Electrophoresis ion signal wireless transmission system

Abstract

The invention relates to an electrophoresis ion signal wireless transmission system, and belongs to the technical field of environment monitoring. Comprising a microchip electrophoresis soil ion signal acquisition terminal, a wireless transmission system based on a Zigbee network, and a remote monitoring platform realized based on a Zigbee network group, collecting ion species and concentration characteristic signals in the soil sample, and meanwhile, providing environment parameters with consistent time and space; stable wireless network connection is established through a Zigbee network, so that data transmission is realized; and remote access is supported. According to the electrophoresis ion signal wireless transmission system provided by the invention, precise monitoring of soil ions is realized through a microchip electrophoresis technology, a Zigbee network is adopted, multi-node data relay is supported, stable wireless transmission is ensured, the transmitting power is adaptively adjusted to enhance environmental adaptability, and data processing efficiency is improved through cloud intelligent classification and concentration prediction; and the remote monitoring platform supports real-time data viewing and decision making.

Description

Technical Field

[0001] This invention relates to a wireless transmission system for electrophoretic ion signals, belonging to the field of environmental monitoring technology. Background Technology

[0002] As modern agriculture rapidly evolves towards intensification and precision, the distributed, real-time, and accurate detection of ions in farmland soil (especially core cations that directly affect crop growth and soil fertility, such as potassium, sodium, calcium, and magnesium ions) has become a key technological support for optimizing fertilization strategies and improving crop yield and quality. This technology belongs to the field of environmental monitoring technology, and its core objective is to solve the entire process of efficient collection, reliable transmission, and accurate analysis of soil ions in large-scale farmland scenarios.

[0003] Currently, extensive research has been conducted on technologies and related equipment for soil ion detection, such as traditional laboratory chemical analysis methods, spectroscopic analysis methods, and early distributed sensing systems. While some progress has been made in detection accuracy and basic functionality, numerous technical shortcomings and application bottlenecks remain in practical large-scale farmland applications. These shortcomings make it difficult to meet the core requirements of modern agriculture for real-time, distributed, low-cost, and highly reliable soil ion monitoring. Specific deficiencies are as follows:

[0004] Current soil ion detection technologies largely rely on laboratory environments, requiring manual collection of farmland soil samples, transportation to a laboratory, and analysis using large analytical equipment such as ion chromatographs and atomic absorption spectrometers. This approach has significant drawbacks: firstly, the detection cycle is long (usually several days to weeks), failing to reflect real-time dynamic changes in soil ions in the field (such as short-term fluctuations in ion concentration after fertilization and the impact of irrigation on ion distribution), leading to significant delays in agricultural management decisions; secondly, laboratory analytical equipment is bulky, with high purchase and maintenance costs (often hundreds of thousands of yuan per unit), and requires specialized technicians for operation, making it difficult to achieve large-scale, multi-site distributed deployment in farmland (e.g., 3-5 monitoring points per hectare), failing to cover the spatial heterogeneity detection needs of vast farmlands, and unable to provide comprehensive data support for refined agricultural management.

[0005] The core principle of existing mainstream detection technologies (such as near-infrared spectroscopy and X-ray fluorescence spectroscopy) is based on the qualitative and quantitative analysis of the absorption or reflection characteristics of soil ions to specific wavelength spectra. However, these technologies are severely limited in their ability to distinguish bioavailable ions (i.e., ions that can be directly absorbed and utilized by crop roots, which are key indicators of soil fertility). The spectral characteristics of impurities such as organic matter, moisture content, and clay particles in farmland soil easily overlap with those of target ions, making it impossible for the technology to accurately distinguish between effective forms of ions (such as soluble potassium and exchangeable calcium) and ineffective forms (such as lattice potassium and precipitated magnesium). The detection results can only reflect the total content of soil ions, which deviates significantly from the actual absorption and utilization by crops. This greatly weakens the guiding value for agricultural production decisions such as adjusting fertilizer application and supplementing nutrients.

[0006] Existing distributed monitoring systems for soil ions in farmland mostly adopt a discrete design pattern of separate sensors, independent transmission modules, and external control units, resulting in extremely low system integration. On the one hand, the ion sensors, wireless transmission modules, and data processing modules need to be adapted through complex hardware interfaces (such as RS485 and USB), leading to a large overall size (usually greater than 1000 cm³) and heavy weight, making it difficult to deploy flexibly in farmland (such as in shallow soil near crop roots or steep slopes in mountain orchards). On the other hand, the independent power supply (such as 5V DC power for sensors and 12V lithium batteries for transmission modules) and independent control of each module not only increase the manufacturing cost of the equipment (the cost of a single node often exceeds 1,000 yuan) but also increase the system failure rate (such as loose interfaces, module compatibility conflicts, and unstable power supply voltage), making it difficult to achieve low-cost widespread application in large-scale farmland.

[0007] Existing transmission schemes are ill-suited to the complex environment and wide-area monitoring needs of farmland: wired transmission is prone to wear and tear or breakage due to agricultural machinery operations and irrigation systems in farmland, and the cost of wiring is high (costing over 10,000 yuan per kilometer), with poor flexibility and inability to adjust the location of monitoring points according to crop planting structure; if wireless technologies such as Wi-Fi and 4G / 5G are used, Wi-Fi has problems such as limited coverage (the coverage radius of a single node is usually less than 100 meters) and high power consumption (standby current often exceeds 10mA), making it difficult to meet the multi-node coverage needs of thousands of acres of farmland; although 4G / 5G has a wide coverage, the communication cost is high, and the signal stability is poor in remote farmland areas (such as mountains and suburbs). Summary of the Invention

[0008] This invention provides a wireless transmission system for electrophoretic ion signals to address the limitations and low efficiency of existing technologies, which cannot meet the needs of real-time field monitoring, have insufficient accuracy in distinguishing bioabsorbable ions, have limited guiding value of detection results, have complex integration of distributed monitoring systems, have high costs and are difficult to deploy, and have poor adaptability of data transmission mechanisms, thus failing to achieve real-time transmission and wide-area coverage.

[0009] This invention provides a wireless transmission system for electrophoretic ion signals, which includes a microchip electrophoretic soil ion signal acquisition terminal, a wireless transmission system based on a Zigbee network, and a remote monitoring platform based on a Zigbee network.

[0010] The microchip electrophoresis soil ion signal acquisition terminal uses microchip electrophoresis technology to collect characteristic signals of ion types and concentrations in soil samples. It also collects geographical location, acquisition time, and temperature and humidity information to provide spatiotemporally consistent environmental parameters for soil ion type and concentration data.

[0011] The Zigbee-based wireless transmission system establishes a stable wireless network connection through the Zigbee network and uses the routing algorithm mechanism of the on-demand distance vector routing protocol to dynamically establish and maintain routes to other network nodes.

[0012] The remote monitoring platform is based on Zigbee networks. The Zigbee network is built using the Z-Stack protocol stack, and the architecture of the Zigbee protocol stack includes the physical layer, media access control layer, network layer, and application layer.

[0013] Preferably, the microchip electrophoresis soil ion signal acquisition terminal includes:

[0014] Microfluidic electrophoresis chip module is used to realize ion separation and concentration electrical signal feature extraction of solution samples;

[0015] The upper integrated control and communication module is used to integrate system parameter setting, real-time monitoring, data processing and export functions, and is also responsible for receiving control commands and synchronizing data through the Zigbee network.

[0016] The lower-end microfluidic chip driver and sampling module receives the electrical signals generated by the micro-electrophoresis chip during the electrophoresis process, and performs amplification, filtering and other processing to ensure the accuracy and stability of the signals;

[0017] The frequency band interference detection module scans the interference intensity of 16 Zigbee channels in real time, automatically switches to the channel with the least interference, and is compatible with the Zigbee protocol.

[0018] Preferably, in the Zigbee-based wireless transmission system, the microchip electrophoresis soil ion signal acquisition terminal realizes Zigbee communication through the chip and is equipped with a voltage regulator module. The voltage regulator module is connected to the DCOUPL interface to provide bias voltage for the radio frequency circuit and the crystal oscillator.

[0019] Preferably, the media access control layer performs frame synchronization, frame assembly and parsing, acknowledgment and retransmission, creates network topology through the network layer, and manages packet forwarding using routing protocols.

[0020] The Zigbee protocol stack includes:

[0021] The coordinator node is the core of the network, responsible for managing the overall functionality and performance of the network.

[0022] End nodes are responsible for data collection and transmission, sending data to other nodes or coordinators.

[0023] Router nodes forward data from other nodes and extend the network's coverage area.

[0024] Preferably, the coordinator node communicates with the sensor node via a serial port, and transmits soil ion information, temperature and humidity, and location to the gateway, as well as return data to the gateway and sends it to the corresponding node;

[0025] The router node configures its own hardware and software facilities through the GenericApp_Init() function, searches for wireless signal channels, and monitors the beacon request frames of the network coordinator.

[0026] Preferably, the microchip electrophoresis soil ion signal acquisition terminal collects rainfall and ambient temperature, judges the interference level based on the frequency band interference detection module, determines environmental anomalies based on the collected data and triggers power correction, and dynamically adjusts the number of retransmissions and retransmission intervals through an ACK waiting timer based on the Z-Stack protocol stack and the bit error rate and interference intensity of data frame transmission.

[0027] Preferably, the microchip electrophoresis soil ion signal acquisition terminal screens four types of agricultural core cations and two types of key characteristics, while providing spatiotemporally consistent background parameters for ion data. The upper-end integrated control and communication module performs 16-bit binary compression on the collected ion type characteristics to form a 16-byte type characteristic matrix. The frequency band interference detection module scans the channel interference intensity in real time. The type characteristic matrix is ​​transmitted to the router, which then forwards it to the coordinator with a delay. The transmission path is dynamically maintained through the AODV routing algorithm.

[0028] Preferably, the initialization process involves uploading the feature matrix of the process type to the cloud; simultaneously, the cloud is triggered to preload the SVM cation classification model, and the cloud outputs the ion category label through the RBF kernel function, calculates the maximum posterior probability of the classification confidence, generates the target ion category instruction and sends it to the terminal. After receiving the instruction, the terminal collects the concentration characteristics of the target ion in a targeted manner.

[0029] Preferably, the concentration characteristics of the directional acquisition target ions are transmitted to the coordinator through the original low-interference Zigbee channel, and then uploaded to the cloud via NB-IoT. The cloud classifies the type feature matrix through an SVM model, and calls the XGBoost concentration model of the target ions, taking the concentration characteristics as input and outputting the ion concentration value, integrating the ion category, concentration value, spatiotemporal parameters, and environmental parameters into a structured dataset.

[0030] The beneficial effects of this invention are:

[0031] This invention provides a wireless transmission system for electrophoretic ion signals. It directly detects soil ion types and collects concentration characteristic signals in the field using microchip electrophoresis technology, avoiding the delays of traditional methods that require bringing soil samples back to the laboratory for analysis. This enables real-time monitoring of soil ions, reflecting short-term fluctuations in ion concentration after fertilization and the impact of irrigation on ion distribution. This provides timely data support for agricultural management decisions. Microchip electrophoresis technology accurately distinguishes between bioabsorbable and non-bioavailable ions in the soil, improving the accuracy of detection results and providing higher guidance for agricultural production decisions such as fertilizer application and nutrient supplementation. A stable wireless network connection is established using a Zigbee network, supporting data relay across multiple nodes, extending the communication range, and ensuring the collection and transmission of data from various terminal sampling points in the field. The system can dynamically adjust the transmission power of the radio frequency module according to environmental conditions, ensuring stable signal transmission even in harsh weather and high-interference scenarios. It can also adjust the transmission power based on rainfall and ambient temperature. The RF module's transmit power is adjusted in real time based on environmental parameters such as interference intensity to ensure stable performance under various environmental conditions. A power limit is set to avoid excessive power consumption. The upper-level integrated control and communication module performs binary compression on the collected ion species characteristics to form a species feature matrix, significantly reducing the amount of transmitted data and alleviating network transmission burden. The cloud uses SVM and XGBoost models to intelligently classify and predict the concentration of transmitted data, directly calling the target ion's specific concentration model without traversing the entire model, thus improving the efficiency and accuracy of concentration calculation. The remote monitoring platform based on Zigbee networking supports remote access, allowing users to monitor and manage soil ion data via the network, view data in real time, and make decisions based on the data, improving the management efficiency of agricultural production. Attached Figure Description

[0032] Figure 1 The circuit diagram of CC2530 is shown in the present invention for a wireless transmission system for electrophoretic ion signals.

[0033] Figure 2 This is a schematic diagram of the AODV routing algorithm discovery process in a wireless transmission system for electrophoretic ion signals according to the present invention.

[0034] Figure 3 This is a flowchart of the coordinator node processing of a wireless transmission system for electrophoretic ion signals according to the present invention.

[0035] Figure 4 This is a flowchart illustrating the design of a router node in a wireless transmission system for electrophoretic ion signals according to the present invention.

[0036] Figure 5 This is a flowchart of the Zigbee terminal node operation of an electrophoretic ion signal wireless transmission system according to the present invention.

[0037] Figure 6 This is a flowchart illustrating the initialization and configuration process of an NB-IoT module in an electrophoretic ion signal wireless transmission system according to the present invention.

[0038] Figure 7 This is a data transmission program design diagram for an NB-IoT module of an electrophoretic ion signal wireless transmission system according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] This invention proposes a wireless transmission system for electrophoretic ion signals, comprising a microchip electrophoretic soil ion signal acquisition terminal, a wireless transmission system based on a Zigbee network, and a remote monitoring platform based on a Zigbee network. The microchip electrophoretic soil ion signal acquisition terminal acquires characteristic signals of ion types and concentrations in soil samples through microchip electrophoresis technology, and simultaneously collects information on geographical location, acquisition time, and temperature and humidity, providing spatiotemporally consistent environmental parameters as important background information for soil ion type and concentration data.

[0042] Specifically, the microchip electrophoresis soil ion signal acquisition terminal includes a microfluidic electrophoresis chip module, an upper integrated control and communication module, a lower microfluidic chip driving and sampling module, and a frequency band interference detection module. The microfluidic electrophoresis chip module is used to realize ion separation and concentration electrical signal feature extraction of solution samples. In microfluidic chips, ions in the sample are distributed in the microchannels at different migration rates according to their charge and mass-to-charge ratio under the influence of an electric field. Subsequently, the migration rate of charged particles in the microchannels can be analyzed, and the types and concentrations of different ions can be identified by utilizing changes in conductivity. The upper-end integrated control and communication module integrates system parameter setting, real-time monitoring, data processing, and export functions. It is also responsible for receiving control commands and synchronization data through the Zigbee network to monitor the operating status of the microelectrophoresis chip, adjust experimental conditions, process experimental results, and send ion type and concentration characteristic signal data, geographic coordinates, and timestamps to the host computer via a wireless network to achieve remote monitoring and data synchronization. The lower-end microfluidic chip drive and sampling module receives the electrical signals generated by the microelectrophoresis chip during electrophoresis and performs amplification, filtering, and other processing to ensure the accuracy and stability of the signals. Its main functions include interface design with micro-electrophoresis chips, signal acquisition and processing, transmission of processed signals to the upper-end integrated control and communication module, and real-time scanning of the interference intensity of 16 Zigbee channels by the frequency band interference detection module, automatically switching to the channel with the lowest interference and being compatible with the Zigbee protocol.

[0043] The Zigbee-based wireless transmission system establishes a stable wireless network connection, enabling efficient and reliable transmission of soil monitoring data and environmental parameters. The system supports data relay across multiple nodes, extending communication range and ensuring the collection and transmission of data from various terminal sampling points in farmland.

[0044] Specifically, such as Figure 1 The diagram shows Zigbee communication implemented using the CC2530 chip. The CC2530 microprocessor integrates a 1.8V voltage regulator module to ensure stable power supply to the internal circuitry. A 1μF C401 capacitor is used to provide a stable, isolated 1.8V power supply to the power amplifier (PA), phase-locked loop (PLL), low-noise amplifier (LNA), and analog-to-digital converter (ADC), achieving power isolation. A 56KΩ precision resistor is connected to the RBIAS pin to provide bias voltage to the RF circuitry and crystal oscillator. The system's main clock signal is generated by an external 32MHz XTAL1 crystal oscillator, while a 32.768kHz XTAL2 crystal oscillator is responsible for the wake-up mechanism when the system enters ultra-low power mode.

[0045] like Figure 2The Zigbee network shown uses a routing algorithm mechanism based on the On-Demand Distance Vector (AODV) protocol, which can dynamically establish and maintain routes to other network nodes as needed.

[0046] To achieve networking capabilities, the Zigbee network was built using the Z-Stack protocol stack from Texas Instruments (TI). Network configuration and management were completed within the IAR software development environment. The protocol stack was developed based on the Z-Stack protocol stack from Texas Instruments, conforming to the Zigbee 2007 / Pro protocol specification. The Z-Stack protocol stack is the implementation of the Zigbee protocol. The Zigbee protocol stack architecture includes the Physical Layer (PHY), Media Access Control Layer (MAC), Network Layer (NWK), and Application Layer (APS). The PHY layer defines communication specifications, supporting multiple frequency bands and data rates to adapt to different application scenarios. The MAC layer is responsible for frame synchronization, frame assembly and parsing, acknowledgment, and retransmission, and also defines device roles. The NWK layer is responsible for routing and device organization to create a self-organizing network topology and uses routing protocols to manage packet forwarding.

[0047] Protocol stack layered structure table and code distribution

[0048] The Zigbee protocol stack mainly includes:

[0049] Coordinator node: such as Figure 3 The coordinator node shown is the core of the network, responsible for managing the overall functionality and performance of the network, establishing and maintaining the network, and configuring necessary security measures.

[0050] Router node: such as Figure 4 The router node shown, in addition to having terminal device functions, can also forward data from other nodes. Router nodes can extend network coverage and enhance network connectivity and stability.

[0051] End node: such as Figure 5 The main function of the terminal node shown is to collect and transmit data, sending the data to other nodes or the coordinator to support real-time data processing and analysis of the network.

[0052] In Zibee IoT, the Coordinator node is responsible for network management, data aggregation, and security control, ensuring the stability and reliability of the IoT. The Coordinator node performs its functions through communication with sensor nodes via serial ports. It is responsible for transmitting information such as soil ion information, temperature and humidity, and location to the gateway for further processing. It can also process the return data from the gateway and send it to the corresponding nodes.

[0053] The router node initialization and network access process can be divided into four stages: initialization, network entry, network communication, and data processing. When a router node is activated and during initialization, it runs the `GenericApp_Init()` function to configure its hardware and software. Next, the node searches for wireless signal channels and monitors beacon request frames from the network coordinator to maintain channel synchronization. Once a suitable channel and beacon pairing are determined, the router responds to the beacon request initiated by the network coordinator and records the coordinator's IEEE MAC 64-bit address. Then, the router sends a connection request frame to the coordinator, initiating the network access process. After the coordinator replies with an acknowledgment frame, the router continues to transmit data request frames to obtain a dedicated 16-bit network ID. In the network communication phase, when the router receives the network address information provided by the coordinator, it indicates that it has successfully joined the network system, and communication with the coordinator can then begin at the application layer. Nodes not only maintain communication with the coordinator but also continuously monitor for incoming data. If the data stream originates from a nearby device, it performs appropriate path selection; otherwise, it discards the data stream, allowing the node to re-enter monitoring mode. Furthermore, routing nodes possess similar functionality to end nodes, collecting information about their environment and transmitting it to the network control center via direct connection or other paths.

[0054] The Zigbee end node device first powers on to initialize its hardware and software, then requests to join the Zigbee network until successful. The end node then begins listening for data input. When data arrives, a hardware interrupt is generated first. Then, the software layer calls the physical layer's interrupt function `spp_rf_IRQ()` and the MAC layer's `dealFrame()` function to process the data. The `readFCF(BYTEn)` function then reads the data processed by the MAC layer, and finally, the `nwkDealFrame()` function extracts the data to the network layer for further processing before sending the data packet to the coordinator.

[0055] Based on real-time environmental perception data from Zigbee terminal nodes and router nodes, the radio frequency module transmit power of the microchip electrophoresis soil ion signal acquisition terminal is dynamically adjusted: in normal weather and low interference scenarios, a low-power baseline power is maintained; in severe weather and high interference scenarios, the power is increased to enhance signal strength, while a power upper limit is set to avoid excessive power consumption.

[0056] Specifically, rainfall and ambient temperature are collected by the microchip electrophoresis soil ion signal acquisition terminal to determine whether it is severe weather, and the average intensity I of the interference signal in the channel is collected by the frequency band interference detection module to determine the interference level.

[0057] The baseline transmit power value for a Zigbee node under normal, low-interference conditions and without severe weather is:

[0058] P 0= 0 dBm.

[0059] Environmental anomalies are determined and power corrections are triggered based on three parameters: rainfall, interference intensity in the same frequency band, and ambient temperature. Adjustments are made using the following function:

[0060]

[0061] in:

[0062] P is the final transmission power;

[0063] P0 is the reference transmit power;

[0064] P max This is the maximum power limit;

[0065] R represents the real-time rainfall;

[0066] R0 is the normal rainfall threshold;

[0067] I represents the real-time interference intensity in the same frequency band;

[0068] I0 is the low interference threshold;

[0069] T represents the ambient temperature;

[0070] T min / T max Within the normal temperature range;

[0071] k1 is the rainfall correction factor;

[0072] k2 is the interference intensity correction coefficient.

[0073] The test involved artificially simulating rainfall and conducting multiple tests on signal attenuation (i.e., power loss from transmitter to receiver) under different rainfall amounts R. When the signal attenuation increases by ΔAttent, the transmission power needs to be increased synchronously to maintain the signal-to-noise ratio at the receiver. ΔP = ΔAttent. A linear regression analysis was performed with the rainfall increment ΔP as the abscissa and the power increment ΔP as the ordinate: ΔP = k1*ΔR + b, where b is the intercept.

[0074] Zigbee-based network communication experiment

[0075]

[0076] Based on the retransmission mechanism of the original AODVjr routing algorithm of the Z-Stack protocol stack, the number of retransmissions and the retransmission interval are dynamically adjusted in combination with the bit error rate and interference intensity I of the data frame transmission. After the terminal / router node sends a data frame, an ACK waiting timer is started. If the timer expires and no ACK confirmation frame is received from the receiver, it is determined that the transmission has failed and a retransmission is immediately triggered.

[0077] The wireless transmission system for microchip electrophoresis soil ion signals integrates a BC26 NB-IoT module. The BC26 module, with its high performance, low power consumption, and multi-band characteristics, provides a stable and reliable foundation for data transmission. Before data transmission, such as… Figure 6 , 7 The NB-IoT module shown requires initialization, including detecting network connectivity, ensuring devices are connected to the internet, and establishing a Socket communication channel. First, the system hardware's network status is checked, including assessing network availability and signal strength. After ensuring all devices have successfully connected to the internet, the module establishes a Socket channel and transmits status information to the host computer. Once the information verification is successful, the module is ready to execute subsequent data transmission tasks. The main task of the NB-IoT module is to upload data from the wireless sensor network to the host computer and cloud platform. After initialization, the module first receives data from the wireless sensor network, converts it into a standard data frame format, and then transmits it directly to the cloud platform. Upon receiving the data frame, the cloud platform parses the wireless sensor data, thus realizing the remote data transmission task of the NB-IoT module.

[0078] This remote monitoring platform, based on Zigbee networks, supports remote access. Users can monitor and manage soil ion data via the network, and the platform provides a data monitoring and analysis interface, allowing users to view data in real time and make decisions based on the data.

[0079] During use, the microchip electrophoresis soil ion signal acquisition terminal collects characteristic signals of ion types and concentrations in soil samples, and simultaneously collects geographical location, collection time, and temperature and humidity information. The wireless transmission system is based on the CC2530 chip, uses the Z-Stack protocol stack to build the network, and integrates an NB-IoT module to achieve communication with the cloud, constructing a low-cost, low-power distributed network to solve the coverage problem of multi-point data transmission in large-scale farmland. It supports multi-hop routing and is more suitable for farmland scenarios than Wi-Fi and 4G / 5G. It wirelessly transmits the collected ion signals, GPS latitude and longitude, temperature and humidity, timestamps, and other data to the cloud and remote monitoring platform, solving the lag of traditional offline analysis. The AODVjr routing algorithm optimizes network energy consumption and reliability, resulting in extremely low packet loss rates in different scenarios. The frequency band interference detection module scans the interference intensity of 16 Zigbee channels in real time and automatically switches to the channel with the least interference. The dynamic power adjustment based on environmental awareness automatically adjusts the transmission power: under normal weather and low interference, the power is maintained at low power consumption; under heavy rain / fog and high interference, the power is increased. At the same time, power threshold control is used to avoid excessive power consumption.

[0080] Compared to existing technologies, the microchip electrophoresis soil ion signal acquisition terminal not only collects characteristic signals of ion types and concentrations in soil samples, but also simultaneously collects geographical location, collection time, and temperature and humidity information. This ensures that the soil ion data has spatiotemporally consistent environmental parameters as background information, providing a more comprehensive and accurate basis for subsequent data analysis. The acquisition terminal adopts a modular design, including a microfluidic electrophoresis chip module, an upper-end integrated control and communication module, a lower-end microfluidic chip driver and sampling module, and a frequency band interference detection module. Each module has a clearly defined function and works collaboratively, facilitating system maintenance, upgrades, and optimization. The lower-end microfluidic chip driver and sampling module amplifies and filters the electrical signals generated by the micro-electrophoresis chip during electrophoresis, ensuring signal accuracy and stability. Simultaneously, this module is responsible for the interface design with the micro-electrophoresis chip, signal acquisition and processing, and transmitting the processed signal to the upper-end module, ensuring signal quality during transmission. The frequency band interference detection module scans the interference intensity of 16 Zigbee channels in real time and automatically switches to the channel with the lowest interference, while also being compatible with the Zigbee protocol. This design effectively avoids the impact of channel interference on data transmission, improving the stability and reliability of data transmission. The Zigbee-based wireless transmission system establishes a stable wireless network connection, supports data relay across multiple nodes, and extends the communication range. In agricultural environments, it ensures the collection and transmission of data from various terminal sampling points, solving the coverage problem of multi-point data transmission in large-scale agricultural fields. Zigbee communication is implemented through the CC2530 chip, which integrates a 1.8V voltage regulator module to ensure stable power supply to the internal circuitry. Simultaneously, a 1μFC capacitor (C401) connected to the DCOUPL interface achieves power isolation, providing bias voltage for the RF circuitry and crystal oscillator. A 56KΩ precision resistor is connected to the RBIAS pin, and an external 32MHz XTAL1 crystal oscillator generates the system's main clock signal. A 32.768KHz XTAL2 crystal oscillator is used for the wake-up mechanism. This optimizes the chip's performance and power consumption, improving system stability and reliability. The Zigbee network employs a routing algorithm based on the On-Demand Distance Vector Routing (AODV) protocol, enabling dynamic establishment and maintenance of routes to other network nodes as needed. By combining the bit error rate and interference intensity I of data frame transmission, the number of retransmissions and the retransmission interval are dynamically adjusted, improving data transmission efficiency and reliability. The system can adjust the RF module's transmit power in real time based on rainfall, ambient temperature, and the average intensity I of the channel interference signal collected by the frequency band interference detection module, all collected by the microchip electrophoresis soil ion signal acquisition terminal.The system maintains a low-power baseline in normal weather and low-interference scenarios, while increasing power to enhance signal strength in severe weather and high-interference scenarios. A power limit is set to prevent excessive power consumption, effectively reducing system energy consumption and extending equipment lifespan. The remote monitoring platform, based on a Zigbee network, supports remote access, allowing users to monitor and manage soil ion data via the network. The platform provides users with a data monitoring and analysis interface, enabling them to view data in real time and make decisions based on the data. This allows users to promptly grasp soil ion status, take appropriate measures, and improve the management efficiency of agricultural production.

[0081] Example 2

[0082] Building upon Example 1, this example uses a microchip electrophoresis soil ion signal acquisition terminal to screen four core cations that directly affect crop growth and soil fertility: potassium, sodium, calcium, and magnesium ions. Subsequently, the microfluidic electrophoresis chip module, based on the ion migration characteristics under an electric field, collects the peak values ​​of ion migration rate and conductivity changes for these four core agricultural cations. The ion migration rate is the speed at which ions migrate within the microchannel under an electric field, and the peak conductivity change is the maximum change in conductivity during ion separation. Simultaneously, the upper-end integrated control and communication module acquires basic environmental temperature and humidity data from the terminal node to prepare for subsequent concentration feature correlation. The upper-end integrated control and communication module performs 16-bit binary compression on the eight characteristic parameters of the four ion types, forming a 16-byte characteristic matrix. Simultaneously, the frequency band interference detection module scans the interference intensity of the 16 Zigbee network channels in real time and automatically switches to the channel with the lowest interference to ensure data integrity. The transmission provides a low-interference channel. The terminal node transmits the compressed species feature matrix to the router node through the Zigbee network built on the CC2530 chip via the upper-end integrated control and communication module. The router node, based on the data relay and network extension functions, forwards the data directly to the coordinator node without additional data processing, with a relay delay of ≤30ms. During this period, the dynamic routing capability of the Zigbee network AODV routing algorithm is maintained to ensure the stability of the transmission path. After receiving the species feature matrix, the coordinator node first completes the initialization through its integrated BC26NB-IoT module. After the network status is verified, the species feature matrix is ​​uploaded to the cloud remote monitoring platform. At the same time, the coordinator node sends a model preloading command to the cloud to prepare for subsequent ion classification. After receiving the species feature matrix, the cloud remote monitoring platform starts the preloaded cation classification model and identifies ion species by ion migration rate + conductivity change. The input is an 8-dimensional feature vector of agricultural core cations.

[0083]

[0084] in, The value represents the migration rate of the target ion in the microchannel.

[0085] This represents the maximum change in conductivity during ion separation.

[0086] The feature similarity is calculated based on the RBF kernel function using the low-power operation logic in Example 1:

[0087]

[0088] in: The kernel function values ​​are the kernel function values ​​of the two eigenvectors. These are the parameters for the kernel function.

[0089] The Euclidean distance between two vectors is as follows: , Let be the k-th dimension element of the two vectors.

[0090] Output ion category labels through kernel function calculation:

[0091]

[0092] in:

[0093] For support vectors Lagrange multipliers;

[0094] For support vectors Category tags;

[0095] For model bias terms;

[0096] This represents the number of support vectors.

[0097] To avoid misclassification leading to invalid transmission of concentration features, a classification confidence verification mechanism is introduced:

[0098]

[0099] in:

[0100] For feature vectors The posterior probability of belonging to class I ions is obtained by... The derivation is performed.

[0101] Set a confidence threshold , Classification is effective; generate target ion category instructions. The classification is invalid; a retransmission instruction is generated.

[0102] After receiving the target ion category instruction from the cloud via the Zigbee network, the upper-end integrated control and communication module triggers the microfluidic electrophoresis chip module to directionally collect three concentration characteristics of the target ion: its migration rate, peak conductivity change, and the real-time ambient temperature and humidity collected synchronously by the lower-end module. These characteristics form a 6-byte concentration feature vector. During the acquisition process, the lower-end microfluidic chip driver and sampling module amplifies and filters the electrical signal again to ensure the accuracy of the concentration feature data. The terminal node transmits the target ion concentration feature vector to the router node via a low-interference channel selected by the previously selected frequency band interference detection module. The router node forwards the vector to the coordinator node using the relay mechanism described in Example 1. The coordinator node then directly uploads the concentration feature vector to the cloud via the initialized BC26NB-IoT module, maintaining the Zigbee node's baseline transmit power throughout the process. After receiving the concentration feature vector in the cloud, it does not need to traverse the entire model; it directly calls the dedicated XGBoost concentration model corresponding to the target ion, using the concentration feature vector as input, and outputs the concentration value of the target ion.

[0103]

[0104] in:

[0105] The target ion concentration.

[0106] The core formula of the XGBoost model for the i-th type of ion is as follows:

[0107] .

[0108] Ultimately, the cloud integrates ion type, concentration value, collection time, geographical location, temperature and humidity data, and synchronizes them to the user interface of the remote monitoring platform for users to view and make decisions in real time.

[0109] In use, the microfluidic electrophoresis chip module collects two key characteristics of agricultural core cations—ion migration rate and conductivity change peaks—based on the charge-to-mass ratio of ions under an electric field. Simultaneously, it collects geographical location, collection timestamp, and ambient temperature and humidity to provide spatiotemporally consistent background parameters for ion data. The lower-level microfluidic chip drive and sampling module amplifies and filters the weak electrical signals generated by electrophoresis, filtering out electromagnetic interference from farmland to ensure detection accuracy. The frequency band interference detection module scans the interference intensity of 16 Zigbee network channels in real time, automatically switching to the channel with the lowest interference to reserve a low-interference channel for subsequent transmission. The upper-level integrated control and communication module performs 16-bit binary compression on the collected ion species characteristics to form a 16-byte species characteristic matrix. Subsequent concentration characteristics are compressed to 8 bytes, significantly reducing the amount of data transmitted. The terminal, based on a Zigbee network built using the CC2530 chip, transmits the compressed species characteristic matrix to the router, which forwards it with a delay of ≤30ms. During the process, the transmission path is dynamically maintained using the AODV routing algorithm to ensure stability. The coordinator starts the BC26NB-IoT module, completes network connection detection, socket channel creation, and information verification. After initialization, the category feature matrix is ​​uploaded to the cloud. Simultaneously, the cloud is triggered to preload the SVM cation classification model to prepare for subsequent classification. The cloud outputs ion category labels through RBF kernel function calculation and calculates the maximum posterior probability of classification confidence. The target ion category command is generated and sent to the terminal. After receiving the command, the terminal collects the concentration features of the target ion in a targeted manner, compresses them into 8 bytes, and transmits them to the coordinator through the original low-interference Zigbee channel. Then, it is uploaded to the cloud via NB-IoT. The cloud classifies the category feature matrix using the SVM model. After the classification is valid, the cloud directly calls the target ion's dedicated XGBoost concentration model, using the concentration features as input, and outputs the ion concentration value. The ion category, concentration value, spatiotemporal parameters, and environmental parameters are integrated into a structured dataset to meet subsequent monitoring requirements.

[0110] Compared with existing technologies, this method selects four core cations that directly affect crop growth and soil fertility for collection, avoiding the collection of a large amount of irrelevant data and improving the targeting and effectiveness of data collection. This allows subsequent analysis to more accurately reflect the soil fertility status. In addition to collecting the peak values ​​of ion migration rate and conductivity changes, it also collects information such as geographical location, collection timestamp, and environmental temperature and humidity, providing spatiotemporally consistent background parameters for ion data. This helps to analyze the relationship between ions and crop growth and soil environment more comprehensively and accurately. The microfluidic chip driving and sampling module at the bottom amplifies and filters the weak electrical signals generated by electrophoresis, effectively filtering out electromagnetic interference in farmland and ensuring detection accuracy. The frequency band interference detection module scans the interference intensity of 16 channels of the Zigbee network in real time and automatically switches to the channel with the least interference, providing a low-interference channel for data transmission. The integrated control and communication module at the top performs 16-bit binary compression on the collected ion type characteristics, and subsequently compresses the concentration characteristics to 8 bytes, significantly reducing the amount of data transmitted and alleviating the network transmission burden. Meanwhile, router nodes forward data with a delay of ≤30ms and dynamically maintain the transmission path through the AODV routing algorithm to ensure the stability of the transmission path. The terminal uses a Zigbee network built on the CC2530 chip for short-distance data transmission, while the coordinator starts the BC26NB-IoT module for long-distance data upload. Combining the advantages of Zigbee network (low cost, low power consumption, self-organizing network) and NB-IoT network (wide coverage, large connectivity), the cloud outputs ion category labels through RBF kernel function calculation and introduces a classification confidence verification mechanism. A confidence threshold is set to determine whether the classification is valid, avoiding invalid transmission of concentration features due to misclassification, thus improving the accuracy and reliability of classification. After classifying the category feature matrix through the SVM model, the cloud directly calls the target ion's dedicated XGBoost concentration model, using the concentration features as input and outputting ion concentration values. This eliminates the need to traverse the entire model, improving the efficiency and accuracy of concentration calculation and enabling faster and more accurate ion concentration values.

[0111] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A wireless transmission system for electrophoretic ion signals, characterized in that: This includes a microchip electrophoresis soil ion signal acquisition terminal, a wireless transmission system based on a Zigbee network, and a remote monitoring platform based on a Zigbee network. The microchip electrophoresis soil ion signal acquisition terminal uses microchip electrophoresis technology to collect characteristic signals of ion types and concentrations in soil samples. It also collects geographical location, acquisition time, and temperature and humidity information to provide spatiotemporally consistent environmental parameters for soil ion type and concentration data. The Zigbee-based wireless transmission system establishes a stable wireless network connection through the Zigbee network and uses the routing algorithm mechanism of the on-demand distance vector routing protocol to dynamically establish and maintain routes to other network nodes. The remote monitoring platform is based on Zigbee networks. The Zigbee network is built using the Z-Stack protocol stack, and the architecture of the Zigbee protocol stack includes the physical layer, media access control layer, network layer, and application layer.

2. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that, The microchip electrophoresis soil ion signal acquisition terminal includes: Microfluidic electrophoresis chip module for ion separation and concentration electrical signal feature extraction of solution samples; The upper integrated control and communication module is used to integrate system parameter setting, real-time monitoring, data processing and export functions, and is also responsible for receiving control commands and synchronizing data through the Zigbee network. The lower microfluidic chip driver and sampling module receives the electrical signals generated by the micro-electrophoresis chip during the electrophoresis process and amplifies and filters them. The frequency band interference detection module scans the interference intensity of 16 Zigbee channels in real time, automatically switches to the channel with the least interference, and is compatible with the Zigbee protocol.

3. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that: The wireless transmission system based on the Zigbee network includes a microchip electrophoresis soil ion signal acquisition terminal that implements Zigbee communication through a chip and is equipped with a voltage regulator module. The voltage regulator module is connected to the DCOUPL interface to provide bias voltage for the radio frequency circuit and crystal oscillator.

4. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that: The media access control layer performs frame synchronization, frame assembly and parsing, acknowledgment and retransmission, creates network topology through the network layer, and manages packet forwarding using routing protocols.

5. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that: The Zigbee protocol stack includes: The coordinator node is the core of the network, responsible for managing the overall functionality and performance of the network. End nodes are responsible for data collection and transmission, sending data to other nodes or coordinators. Router nodes forward data from other nodes and extend the network's coverage area.

6. The wireless transmission system for electrophoretic ion signals according to claim 5, characterized in that: The coordinator node communicates with the sensor node via a serial port, transmits soil ion information, temperature, humidity, and location to the gateway, and receives return data from the gateway and sends it to the corresponding node. The router node configures its own hardware and software facilities through the GenericApp_Init() function, searches for wireless signal channels, and monitors the beacon request frames of the network coordinator.

7. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that: The microchip electrophoresis soil ion signal acquisition terminal collects rainfall and ambient temperature, judges the interference level based on the frequency band interference detection module, determines environmental anomalies based on the collected data and triggers power correction, and dynamically adjusts the number of retransmissions and retransmission intervals through the ACK waiting timer based on the Z-Stack protocol stack and the bit error rate and interference intensity of data frame transmission.

8. The wireless transmission system for electrophoretic ion signals according to claim 1, characterized in that: The process involves using a microchip electrophoresis soil ion signal acquisition terminal to screen four types of core agricultural cations and two key characteristics, while simultaneously providing spatiotemporally consistent background parameters for the ion data. The upper-end integrated control and communication module performs 16-bit binary compression on the collected ion type characteristics to form a 16-byte type characteristic matrix. The frequency band interference detection module scans the channel interference intensity in real time. The type characteristic matrix is ​​transmitted to the router, which then forwards it to the coordinator with a delay. The transmission path is dynamically maintained through the AODV routing algorithm.

9. The wireless transmission system for electrophoretic ion signals according to claim 8, characterized in that: The initialization process involves uploading the feature matrix to the cloud; simultaneously, the cloud is triggered to preload the SVM cation classification model. The cloud then uses the RBF kernel function to output ion category labels and calculates the maximum posterior probability of the classification confidence score. The target ion category instruction is then sent to the terminal. After receiving the instruction, the terminal collects the concentration characteristics of the target ion in a targeted manner.

10. The wireless transmission system for electrophoretic ion signals according to claim 9, characterized in that: The concentration characteristics of the target ions collected in the directional acquisition are transmitted to the coordinator through the original low-interference Zigbee channel, and then uploaded to the cloud via NB-IoT. The cloud classifies the type feature matrix through the SVM model, and calls the XGBoost concentration model of the target ions. Taking the concentration characteristics as input, the cloud outputs the ion concentration value, and integrates the ion category, concentration value, spatiotemporal parameters, and environmental parameters into a structured dataset.

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