Optimization Study On Key Problems In Processing Of Automated Multi-functional Immune Test

With the entironment, work pressure, food safety and other issues trend to worsen, the number of incidences of all kinds of major diseases, such as cancer and Hepatitis B becomes higher and higher. As a result, immunoassay instrument as a detection device for such diseases is widely used in clinical medicine. Manual or semi-automatic testing equipment can no longer satisfy the needs of the times, which promotes the development of automatic immunoassay medical devices. At present, although China has developed a number of immunoassay medical devices and has advantages in terms of costs over foreign products, it is in a backward state concerning similar products from abroad in terms of performance. Therefore, research and development of advanced Chinese immunoassay equipment with international standards is desperately needed. Based on this, the paper processes researches on key issues of optimization study directing on improving inspection efficiency of testing equipment, reliability assessment, optimizing device configuration, and other aspects. Specific works and research results are as follows:1. In order to improve the immune test equipment efficiency, we use the analysis of the scheduling mechanism of immune test to establish a time constraint based Petri net model for the immune test. Minimizing the total completion inspection time as the optimization objective in order to reach test process optimization scheduling. In the process of solving the model, we first use the method of queue theory to consider multiple constraints of the inspection process, and obtain the feasible solution of optimization problem. Then, to satisfy the requirements for further part of the inspection process of waiting time constraint, we calculate the difference between project inspection process waiting time matrix and process time constraint matrix to adjust the corresponding inspection process start time. Finally, using Matlab and CPN Tools to simulate and verify the proposed models and methods. The simulation results show that for a single type of test items that is going through optimized scheduling, the established model and methods have good feasibility.2. Aiming at different types of test items and test equipment efficiency optimization problem, proposed a chaotic particle swarm optimization based on Nolan Model Theory(NMPSO) to solve the inspection process scheduling optimization problem by using the minimization of the total completion time tested as the optimization target. First, the particle swarm optimization(PSO) is analyzed from the new system cognition process and angle, taking into account the Nolan Model emphasizes the importance of the integration of information resources. Therefore, based on the six-phase Nolan Model, a NMPSO algorithm framework is constructed. To ensure the ergodic property of the algorithm, the inertia factor and accelerating factor of PSO are determined by chaotic mapping. Then, based on the information integration, coordinating and balancing ideas of Nolan Model, we use the common information among memory information of particle itself, individual local information and group information to increase information equilibrium, improving PSO’s velocity and position updating formula. In addition, projecting particle group’s distance index to judge and ensure the diversity of initialparticle search and to improve particle’s global search ability. In order to verify the performance of the proposed NMPSO, six Benchmark standard functions are selected to optimize the performance. Comparing the comparison results of the new correlation algorithm, the results show that the proposed method is effective and reasonable. Finally, the method is applied to the scheduling optimization of the immune test process with different types of test items. The simulation results show that the method is effective and reasonable.3. Directing at medical examination in the process of high reliability requirements on the test results to conduct reliability zero failure data from using truncationtest by the confidence limit theory, and apply it to immunoassay process of reliability evaluation. First, considered that test process reliability problems already satisfied the index distribution. In this case, we prove that two-sided M-Bayesian confidence limit’s prior distribution density function increases as hyper parameter index increases and also prove several related theorems of twosided classical confidence limit with the general form. This gives the index of two-sided MBayesian credible limit reliability’s theorem and its proof. The proved theorem shows that two-sided M-Bayesian confidence limits have better results than two-sided classical limits in reliability. Finally, use the reliability method of two-sided M-Bayesian confidence limit in the immune test process. The configuration optimization combination of the equipment can be guided under the high reliability of the testing process.4. Proposed an improved multiple target TOPSIS method based on entropy weight interval to solve uncertain immune test medical equipment cost, test efficiency, and test reliability of multiple objective decision making problems. To ensure multiple objectives optimization, determine the purchase of equipment to meet the needs of hospital. According to the differences and similarities between interval numbers and exact numbers, the initial decision data standardization method was improved by the combination of upper limit, lower limit and median of the initial data of decision problem. From the mathematical theory, we show that the improvement of the standard method meets three principles of standardized management, and prove the interval degradation consistency for the exact numbers, making the standard more reasonable and accurate. In addition, using interval number possibility degree concept and combining with the entropy weighting method can well describe the system of uncertain advantage, presenting improvement method of objective weights of multiple targets. Finally, the method was applied to the multiple objective decision making problem of immune test, and carries on the contrast analysis with multiple objective decision making method from other literatures. The example verification results show that the method in the uncertain interval multiple objective decision making optimization has better superiority and accuracy. Decision results can be used to guide the hospital to determine the optimal scheme of equipment purchase.