optimization problem. Therefore, this application proposes an improved ant colony algorithm to seek an effective solution for the practical application problem of multi-vehicle non-conflicting collaboration with time window constraints. This method can accurately optimize the vehicle's running path and ensure the efficiency and smoothness of the recovery operation.

[0166] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A bacterial strain sampling and transportation control system based on 5G technology, characterized in that: The system includes a controller configured to perform the following steps: Based on 5G communication technology, the status data of each sample sampling point of bacterial strains and transport vehicles are obtained; Based on the acquired status data, a multi-objective optimization model is constructed with transportation cost and transportation workload as the target constraints; Constructing a time window constraint according to the service time range of each sampling point in the state data; Taking the target constraint and the time window constraint as constraint conditions, solving the initial feasible solution, and updating the new pheromone concentration; Input the time window constraint and the updated new pheromone concentration into a preset improved ant colony algorithm, perform iterative solution, and output a planned path; The transport vehicle is controlled to collect the bacterial species and strains at each sample sampling point in sequence along the planned route.

2. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 1 is characterized in that: The controller is also used to perform the following steps: Step 1, initializing the variable parameters of the improved ant colony algorithm; Step 2: Calculate the transition probability based on the new pheromone concentration and time window constraints to determine the next node; Step 3, determining whether the next node to be calculated is a special node that meets the preset conditions; If yes, go to step 4; If not, return to step 2; Step 4, calculate the time to reach the next node; Step 5: Determine whether the time to reach the next node meets the time window constraint; If yes, go to step 6; If not, increase the penalty cost according to the preset penalty model and execute step 6; Step 6, calculate the feasible solution according to the constraints; Step 7, update the global pheromone concentration; Step 8, determining whether the preset iteration requirements are met; If yes, go to step 9; If not, return to step 2; Step 9: Determine the planned path based on the nodes determined by iteration.

3. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 1 is characterized in that: The controller is also used to perform the following steps: Locate the sampling points and transport vehicles through Beidou satellite signals, and obtain status data uploaded by the sampling points and transport vehicles through 5G public network signals; or The sampling points and transport vehicles are located through Beidou satellite signals, and the status data uploaded by the sampling points and transport vehicles are obtained through 5G self-organizing network signals.

4. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 1 is characterized in that: The status data includes the sampling point location, sampling point sampling status, sample real-time dynamic data, sampling point service time window, transport vehicle location and transport vehicle operation status.

5. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 1 is characterized in that: The multi-objective optimization model is: Among them, X ijk is the ergodic decision variable of the transport vehicle, C ij is the value between the i-th sampling point and the j-th sampling point m is the number of transport vehicles, n is the number of sampling points, W i is the penalty for the i-th sampling point, S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time that the sampling point receives, and ε is the penalty factor.

6. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 2 is characterized in that: The controller is also used to perform the following steps: Calculate the waiting time of each sampling point according to the time window constraint of each sampling point; Calculate the transition probability based on the calculated waiting time; The transfer rules of sampling points are determined according to the transfer probability.

7. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 6 is characterized in that: as the waiting time of the ith sampling point of the ith node The transfer probability of vehicle k as the kth ant from the i-th node to the j-th node The kth ant moves from the i-th sampling point to the next sampling point according to the transfer rule. Among them, S i represents the operation time at the i-th sampling point, [ET i , LT i ] represents the time window of the i-th sampling point, ET i Indicates the earliest time when the i-th sampling point starts to receive service; LT i The latest service time accepted by the sampling point, the i-th node selects the service time window span Th i =LT i -ET i , represents the set of nodes to be visited by the kth ant on the i-th node, τ ij is the pheromone concentration between the i-th node and the j-th node, and the expected degree of transfer from the i-th node to the j-th node d ij is the distance between the i-th node and the j-th node, q is a random variable that satisfies uniform distribution on [0,1], τ il is the pheromone concentration between the ith node and the lth node, n il is the expected degree of transfer from the i-th node to the l-th node, T l is the waiting time of the lth sampling point, Th l Select the service time window span for the lth node, q0 is the preset threshold parameter, and a, b, δ are the control variables corresponding to pheromone concentration, expectation level and waiting time respectively.

8. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 2 is characterized in that: The controller is also used to perform the following steps: Calculate the information increment during different ant iterations; Calculate the total information increment based on the information increment during different ant iterations; Update the global pheromone concentration according to the total information increment.

9. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 8, characterized in that: After updating, the pheromone concentration τ from the i-th node to the j-th node at time t+n ij (t+n)=(1-ρ)×τ ij (t)+Δτ ij (t); The pheromone increment when the kth ant iterates from the i-th node to the j-th node at time t The total amount of information increment when iterating from the i-th node to the j-th node at time t Among them, Q is the total amount of pheromone released by the ant after completing a complete path search, d ij is the distance between the i-th node and the j-th node, m is the number of ants, ρ is the pheromone volatility coefficient, and (1-ρ) is the pheromone residual factor.

10. The bacterial strain sampling and transportation control system based on 5G technology as claimed in claim 2, characterized in that: The preset conditions of the special node include: the node has not been visited and meets the capacity limit constraint of the transport vehicle.

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