GMP workshop intelligent monitoring management method and system
A monitoring management and workshop technology, applied in the field of intelligent monitoring and management methods and systems for GMP workshops, can solve problems such as low degree of intelligence
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Embodiment 1
[0027] Such as figure 1 As shown, the embodiment of the present application provides a GMP workshop intelligent monitoring and management method, wherein the method is applied to a GMP workshop intelligent monitoring and management system, and the system is connected to an intelligent camera device through communication. The method includes:
[0028] S100: Obtain a first image information set of the first GMP workshop through the intelligent camera device;
[0029]Specifically, the intelligent camera device is a device for performing all-round video surveillance on various angles of the first GMP workshop, preferably deploying a plurality of intelligent camera devices communicatively connected with the intelligent monitoring and management system of the GMP workshop, deploying The position is set based on the ability to comprehensively monitor the first GMP workshop, especially key areas such as vents and production lines; the first image information set of the first GMP works...
Embodiment approach 1
[0046] The first collaboration system distributes the original model, that is, the first initial model, to each pharmaceutical factory, and each pharmaceutical factory conducts model training, and receives the first pharmaceutical factory, the second pharmaceutical factory, the third pharmaceutical factory up to the Kth pharmaceutical factory. The encryption result of the model parameters after training from the pharmaceutical factory is the first encryption result, because the key is distributed to K pharmaceutical factories by the first collaboration system, so the first collaboration system has Unmasking authority, after removing the encryption masks of all the first encryption results, aggregate all model parameters to obtain the first encryption parameters, and then update the model through the first encryption parameters, because the data The quantitative basis is relatively large, and the analysis result obtained by the first anomaly identification model is also relative...
Embodiment approach 2
[0048] The first pharmaceutical factory, the second pharmaceutical factory, the third pharmaceutical factory until the Kth pharmaceutical factory downloads the original model, that is, the first initial model from the first collaboration system, and uses multiple sets of the first person image information and the The image information of the first device trains the first initial model until the first initial model reaches convergence, extracts model parameters and encrypts them, sends them to the first collaboration system as the first encryption result, and receives the After the first collaboration system aggregates a plurality of information similar to the K training parameter model updates sent by the first pharmaceutical company, that is, the update of the first abnormality recognition model is completed, and when it needs to be called, it sends a message to the first collaboration system Request information, coordinate calls, and process data. The use of intelligent mode...
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