Compound fertilizer production management method and system based on Internet of Things

Abstract

The invention discloses a compound fertilizer production management method and system based on the Internet of Things, and relates to the technical field of production management. During operation of the system, data from various sensors are collected in real time in the production process to be analyzed and processed; according to the method, raw material proportioning, mixing and temperature and humidity control links in the production process are intelligently adjusted and optimized through an intelligent algorithm, a production optimization index IPOI is obtained through calculation, health assessment is conducted on equipment by collecting operation data of the production equipment and applying a machine learning algorithm, the occurrence time and type of equipment faults are predicted, and the production optimization index IPOI is obtained. The block chain technology is used for whole-course tracing, the intelligent energy sensor is installed, the use conditions of various energy sources in the production process are monitored in real time, accurate regulation and optimal configuration of energy consumption in the production process are achieved through optimal power scheduling and energy-saving mode switching, decision support information in the production process is generated, and the production efficiency is improved. And trend analysis and prediction are performed in combination with historical data.

Description

Technical Field

[0001] The present invention relates to the technical field of production management, and in particular to a compound fertilizer production management method and system based on the Internet of Things. Background Art

[0002] With the increasing demand for global agricultural production, compound fertilizer plays a key role in agricultural production as an important means to improve crop yield and quality. The traditional compound fertilizer production management system mainly relies on manual monitoring and manual adjustment, which cannot achieve precise control and real-time data feedback in the production process. This not only leads to low production efficiency, but also easily causes problems such as uneven raw material ratio, unstable environmental control and frequent equipment failure. With the continuous development of information technology, especially the maturity of Internet of Things (IoT) technology, IoT applications have begun to receive widespread attention in the field of compound fertilizer production. IoT technology can collect key data in the production process in real time through sensors, and upload these data to the central control system or cloud platform for processing and analysis through wireless communication technology, thereby realizing the intelligentization, automation and remoteness of the production process. Therefore, the compound fertilizer production management system based on IoT came into being. Combined with advanced technologies such as big data analysis, intelligent algorithms and cloud computing, it realizes precise control and intelligent optimization of each link in the production process, improves production efficiency, reduces resource waste, and ensures the safety and traceability of the production process.

[0003] Although the traditional compound fertilizer production management system can guarantee the continuity and stability of production to a certain extent, the method of relying on manual operation and experience judgment has significant limitations. First, the proportion, mixing, temperature and humidity control of raw materials in the production process often lack real-time feedback, resulting in low raw material utilization and difficulty in improving production efficiency; secondly, due to the inadequate fault prevention and health management of equipment, equipment failures often occur without being discovered in time, resulting in production interruptions and affecting the execution of the overall production plan; thirdly, traditional quality control and traceability methods are relatively backward, and it is impossible to achieve data recording and information traceability throughout the production process, and it is impossible to ensure the quality of each batch of products and the transparency of the source of raw materials. In response to these problems, the compound fertilizer production management system based on the Internet of Things came into being. Through modular designs such as data acquisition modules, intelligent production control modules, and equipment health management modules, the system can monitor the production environment, equipment operation status, and production data in real time, optimize and adjust the production process based on intelligent algorithms, and realize the automation, precision, and intelligence of the production process. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a compound fertilizer production management method and system based on the Internet of Things, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a compound fertilizer production management system based on the Internet of Things, including a data acquisition module, an intelligent production control module, an equipment health management module, a quality control traceability module, an energy management module and a big data analysis module; The data acquisition module is used to collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing through wireless transmission; The intelligent production control module is used to intelligently adjust and optimize the raw material ratio, mixing and temperature and humidity control links in the production process based on the real-time collected data through intelligent algorithms, and calculate and obtain: production optimization index IPOI; The equipment health management module is used to collect the operating data of the production equipment and apply machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, conduct remote diagnosis and maintenance in conjunction with the cloud platform, and provide early warning and arrange intelligent maintenance or spare parts replacement; The quality control traceability module is used to monitor key quality parameters in the production process based on IoT technology and sensor data, and to use blockchain technology for full traceability to ensure the transparency and non-tamperability of information on the production process, raw material sources, and production conditions of each batch of products; The energy management module is used to monitor the use of various energy sources in the production process in real time by installing intelligent energy sensors, and to achieve precise control and optimal configuration of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; The big data analysis module is used to generate decision support information in the production process by performing big data analysis and mining on the data collected from various modules, and to perform trend analysis and prediction in combination with historical data.

[0006] Preferably, the data acquisition module includes a raw material quality monitoring unit, a production environment monitoring unit and an equipment health monitoring unit; The raw material quality monitoring unit is used to collect and monitor the humidity, particle size and proportion of all raw material quality related to the production process in real time through humidity sensors, laser particle size analyzers, near infrared spectroscopy NIR technology and gas detection sensors, and obtain: raw material moisture content RMMC, particle size distribution RPSD, raw material composition ratio RMCR and raw material relative stability RMSI; The production environment monitoring unit is used to monitor the temperature, humidity, gas concentration and air fluidity in the production environment in real time through temperature sensors, humidity sensors, gas sensors, wind speed sensors and wind direction sensors, and obtain: temperature fluctuation coefficient TFC, humidity deviation index HDI, humidity deviation index HDI and air fluidity index AMI; The equipment health monitoring unit is used to monitor the health status of production equipment in real time through vibration sensors, temperature sensors, current sensors and load sensors, and obtain: vibration frequency deviation VFD, equipment temperature rise index ETRI, current fluctuation amplitude CFA and load response stability LRS.

[0007] Preferably, the intelligent production control module includes a raw material ratio optimization unit, an environmental control optimization unit and an equipment health optimization unit; The raw material ratio optimization unit is used to optimize the raw material ratio and mixing process by applying an intelligent algorithm, and calculate and adjust the raw material adaptation coefficient RMF; The environmental control optimization unit is used to adjust the production environment parameters according to the real-time data of the production environment, including temperature and humidity, gas concentration and air flow, through an intelligent algorithm, and calculate the environmental adaptation coefficient EAC; The equipment health optimization unit is used to predict the health status of the equipment through an intelligent algorithm and calculate the equipment health coefficient EHC.

[0008] Preferably, the raw material adaptation coefficient RMF is calculated by the following formula: ; In the formula, RMMC represents the moisture content of the raw material, RPSD represents the particle size distribution of the raw material, RMCR represents the composition ratio of the raw material, RMSI represents the relative stability of the raw material, and RMMC ref , RPSD ref RMCR ref and RMSI ref represents the reference value of each parameter, which is used for standardization. γ represents the constant coefficient, which is used to adjust the overall sensitivity of the model. represents weighting coefficient and exponential factor; The environmental adaptation coefficient EAC is calculated by the following formula: ; In the formula, TFC represents temperature fluctuation coefficient, HDI represents humidity deviation index, GCFC represents gas concentration fluctuation coefficient, AMI represents air mobility index, GCFC represents gas concentration fluctuation coefficient, ref Indicates the reference standard value of the gas concentration fluctuation coefficient, Represents the weight of environmental factors.

[0009] Preferably, the equipment health coefficient EHC is calculated by the following formula: ; In the formula, VFD represents vibration frequency deviation, ETRI represents equipment temperature rise index, CFA represents current fluctuation amplitude, LRS represents load response stability, Represents the weight coefficient, which controls the influence degree of each device health parameter on EHC; Through the comprehensive calculation of the above three coefficients, the production optimization index IPOI is obtained by the following formula: ; In the formula, RMF represents the raw material adaptation coefficient, EAC represents the environment adaptation coefficient, and EHC represents the device health coefficient. are the weight coefficients of the raw material, environment, and device health coefficients respectively, and are dynamically adjusted according to the production environment and system requirements.

[0010] Preferably, the device health management module includes a device status monitoring unit, a health assessment unit, and an intelligent maintenance unit; The device status monitoring unit is used to collect the operation status data of production equipment in real time through sensors, including vibration, temperature, current, and pressure, and perform preliminary denoising, filtering, and standardization on them; The health assessment unit is used to evaluate the health status of the device based on machine learning algorithms, and through historical data analysis and real-time data monitoring, predict the fault type, occurrence time, and possible reasons of the device. By comparing the production optimization index IPOI with the preset health threshold Q and the preset health threshold W, a grade assessment scheme is obtained; IPOI > Q, obtaining the first assessment grade, indicating that the production process is running in an optimized state and the production equipment is in good health. Regularly collect device status data and conduct health assessment in combination with the fault prediction model to ensure the long-term stable operation of the device; Q ≥ IPOI > W, obtaining the second assessment grade, indicating that the production process is in a moderately optimized state, the device operation health degree is within the normal range, but there is still room for optimization. For the deficiencies in raw material ratio, temperature and humidity, and mixing links, moderately adjust through intelligent control algorithms to optimize production efficiency. In extreme environments such as high temperature and high humidity, adjust the temperature and humidity control system; IPOI ≤ W, obtaining the third assessment grade, indicating that the production process has low efficiency and the device may have potential health risks. Use the device health management module to conduct a comprehensive diagnosis of the production equipment, including vibration analysis, temperature rise detection, and load response assessment. According to the health assessment results, arrange intelligent maintenance or spare part replacement in advance to reduce the probability of faults; The intelligent maintenance unit is used to combine with the cloud platform for remote diagnosis and maintenance of the device, analyze the type and location of device faults, provide fault location, cause analysis, and maintenance suggestions, automatically generate an intelligent maintenance plan, and support remote control and guidance for maintenance, reducing the device downtime through remote diagnosis.

[0011] Preferably, the quality control traceability module includes a quality parameter monitoring unit, a blockchain traceability unit and a compliance inspection unit; The quality parameter monitoring unit is used to monitor key quality parameters in the production process in real time through various sensors, including product size, color, density and composition, and transmit these data to the central control system for storage and processing; The blockchain traceability unit is used to use blockchain technology to decentrally store all production data to ensure the non-tamperability and transparency of the production history, raw material sources and production conditions of each batch of products; The compliance checking unit is used to perform quality assessment based on real-time collected quality data and historical data, perform automated quality assessment, check whether the product meets the specified quality standards, and generate a determination report for qualified and unqualified products.

[0012] Preferably, the energy management module includes an energy trend prediction unit; The energy trend prediction unit is used to perform energy efficiency evaluation and optimal scheduling based on real-time monitored energy data, analyze the energy efficiency of each energy use link, and optimize configuration and energy-saving mode switching according to demand. Through big data analysis and prediction algorithms, energy demand trend prediction is performed to help formulate long-term energy management strategies and achieve optimized decision support.

[0013] Preferably, the big data analysis module includes a data mining unit and a prediction analysis unit; The data mining unit is used to apply cluster analysis, association rules and classification prediction technology to extract potential laws and patterns from the cleaned data; The prediction analysis unit is used to combine data mining results with real-time data, use prediction models to perform trend analysis and risk assessment, and generate decision support information to help managers make production adjustments and optimization decisions.

[0014] A compound fertilizer production management method based on the Internet of Things comprises the following steps: Step 1: Collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing via wireless transmission; Step 2: Based on the real-time collected data, the raw material ratio, mixing and temperature and humidity control links in the production process are intelligently adjusted and optimized through intelligent algorithms, and the production optimization index IPOI is calculated and obtained; Step 3: Collect the operating data of production equipment and apply machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, and conduct remote diagnosis and maintenance in combination with the cloud platform, so as to provide early warning and arrange intelligent maintenance or spare parts replacement; Step 4: Monitor key quality parameters in the production process based on IoT technology and sensor data, and use blockchain technology for full traceability to ensure the transparency and immutability of information on the production process, raw material sources, and production conditions of each batch of products; Step 5: Install smart energy sensors to monitor the use of various energy sources in the production process in real time, and achieve precise control and optimal configuration of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; Step 6: Conduct big data analysis and mining on the data collected from each module to generate decision support information for the production process, and conduct trend analysis and prediction based on historical data.

[0015] The present invention provides a compound fertilizer production management method and system based on the Internet of Things, which has the following beneficial effects: (1) When the system is running, it collects data from various sensors in real time during the production process for analysis and processing. It uses intelligent algorithms to intelligently adjust and optimize the raw material ratio, mixing, and temperature and humidity control links in the production process. It calculates and obtains the production optimization index (IPOI). It collects the operating data of production equipment and applies machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, and use blockchain technology to trace the entire process. By installing intelligent energy sensors, it monitors the use of various energy sources in the production process in real time. Through optimal power scheduling and energy-saving mode switching, it achieves precise regulation and optimal configuration of energy consumption in the production process, generates decision support information in the production process, and combines historical data for trend analysis and prediction.

[0016] (2) The compound fertilizer production management system based on the Internet of Things can monitor and accurately control various key parameters in the production process in real time, including raw material ratio, mixing process and temperature and humidity adjustment of the production environment, through core units such as data acquisition module and intelligent production control module. Compared with the traditional manual control method, the system can automatically adjust the production process through intelligent algorithms, eliminate human errors, and ensure the accurate execution of each link. The production optimization index (IPOI) calculated by the intelligent production control module reflects the optimization degree of the production process in real time, greatly improving the production efficiency and stability of the production process. In addition, by optimizing raw material adaptation, environmental control and equipment health management, the system effectively reduces resource waste and energy consumption, thereby improving the overall efficiency of the production process.

[0017] (3) The equipment health management module and intelligent maintenance unit, combined with machine learning algorithms, can monitor the operating status of production equipment in real time, predict the type and time of possible equipment failures, and provide early warning and optimized maintenance through remote diagnosis and intelligent maintenance. This system not only improves the reliability of equipment operation, but also significantly reduces the frequency of equipment failure and downtime. Compared with the traditional method that relies on manual inspections, equipment health management based on the Internet of Things and machine learning is more real-time and accurate. It can take preventive measures before equipment failures occur, greatly extend the service life of equipment, reduce maintenance costs, and improve the overall health of equipment, thereby ensuring the continuity of the production line and the stable execution of production plans.

[0018] (4) The quality control traceability module realizes data collection, monitoring and tamper-proof traceability management of the entire production process through the combination of Internet of Things technology and blockchain technology. The production information, raw material sources, production conditions, etc. of each batch of products are recorded in the blockchain, ensuring the transparency and traceability of product quality. Compared with traditional quality control methods, technologies based on the Internet of Things and blockchain can not only improve the accuracy of product quality testing, but also enhance consumer trust and improve brand competitiveness. The introduction of the energy management module also reduces energy consumption in the production process while accurately monitoring and optimizing energy use, achieving sustainable development. Through comprehensive analysis of the data of each module, the system can make real-time trend predictions, provide scientific decision-making support for production management, further improve the intelligence level of the production process, and promote the development of compound fertilizer production in a more intelligent and green direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a block diagram of a compound fertilizer production management system based on the Internet of Things of the present invention; Figure 2 The present invention is a schematic diagram of the steps of a compound fertilizer production management method based on the Internet of Things. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Example 1 The present invention provides a compound fertilizer production management system based on the Internet of Things. Figure 1 , including data acquisition module, intelligent production control module, equipment health management module, quality control traceability module, energy management module and big data analysis module; The data acquisition module is used to collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing through wireless transmission; The intelligent production control module is used to intelligently adjust and optimize the raw material ratio, mixing and temperature and humidity control links in the production process based on the real-time collected data through intelligent algorithms, and calculate and obtain: production optimization index IPOI; The equipment health management module is used to collect the operating data of the production equipment and apply machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, conduct remote diagnosis and maintenance in conjunction with the cloud platform, and provide early warning and arrange intelligent maintenance or spare parts replacement; The quality control traceability module is used to monitor key quality parameters in the production process based on IoT technology and sensor data, and to use blockchain technology for full traceability to ensure the transparency and non-tamperability of information on the production process, raw material sources, and production conditions of each batch of products; The energy management module is used to monitor the use of various energy sources in the production process in real time by installing intelligent energy sensors, and to achieve precise control and optimal configuration of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; The big data analysis module is used to generate decision support information in the production process by performing big data analysis and mining on the data collected from various modules, and to perform trend analysis and prediction in combination with historical data.

[0022] In this embodiment, data from various sensors are collected in real time during the production process for analysis and processing, and intelligent algorithms are used to intelligently adjust and optimize the raw material ratio, mixing, and temperature and humidity control links in the production process. The production optimization index IPOI is calculated and obtained. The operating data of the production equipment is collected and the health of the equipment is evaluated by applying a machine learning algorithm. The time and type of equipment failure are predicted, and the entire process is traced using blockchain technology. By installing intelligent energy sensors, the use of various energy sources in the production process is monitored in real time. Through optimal power scheduling and energy-saving mode switching, accurate regulation and optimal configuration of energy consumption in the production process are achieved, decision support information for the production process is generated, and trend analysis and prediction are performed in combination with historical data.

[0023] Example 2 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes a raw material quality monitoring unit, a production environment monitoring unit and an equipment health monitoring unit; The raw material quality monitoring unit is used to collect and monitor the humidity, particle size and proportion of all raw material quality related to the production process in real time through humidity sensors, laser particle size analyzers, near infrared spectroscopy NIR technology and gas detection sensors, and obtain: raw material moisture content RMMC, particle size distribution RPSD, raw material composition ratio RMCR and raw material relative stability RMSI; The production environment monitoring unit is used to monitor the temperature, humidity, gas concentration and air fluidity in the production environment in real time through temperature sensors, humidity sensors, gas sensors, wind speed sensors and wind direction sensors, and obtain: temperature fluctuation coefficient TFC, humidity deviation index HDI, humidity deviation index HDI and air fluidity index AMI; The equipment health monitoring unit is used to monitor the health status of production equipment in real time through vibration sensors, temperature sensors, current sensors and load sensors, and obtain: vibration frequency deviation VFD, equipment temperature rise index ETRI, current fluctuation amplitude CFA and load response stability LRS.

[0024] The intelligent production control module includes a raw material ratio optimization unit, an environmental control optimization unit and an equipment health optimization unit; The raw material ratio optimization unit is used to optimize the raw material ratio and mixing process by applying an intelligent algorithm, and calculate and adjust the raw material adaptation coefficient RMF; The environmental control optimization unit is used to adjust the production environment parameters according to the real-time data of the production environment, including temperature and humidity, gas concentration and air flow, through an intelligent algorithm, and calculate the environmental adaptation coefficient EAC; The equipment health optimization unit is used to predict the health status of the equipment through an intelligent algorithm and calculate the equipment health coefficient EHC.

[0025] In this embodiment, the compound fertilizer production management system based on the Internet of Things significantly improves the real-time monitoring and optimization capabilities of various key parameters in the production process through the close cooperation of the data acquisition module and the intelligent production control module. Specifically, the raw material quality monitoring unit of the data acquisition module can accurately monitor and collect key quality data such as raw material moisture content, particle size distribution, and component ratio through high-precision sensing equipment such as humidity sensors and laser particle size analyzers to ensure the stability and uniformity of the raw materials; the production environment monitoring unit tracks production environment parameters such as temperature and humidity, gas concentration, and air flow in real time to provide environmental adaptability data for the production process; the equipment health monitoring unit monitors the equipment status through multiple sensors such as vibration, temperature, and current to promptly discover potential equipment failure risks. These data are processed and adjusted through the optimization unit of the intelligent production control module in combination with advanced intelligent algorithms to ensure the best match between raw material ratio, production environment, and equipment health status, thereby effectively improving the accuracy, stability, and efficiency of the production process, reducing the error of human intervention, and achieving the goal of automated and intelligent production control.

[0026] Example 3 This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the raw material adaptation coefficient RMF is calculated by the following formula: ; In the formula, RMMC represents the moisture content of the raw material, RPSD represents the particle size distribution of the raw material, RMCR represents the composition ratio of the raw material, RMSI represents the relative stability of the raw material, and RMMC ref , RPSD ref RMCR ref and RMSI ref represents the reference value of each parameter, which is used for standardization. γ represents the constant coefficient, which is used to adjust the overall sensitivity of the model. represents weighting coefficient and exponential factor; The environmental adaptation coefficient EAC is calculated by the following formula: ; In the formula, TFC represents temperature fluctuation coefficient, HDI represents humidity deviation index, GCFC represents gas concentration fluctuation coefficient, AMI represents air mobility index, GCFC represents gas concentration fluctuation coefficient, ref Indicates the reference standard value of the gas concentration fluctuation coefficient, Represents the weight of environmental factors.

[0027] The equipment health coefficient EHC is calculated by the following formula: ; In the formula, VFD represents vibration frequency deviation, ETRI represents equipment temperature rise index, CFA represents current fluctuation amplitude, LRS represents load response stability, Represents the weight coefficient, which controls the influence of each equipment health parameter on EHC; Through the comprehensive calculation of the above three coefficients, the production optimization index IPOI is calculated by the following formula: ; In the formula, RMF represents the raw material adaptation coefficient, EAC represents the environmental adaptation coefficient, and EHC represents the equipment health coefficient. are the weight coefficients of raw materials, environment and equipment health factors, , and dynamically adjust according to the production environment and system requirements.

[0028] The equipment health management module includes an equipment status monitoring unit, a health assessment unit and an intelligent maintenance unit; The equipment status monitoring unit is used to collect the operating status data of the production equipment in real time through sensors, including vibration, temperature, current and pressure, and perform preliminary denoising, filtering and standardization on the data; The health assessment unit is used to assess the health status of the equipment based on a machine learning algorithm, and predict the type of equipment failure, occurrence time and possible causes through historical data analysis and real-time data monitoring, and obtain a level assessment scheme by comparing the production optimization index IPOI with a preset health threshold Q and a preset health threshold W; IPOI>Q, the first evaluation level is obtained, indicating that the production process is running in an optimized state and the production equipment is in a good health state. Equipment status data is collected regularly and health assessment is performed in combination with the fault prediction model to ensure the long-term stable operation of the equipment; Q≥IPOI>W, the second evaluation level is obtained, indicating that the production process is in a moderately optimized state, and the equipment operation health is within the normal range, but there is still room for optimization. For the deficiencies in the raw material ratio, temperature and humidity, and mixing links, appropriate adjustments are made through intelligent control algorithms to optimize production efficiency. In extreme environments of high temperature and high humidity, the temperature and humidity control system is adjusted; IPOI≤W, the third assessment level is obtained, indicating that the production process is inefficient and the equipment may have potential health risks. The equipment health management module is used to conduct a comprehensive diagnosis of the production equipment, including vibration analysis, temperature rise detection, and load response evaluation. According to the health assessment results, intelligent maintenance or spare parts replacement can be arranged in advance to reduce the probability of failure. The intelligent maintenance unit is used to perform remote diagnosis and maintenance of equipment in conjunction with the cloud platform, analyze the type and location of equipment failures, provide fault location, cause analysis and repair suggestions, automatically generate intelligent maintenance plans, and support remote control and maintenance guidance, thereby reducing equipment downtime through remote diagnosis.

[0029] In this embodiment, by constructing a comprehensive optimization framework of the raw material adaptation coefficient RMF, the environmental adaptability coefficient EAC and the equipment health coefficient EHC, accurate multi-dimensional optimization management is achieved in the compound fertilizer production process. The raw material adaptation coefficient RMF ensures the quality stability of the raw materials by real-time monitoring of key parameters such as raw material moisture content, particle size, component ratio and stability, thereby improving the accuracy of the production process; the environmental adaptability coefficient EAC is optimized and adjusted according to production environment parameters such as temperature and humidity, gas concentration and air fluidity to ensure the suitability of the production environment and reduce the negative impact of environmental fluctuations on product quality and production efficiency; the equipment health coefficient EHC comprehensively monitors the operating status of the equipment, evaluates the health status of the equipment, and promptly warns and implements maintenance, thereby avoiding production stagnation caused by equipment failure. Combining the above three coefficients, through the comprehensive calculation of the production optimization index IPOI, the present invention can achieve full optimization and intelligent regulation of the production process. When the production optimization index is higher than the preset health threshold, it indicates that the production process and equipment status are in a good optimization state; when the index is in the moderate optimization range, the system will automatically adjust to further improve production efficiency; when the index is lower than the threshold, the system can detect potential equipment failures in advance and perform remote diagnosis and maintenance through the intelligent maintenance unit, thereby ensuring the long-term stable operation of the equipment and the continuous improvement of production efficiency. This intelligent and automated optimization management method significantly improves the reliability of the production system, reduces the equipment failure rate and production downtime, and improves the overall production efficiency. Compared with the traditional manual operation and single parameter monitoring mode, the present invention not only realizes real-time monitoring of the production process through the combination of Internet of Things technology, intelligent algorithms and big data analysis, but also can dynamically adjust according to real-time data to further optimize production management. By implementing multi-dimensional optimization control, production efficiency has been comprehensively improved, equipment management and production environment control have been more refined, and the overall operation and maintenance cost has been effectively reduced. At the same time, the intelligent diagnosis and remote maintenance functions of the system reduce the need for human intervention, improve the autonomy and flexibility of the production system, and make the compound fertilizer production process more efficient, reliable and intelligent.

[0030] Example 4 This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the quality control traceability module includes a quality parameter monitoring unit, a blockchain traceability unit and a compliance inspection unit; The quality parameter monitoring unit is used to monitor key quality parameters in the production process in real time through various sensors, including product size, color, density and composition, and transmit these data to the central control system for storage and processing; The blockchain traceability unit is used to use blockchain technology to decentrally store all production data to ensure the non-tamperability and transparency of the production history, raw material sources and production conditions of each batch of products; The compliance checking unit is used to perform quality assessment based on real-time collected quality data and historical data, perform automated quality assessment, check whether the product meets the specified quality standards, and generate a determination report for qualified and unqualified products.

[0031] The energy management module includes an energy trend prediction unit; The energy trend prediction unit is used to perform energy efficiency evaluation and optimal scheduling based on real-time monitored energy data, analyze the energy efficiency of each energy use link, and optimize configuration and energy-saving mode switching according to demand. Through big data analysis and prediction algorithms, energy demand trend prediction is performed to help formulate long-term energy management strategies and achieve optimized decision support.

[0032] The big data analysis module includes a data mining unit and a prediction analysis unit; The data mining unit is used to apply cluster analysis, association rules and classification prediction technology to extract potential laws and patterns from the cleaned data; The prediction analysis unit is used to combine data mining results with real-time data, use prediction models to perform trend analysis and risk assessment, and generate decision support information to help managers make production adjustments and optimization decisions.

[0033] In this embodiment, the quality control, energy utilization efficiency and decision support capabilities in the production process are further improved through the organic combination of the quality control traceability module, the energy management module and the big data analysis module. The quality control traceability module monitors the key quality parameters in the production process in real time through the quality parameter monitoring unit to ensure that the size, color, density and composition of the product meet the standard requirements. At the same time, blockchain technology is used for decentralized storage to ensure the transparency and immutability of production data, thereby ensuring the quality traceability and product safety of the product. The compliance inspection unit automatically evaluates and determines the product quality, ensures that each link in the production process meets the quality standards, and reports in time when unqualified products appear, effectively reducing quality risks. The energy management module uses the energy trend prediction unit to perform energy efficiency evaluation and optimization scheduling based on real-time monitored energy data, helping the production system to achieve efficient use of energy. The system predicts energy demand trends through big data analysis and prediction algorithms, guides managers to reasonably configure and adjust energy use in production, realizes energy-saving mode switching, reduces energy waste, and saves a lot of energy costs for the enterprise. The intelligent optimization of energy management not only improves production efficiency, but also provides strong support for the sustainable development of enterprises. The big data analysis module extracts potential rules and patterns through the data mining unit, providing deeper insights for production management. Through cluster analysis, association rules and classification prediction technology, the system can mine valuable rules from historical data, and combine real-time data for trend analysis and risk assessment. The predictive analysis unit further uses the data mining results for decision support, helping managers make more accurate production adjustments and optimization decisions, thereby improving the level of intelligent production, reducing unnecessary production costs and waste of resources, and ultimately improving overall operational efficiency. Compared with traditional means, the present invention can achieve comprehensive optimization of the production process, improve energy efficiency and quality control through intelligent and multi-dimensional analysis, providing strong technical support for the intelligent upgrade of modern production systems and green manufacturing.

[0034] Example 5 A compound fertilizer production management method based on the Internet of Things, please refer to Figure 2 , specifically: including the following steps: Step 1: Collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing via wireless transmission; Step 2: Based on the real-time collected data, the raw material ratio, mixing and temperature and humidity control links in the production process are intelligently adjusted and optimized through intelligent algorithms, and the production optimization index IPOI is calculated and obtained; Step 3: Collect the operating data of production equipment and apply machine learning algorithms to conduct a health assessment of the equipment, predict the occurrence time and type of equipment failures, combine with the cloud platform for remote diagnosis and maintenance, and give early warnings and arrange for intelligent repairs or spare part replacements; Step 4: Monitor the key quality parameters in the production process based on Internet of Things technology and sensor data, and use blockchain technology for full-process traceability to ensure the transparency and immutability of information on the production process, raw material sources, and production conditions of each batch of products; Step 5: Install intelligent energy sensors to monitor the usage of various types of energy in the production process in real time, and achieve precise control and optimal allocation of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; Step 6: Conduct big data analysis and mining on the data collected from each module to generate decision-making support information in the production process, and conduct trend analysis and prediction in combination with historical data.

[0035] In this embodiment, the efficiency, quality and sustainability of the production process are significantly improved through the precise integration and intelligent control of six key steps. First, real-time data acquisition and wireless transmission technology ensure the comprehensiveness and real-time nature of the raw material ratio, production environment and equipment status information monitored by various sensors, providing accurate data support for subsequent intelligent adjustment. Based on these data, the production optimization index (IPOI) generated by the adjustment and optimization of the production links through intelligent algorithms can intuitively reflect the optimization state of the production process and guide further adjustment and optimization. Equipment health assessment and fault prediction combined with machine learning technology and cloud platform realize intelligent early warning and maintenance management, effectively reduce equipment failure rate, reduce downtime, and improve the stability and operational efficiency of the production line. In terms of quality control, the key quality parameters in the production process are monitored through the Internet of Things and sensors, and the blockchain technology is combined to ensure the immutability and full traceability of the data, providing comprehensive, transparent and efficient guarantees for product quality control. The transparency of production information, raw material sources and production environment of each batch of products makes product quality traceability more reliable and rapid. In addition, the energy management module uses intelligent energy sensors to monitor and optimize energy use in real time, which not only improves the precise regulation of energy consumption, but also greatly improves energy efficiency in the production process and reduces energy costs through energy-saving mode switching and power optimization configuration. Finally, the big data analysis module conducts in-depth mining and predictive analysis of production data, combines historical data and real-time data to generate decision support information, and provides managers with accurate production trends, optimization plans and risk assessments, thereby further improving the scientificity and intelligence of production decisions. Overall, the present invention can effectively integrate a variety of advanced technologies, such as the Internet of Things, artificial intelligence, blockchain and big data analysis, which not only improves production efficiency and resource utilization, but also provides strong technical support for intelligent and sustainable production, greatly promoting the modernization and intelligent transformation of the production process.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A compound fertilizer production management system based on the Internet of Things, characterized by: It includes data acquisition module, intelligent production control module, equipment health management module, quality control traceability module, energy management module and big data analysis module; The data acquisition module is used to collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing through wireless transmission; The intelligent production control module is used to intelligently adjust and optimize the raw material ratio, mixing and temperature and humidity control links in the production process based on the real-time collected data through intelligent algorithms, and calculate and obtain: production optimization index IPOI; The equipment health management module is used to collect the operating data of the production equipment and apply machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, conduct remote diagnosis and maintenance in conjunction with the cloud platform, and provide early warning and arrange intelligent maintenance or spare parts replacement; The quality control traceability module is used to monitor key quality parameters in the production process based on IoT technology and sensor data, and to use blockchain technology for full traceability to ensure the transparency and non-tamperability of information on the production process, raw material sources, and production conditions of each batch of products; The energy management module is used to monitor the use of various energy sources in the production process in real time by installing intelligent energy sensors, and to achieve precise control and optimal configuration of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; The big data analysis module is used to generate decision support information in the production process by performing big data analysis and mining on the data collected from various modules, and to perform trend analysis and prediction in combination with historical data.

2. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The data acquisition module includes a raw material quality monitoring unit, a production environment monitoring unit and an equipment health monitoring unit; The raw material quality monitoring unit is used to collect and monitor the humidity, particle size and proportion of all raw material quality related to the production process in real time through humidity sensors, laser particle size analyzers, near infrared spectroscopy NIR technology and gas detection sensors, and obtain: raw material moisture content RMMC, particle size distribution RPSD, raw material composition ratio RMCR and raw material relative stability RMSI; The production environment monitoring unit is used to monitor the temperature, humidity, gas concentration and air fluidity in the production environment in real time through temperature sensors, humidity sensors, gas sensors, wind speed sensors and wind direction sensors, and obtain: temperature fluctuation coefficient TFC, humidity deviation index HDI, humidity deviation index HDI and air fluidity index AMI; The equipment health monitoring unit is used to monitor the health status of production equipment in real time through vibration sensors, temperature sensors, current sensors and load sensors, and obtain: vibration frequency deviation VFD, equipment temperature rise index ETRI, current fluctuation amplitude CFA and load response stability LRS.

3. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The intelligent production control module includes a raw material ratio optimization unit, an environmental control optimization unit and an equipment health optimization unit; The raw material ratio optimization unit is used to optimize the raw material ratio and mixing process by applying an intelligent algorithm, and calculate and adjust the raw material adaptation coefficient RMF; The environmental control optimization unit is used to adjust the production environment parameters according to the real-time data of the production environment, including temperature and humidity, gas concentration and air flow, through an intelligent algorithm, and calculate the environmental adaptation coefficient EAC; The equipment health optimization unit is used to predict the health status of the equipment through an intelligent algorithm and calculate the equipment health coefficient EHC.

4. The compound fertilizer production management system based on the Internet of Things according to claim 3 is characterized in that: The raw material adaptation coefficient RMF is calculated by the following formula: ； In the formula, RMMC represents the moisture content of the raw material, RPSD represents the particle size distribution of the raw material, RMCR represents the composition ratio of the raw material, RMSI represents the relative stability of the raw material, and RMMC ref , RPSD ref RMCR ref and RMSI ref represents the reference value of each parameter, which is used for standardization. γ represents the constant coefficient, which is used to adjust the overall sensitivity of the model. represents weighting coefficient and exponential factor; The environmental adaptation coefficient EAC is calculated by the following formula: ； In the formula, TFC represents temperature fluctuation coefficient, HDI represents humidity deviation index, GCFC represents gas concentration fluctuation coefficient, AMI represents air mobility index, GCFC represents gas concentration fluctuation coefficient, ref Indicates the reference standard value of the gas concentration fluctuation coefficient, Represents the weight of environmental factors.

5. The compound fertilizer production management system based on the Internet of Things according to claim 3 is characterized in that: The equipment health coefficient EHC is calculated by the following formula: ； In the formula, VFD represents vibration frequency deviation, ETRI represents equipment temperature rise index, CFA represents current fluctuation amplitude, LRS represents load response stability, Represents the weight coefficient, which controls the influence of each equipment health parameter on EHC; Through the comprehensive calculation of the above three coefficients, the production optimization index IPOI is calculated by the following formula: ； In the formula, RMF represents the raw material adaptation coefficient, EAC represents the environmental adaptation coefficient, and EHC represents the equipment health coefficient. are the weight coefficients of raw materials, environment and equipment health factors, , and dynamically adjust according to the production environment and system requirements.

6. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The equipment health management module includes an equipment status monitoring unit, a health assessment unit and an intelligent maintenance unit; The equipment status monitoring unit is used to collect the operating status data of the production equipment in real time through sensors, including vibration, temperature, current and pressure, and perform preliminary denoising, filtering and standardization on the data; The health assessment unit is used to assess the health status of the equipment based on a machine learning algorithm, and predict the type of equipment failure, occurrence time and possible causes through historical data analysis and real-time data monitoring, and obtain a level assessment scheme by comparing the production optimization index IPOI with a preset health threshold Q and a preset health threshold W; IPOI>Q, the first evaluation level is obtained, indicating that the production process is running in an optimized state and the production equipment is in a good health state. Equipment status data is collected regularly and health assessment is performed in combination with the fault prediction model to ensure the long-term stable operation of the equipment; Q≥IPOI>W, the second evaluation level is obtained, indicating that the production process is in a moderately optimized state, and the equipment operation health is within the normal range, but there is still room for optimization. For the deficiencies in the raw material ratio, temperature and humidity, and mixing links, appropriate adjustments are made through intelligent control algorithms to optimize production efficiency. In extreme environments of high temperature and high humidity, the temperature and humidity control system is adjusted; IPOI≤W, the third assessment level is obtained, indicating that the production process is inefficient and the equipment may have potential health risks. The equipment health management module is used to conduct a comprehensive diagnosis of the production equipment, including vibration analysis, temperature rise detection, and load response evaluation. According to the health assessment results, intelligent maintenance or spare parts replacement can be arranged in advance to reduce the probability of failure. The intelligent maintenance unit is used to perform remote diagnosis and maintenance of equipment in conjunction with the cloud platform, analyze the type and location of equipment failures, provide fault location, cause analysis and repair suggestions, automatically generate intelligent maintenance plans, and support remote control and maintenance guidance, thereby reducing equipment downtime through remote diagnosis.

7. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The quality control traceability module includes a quality parameter monitoring unit, a blockchain traceability unit and a compliance inspection unit; The quality parameter monitoring unit is used to monitor key quality parameters in the production process in real time through various sensors, including product size, color, density and composition, and transmit these data to the central control system for storage and processing; The blockchain traceability unit is used to use blockchain technology to decentrally store all production data to ensure the non-tamperability and transparency of the production history, raw material sources and production conditions of each batch of products; The compliance checking unit is used to perform quality assessment based on real-time collected quality data and historical data, perform automated quality assessment, check whether the product meets the specified quality standards, and generate a determination report for qualified and unqualified products.

8. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The energy management module includes an energy trend prediction unit; The energy trend prediction unit is used to perform energy efficiency evaluation and optimal scheduling based on real-time monitored energy data, analyze the energy efficiency of each energy use link, and optimize configuration and energy-saving mode switching according to demand. Through big data analysis and prediction algorithms, energy demand trend prediction is performed to help formulate long-term energy management strategies and achieve optimized decision support.

9. The compound fertilizer production management system based on the Internet of Things according to claim 1, characterized in that: The big data analysis module includes a data mining unit and a prediction analysis unit; The data mining unit is used to apply cluster analysis, association rules and classification prediction technology to extract potential laws and patterns from the cleaned data; The prediction analysis unit is used to combine data mining results with real-time data, use prediction models to perform trend analysis and risk assessment, and generate decision support information to help managers make production adjustments and optimization decisions.

10. A compound fertilizer production management method based on the Internet of Things, applied to a compound fertilizer production management system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect data from various sensors in real time during the production process, including raw material ratio, production environment conditions and production equipment status, and upload the data to the central control system or cloud platform for analysis and processing via wireless transmission; Step 2: Based on the real-time collected data, the raw material ratio, mixing and temperature and humidity control links in the production process are intelligently adjusted and optimized through intelligent algorithms, and the production optimization index IPOI is calculated and obtained; Step 3: Collect the operating data of production equipment and apply machine learning algorithms to evaluate the health of the equipment, predict the time and type of equipment failure, and conduct remote diagnosis and maintenance in combination with the cloud platform, so as to provide early warning and arrange intelligent maintenance or spare parts replacement; Step 4: Monitor key quality parameters in the production process based on IoT technology and sensor data, and use blockchain technology for full traceability to ensure the transparency and immutability of information on the production process, raw material sources, and production conditions of each batch of products; Step 5: Install smart energy sensors to monitor the use of various energy sources in the production process in real time, and achieve precise control and optimal configuration of energy consumption in the production process through optimal power scheduling and energy-saving mode switching; Step 6: Conduct big data analysis and mining on the data collected from each module to generate decision support information for the production process, and conduct trend analysis and prediction based on historical data.

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