Dynamic environment parameter sensing and self-adaptive information pushing system and method fused with near field communication technology

Abstract

The invention discloses a dynamic environment parameter sensing and self-adaptive information pushing system and method fused with the near field communication technology, and relates to the field of information pushing.The system comprises a terminal, an acquisition end and a cloud server, the radio frequency energy conversion unit comprises a near field communication chip and a radio frequency receiving coil, and the radio frequency receiving coil is coupled with the input end of the near field communication chip and used for capturing radio frequency energy through electromagnetic induction; the near field communication chip is used for storing and sending data information. The system is based on multi-dimensional data, and realizes pushing of different information and data under different scenes, positions and time.

Description

Technical Field

[0001] The present application relates to the field of information push, and in particular to a dynamic environment parameter perception and adaptive information push system and method integrating near-field communication technology. Background Art

[0002] In the field of IoT perception layer technology, near-field communication (NFC) technology has been widely used in areas such as logistics tracking and smart packaging due to its passive nature and convenient interaction. Prior art researchers have attempted to leverage the radio frequency energy harvesting capabilities of NFC tags to drive low-power sensors. For example, existing NFC tags with integrated temperature sensors utilize the radio frequency field energy emitted by the reader to briefly power the sensor for single-parameter measurement. While these solutions surpass the pure data storage capabilities of traditional tags, they still face significant bottlenecks in practical applications. First, existing designs often focus on integrating a single type of sensor (such as temperature or humidity) and lack the ability to simultaneously collect multi-dimensional data such as location coordinates, environmental parameters, and device status, resulting in a single dimensional data acquisition approach. Second, even among the few solutions that attempt to collect multi-parameter data, their data analysis is limited to simple threshold judgments, failing to establish associations with knowledge bases or deploy lightweight AI models, making it impossible to achieve context-aware intelligent decision-making. Summary of the Invention

[0003] The purpose of this application is to overcome at least one of the shortcomings of the existing technology and provide a dynamic environmental parameter perception and adaptive information push system and method that integrates near-field communication technology. The system is based on multi-dimensional data and can push different information and data in different scenarios, locations, and times.

[0004] To achieve the above-mentioned objectives, in the first aspect, the present application discloses a dynamic environmental parameter perception and adaptive information push system that integrates near-field communication technology, the system including a terminal, a collection terminal and a cloud server, wherein the terminal includes a radio frequency energy conversion unit, the radio frequency energy conversion unit includes a near-field communication chip and a radio frequency receiving coil, wherein the radio frequency receiving coil is coupled to the input end of the near-field communication chip for capturing radio frequency energy through electromagnetic induction; the near-field communication chip is used to store and send data information.

[0005] Optionally, the terminal further includes an energy storage circuit connected to the radio frequency energy conversion unit, wherein the energy storage circuit is composed of a rectifier and filter module and an energy storage capacitor, and is used to convert radio frequency energy into direct current energy and establish a power supply circuit.

[0006] Optionally, the terminal further includes a multimodal sensing module connected to the radio energy conversion unit and the energy storage circuit, and the multimodal sensing module includes at least one sensor.

[0007] Furthermore, the sensor is a low-power temperature sensor, a humidity sensor or an acceleration sensor.

[0008] Furthermore, the sensor is connected to the power supply circuit; Optionally, the terminal is further integrated with a micro-execution unit, which is composed of a micro motor or an LED indicator, and its driving circuit is connected to the power supply circuit of the radio frequency energy conversion unit to achieve functional coordination of data acquisition and execution control.

[0009] The acquisition terminal includes a near-field communication module, a data processing module, a satellite positioning module, a communication module, and a display module. These modules are all connected to the data processing module to enable data exchange. The near-field communication module has a built-in resonant frequency matching circuit for establishing a bidirectional data channel with the radio frequency energy conversion unit. Specifically, during near-field communication, the near-field communication module receives terminal data and device status information via radio frequency signals and transmits them to the data processing module.

[0010] Furthermore, the satellite positioning module obtains the geographical location coordinates of the acquisition end in real time and sends them to the data processing module.

[0011] Furthermore, the data processing module is used to receive and process data delivered by the near-field communication module, satellite positioning module, and near-field communication module, and send it to the communication module, which is then sent to the cloud server through the module. The data processing module receives data from the cloud server and sends all or part of it to the display module to display information or data.

[0012] The cloud server performs feature extraction and data cleaning on the data received from the acquisition end, generating a structured multidimensional data vector containing environmental parameters and geographic coordinates.

[0013] Compared with existing technologies, this technical solution realizes real-time perception of multi-dimensional dynamic parameters and intelligent service output through the collaboration of passive sensing architecture and cloud computing, while supporting flexible configuration of sensors and actuators through modular design.

[0014] In the second aspect, the present application discloses a method for dynamic environmental parameter perception and adaptive information push applied to a dynamic environmental parameter perception and adaptive information push system integrating near field communication technology, the specific steps of which are as follows: Step 1: The acquisition end transmits an RF carrier signal, which is coupled in the terminal's RF receiving coil through electromagnetic induction to generate an alternating current. If the terminal is equipped with an energy storage circuit, the RF energy is converted into DC power through the rectification and filtering module and charged to the energy storage capacitor to establish a power supply circuit. When the capacitor voltage reaches the preset threshold, the terminal's optional temperature sensor, humidity sensor, or acceleration sensor is activated. If a micro-executor unit is configured, it is synchronously triggered to enter the initialization state.

[0015] Step 2: The acquisition end uses a time-division multiplexing protocol to divide the RF field energy transmission period and the data communication period. If the terminal is equipped with an energy storage circuit, it maintains a constant carrier power during the energy transmission period to ensure the stability of the sensor power supply. During the data communication period, it switches to the modulated carrier mode and receives the sensor data and device status information sent by the terminal through the near-field communication module. At the same time, the longitude and latitude coordinates of the acquisition end are obtained through the satellite positioning module.

[0016] Step 3: The data processing module at the acquisition end performs spatiotemporal encoding on the environmental parameters collected by the sensor and the geographic location coordinates to construct a composite data frame containing timestamps, spatial coordinates, and multi-dimensional sensing parameters; it eliminates redundant components through a data compression algorithm, and uses an encrypted transmission protocol to perform end-to-end encryption on the data stream, which is then uploaded to the cloud server through the communication module.

[0017] Step 4: The cloud server performs decryption and decompression operations on the encrypted data stream and calls the multi-source data fusion engine to standardize the original data, including data cleaning, dimensional normalization conversion and dynamic filtering through anomaly detection mechanism to generate structured multidimensional data vectors.

[0018] Step 5: Input the multidimensional data vector into the pre-trained basic model for feature extraction, and use the time series analysis mechanism to identify dynamic change trends; match the associated business scenario features through the knowledge graph service system. When the preset trigger conditions are detected, call the lightweight AI reasoning model to generate a decision instruction set and obtain the corresponding data information, and send it to the collection end through the communication module.

[0019] Step 6: The data processing module at the acquisition end parses the decision instruction set, generates visual information, and presents it through the display module; if the terminal is equipped with a micro-execution unit, it generates a control signal and adjusts the execution parameters through the near-field communication reverse channel to achieve physical environment intervention.

[0020] Step 7: The system monitors the terminal sensor data and the response status of the execution unit in real time, and dynamically optimizes the model parameters and transmission strategy through a closed-loop feedback mechanism. If the deviation continues to exceed the tolerance range, the self-check program is triggered to recalibrate the system configuration and complete the adaptive optimization.

[0021] This method achieves flexible configuration of passive terminal nodes and intelligent response to multi-dimensional environmental parameters through modular data processing strategies and hierarchical decision-making mechanisms, optimizing the robustness and scalability of the system in complex scenarios.

[0022] Compared with the prior art, this application has at least one of the following beneficial technical effects: 1. Through the collaboration of passive sensing architecture and cloud computing, real-time perception of multi-dimensional dynamic parameters and intelligent service output are achieved, improving the richness of data acquisition and intelligent decision-making.

[0023] 2. The modular design supports flexible configuration of sensors and actuators to meet the needs of different scenarios and enhance the system's versatility and scalability.

[0024] 3. Adopt a hierarchical decision-making mechanism and closed-loop feedback optimization to ensure the stable operation and efficient execution of the system in complex scenarios, and improve the overall robustness of the system.

[0025] The above-listed beneficial effects are not exhaustive and other potential beneficial effects and detailed technical implementations will be further disclosed in the examples or other description sections of this application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] After reading the following detailed description in conjunction with the accompanying drawings, you will better understand the various aspects of the present disclosure. The positions, sizes, and ranges of various structures shown in the drawings and the like sometimes do not represent the actual positions, sizes, and ranges. In the drawings: Figure 1 This is a three-component block diagram of a system disclosed in this application.

[0027] Figure 2 It is a flow chart of the method disclosed in this application. DETAILED DESCRIPTION

[0028] The present disclosure will be described below with reference to the accompanying drawings, which illustrate several embodiments of the present disclosure. However, it should be understood that the present disclosure can be presented in many different ways and is not limited to the embodiments described below; in fact, the embodiments described below are intended to make the present disclosure more complete and fully illustrate the scope of protection of the present disclosure to those skilled in the art. It should also be understood that the embodiments disclosed herein can be combined in various ways to provide many additional embodiments.

[0029] It should be understood that like reference numerals refer to like elements throughout the drawings. In the drawings, the dimensions of some features may be distorted for clarity.

[0030] It should be understood that the terms used in the specification are only used to describe specific embodiments and are not intended to limit the present disclosure. All terms (including technical and scientific terms) used in the specification have the meanings commonly understood by those skilled in the art unless otherwise defined. For the sake of brevity and / or clarity, the techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered part of the authorization specification.

[0031] Refer to the attached Figure 1 and 2 This example illustrates the application of a dynamic environmental parameter perception and adaptive information push system that integrates near-field communication technology in cold chain logistics monitoring scenarios. This system consists of a terminal, a data collection terminal, and a cloud server. These components, through designed connectivity, enable precise perception and intelligent response to multi-dimensional environmental parameters throughout the cold chain logistics process.

[0032] The core of the terminal is the RF energy conversion unit, which consists of a near-field communication chip and a tightly coupled RF receiving coil. The RF receiving coil is typically made of high-permeability, low-loss magnetic materials, such as permalloy, to enhance electromagnetic induction efficiency. When the acquisition end transmits an RF carrier signal, the coil captures the energy and converts it into an alternating current, providing initial energy for subsequent circuits. For example, a near-field communication chip can be one that complies with the ISO / IEC 14443 standard (other equivalent chips are also available). These chips feature large storage capacity and fast data transmission capabilities, enabling them to store and transmit data.

[0033] To ensure continuous power supply, the terminal is equipped with an energy storage circuit consisting of a rectifier and filter module and an energy storage capacitor. The rectifier and filter module uses high-efficiency rectifier diodes and filter capacitors to convert RF energy into stable DC power, which charges the energy storage capacitor. The energy storage capacitor uses high-capacitance, low equivalent series resistance capacitors to quickly store energy and establish a power supply circuit, providing a stable DC power supply for subsequent sensors and actuators.

[0034] Considering the need to monitor multiple environmental parameters during cold chain logistics, the terminal integrates multimodal sensing modules, including low-power temperature sensors, humidity sensors, and accelerometers. The temperature sensor can be a thermistor, which accurately measures ambient temperature based on the temperature-dependent resistance of semiconductor materials. The humidity sensor uses a capacitive humidity sensor, which monitors ambient humidity by utilizing the capacitance change caused by water molecules adsorbed on the dielectric surface. The accelerometer, based on the piezoelectric effect, detects vibrations during transportation by converting tiny displacements of the mass into electrical signals. These sensors are all connected to the power supply circuit to ensure stable operation when sufficient power is available.

[0035] In addition, the terminal integrates a micro-actuator unit (MCU), consisting of a micromotor or LED indicator. The MCU can be a brushed DC motor, with its drive circuit connected to the output of the RF energy conversion unit. Upon receiving a control signal, it can activate the mechanical switches of the ventilation or refrigeration equipment in the refrigerated compartment, or unlock the compartment, thereby physically intervening in or controlling the opening and closing of the cold chain logistics environment. The LED indicator, whose drive circuit is also connected to the output of the RF energy conversion unit, is used to visually display the terminal's operating status and alarm information, enabling coordinated data acquisition and execution control functions.

[0036] On the data acquisition side, the near-field communication module has a built-in resonant frequency matching circuit, using high-quality inductor and capacitor components to ensure that the resonant frequency is consistent with the terminal's RF energy conversion unit, thereby establishing an efficient two-way data channel. During the near-field communication process, the near-field communication module receives the terminal's data and device status information through RF signals and sends it to the data processing module.

[0037] The data processing module utilizes a high-performance microprocessor with multitasking and data computing capabilities. It receives data from the near-field communication module and satellite positioning module, performs preliminary data processing, such as format conversion and data fusion, and then transmits the processed data to the communication module, which transmits it to the cloud server via wireless communication methods such as 4G / 5G networks or Wi-Fi. Simultaneously, the data processing module receives data from the cloud server and transmits all or part of it to the display module. The display module uses a high-resolution LCD screen to intuitively display information such as environmental parameters, geographic location, and system status, facilitating real-time monitoring by personnel.

[0038] The satellite positioning module uses a high-precision GPS / Beidou dual-mode positioning module, which can obtain the latitude and longitude coordinates of the collection end in real time and send them to the data processing module, providing accurate geographic location information for cold chain logistics monitoring and realizing precise positioning of transport vehicles or goods.

[0039] In cold chain logistics monitoring scenarios, when the acquisition end transmits an RF carrier signal, the terminal receives the signal. If equipped with an energy storage circuit, the rectifier and filter module converts the RF energy into DC power and charges the energy storage capacitor, establishing a power supply circuit. When the capacitor voltage reaches a preset threshold, the terminal's temperature sensor, humidity sensor, and accelerometer are activated. If equipped with a micro-executor unit, this is also triggered to enter its initialization state.

[0040] The acquisition end uses a time-division multiplexing protocol to divide the RF field energy transmission period into the data communication period. During the energy transmission period, a constant carrier power is maintained to ensure stable sensor power supply. During the data communication period, the system switches to a modulated carrier mode, receiving sensor data and device status information from the terminal via the near-field communication module. Simultaneously, the satellite positioning module obtains the longitude and latitude coordinates of the acquisition end.

[0041] The data processing module at the acquisition end codes the spatial and temporal association of environmental parameters collected by the sensors with geographic coordinates, constructing a composite data frame containing timestamps, spatial coordinates, and multidimensional sensor parameters. To improve data transmission efficiency, advanced data compression algorithms, such as those based on discrete cosine transforms, are used to eliminate redundant components, effectively reducing data volume. Furthermore, to ensure data security, the data stream is end-to-end encrypted using the AES encryption transmission protocol and uploaded to the cloud server via the communication module.

[0042] After decrypting and decompressing the received encrypted data stream, the cloud server invokes a multi-source data fusion engine to normalize the raw data. Data cleansing uses anomaly detection mechanisms, such as the statistical 3σ principle, to remove anomalous data. Dimensional normalization transforms data of varying dimensions to the same scale. Dynamic filtering, such as Kalman filtering, smooths data curves and generates structured multidimensional data vectors.

[0043] Multidimensional data vectors are input into a pre-trained basic model for feature extraction. The basic model can use recurrent neural networks (RNNs) or long short-term memory networks (LSTMs) from deep learning to identify time series features in the data. Time series analysis mechanisms, such as moving averages and exponential smoothing, are used to identify dynamic trends. The knowledge graph service system matches associated business scenario features. When a preset trigger condition is detected, such as temperature exceeding a set range or abnormally high humidity, a lightweight AI inference model, such as MobileNet, is invoked to generate a set of decision-making instructions, such as adjusting the cooling power of the refrigerated compartment or sending an alarm. The corresponding data information is then obtained and transmitted to the collection end via the communication module.

[0044] After parsing the decision-making instruction set, the data processing module at the acquisition end generates visual information and presents it through the display module. This information can be displayed on the screen, such as pop-up alarm prompts, current environmental parameters, and recommended operations. If the terminal is equipped with a micro-actuator, it generates control signals and adjusts execution parameters through the near-field communication backchannel. For example, it can control a micro-motor to drive a ventilation device, achieving physical environmental intervention and ensuring the stability of the cold chain logistics environment.

[0045] The system monitors terminal sensor data and actuator response status in real time, dynamically optimizing model parameters and transmission strategies through a closed-loop feedback mechanism. For example, it automatically adjusts the data transmission frequency based on the rate of change of sensor data to reduce data transmission latency. If the deviation persists beyond the tolerance range, such as when environmental parameters deviate from the normal range for a long time and the actuator cannot effectively adjust, a self-check program is triggered to recalibrate the system configuration, completing adaptive optimization and ensuring stable operation and efficient monitoring in the complex cold chain logistics scenarios.

[0046] In practical applications, for example, when transporting a batch of fresh produce that is extremely sensitive to temperature and humidity, the system can accurately sense the temperature, humidity, and vibration conditions within the refrigerated compartment in real time. Combined with geographic location information, through intelligent analysis and decision-making by cloud servers, it can promptly adjust the refrigeration environment to ensure the quality of the fresh produce. Compared with traditional cold chain logistics monitoring methods, this system has achieved a leap from single-parameter monitoring to multi-dimensional dynamic parameter perception. At the same time, through the collaboration of cloud-based intelligent decision-making and terminal execution units, it improves the intelligence level and emergency response capabilities of cold chain logistics monitoring, reduces the risk of cargo loss, and has significant economic and social benefits.

[0047] To further elaborate and facilitate understanding of this embodiment, the technical solution of this application has a wider range of application scenarios. The following is a detailed description of the principle of the differentiated push process of scanning the NFC tag (i.e., terminal) when buying a fishing rod at a mall and scanning the NFC tag at a lake: Imagine a smart fishing service system that uses the aforementioned dynamic environmental parameter perception and adaptive information push system that integrates near-field communication technology. When a user buys a fishing rod at a mall or fishes at a lake, they scan NFC tags in different locations, and the system pushes different practical information based on the scenario.

[0048] Let's first consider the scenario of buying a fishing rod in a shopping mall. Each fishing rod in the mall is equipped with an NFC tag. In this scenario, the RF energy conversion unit primarily consists of a near-field communication chip and an RF receiving coil. NFC chips that comply with the ISO / IEC 14443 standard are typically used, offering stable storage and short-range data transmission capabilities. Furthermore, to better operate in a shopping mall environment, the tag may also incorporate a micro-energy storage circuit capable of storing a small amount of electricity. This circuit includes a simple rectifier and filter module and a small-capacity energy storage capacitor to ensure the tag can temporarily store data and provide power even without sustained external electromagnetic field excitation.

[0049] When a user holds an NFC-enabled smartphone near the NFC tag on a fishing rod, the phone's near-field communication (NFC) module activates. The phone's internal NFC chip transmits a radio frequency (RF) carrier signal, which, through electromagnetic induction, couples to the RF receiving coil on the rod's NFC tag, generating an alternating current. The tag's internal RF energy conversion unit converts this energy into DC power, activating the tag's stored data. This data includes detailed information about the rod, including model, material, length, tonality, intended use (e.g., whether it's suitable for pit fishing or open-water fishing), manufacturer, price, and user reviews.

[0050] This data is modulated and sent back to the user's phone via the NFC communication protocol. The phone's data processing module demodulates and analyzes the received signal, then clearly displays the specific information of the fishing rods on the phone screen. Using the built-in GPS positioning function, the phone can also obtain the specific location of the shopping mall. Combined with the mall's indoor map and the distribution of surrounding fishing shops, it recommends other relevant fishing equipment to the user, such as fishing lines, hooks, and floats that are compatible with the selected fishing rod. A cloud server analyzes the user's browsing history, purchasing preferences, and other big data in the background. Using intelligent algorithms, it predicts related products that the user may be interested in. These personalized recommendations are then pushed to the user, helping them more conveniently select fishing rods and other matching equipment in the mall.

[0051] Consider the lakeside fishing scenario. The NFC tags placed at the lakeside terminals operate similarly to those in shopping malls, but the information they integrate focuses more on environmental parameters and practical fishing advice. When a user brings an NFC-enabled device to the lake and scans the NFC tag, the same data exchange process is triggered.

[0052] The data stored in the tag may include the distribution of fish in the current waters, fish species suitable for fishing in waters of different depths, recent fish dynamics (for example, a certain fish is in its breeding season, is active, and is easier to catch in a specific area), the best fishing time (the active feeding period of fish calculated based on sunrise and sunset times, temperature changes, etc.), weather conditions in the surrounding waters (such as the impact of air pressure changes on fish activity, reminding users that fishing may not be suitable when the air pressure is low), water quality parameters (such as water temperature, pH, dissolved oxygen content, etc., which have an important impact on fish activity), and recommended bait types (recommendations are made based on the taste preferences of different fish), suitable fishing spots (taking into account factors such as terrain, water depth, water flow speed, concealment, etc., to tell users where is more suitable for casting the rod), and other professional information.

[0053] At the same time, the NFC tag (collection terminal) at the lakeside uses a satellite positioning module to accurately obtain the current location coordinates. Combined with the environmental and fish data obtained from the terminal, the data processing module compiles a composite data frame containing geographic location, time, multi-dimensional environmental parameters, and fish analysis. These data frames are compressed and encrypted before being uploaded to the cloud server. The cloud server uses big data analysis and artificial intelligence algorithms to deeply mine and learn from historical fishing data, establishing fishing models for different waters and time periods.

[0054] Based on data requests triggered by the user's scan of an NFC tag near the lake, the cloud server matches the actual lakeside environment with a pre-set fishing model, predicts a more optimal fishing strategy, and generates a set of decision-making instructions. These instructions, such as suggestions for changing bait, adjusting the fishing spot, or altering the fishing depth, are then pushed to the user. If the user's device also incorporates a micro-executor unit (MCU), such as a smart fishing rod with sensors that can sense parameters like water temperature and tension, the cloud server can also adjust the user's device parameters in real time through a reverse control channel, helping the user better navigate complex fishing environments and increasing their fishing success rate and enjoyment.

[0055] In both scenarios, whether buying a fishing rod at the mall or fishing by the lake, the system fully leverages the convenience of near-field communication technology and the powerful processing capabilities of cloud computing. This differentiated information push is tailored to the user's needs and environmental characteristics in each scenario, providing users with precise, personalized services. This context-aware information push method is more tailored to users' actual needs than traditional, single-style information presentation, significantly improving the user experience and enabling users to obtain valuable information to aid decision-making in diverse scenarios.

[0056] It should be noted that some of the material selections, specific numerical parameters, and algorithm models involved in the above embodiments can be flexibly adjusted and optimized according to actual application scenarios and requirements. The adjustment of these details belongs to the common technology and routine operations of those skilled in the art and will not be repeated here. At the same time, for matters not described in detail, such as the specific circuit design of the near-field communication chip and the network protocol details of the communication module, they also fall within the scope of the existing technology well known to those skilled in the art. Those skilled in the art can implement the technical solutions of the present invention based on the above description and their own professional knowledge.

Claims

1. A dynamic environmental parameter perception and adaptive information push system integrating near field communication technology, characterized in that: The system includes a terminal, a collection terminal, and a cloud server. The terminal includes a radio frequency energy conversion unit, which includes a near-field communication chip and a radio frequency receiving coil. The radio frequency receiving coil is coupled to the input end of the near-field communication chip and is used to capture radio frequency energy through electromagnetic induction. The near-field communication chip is used to store and transmit data information. The collection terminal includes a near-field communication module, a data processing module, a satellite positioning module, a communication module, and a display module. The communication module, satellite positioning module, near-field communication module, and display module are all connected to the data processing module to achieve data exchange. The near-field communication module has a built-in resonant frequency matching circuit for establishing a bidirectional data channel with the radio frequency energy conversion unit. The satellite positioning module obtains the geographic location coordinates of the collection terminal in real time and sends them to the data processing module. The data processing module is used to receive and process data sent by the near-field communication module, satellite positioning module, and near-field communication module, and send it to the communication module, which is then sent to the cloud server through the module. The data processing module receives data from the cloud server and sends all or part of it to the display module to realize information or data display. The cloud server performs feature extraction and data cleaning on the data received from the collection terminal to generate a structured multidimensional data vector containing environmental parameters and geographic coordinates.

2. A dynamic environment parameter perception and adaptive information push system integrating near field communication technology as described in claim 1, characterized in that: The terminal also includes an energy storage circuit connected to the radio frequency energy conversion unit. The energy storage circuit consists of a rectifier and filter module and an energy storage capacitor, and is used to convert radio frequency energy into direct current energy and establish a power supply circuit.

3. A dynamic environment parameter perception and adaptive information push system integrating near field communication technology as described in claim 2, characterized in that: The terminal further includes a multimodal sensing module connected to the radio energy conversion unit and the energy storage circuit, and the multimodal sensing module includes at least one sensor.

4. A dynamic environment parameter perception and adaptive information push system integrating near field communication technology as described in claim 3, characterized in that: The sensor is a low-power temperature sensor, a humidity sensor, or an acceleration sensor.

5. A dynamic environment parameter perception and adaptive information push system integrating near field communication technology as described in claim 5, characterized in that: The sensor is connected to the power supply circuit.

6. A dynamic environment parameter perception and adaptive information push system integrating near field communication technology as described in claim 2, characterized in that: The terminal also integrates a micro-execution unit, which is composed of a micro motor or an LED indicator. Its driving circuit is connected to the power supply circuit of the radio frequency energy conversion unit to achieve functional coordination of data acquisition and execution control.

7. A method for dynamic environmental parameter perception and adaptive information push, characterized in that: The system for dynamic environmental parameter perception and adaptive information push integrated with near field communication technology as described in claims 1 to 6 comprises the following specific steps: Step 1: The acquisition end transmits an RF carrier signal, which couples into the terminal's RF receiving coil through electromagnetic induction to generate an alternating current. If the terminal is equipped with an energy storage circuit, the RF energy is converted into DC power through the rectifier and filter module and charged into the energy storage capacitor, establishing a power supply circuit. When the capacitor voltage reaches a preset threshold, the terminal's optional temperature sensor, humidity sensor, or acceleration sensor is activated. If a micro-executor unit is equipped, it is simultaneously triggered to enter the initialization state. Step 2: The acquisition end uses a time-division multiplexing protocol to divide the RF field energy transmission period and the data communication period. If the terminal is equipped with an energy storage circuit, a constant carrier power is maintained during the energy transmission period to ensure the stability of the sensor power supply. During the data communication period, it switches to the modulated carrier mode, receives sensor data and device status information sent by the terminal through the near-field communication module, and obtains the longitude and latitude coordinates of the acquisition terminal through the satellite positioning module; Step 3: The data processing module on the acquisition side performs spatiotemporal encoding of the environmental parameters collected by the sensor and the geographic location coordinates to construct a composite data frame containing a timestamp, spatial coordinates, and multi-dimensional sensing parameters. Redundant components are eliminated using a data compression algorithm, and the data stream is end-to-end encrypted using an encrypted transmission protocol. The data is then uploaded to the cloud server via the communication module. Step 4: The cloud server decrypts and decompresses the encrypted data stream and uses the multi-source data fusion engine to standardize the raw data. This includes data cleaning, dimensional normalization, and dynamic filtering through anomaly detection mechanisms to generate structured multidimensional data vectors. Step 5: Input the multidimensional data vector into the pre-trained basic model for feature extraction, and use the time series analysis mechanism to identify dynamic change trends. The knowledge graph service system matches the associated business scenario features. When the preset trigger conditions are detected, the lightweight AI inference model is called to generate a decision instruction set and obtain the corresponding data information, which is then sent to the collection end through the communication module. Step 6: The data processing module at the acquisition end parses the decision instruction set, generates visual information, and presents it through the display module; if the terminal is equipped with a micro-execution unit, it generates a control signal and adjusts the execution parameters through the near-field communication reverse channel to achieve physical environment intervention.

8. A method for dynamic environmental parameter perception and adaptive information push as claimed in claim 7, characterized in that: It also includes step 7: the system monitors the terminal sensor data and the response status of the execution unit in real time, and dynamically optimizes the model parameters and transmission strategy through a closed-loop feedback mechanism; if the deviation continues to exceed the tolerance range, the self-test program is triggered to recalibrate the system configuration to complete the adaptive optimization.

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