Background communication method and background communication system for juicer system

Abstract

The invention relates to the field of juicer communication, in particular to a background communication method and a background communication system for a juicer system. The method comprises the following steps: obtaining a fruit and vegetable image reserved in a juicer; performing self-adaptive juicing feature analysis on the reserved fruit and vegetable image, performing intelligent juicing parameter adjustment, and constructing a self-adaptive juicing strategy; identifying surrounding environment parameters of the juicer; according to the surrounding environment parameters of the juicer, a self-adaptive juicing strategy is carried out, environment trend influence intelligent adjustment is carried out, juicing control simulation is carried out, and juicing control simulation data are obtained; multi-period juicing effect evolution is carried out on the juicing control simulation data, abnormal state prediction is carried out, and juicer abnormal early warning signals and juicer abnormal fault prediction information are generated; acquiring the network access state of the current juicer; through efficient and rapid background communication, the juicer use experience and performance of a user are improved.

Description

Technical Field

[0001] The present invention relates to the field of juicer communication technology, and in particular to a background communication method and a background communication system for a juicer system. Background Art

[0002] With the popularity of smart homes and kitchen equipment, juicers, as one of the common kitchen appliances in daily life, are widely used in various scenarios such as fruit and vegetable juicing and beverage preparation. With the application of intelligent juicers, many users pay more attention to fresh and intelligent juicing options. Therefore, many users now make appointments for juicing to ensure that they can drink fresh fruit and vegetable juice when they return home. Therefore, during the long-term appointment and use of juicers, they may face different types of problems. For example, in summer, if the user's appointment time is too long, the fruits and vegetables will deteriorate due to weather reasons and high temperatures, resulting in poor user experience, affecting the performance of the juicer, and may also cause equipment damage and even cause safety hazards.

[0003] To meet this challenge, modern juicer systems need to integrate advanced background communication methods to monitor the working status of the equipment in real time through built-in sensors and intelligent algorithms. This method detects the key operating parameters of the juicer (such as motor temperature, speed, pressure, load, etc.) and evaluates the status of the equipment through data analysis. Once an abnormality or failure risk occurs, the system can instantly generate a fault warning signal and upload the relevant information to the cloud platform for further analysis and processing through background communication. This background communication method can not only accurately identify potential failures of the equipment, but also perform real-time remote monitoring and data storage through the cloud platform, providing users with real-time feedback and remote fault diagnosis, thereby effectively avoiding the expansion of faults and improving the service life of the equipment. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a background communication method and a background communication system for a juicer system to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a background communication method for a juicer system, comprising the following steps: Step S1: obtaining fruit and vegetable images reserved in the juicer; performing adaptive juicing feature analysis on the reserved fruit and vegetable images, and performing intelligent juicing parameter adjustment to construct an adaptive juicing strategy; Step S2: Identify the environmental parameters of the juicer; perform an adaptive juicing strategy according to the environmental parameters of the juicer to intelligently adjust the impact of environmental trends, perform juicing control simulation, and obtain juicing control simulation data; Step S3: performing multi-period juicing effect evolution on the juicing control simulation data, and performing abnormal state prediction, generating an abnormal warning signal of the juicer and abnormal fault prediction information of the juicer; Step S4: obtaining the current access network status of the juicer; performing data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; Step S5: uploading the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; Step S6: The user checks the fault prediction information based on the juicer cloud server and performs dynamic transfer learning to generate a smart juicer decision strategy; and executes the background communication and fault handling operations of the juicer according to the smart juicer decision strategy.

[0006] The present invention can accurately identify the type, shape, size and other characteristics of fruits and vegetables by acquiring the fruit and vegetable images reserved in the juicer, and provide detailed input data for the juicer, which is helpful for the juicer to determine the best juicing method. The adaptive juicing feature analysis can adjust the working parameters of the juicer (such as speed, pressure, etc.) according to the physical properties of different fruits and vegetables (such as moisture content, hardness, etc.) to ensure the maximization of the juicing effect. By constructing an adaptive juicing strategy, the juicer can automatically optimize the juicing process without manual intervention, thereby saving time and reducing energy consumption and improving overall efficiency. By identifying factors such as temperature, humidity, air flow and other factors in the environment surrounding the juicer, the system can dynamically adjust the juicing strategy according to these changes to ensure that it can maintain stability even under different environmental conditions. To achieve a certain juicing effect, the intelligent adjustment of environmental factors can identify potential influencing factors in advance. For example, excessively high temperature may cause fruits and vegetables to deteriorate more easily. The juicer system can reduce these adverse effects by adjusting the working parameters. Through the generation of juicing control simulation data, the juicing effect under different environmental conditions can be accurately simulated to provide data support for subsequent adjustments and optimizations. Through multi-period analysis of juicing control simulation data, potential problems in the juicing process or early signs of equipment failure can be found, which provides strong support for preventing equipment failures. According to the simulation data during the operation of the juicer, the system can predict abnormal conditions in advance (such as motor overheating, fruit and vegetable jamming, etc.), and generate abnormal warning signals to remind users to deal with them in time. The prediction of abnormal conditions It can greatly reduce the failure rate of the juicer, ensure the long-term stable operation of the equipment, and reduce unnecessary maintenance and downtime. By real-time monitoring of the network status of the juicer, you can understand the network connection status of the device and ensure stable data transmission between the juicer and the cloud server. Data packet test transmission and dynamic network status evaluation help detect network delays, packet loss and other problems, ensuring that the data uploaded to the cloud is accurate and timely. By dynamically evaluating the network status, you can automatically adjust the communication strategy and select the appropriate transmission method when the network condition is poor to ensure smooth data upload. By uploading the abnormal warning signals and fault prediction information of the juicer to the cloud, users or operation and maintenance personnel can view and remotely monitor the equipment status at any time, and promptly discover and solve potential problems. The cloud server can receive and process data from the juicer in real time to ensure synchronization and timely update of information, and provide a reliable basis for fault warning. Based on cloud data analysis, the system can continuously optimize the performance of the juicer and adjust the juicing strategy to cope with different usage conditions and environmental changes. Users can understand the working status of the juicer based on the fault prediction information provided by the cloud, and take timely measures to adjust it to avoid the problem from worsening. Through dynamic transfer learning, the system can continuously optimize the decision-making strategy based on the feedback from the cloud server, so that the juicer can adapt to the usage needs of different users, different environments, and different fruits and vegetables. Through the generation of intelligent decision-making strategies, the juicer can be adjusted in real time according to actual usage conditions, which not only improves the user experience,It also enhances the intelligence level and self-maintenance capability of the equipment.

[0007] Preferably, step S1 comprises the following steps: Step S11: Acquire the fruit and vegetable images reserved in the juicer; Step S12: performing texture feature enhancement on the reserved fruit and vegetable image to generate a texture enhanced fruit and vegetable image; Step S13: performing visual semantic recognition on the texture enhanced fruit and vegetable images to extract visual features of each fruit and vegetable; Step S14: classifying the types of fruits and vegetables based on the visual features of each fruit and vegetable, thereby generating the type features of fruits and vegetables in the juicer; Step S15: performing adaptive juicing feature analysis on the characteristics of the types of fruits and vegetables in the juicer, thereby generating adaptive juicing features; Step S16: intelligently adjust the juice extraction parameters of the juicer according to the adaptive juice extraction characteristics, and construct an adaptive juice extraction strategy.

[0008] The present invention obtains the reserved fruit and vegetable images in the juicer to provide raw data for subsequent processing and analysis. This step is the basis for the entire intelligent juicing. Accurately obtained images can ensure that subsequent visual analysis and feature extraction are more accurate. Through image acquisition, the juicer can identify different types of fruits and vegetables, which is crucial for subsequent intelligent adjustment and classification. By enhancing the texture features of the image, the details and contrast of the image can be improved, and the visibility of the surface and internal partitions of the fruits and vegetables can be improved. This helps to extract the detailed information of the fruits and vegetables more clearly in subsequent identification. Image texture enhancement technology can help the system better distinguish the slight differences on the surface of fruits and vegetables, especially under complex lighting conditions. The enhanced image can improve the accuracy and robustness of fruit and vegetable recognition. By enhancing the texture features, more abundant and detailed information can be provided for subsequent visual semantic recognition and classification, thereby improving the intelligent level of the juicer. Through visual semantic recognition, the juicer can extract the key visual features of each fruit and vegetable from the enhanced image, such as shape, size, surface texture, etc. This enables the juicer to accurately distinguish different fruits and vegetables and provide accurate input data for the subsequent juicing process. Visual semantic recognition can process a large amount of image data in a short time through algorithm optimization, thus improving the real-time response capability of the system. This technology can be applied to different types of fruits and vegetables, has strong adaptability, is not affected by factors such as the appearance and color of fruits and vegetables, and increases the versatility of the system. According to the visual characteristics of each fruit and vegetable, the type classification can be carried out, and different types of fruits and vegetables (such as apples, oranges, carrots, etc.) can be clearly identified and classified. The juicer can choose different juicing methods according to the characteristics of different fruits and vegetables. Different types of fruits and vegetables have different requirements for the working parameters of the juicer. After classification, the juicing scheme can be accurately selected, avoiding manual intervention and wrong selection, and improving the juicing effect. The classification system can gradually learn more types of fruits and vegetables over time, continuously improve the classification system, and increase the recognition ability of the juicer for new types of fruits and vegetables. Based on the characteristics of fruit and vegetable types, adaptive juicing feature analysis can be carried out, and the juicing parameters can be automatically adjusted according to the hardness, moisture content, texture and other characteristics of each fruit and vegetable, ensuring that each fruit and vegetable can get the most suitable juicing method. Adaptive juicing feature analysis can accurately control the working state of the juicer through real-time data analysis, thereby maximizing the juicing efficiency and improving the quality of the juice. Since each type of fruit and vegetable has its optimal juicing parameters, intelligent adjustment can avoid the waste of ingredients during the juicing process, improve the efficiency of raw material use, and reduce unnecessary electricity and water consumption. According to the adaptive juicing characteristics, the juicer can intelligently adjust parameters such as speed, pressure, and time to ensure that each type of fruit and vegetable can achieve the best juicing effect, eliminating the trouble of manual adjustment. Users do not need to worry about the adjustment of the juicer. The system can automatically adjust according to the characteristics of fruits and vegetables, thereby providing a more convenient and efficient user experience.This intelligent adaptive strategy enables the juicer to provide the most suitable juicing method when faced with fruits and vegetables of different types, maturity and seasons, thereby increasing the adaptability and stability of the juicer.

[0009] Preferably, the specific steps of step S16 are: Get the juicing instructions preset by the user; Analyzing the juicing instructions preset by the user to obtain a preset juicing mode; Calculating the juice extraction speed parameter according to the adaptive juice extraction characteristics to generate the current juice extraction speed parameter; Performing optimal juicing pressure analysis on the adaptive juicing feature to generate optimal juicing pressure; Intelligent juicing parameters are adjusted according to the preset juicing mode, current juicer speed parameters and optimal juicing pressure to build an adaptive juicing strategy.

[0010] The present invention obtains the juicing instructions preset by the user, and the juicer can be personalized according to the user's preferences to provide a customized juicing experience. The user can set different instructions according to the type, quantity, juice concentration, etc. of different fruits and vegetables, and the juicer will automatically execute these settings to meet the user's needs. The user's preset instructions can automate the entire juicing process, avoiding cumbersome settings during each use and improving ease of use. By analyzing the user's preset instructions, the juicer can automatically determine the juicing mode that the user wants to perform (such as efficient juicing, slow juicing, retaining pulp, etc.). The system generates a preset juicing mode based on the analysis result, and adjusts various operating parameters according to the mode to optimize the juicing process. This step can intelligently optimize the juicing process according to the user's preferences. The juicing mode can be set to enable the juicer to handle different types of fruits and vegetables and juicing tasks with different needs (for fruits and vegetables with more pulp, choose a slower juicing mode to retain the pulp), thereby improving the juicing effect. By accurately analyzing the preset instructions, the juicer can automatically identify and execute the best juicing mode, eliminating the trouble of manual adjustment for users and improving the overall user experience. The speed parameters of the juicer are calculated according to the adaptive juicing characteristics (such as the hardness and moisture content of fruits and vegetables), which can ensure that different types of fruits and vegetables are properly processed. Hard fruits and vegetables (such as carrots) may require higher speeds, while soft fruits and vegetables (such as oranges) are suitable for lower speeds. This step can automatically adjust the speed, improve juicing efficiency and reduce energy waste. The juicing of fruits and vegetables requires different rotation speeds. This step can maximize the juicing effect and ensure the efficiency of the juicing process and the purity of the juice. By reasonably setting the speed parameters according to the characteristics of different fruits and vegetables, the excessive load of the juicer can be reduced and the service life of the equipment can be extended. By analyzing the adaptive juicing characteristics to calculate the optimal juicing pressure, it can be ensured that each fruit and vegetable receives the best pressure support during the juicing process to avoid problems such as over-squeezing or insufficient pressure. For fruits and vegetables with high water content, moderately increasing the pressure can effectively squeeze out more juice; for hard fruits and vegetables, the pressure needs to be moderately reduced to avoid excessive damage to the fruits and vegetables. Reasonable juicing pressure helps to improve the quality and taste of the juice, ensure that the squeezed juice is more delicate, and reduce fruit Vegetable residues can improve the accuracy and purity of the juicing process. According to the preset juicing mode, speed parameters and optimal juicing pressure, the juicer can perform comprehensive intelligent adjustments to ensure that all parameters are within an optimal range. This comprehensive adjustment can enable the juicer to maintain an efficient and stable working state in different working environments and different types of fruits and vegetables. By comprehensively considering the preset mode, speed and pressure, an adaptive juicing strategy is constructed. The system can automatically adjust under different juicing needs to achieve the best juicing effect, which makes the juicing process smarter and more efficient. Intelligent adjustment can avoid unnecessary energy waste and excessive consumption in the juicing process, while maximizing the extraction of juice and reducing the waste of fruits and vegetables. It also plays a positive role in energy conservation and environmental protection.

[0011] Preferably, step S2 specifically comprises the following steps: Step S21: Identify the environmental parameters surrounding the juicer; Step S22: analyzing the temperature and humidity change characteristics of the environment parameters around the juicer, thereby generating the environment parameter change characteristics; Step S23: performing an environmental parameter change trend analysis on the environmental parameter change characteristics, thereby generating an environmental parameter change trend curve; Step S24: based on the juice extraction start time point preset by the user, the environmental parameter change trend curve is subjected to the evolution of the environmental trend characteristics at the juice extraction time point, and the environmental trend characteristics at the juice extraction time point are extracted; Step S25: intelligently adjusting the environmental trend impact on the adaptive juicing strategy according to the environmental trend characteristics at the juicing time point, thereby generating an environmental trend juicing strategy; Step S26: Perform juice extraction control simulation based on the environmental trend juice extraction strategy to obtain juice extraction control simulation data.

[0012] By identifying the environmental parameters (such as temperature, humidity, air pressure, etc.) around the juicer, the present invention can enable the system to understand the current environmental status in real time and provide basic data for subsequent analysis and adjustment. This step provides necessary environmental background information for subsequent intelligent adjustment. By identifying these environmental parameters, the juicer can accurately sense the impact of environmental changes on the juicing process, ensuring that subsequent operations can be dynamically adjusted according to the actual environment. By analyzing changes in environmental factors such as temperature and humidity, the system can identify the potential impact of the environmental status on the juicing process. Too high or too low temperature, changes in humidity, etc. may affect the juicing effect of fruits and vegetables, and thus affect the quality of the juice. By analyzing the characteristics of changes in environmental parameters, the juicer can predict the trend of environmental changes and provide guidance for subsequent Make advance preparations for the juicing operation. Excessive humidity may cause moisture on the surface of fruits and vegetables, affecting the efficiency of juicing. The system can make corresponding adjustments through prediction. This analysis can help the system deeply understand the dynamic changes of the environment, provide detailed environmental change characteristic data, provide data support for intelligent adjustment, and improve the stability of the juicing process. Through trend analysis of environmental change characteristics, the juicer can accurately predict environmental change trends and generate environmental parameter change trend curves, which enables the system to adjust the parameters of the juicing process according to the trends. When the temperature is about to rise, the juicer can reduce the speed or adjust the pressure in advance to cope with environmental changes. After generating the environmental parameter change trend curve, the juicer can intelligently predict environmental changes in the future, so as to make adjustments. Make appropriate adjustments to ensure that the entire juicing process is smooth and avoid negative impacts of environmental fluctuations on the juicing effect. At the user-preset juicing start time, the juicer can analyze and evolve environmental trends before and after the specified time point. This step helps the juicer to accurately predict changes in environmental conditions at the juicing time point based on actual usage scenarios and make real-time adjustments. By extracting the environmental trend characteristics at the juicing time point, the juicer can understand and respond to special environmental changes at the time point (such as temperature rise or humidity increase during juicing), making the juicing process more refined and avoiding the impact of environmental changes on juicing efficiency and quality. Based on the environmental trend characteristics at the juicing time point, the juicer can intelligently adjust the adaptive juicing strategy to ensure that under different environmental conditions , the juicer can still maintain the best working state. When the temperature is high, the system can automatically lower the speed to reduce the burden on the juicer. By analyzing environmental trends in real time and adjusting the juicing strategy, the juicer can effectively respond to environmental fluctuations and reduce the impact of environmental changes on the juicing process, thereby improving the stability and long-term operation capability of the system. Through juicing control simulation, the juicer can predict environmental trends and control strategies before actual juicing, and simulate the juicing process under different environmental conditions. This allows the juicer to pre-adjust parameters before actual operation to avoid interference with the juicing process caused by sudden environmental changes. Simulation data can help the juicer optimize the control strategy in advance to ensure that there are no unexpected operating errors or poor environmental adaptation during the actual juicing process.Through continuous simulation and adjustment, the system can achieve the best juicing effect. Through simulation, the juicer can enhance its adaptability to environmental changes. When facing different environmental changes, it can still maintain high-quality juicing effects and reduce unnecessary errors and failures.

[0013] Preferably, step S3 specifically comprises the following steps: Step S31: performing multi-time period juicing effect evolution on the juicing control simulation data to obtain juicing effect evolution data in multiple time periods; Step S32: Calculate the key index of juicing for each time period based on the juicing effect evolution data of multiple time periods, and extract the key index of juicing for each time period; Step S33: predicting the juicing effect situation of the key juicing indicators in each time period, thereby generating the juicing effect situation in the future time period; Step S34: predicting abnormal conditions of the juice extraction effect in the future period, thereby generating abnormal juice extraction state prediction data; Step S35: calculating the abnormal state time point of the abnormal juice extraction state prediction data, and marking the abnormal state prediction time point; Step S36: Generate a juicer abnormal warning signal and juicer abnormal failure prediction information based on the abnormal state prediction time point.

[0014] By analyzing the evolution of juicing effects in multiple time periods, the system can fully understand the effect changes in the juicing process. The juicing effects in different time periods may vary due to the state of fruits and vegetables, environmental conditions or machine loads. This analysis can help understand the various stages of the juicing process, so as to adjust the strategy in time. Through multi-time period analysis, the system can identify the performance changes of the juicer during long-term operation, and optimize according to the juicing effects in different stages to ensure the efficiency and consistency of the entire juicing process. The effect evolution data of multiple time periods helps to accumulate more real-time data, so that the juicer can perform performance analysis from a longer-term perspective and identify potential performance bottlenecks. By extracting key juicing indicators (such as juicing performance) time by time period, the system can identify the performance changes of the juicer during long-term operation and optimize according to the juicing effects in different stages to ensure the efficiency and consistency of the entire juicing process. The effect evolution data of multiple time periods helps to accumulate more real-time data, so that the juicer can perform performance analysis from a longer-term perspective and identify potential performance bottlenecks. The system can quantify the juicing effect of each time period (such as juice volume, juice purity, and pomace amount). This analysis can help the juicer understand its specific performance at different time points, further optimize the operation of each stage, and extract key indicators so that the system can identify which time periods have better juicing effects and which stages may have problems. Based on this data, the system can make real-time adjustments to ensure that the juicing quality remains consistent in each time period. By predicting the key juicing indicators for each time period, the system can generate the juicing effect trend for future time periods. This prediction allows the juicer to prepare in advance to ensure that the juicing quality and efficiency of future time periods are not affected. By predicting the effects of future time periods, the system can intelligently adjust the parameters of the juicer (such as speed, pressure, etc.), to respond to potential changes in advance. If it is predicted that the juicing effect may decrease in the next few minutes, the system can adjust parameters in advance to prevent the quality of the juice from decreasing. By analyzing the juicing effect trend in the future period, the system can predict possible abnormal conditions (such as a sudden decrease in juice production, a decrease in juice purity, etc.). This early warning can help the system take measures early to avoid the problem from expanding. Through abnormal state prediction, the system can promptly identify possible signs of failure (such as overload, abnormal pressure, etc.) and prevent equipment damage by adjusting operations or issuing alarms. By calculating the specific time point of the abnormal state, the system can accurately mark the moment when the abnormality occurs in the juicing process, and the system can take intervention measures at the moment of the abnormality. , to avoid further development of the problem. After marking the predicted time point of the abnormal state, users or maintenance personnel can find and repair faults more quickly. If the juice production drops abnormally in a certain period of time, the system can provide accurate time point data to help users analyze the fault. By accurately marking the time when the abnormal state occurs, the system can provide data basis for subsequent equipment maintenance and inspection, which helps to formulate more efficient maintenance plans. By generating abnormal warning signals and fault prediction information, the system can notify users or technicians in advance before the abnormality occurs. This early warning mechanism can minimize the impact of equipment failures and generate fault prediction information in advance, so that the juicer can be adjusted or repaired before the abnormal state occurs, reducing the probability of sudden failures.The overall reliability and stability of the equipment are improved, and the generation of abnormal warning and fault prediction information allows users to grasp the status of the equipment in real time, avoiding inconvenience and losses caused by equipment failure. Users can take corresponding measures in advance according to the warning signals, thus obtaining a smoother and more efficient juicing experience.

[0015] Preferably, the specific steps of step S4 are: Step S41: obtaining the current network access status of the juicer; Step S42: performing data packet test transmission on the access network status to generate transmission test parameters; Step S43: Calculate the network real-time bandwidth according to the transmission test parameters to generate the current network real-time bandwidth parameters; Step S44: performing network load analysis on the transmission test parameters, thereby generating real-time network load characteristics of the juicer; Step S45: Perform dynamic network status evaluation on the current network real-time bandwidth parameters and the juicer real-time network load characteristics, thereby generating a dynamic network status evaluation result.

[0016] By acquiring the access network status of the juicer, the system can understand in real time whether the current device is connected to the network and whether the network connection is stable. This step provides basic data for subsequent network optimization and adjustment, ensuring smooth communication between the juicer and the backend cloud server or other devices. Only on the basis of correctly identifying the network status can effective data transmission and remote operation be performed. By performing data packet test transmission, the system can test the stability and transmission speed of the current network. The transmission test parameters (such as delay, packet loss rate, throughput, etc.) generated by the test results provide a quantitative basis for further optimizing the network connection. Through test transmission, problems existing in the network, such as high delay or packet loss, can be identified, which provides data support for subsequent network optimization and troubleshooting. By calculating the real-time bandwidth, the system can accurately understand the actual bandwidth capacity of the current network, especially the bandwidth demand of the juicer when processing data transmission, which is crucial for judging whether the network is sufficient to support backend communication. By dynamically calculating the bandwidth, the system can adjust the performance of the juicer under different network conditions. In a network environment with low bandwidth, the system can reduce the amount of data transmission to avoid data delay or loss due to insufficient bandwidth. By analyzing the load information (such as network utilization, traffic fluctuation, etc.) in the transmission test parameters, the current network can be evaluated. Load conditions. If the network load is too high, the data transmission speed may be reduced, affecting the communication efficiency between the juicer and the cloud server. By understanding the real-time network load characteristics of the juicer, the system can automatically adjust the frequency or size of data transmission to reduce network congestion. When the network load is peak, the system may delay the upload of some non-real-time data to ensure that real-time data (such as juicing process monitoring) is transmitted first. Load analysis can help identify network bottlenecks and inefficient links, thereby improving the efficiency of data transmission and ensuring that the background communication system of the juicer will not be stuck or delayed due to excessive network load. By combining real-time bandwidth and network load characteristics, the system can perform full The dynamic network status evaluation is used to evaluate whether the current network meets the communication needs of the juicer. Sufficient bandwidth but excessive load may cause transmission delays or failures. The evaluation results can provide a basis for subsequent decisions of the system. Dynamic evaluation can identify actual performance fluctuations of the network and dynamically adjust the background communication strategy of the juicer according to the evaluation results. For example, when the bandwidth decreases, the system may choose to compress data or reduce the frequency of data upload to avoid network crashes or delays. Through dynamic evaluation, the system can adapt to different network conditions in real time and automatically optimize communication strategies and data transmission methods to ensure the communication stability and data transmission efficiency of the juicer in various network environments.

[0017] Preferably, the specific steps of step S44 are: Calculate the data transmission rate for the transmission test parameters to obtain the real-time data transmission rate; Performing data transmission delay analysis based on the real-time data transmission rate to generate data transmission delay parameters; Calculate an initial transmission data packet based on the transmission test parameters; Detect the number of data packets finally received; Based on the number of data packets finally received and the number of data packets initially sent, packet loss rate statistics are performed to generate a real-time transmission packet loss rate; The network load analysis is performed on the real-time data transmission rate, data transmission delay parameters and real-time transmission packet loss rate to generate the real-time network load characteristics of the juicer.

[0018] By calculating the data transmission rate, the system of the present invention can accurately evaluate the transmission capacity of the current network and determine whether the network can support efficient communication between the juicer and the cloud. The real-time data transmission rate helps to determine whether the current network is stable enough to support data transmission. Especially when performing large data transmission, by analyzing the relationship between the data transmission rate and the delay, the response time of the network connection can be evaluated. The delay analysis helps to discover potential delay problems (network congestion or bandwidth bottleneck) in the network, thereby improving the real-time performance of data transmission. For the smart juicer, it is very important to upload certain data (such as the juice extraction process, fruit and vegetable status, etc.) to the cloud in real time. The delay analysis helps to ensure that there is no significant delay in the data upload process, and ensures the instant synchronization between the device and the cloud. The calculation of the number of data packets initially sent provides a standard benchmark for subsequent data transmission quality evaluation. By knowing the amount of data initially sent, it is possible to evaluate whether the data flow in the entire transmission process meets expectations. This step provides a starting point for the entire data transmission process. By comparing with the number of received data packets, it can help identify the situation of data packet loss or delay. By tracking the sending of the initial data packet, the data transmission flow can be accurately grasped, providing data support for subsequent network optimization. Detecting the number of data packets finally received helps to confirm whether packet loss occurs during data transmission. The change in the number of data packets is a good way to judge the network status and A direct indicator of transmission quality, ensuring that the number of received packets matches the number of sent packets, can help identify packet loss or data loss problems during network transmission. Especially for real-time applications, data integrity is crucial. Calculating the packet loss rate is an important means to judge the quality of network transmission. If the packet loss rate is too high, it means that the network stability is poor, which may cause delays in the control commands of the juicer or failure to upload data. The real-time packet loss rate generation helps to detect transmission problems in a timely manner. The real-time packet loss rate data can help automatically optimize the data transmission method. When the packet loss rate is high, the system may adopt a retransmission strategy or adjust the data flow to ensure data integrity and reduce transmission delays. By combining real-time data transmission rate, data Based on a comprehensive analysis of transmission delays and packet loss rates, the system can fully understand the current network load status. This assessment can help discover network bottlenecks, identify delay problems in transmission, and provide guidance for subsequent network optimization and adjustments. Through real-time load analysis, the juicer system can automatically adjust the background communication strategy according to network conditions. In a network environment with low bandwidth or high load, the system can reduce the frequency of data uploads, delay the transmission of non-real-time data, and ensure that the transmission of critical data is not affected. Through real-time analysis of network load, the juicer can dynamically adjust the data transmission method according to different network states, reduce the freeze or delay perceived by users due to network instability, and improve the overall user experience.

[0019] Preferably, the specific steps of step S5 are: The dynamic network status evaluation result includes a general network status, a good network status and a good network status; when the dynamic network status evaluation result is a general network status, an abnormal warning signal of the juicer is uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the key information of the abnormal fault prediction information of the juicer is analyzed to extract the key part fault prediction information; the key abnormal fault prediction information and the abnormal warning signal of the juicer are uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the juicer abnormal warning signal and the juicer abnormal fault prediction information are uploaded to the juicer cloud server.

[0020] When the network status of the present invention is "general", there may be network fluctuations or bandwidth limitations. In this case, the system will give priority to uploading the "abnormal warning signal" to ensure that key warning information can reach the cloud in time. This helps to ensure that even when the network condition is poor, the system can still perform remote monitoring and detect abnormal conditions of the juicer in time. Uploading the warning signal first can ensure that even if the network is unstable, potential faults detected by the system will not be missed. Even if the uploading of other data may be delayed due to network problems, the warning signal can still be guaranteed to be transmitted. This step allows the juicer to actively respond when the network condition is poor, and reduces the risk of system failure expansion by transmitting the most important warning information, thereby improving the fault tolerance of the equipment. When the network condition is good, the system can process more data, but still control the data volume within a reasonable range. By analyzing the key information of the abnormal fault prediction information, the system extracts the most critical fault prediction part and avoids uploading redundant data. This not only ensures the timely uploading of key prediction information, but also effectively reduces the network load. By extracting key information, the system can concentrate on uploading important faults. Prediction information, which helps the cloud server to receive and analyze the risk of equipment failure more quickly and accurately, and then take appropriate preventive measures. This step uses data analysis technology to optimize the uploaded data content, making network utilization more efficient. Even when the network is good, the system will not send irrelevant information, ensuring the accuracy and effectiveness of background communication. When the network is in good condition, the juicer can transmit data more stably and will not be significantly limited by bandwidth or delay. In this case, the system will upload complete abnormal warning signals and detailed abnormal fault prediction information at the same time, which ensures that the cloud server can receive comprehensive data and conduct more in-depth analysis and prediction. By uploading all relevant fault information to the cloud in a timely manner, the cloud server can predict faults and conduct risk assessments in advance, helping users or maintenance personnel to take corresponding measures in time to reduce the incidence of equipment failures. The data upload is more comprehensive, providing sufficient information for the cloud intelligent decision-making system, helping the cloud server to make more accurate judgments and predictions, and the juicer can respond and handle potential faults quickly.

[0021] Preferably, step S6 comprises the following steps: Step S61: The user checks the fault prediction information based on the juicer cloud server and performs feedback operation to generate a user feedback instruction; Step S62: Perform personalized fault handling evolution on the user feedback instruction to generate a personalized fault handling habit for the user; Step S63: Dynamically transfer learning of the user's personalized fault handling habits to generate a smart juicer decision strategy; Step S64: executing the background communication and fault handling operations of the juicer according to the intelligent juicer decision strategy.

[0022] The present invention allows users to view the fault prediction information of the juicer in real time and provide feedback according to their needs, which provides users with more control rights, enables them to actively participate in the management and fault handling of the equipment, and enhances user experience. Through user feedback, the system can identify the user's processing preferences for fault prediction information, such as whether they want the system to automatically repair or whether they want manual user intervention. This personalized feedback mechanism helps the juicer respond to user needs more accurately. According to user feedback instructions, the system can evolve personalized fault handling solutions. Some users may hope that the device can automatically repair itself when a fault occurs, while other users hope to get detailed manual guidance or direct intervention when a fault occurs. By evolving these preferences, the system can better meet the needs of different users. The system can continuously adjust and optimize the fault handling process to make it more in line with user habits. If users report that they often choose a certain operation method, the system will learn this and use it as the default fault handling strategy. The juicer will become more intelligent in subsequent fault handling, reducing the user's operating burden and improving user satisfaction. The personalized fault handling evolution allows the system to adapt to the behaviors and preferences of different users and adjust the fault handling mode at any time, thereby improving the flexibility and intelligence of the entire device. Based on personalized user feedback, the system continuously optimizes and improves the fault handling strategy through dynamic transfer learning. Transfer learning allows the system to not only learn from a single user, but also improve the overall performance of the system. The system can learn from the feedback of users and transfer this learning to other similar usage scenarios to enhance the adaptability and breadth of the system. Through transfer learning, the system can integrate the behaviors and feedback of different users to form a more intelligent decision-making strategy. The smart juicer can automatically make the best decision in multiple scenarios, such as automatically adjusting the juicing mode or selecting the appropriate fault handling method according to the user's habits, thereby improving the response efficiency of the system. Over time, the system can perform long-term optimization based on the accumulated user feedback. Transfer learning enables the juicer to gradually adapt to and meet the needs of individual users when handling faults and performing background communications, thereby improving the intelligence level and operating efficiency of the equipment. According to the intelligent decision-making strategy, the juicer can automatically Perform fault handling operations without user intervention. When a certain fault is detected, the juicer can automatically select the most appropriate repair method or notify the user to perform an operation. This greatly reduces the need for manual intervention by the user and improves the automation level of the equipment. The decision-making strategy of the intelligent juicer not only includes fault handling, but also involves real-time data exchange with the cloud server. The system can intelligently adjust the communication frequency and content according to the network status and device status to ensure stable and efficient background communication in different network environments. Through intelligent decision-making based on personalized learning, the system can quickly perform background operations when a fault occurs, improve the response speed and accuracy of fault handling, and users can also experience more stable and efficient equipment performance, improving their usage experience.Through feedback loops and intelligent decision-making, the juicer can continuously optimize itself and improve its performance in various usage scenarios. Every fault handling and user feedback provides valuable data for future decisions of the device, thus helping the device to continuously optimize the fault handling process and background communication mechanism.

[0023] In this specification, a background communication system is provided, which is used to execute the background communication method for a juicer system as described above, including: A juicing parameter adjustment module is used to obtain the fruit and vegetable images reserved in the juicer; perform adaptive juicing feature analysis on the reserved fruit and vegetable images, and perform intelligent juicing parameter adjustment to construct an adaptive juicing strategy; The juice extraction control simulation module is used to identify the environmental parameters around the juice extractor; according to the environmental parameters around the juice extractor, an adaptive juice extraction strategy is implemented to intelligently adjust the environmental trend influence, and juice extraction control simulation is performed to obtain juice extraction control simulation data; The juicing effect evolution module is used to perform multi-period juicing effect evolution on the juicing control simulation data, and to predict abnormal states, thereby generating abnormal warning signals for the juicer and abnormal fault prediction information for the juicer; The network status evaluation module is used to obtain the current access network status of the juicer; perform data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; A cloud information uploading module is used to upload the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; The dynamic transfer learning module is used for users to view fault prediction information based on the juicer cloud server and perform dynamic transfer learning to generate an intelligent juicer decision strategy; the juicer's background communication and fault handling operations are executed according to the intelligent juicer decision strategy.

[0024] The present invention acquires images of fruits and vegetables, and the module can analyze the characteristics of fruits and vegetables (such as type, size, hardness, etc.) based on image recognition technology, so as to obtain suitable juicing parameters. This ensures that the juicer is optimized and adjusted according to different types of fruits and vegetables. The juicing parameters (such as rotation speed, pressure, etc.) are adaptively adjusted according to the characteristics of fruits and vegetables, which can make the juicing process more efficient and maximize the extraction of juice while avoiding waste. The intelligent adjustment of juicing parameters not only adapts to different types of fruits and vegetables, but can also be customized according to user needs to provide personalized juicing solutions, improve user experience, monitor and identify environmental parameters around the juicer (such as temperature, humidity, etc.) in real time, and adjust the juicing strategy based on these environmental parameters. In a high temperature environment, the juicing temperature may need to be adjusted or time to avoid overheating of fruits and vegetables. By conducting juicing control simulation, the juicing effect in different environments can be predicted to ensure that the juicer always maintains the best working state under various environmental conditions. The juicing strategy can be adjusted in real time according to the simulation results to ensure that the juicer can operate efficiently and stably. No matter how the external environment changes, multiple time periods in the juicing process are monitored and the changes in juicing effect in different time periods are analyzed. This helps to discover possible performance degradation or failure risks in advance. Through the evolution analysis of simulation data, the system can predict abnormal conditions and generate fault warning signals. The system can take preventive measures before a failure occurs to reduce the occurrence of unexpected failures. It can identify possible fault types in advance and generate corresponding fault prediction information, thereby helping Help users deal with potential problems in a timely manner, improve the stability and reliability of the juicer, detect the network connection status of the juicer in real time, and evaluate the quality of data transmission. If the network quality is poor, it may cause data upload delays or losses. Through data packet testing and dynamic evaluation, the system can determine whether the current network status is suitable for large data transmission or real-time fault handling. If the network status is poor, the system can choose to upload important information first to ensure the stability of data transmission. By evaluating the network status in real time, the system can adjust the data transmission strategy when the network is unstable to ensure that key data (such as fault warning signals) can be uploaded in time, and information will not be lost or delayed due to network problems. According to the network status evaluation results, the system can dynamically adjust data upload Strategy, when the network is not good, it will give priority to uploading important abnormal signals and prediction information to ensure the timely upload of key data. Regardless of the network status, the system can ensure the upload of abnormal warning signals and fault prediction information. Users can obtain equipment status and fault information in a timely manner through the cloud platform. By uploading information to the cloud, users and maintenance personnel can remotely monitor the operating status of the juicer and grasp the health status of the equipment in a timely manner, so as to make timely processing. The user's operation feedback and historical data can help the system learn and generate personalized decision-making strategies. The fault handling method and processing speed fed back by the user will affect the learning process of the system, so that the juicer can be optimized according to the preferences of different users. Through transfer learning, the system can not only learn the user's operating habits,It can also transfer the learned knowledge to other devices or scenarios and optimize decision-making strategies. This dynamic learning ability helps the juicer to continuously adapt to new usage environments and needs. Based on intelligent decision-making strategies, the system can automatically adjust the background communication frequency and content to ensure real-time data transmission and fault handling. When a fault occurs, the system can automatically select the best fault handling method to reduce user intervention and improve the intelligence level of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the steps of a background communication method for a juice extractor system according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0026] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0027] The present application example provides a background communication method and background communication system for a juicer system. The execution subjects of the background communication method and background communication system for a juicer system include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0028] See also Figures 1 to 4 The present invention provides a background communication method for a juicer system, and the background communication method for a juicer system comprises the following steps: Step S1: obtaining fruit and vegetable images reserved in the juicer; performing adaptive juicing feature analysis on the reserved fruit and vegetable images, and performing intelligent juicing parameter adjustment to construct an adaptive juicing strategy; Step S2: Identify the environmental parameters of the juicer; perform an adaptive juicing strategy according to the environmental parameters of the juicer to intelligently adjust the impact of environmental trends, perform juicing control simulation, and obtain juicing control simulation data; Step S3: performing multi-period juicing effect evolution on the juicing control simulation data, and performing abnormal state prediction, generating an abnormal warning signal of the juicer and abnormal fault prediction information of the juicer; Step S4: obtaining the current access network status of the juicer; performing data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; Step S5: uploading the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; Step S6: The user checks the fault prediction information based on the juicer cloud server and performs dynamic transfer learning to generate a smart juicer decision strategy; and executes the background communication and fault handling operations of the juicer according to the smart juicer decision strategy.

[0029] The present invention can accurately identify the type, shape, size and other characteristics of fruits and vegetables by acquiring the fruit and vegetable images reserved in the juicer, and provide detailed input data for the juicer, which is helpful for the juicer to determine the best juicing method. The adaptive juicing feature analysis can adjust the working parameters of the juicer (such as speed, pressure, etc.) according to the physical properties of different fruits and vegetables (such as moisture content, hardness, etc.) to ensure the maximization of the juicing effect. By constructing an adaptive juicing strategy, the juicer can automatically optimize the juicing process without manual intervention, thereby saving time and reducing energy consumption and improving overall efficiency. By identifying factors such as temperature, humidity, air flow and other factors in the environment surrounding the juicer, the system can dynamically adjust the juicing strategy according to these changes to ensure that it can maintain stability even under different environmental conditions. To achieve a certain juicing effect, the intelligent adjustment of environmental factors can identify potential influencing factors in advance. For example, excessively high temperature may cause fruits and vegetables to deteriorate more easily. The juicer system can reduce these adverse effects by adjusting the working parameters. Through the generation of juicing control simulation data, the juicing effect under different environmental conditions can be accurately simulated to provide data support for subsequent adjustments and optimizations. Through multi-period analysis of juicing control simulation data, potential problems in the juicing process or early signs of equipment failure can be found, which provides strong support for preventing equipment failures. According to the simulation data during the operation of the juicer, the system can predict abnormal conditions in advance (such as motor overheating, fruit and vegetable jamming, etc.), and generate abnormal warning signals to remind users to deal with them in time. The prediction of abnormal conditions It can greatly reduce the failure rate of the juicer, ensure the long-term stable operation of the equipment, and reduce unnecessary maintenance and downtime. By real-time monitoring of the network status of the juicer, you can understand the network connection status of the device and ensure stable data transmission between the juicer and the cloud server. Data packet test transmission and dynamic network status evaluation help detect network delays, packet loss and other problems, ensuring that the data uploaded to the cloud is accurate and timely. By dynamically evaluating the network status, you can automatically adjust the communication strategy and select the appropriate transmission method when the network condition is poor to ensure smooth data upload. By uploading the abnormal warning signals and fault prediction information of the juicer to the cloud, users or operation and maintenance personnel can view and remotely monitor the equipment status at any time, and promptly discover and solve potential problems. The cloud server can receive and process data from the juicer in real time to ensure synchronization and timely update of information, and provide a reliable basis for fault warning. Based on cloud data analysis, the system can continuously optimize the performance of the juicer and adjust the juicing strategy to cope with different usage conditions and environmental changes. Users can understand the working status of the juicer based on the fault prediction information provided by the cloud, and take timely measures to adjust it to avoid the problem from worsening. Through dynamic transfer learning, the system can continuously optimize the decision-making strategy based on the feedback from the cloud server, so that the juicer can adapt to the usage needs of different users, different environments, and different fruits and vegetables. Through the generation of intelligent decision-making strategies, the juicer can be adjusted in real time according to actual usage conditions, which not only improves the user experience,It also enhances the intelligence level and self-maintenance capability of the equipment.

[0030] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a background communication method for a juice extractor system of the present invention. In this example, the steps of the method include: Step S1: obtaining fruit and vegetable images reserved in the juicer; performing adaptive juicing feature analysis on the reserved fruit and vegetable images, and performing intelligent juicing parameter adjustment to construct an adaptive juicing strategy; In this embodiment, it is ensured that the juicer is equipped with a high-resolution camera that can capture images of fruits and vegetables before juicing. A suitable camera (such as Raspberry, Pi, Camera, Module) is selected and connected to the juicer control system to ensure stable data transmission. The camera is calibrated to ensure the quality and clarity of the acquired image, especially under different lighting conditions. An image storage system is designed to ensure that the captured images of fruits and vegetables can be classified and stored by time and type. Local storage or cloud storage can be used to ensure data security and accessibility. Before each juicing, the system automatically captures and saves images of the current fruits and vegetables, and records the image. Timestamp and related information for subsequent analysis, ensure that image data is saved in a standard format (such as JPEG or PNG) for subsequent processing, use image processing libraries (such as OpenCV) for image format conversion and preprocessing to ensure image uniformity, perform basic preprocessing on acquired images, such as denoising, adjusting contrast and brightness to improve the effectiveness of subsequent analysis, perform quality checks after image storage to ensure the clarity and integrity of each image, use image processing algorithms (such as edge detection) to assess the quality of the image, record any anomalies, and retake images if problems are found to ensure data accuracy and reliability, select Suitable image processing and machine learning algorithms are used for feature extraction. Convolutional neural network (CNN) models can be used because of their excellent performance in image classification and feature extraction. The models are trained to identify and classify different types of fruits and vegetables, including different features of fruits and vegetables (such as color, texture, and shape), to ensure that the model can accurately identify and extract key information. The trained CNN model is used to analyze the reserved fruit and vegetable images to extract feature vectors for each fruit and vegetable. These feature vectors can include color distribution, shape features, and texture information, etc. Image processing techniques (such as color histograms, edge detection, etc.) are used to further analyze the characteristics of fruits and vegetables to ensure the extracted The features are representative and accurate. According to the extracted features, the juicing parameters, such as juicing temperature, speed and time, can be intelligently adjusted. A rule engine can be built to automatically generate parameter adjustment strategies based on the feature values. For high-moisture fruits, the system can automatically reduce the juicing speed to reduce foam, or adjust the juicing time to prevent over-ripening. The adjusted juicing parameters are integrated into an adaptive juicing strategy and stored in the system for real-time call. Before each juicing, the system automatically calls the corresponding strategy for operation, conducts multiple experiments to verify the effect of the adaptive juicing strategy, and records the juicing efficiency, juice quality and user feedback to ensure the effectiveness and stability of the strategy.

[0031] Step S2: Identify the environmental parameters of the juicer; perform an adaptive juicing strategy according to the environmental parameters of the juicer to intelligently adjust the impact of environmental trends, perform juicing control simulation, and obtain juicing control simulation data; In this embodiment, a variety of sensors are installed around the juicer to collect necessary environmental parameters. Common sensors include temperature sensors (such as DS18B20), humidity sensors (such as DHT22), light sensors (such as BH1750) and air pressure sensors (such as BMP180). Ensure that the sensor is well connected to the control system of the juicer and can transmit data in real time. Arduino or Raspberry, Pi can be used as a data acquisition platform to write a driver to read sensor data. Environmental parameter data is obtained from the sensor regularly, for example, once every 5 seconds. The recorded data includes current temperature, humidity, light intensity, and air pressure value. The acquired data is basically cleaned to remove outliers and noise to ensure the accuracy and reliability of the data. A simple threshold check method can be used to identify and exclude unreasonable data points. The collected environmental parameter data is stored in a database for subsequent analysis and query. Ensure that each data point recorded has a timestamp to support time series analysis. Use visualization tools (such as Grafana or Matplotlib) to generate a real-time monitoring interface for environmental parameters to help users intuitively understand the current environmental conditions. Analyze the collected environmental parameter data and identify key influencing factors. For example, temperature and humidity may affect the texture and moisture content of fruits and vegetables, thereby affecting the juicing effect. Generate descriptive statistics of environmental parameters to help the team understand the performance of fruits and vegetables under different environmental conditions and provide a basis for subsequent adaptive strategies. Design an adaptive juicing strategy model to consider the impact of various environmental parameters on the juicing effect. The model can be built based on machine learning algorithms (such as decision trees or random forests), and the model can be trained using historical data to identify the best parameter settings. Define the adjustment rules for juicing parameters under different environmental conditions, such as reducing the juicing time to prevent juice fermentation in high temperature and high humidity environments. Automatically adjust the working parameters of the juicer, such as speed, temperature, and juicing time, according to real-time environmental parameters. For example, when the humidity is high, increase the speed to improve the juicing efficiency; while in a low temperature environment, appropriately extend the juicing time to ensure full extraction. Establish a feedback mechanism to monitor the juicing effect in real time, and further adjust the strategy based on the effect data to ensure the flexibility and adaptability of the juicing process. After intelligently adjusting the parameters, perform a juicing control simulation and use computer simulation software (such as MATLAB or Simulink) to simulate the juicing process. Input the adjusted parameters and observe the changes in the juicing effect. Record key data from the simulation process, including juice yield, quality, and energy consumption, to ensure that the juicing effect under different environmental conditions can be compared. Analyze the collected control simulation data to evaluate the effectiveness of the adaptive juicing strategy. Compare the juicing effect under different environmental parameters and identify the best adjustment strategy. Based on the analysis results, optimize the adaptive strategy model and update the parameter settings to provide better results in future juicing processes.

[0032] Step S3: performing multi-period juicing effect evolution on the juicing control simulation data, and performing abnormal state prediction, generating an abnormal warning signal of the juicer and abnormal fault prediction information of the juicer; In this embodiment, the juice extraction control simulation data from different time periods are collected and integrated, including the juice extraction parameters (such as rotation speed, temperature, pressure) and juice yield and quality results of each time period. The data is cleaned to remove incomplete or abnormal data points to ensure the accuracy of the data. The Pandas library can be used for data processing to ensure that the data format is unified and can be used for analysis. The integrated data is divided into time periods, such as hourly, daily or each stage of the juice extraction process. It is ensured that the amount of data in each time period is sufficient for effective statistical analysis. Labels are created for each time period for subsequent comparison and analysis. The juicing effect is calculated, including key indicators such as juice yield, taste score and nutritional content. Statistical methods (such as mean and variance) can be used to summarize the changes in juicing effect in each time period, record the evolution data of juicing effect in each time period, and generate effect change charts to help the team intuitively understand the trend of juicing performance. Based on the effect evolution data, abnormal fluctuations in juicing effect can be identified. For example, the standard deviation method is used to set a normal fluctuation range. Data points outside this range are abnormal. The occurrence time, duration and impact of each abnormal state are recorded to provide a basis for subsequent fault prediction and select appropriate anomaly detection algorithms, such as isolation forest and support vector The abnormal state prediction model is constructed by using a support vector machine (SVM) or an LSTM model based on deep learning to ensure that the model can effectively identify potential abnormal patterns. When training the model, historical data is used for training, and the data of normal and abnormal states are labeled to ensure that the model can learn effective judgment criteria. In actual operation, the operating state of the juicer is monitored in real time, and the real-time data is input into the trained abnormal prediction model. The model will determine whether the current state is abnormal and give the corresponding prediction results. The results of each prediction, including the prediction time, abnormal type and prediction confidence, are recorded for subsequent analysis and evaluation. When the model detects an abnormal state , automatically generate corresponding warning signals, and can design multiple warning levels (such as minor, moderate, and severe) to classify according to the severity of the abnormality. The warning signal can be sent to the user in a variety of ways, such as mobile phone notifications, SMS reminders, or prompts through the juicer's display screen to ensure that users can obtain information in a timely manner. Based on the predicted abnormal state, generate fault prediction information for the juicer, including possible fault types, probability of occurrence, and recommended maintenance measures, etc. The fault prediction information is recorded in the database to ensure the queryability of the data for subsequent maintenance and management. At the same time, the prediction model is continuously optimized based on user feedback to improve the accuracy and reliability of the prediction.

[0033] Step S4: obtaining the current access network status of the juicer; performing data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; In this embodiment, the network module of the juicer needs to be initialized. According to the network access mode of the device (such as Wi-Fi or Ethernet), use the corresponding library or API to set it. For example, for Wi-Fi connection, the Wi-Fi library of the development platform such as Arduino or Raspberry, Pi, etc. can be used to initialize the network interface. Next, by calling the network status query interface provided by the system, check whether the juicer has been successfully connected to the network. This can be verified by sending a simple request (such as HTTP, GET request) to a known server (for example, Google's public DNS server). If the server responds normally, it means that the network connection status is good; if it times out or returns an error, it means that there is a network connection problem. Once it is confirmed that the device is connected to the network, it is necessary to collect relevant network parameters, including IP address, subnet mask, gateway and DNS server address. This information can help with subsequent network status evaluation and troubleshooting. Design a simple data packet test mechanism that can send a "ping" request using the ICMP protocol. This process can be implemented by writing a script (such as Python or C language) to send a data packet to a specific target address (such as a local gateway or an external server). After sending the data packet, record the sending time and receiving time of each data packet. You can set multiple data packets (such as sending 10) for testing to obtain more accurate results. The round-trip time (RTT) of each data packet will be used as an indicator of network latency. In the received replies, the packet loss rate is calculated. If some of the sent data packets fail to receive a response, it can be considered as packet loss. According to the results of the data packet test, the round-trip time and packet loss rate of each tested data packet are counted. The average delay, maximum delay, and minimum delay can be calculated to understand the overall situation of network performance. At the same time, the network performance in different time periods is recorded for trend analysis. Combined with the statistical results, set network quality evaluation indicators, including: Latency: Ideally, it should be lower than a specific threshold (such as 100 milliseconds). Packet loss rate (Packet, Loss): It should be as low as possible. Bandwidth (Bandwidth): It can be measured by download and upload speed test tools (such as iperf). Finally, all collected data is integrated to generate dynamic network status evaluation results. This can be displayed through visualization tools (such as charts or dashboards) to help users intuitively understand the current network status.

[0034] Step S5: uploading the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; In this embodiment, before uploading data, it is first necessary to confirm whether the network connection status of the device is good. This can be done by calling the network status detection interface again to ensure that the device is still online and can access the cloud server. If the network status is not good, a retry mechanism should be set to try to connect again after a few seconds. Design a data upload interface, usually using the HTTP protocol, to send data to the cloud server through a POST request. RESTful, API can be used for data interaction. Set the request header, specify the content type (such as application / json), and send the prepared data as the request body. Determine the API endpoint URL of the cloud server, and perform the data upload operation after building the HTTP request. Send the request and wait for the server's response. If the upload is successful, the server usually returns a status code (such as 200, OK) and related response information. After successfully uploading the data, it can be recorded (such as saving the upload time and status in the device log) for subsequent auditing and monitoring. If the upload fails (such as network interruption, server error, etc.), it should be handled accordingly according to the returned status code. A retry mechanism can be set to try to re-upload the data, and record the error information after multiple failures, triggering a local alarm or notifying the user.

[0035] Step S6: The user checks the fault prediction information based on the juicer cloud server and performs dynamic transfer learning to generate a smart juicer decision strategy; and executes the background communication and fault handling operations of the juicer according to the smart juicer decision strategy.

[0036] In this embodiment, a user-friendly interface is designed on the cloud server so that users can easily view the device status, fault prediction information and historical data. Data visualization tools (such as charts and dashboards) can be used to display key indicators such as network latency, fault probability and fault type. After logging in, the user can view the fault prediction information and early warning signals of the juicer. The user selects a fault handling method by browsing the prediction information and generates a fault handling feedback instruction. Based on the fault prediction and network status, corresponding decision rules are formulated, such as reducing the juicing speed when the network is unstable, or automatically stopping the operation when a fault is detected. Based on the learned information, the operation process of the juicer is optimized to ensure a quick response when a fault occurs. The juicer should periodically send its current status to the cloud server to ensure that the server can monitor the operation of the device in real time. The cloud server can send operating instructions to the juicer based on the fault prediction and the decision strategy set by the user, such as adjusting the juicing parameters or performing self-detection.

[0037] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Acquire the fruit and vegetable images reserved in the juicer; Step S12: performing texture feature enhancement on the reserved fruit and vegetable image to generate a texture enhanced fruit and vegetable image; Step S13: performing visual semantic recognition on the texture enhanced fruit and vegetable images to extract visual features of each fruit and vegetable; Step S14: classifying the types of fruits and vegetables based on the visual features of each fruit and vegetable, thereby generating the type features of fruits and vegetables in the juicer; Step S15: performing adaptive juicing feature analysis on the characteristics of the types of fruits and vegetables in the juicer, thereby generating adaptive juicing features; Step S16: intelligently adjust the juice extraction parameters of the juicer according to the adaptive juice extraction characteristics, and construct an adaptive juice extraction strategy.

[0038] In this embodiment, a suitable environment is selected for image acquisition to ensure that the light is uniform and without shadows, so as to obtain clear images of fruits and vegetables. Natural light or artificial lighting can be selected to avoid strong direct light. The juicer is configured so that it can stably place fruits and vegetables to ensure that the fruits and vegetables do not move during shooting. A fixed bracket can be used to keep the camera stable. A suitable camera device is selected. Usually, a high-definition camera is used to ensure image quality. A digital camera, a mobile phone camera or a dedicated industrial camera can be selected to ensure that the device can capture enough details. Different fruits and vegetables are placed in the juicer, and multiple images are taken by the camera to ensure that images are obtained from different directions and angles so that there is diversity in subsequent processing. The shooting conditions of each image (such as lighting, angle) for reference in subsequent analysis. Store the collected images on a computer or cloud storage, ensuring that the image format is uniform (such as JPEG or PNG). You can use a folder system to classify by fruit and vegetable type for subsequent processing. Generate metadata files to record detailed information about each image, including shooting time, fruit and vegetable type, image quality, etc., for reference in subsequent analysis. Choose appropriate image processing software or programming libraries, such as OpenCV or Pillow, to perform image processing. Using Python for programming can simplify the processing process. Preprocess each image, including resizing, denoising, and color space conversion (such as from RGB to grayscale) for subsequent feature enhancement. You can use Gaussian model Blur to smooth the image and remove small noise points. Apply edge enhancement algorithms (such as Sobel operator or Laplace operator) to highlight the texture features in the image. By calculating the gradient of the image, the edge detection effect is enhanced to make the shape and texture of fruits and vegetables more obvious. Use histogram equalization technology to enhance the contrast of the image, make the brightness distribution of the image more uniform, and improve the visibility of details. Save the processed image as a new file and keep the original image unchanged for subsequent comparison when needed. Make sure to save the image in a high-quality format (such as PNG) to avoid information loss caused by compression. Choose a suitable visual semantic recognition model, such as a convolutional neural network (CNN). You can use a pre-trained model (such as VGG16, ResNet). Perform feature extraction to reduce training time and resources. Input the texture-enhanced fruit and vegetable images into the selected visual recognition model. Extract the feature vectors of the intermediate layer, which can effectively represent the visual information of the image. Use Python's deep learning framework (such as TensorFlow or PyTorch) to implement the feature extraction process, ensure the correctness of model loading and image preprocessing, organize the extracted feature vectors into a structured data format (such as CSV or database) for subsequent analysis and classification, record the relationship between each feature vector and the corresponding fruit and vegetable label, reduce the dimension and visualize the extracted features through visualization methods (such as t-SNE or PCA), and evaluate the effectiveness and distribution of the features.Ensure that different types of fruits and vegetables can be clearly separated in the feature space, select a suitable classification algorithm, such as support vector machine (SVM), random forest or deep learning model (such as fully connected neural network), to classify the extracted features, separate the extracted feature vectors from the corresponding fruit and vegetable type labels, build training sets and test sets, usually 70% as training sets and 30% as test sets, to ensure the randomness and representativeness of the data set, use the training set to train the selected classification model, optimize the model parameters to improve the classification accuracy, record the loss and accuracy of each training for subsequent evaluation, and use the cross-validation method to ensure the generalization ability of the model and avoid over-validation. Fitting: Use the test set to evaluate the classification performance of the model, calculate indicators such as accuracy, recall, and F1 score to understand the effect of the model, adjust and retrain the model based on the evaluation results, ensure the classification accuracy of the model on different types of fruits and vegetables, establish a framework for adaptive feature analysis, define key parameters (such as fruit and vegetable type, hardness, juice content, etc.) and prepare data collection tools to collect data related to the juicing of each fruit and vegetable, including the physical properties of fruits and vegetables (such as weight, hardness, water content), juicing process parameters (such as speed, time), etc., identify the correlation between fruit and vegetable type and juicing effect through data analysis methods (such as regression analysis or cluster analysis), and generate features Analyze the model, and based on the analysis results, generate adaptive juicing features for each fruit and vegetable, including the optimal juicing parameters and expected juice yield. Record the optimal juicing strategy for each fruit and vegetable, form a database for subsequent calls, verify the effectiveness of the adaptive features in the actual juicing process, record the juice yield and quality, evaluate the match with the expected results, adjust the adaptive features based on feedback, improve the juicing effect, design an intelligent parameter adjustment model, formulate parameter adjustment rules based on the adaptive juicing features, determine the impact of key parameters (such as speed, time, pressure, etc.) on the juicing effect, and monitor the operating status and juice yield of the juicer in real time during the juicing process, and establish a data feedback mechanism , Use sensors to collect data and transmit it to the central processing unit. Compare the real-time monitoring data with the adaptive feature model to automatically adjust the parameters of the juicer to optimize the juicing effect. For example, when it is detected that the juice yield of a certain fruit or vegetable is lower than expected, the speed is automatically increased or the juicing time is extended. Automatic parameter adjustment is achieved through a control system (such as a PLC or embedded system) to ensure the flexibility and efficiency of the juicing process. Record the parameter adjustment and final yield of each juicing process, analyze the adjustment effect, evaluate the effectiveness of the intelligent adjustment strategy based on the actual results, and regularly update the adaptive juicing strategy. Based on new experimental data and user feedback, a closed-loop system with continuous optimization is formed. ,

[0039] In this embodiment, the specific steps of step S16 are: Get the juicing instructions preset by the user; Analyzing the juicing instructions preset by the user to obtain a preset juicing mode; Calculating the juice extraction speed parameter according to the adaptive juice extraction characteristics to generate the current juice extraction speed parameter; Performing optimal juicing pressure analysis on the adaptive juicing feature to generate optimal juicing pressure; Intelligent juicing parameters are adjusted according to the preset juicing mode, current juicer speed parameters and optimal juicing pressure to build an adaptive juicing strategy.

[0040] In this embodiment, after the user inputs the juice extraction instruction through the interface, the system should verify the input to ensure that all required items have been filled in and the selection is reasonable. For example, if the user selects a certain fruit or vegetable, the system should check whether the fruit or vegetable is available and use the input verification algorithm to ensure that the data entered by the user conforms to the preset format and range. If there is an error, the system should give a friendly prompt to guide the user to make corrections. The user's preset juice extraction instructions are stored in the system database for subsequent analysis and execution. SQLite or other lightweight databases can be used to manage these instructions, and the timestamp of each instruction is recorded to track the user's operation history, which is convenient for subsequent personalized recommendations and improvements. After the user successfully enters the instruction, the system should provide instant feedback and display the user's settings. A summary of the juicing instructions, which can be achieved through a pop-up window or confirmation page to ensure that the user's instructions have been accurately received, and a variety of juicing modes are defined, such as "fast juicing", "fine juicing", "low-speed juicing", etc. Each mode corresponds to a different speed, pressure and juicing time. According to the type of fruits and vegetables and the user's choice, the specific parameters of each mode are set. These parameters can be obtained through experiments and data analysis to ensure the best juicing effect. The juicing instructions entered by the user are parsed to extract key information, such as the type of fruits and vegetables, juicing concentration and time. Natural language processing (NLP) technology can be used to analyze the user's text input, match the parsed results with the predefined juicing mode, determine the juicing mode that best suits the current user's instructions, and implement the selection. Selection logic, automatically generate preset juicing modes based on the parameters input by the user. This process can be implemented using a decision tree or rule engine to select the appropriate mode according to different conditions, and record the selected mode and its corresponding parameters for reference in the subsequent juicing process. After determining the preset juicing mode, the system should feedback the selected mode and its parameters to the user, which can be displayed on the user interface to ensure user satisfaction and confirmation that the mode meets their expectations. Adaptive juicing features are extracted from the previous image processing and fruit and vegetable feature analysis, such as the hardness, fiber structure and moisture content of fruits and vegetables. These features will directly affect the efficiency and quality of juicing. The extracted features are analyzed using machine learning algorithms (such as support vector machines or decision trees) to evaluate their effects on the quality of the juicing process. The degree of influence on the juicing process, based on the adaptive features, builds a speed parameter calculation model, and can use linear regression, neural network and other methods to predict the optimal speed. The model input is determined as the fruit and vegetable characteristics, and the output is the speed parameter. The model is trained with historical data to improve its prediction accuracy. The extracted adaptive features are input into the model to calculate the current juicer speed parameters to ensure that the speed parameters are within the safety range of the equipment and meet the preset juicing mode requirements. The parameters and results of the calculation process are recorded for subsequent analysis and optimization. The calculated speed parameters are verified to ensure their rationality and effectiveness. The juicing effects at different speeds can be measured experimentally, and comparative analysis can be performed. According to the verification results, if necessary, the calculation model or parameters can be adjusted.In order to ensure the best juicing effect, the response of different fruits and vegetables to pressure during the juicing process is studied, including the compressibility, juice content and fiber structure of fruits and vegetables. This can be achieved through experimental design and data analysis. The juicing effect data of different fruits and vegetables under different pressures are collected, and the relationship between them and juice extraction rate, taste and nutritional components is analyzed. Based on the adaptive juicing characteristics, an optimal juicing pressure parameter model is established. Regression analysis or machine learning algorithms (such as random forests) can be used to predict the optimal pressure. The model input is determined as the fruit and vegetable characteristics and the current speed, and the output is the optimal juicing pressure parameter. The adaptive characteristics and current speed parameters are input into the pressure model to calculate the optimal juicing pressure. This process should ensure that the pressure parameters are within the safety range of the equipment and In accordance with the requirements of the juicing mode, the parameters and results of the calculation process are recorded for subsequent analysis and optimization. In the actual juicing process, the calculated optimal pressure parameters are verified. By comparing the juicing effects under different pressures, the impact on the quality of the juice is analyzed. According to the verification results, the model or parameters are adjusted to ensure the best juicing effect and equipment safety. The user's preset juicing mode, the calculated speed parameters and the optimal juicing pressure are integrated to form a complete juicing parameter set. This process can be achieved through programming to ensure the accuracy and consistency of the parameters. Priorities are set according to the needs of different modes. For example, in the "fast juicing" mode, higher speed and pressure may be required. An intelligent algorithm is designed to dynamically adjust the operation of the juicer according to the integrated parameters. The PID control algorithm or fuzzy control algorithm can be used to achieve real-time parameter adjustment, ensuring that the algorithm can automatically adjust the parameters according to real-time data feedback (such as changes in vegetable characteristics, monitoring data during the juicing process) to optimize the juicing effect. During the juicing process, the operating status of the equipment and the juice extraction effect are monitored in real time, and the speed and pressure parameters are adjusted dynamically. Automatic control is achieved through a control system (such as Arduino or Raspberry, Pi) to ensure the stability of the adjustment process and avoid the decline of juicing effect due to parameter fluctuations. Monitor the results of the juicing process, including the extraction rate, taste and quality of the juice, to ensure that the adjusted parameters can achieve the expected results. Collect user feedback and record the juicing effect and The relationship between the model and user satisfaction is designed to optimize the model and parameter settings in the future. The framework of the adaptive juicing strategy is designed, including data collection, analysis and processing, parameter adjustment and feedback mechanism, to ensure that the various components of the strategy can work together effectively, to determine the goals of the strategy, such as optimizing juicing efficiency, improving juice quality and user satisfaction, etc., to integrate various modules (such as image processing, parameter calculation, intelligent adjustment) to ensure smooth information flow, to use microservice architecture or modular design to improve the flexibility and scalability of the system, to ensure that the system can process user input and monitoring data in real time, and to make intelligent decisions based on the analysis results, to test the adaptive juicing strategy in actual use, and to monitor its performance under different fruit and vegetable types and user instructions.Record the juicing effect and user feedback to evaluate the effectiveness of the strategy. According to the test results, adjust the parameters and algorithms of the strategy to improve its adaptability and stability.

[0041] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Identify the environmental parameters surrounding the juicer; Step S22: analyzing the temperature and humidity change characteristics of the environment parameters around the juicer, thereby generating the environment parameter change characteristics; Step S23: performing an environmental parameter change trend analysis on the environmental parameter change characteristics, thereby generating an environmental parameter change trend curve; Step S24: based on the juice extraction start time point preset by the user, the environmental parameter change trend curve is subjected to the evolution of the environmental trend characteristics at the juice extraction time point, and the environmental trend characteristics at the juice extraction time point are extracted; Step S25: intelligently adjusting the environmental trend impact on the adaptive juicing strategy according to the environmental trend characteristics at the juicing time point, thereby generating an environmental trend juicing strategy; Step S26: Perform juice extraction control simulation based on the environmental trend juice extraction strategy to obtain juice extraction control simulation data.

[0042] In this embodiment, appropriate sensors are selected to measure various parameters of the surrounding environment. Commonly used sensors include temperature sensors (such as DS18B20), humidity sensors (such as DHT22) and air quality sensors (such as MQ series). The sensors are installed near the juicer to ensure that environmental changes can be monitored in real time. The sensors should be able to be connected to the control system (such as Arduino or Raspberry, Pi) to facilitate data collection. The data acquisition system is designed to ensure that the sensors can read environmental parameters regularly and send the data to the central processing unit. The data can be read every 5 seconds or 10 seconds and stored in the database. Data logging software (such as Flask in Python) is used to create Build a simple interface to facilitate real-time viewing and monitoring of environmental parameters, store the collected environmental parameter data in a database (such as SQLite or MongoDB), ensure data structure, facilitate subsequent query and analysis, record the timestamp of each data collection, ensure the timing and integrity of the data for reference in subsequent analysis, create a preliminary data visualization interface, and use a chart library (such as Matplotlib or Plotly) to display real-time temperature and humidity changes. This process helps to quickly identify abnormal situations and ensure the normal operation of the system. Preprocess the environmental parameter data stored in the database, including removing outliers and filling missing values. You can use the Pandas library for data cleaning to ensure data integrity. The quality and accuracy of the data are improved. The data are sorted in time series to ensure that the temperature and humidity data at each time point can correspond. The changing characteristics of temperature and humidity are extracted through statistical analysis methods. For example, the mean, standard deviation and change rate of temperature and humidity are calculated to help understand their changing trends. The sliding window technology is used to analyze short-term characteristics that change over time and identify possible patterns, such as seasonal changes or day and night changes. The extracted changing characteristics are saved in a new data table to ensure their traceability with the original data. The calculation method and time period of each feature are recorded for subsequent analysis. A feature analysis report is generated to show the changing characteristics of temperature and humidity and their potential impact on the juicing process. Visualization tools (such as Seaborn) are used. or Matplotlib) to display the changing characteristics of temperature and humidity, help the team and users understand the changing rules of these parameters, display the trend of feature changes through charts, provide intuitive basis for subsequent analysis and decision-making, select appropriate trend analysis methods, such as linear regression, moving average method or exponential smoothing method, which can help identify long-term trends and short-term fluctuations, use StatsModels or SciPy library in Python to implement trend analysis, ensure the scientificity and accuracy of the analysis process, calculate the trend of temperature and humidity change characteristics, and identify their long-term change trends. For example, use linear regression model to fit temperature and humidity data, calculate the slope and intercept of trend line, and record the results of trend analysis.Include the trend type (rising, falling or stable) and change rate of each parameter, generate a trend curve based on the trend analysis results, use visualization tools to draw a time series graph of temperature and humidity to show its changing trend, mark obvious change points and trend turning points in the graph for subsequent analysis and discussion, extract the juicing start time point from the user's preset juicing instructions, and ensure that it is consistent with the timestamp of the environmental parameter data. This process can be achieved by querying the user instruction records in the database to ensure that the format of the time point is consistent, such as unifying it into ISO, 8601 format, to facilitate subsequent processing, and set a certain time window before and after the juicing start time point (such as 30 minutes or 1 hour) to analyze the changing characteristics of the environmental parameters within this time period. , extract the temperature, humidity and other related parameters within the time window from the environmental parameter change trend curve, analyze the environmental parameter change characteristics within the set time window, extract the characteristic data of the juicing time point, including the highest value, lowest value, mean value and change rate of the temperature, etc., ensure that the extracted characteristics can reflect the environmental changes within the time period, so as to carry out subsequent analysis, record the environmental trend characteristics of the juicing time point into the database, ensure that the data is structured and queryable, record the calculation method and time period of each feature for subsequent analysis, and feedback the extracted environmental trend characteristics to the user to ensure that he understands the impact of the current environment on the juicing process, analyze the environmental trend characteristics of the juicing time point, and identify its possible impact on the juicing process, for example, high Temperature may cause increased evaporation of water in fruits and vegetables, and high humidity may affect the juicing effect. Use rule engines or decision tree models to automatically generate adjustment suggestions based on the extracted features, design intelligent adjustment logic, and automatically adjust the parameters in the juicing strategy based on environmental trend characteristics, such as speed, pressure, and juicing time. Ensure that the logic can be dynamically adjusted according to real-time data feedback to adapt to environmental changes. During the juicing process, adjust the speed and pressure parameters in real time according to the analysis results to ensure the optimal juicing effect. PID control algorithms can be used to achieve automatic adjustment, record the parameter changes and corresponding environmental characteristics of each adjustment for subsequent analysis and optimization, monitor the results of the juicing process, and ensure that the adjusted strategy can effectively improve the quality and efficiency of juicing. Record Juicing effect and user feedback, evaluate the effect of intelligent adjustment, feed back the adjustment results to the user, ensure that he understands the current state of the juicing process and the reasons for the adjustment, use computer simulation software (such as MATLAB or Simulink) to build a simulation environment for the juicing control system, ensure that the simulation environment can truly reflect the actual situation, including equipment parameters and environmental factors, set the same parameters and algorithms as the real system in the simulation environment for effective comparison, input the parameters of the adaptive juicing strategy into the simulation system, including speed, pressure, juicing time and environmental trend characteristics, ensure that the format and range of the input data meet the requirements of the simulation system to avoid errors in the simulation process, run the simulation system, and observe each stage of the juicing process.Record the efficiency, quality and other relevant data of juice extraction. You can set up multiple operation scenarios to test different environmental conditions and parameter settings, monitor key indicators in the simulation process, such as juice extraction rate, juice quality and equipment response, etc., to ensure the effectiveness of the simulation results.

[0043] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing multi-time period juicing effect evolution on the juicing control simulation data to obtain juicing effect evolution data in multiple time periods; Step S32: Calculate the key index of juicing for each time period based on the juicing effect evolution data of multiple time periods, and extract the key index of juicing for each time period; Step S33: predicting the juicing effect situation of the key juicing indicators in each time period, thereby generating the juicing effect situation in the future time period; Step S34: predicting abnormal conditions of the juice extraction effect in the future period, thereby generating abnormal juice extraction state prediction data; Step S35: calculating the abnormal state time point of the abnormal juice extraction state prediction data, and marking the abnormal state prediction time point; Step S36: Generate a juicer abnormal warning signal and juicer abnormal failure prediction information based on the abnormal state prediction time point.

[0044] In this embodiment, the juice extraction control data generated during the simulation process is sorted by time period to ensure that the data of each time period contains relevant juice extraction parameters (such as rotation speed, pressure, temperature, etc.) and corresponding juice extraction effects (such as juice output, quality, etc.). Data cleaning technology is used to remove outliers and missing values ​​to ensure the integrity and accuracy of the data. The Pandas library in Python is used for operation. According to the total length of the simulation time, the data is divided into multiple time periods (such as every hour, every minute, or each stage of the juice extraction process) to ensure that the amount of data in each time period is sufficient for effective analysis. A unified time label is set for each time period for subsequent comparison and analysis. For each time period, the change in the juice extraction effect is calculated, including Including key indicators such as juice yield, taste rating, and nutritional ingredients, use statistical analysis methods (such as mean, variance, etc.) to summarize the juicing effect of each time period, record the evolution of each time period, and store the juicing effect evolution data of each time period in the database to ensure that the data is structured and queryable. Use visualization tools (such as Matplotlib or Seaborn) to draw a chart of the juicing effect over time to help the team intuitively understand the evolution trend of the juicing effect, determine the key indicators of the juicing effect, including but not limited to juice yield, juicing efficiency, nutritional content, taste score and juice color, etc. These indicators will be used for subsequent effect analysis and prediction, formulate the calculation formula for each key indicator, and ensure that each indicator The calculation method is clear and consistent. The evolution data of the juicing effect in each time period are analyzed one by one to calculate its key indicators. Python scripts can be used to automate this process and improve efficiency. For example, when calculating the juice output in each time period, it is combined with parameters such as juicing time and rotation speed to obtain the corresponding juicing efficiency. The key indicator results of each time period are recorded in a new data table to ensure the traceability and structure of the data, which is convenient for subsequent analysis and query. A summary report is generated to describe the key indicators and their changes in each time period, which provides a basis for subsequent situation forecasting. Appropriate forecasting models are selected, such as time series analysis (ARIMA model), machine learning methods (such as random forest or LSTM neural network Network), etc., in order to predict the future juicing effect situation based on historical data, ensure that the model can capture the time dependence and nonlinear characteristics in the data, improve the accuracy of the prediction, use the historical juicing key indicator data to train the selected model, and perform cross-validation to evaluate the performance of the model. The SciKit-Learn library can be used to train and verify the model, adjust the model parameters (such as learning rate, number of trees, etc.) to optimize the prediction effect, apply the trained model to the key indicator data of each time period, and predict the juicing effect situation in the future period. The prediction results should include future juice production, quality and other key indicators, and record the predicted confidence interval to evaluate the reliability of the prediction, select a suitable anomaly detection method,Such as statistical methods (such as Z-score method), machine learning methods (such as isolation forest algorithm), etc., which can effectively identify abnormal patterns in data, ensure that the selected method can process time series data, and has high efficiency and accuracy, apply the anomaly detection model to the predicted juicing effect situation data, identify potential abnormal conditions, for example, monitor the sudden decrease in juice production or quality, record the occurrence time, duration and impact of each abnormal condition for subsequent analysis, analyze the detected abnormal conditions, evaluate their impact on the overall juicing effect, record relevant data, generate abnormal condition reports, describe the characteristics of each abnormal condition and its possible causes , help the team understand potential risks, extract the timestamps of all abnormal events from the abnormal state prediction data, identify the specific time point when each abnormal state occurs, use conditional screening methods to extract the time period marked as abnormal from the prediction data and record the time when it occurs, add tags to each identified abnormal time point to ensure that these time points are clearly visible in the data set, and use new columns or labels to mark abnormal states, record relevant information for each abnormal time point, such as abnormal type, duration, and impact, to ensure the integrity of subsequent analysis, store the marked abnormal time point data in the database, ensure the structure and queryability of the data, and record detailed information for each time point. For subsequent analysis and response, a summary report of abnormal state time points is generated, describing the characteristics of each time point and its possible impact, providing support for subsequent decision-making, designing a monitoring mechanism, real-time monitoring of marked abnormal time points, ensuring that potential problems can be discovered and responded to in a timely manner, and formulating emergency plans to ensure that appropriate measures can be taken quickly when abnormal states occur to reduce the impact on the juicing process. According to the marked abnormal state time points, an early warning signal mechanism is designed. When the system detects an abnormal time point, it automatically generates a corresponding early warning signal. The early warning signal may include sound alarms, visual prompts (such as flashing lights) or notifications sent through mobile phone applications. Based on the abnormal state time points and related data, a juicer fault alarm is generated. Prediction information, you can use machine learning models (such as decision trees or support vector machines) to predict possible fault types and probabilities, record each predicted fault information, including fault type, probability of occurrence, and recommended maintenance measures, store the generated warning signals and fault prediction information in the database, ensure the structure and queryability of the data, record the sending time and response status of each warning signal, so as to evaluate the effect and subsequent optimization, implement a real-time monitoring mechanism, continuously track the operating status of the juicer, ensure that abnormal situations can be discovered and responded to in a timely manner, regularly evaluate the effectiveness of warning signals and the accuracy of fault predictions, and continuously optimize the monitoring and response mechanism based on feedback to ensure the stability and reliability of the system.

[0045] In this embodiment, step S4 includes the following steps: Step S41: obtaining the current network access status of the juicer; Step S42: performing data packet test transmission on the access network status to generate transmission test parameters; Step S43: Calculate the network real-time bandwidth according to the transmission test parameters to generate the current network real-time bandwidth parameters; Step S44: performing network load analysis on the transmission test parameters, thereby generating real-time network load characteristics of the juicer; Step S45: Perform dynamic network status evaluation on the current network real-time bandwidth parameters and the juicer real-time network load characteristics, thereby generating a dynamic network status evaluation result.

[0046] In this embodiment, ensure that the network interface (such as Wi-Fi or Ethernet) of the juicer is correctly configured, and use an embedded system (such as Raspberry, Pi or Arduino) to manage the network connection, ensure that the device can connect to the available network, install the necessary network drivers on the device so that it can identify and connect to the surrounding network, use a network monitoring tool (such as Ping command or network status detection library) to check the network connection status of the juicer, confirm the stability of the connection by sending test data packets to other devices in the network (such as routers or servers), record the response time and packet loss rate of the network connection to evaluate the health of the network, and obtain the current access network information, including IP address, subnet mask, etc. code, gateway, and DNS server, etc. This information can be obtained through system commands (such as ifconfig or ip, addr). The obtained network status information is stored in the device's memory or sent to a remote server for subsequent analysis and monitoring. The network status information is recorded in the database, ensuring that the timestamp and related information of the access status are saved for subsequent query and analysis. A user interface is provided to display summary information of the current network access status, including connection status, IP address, and signal strength, etc., to help users understand the network status of the device. A network programming library (such as Python's socket library) is used to generate test data packets, and a reasonable packet size and content are designed to simulate real data transmission conditions. Different Test packet sizes (e.g. 64 bytes, 128 bytes, 512 bytes, etc.) to analyze network performance under different loads. Send test packets to the target address (e.g. router or other network device) and record the sending and receiving time of each packet. You can use UDP or TCP protocol for testing. The specific choice depends on the network characteristics that need to be evaluated. Use multiple sending methods to ensure that the results of each test packet are statistically significant to obtain a more accurate evaluation. Organize the transmission parameters recorded for each test, including the number of packets sent, the number of packets successfully received, the packet loss rate, the average round-trip time (RTT) and the maximum delay, etc. Store these parameters in the database to ensure that subsequent analysis can be easily performed to determine the bandwidth calculation. Calculation formula, usually used formula: Bandwidth =, amount of data successfully transmitted / transmission time, calculate the bandwidth according to the data volume and time information in the transmission test results, extract the amount of data successfully sent and the corresponding transmission time from the previous transmission test results, ensure the accuracy of the data, so as to calculate the real bandwidth value, calculate the bandwidth of each test, and record the results of each test for subsequent analysis, store the calculated real-time bandwidth value in the database for monitoring and query, ensure that the recorded bandwidth value includes a timestamp, so as to perform time series analysis, set thresholds, mark abnormal bandwidth changes, such as when the bandwidth is lower than a critical value, trigger an alarm, use visualization tools to draw the current network real-time bandwidth change curve,Help users to intuitively understand the fluctuation of network bandwidth, provide a user interface, display the summary information of real-time bandwidth, including current bandwidth, historical highest and lowest bandwidth, etc., help users monitor network status, determine the definition of network load, including the number of concurrent connections, data transmission rate, network delay and packet loss rate, etc., formulate the calculation method of each indicator to ensure the accuracy of analysis, record the changes of network load, especially the performance during peak and off-peak hours, extract relevant data from transmission test parameters, including packet loss rate, delay and bandwidth of each test, as the basic data for load analysis, combine with historical data, analyze the load performance of the network in different time periods, identify high-load periods and low-load periods, use statistical analysis methods (such as variance analysis) to analyze the collected load data, identify the key characteristics and patterns of network load, and record the results of network load analysis, including load The average, maximum value and change trend, etc., are used for subsequent evaluation. The key indicators for dynamic network status evaluation are determined, including real-time bandwidth, load characteristics, delay, packet loss rate, etc., to ensure that each indicator has a clear calculation method in the evaluation, and set reasonable thresholds to facilitate the judgment of the quality of the network status. For example, the normal range of bandwidth and load is set based on historical data, and the real-time bandwidth parameters and network load characteristic data are integrated to form a comprehensive network status evaluation data set. The timestamps of the data are ensured to be consistent for effective comparison and analysis, and the current value of each indicator and its changes are recorded for subsequent evaluation. A weighted scoring system is used to evaluate each indicator, and the score is calculated according to the set threshold. For example, the weights of bandwidth and load can be set and adjusted according to the actual situation to generate a comprehensive evaluation result, determine whether the network status is normal, warning or abnormal, and record the evaluation results and timestamps.

[0047] In this embodiment, the specific steps of step S44 are: Calculate the data transmission rate for the transmission test parameters to obtain the real-time data transmission rate; Performing data transmission delay analysis based on the real-time data transmission rate to generate data transmission delay parameters; Calculate an initial transmission data packet based on the transmission test parameters; Detect the number of data packets finally received; Based on the number of data packets finally received and the number of data packets initially sent, packet loss rate statistics are performed to generate a real-time transmission packet loss rate; The network load analysis is performed on the real-time data transmission rate, data transmission delay parameters and real-time transmission packet loss rate to generate the real-time network load characteristics of the juicer.

[0048] In this embodiment, when performing a data packet test, the size of each data packet sent (for example, assuming that each packet is 128 bytes) and the time taken for the transmission to be completed are recorded. The sending and receiving time of each packet can be recorded using a timestamp. The total size of the test data packet is generated, usually in bytes. Transmission rate, (bps) =, total data size, (bytes) / transmission time, (seconds), and the obtained rate is converted into a more readable unit, such as kilobits per second (Kbps) or megabits per second (Mbps). The calculated real-time transmission rate is stored in a database for subsequent analysis. The timestamp and calculation method are recorded to ensure the traceability of the data. A real-time monitoring interface is implemented to display the current transmission rate to help users intuitively understand network performance. Use visualization tools (such as Grafana or Matplotlib) to draw a curve of the change in real-time transmission rate to facilitate the identification of fluctuations and trends in network performance. Generate a report based on real-time data, describing the current transmission rate and its impact on the performance of the juicer, and provide it to the user for reference. Define data transmission delay, which usually includes round-trip time (RTT) and one-way delay. Round trip time refers to the time it takes for a data packet to be sent, received and returned, while one-way latency refers to the time it takes for a data packet to be sent and received. Collect the send and receive timestamps recorded in the data packet test to calculate latency. One-way latency is obtained by calculating the time it takes from the data packet being sent to being received. Latency, (ms) = receive time − send time. For round trip time (RTT), the round trip latency of two identical data packets can be calculated and averaged. The calculated latency parameters are stored in the database, including indicators such as average latency, maximum latency, and minimum latency. Generate a latency report to analyze the changing trend of the current latency and historical data to provide a basis for subsequent evaluation. Before conducting a data transmission test, set the number of packets to be initially sent. For example, you can choose to send 100 packets for testing. Determine the size of each packet (such as 128 bytes) and calculate the total amount of data. During the test, use a network programming library (such as Python's socket library) to send a set number of packets. Record the sending status of each packet to ensure the integrity of the sending process. Count whether there are any errors or unsuccessfully sent packets during the sending process. Record the number of packets initially sent and their size in a database, including detailed information about each packet, such as the time it was sent, the size of the data, etc. Generate a packet sending report, recording the time period and number of packets sent for subsequent analysis. Use a network programming library (such as Python's socket library) on the receiving end to monitor the number of packets received. Record the reception time and status of each packet. For each successfully received packet, update the counter to ensure that the number of packets received can be accurately counted. During the reception test, record the identification of any lost packets (if any). Lost packets can be identified by comparing the sent and received packets.Count the number of packets received at the end and compare it with the number initially sent. Record the number of packets received at the end in the database and associate it with the number initially sent for subsequent analysis. Generate a reception result report that describes the reception time period and the number of packets successfully received to help users understand network performance. Provide a user interface to display the number of packets successfully received and the packet loss situation to help users intuitively understand the transmission effect. Use visualization tools to display the changes in received packets to help identify problems in the transmission process. Packet loss rate = (initial number of packets sent − number of packets received) / initial number of packets sent × 100%. Extract the number of packets initially sent and the number of packets finally received from the database to ensure data accuracy. Apply these data to the packet loss rate calculation formula to obtain the real-time packet loss rate. Record the calculated packet loss rate in the database to ensure data structure and queryability. Generate a packet loss rate analysis report that describes the current packet loss rate and its impact on network performance to help users understand network stability. Determine the comprehensive indicators for network load analysis, including real-time data transmission rate, data transmission delay parameters, and real-time transmission packet loss rate. Formulate the weight of each indicator to facilitate subsequent comprehensive evaluation. Set reasonable thresholds to facilitate judgment of whether the network load is normal or not. Integrate the data of real-time data transmission rate, data transmission delay and packet loss rate to form a comprehensive network load analysis data set. Use a weighted scoring system to evaluate each indicator and generate a comprehensive load score based on the set threshold and weight. Based on the comprehensive scoring results, generate real-time network load characteristics, including load status (normal, warning or abnormal) and load change trends. Record each evaluation result to make it time-series for subsequent analysis and comparison.

[0049] In this embodiment, the specific steps of step S5 are: The dynamic network status evaluation result includes a general network status, a good network status and a good network status; when the dynamic network status evaluation result is a general network status, an abnormal warning signal of the juicer is uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the key information of the abnormal fault prediction information of the juicer is analyzed to extract the key part fault prediction information; the key abnormal fault prediction information and the abnormal warning signal of the juicer are uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the juicer abnormal warning signal and the juicer abnormal fault prediction information are uploaded to the juicer cloud server.

[0050] In this embodiment, key indicators for evaluating the network status are determined, including real-time data transmission rate, data transmission delay, and packet loss rate. These indicators can effectively reflect the health of the network, and a normal threshold range for each indicator is set. For example, when the data transmission rate is lower than a certain value, it can be regarded as "the network status is general", and when the packet loss rate is higher than a certain ratio, it can be regarded as "the network status is good". The current values ​​of these indicators are obtained from the monitoring system in real time. The latest data required for network status evaluation can be obtained by API call or database query. The obtained real-time data is compared with the set threshold to determine the category of the current network status. According to the comparison result, the current network status is determined and recorded as the corresponding category. For example: if the data transmission rate is lower than a certain value, the network status is normal, and the packet loss rate is higher than a certain ratio, the network status is good. If the rate is normal but the packet loss rate is high, it is evaluated as "normal network status". If all indicators are within the normal range, it is evaluated as "good network status". The evaluation results are stored in the system for subsequent operations and records. The dynamic network status evaluation results are recorded in the database to ensure the traceability of the data, including the evaluation time and specific status information. An evaluation report is generated to provide the user or management system with a summary of the current network status for easy monitoring and management. When the system detects that the network status is normal, it immediately generates an abnormal warning signal for the juicer. The signal contains detailed information about the current network status, such as connection quality, delay, and packet loss rate. The abnormal warning signal is uploaded to the juicer cloud server through the background communication mechanism, and can be used for HT TP,POST request sends data to the server-side API interface to ensure timely data transmission and record the results of the upload operation for subsequent audit and troubleshooting. When the system detects that the network status is good, it performs key information analysis of the abnormal fault prediction information of the juicer. This step involves analyzing the prediction data and extracting the most critical information for fault handling. The specific operation can use natural language processing technology to identify the fault type, possible impact and recommended treatment measures to ensure that the extracted information can effectively guide subsequent fault handling. The extracted key abnormal fault prediction information is uploaded to the juicer cloud server together with the abnormal warning signal of the juicer to ensure the integrity and accuracy of the data. The same HTTP,POST is used. Request, but the data body contains two parts of information. It also records the results of the upload operation to ensure that the information processing process can be traced. When the network status is good, the system will upload all the abnormal warning signals and abnormal fault prediction information of the juicer to the juicer cloud server. At this time, the stability of data transmission is relatively high, which can ensure the integrity of the information. Use HTTP, POST request to send all relevant data (including warning signals and fault prediction information) to the cloud server to ensure that the server can receive it in time and perform corresponding processing. Record the detailed information of this upload operation, including upload time, data content and server response, etc., for subsequent analysis and optimization. The system can set up a monitoring mechanism to track the data processing status after upload in real time.Ensure that all information is received and stored correctly.

[0051] In this embodiment, the specific steps of step S6 are: Step S61: The user checks the fault prediction information based on the juicer cloud server and performs feedback operation to generate a user feedback instruction; Step S62: Perform personalized fault handling evolution on the user feedback instruction to generate a personalized fault handling habit for the user; Step S63: Dynamically transfer learning of the user's personalized fault handling habits to generate a smart juicer decision strategy; Step S64: executing the background communication and fault handling operations of the juicer according to the intelligent juicer decision strategy.

[0052] In this embodiment, the fault prediction information collected from the juicer is displayed in real time on the cloud interface to ensure the timeliness of the data. The probability and impact of the fault are displayed using data visualization technology (such as charts or dashboards). Feedback options are provided to allow users to evaluate and provide feedback on the fault prediction information, such as "confirm", "disconfirm" or "need more information". When the user submits the feedback, the system will generate corresponding user feedback instructions based on the user's selection. These instructions may include the need for further fault handling, requesting more information or confirming the fault status. The user feedback instructions are sent to the management system of the juicer using an API for subsequent processing. The user's feedback instructions and related information are recorded in the database to ensure the traceability of the information and the integrity of the historical records. The frequency and type of user feedback are analyzed to identify potential improvement points. The user's feedback instructions are analyzed to identify common fault handling requirements and preferences. Natural language processing (NLP) technology can be used to classify and mark the instructions. The feedback instructions of each user are recorded and classified according to their processing methods, such as "restart the device", "check the connection", etc. Based on the analysis results, personalized fault handling habits are generated for each user. Machine learning algorithms (such as K-means clustering) can be used to identify user behavior patterns and preferences. Personalized habits are recorded in the user's profile to ensure that the system can perform corresponding fault handling according to these habits. Design a mechanism to regularly update the user's personalized fault handling habits to reflect the user's latest behavior and feedback. A time window (such as monthly or quarterly) can be set to review and update habits. Collect new feedback data and compare it with old habits to identify changes and trends. Select a suitable transfer learning model (such as a deep neural network for transfer learning) to analyze the user's personalized fault handling habits. The model should be able to process user behavior data and extract effective features. Ensure that the model can transfer knowledge between different users to cope with new users or new fault situations. Collect users' personalized fault handling habit data and input it into the transfer learning model as a training set. You can use historical user feedback, fault handling results, and time information. Train the model and adjust the model parameters to optimize the learning effect. Use cross-validation to evaluate the performance of the model to ensure its generalization ability. Generate a decision strategy for the smart juicer based on the trained model. This strategy should include processing steps and suggestions for different fault types to ensure that user needs can be responded to quickly. Record the generation process of each decision strategy for subsequent analysis and optimization. Evaluate the generated decision strategy and analyze its effect in actual fault handling to ensure that it meets user needs and fault handling efficiency. Update the decision strategy regularly, combine the latest user feedback and fault data, and improve the responsiveness of the smart juicer. Design the background communication mechanism of the juicer to ensure that the device can interact with the cloud server in real time. You can use MQTT or HTTP protocol for communication to ensure the reliability and timeliness of data transmission.Ensure that the communication mechanism can handle the receipt and execution of fault handling instructions. Perform the corresponding fault handling tasks according to the intelligent decision-making strategy. For example, when a fault is detected, automatically restart the device, send an alarm, or instruct the user on how to handle it manually. Record the execution of each fault handling task, including execution time, success or failure, and user feedback, for subsequent analysis and improvement. Implement a real-time monitoring mechanism to track the operating status of the juicer and the effect of fault handling to ensure that new problems can be discovered and responded to in a timely manner. Provide real-time feedback information to users to inform them of the progress and results of fault handling to enhance the user experience.

[0053] In this embodiment, a background communication system is provided, which is used to execute the background communication method for the juice extractor system as described above, including: A juicing parameter adjustment module is used to obtain the fruit and vegetable images reserved in the juicer; perform adaptive juicing feature analysis on the reserved fruit and vegetable images, and perform intelligent juicing parameter adjustment to construct an adaptive juicing strategy; The juice extraction control simulation module is used to identify the environmental parameters around the juice extractor; according to the environmental parameters around the juice extractor, an adaptive juice extraction strategy is implemented to intelligently adjust the environmental trend influence, and juice extraction control simulation is performed to obtain juice extraction control simulation data; The juicing effect evolution module is used to perform multi-period juicing effect evolution on the juicing control simulation data, and to predict abnormal states, thereby generating abnormal warning signals for the juicer and abnormal fault prediction information for the juicer; The network status evaluation module is used to obtain the current access network status of the juicer; perform data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; A cloud information uploading module is used to upload the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; The dynamic transfer learning module is used for users to view fault prediction information based on the juicer cloud server and perform dynamic transfer learning to generate an intelligent juicer decision strategy; the juicer's background communication and fault handling operations are executed according to the intelligent juicer decision strategy.

[0054] The present invention acquires images of fruits and vegetables, and the module can analyze the characteristics of fruits and vegetables (such as type, size, hardness, etc.) based on image recognition technology, so as to obtain suitable juicing parameters. This ensures that the juicer is optimized and adjusted according to different types of fruits and vegetables. The juicing parameters (such as rotation speed, pressure, etc.) are adaptively adjusted according to the characteristics of fruits and vegetables, which can make the juicing process more efficient and maximize the extraction of juice while avoiding waste. The intelligent adjustment of juicing parameters not only adapts to different types of fruits and vegetables, but can also be customized according to user needs to provide personalized juicing solutions, improve user experience, monitor and identify environmental parameters around the juicer (such as temperature, humidity, etc.) in real time, and adjust the juicing strategy based on these environmental parameters. In a high temperature environment, the juicing temperature may need to be adjusted or time to avoid overheating of fruits and vegetables. By conducting juicing control simulation, the juicing effect in different environments can be predicted to ensure that the juicer always maintains the best working state under various environmental conditions. The juicing strategy can be adjusted in real time according to the simulation results to ensure that the juicer can operate efficiently and stably. No matter how the external environment changes, multiple time periods in the juicing process are monitored and the changes in juicing effect in different time periods are analyzed. This helps to discover possible performance degradation or failure risks in advance. Through the evolution analysis of simulation data, the system can predict abnormal conditions and generate fault warning signals. The system can take preventive measures before a failure occurs to reduce the occurrence of unexpected failures. It can identify possible fault types in advance and generate corresponding fault prediction information, thereby helping Help users deal with potential problems in a timely manner, improve the stability and reliability of the juicer, detect the network connection status of the juicer in real time, and evaluate the quality of data transmission. If the network quality is poor, it may cause data upload delays or losses. Through data packet testing and dynamic evaluation, the system can determine whether the current network status is suitable for large data transmission or real-time fault handling. If the network status is poor, the system can choose to upload important information first to ensure the stability of data transmission. By evaluating the network status in real time, the system can adjust the data transmission strategy when the network is unstable to ensure that key data (such as fault warning signals) can be uploaded in time, and information will not be lost or delayed due to network problems. According to the network status evaluation results, the system can dynamically adjust data upload Strategy, when the network is not good, it will give priority to uploading important abnormal signals and prediction information to ensure the timely upload of key data. Regardless of the network status, the system can ensure the upload of abnormal warning signals and fault prediction information. Users can obtain equipment status and fault information in a timely manner through the cloud platform. By uploading information to the cloud, users and maintenance personnel can remotely monitor the operating status of the juicer and grasp the health status of the equipment in a timely manner, so as to make timely processing. The user's operation feedback and historical data can help the system learn and generate personalized decision-making strategies. The fault handling method and processing speed fed back by the user will affect the learning process of the system, so that the juicer can be optimized according to the preferences of different users. Through transfer learning, the system can not only learn the user's operating habits,It can also transfer the learned knowledge to other devices or scenarios and optimize decision-making strategies. This dynamic learning ability helps the juicer to continuously adapt to new usage environments and needs. Based on intelligent decision-making strategies, the system can automatically adjust the background communication frequency and content to ensure real-time data transmission and fault handling. When a fault occurs, the system can automatically select the best fault handling method to reduce user intervention and improve the intelligence level of the device.

[0055] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0056] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A background communication method for a juicer system, characterized in that: The following steps are involved: Step S1: obtaining fruit and vegetable images reserved in the juicer; performing adaptive juicing feature analysis on the reserved fruit and vegetable images, and performing intelligent juicing parameter adjustment to construct an adaptive juicing strategy; Step S2: Identify the environmental parameters of the juicer; perform an adaptive juicing strategy according to the environmental parameters of the juicer to intelligently adjust the impact of environmental trends, perform juicing control simulation, and obtain juicing control simulation data; Step S3: performing multi-period juicing effect evolution on the juicing control simulation data, and performing abnormal state prediction, generating an abnormal warning signal of the juicer and abnormal fault prediction information of the juicer; Step S4: obtaining the current access network status of the juicer; performing data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; Step S5: uploading the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; Step S6: The user checks the fault prediction information based on the juicer cloud server and performs dynamic transfer learning to generate a smart juicer decision strategy; The background communication and fault handling of the juicer are performed according to the intelligent juicer decision strategy.

2. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: Acquire the fruit and vegetable images reserved in the juicer; Step S12: performing texture feature enhancement on the reserved fruit and vegetable image to generate a texture enhanced fruit and vegetable image; Step S13: performing visual semantic recognition on the texture enhanced fruit and vegetable images to extract visual features of each fruit and vegetable; Step S14: classifying the types of fruits and vegetables based on the visual features of each fruit and vegetable, thereby generating the type features of fruits and vegetables in the juicer; Step S15: performing adaptive juicing feature analysis on the characteristics of the types of fruits and vegetables in the juicer, thereby generating adaptive juicing features; Step S16: intelligently adjust the juice extraction parameters of the juicer according to the adaptive juice extraction characteristics, and construct an adaptive juice extraction strategy.

3. The background communication method for a juicer system according to claim 2, characterized in that: The specific steps of step S16 are: Get the juicing instructions preset by the user; Analyzing the juicing instructions preset by the user to obtain a preset juicing mode; Calculating the juice extraction speed parameter according to the adaptive juice extraction characteristics to generate the current juice extraction speed parameter; Performing optimal juicing pressure analysis on the adaptive juicing feature to generate optimal juicing pressure; Intelligent juicing parameters are adjusted according to the preset juicing mode, current juicer speed parameters and optimal juicing pressure to build an adaptive juicing strategy.

4. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: Identify the environmental parameters surrounding the juicer; Step S22: Analyze the temperature and humidity change characteristics of the environment parameters around the juicer to generate the environment parameter change characteristics; Step S23: performing an environmental change trend analysis on the environmental parameter change characteristics, thereby generating an environmental parameter change trend curve; Step S24: based on the juice extraction start time point preset by the user, the environmental parameter change trend curve is subjected to the evolution of the environmental trend characteristics at the juice extraction time point, and the environmental trend characteristics at the juice extraction time point are extracted; Step S25: intelligently adjusting the environmental trend impact on the adaptive juicing strategy according to the environmental trend characteristics at the juicing time point, thereby generating an environmental trend juicing strategy; Step S26: Perform juice extraction control simulation based on the environmental trend juice extraction strategy to obtain juice extraction control simulation data.

5. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: performing multi-time period juicing effect evolution on the juicing control simulation data to obtain juicing effect evolution data in multiple time periods; Step S32: Calculate the key index of juicing for each time period based on the juicing effect evolution data of multiple time periods, and extract the key index of juicing for each time period; Step S33: predicting the juicing effect situation of the key juicing indicators in each time period, thereby generating the juicing effect situation in the future time period; Step S34: predicting abnormal conditions of the juice extraction effect in the future period, thereby generating abnormal juice extraction state prediction data; Step S35: calculating the abnormal state time point of the abnormal juice extraction state prediction data, and marking the abnormal state prediction time point; Step S36: Generate a juicer abnormal warning signal and juicer abnormal failure prediction information based on the abnormal state prediction time point.

6. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: obtaining the current network access status of the juicer; Step S42: performing data packet test transmission on the access network status to generate transmission test parameters; Step S43: Calculate the network real-time bandwidth according to the transmission test parameters to generate the current network real-time bandwidth parameters; Step S44: performing network load analysis on the transmission test parameters, thereby generating real-time network load characteristics of the juicer; Step S45: Perform dynamic network status evaluation on the current network real-time bandwidth parameters and the juicer real-time network load characteristics, thereby generating a dynamic network status evaluation result.

7. The background communication method for a juicer system according to claim 6, characterized in that: The specific steps of step S44 are: Calculate the data transmission rate for the transmission test parameters to obtain the real-time data transmission rate; Performing data transmission delay analysis based on the real-time data transmission rate to generate data transmission delay parameters; Calculate an initial transmission data packet based on the transmission test parameters; Detect the number of data packets finally received; Based on the number of data packets finally received and the number of data packets initially sent, packet loss rate statistics are performed to generate a real-time transmission packet loss rate; The network load analysis is performed on the real-time data transmission rate, data transmission delay parameters and real-time transmission packet loss rate to generate the real-time network load characteristics of the juicer.

8. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S5 are: The dynamic network status evaluation result includes a general network status, a good network status and a good network status; when the dynamic network status evaluation result is a general network status, an abnormal warning signal of the juicer is uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the key information of the abnormal fault prediction information of the juicer is analyzed to extract the key part fault prediction information; the key abnormal fault prediction information and the abnormal warning signal of the juicer are uploaded to the juicer cloud server; When the dynamic network status evaluation result shows that the network status is good, the juicer abnormal warning signal and the juicer abnormal fault prediction information are uploaded to the juicer cloud server.

9. The background communication method for a juicer system according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: The user checks the fault prediction information based on the juicer cloud server and performs feedback operation to generate a user feedback instruction; Step S62: Perform personalized fault handling evolution on the user feedback instruction to generate a personalized fault handling habit for the user; Step S63: Dynamically transfer learning of the user's personalized fault handling habits to generate a smart juicer decision strategy; Step S64: Execute the background communication and fault handling operations of the juicer according to the intelligent juicer decision strategy.

10. A background communication system, characterized in that: The method for executing the background communication method for a juice extractor system according to claim 1 comprises: A juicing parameter adjustment module is used to obtain the fruit and vegetable images reserved in the juicer; perform adaptive juicing feature analysis on the reserved fruit and vegetable images, and perform intelligent juicing parameter adjustment to construct an adaptive juicing strategy; The juice extraction control simulation module is used to identify the environmental parameters around the juice extractor; according to the environmental parameters around the juice extractor, an adaptive juice extraction strategy is implemented to intelligently adjust the environmental trend influence, and juice extraction control simulation is performed to obtain juice extraction control simulation data; The juicing effect evolution module is used to perform multi-period juicing effect evolution on the juicing control simulation data, and to predict abnormal states, thereby generating abnormal warning signals for the juicer and abnormal fault prediction information for the juicer; The network status evaluation module is used to obtain the current access network status of the juicer; perform data packet test transmission and dynamic network status evaluation on the access network status, thereby generating a dynamic network status evaluation result; A cloud information uploading module is used to upload the abnormal warning signal of the juicer and the abnormal fault prediction information of the juicer to the juicer cloud server based on the dynamic network status evaluation result; The dynamic transfer learning module is used for users to view fault prediction information based on the juicer cloud server and perform dynamic transfer learning to generate an intelligent juicer decision strategy; the juicer's background communication and fault handling operations are executed according to the intelligent juicer decision strategy.

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