Production progress real-time tracking platform and real-time tracking method thereof

Abstract

The invention relates to the field of data monitoring, in particular to a production progress real-time tracking platform and a real-time tracking method thereof, which integrate an intelligent material management module, an emergency production scheduling module, a production efficiency and quality analysis module, a personnel state and safety monitoring module, an equipment state and maintenance prediction module and the like. The platform comprehensively guarantees the smoothness and safety of the production process by monitoring raw material inventory in real time, predicting the consumption trend, intelligently coping with equipment faults and raw material shortage, automatically generating a production report, monitoring personnel safety and equipment states and predicting maintenance requirements, effectively improves the production efficiency and the product quality, and guarantees the smooth execution of a production plan.

Description

Technical Field

[0001] The present application relates to the field of data monitoring, and in particular to a production progress real-time tracking platform and a real-time tracking method thereof. Background Art

[0002] In today's global manufacturing competition environment, the hardware and mold manufacturing industry faces unprecedented challenges and opportunities. As customers' requirements for product quality, delivery time and cost control continue to increase, companies must continuously improve production efficiency and management level to maintain market competitiveness.

[0003] At present, the hardware and mold manufacturing industry mainly relies on traditional production management platforms in terms of production management, including ERP (Enterprise Resource Planning), MES (Manufacturing Execution Platform), etc. These platforms have achieved the collection and preliminary analysis of production data to a certain extent, providing basic support for production scheduling and resource management.

[0004] Regarding the above-mentioned related technologies, the inventors believe that the following problems exist: the existing platforms often face emergencies, such as equipment failure or shortage of raw materials. The existing platforms rely on manual decision-making, have slow response speed, poor flexibility, and it is difficult to quickly adjust production plans. Summary of the invention

[0005] In order to improve production efficiency, product quality and production safety, the present application provides a production progress real-time tracking platform and a real-time tracking method thereof.

[0006] In a first aspect, the present application provides a real-time production progress tracking platform, including the following modules: Intelligent material management and forecasting module monitors raw material inventory in real time, predicts consumption trends, and when it detects that raw materials are about to be in short supply, intelligently analyzes the availability of alternative materials and automatically triggers the procurement process; Emergency production scheduling and optimization module: when emergencies such as equipment failure or raw material shortage occur, the platform automatically analyzes the impact range and re-plans the production sequence; The production efficiency and quality analysis module automatically generates production reports containing key indicators such as equipment utilization, raw material consumption, and production efficiency, paying special attention to the impact of emergencies on production and providing data support; The personnel status and safety monitoring module uses biometrics and behavioral analysis technology to monitor personnel changes on the production line, including operator attendance, training status, and safety behavior, while also monitoring safety indicators in the production environment; The equipment status and maintenance prediction module monitors the operating status of production equipment in real time, predicts potential failures, performs maintenance in advance, and reduces unplanned downtime.

[0007] Optionally, a method for real-time tracking of production progress is performed using the above-mentioned real-time tracking platform for production progress, and includes the following steps: S1: Deploy sensors and IoT devices on the production line to monitor raw material inventory levels in real time, use historical data and current production plans to predict material consumption trends, and automatically analyze the availability of alternative materials and evaluate their impact on production plans when raw material inventory is detected approaching a preset minimum threshold. S2: When emergencies such as equipment failure and raw material shortage occur, the platform immediately analyzes the scope of impact, assesses the potential impact on the production plan, re-plans the production sequence, and adjusts the configuration of the production line and resource allocation; S3: Use the production data management platform to automatically collect key data in the production process, including equipment utilization, raw material consumption, and production efficiency indicators, and generate real-time production reports; S4: Deploy a biometric and behavioral analytics platform on the production line to monitor operators’ attendance, training status, and safety behaviors in real time; S5: Monitor the operating status of the equipment in real time and collect equipment operating data, including temperature, vibration and energy consumption.

[0008] Optionally, the biometric recognition and behavior analysis platform is deployed on the production line to monitor the attendance, training status and safety behavior of operators in real time, including the following sub-steps: S41: The biometric platform records operator status, including attendance, training status, and health status, to ensure that operators meet production requirements; S42: Use behavioral analysis technology to monitor operator safety behavior, identify unsafe behaviors through video analysis and sensor data, and prevent accidents; S43: The environmental monitoring platform checks the production environment, including temperature, humidity and air quality, to ensure the safety and comfort of the production environment; S44: The early warning platform is automatically triggered when unsafe behavior is detected. The unsafe behavior is based on the operator's operating behavior and environmental monitoring data. The operator and management personnel are promptly reminded through sound and light alarms and SMS notifications.

[0009] Optionally, the biometric platform records the operator status, including attendance, training status, and health status, to ensure that the operator meets production requirements, including the following steps: S411: Combine environmental monitoring data to intelligently adjust operator attendance time; S412: Collect the operator's health data through wearable devices, combine it with environmental parameters, analyze the impact of the environment on health, and warn of potential health risks; S413: Based on virtual reality technology, simulate work scenarios under different environmental conditions and conduct safety training.

[0010] Optionally, the use of behavioral analysis technology to monitor the operator's safety behavior, identify unsafe behaviors through video analysis and sensor data, and prevent accidents includes the following steps: S421: Study the impact of environmental parameters on operator behavior and optimize the production environment; S422: When unsafe behavior is detected, the platform not only sends a warning to the operator, but also automatically adjusts environmental parameters, including increasing lighting, reducing noise, and changing ambient temperature, to create safer working conditions; S423: Through machine learning, the platform learns patterns of safe operation under different environmental conditions, optimizes behavior analysis algorithms, and improves the accuracy and environmental adaptability of early warnings.

[0011] Optionally, the environmental monitoring platform checks the production environment, including temperature, humidity and air quality, to ensure the safety and comfort of the production environment, including the following steps: S431: While monitoring environmental parameters, analyze their impact on operator health and work efficiency, and dynamically adjust the production environment to ensure optimal working conditions; S432: When environmental parameters exceed the safety range, the platform automatically triggers an early warning and sends out dispatch instructions through the mobile terminal to intelligently dispatch operators; S433: Regularly carry out environmental improvements based on environmental monitoring data and provide environmental safety training to operators.

[0012] Optionally, the early warning platform is automatically triggered when unsafe behavior is detected. The unsafe behavior is based on the operator's operation behavior and environmental monitoring data, and the operator and management personnel are promptly reminded through sound and light alarms and SMS notifications, including the following steps: S441: Using situational awareness technology, combined with environmental parameters, operator status and production data, intelligently predict potential safety risks and trigger early warnings; S442: When an early warning is triggered, the platform not only sends a warning to the operator, but also automatically dispatches emergency resources, including automatically starting emergency equipment and intelligently adjusting the production line layout; S443: Through deep learning algorithms, the platform continuously learns and optimizes security policies, including warning thresholds, response processes, and resource allocation.

[0013] Preferably, before deploying the biometric recognition and behavior analysis platform on the production line to monitor the operator's attendance, training status and safety behavior in real time, the following steps are included: A1: Deploy necessary smart devices and sensor networks, establish integrated data and analysis platforms, and ensure network and information security to provide comprehensive support for data collection, analysis, and protection; A2: Train operators on the use of smart devices, safety awareness and emergency response, as well as situational awareness and intelligent response, to ensure that operators can operate the equipment correctly, understand the principles of early warning, and respond quickly when an early warning is triggered; A3: Develop detailed safety policies and standards, emergency response plans, and establish continuous improvement and feedback mechanisms.

[0014] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a method for real-time tracking of production progress as described in any one of the above items are implemented.

[0015] In a fourth aspect, the present application provides a computer storage medium, which adopts the following technical solution: A computer storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a method for real-time tracking of production progress as described in any one of the above items are implemented.

[0016] In summary, this application includes the following beneficial technical effects: 1. Through the intelligent material management and forecasting module, the platform can monitor raw material inventory in real time, predict consumption trends, automatically analyze the availability of alternative materials, and trigger the procurement process to effectively avoid production interruptions. At the same time, the emergency production scheduling and optimization module can quickly analyze the impact scope and re-plan the production sequence when emergencies such as equipment failure or raw material shortage occur, significantly improving the flexibility and response speed of the production platform; 2. The production efficiency and quality analysis module automatically generates production reports containing key indicators such as equipment utilization, raw material consumption, and production efficiency, paying special attention to the impact of emergencies on production, and providing real-time and accurate data support for production management; 3. The personnel status and safety monitoring module uses biometric recognition and behavior analysis technology to monitor personnel changes on the production line and safety indicators of the production environment in real time to ensure production safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a principle block diagram of a real-time production progress tracking platform in an embodiment of the present application.

[0018] Figure 2 It is a flow chart of a method for real-time tracking of production progress in an embodiment of the present application.

[0019] Figure 3It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0020] Figure 4 It is a flowchart of the sub-steps of step S41 in the embodiment of the present application.

[0021] Figure 5 It is a flowchart of the sub-steps of step S42 in the embodiment of the present application.

[0022] Figure 6 It is a flowchart of the sub-steps of step S43 in the embodiment of the present application.

[0023] Figure 7 It is a flowchart of the sub-steps of step S44 in the embodiment of the present application.

[0024] Figure 8 It is a flowchart of steps A1-A3 in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1-Figure 8 This application is described in further detail.

[0026] Reference Figure 1 The present application embodiment discloses a real-time production progress tracking platform, including the following modules: Intelligent material management and forecasting module monitors raw material inventory in real time, predicts consumption trends, and when it detects that raw materials are about to be in short supply, intelligently analyzes the availability of alternative materials and automatically triggers the procurement process; Emergency production scheduling and optimization module: when emergencies such as equipment failure or raw material shortage occur, the platform automatically analyzes the impact range and re-plans the production sequence; The production efficiency and quality analysis module automatically generates production reports containing key indicators such as equipment utilization, raw material consumption, and production efficiency, paying special attention to the impact of emergencies on production and providing data support; The personnel status and safety monitoring module uses biometrics and behavioral analysis technology to monitor personnel changes on the production line, including operator attendance, training status, and safety behavior, while also monitoring safety indicators of the production environment; The equipment status and maintenance prediction module monitors the operating status of production equipment in real time, predicts potential failures, performs maintenance in advance, and reduces unplanned downtime.

[0027] Specifically, the real-time production progress tracking platform integrates multiple intelligent modules to achieve comprehensive monitoring and optimization of the production process. The intelligent material management and forecasting module monitors raw material inventory in real time, combines historical consumption data and production plans, and predicts future demand. When inventory approaches a critical value, it automatically analyzes the feasibility of alternative materials and triggers the procurement process to ensure production continuity. When equipment failure or raw material shortage is detected, the emergency production scheduling and optimization module quickly assesses the scope of impact, re-plans the production sequence, and reduces production interruptions.

[0028] In addition, the production efficiency and quality analysis module automatically generates key production indicator reports, including equipment utilization, raw material consumption and production efficiency, with special attention to the impact of emergencies on production, providing data support for continuous improvement. The personnel status and safety monitoring module uses biometrics and behavioral analysis technology to monitor operators' attendance, training status and safety behavior, while monitoring the safety indicators of the production environment to ensure production safety. The equipment status and maintenance prediction module monitors the operating status of equipment in real time, predicts potential failures, arranges maintenance in advance, reduces unplanned downtime, and ensures production efficiency.

[0029] Correspondingly, taking the production and processing of mold hardware as an example, suppose that during the production process, the intelligent material management and prediction module detects that the inventory of a specific steel used to manufacture molds is about to run out. The platform automatically analyzes the availability of alternative materials and finds that another steel is replaceable in performance and has sufficient inventory. The platform then automatically triggers the procurement process and notifies the emergency production scheduling and optimization module. This module evaluates the impact of steel shortages on production, replans the production sequence, and prioritizes production tasks using alternative materials to ensure the continuous operation of the production line.

[0030] Furthermore, the production efficiency and quality analysis module automatically generated a production report showing key indicators such as raw material consumption, equipment utilization and production efficiency, with special attention paid to the impact of the raw material shortage on production, providing data support for subsequent production planning and material management.

[0031] At the same time, the equipment status and maintenance prediction module detected abnormal operating parameters of a key processing equipment and predicted potential failure risks. The platform arranged maintenance in advance, avoided unplanned downtime, and ensured the smooth execution of the production plan.

[0032] In addition, the personnel status and safety monitoring module confirms the operator's attendance status through biometric technology and monitors the operator's safe operation behavior through behavioral analysis technology, ensuring the safety of personnel during the production process. Through the collaborative work of these modules, the real-time production progress tracking platform significantly improves production efficiency, reduces production interruptions, ensures production safety, and realizes intelligent production management.

[0033] Reference Figure 2 The embodiment of the present application also discloses a method for real-time tracking of production progress, which is executed by using the above-mentioned real-time tracking platform for production progress, and includes the following steps: S1: Deploy sensors and IoT devices on the production line to monitor raw material inventory levels in real time, use historical data and current production plans to predict material consumption trends, and automatically analyze the availability of alternative materials and evaluate their impact on production plans when it is detected that raw material inventory is approaching a preset minimum threshold.

[0034] Correspondingly, for step S1, this application provides the following example.

[0035] Specifically, in the scenario of mold hardware production and processing, sensors and IoT devices are deployed on the production line, which monitor the inventory level of raw materials in real time, such as the inventory of specific steel used to make molds. The platform uses historical consumption data and current production plans to predict material consumption trends through algorithms. When it is detected that the inventory of a specific steel is close to the preset minimum threshold, the intelligent material management and prediction module is automatically started to analyze the availability of alternative materials, such as another steel with similar performance, and evaluate its impact on the production plan to ensure that production continuity is not affected.

[0036] The specific examples are described as follows: Suppose that during the production process, the platform detects that the inventory of a specific steel material used to make molds is about to run out, while there are still a large number of molds that need to be processed in the current production plan. The intelligent material management and forecasting module is immediately launched, analyzing historical consumption data and current production needs, and predicting that the steel inventory will drop to a critical value in the next few days. The platform automatically analyzes the availability of alternative materials and finds that another steel material is substitutable in performance, has sufficient inventory, and has similar cost and processing technology to the original steel. The platform evaluates the impact of using alternative materials on the production plan and confirms that it will not have a significant impact on product quality and production efficiency.

[0037] Subsequently, the platform automatically triggered the procurement process and issued an order to the supplier to ensure the timely arrival of the replacement materials. At the same time, the emergency production scheduling and optimization module intervened to re-plan the production sequence and prioritize the production tasks using replacement materials to ensure the continuous operation of the production line. The production efficiency and quality analysis module automatically generated a production report, showing key indicators such as raw material consumption, equipment utilization, and production efficiency, with special attention to the impact of the raw material shortage on production, providing data support for subsequent production planning and material management.

[0038] In addition, the equipment status and maintenance prediction module detected abnormal operating parameters of a key processing equipment and predicted potential failure risks. The platform arranged maintenance in advance, avoiding unplanned downtime and ensuring the smooth implementation of the production plan. The personnel status and safety monitoring module confirmed the operator's attendance status through biometric technology and monitored the operator's safe operation behavior through behavioral analysis technology, ensuring the safety of personnel during the production process.

[0039] After step S1, the method further includes the following steps: S2: When emergencies such as equipment failure and raw material shortage occur, the platform immediately analyzes the scope of impact, assesses the potential impact on the production plan, re-plans the production sequence, and adjusts the configuration of the production line and resource allocation.

[0040] Similarly, for step S2, the following example is given.

[0041] First, the equipment status and maintenance prediction module monitors the abnormal operation parameters of the equipment in real time and immediately triggers an alarm. The platform analyzes the location and function of the faulty equipment in the production line and assesses its impact on the current production tasks, including the possible production interruption time, the number of production tasks affected, and the potential economic losses.

[0042] Furthermore, the emergency production scheduling and optimization module re-plans the production sequence according to the impact range of the faulty equipment. The platform automatically identifies which production tasks can be transferred to other available equipment, which tasks need to be postponed, and which tasks can be completed in advance to make up for the lost time. At the same time, the platform adjusts the configuration and resource allocation of the production line to ensure the efficient operation of other equipment and production lines to minimize the impact of production interruptions.

[0043] In addition, the platform automatically analyzes resource allocation on the production line, including personnel, materials and equipment, to ensure the rational use of resources. For example, if a faulty device is used to process a specific raw material, the platform will automatically look for alternative materials or adjust the production plan to use other materials in the existing inventory to avoid production stagnation.

[0044] At the same time, the platform automatically sends notifications to relevant personnel, including production managers, operators and maintenance teams, informing them of equipment failures, adjustments to production plans and new task arrangements. At the same time, it coordinates the maintenance team to repair the equipment and ensure that the equipment is restored to operation as soon as possible.

[0045] During equipment maintenance, the platform continuously monitors production progress and dynamically adjusts production plans based on actual conditions. Once the faulty equipment is repaired, the platform will re-evaluate the production plan to ensure smooth execution of production tasks.

[0046] After step S2, the corresponding steps also include: S3: Use the production data management platform to automatically collect key data in the production process, including equipment utilization, raw material consumption, and production efficiency indicators, and generate real-time production reports.

[0047] Assume that in a factory that specializes in mold hardware production, the production line is equipped with advanced sensors and IoT devices to monitor key indicators of the production process in real time. These devices can automatically collect the following data: Equipment utilization: By monitoring the operating status of the equipment, the platform can calculate the ratio of the actual operating time of the equipment within a specific time to the planned operating time, thereby evaluating the equipment utilization. For example, a stamping machine is planned to run for 7.5 hours within 8 hours, but actually runs for 7 hours, then the equipment utilization is 93.3% (7 / 7.5*100%).

[0048] Raw material consumption: The platform monitors the use of raw materials and records the consumption of each material, including steel and copper used to produce molds. For example, the platform records that 1,000 kg of steel and 200 kg of copper were consumed in the production of a specific batch of mold hardware.

[0049] Production efficiency: By analyzing the completion of production tasks, the platform can calculate the actual production efficiency. This includes comparing the planned output with the actual output, and evaluating the production cycle time. For example, if it is planned to produce 1,000 mold hardware parts within 8 hours, but 950 pieces are actually produced, then the production efficiency is 95% (950 / 1,000*100%).

[0050] Based on the above data, the production data management platform processes and analyzes the collected data and automatically generates a real-time production report. Correspondingly, the report contains the following information: Equipment Status Overview: Lists the current status of all key equipment, including operation, standby, maintenance or fault status, as well as equipment utilization statistics.

[0051] Raw material consumption report: Lists in detail the consumption of each raw material and the remaining inventory to help production managers replenish inventory in a timely manner.

[0052] Production efficiency analysis: Provides real-time data on production efficiency, including the completion status of production tasks, production cycle time, fluctuation trends of production efficiency, etc.

[0053] Abnormal situation alarm: If the equipment utilization rate is lower than the preset threshold, or the raw material consumption is abnormal, the platform will automatically issue an alarm to prompt production managers to take appropriate measures.

[0054] In the above example of mold hardware production and processing, the production data management platform helped the factory achieve the following goals through real-time monitoring and analysis: Optimize production plans: By understanding equipment utilization and raw material consumption in real time, production managers can adjust production plans in a timely manner to ensure the rational allocation of resources.

[0055] Improve production efficiency: Real-time production reports provide immediate feedback on production efficiency, helping factories identify production bottlenecks and take measures to improve production efficiency.

[0056] Reduced waste: By monitoring raw material consumption, factories are able to control inventory more accurately, reducing over-purchasing and waste.

[0057] Quick response to anomalies: The platform automatically detects abnormal situations, such as equipment failure or raw material shortage, and promptly notifies relevant personnel to reduce production interruption time.

[0058] Correspondingly, after step S3, the following steps are also included: S4: Deploy a biometric and behavioral analytics platform on the production line to monitor operators’ attendance, training status, and safety behaviors in real time.

[0059] Reference Figure 3 , step S4 correspondingly includes the following sub-steps: S41: The biometric platform records operator status, including attendance, training status, and health status, ensuring that operators meet production requirements.

[0060] Reference Figure 4 , step S41 correspondingly includes the following sub-steps: S411: Combine environmental monitoring data to intelligently adjust operator attendance time; S412: Collect the operator's health data through wearable devices, combine it with environmental parameters, analyze the impact of the environment on health, and warn of potential health risks; S413: Based on virtual reality technology, simulate work scenarios under different environmental conditions and conduct safety training.

[0061] Correspondingly, steps S411-S413 are described by way of example below.

[0062] Specifically, in the scenario of mold hardware production and processing, the personnel status and safety monitoring module further ensures the health and safety of operators by intelligently adjusting the operator's attendance time, collecting health data and warning of potential health risks, and conducting safety training based on virtual reality technology. The following is a detailed description of the specific example: Combined with environmental monitoring data, intelligently adjust the operator's attendance time The environmental monitoring platform continuously monitors parameters such as temperature, humidity, noise level and air quality in the production environment. When environmental conditions reach the preset threshold of unsuitable working conditions, such as high temperature or poor air quality, the platform will intelligently adjust the operator's attendance time to avoid working in harsh environments. For example, if the temperature in the production area is monitored to be over 35°C, the platform will automatically adjust the operator's work shift to work during a period of lower temperature or provide additional rest time to ensure the health and safety of the operator.

[0063] Collect operator health data through wearable devices to warn of potential health risks Wearable devices worn by operators, such as smart watches or health monitoring bracelets, continuously collect health data such as heart rate, blood pressure, body temperature, and exercise volume. The platform combines this data with environmental parameters to analyze the impact of the environment on the operator's health. For example, if the operator's heart rate is abnormally high when working in a high temperature environment, the platform will warn of potential heat stroke risks, remind the operator to rest and replenish water in time, and notify management to take measures to improve the working environment.

[0064] Based on virtual reality technology, simulate working scenarios under different environmental conditions and conduct safety training Virtual reality (VR) technology is used to simulate work scenarios under different environmental conditions and provide immersive safety training for operators. For example, the platform can simulate environments with high temperatures, high noise or toxic gas leaks, allowing operators to learn how to properly use personal protective equipment, how to escape in an emergency, and how to perform first aid in a virtual environment. Through VR training, operators can practice responding to various emergency situations in a safe environment, improving their safety awareness and emergency response capabilities.

[0065] Replay Figure 3 , after step S41, the following steps are also included: S42: Use behavioral analysis technology to monitor operator safety behavior and identify unsafe behaviors through video analysis and sensor data to prevent accidents.

[0066] Reference Figure 5 , step S42 correspondingly includes the following sub-steps: S421: Study the impact of environmental parameters on operator behavior and optimize the production environment; S422: When unsafe behavior is detected, the platform not only sends a warning to the operator, but also automatically adjusts environmental parameters, including increasing lighting, reducing noise, and changing ambient temperature, to create safer working conditions; S423: Through machine learning, the platform learns patterns of safe operation under different environmental conditions, optimizes behavior analysis algorithms, and improves the accuracy and environmental adaptability of early warnings.

[0067] In the scenario of mold hardware production and processing, the personnel status and safety monitoring module studies the impact of environmental parameters on operator behavior, automatically adjusts environmental parameters to optimize the production environment, and optimizes the behavior analysis algorithm through machine learning to ensure that operators work in safer and more suitable conditions. The following is a detailed description of a specific example: First, the platform continuously collects environmental parameters such as temperature, humidity, lighting intensity and noise level, as well as operator behavior data. Through data analysis, the platform studies the impact of environmental parameters on operator behavior, for example, finding that a high noise environment may cause operators to be distracted and increase the risk of operating errors. Based on these findings, the platform optimizes the production environment, for example, by adding sound insulation materials or adjusting equipment layout to reduce noise levels and create safer and more focused working conditions.

[0068] When behavioral analysis technology detects unsafe operator behavior, the platform not only sends a warning to the operator, but also automatically adjusts environmental parameters to create safer working conditions. For example, if the platform recognizes that the operator is operating precision equipment in a low-light environment, the platform will automatically increase lighting to improve the operator's visual clarity and reduce operating errors. If the operator is detected working in a high-noise environment, the platform will automatically reduce the noise level, such as by activating noise reduction equipment or adjusting the equipment's operating hours to reduce the impact of noise on the operator.

[0069] In addition, the platform uses machine learning technology to continuously learn safe operation patterns under different environmental conditions, optimize behavior analysis algorithms, and improve the accuracy and environmental adaptability of early warnings. For example, the platform learns that in a high-humidity environment, operators are more likely to slip when operating metal equipment. The platform will adjust the algorithm to more sensitively identify such unsafe behaviors and issue early warnings in a timely manner. Through continuous learning and optimization, the platform can more accurately identify unsafe behaviors, while reducing false alarms and improving the effectiveness of early warnings. Replay Figure 3 , after step S42, the following steps are also included: S43: The environmental monitoring platform checks the production environment, including temperature, humidity and air quality, to ensure the safety and comfort of the production environment.

[0070] Reference Figure 6 Correspondingly, step S43 includes the following sub-steps: S431: While monitoring environmental parameters, analyze their impact on operator health and work efficiency, and dynamically adjust the production environment to ensure optimal working conditions; S432: When environmental parameters exceed the safety range, the platform automatically triggers an early warning and sends out dispatch instructions through the mobile terminal to intelligently dispatch operators; S433: Regularly carry out environmental improvements based on environmental monitoring data and provide environmental safety training to operators.

[0071] The following examples are given for steps S431-S433.

[0072] First, the environmental monitoring platform continuously monitors the temperature, humidity, air quality, noise level and other parameters of the production environment. The platform analyzes the impact of these parameters on the health and work efficiency of operators. For example, it finds that a high humidity environment may cause discomfort to operators and affect work efficiency. Based on these analyses, the platform dynamically adjusts the production environment, for example, by automatically adjusting the air conditioning platform to maintain appropriate temperature and humidity to ensure that operators can produce in the best working condition.

[0073] Furthermore, when the environmental monitoring platform detects that environmental parameters are beyond the safe range, for example, the air quality index (AQI) in the production area exceeds the preset safety threshold, the platform automatically triggers an early warning. The early warning information is not only issued through the sound and light alarm on site, but also sends dispatch instructions to operators and managers through mobile terminals. The platform intelligently dispatches operators, for example, arranging operators to temporarily evacuate to a safe area, or adjusting work shifts to avoid working in an unsafe environment and ensure the safety of operators.

[0074] Based on long-term environmental monitoring data, the platform regularly analyzes the need to improve the production environment. For example, if it finds that the noise level in the production area is persistently high, it may affect the hearing health of the operators. The platform will make suggestions for environmental improvement, such as adding sound insulation materials or adjusting the equipment layout to reduce the noise level. At the same time, the platform provides environmental safety training for operators, educating them on how to protect their health under different environmental conditions and how to properly use personal protective equipment to improve their environmental safety awareness.

[0075] Replay Figure 3 , after step S43, the following steps are also included: S44: The early warning platform is automatically triggered when unsafe behavior is detected. The unsafe behavior is based on the operator's operating behavior and environmental monitoring data. The operator and management personnel are promptly reminded through sound and light alarms and SMS notifications.

[0076] Reference Figure 7 , step S44 correspondingly includes the following sub-steps: S441: Using situational awareness technology, combined with environmental parameters, operator status and production data, intelligently predict potential safety risks and trigger early warnings; S442: When an early warning is triggered, the platform not only sends a warning to the operator, but also automatically dispatches emergency resources, including automatically starting emergency equipment and intelligently adjusting the production line layout; S443: Through deep learning algorithms, the platform continuously learns and optimizes security policies, including warning thresholds, response processes, and resource allocation.

[0077] In the scenario of mold hardware production and processing, the personnel status and safety monitoring module uses situational awareness technology, combined with environmental parameters, operator status and production data, to intelligently predict potential safety risks, trigger early warnings, and ensure production safety. The following is a detailed description of a specific example: First, situational awareness technology comprehensively analyzes environmental parameters (such as temperature, humidity, and air quality), operator status (such as health data and behavior patterns), and production data (such as equipment operating status and production efficiency), and intelligently predicts potential safety risks through deep learning algorithms. For example, the platform may predict that under certain environmental conditions, operators may become fatigued due to long hours of work, increasing the risk of operational errors. Based on these predictions, the platform triggers early warnings to remind operators and managers to take preventive measures, such as arranging breaks or adjusting work intensity, to avoid potential safety accidents.

[0078] Furthermore, when the platform detects a potential safety risk and triggers an early warning, it not only sends a warning message to the operator, but also automatically dispatches emergency resources. For example, the platform can automatically start emergency equipment, such as a fire fighting platform or an emergency ventilation platform, to respond to possible fires or leaks of harmful gases. At the same time, the platform intelligently adjusts the layout of the production line, for example, automatically stops or isolates the affected production area to avoid the spread of risks and ensure the safety of other operators.

[0079] In addition, the platform continuously learns and optimizes safety strategies, including warning thresholds, response processes, and resource allocation, through deep learning algorithms. For example, the platform learns the correlation between changes in operator health data and decreased production efficiency under specific environmental conditions, and can adjust the warning threshold to more accurately predict potential safety risks. Through continuous learning, the platform is able to optimize the response process, for example, automatically adjust the allocation of emergency resources to ensure that when the warning is triggered, the most appropriate emergency resources can be dispatched quickly and effectively, improving the ability to respond to safety risks.

[0080] Reference Figure 8 Before step S4, the following pre-steps need to be performed: A1: Deploy necessary smart devices and sensor networks, establish integrated data and analysis platforms, and ensure network and information security to provide comprehensive support for data collection, analysis, and protection; A2: Train operators on the use of smart devices, safety awareness and emergency response, as well as situational awareness and intelligent response, to ensure that operators can operate the equipment correctly, understand the principles of early warning, and respond quickly when an early warning is triggered; A3: Develop detailed safety policies and standards, emergency response plans, and establish continuous improvement and feedback mechanisms.

[0081] Correspondingly, first of all, enterprises need to deploy a series of intelligent devices and sensors at the production site, including temperature sensors, humidity sensors, air quality monitors, noise monitors, vibration sensors, etc., to monitor environmental parameters and equipment status in real time. At the same time, an integrated data platform should be established to collect, store and analyze these sensor data. The data platform should have strong data processing capabilities, be able to process large amounts of data in real time, and support the operation of deep learning algorithms. In addition, enterprises also need to pay attention to network and information security, use encryption technology to protect data transmission, set access control, prevent data leakage and unauthorized access, and ensure the security and integrity of data.

[0082] Furthermore, in order to ensure that operators can correctly operate smart devices, understand the principles of early warning, and respond quickly when early warnings are triggered, companies need to train operators on the platform. The training content includes how to use smart devices, such as how to read and understand sensor data, how to operate emergency equipment; safety awareness education, such as identifying potential safety risks, understanding safety policies and standards; and emergency response training, such as emergency evacuation procedures when early warnings are triggered, and how to use personal protective equipment. In addition, operators also need to learn the basic principles of situational awareness and intelligent response, understand how the platform predicts safety risks based on environmental parameters and production data, and how to adjust work plans under intelligent scheduling to ensure that they can respond correctly in various situations.

[0083] In addition, enterprises should formulate detailed safety policies and standards, including health and safety regulations for operators, equipment operating procedures, environmental safety standards, etc., to ensure that all operating activities meet safety requirements. At the same time, formulate emergency response plans to clarify the responsibilities and action steps of operators and managers when different types of warnings are triggered, such as emergency evacuation routes, emergency resource dispatch processes, etc. In addition, enterprises need to establish a continuous improvement and feedback mechanism, regularly evaluate the implementation of safety policies and standards, collect feedback from operators, adjust safety strategies according to actual operating conditions, and continuously optimize emergency response plans to ensure the dynamic adaptability and effectiveness of the safety management system.

[0084] Replay Figure 2 , after step S4, the following steps are also included: S5: Monitor the operating status of the equipment in real time and collect equipment operating data, including temperature, vibration and energy consumption.

[0085] It should be noted that in the scenario of mold hardware production and processing, the equipment status and performance monitoring module monitors the operating status of key production equipment in real time, collects and analyzes equipment operation data, including key indicators such as temperature, vibration, and energy consumption. For example, the platform continuously monitors the temperature of the stamping machine to ensure that it operates within a safe range to avoid overheating and damage to the equipment or deterioration of production quality. At the same time, the platform collects vibration data, analyzes the operating stability of the equipment, and promptly detects potential mechanical failures such as bearing wear or unbalanced loads, and avoids production interruptions caused by equipment failures through early warning. In addition, the platform also monitors the energy consumption of the equipment, and by analyzing energy consumption data, optimizes the operating efficiency of the equipment, reduces energy waste, and achieves green production. Through real-time monitoring and data analysis, the equipment status and performance monitoring module ensures the efficient and safe operation of production equipment, improves production efficiency and product quality.

[0086] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a platform bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating platform, a computer program and a database. The internal memory provides an environment for the operation of the operating platform and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a real-time tracking method for production progress is implemented: S1: Deploy sensors and IoT devices on the production line to monitor raw material inventory levels in real time, use historical data and current production plans to predict material consumption trends, and automatically analyze the availability of alternative materials and evaluate their impact on production plans when raw material inventory is detected approaching a preset minimum threshold. S2: When emergencies such as equipment failure and raw material shortage occur, the platform immediately analyzes the scope of impact, assesses the potential impact on the production plan, re-plans the production sequence, and adjusts the configuration of the production line and resource allocation; S3: Use the production data management platform to automatically collect key data in the production process, including equipment utilization, raw material consumption, and production efficiency indicators, and generate real-time production reports; S4: Deploy a biometric and behavioral analytics platform on the production line to monitor operators’ attendance, training status, and safety behaviors in real time; S5: Monitor the operating status of the equipment in real time and collect equipment operating data, including temperature, vibration and energy consumption.

[0087] When the computer program is executed by a processor, it can implement any one of the production progress real-time tracking methods in the above method embodiments.

[0088] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S1: Deploy sensors and IoT devices on the production line to monitor raw material inventory levels in real time, use historical data and current production plans to predict material consumption trends, and automatically analyze the availability of alternative materials and evaluate their impact on production plans when raw material inventory is detected approaching a preset minimum threshold. S2: When emergencies such as equipment failure and raw material shortage occur, the platform immediately analyzes the scope of impact, assesses the potential impact on the production plan, re-plans the production sequence, and adjusts the configuration of the production line and resource allocation; S3: Use the production data management platform to automatically collect key data in the production process, including equipment utilization, raw material consumption, and production efficiency indicators, and generate real-time production reports; S4: Deploy a biometric and behavioral analytics platform on the production line to monitor operators’ attendance, training status, and safety behaviors in real time; S5: Monitor the operating status of the equipment in real time and collect equipment operating data, including temperature, vibration and energy consumption.

[0089] When the computer program is executed by a processor, it can implement any one of the production progress real-time tracking methods in the above method embodiments.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0091] Technical personnel in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the platform can be divided into different functional units or modules to complete all or part of the functions described above.

[0092] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A real-time production progress tracking platform, characterized in that: Includes the following modules: Intelligent material management and forecasting module monitors raw material inventory in real time, predicts consumption trends, and when it detects that raw materials are about to be in short supply, intelligently analyzes the availability of alternative materials and automatically triggers the procurement process; Emergency production scheduling and optimization module: when emergencies such as equipment failure or raw material shortage occur, the platform automatically analyzes the impact range and re-plans the production sequence; The production efficiency and quality analysis module automatically generates production reports containing key indicators such as equipment utilization, raw material consumption, and production efficiency, paying special attention to the impact of emergencies on production and providing data support; The personnel status and safety monitoring module uses biometrics and behavioral analysis technology to monitor personnel changes on the production line, including operator attendance, training status, and safety behavior, while also monitoring safety indicators of the production environment; The equipment status and maintenance prediction module monitors the operating status of production equipment in real time, predicts potential failures, performs maintenance in advance, and reduces unplanned downtime.

2. A method for real-time tracking of production progress, implemented by a real-time tracking platform for production progress as claimed in claim 1, characterized in that: The following steps are involved: S1: Deploy sensors and IoT devices on the production line to monitor raw material inventory levels in real time, use historical data and current production plans to predict material consumption trends, and automatically analyze the availability of alternative materials and evaluate their impact on production plans when raw material inventory is detected approaching a preset minimum threshold. S2: When emergencies such as equipment failure and raw material shortage occur, the platform immediately analyzes the scope of impact, assesses the potential impact on the production plan, re-plans the production sequence, and adjusts the configuration of the production line and resource allocation; S3: Use the production data management platform to automatically collect key data in the production process, including equipment utilization, raw material consumption, and production efficiency indicators, and generate real-time production reports; S4: Deploy a biometric and behavioral analytics platform on the production line to monitor operators’ attendance, training status, and safety behaviors in real time; S5: Monitor the operating status of the equipment in real time and collect equipment operating data, including temperature, vibration and energy consumption.

3. A method for real-time tracking of production progress according to claim 2, characterized in that: The deployment of a biometric and behavioral analysis platform on the production line to monitor the operator's attendance, training status, and safety behavior in real time includes the following sub-steps: S41: The biometric platform records operator status, including attendance, training status, and health status, to ensure that operators meet production requirements; S42: Use behavioral analysis technology to monitor operator safety behavior, identify unsafe behaviors through video analysis and sensor data, and prevent accidents; S43: The environmental monitoring platform checks the production environment, including temperature, humidity and air quality, to ensure the safety and comfort of the production environment; S44: The early warning platform is automatically triggered when unsafe behavior is detected. The unsafe behavior is based on the operator's operating behavior and environmental monitoring data. The operator and management personnel are promptly reminded through sound and light alarms and SMS notifications.

4. A method for real-time tracking of production progress according to claim 2, characterized in that: The biometric platform records operator status, including attendance, training status, and health status, to ensure that operators meet production requirements, including the following steps: S411: Combine environmental monitoring data to intelligently adjust operator attendance time; S412: Collect the operator's health data through wearable devices, combine it with environmental parameters, analyze the impact of the environment on health, and warn of potential health risks; S413: Based on virtual reality technology, simulate work scenarios under different environmental conditions and conduct safety training.

5. A method for real-time tracking of production progress according to claim 2, characterized in that: The method of using behavioral analysis technology to monitor operator safety behaviors, identifying unsafe behaviors through video analysis and sensor data, and preventing accidents includes the following steps: S421: Study the impact of environmental parameters on operator behavior and optimize the production environment; S422: When unsafe behavior is detected, the platform not only sends a warning to the operator, but also automatically adjusts environmental parameters, including increasing lighting, reducing noise, and changing ambient temperature, to create safer working conditions; S423: Through machine learning, the platform learns patterns of safe operation under different environmental conditions, optimizes behavior analysis algorithms, and improves the accuracy and environmental adaptability of early warnings.

6. A method for real-time tracking of production progress according to claim 2, characterized in that: The environmental monitoring platform checks the production environment, including temperature, humidity and air quality, to ensure the safety and comfort of the production environment, including the following steps: S431: While monitoring environmental parameters, analyze their impact on operator health and work efficiency, and dynamically adjust the production environment to ensure optimal working conditions; S432: When environmental parameters exceed the safety range, the platform automatically triggers an early warning and sends out dispatch instructions through the mobile terminal to intelligently dispatch operators; S433: Regularly carry out environmental improvements based on environmental monitoring data and provide environmental safety training to operators.

7. A method for real-time tracking of production progress according to claim 2, characterized in that: The early warning platform is automatically triggered when unsafe behavior is detected. The unsafe behavior is based on the operator's operation behavior and environmental monitoring data. The notification method of sound and light alarm and SMS notification is used to timely remind operators and managers, including the following steps: S441: Using situational awareness technology, combined with environmental parameters, operator status and production data, intelligently predict potential safety risks and trigger early warnings; S442: When an early warning is triggered, the platform not only sends a warning to the operator, but also automatically dispatches emergency resources, including automatically starting emergency equipment and intelligently adjusting the production line layout; S443: Through deep learning algorithms, the platform continuously learns and optimizes security policies, including warning thresholds, response processes, and resource allocation.

8. A method for real-time tracking of production progress according to claim 2, characterized in that: The deployment of the biometric and behavioral analysis platform on the production line to monitor the operator's attendance, training status and safety behavior in real time includes the following steps: A1: Deploy necessary smart devices and sensor networks, establish integrated data and analysis platforms, and ensure network and information security to provide comprehensive support for data collection, analysis, and protection; A2: Train operators on the use of smart devices, safety awareness and emergency response, as well as situational awareness and intelligent response, to ensure that operators can operate the equipment correctly, understand the principles of early warning, and respond quickly when an early warning is triggered; A3: Develop detailed safety policies and standards, emergency response plans, and establish continuous improvement and feedback mechanisms.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for real-time tracking of production progress as described in any one of claims 2 to 8 are implemented.

10. A computer storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a method for real-time tracking of production progress as described in any one of claims 2 to 8 are implemented.

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