Method for realizing remote monitoring in product production process
By deploying cameras and intelligent video AI management platforms in pharmaceutical, cosmetic, and medical device manufacturing enterprises, and customizing inspection forms and processes, remote monitoring and anomaly handling are achieved, solving the problem of low regulatory efficiency and ensuring product quality and safety.
Patent Information
- Application Number
- CN202511600330.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-04
- Publication Date
- 2026-02-10
AI Technical Summary
The existing regulatory methods for pharmaceuticals, cosmetics, and medical devices are too simplistic, resulting in low regulatory efficiency, difficulty in timely detection of potential risks, challenges in cross-regional cooperation, and insufficient data processing capabilities, making it impossible to fully and accurately grasp the quality status and potential risks.
By deploying cameras in manufacturing enterprises, connecting them to an intelligent video AI management platform, customizing inspection forms, configuring business processes, and utilizing big data analysis and real-time video monitoring, remote inspection and anomaly handling can be achieved, supporting multi-terminal collaboration and instant communication, thus forming a closed-loop management system.
It enables efficient remote monitoring of the production processes of pharmaceuticals, cosmetics, and medical devices, improves the coordination and transparency of regulatory work, ensures controllable product quality, promptly detects and handles anomalies, and safeguards public safety in the use of pharmaceuticals and medical devices.
Smart Images
Figure CN121504145A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of remote monitoring technology, and in particular to a method for achieving remote monitoring during product manufacturing. Background Technology
[0002] In today's era, information technology is developing rapidly at an unprecedented pace and permeating all fields, profoundly changing the way society operates and works. This wave of change has also had a significant impact on the regulation of the pharmaceutical, cosmetic, and medical device industries. However, in reality, the departments under the State Drug Administration responsible for regulating these categories face many pressing problems in fulfilling their regulatory duties, among which the most prominent are the limited range of regulatory methods and the resulting low efficiency.
[0003] Traditional regulatory models often rely excessively on basic methods such as manual on-site inspections and paper document review. For example, when inspecting pharmaceutical manufacturers, staff mainly rely on on-site visits to check the production environment, equipment operation, and reviewing a large number of paper production records and inspection reports to assess the company's compliance. This approach not only consumes a lot of manpower, resources, and time, but is also prone to oversights or judgment biases due to human factors. In the cosmetics industry, with its diverse product range and rapid product updates, limited sampling is insufficient to comprehensively and accurately grasp the overall quality situation, while single sampling methods cannot effectively track the flow and usage of potentially risky products. In the medical device field, with continuous technological advancements and the emergence of new, sophisticated, and complex equipment, traditional simple testing tools and methods are insufficient to meet the needs for precise assessment of performance, safety, and other aspects.
[0004] Meanwhile, with the continuous expansion of the industry and the increasing frequency of market transactions, the number of various products has grown exponentially. A massive amount of data is flooding in, including enterprise registration documents, production and operation data, product quality testing results, adverse reaction reports, and so on. However, the existing regulatory system is clearly insufficient in data processing capabilities, lacking an efficient data analysis platform and an intelligent information integration mechanism. A large amount of valuable data is stored in isolation, failing to be fully explored and utilized, making it difficult for regulatory authorities to accurately grasp industry trends from a macro perspective and to promptly identify potential systemic risks.
[0005] Furthermore, difficulties in cross-regional regulatory cooperation are also a significant factor hindering the improvement of regulatory effectiveness. Poor information communication and a tendency for regulatory departments in different regions to operate independently are common. When problems arise with products produced and sold in different locations, the lack of convenient information sharing channels and a sound collaborative working mechanism often leads to delayed responses and untimely handling, preventing the problems from being resolved quickly and effectively, further impacting the overall regulatory outcome.
[0006] In conclusion, against the backdrop of rapid development in information technology, the existing single regulatory methods used by drug, cosmetic, and medical device regulatory departments are no longer adequate to meet the increasingly complex market environment and the massive regulatory demands. The problem of low regulatory efficiency is becoming increasingly prominent, making it imperative to innovate and transform using advanced information technology to build a more scientific, efficient, and intelligent regulatory system in order to safeguard the public's safety and health rights in the use of drugs and medical devices.
[0007] Based on the above problems, this invention proposes a method for remote monitoring during product manufacturing. Summary of the Invention
[0008] To overcome the shortcomings of existing technologies, this invention provides a simple and efficient method for remote monitoring during product manufacturing.
[0009] This invention is achieved through the following technical solution:
[0010] A method for remote monitoring during product manufacturing includes the following steps:
[0011] Step S1: Preliminary Preparations and System Integration
[0012] Deploy cameras in relevant manufacturing enterprises and connect them to an intelligent video AI management platform to ensure normal and stable network deployment;
[0013] Enterprises upload batch record data to the regulatory platform in accordance with unified standards. Batch records are used to record key information in the drug production process, including raw material procurement, production process parameters and quality inspection results, for subsequent inspection and retrieval.
[0014] Step S2: Customize the inspection form
[0015] Given the differences in production and operation characteristics among different types of enterprises, customized inspection forms are designed for each type. Customized inspection forms are created for different enterprise types, generating a consistent runtime page. This page features fast rendering speed, flexibility, and easy code maintenance, effectively improving the efficiency and accuracy of inspection work.
[0016] Step S3, Process Configuration
[0017] Considering that the regulatory methods and processes vary for different types of enterprises, the system supports the rapid generation and adjustment of business processes through configuration, and supports unified configuration management of custom condition rules, enabling more flexible and autonomous process flow and branch control.
[0018] In step S3, a custom scheduled task is defined to periodically review and summarize the entire regulatory process, collect feedback from relevant enterprises and regulatory personnel, and continuously optimize system functions and operating procedures.
[0019] Step S4: Review and Exception Handling
[0020] The system reviews batch records uploaded by enterprises. If abnormal batches are found, an alarm is triggered, detailed information of the abnormal batches is recorded, and the relevant enterprises are notified in a timely manner to rectify the issues. At the same time, the system tracks the rectification progress of enterprises to form a closed-loop management system and ensure that product quality is always under control.
[0021] Real-time video monitoring of key areas and critical processes in manufacturing enterprises is conducted through an intelligent video AI management platform.
[0022] In step S4, the system supports remote mobile inspections between relevant enterprise personnel and regulatory personnel, and enables real-time communication between them using instant messaging tools. This allows regulatory personnel to ask questions and provide guidance at any time, while relevant enterprise personnel can promptly answer questions and provide supplementary materials.
[0023] In step S4, the intelligent video AI management platform uses image recognition technology and behavior analysis technology to automatically identify potential violations and safety hazards, and issues early warning signals in a timely manner.
[0024] Step S5: Summary of Results
[0025] The remote command and dispatch center summarizes and organizes the inspection results from batch record review, process supervision and real-time video monitoring, and uses big data analysis technology to conduct in-depth mining and comprehensive evaluation of the summarized data; through the analysis and comparison of massive data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, and provides a strong basis for subsequent regulatory decisions.
[0026] In step S5, the on-site images and data are centrally displayed and analyzed through the remote command and dispatch center, so that regulatory personnel can fully grasp the inspection progress and overall situation, make timely decisions and deployments, and improve the coordination and efficiency of regulatory work.
[0027] Step S6: Risk Warning and Handling Measures
[0028] Based on the comprehensive assessment of the aggregated data, risk warnings are promptly issued for enterprises or products with high risks. At the same time, personalized handling measures are tailored to the specific circumstances of the enterprises and historical data, including ordering rectification, suspending production and sales, and recalling substandard products, to ensure that the safety of public use of medicines and medical devices is effectively guaranteed.
[0029] A system for remote monitoring during product manufacturing, used to implement the above method, includes:
[0030] The data acquisition module is responsible for collecting video and image information of the production process in real time through cameras deployed in relevant production enterprises. It also collects key information about the drug production process, including raw material procurement, production process parameters and quality inspection results, through batch record data collection, for subsequent inspection and retrieval.
[0031] The checklist customization module is responsible for creating custom checklists for different enterprise types and generating consistent runtime pages.
[0032] The process configuration module is responsible for generating and adjusting business processes with custom configurations, supporting unified configuration management of custom condition rules, and enabling more flexible and autonomous process flow and branch control.
[0033] The intelligent video AI management platform is responsible for real-time video monitoring of key areas and critical processes in production enterprises, automatically identifying potential violations and safety hazards, and issuing timely warning signals.
[0034] The batch record review module is responsible for reviewing the batch records uploaded by enterprises. If an abnormal batch is found, an alarm is triggered, the detailed information of the abnormal batch is recorded, and the relevant enterprise is notified in a timely manner to make rectification. At the same time, the rectification status of the enterprise is tracked to form a closed-loop management.
[0035] The results aggregation module (remote command and dispatch center) is responsible for summarizing and organizing the inspection results from batch record review, process supervision and real-time video monitoring. It uses big data analysis technology to deeply mine and comprehensively evaluate the aggregated data. Through the analysis and comparison of massive amounts of data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, providing a strong basis for subsequent regulatory decisions.
[0036] The risk warning and response module is responsible for issuing timely risk warnings to enterprises or products with high risks based on the comprehensive evaluation results of the aggregated data. At the same time, it can customize and formulate personalized response measures based on the specific circumstances of the enterprise and historical data, including ordering rectification, suspending production and sales, and recalling substandard products, to ensure that the safety of public use of medicines and medical devices is effectively guaranteed.
[0037] A device for remote monitoring during product manufacturing includes a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the above-described method steps.
[0038] A readable storage medium storing a computer program that, when executed by a processor, implements the above-described method steps.
[0039] The beneficial effects of this invention are: the method for remote monitoring during product manufacturing utilizes information technology to achieve efficient supervision of pharmaceutical and medical device enterprises, effectively solving the problems of single methods and low efficiency under the traditional supervision model, and providing strong support for ensuring the safety of public use of medicines and medical devices. Attached Figure Description
[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0041] Appendix Figure 1 This is a schematic diagram illustrating the method for remote monitoring during product manufacturing according to the present invention.
[0042] Appendix Figure 2 This is a schematic diagram of the process configuration method of the present invention. Detailed Implementation
[0043] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions in the embodiments of this invention will be clearly and completely described below in conjunction with the embodiments of this invention. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.
[0044] The method for achieving remote monitoring during product manufacturing includes the following steps:
[0045] Step S1: Preliminary Preparations and System Integration
[0046] Deploying cameras in relevant manufacturing enterprises and connecting them to an intelligent video AI management platform to ensure normal and stable network deployment is a fundamental condition for conducting subsequent remote inspections. Only when the enterprise's hardware facilities and network environment meet the requirements can the multi-terminal integrated remote inspection process proceed smoothly.
[0047] Enterprises upload batch record data to the regulatory platform in accordance with unified standards. Batch records are used to record key information in the drug production process, including raw material procurement, production process parameters and quality inspection results, for subsequent inspection and retrieval.
[0048] Step S2: Customize the inspection form
[0049] Given the differences in production and operation characteristics among different types of enterprises, customized inspection forms are designed for each type. Customized inspection forms are created for different enterprise types, generating a consistent runtime page. This page features fast rendering speed, flexibility, and easy code maintenance, effectively improving the efficiency and accuracy of inspection work.
[0050] For example, chemical pharmaceutical companies and biopharmaceutical companies have different production processes and quality control points, so the content of the corresponding inspection forms will also have different focuses. However, they can all be presented in the same system with a unified interface, which is convenient for regulatory personnel to use.
[0051] Step S3, Process Configuration
[0052] Considering that the regulatory methods and processes vary for different types of enterprises, the system supports the rapid generation and adjustment of business processes through configuration, and supports unified configuration management of custom condition rules, enabling more flexible and autonomous process flow and branch control.
[0053] For example, when inspecting medical device manufacturers, if a certain type of product is found to have specific risk factors, the corresponding in-depth inspection process or rectification requirements can be automatically triggered according to preset conditions and rules, ensuring the pertinence and effectiveness of the regulatory work.
[0054] In step S3, a custom scheduled task is defined to periodically review and summarize the entire regulatory process, collect feedback from relevant enterprises and regulatory personnel, and continuously optimize system functions and operating procedures. Through continuous improvement, the scientific nature, accuracy, and effectiveness of regulatory work are further enhanced, forming a virtuous cycle and promoting the healthy development of the pharmaceutical, cosmetic, and medical device industries.
[0055] Step S4: Review and Exception Handling
[0056] The system reviews batch records uploaded by enterprises. If abnormal batches are found, an alarm is triggered, detailed information of the abnormal batches is recorded, and the relevant enterprises are notified in a timely manner to rectify the issues. At the same time, the system tracks the rectification progress of enterprises to form a closed-loop management system and ensure that product quality is always under control.
[0057] For example, if a certain quality indicator of a batch of drugs exceeds the specified range, the system will automatically mark it and notify the company to find out the cause and take corrective measures until the problem is completely resolved.
[0058] Real-time video monitoring of key areas and critical processes in manufacturing enterprises is conducted through an intelligent video AI management platform.
[0059] In step S4, the system supports remote mobile inspections between relevant enterprise personnel and regulatory personnel, and enables real-time communication between them using instant messaging tools. This allows regulatory personnel to ask questions and provide guidance at any time, while relevant enterprise personnel can promptly answer questions and provide supplementary materials.
[0060] Compared to traditional regulatory procedures, this interactive inspection method combines various technologies such as instant messaging, intelligent video AI management platforms, and remote command and dispatch centers to achieve multi-terminal collaboration and simultaneous multi-person inspections. It breaks through the limitations of time, space, and distance, realizing an innovative new regulatory model for off-site drug supervision and full-process monitoring of key links. This not only improves work efficiency but also enhances the transparency and fairness of supervision.
[0061] In step S4, the intelligent video AI management platform uses image recognition technology and behavior analysis technology to automatically identify potential violations and safety hazards, and issues early warning signals in a timely manner.
[0062] For example, when the system detects that personnel are not wearing protective equipment as required when entering a clean area, it will immediately remind the relevant personnel to correct the wrong behavior, effectively reducing safety risks.
[0063] Step S5: Summary of Results
[0064] The remote command and dispatch center summarizes and organizes the inspection results from batch record review, process supervision and real-time video monitoring, and uses big data analysis technology to conduct in-depth mining and comprehensive evaluation of the summarized data; through the analysis and comparison of massive data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, and provides a strong basis for subsequent regulatory decisions.
[0065] In step S5, the on-site images and data are centrally displayed and analyzed through the remote command and dispatch center, so that regulatory personnel can fully grasp the inspection progress and overall situation, make timely decisions and deployments, and improve the coordination and efficiency of regulatory work.
[0066] Step S6: Risk Warning and Handling Measures
[0067] Based on the comprehensive assessment of the aggregated data, risk warnings are promptly issued for enterprises or products with high risks. At the same time, personalized handling measures are tailored to the specific circumstances of the enterprises and historical data, including ordering rectification, suspending production and sales, and recalling substandard products, to ensure that the safety of public use of medicines and medical devices is effectively guaranteed.
[0068] The system for remote monitoring during product manufacturing, used to implement the above methods, includes:
[0069] The data acquisition module is responsible for collecting video and image information of the production process in real time through cameras deployed in relevant production enterprises. It also collects key information about the drug production process, including raw material procurement, production process parameters and quality inspection results, through batch record data collection, for subsequent inspection and retrieval.
[0070] The checklist customization module is responsible for creating custom checklists for different enterprise types and generating consistent runtime pages.
[0071] The process configuration module is responsible for generating and adjusting business processes with custom configurations, supporting unified configuration management of custom condition rules, and enabling more flexible and autonomous process flow and branch control.
[0072] The intelligent video AI management platform is responsible for real-time video monitoring of key areas and critical processes in production enterprises, automatically identifying potential violations and safety hazards, and issuing timely warning signals.
[0073] The batch record review module is responsible for reviewing the batch records uploaded by enterprises. If an abnormal batch is found, an alarm is triggered, the detailed information of the abnormal batch is recorded, and the relevant enterprise is notified in a timely manner to make rectification. At the same time, the rectification status of the enterprise is tracked to form a closed-loop management.
[0074] The results aggregation module (remote command and dispatch center) is responsible for summarizing and organizing the inspection results from batch record review, process supervision and real-time video monitoring. It uses big data analysis technology to deeply mine and comprehensively evaluate the aggregated data. Through the analysis and comparison of massive amounts of data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, providing a strong basis for subsequent regulatory decisions.
[0075] The risk warning and response module is responsible for issuing timely risk warnings to enterprises or products with high risks based on the comprehensive evaluation results of the aggregated data. At the same time, it can customize and formulate personalized response measures based on the specific circumstances of the enterprise and historical data, including ordering rectification, suspending production and sales, and recalling substandard products, to ensure that the safety of public use of medicines and medical devices is effectively guaranteed.
[0076] The device for remote monitoring during product manufacturing includes a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the above-described method steps.
[0077] The readable storage medium stores a computer program that, when executed by a processor, implements the above-described method steps.
[0078] The embodiments described above are merely one specific implementation of the present invention. Ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for remote monitoring during product manufacturing, characterized in that: Includes the following steps: Step S1: Preliminary Preparations and System Integration Deploy cameras in relevant manufacturing enterprises and connect them to an intelligent video AI management platform to ensure normal and stable network deployment; Enterprises upload batch record data to the regulatory platform in accordance with unified standards. Batch records are used to record key information in the drug production process, including raw material procurement, production process parameters and quality inspection results, for subsequent inspection and retrieval. Step S2: Customize the inspection form Custom checklists can be created for different enterprise types, and consistent runtime pages can be generated. Step S3, Process Configuration It supports generating and adjusting business processes through configuration, and supports unified configuration management of custom condition rules, enabling autonomous process flow and branch control; Step S4: Review and Exception Handling The system reviews batch records uploaded by enterprises. If abnormal batches are found, an alarm is triggered, detailed information of the abnormal batches is recorded, and the relevant enterprises are notified in a timely manner to rectify the issues. At the same time, the system tracks the rectification progress of enterprises to form a closed-loop management system. Real-time video monitoring of key areas and critical processes in manufacturing enterprises is conducted through an intelligent video AI management platform. Step S5: Summary of Results The remote command and dispatch center summarizes and organizes the inspection results from batch record review, process supervision and real-time video monitoring, and uses big data analysis technology to conduct in-depth mining and comprehensive evaluation of the summarized data; through the analysis and comparison of massive data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, and provides a strong basis for subsequent regulatory decisions. Step S6: Risk Warning and Handling Measures Based on the comprehensive evaluation results of the aggregated data, risk warning information will be issued in a timely manner for enterprises or products that pose risks. At the same time, based on the specific circumstances and historical data of the enterprise, customized disposal measures are formulated, including ordering rectification, suspending production and sales, and recalling substandard products.
2. The method for remote monitoring during product manufacturing according to claim 1, characterized in that: In step S3, a custom scheduled task is defined to periodically review and summarize the entire regulatory process, collect feedback from relevant enterprises and regulatory personnel, and continuously optimize system functions and operating procedures.
3. The method for remote monitoring during product manufacturing according to claim 1, characterized in that: In step S4, the system supports remote mobile inspections between relevant enterprise personnel and regulatory personnel, and enables real-time communication between them using instant messaging tools. This allows regulatory personnel to ask questions and provide guidance at any time, while relevant enterprise personnel can promptly answer questions and provide supplementary materials.
4. The method for remote monitoring during product manufacturing according to claim 1, characterized in that: In step S4, the intelligent video AI management platform uses image recognition technology and behavior analysis technology to automatically identify violations and safety hazards, and issues early warning signals in a timely manner.
5. The method for remote monitoring during product manufacturing according to claim 1, characterized in that: In step S5, the on-site images and data are centrally displayed and analyzed through the remote command and dispatch center, so that regulatory personnel can fully grasp the inspection progress and overall situation and make timely decisions and deployments.
6. A system for remote monitoring during product manufacturing, characterized in that: To implement the method according to any one of claims 1 to 5, comprising: The data acquisition module is responsible for collecting video and image information of the production process in real time through cameras deployed in relevant production enterprises. It also collects key information about the drug production process, including raw material procurement, production process parameters and quality inspection results, through batch record data collection, for subsequent inspection and retrieval. The checklist customization module is responsible for creating custom checklists for different enterprise types and generating consistent runtime pages. The process configuration module is responsible for generating and adjusting business processes with custom configurations, supporting unified configuration management of custom condition rules, and enabling autonomous process flow and branch control. The intelligent video AI management platform is responsible for real-time video monitoring of key areas and critical processes in production enterprises, automatically identifying violations and safety hazards, and issuing timely warning signals. The batch record review module is responsible for reviewing the batch records uploaded by enterprises. If an abnormal batch is found, an alarm is triggered, the detailed information of the abnormal batch is recorded, and the relevant enterprise is notified in a timely manner to make rectification. At the same time, the rectification status of the enterprise is tracked to form a closed-loop management. The results aggregation module is responsible for summarizing and organizing the inspection results from batch record review, process supervision, and real-time video monitoring. It uses big data analysis technology to deeply mine and comprehensively evaluate the aggregated data. Through the analysis and comparison of massive amounts of data, combined with pre-set conditions and rules, it judges the compliance status and risk level of enterprises, providing a strong basis for subsequent regulatory decisions. The risk warning and handling module is responsible for issuing timely risk warnings for enterprises or products with risks based on the comprehensive evaluation results of the aggregated data. At the same time, it can customize and formulate personalized handling measures based on the specific circumstances of the enterprise and historical data, including ordering rectification, suspending production and sales, and recalling substandard products.
7. A device for remote monitoring during product manufacturing, characterized in that: It includes a memory and a processor; the memory is used to store a computer program, and the processor is used to execute the computer program to implement the method as described in any one of claims 1 to 5.
8. A readable storage medium, characterized in that: The readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.