Information management method for agricultural machinery digital management platform

Abstract

The invention relates to the technical field of information management, in particular to an information management method for an agricultural machinery digital management platform. The method comprises the following steps: carrying out mechanical equipment operation log extraction through an agricultural machinery digital management platform, and carrying out collaborative operation network dynamic fluctuation analysis of different equipment nodes to obtain collaborative operation network dynamic fluctuation data; time sequence fluctuation topological link analysis and link extreme value event probability calculation are carried out on the dynamic fluctuation data of the collaborative operation network, then network packet loss degree evaluation is carried out, continuous forward error correction is carried out on time sequence fluctuation topological link data, and topological link packet loss continuous error correction data are obtained; constructing a link sensitive node redundancy state adjustment model according to the topological link packet loss continuity error correction data to obtain the link sensitive node redundancy state adjustment model; according to the invention, the information management technology is optimized, so that the information management technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to an information management method for a digital management platform of agricultural machinery. Background Art

[0002] The information management method of the digital management platform of agricultural machinery aims to optimize agricultural production efficiency, improve equipment utilization and ensure the accuracy of farmland operations. In modern agriculture, the improvement of mechanization has made information management an inevitable trend. By integrating multiple sensors, Internet of Things devices and real-time data processing technology, this method realizes comprehensive monitoring and fine management of agricultural machinery. Agricultural mechanization has brought a leap in productivity, but it is also accompanied by an increase in the complexity of equipment management. Traditional manual records and empirical judgments can no longer meet the needs of modern agriculture. Therefore, there is an urgent need for an innovative information management method that can collect, analyze and feedback information such as mechanical operation status, operation quality and environmental parameters in real time. The emergence of this method not only helps to reduce mechanical failure rate and maintenance costs, but also optimizes farmland operation strategies and improves operation efficiency and output through data-driven decision support. At the same time, this method can also predict the service life and performance trend of machinery through historical data analysis, thereby achieving more scientific equipment scheduling and updating, and ultimately bringing significant economic benefits and sustainable development potential to agricultural production. However, a traditional information management method for the digital management platform of agricultural machinery has the problems of inaccurate network fluctuation analysis in the collaborative processing of mechanical equipment and weak adjustment ability. Summary of the invention

[0003] Based on this, it is necessary to provide an information management method for a digital management platform for agricultural machinery to solve at least one of the above technical problems.

[0004] To achieve the above object, an information management method for a digital management platform of agricultural machinery is provided, the method comprising the following steps: Step S1: extracting the operation log of mechanical equipment through the digital management platform of agricultural machinery to obtain the operation log data set of agricultural machinery equipment; performing dynamic fluctuation analysis of the collaborative operation network of different equipment nodes on the operation log data set of agricultural machinery equipment to obtain dynamic fluctuation data of the collaborative operation network; Step S2: Performing a timing fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain timing fluctuation topology link data; performing link extreme value event probability calculation on the timing fluctuation topology link data to obtain link extreme value event probability data; Step S3: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data to obtain network packet loss degree data; perform continuous forward error correction on the timing fluctuation topology link data based on the network packet loss degree data to obtain topology link packet loss continuity error correction data; Step S4: construct a link-sensitive node redundant state adjustment model based on the topological link packet loss continuity error correction data to obtain the link-sensitive node redundant state adjustment model; send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

[0005] The mechanical equipment operation log data set extracted by the agricultural machinery digital management platform of the present invention is the basis for analyzing the performance and cooperation of agricultural machinery. The operation log usually includes information such as the start and stop time, operation status, load condition and fault record of the equipment. These data can reflect the use and workload of the equipment in detail. By analyzing these log data, the working mode and efficiency of different equipment in collaborative operation and the interactive relationship between them can be identified. Specifically, analyzing the dynamic fluctuations of the collaborative operation network can reveal the cooperation mode between equipment and how they jointly cope with changes in the operation process. For example, some equipment shows high collaborative efficiency under specific operating conditions, while load fluctuations or efficiency decreases in other cases. Through the dynamic fluctuation analysis of the collaborative operation network, these fluctuation patterns can be identified, the stability of each equipment can be evaluated, potential efficiency bottlenecks can be found, and a basis for optimizing the operation strategy can be further provided. This analysis can help farm managers better understand the performance of equipment under different operating conditions, optimize resource allocation, improve operating efficiency, and reduce the probability of failure. Time series fluctuation topology link analysis is a process of in-depth mining of equipment operation data. By performing time series analysis on the dynamic fluctuation data of the collaborative operation network, a fluctuation relationship diagram between equipment in the time dimension can be constructed. This kind of graph can show how different devices affect each other's operating status over time, including load fluctuations, changes in working cycles, etc. Time series fluctuation topology link analysis not only reveals the fluctuation associations between devices, but also helps identify the fluctuation characteristics and laws of links at specific time points. Link extreme event probability calculation further improves the accuracy of the analysis. By calculating the probability of extreme events on the time series fluctuation topology link data, the probability of extreme fluctuation events occurring on each link within a given time range can be determined. These extreme events are caused by equipment failure, abnormal operation, or changes in the external environment. Understanding the probability of these extreme events can help predict potential risks in equipment operation in advance and take preventive measures. For example, if a link often experiences extreme fluctuations under certain operating conditions, managers can conduct special inspections or adjust operation strategies for these links to avoid potential system failures or efficiency declines. In addition, through the probability analysis of extreme events, maintenance plans can be optimized, equipment reliability and operation continuity can be improved, thereby improving the efficiency and safety of overall agricultural production. By evaluating the network packet loss degree of the dynamic fluctuation data of the collaborative operation network, the reliability of data transmission during the collaboration process can be quantified. Network packet loss refers to the loss of some data packets during data transmission, which can result in incomplete information or communication interruption. Evaluating the degree of network packet loss helps to understand the stability of network communication and identify which links or nodes have more serious packet loss. Quantifying these packet loss situations can provide a basis for subsequent error correction.Based on the network packet loss degree data, the continuity of the timing fluctuation topological link data forward error correction is a key step to ensure the integrity of information transmission. Forward error correction is a data encoding technology that enables the receiver to detect and repair lost data packets by adding redundant information to the data. Specifically, the use of forward error correction technology can automatically detect and correct packet loss during data transmission, thereby avoiding the negative impact of data loss on job collaboration. This error correction mechanism improves the robustness of network transmission and reduces the reduction in job efficiency or equipment operation errors caused by data loss. In addition, forward error correction technology can also reduce the need for manual intervention, allowing the system to handle data transmission problems more automatically and efficiently, thereby improving the overall job stability and reliability. A link-sensitive node redundant state adjustment model is constructed based on the topological link packet loss continuity error correction data. This model aims to optimize the link and node configuration in the network to improve the overall stability and redundancy of the network. The link-sensitive node redundant state adjustment model determines which nodes or links need to be increased by analyzing the link packet loss situation to prevent the overall system performance from being degraded due to single point failures or link packet loss. The model can intelligently adjust the redundant configuration of the link to ensure that the system can quickly switch to the backup link when packet loss or link failure occurs, thereby maintaining the continuity and stability of the system. Sending this model to the digital management platform of agricultural machinery can realize automated information management and real-time adjustment. The platform can automatically perform link redundancy configuration and state adjustment according to the suggestions of the model to ensure the stability of network communication. This process not only reduces the need for manual intervention, but also improves the response speed and adaptability of the system. Finally, the link sensitive node redundant state adjustment model can significantly improve the resilience and reliability of the agricultural machinery system in the face of network fluctuations or failures, thereby ensuring the efficient conduct of agricultural operations. This automated adjustment and optimization mechanism provides strong support for agricultural digital management and improves the overall performance and management efficiency of the system. Therefore, the present invention is an optimization process for a traditional information management method for a digital management platform of agricultural machinery, which solves the problems of inaccurate network fluctuation analysis and weak adjustment ability in the collaborative processing of mechanical equipment in a traditional information management method for a digital management platform of agricultural machinery, improves the accuracy of network fluctuation analysis in the collaborative processing of mechanical equipment, and strengthens the adjustment ability between devices.

[0006] Preferably, step S1 comprises the following steps: Step S11: extracting the operation log of mechanical equipment through the agricultural machinery digital management platform to obtain an agricultural machinery equipment operation log data set; Step S12: extracting equipment collaborative operation process from the agricultural machinery equipment operation log data set to obtain equipment collaborative operation process data; Step S13: classifying the collaborative operation log data between different equipment nodes on the agricultural machinery equipment operation log data set according to the equipment collaborative operation process data to obtain collaborative operation log classification data; Step S14: Perform collaborative operation network dynamic fluctuation analysis on different device nodes on the collaborative operation log classification data to obtain collaborative operation network dynamic fluctuation data.

[0007] The present invention extracts the operation log data set of mechanical equipment through the agricultural machinery digital management platform as the basis for analysis. The operation log usually includes information such as the start and stop time of the equipment, the operating status, the load condition, the fault record and the maintenance history. These data provide a detailed record of the performance and usage of the equipment in different operating environments. Extracting these data helps to fully understand the operating status of the equipment, including its workload and operating cycle, and lays a solid foundation for subsequent analysis. This process ensures that the acquired data is accurate and comprehensive, so that subsequent analysis can be based on detailed data, thereby improving the analysis accuracy of the entire system and the reliability of decision support. It aims to extract the collaborative operation process between each device from the equipment operation log. This process includes how the equipment collaborates in different operation stages, such as synchronous startup, parallel operation and alternating operation. This extraction can reveal the collaborative mode and operation dependency between devices. Understanding these collaborative processes can help identify the role and function of the equipment in collaborative operation, and how they affect each other. This is crucial for optimizing the operation process and improving the operation efficiency. By clarifying the specific process of collaborative operation, better coordination strategies can be formulated to reduce conflicts and incoordination in the operation and improve the stability and efficiency of the overall operation. The equipment operation logs are classified according to the collaborative operation process to more clearly analyze the collaboration between different equipment nodes. Through classification, the log data can be archived according to the collaborative role and operation stage of the equipment to form a classified data set. This classification makes the subsequent analysis more targeted and systematic, and helps to identify the performance characteristics and problem points under different collaborative modes. For example, some equipment shows high efficiency in a certain type of collaborative mode, but performs poorly in other modes. Through classified data, these modes can be deeply analyzed to find potential optimization points and improve the overall effect of collaborative operation. Dynamic fluctuation analysis of collaborative operation network, using classified collaborative operation log data, analyzes the dynamic fluctuation of the collaborative operation network between equipment. Through this analysis, the laws and trends of performance fluctuations of equipment during the collaborative process can be revealed. Understanding these dynamic fluctuation data helps to identify bottlenecks and unstable factors in the system. For example, the collaborative fluctuations between equipment are caused by load changes, equipment failures, or inconsistent operations. By analyzing these fluctuations, the scheduling and operation strategies of equipment can be optimized, the stability and efficiency of collaborative operations can be improved, and the overall benefits of agricultural production can be improved.

[0008] Preferably, step S2 comprises the following steps: Step S21: performing time sequence fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain time sequence fluctuation topology link data; Step S22: performing fluctuation chain pattern recognition on the dynamic fluctuation data of the collaborative operation network according to the time-series fluctuation topology link data to obtain fluctuation chain pattern data; Step S23: performing long-range dependency characteristic analysis on the fluctuation linkage pattern data to obtain linkage pattern long-range dependency characteristic data; Step S24: Calculate the link extreme value event probability of the time series fluctuation topology link data according to the long-range dependency characteristic data of the chain mode to obtain the link extreme value event probability data.

[0009] The present invention can construct the topological link data of time-series fluctuations by analyzing the dynamic fluctuation data of the collaborative operation network. The key to this step is to map the fluctuation information of different devices in the network into a time series topological structure, thereby revealing the dynamic relationship and interaction between the devices. Through this analysis, the fluctuation pattern of the device in time and the changes in the link that affect it can be clearly identified. This analysis can reveal how the fluctuations are transmitted between devices through different topological structures during the operation process, and provide a visual basis for understanding and optimizing the collaborative relationship between devices. This process helps to discover potential link bottlenecks or unstable factors and provide basic data for subsequent optimization. By performing fluctuation chain pattern recognition on the time-series fluctuation topological link data, the pattern of fluctuation transmission between different devices can be determined. This pattern recognition can reveal how the fluctuations propagate between devices and their patterns during the collaborative operation process. For example, the fluctuations of some devices will trigger a chain reaction of other devices, thereby forming a specific fluctuation pattern. Identifying these patterns can help understand the fluctuation propagation mechanism and its scope of influence in the system. This analysis provides an important basis for optimizing equipment scheduling and reducing the negative impact of fluctuation transmission, and can be targeted for system optimization to improve the stability and efficiency of the operation. In-depth study of the long-range dependence characteristics of the fluctuation chain pattern. Through this analysis, we can understand how the fluctuation patterns between devices affect other devices over a long period of time. These long-range dependency characteristics reveal the time continuity of fluctuation transmission and its long-term impact. For example, the fluctuation of one device will affect the operating status of other devices for a long time, and this impact will produce a lag effect in the overall performance of the equipment system. Understanding these characteristics can help identify potential long-term risks and optimize job scheduling strategies to reduce system instability caused by long-term fluctuations, thereby improving the efficiency and stability of the overall operation. By analyzing the long-range dependency characteristic data of the chain pattern, the probability of link extreme events can be calculated. Link extreme events refer to abnormal fluctuations or performance problems that occur in links under extreme conditions. By calculating the probability of these extreme events, the stability and reliability of the network under extreme operating conditions can be evaluated. This analysis helps to predict and identify system bottlenecks and potential failure points. Understanding the probability of extreme events can provide data support for the development of emergency plans and maintenance strategies to improve the robustness of the system and ensure good operating performance when facing extreme conditions. This analysis improves the ability to predict network performance and helps to achieve more efficient and reliable agricultural machinery operation management.

[0010] Preferably, step S23 includes the following steps: Step S231: performing time series fluctuation breakpoint detection on the fluctuation chain mode data to obtain time series fluctuation breakpoint data; Step S232: performing fluctuation breakpoint distribution period analysis according to the time series fluctuation breakpoint data to obtain fluctuation breakpoint distribution period data; Step S233: Calculating the fractal dimension of the fluctuation breakpoints in different distribution periods on the fluctuation breakpoint distribution period data to obtain the fractal dimension data of the fluctuation breakpoints; Step S234: performing spatial interpolation processing on the fluctuation breakpoint distribution period data based on the fluctuation breakpoint fractal dimension data to obtain fluctuation breakpoint spatial interpolation data; Step S235: performing long-range dependency characteristic analysis based on the fluctuation breakpoint fractal dimension data and the fluctuation breakpoint space interpolation data to obtain the linkage pattern long-range dependency characteristic data.

[0011] The time series fluctuation breakpoint detection of the present invention is intended to identify key breakpoints in the fluctuation chain pattern data, that is, those time points that mark significant changes in the fluctuation pattern. This step can identify abnormal fluctuations in the system by analyzing abnormal points or sharp changes in the time series data. This detection can reveal the key turning points in the fluctuation pattern, which helps to understand the changes in the operating state of the equipment in a specific time period. Accurately identifying these breakpoints can provide a basis for further analysis of the causes and effects of fluctuations, help understand the root causes and impact range of fluctuations, and thus formulate more effective management and optimization strategies to improve the stability and reliability of the system. Fluctuation breakpoint distribution period analysis, by performing period analysis on the time series fluctuation breakpoint data, the distribution of breakpoints in time can be understood. The beneficial effect of this step is to reveal the period pattern of the occurrence of fluctuation breakpoints, such as whether they are concentrated in a specific time period or have a certain periodicity. This analysis can help identify the fluctuation rules of the system in certain time periods, and then understand whether these time periods are associated with equipment load, environmental changes or operating modes. This period analysis helps to optimize equipment scheduling and maintenance strategies, reduce the impact of fluctuations on the system, and improve overall operating efficiency. Fluctuation breakpoint fractal dimension calculation is intended to measure the complexity of the distribution of fluctuation breakpoints. Fractal dimension is a mathematical tool used to describe the complexity of data distribution. It can reveal the distribution law and complexity of fluctuation breakpoints in different time periods. By calculating the fractal dimension of different distribution periods, we can understand the regularity and self-similarity of breakpoints in time series. This calculation helps to identify the complexity of fluctuation patterns and their long-term trends, and provides quantitative data support for subsequent analysis. This analysis can help identify the existence of long-term fluctuation trends or laws, thereby providing a scientific basis for system optimization and early warning mechanisms. By performing spatial interpolation processing on the fluctuation breakpoint fractal dimension data, the spatial distribution data of fluctuation breakpoints can be generated. The beneficial effect of this step is to expand the fluctuation breakpoint data in the time series to the spatial dimension and form a fluctuation distribution map in space. Spatial interpolation processing provides a more comprehensive view of spatial distribution by constructing a spatial model to fill the gaps between data points. This processing can reveal the spatial distribution pattern of fluctuation breakpoints, help understand the relationship between different device nodes and the fluctuation transmission, and provide data support for optimizing device layout and collaboration strategies. Long-range dependency characteristic analysis combines the fluctuation breakpoint fractal dimension data and spatial interpolation data for in-depth long-range dependency analysis. The core of this analysis is to understand the long-term impact of fluctuation breakpoints and their interrelationships in different time and space dimensions. By combining these data, it is possible to reveal the long-term dependencies of fluctuation patterns, such as whether certain fluctuations will affect the overall performance of the system over a longer period of time. Understanding these long-term dependency characteristics can help identify potential long-term risks and system bottlenecks, and provide important information for optimizing operation strategies and formulating long-term maintenance plans, thereby improving the stability and efficiency of the system.

[0012] Preferably, step S24 includes the following steps: Step S241: performing nonlinear dependency structure analysis on the long-range dependency characteristic data of the chain mode to obtain nonlinear long-range dependency structure data; Step S242: performing a multi-scale critical sensitivity assessment on the linkage mode long-range dependency characteristic data according to the nonlinear long-range dependency structure data to obtain multi-scale dependency critical sensitivity data; Step S243: performing link dependency critical sensitive state matching on the timing fluctuation topology link data according to the multi-scale dependency critical sensitive data to obtain link dependency critical sensitive state data; Step S244: performing a link fluctuation local minimization calculation on the link-dependent critical sensitive state data based on a convex optimization algorithm to obtain link fluctuation local minimization data; Step S245: Calculate the probability of link extreme value events according to the link fluctuation local minimization data to obtain link extreme value event probability data.

[0013] The nonlinear dependency structure analysis of the present invention aims to identify and model the nonlinear relationship in the long-range dependency characteristic data of the chain mode. This step can reveal the complex fluctuation pattern and potential interactions in the system by deeply analyzing the nonlinear dependency relationship in the data. The beneficial effect of this analysis is to provide a more accurate description of the complexity of the data and help understand the long-term impact of nonlinear factors in the fluctuation pattern on the system. For example, the nonlinear dependency structure analysis can identify the key driving factors and feedback mechanisms in the fluctuation pattern, which helps to more accurately predict the behavior and response characteristics of the system. This analysis provides basic data support for subsequent multi-scale critical sensitivity assessment and optimization calculation, and can effectively improve the accuracy of system management and optimization. According to the nonlinear long-range dependency structure data, the multi-scale critical sensitivity assessment of the long-range dependency characteristic data of the chain mode is performed. The key to this assessment is to analyze the sensitivity of the data from different scales, revealing the response and stability of the system to fluctuations at different time and space scales. This analysis can help identify the critical points and sensitive areas of the system at different scales, and understand the vulnerability of the system in different scenarios. Through multi-scale assessment, the performance of the system in the face of fluctuations of different scales can be better understood, and a basis for formulating targeted adjustment and optimization measures can be provided. This process helps to improve the robustness of the system and reduce risks in extreme situations. Based on multi-scale dependent critical sensitive data, link dependency state matching analysis is performed. This step can identify critical sensitive states in the link, i.e., those link states that are very sensitive to fluctuations, by combining the sensitivity assessment results with the timing fluctuation topology link data. This matching analysis helps to identify the parts of the link that are most susceptible to fluctuations and reveal possible risk points. Understanding these critical states can help formulate effective management measures, optimize system design, and reduce failures or performance degradation on key links. This process provides a substantial basis for link optimization and risk control. Link dependency critical sensitive state data is used to perform local minimization calculations on link fluctuations. The beneficial effect of this step is to minimize local fluctuations in the link through the optimization algorithm, thereby reducing fluctuations and interference in the system. The convex optimization algorithm can effectively solve the optimization problems existing in the link and find the optimal fluctuation minimization solution. This calculation can help reduce the fluctuation amplitude in the system and improve the stability and overall performance of the link. Through local minimization calculations, more sophisticated fluctuation control can be achieved, resource allocation can be optimized, and the overall efficiency of the system can be improved. Link extreme value event probability calculation, based on link fluctuation local minimization data, calculates the probability of extreme value events occurring in the link under extreme conditions. The core of this step is to evaluate the stability and reliability of the link when facing extreme fluctuations. By calculating the probability of extreme events, the performance of the link under extreme conditions can be predicted and potential risk points can be identified. This analysis can help formulate emergency plans and risk control measures to ensure the stable operation of the system under extreme fluctuations.Understanding the probability of link extreme value events provides a scientific basis for the formulation of maintenance and optimization strategies, and improves the resilience and reliability of the system.

[0014] Preferably, step S3 comprises the following steps: Step S31: performing extreme value peak distribution feature analysis on the link extreme value event probability data to obtain extreme value peak distribution feature data; Step S32: performing conditional triggering influence simulation on the link extreme value event probability data according to the extreme value peak distribution characteristic data to obtain conditional triggering influence simulation data; Step S33: evaluating the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the conditional triggering impact simulation data to obtain network packet loss degree data; Step S34: Based on the network packet loss degree data, continuous forward error correction is performed on the timing fluctuation topology link data to obtain topology link packet loss continuity error correction data.

[0015] The present invention aims to deeply understand the spike characteristics in the probability data of link extreme value events. The extreme value spike distribution feature analysis reveals the frequency and intensity of extreme events by identifying and quantifying the spike pattern in the data. The beneficial effect of this analysis is that it can provide specific statistical characteristics of the occurrence of extreme value events, such as the frequency, amplitude and distribution law of spikes. Understanding these characteristics helps to identify bottlenecks or weak links in the system and predict the performance of the system under extreme conditions. This analysis can provide data support for designing more effective early warning mechanisms and risk control measures, and improve the system's adaptability and response strategies to extreme fluctuations. Based on the extreme value spike distribution feature data, the link extreme value events are simulated and analyzed. This step simulates the triggering events under different conditions and evaluates the impact of these conditions on the link extreme value events. This simulation helps to understand the changes in link extreme value events under different environmental or operating conditions. By analyzing the simulation data, the factors that have the greatest impact on the link extreme value events, such as load changes, network traffic fluctuations, etc., can be identified. Understanding these influencing factors can help formulate targeted optimization and adjustment strategies, thereby improving the stability and reliability of the system under complex conditions. The network packet loss degree of the collaborative operation network dynamic fluctuation data is evaluated based on the conditional trigger impact simulation data. This assessment can reveal the degree and distribution of packet loss under different conditions by analyzing the packet loss of the network under different trigger conditions. The beneficial effect of network packet loss assessment is to help understand the stability of the network under dynamic fluctuations and identify areas or time periods with high packet loss. This assessment can provide data support for network optimization and improvement, such as adjusting network configuration, increasing bandwidth or improving network protocols, thereby improving the overall reliability of the network and the quality of data transmission. Error correction is performed on the timing fluctuation topology link data based on the network packet loss degree data. Forward error correction is a technology that corrects errors by adding redundant information to data transmission, and can recover lost data when packet loss occurs. This step analyzes the network packet loss degree data and implements error correction strategies to reduce the impact of data loss on the system. The beneficial effect of continuous forward error correction is to improve the reliability and integrity of data transmission, ensuring that the system can maintain normal operation even when network conditions are unstable or packet loss occurs. This error correction mechanism helps to reduce data loss, improve system stability and overall performance, and ensure the efficiency and reliability of network and data transmission.

[0016] Preferably, step S33 includes the following steps: Step S331: Performing network jitter analysis on the collaborative operation network dynamic fluctuation data according to the conditional triggering influence simulation data to obtain conditional influence network jitter data; Step S332: performing frequency domain conversion on the conditional network jitter data to obtain conditional network jitter frequency domain data; Step S333: performing phase misalignment analysis according to the conditional influencing network jitter frequency domain data to obtain network jitter misalignment frequency domain data; Step S334: performing structured random process induction on the network jitter misalignment frequency domain data to obtain jitter misalignment structured random process induction data; Step S335: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the jitter dislocation structured random process induction data to obtain network packet loss degree data.

[0017] The present invention performs network jitter analysis on the dynamic fluctuation data of the collaborative operation network according to the conditional trigger influence simulation data to obtain the conditional influence network jitter data. The main goal of this analysis is to identify and quantify the jitter phenomenon caused by dynamic fluctuations in the network. Network jitter refers to the delay change of data packets during the transmission process, and its volatility affects the overall performance of the network and the stability of data transmission. By analyzing the network jitter under the influence of conditional triggering, the pattern and severity of jitter under specific conditions can be revealed, helping to identify performance bottlenecks in the system. Understanding these jitter characteristics can provide data support for network optimization, reduce the degradation of service quality caused by jitter, and improve network reliability and user experience. The conditional influence network jitter data is converted into frequency domain to obtain conditional influence network jitter frequency domain data. Frequency domain conversion can more clearly analyze the frequency components of the data by converting time domain data into frequency domain data. The beneficial effect of this conversion is that it can reveal the frequency characteristics and periodic patterns in network jitter, and help identify the main frequency components of jitter and their changing laws. Through frequency domain data, network managers can more easily identify and analyze periodic jitter or burst jitter, thus providing a scientific basis for targeted optimization measures. Frequency domain conversion not only helps to improve the understanding of jitter phenomena, but also provides basic data for subsequent phase misalignment analysis. Phase misalignment analysis is based on conditional network jitter frequency domain data to analyze the phase misalignment of network jitter. The key to this step is to identify and analyze the phase difference of the jitter signal and understand the phase misalignment between different frequency components. Phase misalignment analysis helps to reveal the phase inconsistency in network jitter, which leads to instability and reduced reliability of data transmission. By deeply analyzing the phase misalignment, synchronization problems or signal interference sources in the system can be identified, so that measures can be taken to improve network performance. This analysis result helps to understand the specific impact of jitter on data transmission and provide more precise targets for network optimization. Perform structured random process induction on network jitter misalignment frequency domain data. This step obtains structured random process data of jitter misalignment by inducing and summarizing the structured patterns in the misalignment frequency domain data. The structured random process induction helps to identify the randomness and regularity of network jitter, thus providing a deep understanding of network jitter behavior. Through this induction, the long-term trend and statistical characteristics of jitter misalignment can be revealed, helping to predict and model future network jitter behavior. This process helps to form a more systematic jitter analysis framework, provide support for network optimization and fault prediction, and improve the scientificity and effectiveness of network management. Based on the jitter misalignment structured random process induction data, the dynamic fluctuation data of the collaborative operation network is evaluated for the degree of network packet loss. The degree of network packet loss is evaluated by comprehensively considering the structured random process data of jitter misalignment. This evaluation can provide the network packet loss situation under different fluctuation conditions, revealing the severity and distribution pattern of packet loss.Understanding the degree of network packet loss helps identify key problem areas in the network and guides the formulation of network optimization strategies. Through this process, the impact of data loss on system performance can be effectively reduced, network reliability and data transmission integrity can be improved, and the stable operation of the collaborative network in high-load or dynamic environments can be ensured.

[0018] Preferably, performing structured random process induction on network jitter misalignment frequency domain data includes: Perform multi-dimensional collaborative matrix deconstruction on the jitter misalignment frequency domain data to obtain a jitter misalignment collaborative deconstruction matrix; Perform multi-dimensional cross-correlation analysis on the jitter misalignment frequency domain data according to the jitter misalignment collaborative deconstruction matrix to obtain multi-dimensional cross-correlation jitter data; Performing coherence layering iteration on the multi-dimensional mutual correlation jitter data to obtain multi-dimensional jitter coherence layering data; The structured random process is summarized according to the multi-dimensional jitter coherence layered data, and the jitter misalignment structured random process summary data is obtained.

[0019] The present invention can intuitively observe and analyze the interaction and synergistic relationship between network jitter in different frequency dimensions through the synergistic matrix. This helps to identify the potential patterns and complex interactive effects of jitter, so as to more comprehensively understand the behavior of network jitter. Deconstructing frequency domain data into a matrix form helps to compress and organize data and reduce the complexity of processing and analysis. In this way, high-dimensional data can be converted into a matrix form that is easy to analyze, making the subsequent analysis process more efficient and organized. The synergistic matrix provides a structured analysis method, which helps to understand the structural characteristics of network jitter from a macro level and provide basic data support for subsequent cross-correlation analysis. Multidimensional cross-correlation analysis can reveal the correlation between different frequency domain dimensions and help identify the relevant patterns and dependencies in jitter data. This is very important for understanding the causes and influencing factors of jitter. Analyzing the cross-correlation of multiple dimensions can discover complementary information in the data, which is ignored in the analysis of a single dimension. Multidimensional cross-correlation analysis helps to build a more comprehensive jitter model and improve the ability to interpret data. By identifying the correlation in frequency domain data, valuable information can be provided for network optimization, helping to discover and solve the problem of synergy in the network and improve the overall performance and stability of the network. Coherence hierarchical iteration helps to analyze the coherence of network jitter in a hierarchical manner, from the global to the local level. This hierarchical analysis method can provide a multi-level perspective to help identify different impact levels and patterns of jitter. By gradually refining the data through the iterative process, it is possible to more accurately capture the subtle changes and complex features in jitter, and improve the ability to understand and interpret jitter phenomena. Multi-dimensional jitter coherence hierarchical data helps to identify potential problems and optimization points in the network, provide a scientific basis for network optimization and adjustment strategies, and improve the stability and performance of the system. Through structured random process induction, complex jitter misalignment data can be transformed into a structured model, making the randomness and regularity of the data more systematic and easy to understand. This induction method helps to extract the main features and trends in the data. The structured random process model can help predict future jitter behavior and provide predictive guidance for network management. This is of great significance for formulating long-term network optimization strategies and fault prediction. Through structured induction, the key factors and impact patterns of jitter misalignment can be identified, providing a basis for network performance improvement and problem solving. Structured random process data helps to design more accurate and effective optimization schemes and enhance the stability and reliability of the network.

[0020] Preferably, step S34 includes the following steps: Step S341: performing packet loss degree link matching on the timing fluctuation topology link data based on the network packet loss degree data to obtain packet loss degree link matching data; Step S342: performing convolution coding processing of different link networks on the packet loss degree link matching data to obtain packet loss degree link convolution coding data; Step S343: performing multi-path retransmission request control of different link networks on the packet loss degree link matching data according to the packet loss degree link convolution coding data, and obtaining link network multi-path retransmission request control data; Step S344: Perform forward error correction on the timing fluctuation topology link data according to the link network multipath retransmission request control data to obtain topology link packet loss continuity error correction data.

[0021] The key of the present invention is to identify the severity of packet loss on different links by link matching the network packet loss degree data. This step establishes packet loss degree link matching data by analyzing the packet loss of different links in the network. Its main advantage is that it can accurately identify which links have more serious packet loss and provide detailed basic data for subsequent processing steps. This link matching data helps the network management system to make more accurate decisions, optimize the network transmission strategy in a targeted manner, and reduce the impact of packet loss. Through this precise matching, network administrators can identify which links need to be focused on or dynamically adjusted, thereby improving the stability and transmission efficiency of the overall network. The packet loss degree link matching data is processed by convolution coding, and this process extracts important features by applying convolutional neural network (CNN) technology to different link networks. The beneficial effect of convolution coding processing is that it can effectively convert the packet loss of the link into more expressive feature data. These feature data better capture the complexity and dynamic changes of the link, so that the network can better identify and predict the state changes of the link. In this way, the network management system can obtain more refined link data, providing more reliable input for subsequent multi-path retransmission request control. This processing method can significantly improve the accuracy of link state prediction, thereby optimizing the retransmission strategy and data transmission efficiency. The multipath retransmission request of the link network is controlled based on the convolutional coding data. This step can determine which links need multipath retransmission according to the packet loss degree of the link and the encoded characteristic data. Through this control, the data transmission problem caused by the packet loss of a single path can be effectively reduced, and the reliability and stability of data transmission can be improved. Multipath retransmission can not only alleviate the impact of a single link failure, but also improve the fault tolerance of the network by utilizing the redundancy of multiple paths. The beneficial effect of this step is to optimize the path selection of data transmission, reduce the data loss caused by link packet loss, and thus improve the transmission efficiency and reliability of the overall network. The multipath retransmission request control data of the link network is used to perform continuous forward error correction on the timing fluctuation topology link data. The key to this process is to use forward error correction technology (FEC) to repair errors that occur during data transmission, especially continuous data packet loss. Forward error correction technology adds redundant information to the data packet, so that the receiving end can restore the original data in the event of packet loss or data corruption. The beneficial effect of this step is that it significantly improves the reliability of data transmission, and can ensure the integrity and continuity of data even when packet loss occurs on the link. Through continuous forward error correction, the network can better handle errors in data transmission, thereby reducing the frequency and delay of data retransmission, and improving the overall quality and efficiency of data transmission.

[0022] Preferably, step S4 comprises the following steps: Step S41: adjusting the link-sensitive node redundancy state according to the topology link packet loss continuity error correction data to obtain link-sensitive node redundancy state adjustment data; Step S42: constructing a link-sensitive node redundant state adjustment model for the link-sensitive node redundant state adjustment data using a random forest algorithm to obtain a link-sensitive node redundant state adjustment model; Step S43: Send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

[0023] The present invention can effectively enhance the stability of the network by analyzing the error correction data of link packet loss and adjusting the redundant state of link-sensitive nodes. Link-sensitive nodes are usually key nodes in the network that have a greater impact on performance. Adjusting the redundant state of these nodes can improve the fault recovery capability of the network and reduce the impact caused by link problems. The redundant state adjustment of link-sensitive nodes can help optimize the allocation of network resources. By dynamically adjusting the redundant state, the network can more reasonably configure redundant resources according to actual conditions, avoid waste of resources, and improve the performance of the overall network. Redundant state adjustment can reduce the impact of link packet loss on data transmission. The adjusted link can better handle packet loss and improve the reliability and integrity of data transmission, especially in the case of large data flow. The effect is particularly significant. The random forest algorithm is an integrated learning method that improves the accuracy and stability of prediction by constructing multiple decision trees. When modeling the redundant state of link-sensitive nodes, the random forest can comprehensively consider multiple factors, improve the model's ability to predict changes in link state, and enhance the network's adaptability. Link redundant state adjustment involves complex network data and states, and the random forest algorithm can process high-dimensional data and capture nonlinear relationships in the data. This algorithm can effectively extract useful features from complex error correction data and establish an accurate adjustment model. The constructed model can provide a scientific basis for network management, help optimize link redundancy configuration, and improve the overall performance of the network. The decision results of the random forest model are more reliable and can effectively support actual network adjustment and optimization decisions. Integrating the adjustment model into the digital management platform of agricultural machinery can realize the comprehensive digital management of agricultural machinery networks. By automatically executing the model through the platform, management efficiency can be improved, manual intervention can be reduced, and more intelligent network management can be achieved. Applying the redundant state adjustment model to the agricultural machinery management platform can enhance the platform's functions and provide more accurate and timely information management services. This not only improves the practicality of the platform, but also increases the speed of data processing and analysis. The digital management platform of agricultural machinery combined with the adjustment model can realize intelligent decision-making and optimization. The platform can monitor the network status in real time, automatically adjust the link configuration according to the model's suggestions, optimize resource usage, and improve the operating efficiency and stability of agricultural machinery.

[0024] The beneficial effect of the present invention is to optimize the traditional information management method for the digital management platform of agricultural machinery, solve the problems of inaccurate network fluctuation analysis and weak adjustment ability in the collaborative processing of mechanical equipment in the traditional information management method for the digital management platform of agricultural machinery, improve the accuracy of network fluctuation analysis in the collaborative processing of mechanical equipment, and enhance the adjustment ability between equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A schematic diagram of the steps of an information management method for a digital management platform of agricultural machinery; Figure 2 for Figure 1 Detailed implementation steps of step S2 in FIG.

[0026] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0027] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0028] To achieve this, please refer to Figure 1 to Figure 2 , an information management method for a digital management platform of agricultural machinery, in an embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of an information management method for a digital management platform of agricultural machinery according to the present invention. In this example, the information management method for a digital management platform of agricultural machinery includes the following steps: Step S1: extracting the operation log of mechanical equipment through the digital management platform of agricultural machinery to obtain the operation log data set of agricultural machinery equipment; performing dynamic fluctuation analysis of the collaborative operation network of different equipment nodes on the operation log data set of agricultural machinery equipment to obtain dynamic fluctuation data of the collaborative operation network; In the embodiment of the present invention, the digital management platform for agricultural machinery collects and stores all operation logs of agricultural machinery equipment in the working process in real time through the built-in log extraction module. First, the system connects to the control unit of the agricultural machinery equipment and continuously monitors the status of the equipment, including engine operating parameters, geographic location information, operating load, etc., through standard communication protocols such as CAN bus protocols. The collected data is automatically organized into a time series format and assigned to different equipment nodes, each of which corresponds to an independent mechanical equipment. Then, the data of these sub-nodes are integrated using the log analysis engine of the platform to form a complete agricultural machinery equipment operation log data set. The data set is indexed by high-precision timestamps and covers all state change information of equipment operation. Next, the system uses the collaborative operation analysis module to construct a collaborative operation network by analyzing the real-time data stream between multiple equipment nodes. A specific dynamic fluctuation detection algorithm is used to analyze the fluctuation of each equipment node in the collaborative operation network, quantify the spatiotemporal distribution characteristics of these fluctuations, and finally generate dynamic fluctuation data of the collaborative operation network. This data can reflect the collaborative operation behavior and fluctuation pattern between different mechanical equipment, providing a basis for subsequent analysis.

[0029] Step S2: Performing a timing fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain timing fluctuation topology link data; performing link extreme value event probability calculation on the timing fluctuation topology link data to obtain link extreme value event probability data; In the embodiment of the present invention, the dynamic fluctuation data of the collaborative operation network generated in the previous step is deeply processed by the timing analysis tool of the agricultural machinery digital management platform. First, the system uses the timing fluctuation topology analysis algorithm to model each node in the collaborative operation network and the link connected to it. Each link represents the information exchange and collaborative operation between different device nodes. The focus of the analysis is to identify the timing fluctuation characteristics of the link, that is, the influence of dynamic factors such as link load and state change in the time dimension. The platform uses time series analysis technology to refine the data of each link, decompose high-frequency fluctuations and low-frequency fluctuations, and visualize these fluctuations in the form of a topological link diagram to obtain timing fluctuation topological link data. This data is highly structured and can clearly indicate the fluctuation state of each link at different times. Next, the system uses a probability calculation module to analyze the extreme value events in the timing fluctuation topological link data. Specifically, the calculation module first identifies the fluctuation extreme points of each link through the extreme value statistical method, and uses a non-parametric estimation method to accurately calculate the probability of occurrence of these extreme value events. This step focuses on predicting the possible fluctuation range of the link during future operations, especially under extreme conditions (such as high load or complex terrain). The resulting link extreme event probability data provides a probability assessment of extreme fluctuations in each link under different operating conditions, providing key data support for the operational risk management of agricultural machinery and equipment.

[0030] Step S3: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data to obtain network packet loss degree data; perform continuous forward error correction on the timing fluctuation topology link data based on the network packet loss degree data to obtain topology link packet loss continuity error correction data; In an embodiment of the present invention, in the digital management platform for agricultural machinery, a dedicated network analysis module is used to evaluate the degree of network packet loss of the dynamic fluctuation data of the collaborative operation network. First, the platform uses a data packet tracker to analyze the network data packet by packet to identify packet loss events that may occur during data transmission. By comparing each data packet, the system can detect the location and number of packet loss, and count the packet loss ratio on each link to generate network packet loss degree data. The data reflects the reliability differences of each collaborative operation network node in information transmission, and quantitatively analyzes the factors affecting packet loss. Next, the platform combines the network packet loss degree data with the timing fluctuation topological link data to start the continuity forward error correction algorithm. The algorithm detects the packet loss link on a link-by-link basis, and repairs the lost data packets through technical means such as interpolation, data redundancy and forwarding mechanism to ensure data continuity. In specific operations, the platform will use the comparison and compensation mechanism of redundant data according to the timestamp and data structure in the timing fluctuation topological link to repair the blank points of the packet loss link, and finally generate the topological link packet loss continuity error correction data. These data are highly accurate and can ensure the integrity and continuity of information transmission in the data link, providing lossless basic data support for further analysis.

[0031] Step S4: construct a link-sensitive node redundant state adjustment model based on the topological link packet loss continuity error correction data to obtain the link-sensitive node redundant state adjustment model; send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

[0032] In the embodiment of the present invention, through the in-depth analysis of the topological link packet loss continuity error correction data, the platform constructs an adjustment model for the redundant state of sensitive nodes in the link. First, based on the historical data and error correction data of the link, the platform uses a sensitive node identification algorithm to determine the key nodes in the network that have the greatest impact on the reliability of data transmission. Subsequently, according to the current state of these sensitive nodes and the predicted load conditions, a redundant state adjustment model is constructed. The model calculates the optimal redundant configuration strategy based on the state of each sensitive node and the connection relationship of the surrounding nodes, such as by adding redundant links or optimizing the node load distribution to improve the stability and anti-interference ability of the overall network. In the process of model construction, a redundant optimization algorithm is used, which automatically generates a set of redundant configuration strategies based on the network topology and the correlation between nodes. These strategies not only take into account the current network structure, but also integrate possible future fluctuations to ensure the efficiency and stability of the network. The link sensitive node redundant state adjustment model finally generated can dynamically adjust the redundant configuration of sensitive nodes in real-time operation to ensure that the network can maintain optimal performance under any conditions. After completing the construction of the model, the platform sends the model to the execution layer of the agricultural machinery digital management platform, and the instruction system performs actual operation management of agricultural machinery equipment according to the model. Specific operations include real-time monitoring of sensitive nodes, adjusting the configuration of redundant links, and automatically executing optimization strategies during device operation to maximize network stability and data transmission reliability.

[0033] Preferably, step S1 comprises the following steps: Step S11: extracting the operation log of mechanical equipment through the agricultural machinery digital management platform to obtain an agricultural machinery equipment operation log data set; Step S12: extracting equipment collaborative operation process from the agricultural machinery equipment operation log data set to obtain equipment collaborative operation process data; Step S13: classifying the collaborative operation log data between different equipment nodes on the agricultural machinery equipment operation log data set according to the equipment collaborative operation process data to obtain collaborative operation log classification data; Step S14: Perform collaborative operation network dynamic fluctuation analysis on different device nodes on the collaborative operation log classification data to obtain collaborative operation network dynamic fluctuation data.

[0034] In an embodiment of the present invention, the agricultural machinery digital management platform extracts the operation log of agricultural machinery equipment in real time through a high-precision data acquisition module. In the specific operation process, the platform first connects the sensor interface of various mechanical equipment to obtain the real-time operation data of the equipment through the CAN bus protocol or other standardized industrial protocols. These data include but are not limited to multi-dimensional information such as engine speed, oil pressure, temperature, geographical location, operation depth and operation speed. The data acquisition module will synchronously store these real-time data in the system's log database to form an agricultural machinery equipment operation log data set containing a timestamp and a device identifier. In order to ensure the accuracy of the data, the system introduces an error detection mechanism in the data extraction process. By comparing the checksum of the data with the preset standard, any possible acquisition errors are eliminated, and finally a high-integrity and high-precision agricultural machinery equipment operation log data set is formed. This data set is a full record of the operation process of agricultural machinery equipment, which provides a basis for subsequent analysis. After the construction of the agricultural machinery equipment operation log data set is completed, the agricultural machinery digital management platform uses the built-in operation process extraction module to further extract the equipment collaborative operation process from these log data. The operation steps include: first, the platform identifies the working association of different devices in the same time period by analyzing the time series in the log data. Subsequently, the system extracts the collaborative operation process of the equipment according to the overlap degree of these time periods and the task relationship between the equipment. The extraction process uses an event sequence comparison algorithm to automatically identify and mark each collaborative operation step and construct a complete operation flow chart. In order to improve the extraction accuracy, the system performs multiple iterations to optimize the extracted process. By comparing the log data of different time periods, the detailed errors in the process are adjusted and corrected, and finally the equipment collaborative operation process data is obtained. These data reflect the collaborative working mode between agricultural machinery and equipment, and provide strong support for further classification and analysis. Based on the equipment collaborative operation process data, the agricultural machinery digital management platform classifies the collaborative operation log data between equipment nodes in the agricultural machinery equipment operation log data set. The specific operation process is as follows: First, the system divides the agricultural machinery equipment operation log data set according to the equipment node according to the flowchart in the equipment collaborative operation process data. Each equipment node represents a specific type of mechanical equipment or a specific operation unit. The system sequentially selects the operation logs of the corresponding equipment through the equipment nodes identified in the flow chart to ensure that the log data of each node is accurately classified. Next, the system uses an automatic classification algorithm to further classify these log data according to the collaborative operation relationship between equipment nodes to generate collaborative operation log classification data. The classification process is based on the timestamp, task type and device identifier of the log data, ensuring that the classified data structure is clear and can accurately reflect the collaborative working status between devices. The classified data will be used for subsequent dynamic fluctuation analysis to further reveal the collaborative efficiency and potential problems between devices.The digital management platform for agricultural machinery uses the data analysis module to analyze the dynamic fluctuations of the collaborative operation network of the equipment nodes for the collaborative operation log classification data. First, the system uses the time series analysis tool to perform dynamic fluctuation detection on the log data of each equipment node, and identifies the fluctuation factors that may affect the overall efficiency during the collaborative operation process, such as sudden changes in the equipment operation status or delays in data transmission. Next, the platform maps these fluctuation factors to the connection relationship between different equipment nodes by building a collaborative operation network, and generates a dynamic fluctuation graph that reflects the real-time status of the collaborative operation. During the analysis process, the system uses dynamic network modeling technology to calculate the fluctuation amplitude and frequency of each link based on the communication frequency and data exchange volume between equipment nodes. The dynamic fluctuation data of the collaborative operation network finally obtained not only reveals the potential bottlenecks and risks in collaborative operations, but also provides data support for further optimizing the collaboration between equipment.

[0035] Preferably, as an example of the present invention, reference is made to Figure 2 As shown, in this example, step S2 includes: Step S21: performing time sequence fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain time sequence fluctuation topology link data; In an embodiment of the present invention, the agricultural machinery digital management platform uses a built-in timing analysis tool to perform timing fluctuation topological link analysis on the dynamic fluctuation data of the collaborative operation network. First, the system uses frequency domain analysis methods such as Fourier transform and wavelet transform to convert the original dynamic fluctuation data into a frequency domain representation so as to identify the periodic fluctuation characteristics in the data. Subsequently, the platform combines the topological analysis tool to construct a topological network model that reflects the connection relationship between the nodes of each device. Each node represents a device, and each link represents the data transmission or collaboration relationship between the devices. The system identifies the key paths and vulnerable links in the topological network by analyzing the fluctuation amplitude and frequency changes of these links. In specific operations, the platform normalizes the timing fluctuations of the links to ensure the accuracy of data analysis and avoid misleading due to differences in data volume between devices. The timing fluctuation topological link data finally generated provides a basis for the subsequent fluctuation chain pattern recognition, showing the chain effects and abnormal fluctuation paths that may exist in the collaborative operation between devices.

[0036] Step S22: performing fluctuation chain pattern recognition on the dynamic fluctuation data of the collaborative operation network according to the time-series fluctuation topology link data to obtain fluctuation chain pattern data; In the embodiment of the present invention, based on the time-series fluctuation topological link data, the agricultural machinery digital management platform carries out fluctuation chain pattern recognition. First, the platform uses the dynamic time warping (DTW) algorithm to time align the topological link data to capture the synchronization of the fluctuation patterns between links. Then, the system uses the association rule mining technology to identify the fluctuation chain relationship between links, that is, when a link fluctuates, whether other related links will have a chain reaction. To ensure the accuracy of identification, the system adopts a variety of matching filtering mechanisms, including fluctuation intensity threshold setting and time delay window adjustment, to accurately locate the fluctuation chain pattern between links. The process also combines the time-series association diagram to present the identified chain pattern in a graphical way for easy analysis. The fluctuation chain pattern data finally generated shows the fluctuation association between the links in the collaborative operation network, laying a data foundation for further long-range dependency analysis.

[0037] Step S23: performing long-range dependency characteristic analysis on the fluctuation linkage pattern data to obtain linkage pattern long-range dependency characteristic data; In an embodiment of the present invention, after obtaining the fluctuating linkage pattern data, the digital management platform for agricultural machinery carries out a long-range dependency characteristic analysis. During the operation, the platform first performs a preliminary analysis of the fluctuating linkage pattern data through the autocorrelation function (ACF) and the partial autocorrelation function (PACF) to identify the long-range dependency in the data. Subsequently, the system uses the Hurst index calculation method to further quantify the time series of the linkage pattern. The index reveals the strength of the long-range dependency characteristics in the fluctuating linkage pattern by measuring the fractal dimension of the time series. In order to improve the accuracy of the analysis, the platform also introduces fractal analysis and multifractal spectrum analysis techniques to explore the changes in the dependency characteristics of the fluctuating linkage pattern at different time scales. The long-range dependency characteristic data of the linkage pattern finally generated provides a prediction basis for potential extreme value events in the topological links, and clarifies which links are most susceptible to linkage fluctuations over a long time span.

[0038] Step S24: Calculate the link extreme value event probability of the time series fluctuation topology link data according to the long-range dependency characteristic data of the chain mode to obtain the link extreme value event probability data.

[0039] In an embodiment of the present invention, based on the long-range dependency characteristic data of the chain mode, the digital management platform of agricultural machinery calculates the probability of link extreme value events for the time-series fluctuation topological link data. The system first models the state transition probability of the topological link through the Markov chain model, and combines the aforementioned long-range dependency characteristic data to identify the probability of extreme value events occurring in each link over a long period of time. In specific operations, the platform uses Monte Carlo simulation technology to repeatedly simulate the state changes of the link under various fluctuation scenarios to estimate the frequency of occurrence of its extreme value events. In order to improve the accuracy of the calculation, the system performs Bayesian correction on the sample data generated during the simulation process to eliminate potential statistical deviations. The link extreme value event probability data finally obtained provides an important basis for optimizing the stability of the collaborative operation of agricultural machinery and equipment, clarifies the links and nodes that need to be monitored, and further improves the fault tolerance and reliability of the system.

[0040] Preferably, step S23 includes the following steps: Step S231: performing time series fluctuation breakpoint detection on the fluctuation chain mode data to obtain time series fluctuation breakpoint data; Step S232: performing fluctuation breakpoint distribution period analysis according to the time series fluctuation breakpoint data to obtain fluctuation breakpoint distribution period data; Step S233: Calculating the fractal dimension of the fluctuation breakpoints in different distribution periods on the fluctuation breakpoint distribution period data to obtain the fractal dimension data of the fluctuation breakpoints; Step S234: performing spatial interpolation processing on the fluctuation breakpoint distribution period data based on the fluctuation breakpoint fractal dimension data to obtain fluctuation breakpoint spatial interpolation data; Step S235: performing long-range dependency characteristic analysis based on the fluctuation breakpoint fractal dimension data and the fluctuation breakpoint space interpolation data to obtain the linkage pattern long-range dependency characteristic data.

[0041] In the embodiment of the present invention, the time series fluctuation breakpoint detection is performed on the fluctuation chain pattern data, and a method based on change point detection (CPD) is used in the operation. In the specific implementation, the platform first divides the time series data into multiple subsequences of fixed length using the sliding window technology, and then calculates the cost function of each subsequence through the dynamic programming algorithm. In order to improve the detection accuracy, the platform performs first-order and second-order difference analysis on the cost function to capture the small fluctuation changes that may exist in the time series. Then, the platform applies the step-by-step segmentation method to recursively segment the entire sequence and identify the breakpoint positions therein. Each breakpoint represents a possible fluctuation abnormal node, and the appearance of these nodes usually means that the collaboration between devices has changed significantly. The time series fluctuation breakpoint data finally obtained provides an accurate positioning basis for the subsequent fluctuation breakpoint distribution period analysis. The fluctuation breakpoint distribution period analysis is performed on these breakpoints. The specific operation includes first arranging all breakpoints according to the time axis, and dividing the entire time series into several continuous time periods according to the preset time interval. The platform then counted the number of fluctuation breakpoints that appeared in each time period, and used statistical methods such as Poisson distribution fitting and chi-square test to analyze the frequency distribution characteristics of the breakpoints in each time period. In order to further clarify the time period concentration of these breakpoints, the platform also performed a cluster analysis on the breakpoints in the time period, and identified the dense intervals of fluctuation breakpoints in different time periods through the K-Means clustering algorithm. The fluctuation breakpoint distribution time period data finally generated revealed the distribution law of fluctuation breakpoints in the time dimension, laying a data foundation for the subsequent fractal dimension calculation. The agricultural machinery digital management platform calculates the fractal dimension of fluctuation breakpoints in different distribution periods based on the fluctuation breakpoint distribution time period data. First, the platform performs fractal analysis on the fluctuation breakpoints in each time period by the box-counting method. In the specific operation, the platform maps the fluctuation breakpoint data in each time period to a high-dimensional space and calculates the minimum number of boxes containing these breakpoints in the space. By performing linear regression on the number of boxes and scale size at different scales, the platform obtains the fractal dimension of the breakpoint distribution. In order to ensure the stability of the calculation results, the platform also introduces multi-scale analysis and cross-validates the fractal dimensions at different time scales to reduce possible calculation deviations. The resulting fluctuation breakpoint fractal dimension data provides a quantitative description of the complexity of the spatial distribution of fluctuation breakpoints, and provides an important reference for the next step of spatial interpolation processing. In the digital management platform for agricultural machinery, spatial interpolation processing is performed on the fluctuation breakpoint distribution period data based on the fluctuation breakpoint fractal dimension data. The platform uses the Kriging interpolation method to perform interpolation analysis on fluctuation breakpoints in different time periods. During the operation, the semivariogram function is first fitted to the spatial distribution of the fluctuation breakpoints, and the most suitable interpolation model is determined by optimizing the parameter settings.Subsequently, the platform calculates the possible positions and probability distributions of the unobserved breakpoints in each period based on the semivariogram. In order to improve the accuracy of the interpolation results, the platform also combines the fractal dimension data of the fluctuation breakpoints to adjust the weight of the interpolation process, so that the areas with higher fractal complexity occupy a greater weight in the interpolation. The final obtained spatial interpolation data of the fluctuation breakpoints provides a new data dimension for the study of the continuity of the time series fluctuation breakpoints in the time axis and space. The digital management platform of agricultural machinery analyzes the long-range dependence characteristics based on the fractal dimension data of the fluctuation breakpoints and the spatial interpolation data of the fluctuation breakpoints. First, the platform analyzes the time series of the fluctuation breakpoints by calculating the Hurst exponent. The Hurst exponent can reveal the long-range dependence of the time series, that is, the persistence of the fluctuation characteristics over a long span. In the specific operation, the platform uses the spatial interpolation data of the fluctuation breakpoints obtained previously to decompose the fluctuation data at multiple scales and extract the long-range dependence characteristics at different scales. In order to further verify these long-term dependence characteristics, the platform also applied the R / S analysis method to analyze the extreme value distribution of the fluctuation breakpoint sequence to identify the probability of extreme events in the long time series. The long-term dependence characteristic data of the chain mode finally generated provides a deep theoretical basis for understanding the complex synergistic relationship between agricultural machinery and equipment, and also provides accurate data support for the next step of decision optimization.

[0042] Preferably, step S24 comprises the following steps: Step S241: performing nonlinear dependency structure analysis on the long-range dependency characteristic data of the chain mode to obtain nonlinear long-range dependency structure data; Step S242: performing a multi-scale critical sensitivity assessment on the linkage mode long-range dependency characteristic data according to the nonlinear long-range dependency structure data to obtain multi-scale dependency critical sensitivity data; Step S243: performing link dependency critical sensitive state matching on the timing fluctuation topology link data according to the multi-scale dependency critical sensitive data to obtain link dependency critical sensitive state data; Step S244: performing a link fluctuation local minimization calculation on the link-dependent critical sensitive state data based on a convex optimization algorithm to obtain link fluctuation local minimization data; Step S245: Calculate the probability of link extreme value events according to the link fluctuation local minimization data to obtain link extreme value event probability data.

[0043] In an embodiment of the present invention, in the digital management platform for agricultural machinery, nonlinear dependency structure analysis is performed on the long-range dependency characteristic data of the linkage mode. The platform adopts the phase space reconstruction method in chaos theory to reveal the nonlinear characteristics hidden in the time series. The specific operation includes firstly using the delayed embedding method to embed the long-range dependency characteristic data into the high-dimensional phase space, and then determining the optimal embedding dimension by the pseudo nearest neighbor algorithm. On this basis, the platform applies the Lyapunov exponent calculation method to quantify the nonlinear dependency degree of the system, and judges the chaos of the system by calculating the maximum Lyapunov exponent. In order to further understand the nonlinear structure, the platform also implements recurrence plot analysis (Recurrence Plot Analysis) to identify the nonlinear dependency structure in the complex dynamic system by observing the recursive pattern of the data. The nonlinear long-range dependency structure data finally generated provides a profound nonlinear analysis for the collaborative dynamic behavior of agricultural machinery and equipment. The digital management platform for agricultural machinery performs multi-scale critical sensitivity evaluation on the long-range dependency characteristic data of the linkage mode based on the nonlinear long-range dependency structure data. The platform uses the multiscale entropy analysis method to quantitatively evaluate the dependency characteristics at different time scales. First, the long-range dependency characteristic data is decomposed into multiple subsequences of different time scales, each of which corresponds to a specific time scale. Then, the platform evaluates the complexity and sensitivity of each subsequence at different time scales by calculating the sample entropy of each subsequence. At each time scale, the platform further introduces a local sensitivity index to identify the critical sensitive points of the system by analyzing the response intensity of the system to external disturbances at different scales. The multi-scale dependency critical sensitive data finally obtained can reveal the key dependency relationships and their critical points in the collaborative operation process between agricultural machinery and equipment. In the digital management platform for agricultural machinery, the link dependency critical sensitive state data is calculated based on the convex optimization algorithm to perform local minimization of link fluctuations. The platform first establishes a mathematical model for the link dependency state data, sets the objective function as the total energy of the link fluctuation, and introduces constraints to ensure the stability of the link state. Then, the platform solves the optimization problem by the interior point method to achieve local minimization of link fluctuations. In the specific operation, the platform uses the Lagrange multiplier method to perform Lagrange transformation on the objective function, and gradually approaches the optimal solution through an iterative algorithm. In each iteration, the platform updates the link status data to ensure the convergence and stability of the solution. The link fluctuation local minimization data finally generated provides an optimization basis for further evaluating the probability of extreme events in the collaborative operation of agricultural machinery and equipment. The agricultural machinery digital management platform calculates the probability of link extreme value events based on the link fluctuation local minimization data.The platform uses the Generalized Extreme Value Distribution (GEV) method in Extreme Value Theory (EVT) to evaluate the probability of extreme events in link fluctuations. First, the platform performs distribution fitting on the local minimization data of link fluctuations to determine the shape parameters, location parameters, and scale parameters of the generalized extreme value distribution. Then, the platform estimates these parameters through the Maximum Likelihood Estimation (MLE) method and generates a probability distribution diagram of extreme value events. In order to improve the accuracy of the calculation, the platform also introduces a block extreme value model to perform block extreme value sampling on the link data to capture extreme fluctuation events in the data. The link extreme value event probability data finally generated can provide a scientific basis for preventive maintenance and fault warning of agricultural machinery and equipment.

[0044] Preferably, step S3 comprises the following steps: Step S31: performing extreme value peak distribution feature analysis on the link extreme value event probability data to obtain extreme value peak distribution feature data; Step S32: performing conditional triggering influence simulation on the link extreme value event probability data according to the extreme value peak distribution characteristic data to obtain conditional triggering influence simulation data; Step S33: evaluating the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the conditional triggering impact simulation data to obtain network packet loss degree data; Step S34: Based on the network packet loss degree data, continuous forward error correction is performed on the timing fluctuation topology link data to obtain topology link packet loss continuity error correction data.

[0045] In the embodiment of the present invention, the extreme value spike distribution characteristics of the link extreme value event probability data are analyzed. First, the platform uses the fluctuation analysis tool to capture the peak data of the extreme value event, and uses the local maximum extraction algorithm to calibrate the extreme value spike. Next, the platform quantifies its distribution characteristics by analyzing the frequency distribution, amplitude and occurrence rules of the spike value in different time periods. In the specific operation, the platform uses the Gumbel distribution model to fit the peak distribution, and uses the maximum likelihood estimation method to optimize the model parameters to ensure the accuracy of the peak distribution. In order to further verify the analysis results, the platform introduces the KS test (Kolmogorov-Smirnov Test) method to evaluate the goodness of distribution fitting. The extreme value spike distribution characteristic data finally generated provides data support for the subsequent simulation of the influence of the conditional triggering of extreme events. Conditional triggering influence simulation is performed on the link extreme value event probability data. The platform first analyzes the triggering conditions of the extreme value event and identifies the key factors and conditional variables that may cause the extreme value event. Then, the platform uses the conditional probability calculation method to associate these conditional variables with the extreme value event probability data to establish a conditional triggering model. In specific operations, the platform repeatedly simulates the probability of extreme events under different conditions through the Monte Carlo simulation method to quantify the impact of conditional triggers on link status. To improve the simulation accuracy, the platform also introduces Latin Hypercube Sampling to ensure that more possible conditions are covered with a smaller sample size. The resulting conditional trigger impact simulation data can reveal the link risk distribution of agricultural machinery under different operating conditions. The dynamic fluctuation data of the collaborative operation network is evaluated for the degree of network packet loss. First, the platform collects dynamic fluctuation data in real time and tracks the transmission path of the data packet to identify potential packet loss points. Then, the platform uses the key parameters in the conditional trigger impact simulation data to predict the possible packet loss during the transmission of the data packet. In specific operations, the platform quantifies the degree of packet loss by calculating indicators such as packet loss rate, packet loss interval time, and packet loss distribution density. To improve the accuracy of the evaluation, the platform also combines jitter analysis to capture the instability factors in network transmission. The resulting network packet loss data provides a quantitative basis for evaluating the network reliability of agricultural machinery and equipment in collaborative operations. Continuous forward error correction is performed on the timing fluctuation topology link data. The platform first analyzes the packet loss data to determine the key time periods and link nodes where packet loss occurs. Then, the platform implements forward error correction coding (FEC) technology to redundantly encode data packets before data transmission to resist the potential impact of packet loss.In specific operations, the platform uses the low-density parity check (LDPC) coding method to generate redundant codes to improve the anti-packet loss capability of data transmission. During the error correction process, the platform uses sliding window technology to monitor link data in real time to ensure the effectiveness of the error correction algorithm and the continuity of data transmission. The topological link packet loss continuity error correction data finally generated helps to ensure the stability of data transmission of agricultural machinery and equipment in complex network environments.

[0046] Preferably, step S33 includes the following steps: Step S331: Performing network jitter analysis on the collaborative operation network dynamic fluctuation data according to the conditional triggering influence simulation data to obtain conditional influence network jitter data; Step S332: performing frequency domain conversion on the conditional network jitter data to obtain conditional network jitter frequency domain data; Step S333: performing phase misalignment analysis according to the conditional influencing network jitter frequency domain data to obtain network jitter misalignment frequency domain data; Step S334: performing structured random process induction on the network jitter misalignment frequency domain data to obtain jitter misalignment structured random process induction data; Step S335: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the jitter dislocation structured random process induction data to obtain network packet loss degree data.

[0047] In an embodiment of the present invention, the network jitter analysis is performed on the dynamic fluctuation data of the collaborative operation network according to the conditional triggering influence simulation data. First, the platform uses the packet capture tool to continuously collect real-time data packets in the network and record their transmission delay and jitter amplitude. Then, the platform analyzes the jitter of the data packet in the network by calculating the time deviation and change rate of the data packet transmission. In the specific operation process, the platform uses a moving average filter to smooth the collected data, eliminate high-frequency noise interference, and ensure the accuracy of the jitter analysis. Subsequently, the platform introduces the jitter histogram analysis method to quantify the overall characteristics of the network jitter by counting the distribution frequencies of different jitter amplitudes. The conditional influence network jitter data finally generated provides basic data support for subsequent frequency domain analysis. The conditional influence network jitter data is converted to the frequency domain. The agricultural machinery digital management platform first converts the time domain jitter data to the frequency domain through Fourier Transform to analyze the frequency components of the network jitter. In the specific operation, the platform uses the fast Fourier transform (FFT) algorithm to map the jitter data from the time domain to the frequency domain, thereby revealing the dominant frequency and harmonic components of the network jitter. During the conversion process, the platform normalizes the amplitude of the frequency components for subsequent analysis. The platform further analyzes the converted frequency domain data through spectrum analysis tools to identify the main frequency ranges that affect network performance. The conditional network jitter frequency domain data generated in the end can accurately locate the frequency characteristics of network jitter, laying the foundation for phase misalignment analysis. The agricultural machinery digital management platform performs phase misalignment analysis based on the conditional network jitter frequency domain data. The platform first segments the frequency domain data and extracts the phase information of different frequency components. Then, the platform uses phase alignment technology to identify the frequency bands with misalignment by comparing the phase differences of different frequency components. In the specific operation, the platform uses the phase envelope comparison method to calculate the phase difference between each frequency band and draw a phase difference curve to visualize the degree of misalignment. On this basis, the platform classifies and annotates the identified phase misalignment and further analyzes its impact on network transmission stability. The network jitter misalignment frequency domain data generated in the end provides the necessary data support for the subsequent structured random process induction. The digital management platform for agricultural machinery performs structured random process induction on the frequency domain data of network jitter dislocation. The platform first performs distribution analysis on the jitter dislocation data to identify its distribution pattern in time and frequency. Then, the platform uses random process theory to model the dynamic behavior of jitter dislocation, and uses Markov chain analysis tools to summarize the structured random process characteristics of network jitter dislocation. In specific operations, the platform uses the autoregressive moving average model (ARMA) to parameterize the random process to capture the time series characteristics of jitter dislocation. Subsequently, the platform verifies the stability and applicability of the random process model through multiple Monte Carlo simulations.The resulting jitter misalignment structured random process summary data provides a reliable theoretical basis for evaluating the degree of network packet loss. Based on the jitter misalignment structured random process summary data, the agricultural machinery digital management platform evaluates the degree of network packet loss on the dynamic fluctuation data of the collaborative operation network. The platform first integrates the summarized random process model with the dynamic fluctuation data, and estimates the probability of packet loss by predicting the potential impact of jitter misalignment on data packet transmission. In specific operations, the platform uses the Poisson distribution model to model packet loss events and calculate the packet loss rate under different fluctuation conditions. At the same time, the platform introduces a link status analysis tool to monitor the transmission path and node status of data packets in real time, and identify key links prone to packet loss. The resulting network packet loss degree data provides a scientific basis for further improving the data transmission reliability of agricultural machinery equipment in collaborative operations.

[0048] Preferably, performing structured random process induction on network jitter misalignment frequency domain data includes: Perform multi-dimensional collaborative matrix deconstruction on the jitter misalignment frequency domain data to obtain a jitter misalignment collaborative deconstruction matrix; Perform multi-dimensional cross-correlation analysis on the jitter misalignment frequency domain data according to the jitter misalignment collaborative deconstruction matrix to obtain multi-dimensional cross-correlation jitter data; Performing coherence layering iteration on the multi-dimensional mutual correlation jitter data to obtain multi-dimensional jitter coherence layering data; The structured random process is summarized according to the multi-dimensional jitter coherence layered data, and the jitter misalignment structured random process summary data is obtained.

[0049] In an embodiment of the present invention, the network jitter misalignment frequency domain data is first deconstructed by a multidimensional collaborative matrix. In the specific implementation, the platform constructs a multidimensional collaborative matrix, divides the frequency domain data according to different frequency components and time axes, and performs matrix processing. The core of this step is to separate the collaborative relationship between the various dimensions in the frequency domain data by deconstructing the matrix. During the operation, the platform uses a high-dimensional data decomposition tool, such as the singular value decomposition (SVD) method, to decompose the multidimensional matrix, extract the main collaborative components and the secondary collaborative components, and store them in different matrices respectively. This deconstruction method can clarify the relationship between different frequency bands in time and frequency, and provide basic data support for subsequent cross-correlation analysis. The jitter misalignment collaborative deconstruction matrix finally generated becomes the key data source for further analysis of network jitter. Next, a multidimensional cross-correlation analysis is performed to further understand the mutual influence between the various frequency components. In the specific operation, the platform applies a cross-correlation function to identify high-correlation areas in the frequency domain data by calculating the cross-correlation coefficients between matrices of different dimensions. The operation steps include matching the correlation of each dimension of the deconstruction matrix with other dimensions respectively, and using the correlation coefficient matrix to analyze the mutual influence of frequency components. The identification of high correlation areas can reveal the temporal dependence and synchronization of different frequency components. The platform generates a correlation heat map through visualization tools, and displays the correlation strength of different dimensions in the form of color gradients. The multi-dimensional correlation jitter data finally output lays the foundation for the hierarchical iterative analysis of jitter characteristics. After obtaining the multi-dimensional correlation jitter data, the platform begins to perform coherence hierarchical iteration on these data to analyze the deep structure of network jitter characteristics. The operation process includes using a multi-level hierarchical algorithm to group the jitter data according to coherence, and each group represents jitter behavior with similar coherence characteristics in the same frequency band. The platform iteratively calculates the coherence coefficient of each group of data, and further hierarchizes the groups with higher coherence coefficients to improve the accuracy of data analysis. In this process, the platform introduces a weighted average algorithm to weight the coherence of each level to ensure the stability and reliability of hierarchical analysis. After each iteration, the platform verifies the coherence stratification results and adjusts the stratification parameters to achieve the best analysis results. The multi-dimensional jitter coherence stratification data finally generated provides the basis for a deep understanding of network jitter behavior. Finally, the platform summarizes the network jitter behavior as a structured random process based on the multi-dimensional jitter coherence stratification data. During the implementation process, the platform uses random process theory to mathematically model the stratified data and identify the random characteristics of jitter behavior at each level. The specific operation includes inputting the stratified data into the random process analysis tool, modeling the time series of jitter behavior through the Markov chain analysis method, and capturing its transition probability and state distribution characteristics. The platform further verifies and optimizes the summarized random process model through the statistical process control (SPC) method to ensure that the model can accurately reflect the complexity of network jitter behavior.The jitter misalignment structured random process induction data finally generated provides a scientific basis for the prediction and control of network jitter.

[0050] Preferably, step S34 includes the following steps: Step S341: performing packet loss degree link matching on the timing fluctuation topology link data based on the network packet loss degree data to obtain packet loss degree link matching data; Step S342: performing convolution coding processing of different link networks on the packet loss degree link matching data to obtain packet loss degree link convolution coding data; Step S343: performing multi-path retransmission request control of different link networks on the packet loss degree link matching data according to the packet loss degree link convolution coding data, and obtaining link network multi-path retransmission request control data; Step S344: Perform forward error correction on the timing fluctuation topology link data according to the link network multipath retransmission request control data to obtain topology link packet loss continuity error correction data.

[0051] In an embodiment of the present invention, the network packet loss degree data is matched with the timing fluctuation topology link data to identify and record the packet loss in the link. In operation, the platform first extracts the link ID and packet loss amount information in the network packet loss degree data, and then compares these data with the corresponding link information in the timing fluctuation topology link data. By writing a matching algorithm, the packet loss amount in the packet loss degree data is matched to the corresponding link in the topology link data according to the link ID. The data processing tools used in this process include data matching scripts and database query tools to ensure that the packet loss information is accurately associated with the corresponding link. The packet loss degree link matching data finally generated reflects the actual situation of link packet loss and lays the foundation for subsequent convolution coding processing. Convolution coding is performed on the packet loss degree link matching data to enhance the expression ability of the data. During the implementation process, the platform first selects the convolution kernel for each link, and designs a suitable convolution kernel for the packet loss characteristics in the link. The link information in the matching data is processed using a convolution operation to extract the local features of the packet loss data. The specific operation includes applying the convolution kernel to the link data, performing a one-dimensional convolution operation, and obtaining convolution coding data. The tools used in this step include convolution operation algorithms and data processing software, which are used to process link data and generate convolution coding results. The generated packet loss link convolution coding data is helpful for the subsequent multi-path retransmission request control. According to the packet loss link convolution coding data, multi-path retransmission request control is performed on the packet loss in the link. The platform first analyzes the convolution coding data to identify the high packet loss rate link and its corresponding multi-path. Using the preset retransmission strategy, retransmission requests for multiple paths are issued to each link to ensure that the lost packet data can be retransmitted through the backup link. In the specific operation, the platform configures the retransmission strategy, including the retransmission frequency and path selection rules, and sends a retransmission request to the link through the network control module. The operation tool includes a network management system and a retransmission control protocol to realize the dynamic management of link packet loss. The obtained link network multi-path retransmission request control data ensures the reliability of link data transmission. According to the link network multi-path retransmission request control data, continuous forward error correction is performed on the timing fluctuation topology link data. First, the platform adjusts the forward error correction strategy in the link according to the retransmission request control data, including selecting a suitable error correction code type and adjusting the error correction parameters. The forward error correction algorithm is implemented on the topological link data to ensure the integrity and reliability of the data in the case of packet loss by adding redundant information and error correction codes. The specific operation includes calculating the error correction code and embedding it into the link data, and using the forward error correction algorithm to repair the link data. The tools used include the error correction code generator and the data repair algorithm. The resulting topological link packet loss continuity error correction data effectively improves the continuity and reliability of data transmission.

[0052] Preferably, step S4 comprises the following steps: Step S41: adjusting the link-sensitive node redundancy state according to the topology link packet loss continuity error correction data to obtain link-sensitive node redundancy state adjustment data; Step S42: constructing a link-sensitive node redundant state adjustment model for the link-sensitive node redundant state adjustment data using a random forest algorithm to obtain a link-sensitive node redundant state adjustment model; Step S43: Send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

[0053] In an embodiment of the present invention, first, the redundant state of the link sensitive node is adjusted according to the topological link packet loss continuity error correction data. In this step, the packet loss information and redundant state of the link sensitive node are first extracted from the topological link packet loss continuity error correction data. Next, it is analyzed whether the redundant nodes of each link need to be adjusted to cope with the impact of packet loss. By setting redundant state adjustment rules, such as link load balancing and node backup strategy, the redundancy adjustment of the sensitive nodes in the link is performed. This process includes calculating the load and redundancy of each node in the link, and reconfiguring the redundant nodes to improve the stability and reliability of the link. The tools used in the operation include a redundant configuration management tool and a link performance monitoring system. The link sensitive node redundant state adjustment data finally generated indicates the adjusted node redundant state. Next, a link sensitive node redundant state adjustment model is constructed using a random forest algorithm. Although the title does not require the use of a specific model, in actual operation, the random forest algorithm can effectively process the data of the link sensitive node. The steps include: first, collecting the characteristic variables and target variables in the link sensitive node redundant state adjustment data; then, using the random forest algorithm to train these data and establish a model. The algorithm constructs multiple decision trees and combines the results of each tree for prediction to obtain the redundant state adjustment model of link-sensitive nodes. The tools used in the specific implementation include data processing software and random forest algorithm library for data training and model generation. The generated redundant state adjustment model of link-sensitive nodes is used to predict and adjust the redundant state in future links. Finally, the redundant state adjustment model of link-sensitive nodes is sent to the digital management platform for agricultural machinery. First, the model data is formatted into a format supported by the platform, such as CSV or JSON. Then, the formatted model data is uploaded to the platform using a data transmission tool. The upload process includes connecting to the platform through a network interface to ensure the integrity and security of data transmission. After receiving the model, the platform is automatically integrated into its information management system to perform dynamic adjustment of the redundant state of link-sensitive nodes. The data transmission protocol and network interface used in the operation ensure the correct transmission and application of the model. Through this process, the digital management platform for agricultural machinery can manage and optimize the redundant state of link-sensitive nodes in real time and improve the stability and efficiency of the overall system.

[0054] The above description 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 should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An information management method for a digital management platform for agricultural machinery, characterized in that: The following steps are involved: Step S1: extracting the operation log of mechanical equipment through the agricultural machinery digital management platform to obtain an agricultural machinery equipment operation log data set; The dynamic fluctuation analysis of the collaborative operation network of different equipment nodes is performed on the agricultural machinery equipment operation log data set to obtain the dynamic fluctuation data of the collaborative operation network; Step S2: Performing a timing fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain timing fluctuation topology link data; performing link extreme value event probability calculation on the timing fluctuation topology link data to obtain link extreme value event probability data; Step S3: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data to obtain network packet loss degree data; Based on the network packet loss degree data, continuous forward error correction is performed on the timing fluctuation topology link data to obtain the topology link packet loss continuous error correction data; Step S4: construct a link-sensitive node redundant state adjustment model based on the topological link packet loss continuity error correction data to obtain the link-sensitive node redundant state adjustment model; send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

2. The information management method for the digital management platform of agricultural machinery according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: extracting the operation log of mechanical equipment through the agricultural machinery digital management platform to obtain an agricultural machinery equipment operation log data set; Step S12: extracting equipment collaborative operation process from the agricultural machinery equipment operation log data set to obtain equipment collaborative operation process data; Step S13: classifying the collaborative operation log data between different equipment nodes on the agricultural machinery equipment operation log data set according to the equipment collaborative operation process data to obtain collaborative operation log classification data; Step S14: Perform collaborative operation network dynamic fluctuation analysis on different device nodes on the collaborative operation log classification data to obtain collaborative operation network dynamic fluctuation data.

3. The information management method for the digital management platform of agricultural machinery according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing time sequence fluctuation topology link analysis on the dynamic fluctuation data of the collaborative operation network to obtain time sequence fluctuation topology link data; Step S22: performing fluctuation chain pattern recognition on the dynamic fluctuation data of the collaborative operation network according to the time-series fluctuation topology link data to obtain fluctuation chain pattern data; Step S23: performing long-range dependency characteristic analysis on the fluctuation linkage pattern data to obtain linkage pattern long-range dependency characteristic data; Step S24: Calculate the link extreme value event probability of the time series fluctuation topology link data according to the long-range dependency characteristic data of the chain mode to obtain the link extreme value event probability data.

4. The information management method for the digital management platform of agricultural machinery according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing time series fluctuation breakpoint detection on the fluctuation chain mode data to obtain time series fluctuation breakpoint data; Step S232: performing fluctuation breakpoint distribution period analysis according to the time series fluctuation breakpoint data to obtain fluctuation breakpoint distribution period data; Step S233: Calculating the fractal dimension of the fluctuation breakpoints in different distribution periods on the fluctuation breakpoint distribution period data to obtain the fractal dimension data of the fluctuation breakpoints; Step S234: performing spatial interpolation processing on the fluctuation breakpoint distribution period data based on the fluctuation breakpoint fractal dimension data to obtain fluctuation breakpoint spatial interpolation data; Step S235: performing long-range dependency characteristic analysis based on the fluctuation breakpoint fractal dimension data and the fluctuation breakpoint space interpolation data to obtain the linkage pattern long-range dependency characteristic data.

5. The information management method for the digital management platform of agricultural machinery according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing nonlinear dependency structure analysis on the long-range dependency characteristic data of the chain mode to obtain nonlinear long-range dependency structure data; Step S242: performing a multi-scale critical sensitivity assessment on the linkage mode long-range dependency characteristic data according to the nonlinear long-range dependency structure data to obtain multi-scale dependency critical sensitivity data; Step S243: performing link dependency critical sensitive state matching on the timing fluctuation topology link data according to the multi-scale dependency critical sensitive data to obtain link dependency critical sensitive state data; Step S244: performing a link fluctuation local minimization calculation on the link-dependent critical sensitive state data based on a convex optimization algorithm to obtain link fluctuation local minimization data; Step S245: Calculate the probability of link extreme value events according to the link fluctuation local minimization data to obtain link extreme value event probability data.

6. The information management method for the digital management platform of agricultural machinery according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing extreme value peak distribution feature analysis on the link extreme value event probability data to obtain extreme value peak distribution feature data; Step S32: performing conditional triggering influence simulation on the link extreme value event probability data according to the extreme value peak distribution characteristic data to obtain conditional triggering influence simulation data; Step S33: evaluating the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the conditional triggering impact simulation data to obtain network packet loss degree data; Step S34: Based on the network packet loss degree data, continuous forward error correction is performed on the timing fluctuation topology link data to obtain topology link packet loss continuity error correction data.

7. The information management method for the digital management platform of agricultural machinery according to claim 6, characterized in that: Step S33 includes the following steps: Step S331: Performing network jitter analysis on the collaborative operation network dynamic fluctuation data according to the conditional triggering influence simulation data to obtain conditional influence network jitter data; Step S332: performing frequency domain conversion on the conditional network jitter data to obtain conditional network jitter frequency domain data; Step S333: performing phase misalignment analysis according to the conditional influencing network jitter frequency domain data to obtain network jitter misalignment frequency domain data; Step S334: performing structured random process induction on the network jitter misalignment frequency domain data to obtain jitter misalignment structured random process induction data; Step S335: Evaluate the network packet loss degree of the collaborative operation network dynamic fluctuation data according to the jitter dislocation structured random process induction data to obtain network packet loss degree data.

8. The information management method for the digital management platform of agricultural machinery according to claim 7, characterized in that: The structured random process induction of network jitter misalignment frequency domain data includes: Perform multi-dimensional collaborative matrix deconstruction on the jitter misalignment frequency domain data to obtain a jitter misalignment collaborative deconstruction matrix; Perform multi-dimensional cross-correlation analysis on the jitter misalignment frequency domain data according to the jitter misalignment collaborative deconstruction matrix to obtain multi-dimensional cross-correlation jitter data; Performing coherence layering iteration on the multi-dimensional mutual correlation jitter data to obtain multi-dimensional jitter coherence layering data; The structured random process is summarized according to the multi-dimensional jitter coherence layered data, and the jitter misalignment structured random process summary data is obtained.

9. The information management method for the digital management platform of agricultural machinery according to claim 6, characterized in that: Step S34 includes the following steps: Step S341: performing packet loss degree link matching on the timing fluctuation topology link data based on the network packet loss degree data to obtain packet loss degree link matching data; Step S342: performing convolution coding processing of different link networks on the packet loss degree link matching data to obtain packet loss degree link convolution coding data; Step S343: performing multi-path retransmission request control of different link networks on the packet loss degree link matching data according to the packet loss degree link convolution coding data, and obtaining link network multi-path retransmission request control data; Step S344: Perform forward error correction on the timing fluctuation topology link data according to the link network multipath retransmission request control data to obtain topology link packet loss continuity error correction data.

10. The information management method for the digital management platform of agricultural machinery according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: adjusting the link-sensitive node redundancy state according to the topology link packet loss continuity error correction data to obtain link-sensitive node redundancy state adjustment data; Step S42: constructing a link-sensitive node redundant state adjustment model for the link-sensitive node redundant state adjustment data using a random forest algorithm to obtain a link-sensitive node redundant state adjustment model; Step S43: Send the link-sensitive node redundant state adjustment model to the agricultural machinery digital management platform to perform information management of the agricultural machinery digital management platform.

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