Agricultural product traceability data acquisition method and system based on Internet of Things

Abstract

The embodiment of the invention provides an agricultural product traceability data acquisition method and system based on the Internet of Things. The method comprises the following steps: acquiring farmland environment data in real time based on a distributed sensor network; the data transmission stability between the sensors is enhanced by using a wireless communication technology; recording data transmitted by the sensor based on a chain type data storage structure so as to ensure data integrity; when a high-humidity environment is detected, a redundant data verification mechanism is started, and data loss is reduced; the traceability data is integrated and analyzed based on the cloud data analysis platform, and the data acquisition accuracy is improved. Through the scheme of the embodiment of the invention, the problem of data loss caused by unstable sensor data transmission in a high-humidity farmland environment can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a method and system for collecting agricultural product traceability data based on the Internet of Things. Background Art

[0002] The agricultural product traceability data collection method based on the Internet of Things utilizes modern sensing technology and network communication technology. By deploying various types of sensors (such as temperature, humidity, etc.) at agricultural production sites, it collects various environmental data related to crop growth in real time, and transmits these data to the central management system for processing and analysis to ensure the quality and safety of agricultural products and full traceability. However, this technology also faces certain challenges in practical application. For example, in a high-humidity farmland environment, excessive air humidity often causes wireless signal attenuation or interference, causing the data collected by the sensor to become unstable or even lost during the transmission process. This problem has caused obvious obstacles to the continuous operation of the system and data integrity. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for collecting agricultural product traceability data based on the Internet of Things to solve the problems existing in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for collecting agricultural product traceability data based on the Internet of Things, the method comprising:

[0005] S1, real-time acquisition of farmland environmental data based on distributed sensor networks;

[0006] S2, using wireless communication technology to enhance the stability of data transmission between sensors;

[0007] S3, recording the data transmitted by the sensor based on the chain data storage structure to ensure data integrity;

[0008] S4. Start the redundant data verification mechanism when a high humidity environment is detected to reduce data loss;

[0009] S5. Integrate and analyze traceability data based on the cloud data analysis platform to improve the accuracy of data collection.

[0010] Preferably, the method of starting the redundant data verification mechanism when a high humidity environment is detected specifically includes:

[0011] When the farmland environmental humidity H is greater than the preset high humidity threshold H1, the redundant data verification mechanism is triggered;

[0012] According to the sensor node ID, the corresponding redundant backup node is assigned;

[0013] The redundant backup node acquires data synchronously with the original sensor node and transmits it through different paths;

[0014] The chain data storage structure is used to ensure the consistency and integrity of data transmitted to the cloud.

[0015] Preferably, the specific steps of allocating corresponding redundant backup nodes include:

[0016] Calculate the actual load of each node in the sensor network: compare the current load L of the sensor node with the historical average load Lavg to determine the load of the node;

[0017] If L>Lavg+ε, the node is judged to be in an overloaded operation state;

[0018] For sensors in overload state, the system automatically selects a nearby node within the standard load as its redundant support node based on the distance algorithm;

[0019] Set up redundant backup paths: Use the optimal routing scheme to connect the main sensor and its backup supporting sensor nodes to increase the probability of reliable data transmission.

[0020] Preferably, the process of adopting the optimal routing solution specifically includes:

[0021] The optimal path is calculated based on the link cost Ci j and link availability Ai j, where Ci j refers to the cost indicator of the communication link segment from two adjacent sensor points i to j, and Ai j reflects the percentage of the effective available time period of this chain;

[0022] Evaluate the reliability level of each possible data transmission path according to the Ci j + (1 - Ai j) weight function, and select the one with the highest reliability as the actual communication connection mode;

[0023] Regularly re-evaluate the effective parameters Ci j and Ai j of each link and update the routing according to the actual situation;

[0024] If a sudden connection failure occurs or the signal quality drops significantly, the system automatically switches to an alternative suboptimal path.

[0025] Preferably, the operation steps included in the process of periodically re-evaluating the effective parameters of each connection are specifically:

[0026] Based on the historical communication performance index HPI in the past week and the potential influencing factor P under the predicted future weather conditions, the payload prediction capability PEF_i of each communication path in the next week is predicted. Here, HPI may be composed of multiple factors such as delay and packet loss rate;

[0027] If the predicted load PEFi drops to the lower limit of the warning line L l im (L l im is determined by the test and is less than the minimum capacity required by the system), the emergency plan is immediately activated to check whether there is a better path;

[0028] The linkage system control center adjusts the routing table and related redundancy mechanism parameter settings according to the plan;

[0029] The newly revised configuration information is synchronized to all network terminal devices through the cloud server, and the latter are required to complete real-time updates to maintain the robustness and efficient operation of the overall mesh network architecture.

[0030] Preferably, the following actions need to be implemented in the process of checking whether there is a better path to optimize the path selection strategy:

[0031] According to the actual network status obtained by the latest detection, the comprehensive health index CH I of all communication links under the current path is calculated, and this is compared with the predetermined target minimum health threshold TH I;

[0032] When CH I is lower than TH I, the fitness function of the alternative route is evaluated using the following equation: F = W × TH I / CH I, where W represents the importance coefficient (e.g., 5 in a humid environment) and is used to balance the differences in importance of different scenarios;

[0033] If the fitness F of a candidate route is higher than the existing best one and can maintain data connectivity, it will be considered as a new optimal path;

[0034] After final confirmation of the changes, all connection statuses are retested until the system reaches the optimal or acceptable operating level, and the complete process is recorded for subsequent review reference.

[0035] Preferably, the adjustments made to continuously monitor overall communication quality and data reliability include the following measures:

[0036] Dynamically generate real-time network health trend HTT based on accumulated historical data, and timely identify nodes or areas with impending or existing hidden dangers based on the curve, that is, when HTT shows a rapid landslide trend or falls below the critical lower bound CBH;

[0037] If HTT falls below CBH (both HTT and CBH are rate of change indicators defined based on network robustness), the preventive maintenance mode is activated, giving priority to strengthening the design of data transmission protection mechanisms at the above high-risk locations;

[0038] Automatically increase the frequency and intensity of redundant inspections within a specified range, striving to eliminate all potential sources of hidden dangers such as location problems or connection defects before reducing potential risks to negligible levels.

[0039] An agricultural product traceability data collection system based on the Internet of Things, the system comprising:

[0040] An acquisition module is used to acquire farmland environmental data in real time based on a distributed sensor network;

[0041] A stabilization module for enhancing the stability of data transmission between sensors using wireless communication technology;

[0042] A recording module is used to record the data transmitted by the sensor based on a chain data storage structure to ensure data integrity;

[0043] A verification module is used to start a redundant data verification mechanism when a high humidity environment is detected to reduce data loss;

[0044] The analysis module is used to integrate and analyze traceability data based on the cloud data analysis platform to improve the accuracy of data collection.

[0045] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0046] The agricultural product traceability data collection method and system based on the Internet of Things obtains farmland environment data in real time based on a distributed sensor network; uses wireless communication technology to enhance the stability of data transmission between sensors; records data transmitted by sensors based on a chain data storage structure to ensure data integrity; starts a redundant data verification mechanism when a high humidity environment is detected to reduce data loss; integrates and analyzes traceability data based on a cloud data analysis platform to improve data collection accuracy. The solution of the disclosed embodiment can solve the problem of data loss caused by unstable sensor data transmission in a high-humidity farmland environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the process of the present invention;

[0048] Figure 2 This is a module connection diagram of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] like Figure 1As shown, a method for collecting agricultural product traceability data based on the Internet of Things is presented. The core of the invention is to solve the problem of unstable data collection in a high humidity environment. By introducing Internet of Things technology and optimizing data management, it ensures that data can be efficiently and stably transmitted from the source to the cloud. The specific implementation includes the following aspects.

[0051] First, a series of sensors are installed in the farmland environment to form a distributed perception network. Each node in the system is responsible for monitoring specific parameters in a local area of ​​the farmland, such as soil moisture, air temperature, light intensity, etc. The sensor can be a combination of soil moisture sensors, air temperature and humidity sensors, and light intensity sensors. In one embodiment, it is assumed that the sensors in a smart greenhouse report data to the central router every 15 minutes. In this case, if the ambient humidity rises sharply, it may affect the normal communication between these sensors. To this end, the developers fully considered how to deal with the weakening or even loss of signals under such extreme weather conditions during the design phase.

[0052] Secondly, use advanced wireless communication protocols and technologies to improve the ability of each component in the entire system to communicate with each other. For example, the signal attenuation caused by interference caused by external factors can be reduced by adjusting the RF operating frequency or adding redundant links. For example, in this scenario, Zigbee or Lora wireless communication technology can be used to achieve data interaction. When encountering high humidity conditions, the system automatically selects the best link path to ensure that the data upload and download process can be completed even if the network in some areas is damaged. This not only improves the efficiency of channel resource utilization, but also enhances the overall system's ability to resist failure risks, effectively alleviates the adverse effects caused by poor signal coverage, and avoids the loss of key production guidance information.

[0053] Then, using blockchain-like technology, all the information obtained from the fields is organized into a complete record sequence according to a certain structure, ensuring that each operation step can be tracked and queried. Specifically, in actual application scenarios, each time a new measurement result is received, it will be compared and verified with the last archived version; only when there is no difference between the two and the format complies with the regulations will it be added to the main chain file and become an unmodifiable part; if an abnormal network disconnection or other accident occurs during the period and the data packet is lost, the system will automatically roll back to find a neighboring node and try to reload until the task is completed. The benefits of this are obvious: even if an individual device temporarily fails, it cannot prevent the remaining functional components from continuing to carry out subsequent work without interruption; at the same time, it can also remove invalid redundant content to a large extent and reduce the operating pressure of storage devices.

[0054] Next, a preventive compensation function specially designed for harsh working conditions is embedded in the monitoring program. When the humidity sensor detects that the current environmental value exceeds the normal range, an alarm will be issued to remind the user to take emergency measures and activate multiple calibration methods to reduce the error rate. For example: when the rainy season comes, the soil may be too moist, causing the risk of plant root hypoxia or even rot. In this case, the early warning device will start collecting relevant parameters a few hours in advance and determine whether the precipitation poses a potential threat to the overall planting operation by comparing the historical statistical data in the database. At the same time, other types of auxiliary sensors will also be awakened to participate in the monitoring operation. For example, carbon dioxide sensors may help researchers analyze the degree of change in soil fertility caused by precipitation.

[0055] Finally, the collected indicators are uploaded to the big data analysis platform on the Internet so that a more professional team can process and analyze them, and formulate a more scientific and reasonable agricultural cultivation plan based on this and feedback it to the grassroots. For example, technicians found through observation that some varieties have weak disease resistance and have not been able to recover their growth for a period of time. It is likely that the pathogenic microorganisms have invaded the plants themselves. At this time, the fertilizer ratio should be adjusted in combination with the pesticide spraying strategy to improve the current situation. Or use artificial intelligence algorithms to deduce the optimal planting density and harvest season to achieve the goal of maximum economic benefit output.

[0056] The above process constitutes a complete crop production activity tracking and management system based on the framework of the IOT system, which largely solves the problem that conventional methods are difficult to fully take care of the defects in each link due to the complex and diverse climate. The above design ideas not only meet the needs of modern agricultural informatization construction, but also improve food safety supervision capabilities.

[0057] Next, the present invention is described in detail for starting the redundant data verification mechanism when a high humidity environment is detected. The first step is to immediately trigger the redundant data verification mechanism when the farmland environmental humidity H is greater than the preset high humidity threshold H1. Among them, H represents the humidity percentage in the actual environment, and H1 is a specific value established after a series of tests or according to actual needs, which is used as a benchmark for determining whether the humidity is too high. Such a threshold is set in order to be able to take timely countermeasures when encountering conditions that are not conducive to data collection and transmission. In one embodiment, if the preset H1 value is 80%, it means that when the real-time humidity in the monitored area reaches or exceeds this setting, the mechanism will be activated.

[0058] The next step is to assign corresponding redundant backup nodes based on the sensor node ID to enhance the system's fault tolerance and ensure data quality. Each device is equipped with a unique number to distinguish each other, which helps to accurately assign the corresponding auxiliary collector to work together. Assuming that there are several sensors responsible for monitoring key indicators such as humidity distributed in the farmland, once the humidity in a certain area exceeds the expected trigger condition, the unique ID matching the problem node can guide the backup device to intervene and provide necessary support.

[0059] The next step is to let the redundant backup node and the original sensor node synchronize the data and send it out through different paths to ensure that the information can be effectively transmitted even if a communication path is blocked. Specifically, if a sensor point affects the regular transmission function due to moisture, the alternative option prepared in advance at the other end will receive the same data at the same time, and then feed the collected results back to the system platform through the alternative channel.

[0060] Finally, the chain data storage structure is used to ensure the consistency and integrity of the data transmitted to the cloud. With the support of this architecture, there is no need to worry about the loss of overall data even if a single link is damaged. This means, for example, that the information transmitted from each batch of crop growth environment is connected in a certain order to form a long chain and stored in the cloud warehouse; the advantage of this is that any missing parts can be more intuitively and effectively found and corrected during subsequent review or verification. The purpose of the overall design is to reliably collect important parameters such as the growth status of agricultural products in seasons with high humidity.

[0061] Next, the specific steps of allocating corresponding redundant backup nodes in the present invention are described, including: calculating the actual load of each node in the sensor network: comparing the current load L of the sensor node with the historical average load L_avg to determine the load condition of the node; if L>Lavg+ε, the node is determined to be in an overloaded operating state; for the sensor in the overloaded state, the system automatically selects a nearby node within the standard load as its redundant support node based on the distance algorithm; setting a redundant backup path: using the optimal routing solution to connect the main sensor and its backup support sensor node to improve the probability of reliable data transmission.

[0062] The step of calculating the actual load of each node in the sensor network refers to quantifying the current load by monitoring the amount of data processed or the tasks completed by each node, and comparing it with the average load in the past period of time. Specifically, L represents the current load, Lavg is the historical average value, and ε is the threshold used to determine whether it is overloaded. This comparison can quickly and accurately evaluate the working pressure level of each node. For example, if ε=Lavg×10% is set, it will only be judged as overloaded when it exceeds the average plus 10%. This setting is to filter out the impact of small load changes caused by accidental factors in a short period of time, and improve the stability of the system in determining the working status of nodes;

[0063] If a sensor actually measures L>Lavg+ε, it means that the sensor is currently overloaded, which may lead to performance degradation or even malfunction and damage. At this time, emergency processing is required to prevent data transmission interruption from affecting subsequent data analysis. For example, in one embodiment, an IoT device is deployed to detect the crop growth environment. If a humidity monitoring node records more than twice the amount of update information for two consecutive days, the node is considered to be overloaded, and the system needs to take action to protect the normal operation of the device;

[0064] For devices under overload conditions, suitable new nodes should be found immediately to assist in the work, reduce the workload of the original facilities and ensure the overall operation is efficient and smooth. The system usually uses the nearest neighbor node priority strategy to search for candidate helpers, and requires that the latter must not reach or exceed the normal tolerance range. This operation ensures that a new round of load imbalance will not be triggered while making data collection more uniform and reliable;

[0065] Setting the optimal route to connect the original primary and backup support sites is to build a stable communication path to reduce the delays and low packet loss rates that may occur during transmission. Through intelligent path optimization technology, the optimal transmission cable is dynamically selected to achieve an efficient interconnection mechanism, further enhancing the overall system's robustness and ability to resist risk shocks. Specifically, in the multiple node network that monitors field air quality and temperature, if it is detected that the air quality sensor at position A is experiencing a sudden surge in information volume (possibly due to bad weather that causes the detection frequency to increase, etc.), then after confirming that the point is indeed under high load through the above process, the algorithm will search for and lock on node B as a source of additional power. Assuming that B is located about 20 meters away from A, the current load is moderate, and similar high-load conditions have rarely occurred in history, a healthy unit will be established between the two to ensure smooth data flow.

[0066] This method can effectively avoid the possibility of a single sensor crashing due to long-term overclocking, which in turn causes the entire framework to fall into trouble. At the same time, preventive plans can be formulated in advance for key areas to ensure that agricultural production information is captured without omissions.

[0067] Next, the present invention is described in particular in the process of adopting the optimal routing scheme. This process includes calculating the optimal path, evaluating the reliability level of the path and selecting the most reliable path, regularly re-evaluating the effective parameters and updating the route, and automatically performing path switching. The optimal path is calculated based on the link cost Ci j and the link availability Ai j, where Ci j represents the energy consumption, distance or other cost index that affects the transmission efficiency of the connecting part of the adjacent sensor points i and j. The larger the parameter, the higher the communication cost; the link availability Ai j represents the percentage of the communication link segment between i and j that works effectively in a specific period. In order to obtain a more robust and stable data transmission channel, the path cost Ci j and (1-Ai j), that is, the proportion of unavailable time periods, are combined to comprehensively evaluate each transmission path. The one with the smallest result of Ci j plus 1 minus Ai j is selected as the best path selection scheme. This combined weighting method reflects the fact that it is necessary to balance the requirements of low transmission cost and high service stability when selecting. The path with lower cost and higher availability will be regarded as a data transmission path with higher priority.

[0068] For example, in a farm's environmental monitoring system, there is an IoT network formed by many nodes of data collection devices that monitor soil moisture and temperature. In order to transmit information from different places back to the central control node for analysis and decision-making, it is necessary to decide which direction has the highest efficiency and the lowest loss. If the monitoring node located in the north of the orchard needs to send information to the center, there are three optional channel paths: a to b and then to e, a directly connected to c and then jumped to d via c to reach the final e; there is also a shorter path from ab to f and then to e, which may cause unstable communication due to the surrounding high-density vegetation. According to the pre-recorded cost between each adjacent pair ab (such as the degree of signal attenuation or the estimated value of power consumption) and the available time period frequency information, the middle option is determined as the current best route after calculation using the described rules; if the communication quality between bc is found to be reduced due to weather reasons in the next few days, the pre-defined secondary route switching process will be quickly started to keep the network connectivity from being interrupted, that is, the alternative channel af, which is relatively more expensive, will be chosen to complete the task.

[0069] In one embodiment, at regular intervals (for example, after farm operations are completed every night), the adaptive module of the system automatically collects the cost changes of each connection edge under the actual operation status of the network in the recent period (for example, the battery life may gradually decrease with the change of seasons, and the newly installed relay points may enhance the coverage of certain areas, etc.), as well as the temporary failure of a link due to an emergency, and corrects the previously established Ci j table according to the newly measured values, and adjusts the optimized path selection. When an emergency occurs, such as signal loss, the system will quickly identify the behavior that deviates from the expected behavior and immediately start the recovery algorithm to check whether there is a backup path nearby that can meet the minimum requirements for the continued operation of the business.

[0070] Specifically, the purpose of designing the weight function in the formula is to quantify the capabilities and characteristics of a single or a series of link combinations in various situations, so that the system has the ability to dynamically screen out the methods that minimize energy consumption or improve the certainty and security of message transmission while ensuring the overall system functionality; doing so will not only help improve resource utilization and avoid unnecessary waste, but also enhance the ability to respond to disturbances caused by uncertainty. Therefore, it is a very meaningful design consideration for creating an intelligent Internet of Things application environment.

[0071] Next, the operation steps included in the process of regularly re-evaluating the effective parameters of each connection of the present invention are described as follows: based on the historical communication performance index HPI of the most recent week and the potential influencing factor P under predicted future weather conditions, the effective load prediction capacity PEFi of each communication path in the next week is predicted, where HPI may be composed of multiple factors such as delay and packet loss rate; if the predicted load PEFi drops to the lower limit of the warning line L l im (L l im is determined by testing and is less than the minimum capacity required by the system), the emergency plan is immediately activated to check whether there is a better path; the linkage system control center adjusts the routing table and related redundancy mechanism parameter settings according to the plan; synchronizes the newly revised configuration information to all network terminal devices through the cloud server, and requires the latter to complete real-time updates to maintain the robustness and efficient operation of the overall mesh network architecture.

[0072] Each step in this process is designed around maintaining the efficiency and availability of the communication system. Specifically, the first step requires analyzing the existing communication environment using data from the past week. For example, HPI (historical communication performance index) may include the average delay time Tde l ay and the average packet loss rate DPL. On this basis, it is also necessary to predict the potential factors P that may affect communication performance in the next week, which usually include possible weather changes such as future rainfall. The payload prediction capacity PEFi is an estimate of the maximum useful data flow capacity that can be reliably transmitted by a specific link in a period of time in the future under existing and predicted conditions. For example, in the field of agricultural applications, if it is monitored that continuous rainy weather is about to occur, the system will combine HPI to calculate a new PEFi to evaluate whether the carrying capacity of the farmland sensor network will be reduced in this case.

[0073] The next action is triggered when the predicted PEF_i indicator falls to the preset safety bottom line L l im. The value of L l im is a standard threshold determined in advance based on a large number of experiments, which is generally less than the set value of the minimum capacity required by the network under normal operation. If PEFi crosses this line, the backup plan will be activated immediately. For example, in the process of collecting information on the source of agricultural products, when a certain communication link is greatly affected by severe weather interference and the effective communication bandwidth is insufficient to support the data transmission needs of all IoT devices, this type of early warning response will be triggered, and the system will automatically detect and select other healthy working paths as substitutes.

[0074] Next, in order to ensure the continued normal service of the network, the linkage control system center will be notified and intervene. Updating router rules or adding redundancy measures according to the emergency plan to improve the impact resistance of communication is the key to this operation. For example, in one implementation, if the monitoring platform of the detection center identifies that the signal transmission performance of a node has decreased due to climate problems, the switching process will be initiated, switching from the main route to an alternative link with similar or even better transmission conditions, and optimizing the redirection strategy to adapt to the current best connection method.

[0075] The last important step is to quickly distribute the new configuration adjusted through the above process to ensure that the terminal equipment can also adjust its own sending / receiving frequency or strength in time to match the overall changes. Assume that in an example, various sensors and mobile devices widely deployed in the field need to perform necessary calibration according to the latest parameter instructions pushed by the cloud to ensure smooth information exchange in the entire wireless network coverage area. This step strengthens the ability of the distributed architecture to offset the negative impact of uncertain factors while maintaining the operating efficiency and quality of the entire system.

[0076] In general, this set of dynamic parameter re-evaluation mechanisms for periodic execution ensures that even in the face of complex and changing work scenarios, the Internet of Things technology can still effectively support the effective execution of stable interactions and efficient collaborative processing mechanisms at each point in the agricultural information chain.

[0077] Next, the present invention describes that the following actions need to be implemented in the process of checking whether there is a better path to optimize the path selection strategy: according to the actual network status obtained by the latest detection, the comprehensive health index CH I of all communication links under the current path is calculated, and this is compared with the predetermined target minimum health threshold TH I; when CH I is lower than TH I, if the fitness F of a candidate line is higher than the existing best and can maintain data connectivity, it is regarded as a new preferred path; after the final confirmation of the change, all connection states are checked again until the system reaches the optimal or acceptable operating level, and the complete process is recorded for subsequent review reference.

[0078] Step 1 involves making a comprehensive evaluation of the communication conditions of each link in the currently used information transmission path based on the latest network monitoring results to calculate the comprehensive health index CHI under the current path, and compare it with the preset target minimum health standard value THI. In this process, CHI considers the result of the weighted average of multiple communication indicators. For example, when the agricultural product traceability data collection system based on the Internet of Things is working, assuming that the detection results at a certain moment show that the stability of the current information flow channel is poor, the calculation of CHI may result in a lower value.

[0079] The second step is initiated when CH I does not meet the minimum requirement TH I in the first step. The quality level of potential replacement lines is evaluated using an equation, where W represents the importance parameter value of a specific factor, which may vary in different application scenarios or environments to adjust the proportion of the impact of the corresponding conditions. In this scenario, W is set to five under certain conditions to represent the improvement of the importance of source protection in the context of significant humidity changes. The higher the fitness measurement value, the more effective the candidate line plan is under a given weight system.

[0080] In the third stage, the best option is selected from all the alternative transmission paths that have been reviewed. If the adaptation score of one or more lines exceeds that of the identified excellent ones while maintaining the reliable transmission link unchanged, the line will be adopted as the main connection plan for the next step to update the current settings.

[0081] Finally, after completing the above conversion, make sure to review the entire network connection status again until the highest efficiency or a tolerable standard is achieved, and record the overall implementation process for future retrospective learning and analysis and improvement. For example, if the original CH I reading in a certain case system is low, the above rules are executed and it is found that another line not only has a higher F but also meets the requirement of no breakpoint connection, then the replacement strategy is executed and the whole process is recorded until it is stable and archived. These experience data will help improve the optimization algorithm in similar operation decision-making processes in the future.

[0082] Next, the present invention is described as continuously monitoring the overall communication quality and data reliability adjustment including the following measures: dynamically generating a real-time network health trend HTT based on accumulated historical data, and timely identifying nodes or areas with impending or existing hidden dangers according to the curve, that is, when HTT shows a rapid landslide trend or falls below the critical lower bound CBH; if HTT slides below CBH (here HTT and CBH are both a series of rate of change indicators defined with network sound deployment as the benchmark), starting the preventive maintenance mode, giving priority to strengthening the design of the data stable transmission protection mechanism at the above-mentioned high-risk locations; automatically increasing the frequency and intensity of redundant checks within a specified range, striving to eliminate all location problems or connection defects at the source of all hidden dangers before reducing potential risks to a negligible level; combined with long-term monitoring.

[0083] First, in order to ensure that the communication system is in the most ideal state and to detect possible failures in advance, the status of the real-time communication network is dynamically analyzed based on the data of past records and a real-time trend curve representing the current communication status is generated. This trend is named the real-time network health trend (HTT). This step predicts the future network health status by statistically analyzing the changes in indicators such as error code counts, delays, and packet loss rates in historical data. Once it is identified that HTT shows a clear trend of rapid decline or landslide and crosses the pre-set lower threshold, the critical lower bound (CBH), it indicates that the system may have potential problem points or network bottlenecks. HTT can be reflected by calculating the average value of data packet loss and communication delay over a period of time to obtain its decline rate, while CBH serves as a limit value of the safe operating range.

[0084] When HTT falls below this set critical point, the preventive maintenance operation mechanism will be activated immediately. In this mode, the system will actively detect the high-incidence areas or nodes analyzed previously, strengthen the data communication redundancy backup measures between them and enhance the signal transmission strength to prevent data loss or other abnormal problems caused by a single failure, and improve the stability and safety factor of the overall data transmission link.

[0085] Specifically, in a modern agricultural environment supported by IoT technology, such as in the tracking and tracing management system for agricultural products, it is assumed that many sensors are deployed inside the farm to monitor the temperature, humidity, irrigation conditions and other data required for the growth of various crops, and send them back to the server through a wireless sensor network for processing and storage for user query. Once a sensor group begins to frequently report the loss of information, a significant downward trend can be seen on the HTT curve generated by the system; if further detection shows that the HTT value falls out of the specified CBH warning range (the optimal CBH value here can be determined based on actual tests), the area will be marked as a high-risk area, and actions such as adding additional wireless transceivers, optimizing antenna deployment methods, or changing communication protocols to enhance stability and anti-interference capabilities will be taken immediately to improve the current situation and prevent data loss from affecting the accuracy and completeness of the final crop information record.

[0086] Automatically increasing the frequency and level of redundancy checks in specific areas is to ensure that the above measures can actually solve the root cause of the problem rather than just alleviate the temporary symptoms. Only after multiple rounds of rigorous verification prove that the hidden danger has been completely eliminated can the normal maintenance plan be gradually resumed.

[0087] Long-term monitoring can not only better understand the law of communication quality changes, but also provide timely feedback to the design department to improve hardware layout and software logic, so that the next generation version has stronger vitality. For example, if it is found that some network nodes are prone to synchronization distortion under certain conditions during a maintenance process, after accumulating sufficient experience data and correcting the algorithm, similar errors can be prevented from occurring again and the overall performance optimization process can be continuously promoted until the optimal level is reached.

[0088] The present invention provides a method for collecting agricultural product traceability data based on the Internet of Things, comprising:

[0089] The first step is to deploy a distributed sensor network in a specific farmland environment. Through dense node deployment, the sensor network can continuously acquire the parameters of the entire crop growth cycle and the environment in which it is located, and send the acquired data to designated nodes for later data fusion and information push. These farmland environmental parameters include but are not limited to temperature, humidity, light, wind speed, soil fertility level and pH, etc., so as to have a more comprehensive control of the crop production status and provide raw data support for subsequent information analysis and utilization.

[0090] The second step is to use wireless communication technology to ensure that the sensors can effectively interconnect, share resources and work together under complex natural environment conditions. In the actual cultivation of crops, there will be problems such as severe weather or geographical conditions (such as frequent thunderstorms or undulating terrain, etc.), interference caused by the operation of mechanical facilities, etc., making it difficult for traditional single communication methods to ensure the quality or reliability of information exchange. Therefore, the use of a variety of communication technologies can effectively improve the performance of sensor networks; specifically, when encountering the above extreme environments, corresponding enhancement mechanisms are activated - such as adaptive frequency selection, multi-route path planning, relay forwarding device assistance or redundant information backup strategies, etc., to deal with possible problems;

[0091] The third step is to use the idea of ​​blockchain to record data in the entire data exchange process in a data organization structure similar to a linked list. This can not only avoid the global failure problem caused by the damage of a certain storage space, but also facilitate the traceability verification according to the cause and effect relationship in the future. Whenever a new data sample is received from the next-level device, it will first determine whether the current block already has a duplicate sample of the same time fragment based on its timestamp. If so, the existing entry will be updated to reflect the current latest version; otherwise, a new link will be created at the end to save the newly added sample and ensure its sequence; in addition, special measures for redundant data packet inspection will be initiated under certain circumstances, especially when the signal source is triggered by a high humidity weather event, it is considered that the probability of information flow interruption is greatly increased at this time, so extra attention should be paid, so that the corresponding remedial actions can be quickly taken to restore consistency before data loss or after incomplete transmission occurs; the introduction of this series of mechanisms is intended to ensure that even in the face of a more severe or volatile actual planting environment, the timeliness and credibility of information acquisition can be ensured to a certain extent, laying a solid foundation for subsequent large-scale integrated analysis based on the cloud computing platform;

[0092] In the fourth step, after all relevant parties have submitted their respective observation data, they will upload them to a pre-established virtual space, where comprehensive statistical inferences can be carried out with the support of powerful backend computing power. On the one hand, the trend of a variable's change over time and the characteristics of seasonal patterns can be examined from the time dimension. On the other hand, the differences in geographical distribution can be considered, and then the conclusions of which regions are more susceptible to which specific unfavorable factors, which periods need to strengthen preventive measures, or which factors may have some potential correlation or regularity with each other can be summarized. In this way, the control accuracy of each link in the front end of the agricultural production chain can be further improved, and strong technical support can be provided for the realization of precise agricultural decision-making, efficient resource allocation, and the creation of fully controllable supply chain management in the future.

[0093] In summary, in order to deal with the problem of data loss caused by unstable sensor data transmission in a high-humidity farmland environment, the present invention adopts an enhanced version of wireless sensor technology to ensure signal transmission stability, combines a blockchain-style database model to achieve data immutability, and then uses a high-efficiency cloud analysis platform to play the value of a macro perspective - that is, a multi-level comprehensive solution system is formed. This multi-angle synergistic method not only greatly optimizes the performance of local details within an individual, but also significantly enhances the robustness of the entire system, allowing it to maintain the integrity of its core functions when facing greater risks and challenges under similar adverse conditions, and can better serve the needs of the intelligent transformation of agricultural production, and escort goals such as food safety, environmental supervision, and even increased income for farmers.

[0094] like Figure 2 As shown, a system for collecting agricultural product traceability data based on the Internet of Things is also provided, and the system includes:

[0095] An acquisition module is used to acquire farmland environmental data in real time based on a distributed sensor network;

[0096] A stabilization module for enhancing the stability of data transmission between sensors using wireless communication technology;

[0097] A recording module is used to record the data transmitted by the sensor based on a chain data storage structure to ensure data integrity;

[0098] A verification module is used to start a redundant data verification mechanism when a high humidity environment is detected to reduce data loss;

[0099] The analysis module is used to integrate and analyze traceability data based on the cloud data analysis platform to improve the accuracy of data collection.

[0100] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collecting agricultural product traceability data based on the Internet of Things, characterized in that: The method comprises: S1, real-time acquisition of farmland environmental data based on distributed sensor networks; S2, using wireless communication technology to enhance the stability of data transmission between sensors; S3, recording the data transmitted by the sensor based on the chain data storage structure to ensure data integrity; S4. Start the redundant data verification mechanism when a high humidity environment is detected to reduce data loss; S5. Integrate and analyze traceability data based on the cloud data analysis platform to improve the accuracy of data collection.

2. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 1, characterized in that: The method of starting the redundant data verification mechanism when a high humidity environment is detected specifically includes: When the farmland environmental humidity H is greater than the preset high humidity threshold H1, the redundant data verification mechanism is triggered; According to the sensor node ID, the corresponding redundant backup node is assigned; The redundant backup node acquires data synchronously with the original sensor node and transmits it through different paths; The chain data storage structure is used to ensure the consistency and integrity of data transmitted to the cloud.

3. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 2 is characterized in that: The specific steps of allocating corresponding redundant backup nodes include: Calculate the actual load of each node in the sensor network: compare the current load L of the sensor node with the historical average load Lavg to determine the load of the node; If L>Lavg+ε, the node is judged to be in an overloaded operation state; For sensors in overload state, the system automatically selects a nearby node within the standard load as its redundant support node based on the distance algorithm; Set up redundant backup paths: Use the optimal routing scheme to connect the main sensor and its backup supporting sensor nodes to increase the probability of reliable data transmission.

4. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 3 is characterized in that: The process of adopting the optimal routing solution specifically includes: The optimal path is calculated based on the link cost Cij and the link availability Aij, where Cij refers to the cost index of the communication link segment from two adjacent sensor points i to j, and Aij reflects the percentage of the effective available time period of this chain; Evaluate the reliability level of each possible data transmission path according to the Cij+(1-Aij) weight function, and select the one with the highest reliability as the actual communication connection mode; Regularly re-evaluate the effective parameters Cij and Aij of each link and update the routing according to the actual situation; If a sudden connection failure occurs or the signal quality drops significantly, the system automatically switches to an alternative suboptimal path.

5. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 4 is characterized in that: The specific steps involved in the process of periodically re-evaluating the effective parameters of each connection are: Based on the historical communication performance index HPI in the past week and the potential influencing factor P under the predicted future weather conditions, the payload prediction capability PEF_i of each communication path in the next week is predicted. Here, HPI may be composed of multiple factors such as delay and packet loss rate; If the predicted load PEFi drops to the lower limit of the warning line Llim (Llim is determined by the test and is less than the minimum capacity required by the system), the emergency plan is immediately activated to check whether there is a better path; The linkage system control center adjusts the routing table and related redundancy mechanism parameter settings according to the plan; The newly revised configuration information is synchronized to all network terminal devices through the cloud server, and the latter are required to complete real-time updates to maintain the robustness and efficient operation of the overall mesh network architecture.

6. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 5, characterized in that: In the process of checking whether there is a better path, the following actions need to be implemented to optimize the path selection strategy: According to the actual network status obtained by the latest detection, the comprehensive health index CHI of all communication links under the current path is calculated, and this is compared with the predetermined target minimum health threshold THI; When CHI is lower than THI, the fitness function of the alternative route is evaluated using the following equation: F = W × THI / CHI, where W represents the importance coefficient (e.g., 5 in a humid environment) and is used to balance the differences in importance of different scenarios; If the fitness F of a candidate route is higher than the existing best one and can maintain data connectivity, it will be considered as a new optimal path; After final confirmation of the changes, all connection statuses are retested until the system reaches the optimal or acceptable operating level, and the complete process is recorded for subsequent review reference.

7. The method for collecting agricultural product traceability data based on the Internet of Things according to claim 6 is characterized in that: The adjustments described to continually monitor overall communication quality and data reliability include the following: Dynamically generate real-time network health trend HTT based on accumulated historical data, and timely identify nodes or areas with impending or existing hidden dangers based on the curve, that is, when HTT shows a rapid landslide trend or falls below the critical lower bound CBH; If HTT falls below CBH (both HTT and CBH are rate of change indicators defined based on network robustness), the preventive maintenance mode is activated, giving priority to strengthening the design of data transmission protection mechanisms at the above high-risk locations; Automatically increase the frequency and intensity of redundant inspections within a specified range, striving to eliminate all potential sources of hidden dangers such as location problems or connection defects before reducing potential risks to negligible levels.

8. An agricultural product traceability data collection system based on the Internet of Things, characterized by: The system comprises: An acquisition module is used to acquire farmland environmental data in real time based on a distributed sensor network; A stabilization module for enhancing the stability of data transmission between sensors using wireless communication technology; A recording module is used to record the data transmitted by the sensor based on a chain data storage structure to ensure data integrity; A verification module is used to start a redundant data verification mechanism when a high humidity environment is detected to reduce data loss; The analysis module is used to integrate and analyze traceability data based on the cloud data analysis platform to improve the accuracy of data collection.

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