Multi-Mode Dynamic Positioning and Tracking Method and System for Recyclable Packaging Appliances

Abstract

The present invention relates to the field of intelligent logistics technology, and particularly to a multi-mode dynamic positioning and tracking method and system for reusable packaging appliances. The method includes the following steps: controlling a multi-mode communication unit by a microcontroller unit to perform multi-mode monitoring signal scanning, performing multi-mode collaborative positioning according to multi-mode communication signal data, and optimizing a multi-target positioning mode according to the multi-mode collaborative positioning data, so as to realize a positioning mode pre-switching decision for the reusable packaging appliance. The present invention proposes a dynamic switching and collaborative positioning mechanism, automatically selects an optimal positioning mode according to environmental conditions, evaluates the positioning signal strength and cost in real time, dynamically adjusts the technology combination, and ensures the continuity and accuracy of positioning data. The present invention greatly improves the tracking reliability of reusable packaging appliances in complex scenarios, reduces communication energy consumption, and is applicable to global supply chain management.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics technology, and particularly relates to a multi-mode dynamic positioning and tracking method and system for recyclable packaging appliances. Background Art

[0002] As an important part of the modern supply chain, the efficient management and tracking of recyclable packaging appliances have become key factors in optimizing the logistics system. Recyclable packaging appliances can effectively reduce resource waste, environmental pollution, and improve the operational efficiency of enterprises. Therefore, in the actual application process, how to achieve accurate positioning and efficient tracking of these packaging appliances has become a key problem to be solved urgently. However, the traditional tracking of recyclable packaging appliances mainly relies on single technical means, such as RFID tags, barcode scanning, or simple GPS positioning systems. For example, although GPS has high positioning accuracy in outdoor open environments, its positioning performance will significantly decline, or even be unable to position, in indoor or severely signal-blocked areas. While positioning technologies such as WiFi and LBS (Location Based Services) have certain availability in indoor environments, their positioning accuracy is relatively low, and they are easily affected by the environment, making it difficult to meet the requirements of high-precision positioning. Although BLE (Bluetooth Low Energy) low-power Bluetooth technology is suitable for indoor near-field positioning, its range is limited, and it is easily affected by multipath effects and signal attenuation in complex environments. A single positioning technology often fails to cover the various complex environments that recyclable packaging appliances experience during the supply chain transfer process, such as indoor warehouses, outdoor transport vehicles, urban streets, cross-border logistics centers, etc. On the other hand, the selection of positioning technology needs to comprehensively consider factors such as accuracy, cost, and energy consumption. For recyclable packaging appliances, long-term operation and low power consumption are crucial. Therefore, it is necessary to reduce the power consumption of the device while ensuring positioning accuracy. Traditional positioning solutions often adopt a single positioning technology. For example, they always rely on GPS for positioning. Even in indoor or poor network signal environments, they still try to perform GPS positioning, resulting in positioning failures, reduced accuracy, and consuming a large amount of electrical energy. It is difficult to cover all links of recyclable packaging appliances in the entire supply chain, easily leading to tracking interruptions and information gaps. Summary of the Invention

[0003] Based on this, the present invention provides a multi-mode dynamic positioning and tracking method and system for recyclable packaging appliances to solve at least one of the above technical problems.

[0004] To achieve the above object, a multi-mode dynamic positioning and tracking method for recyclable packaging appliances is applied to recyclable packaging appliances. The recyclable packaging appliances are embedded with a microcontroller unit, a low-power inertial measurement unit, and a multi-mode communication unit. The multi-mode communication unit includes a BLE communication component, a WiFi communication component, a 4G Cat1 communication component, and a GPS communication component. The microcontroller unit is electrically connected to the low-power inertial measurement unit and the multi-mode communication unit. The multi-mode dynamic positioning and tracking method for the recyclable packaging appliances includes the following steps:

[0005] Step S1: Use the microcontroller unit to control the multi-mode communication unit to perform multi-mode monitoring signal scanning to obtain multi-mode communication signal data; use the low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data;

[0006] Step S2: Perform positioning scenario label processing based on the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data;

[0007] Step S3: Perform motion position prediction based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning tracking trajectory prediction data;

[0008] Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to realize the positioning mode pre-switching decision for the recyclable packaging appliance.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Use the microcontroller unit to configure a low-power timer to obtain timer configuration parameters;

[0011] Step S12: Trigger a periodic interrupt on the microcontroller unit through the timer configuration parameters to obtain a wake-up interrupt signal;

[0012] Step S13: Activate the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up status signal;

[0013] Step S14: Use the wake-up status signal to perform multi-mode power supply control on the multi-mode communication unit to generate multi-mode power supply status data;

[0014] Step S15: Perform multi-mode monitoring signal scanning based on the multi-mode power supply status data to obtain multi-mode communication signal data;

[0015] Step S16: Use the low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data.

[0016] Preferably, the multi-mode communication signal data includes a BLE signal strength list, a WiFi signal list, 4G base station information, and GPS signal data. Step S15 includes the following steps:

[0017] Step S151: Send a beacon scanning instruction to the BLE communication component based on the multi-mode power supply status data, and then perform a complete beacon scan to obtain complete BLE scan data;

[0018] Step S152: Analyze the signal strength and beacon ID of the complete BLE scan data to obtain a BLE signal strength list;

[0019] Step S153: Perform a WiFi AP scan on the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scan data;

[0020] Step S154: Analyze the signal strength and SSID of the initial WiFi scan data to obtain a WiFi signal list;

[0021] Step S155: Activate the 4G Cat1 communication component based on the multi-mode power supply status data to obtain a 4G module activation signal;

[0022] Step S156: Query the base station information according to the 4G module activation signal to obtain 4G base station information;

[0023] Step S157: Start the GPS communication component based on the multi-mode power supply status data, and then collect GPS data to obtain raw GPS data;

[0024] Step S158: Analyze the number of satellites and HDOP value of the raw GPS data to generate GPS signal data.

[0025] Preferably, step S2 includes the following steps:

[0026] Step S21: Query the indoor beacon database according to the BLE signal strength list in the multi-mode communication signal data to obtain a matching beacon list;

[0027] Step S22: Calculate the BLE indoor matching degree according to the matching beacon list to generate a BLE indoor matching degree;

[0028] Step S23: Compare the WiFi signal list in the multi-mode communication signal data with the preset WiFi white list, and then perform an AP validity evaluation to obtain a WiFi confidence level;

[0029] Step S24: Evaluate the signal strength according to the 4G base station information in the multi-mode communication signal data to obtain a 4G signal strength score;

[0030] Step S25: Evaluate the GPS positioning accuracy based on the GPS signal data in the multi-mode communication signal data to obtain the GPS signal quality score;

[0031] Step S26: Process the positioning scenario tags according to the multi-mode communication signal data and the appliance motion monitoring data to obtain the real-time positioning scenario tag data;

[0032] Step S27: Perform multi-mode collaborative positioning on the BLE indoor matching degree, WiFi confidence, 4G signal strength score, and GPS signal quality score through the real-time positioning scenario tag data to generate multi-mode collaborative positioning data.

[0033] Preferably, step S26 includes the following steps:

[0034] Step S261: Judge the indoor / outdoor environment according to the BLE indoor matching degree to generate indoor / outdoor environment judgment data;

[0035] Step S262: Query the base station coverage range according to the 4G base station information in the multi-mode communication signal data to obtain the base station coverage range data;

[0036] Step S263: Match the geographical location of the base station coverage range data through the preset geographical fence information to obtain the geographical location matching degree;

[0037] Step S264: Identify the motion state based on the appliance motion monitoring data to generate the appliance motion state data;

[0038] Step S265: Judge the region type according to the geographical location matching degree to obtain the region type label;

[0039] Step S266: Classify the motion mode according to the appliance motion state data to obtain the motion mode label;

[0040] Step S267: Integrate the positioning scenario tags based on the indoor / outdoor environment judgment data, region type label, and motion mode label to obtain the real-time positioning scenario tag data.

[0041] Preferably, step S27 includes the following steps:

[0042] Step S271: Configure the availability weight of the positioning mode according to the BLE indoor matching degree, WiFi confidence, 4G signal strength score, and GPS signal quality score to obtain the initial communication positioning mode weight coefficient;

[0043] Step S272: Sort the priorities of the positioning modes according to the initial communication positioning mode weight coefficient to obtain the priority scores of each mode;

[0044] Step S273: Based on the real-time positioning scenario tag data, perform decision tree multi-mode combination reasoning through the priority scores of each mode to obtain the priority positioning mode combination data;

[0045] Step S274: Perform collaborative positioning processing for each mode according to the priority positioning mode combination data to generate multi-mode collaborative positioning data.

[0046] Preferably, the step S274 includes:

[0047] Activate the current collaborative positioning component according to the priority positioning mode combination data to obtain the collaborative positioning component activation status data;

[0048] Extract multi-mode positioning data according to the collaborative positioning component activation status data to obtain the positioning input data for each mode;

[0049] Perform parallel positioning calculations for each mode based on the positioning input data for each mode to obtain the independent positioning result data for each mode;

[0050] Evaluate the confidence of the positioning results for each mode by using the initial communication positioning mode weight coefficient for the independent positioning result data of each mode to obtain the positioning confidence for each mode;

[0051] Perform weighted collaborative positioning calculations on the independent positioning result data of each mode by using the positioning confidence for each mode to generate multi-mode collaborative positioning data.

[0052] Preferably, step S3 includes the following steps:

[0053] Step S31: Extract the timestamp from the multi-mode collaborative positioning data to obtain the current positioning time data;

[0054] Step S32: Retrieve historical positioning data for the current positioning time data through a preset historical retrieval time window to obtain historical trajectory data;

[0055] Step S33: Extract the motion feature parameters of the appliance by using a low-power inertial measurement unit;

[0056] Step S34: Identify the motion mode of the motion feature parameters through the historical trajectory data to generate motion mode identification data;

[0057] Step S35: Predict the motion position at the next moment for the multi-mode collaborative positioning data through the motion mode identification data to obtain the predicted position coordinates;

[0058] Step S36: Obtain the operation requirement data of the reusable packaging appliance; perform short-term trajectory tracking on the predicted position coordinates through the operation requirement data of the reusable packaging appliance to obtain the positioning tracking trajectory prediction data.

[0059] Preferably, step S4 includes the following steps:

[0060] Step S41: Analyze future trajectory points based on the positioning and tracking trajectory prediction data to generate future positioning trajectory point data;

[0061] Step S42: Conduct future environmental scenario analysis based on the real-time positioning scenario tag data and the future positioning trajectory point data to generate future scenario sequence data;

[0062] Step S43: Evaluate the availability of each positioning mode according to the future scenario sequence data to obtain the future availability scores of each mode;

[0063] Step S44: Obtain the energy consumption data per unit time of each positioning component and the communication cost data per unit data volume of each positioning component;

[0064] Step S45: Optimize the multi-objective positioning mode based on the future availability scores of each mode, the energy consumption data per unit time of each positioning component, and the communication cost data per unit data volume of each positioning component, so as to make a pre-switching decision on the positioning mode of the recyclable packaging appliance.

[0065] Preferably, the present invention also provides a multi-mode dynamic positioning and tracking system for a recyclable packaging appliance, which executes the multi-mode dynamic positioning and tracking method for a recyclable packaging appliance as described above. The multi-mode dynamic positioning and tracking system for a recyclable packaging appliance includes:

[0066] A multi-source data acquisition module, configured to use a microcontroller unit to control a multi-mode communication unit to perform multi-mode monitoring signal scanning to obtain multi-mode communication signal data; use a low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data;

[0067] A collaborative positioning processing module, configured to perform positioning scenario tag processing according to the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario tag data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario tag data to generate multi-mode collaborative positioning data;

[0068] A trajectory prediction and tracking module, configured to predict the motion position according to the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning and tracking trajectory prediction data;

[0069] A multi-mode dynamic positioning module, configured to optimize the multi-objective positioning mode according to the positioning and tracking trajectory prediction data, so as to make a pre-switching decision on the positioning mode of the recyclable packaging appliance.

[0070] The present invention can scan and acquire multiple positioning signals including but not limited to BLE, Wi-Fi, 4G Cat1 and GPS by integrating a multi-mode communication unit, thus overcoming the limitations of a single technology in a specific scenario. For example, in an indoor environment where the GPS signal is blocked, the system can automatically switch to WiFi or BLE mode, and use indoor base stations or beacons for positioning to ensure the continuity of positioning information. In a wide outdoor area, it can flexibly switch to 4G Cat1 or GPS mode to achieve long-distance and high-precision tracking. This multi-mode signal acquisition capability greatly expands the coverage of the positioning system, reduces the probability of the occurrence of positioning blind spots, and ensures the real-time visibility of recyclable packaging equipment during the entire supply chain circulation process. Even if the equipment switches between different environments, it can always maintain the tracking state to avoid information faults caused by signal loss. A low-power inertial measurement unit is used to perform periodic motion sampling to obtain the motion data of the equipment. These motion data can not only assist in positioning, but also be used to judge the motion state and environmental characteristics of the equipment. For example, by analyzing the data of the inertial measurement unit, it can be judged whether the equipment is in a transport state, a stationary state, and even the type of transport vehicle in which the equipment is located can be inferred. This provides more abundant input information for subsequent positioning scene label processing and improves the accuracy of scene recognition. More importantly, positioning in combination with motion data can reduce dependence on external signals, especially in areas with weak or unstable signals. The data of the inertial measurement unit can play a role in smoothing and correction, improving the stability and reliability of positioning. According to the multi-mode communication signal data and the equipment motion monitoring data, the type of environment and positioning requirements of the equipment can be judged in real time. For example, the system can automatically identify whether the equipment is in an indoor warehouse, an outdoor transport vehicle or a cross-border logistics center, and select the optimal positioning mode combination according to different scenarios. This intelligent scene recognition capability avoids the waste of resources and the reduction of positioning accuracy caused by the operation of traditional systems in fixed mode. By analyzing the advantages and disadvantages of different positioning modes and combining positioning scene labels, the system can select a suitable fusion algorithm to perform weighted averaging, Kalman filtering and other processing on the data of different positioning modes to improve positioning accuracy and stability. For example, in an indoor environment, the positioning data of WiFi and BLE can be combined, and the global positioning information of WiFi and the near-field positioning information of BLE can be used to achieve more accurate positioning. In outdoor environments, the positioning data of GPS and LBS can be combined, and the high-precision positioning information of GPS and the fast positioning information of LBS can be used to improve the positioning speed and accuracy, making full use of the advantages of various positioning modes, achieving the effect of learning from each other's strengths and making up for each other's weaknesses, and significantly improving the overall performance of the positioning system. By analyzing the movement trajectory and future movement trends of the device, the system can predict the type of environment and positioning requirements that the device will enter in advance, and switch to the optimal positioning mode combination in advance.For example, when the device is about to enter the indoor environment, the system can turn on the WiFi and BLE modules in advance to achieve seamless switching. This pre-switching ability avoids the latency and accuracy degradation problems of traditional systems during positioning mode switching, ensuring the continuity and accuracy of positioning information. In addition, through the optimization of the positioning mode, the system can also achieve refined power consumption management. For example, when the device is stationary, high-power consumption modules such as GPS can be turned off to reduce power consumption. Through pre-switching and power consumption management, this solution can maximize the battery life of the device and reduce maintenance costs. Therefore, a multi-mode dynamic positioning and tracking method for recyclable packaging devices of the present invention can automatically select the optimal positioning mode according to the specific environment where the recyclable packaging device is located (such as indoor / outdoor, regional network coverage) by introducing a dynamic switching and collaborative positioning mechanism. The system evaluates the positioning signal strength and cost in real time, dynamically adjusts the technology combination, and ensures the continuity and accuracy of positioning data, thereby achieving continuous and precise positioning of recyclable packaging devices in the supply chain. It effectively solves the problems of positioning blind spots, accuracy degradation, and excessive energy consumption that easily occur in traditional single tracking technologies when facing complex scenarios of indoor / outdoor switching and network signal changes, resulting in interruption of packaging device tracking and increased management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 It is a schematic flowchart of the steps of the multi-mode dynamic positioning and tracking method for the recyclable packaging device of the present invention;

[0072] Figure 2 is Figure 1 a detailed implementation step flowchart of step S3 in

[0073] Figure 3 is Figure 1 a detailed implementation step flowchart of step S4 in

[0074] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0076] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0077] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0078] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a multi-mode dynamic positioning and tracking method for recyclable packaging appliances, which is applied to recyclable packaging appliances. The recyclable packaging appliances are embedded with a microcontroller unit, a low-power inertial measurement unit, and a multi-mode communication unit. The multi-mode communication unit includes a BLE communication component, a WiFi communication component, a 4G Cat1 communication component, and a GPS communication component. The microcontroller unit is electrically connected to the low-power inertial measurement unit and the multi-mode communication unit. The multi-mode dynamic positioning and tracking method for the recyclable packaging appliances includes the following steps:

[0079] Step S1: Use the microcontroller unit to control the multi-mode communication unit to perform multi-mode monitoring signal scanning to obtain multi-mode communication signal data; use the low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data;

[0080] Step S2: Perform positioning scenario label processing based on the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data;

[0081] Step S3: Perform motion position prediction based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning tracking trajectory prediction data;

[0082] Step S4: Perform multi-target positioning mode optimization based on the positioning tracking trajectory prediction data to implement a positioning mode pre-switching decision for the recyclable packaging appliance.

[0083] In an embodiment of the present invention, the multi-mode dynamic positioning and tracking method for the recyclable packaging appliance includes the following steps:

[0084] Step S1: Use a microcontroller unit to control a multi-mode communication unit to perform multi-mode monitoring signal scanning to obtain multi-mode communication signal data; use a low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data;

[0085] In the embodiment of the present invention, a microcontroller unit (MCU) integrating a 32-bit ARM Cortex-M0+ core is selected. The MCU is built-in with a low-power timer module and multiple communication interfaces (UART, SPI, I2C). The multi-mode communication unit includes a BLE module, a WiFi module, a 4G Cat1 module, and a GPS module, and each module has an independent power control pin. The low-power inertial measurement unit (IMU) is a six-axis sensor, including a three-axis accelerometer and a three-axis gyroscope. The low-power timer is configured by directly operating the MCU registers. Enable the peripheral clock of the timer (write 1 to the corresponding bit of the clock control register), configure the prescaler (for example, the division factor is 32, write 31 to the prescaler register), configure the auto-reload register (for example, a 1-second period, write 1023), and enable the update interrupt (write 1 to the corresponding bit of the interrupt enable register). When the timer counter reaches the auto-reload value, an update event is generated, triggering an interrupt. In the interrupt service routine (ISR), clear the interrupt flag bit (write 1 to the corresponding bit of the interrupt status register), and set the global wake-up flag variable to 1. After the MCU is initialized, it enters the low-power sleep mode. In the main loop, check the global wake-up flag variable. If it is 0, execute the WFI instruction to continue sleeping; if it is 1, exit the sleep, set the wake-up status indication variable to 1, and clear the global wake-up flag variable. When the wake-up status indication variable is 1, trigger the power supply control of the multi-mode communication unit. According to the preset strategy (for example, NB-IoT first, switch to LoRa in case of failure, and finally GNSS), control the power supply of each module through GPIO. For example, first set the power control pin of the NB-IoT module to high level to turn on the NB-IoT power supply. According to the multi-mode power supply status data, control the corresponding module to scan for signals. For example, when "NB-IoT is powered on", the MCU sends AT commands to the NB-IoT module through UART to search for NB-IoT base station signals, and stores the results (signal strength, cell ID, etc.) in the memory as NB-IoT communication signal data. Similarly, control the WiFi module to scan for WiFi APs, obtain the SSID and RSSI, and store them as a WiFi signal list; control the GPS module to search for satellites and obtain NMEA sentences. After the MCU wakes up, send configuration commands to the IMU through the I2C interface to set the sampling rate (for example, 10Hz), measurement range, and low-power mode. The IMU collects acceleration and angular velocity data at the set sampling rate, and the MCU periodically reads the FIFO data through I2C and stores it as appliance motion monitoring data.

[0086] Step S2: Perform positioning scenario label processing on the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data;

[0087] In an embodiment of the present invention, the motion state of the appliance is calculated using the three-axis acceleration data in the appliance motion monitoring data, such as stationary, uniform motion, or accelerated motion. Through preset threshold judgment, the motion state of the appliance is converted into motion labels, such as "stationary", "low-speed movement", and "high-speed movement". At the same time, according to the GPS signal strength in the multi-mode communication signal data, it is judged whether the GPS signal is available. The GPS signal availability is combined with the motion label to generate real-time positioning scenario label data. For example, when the GPS signal is available and the appliance is in a stationary state, the real-time positioning scenario label is "outdoor stationary"; when the GPS signal is unavailable and the appliance is in a low-speed movement state, the real-time positioning scenario label is "indoor low-speed movement". According to the real-time positioning scenario label data, a suitable positioning algorithm is selected for multi-mode collaborative positioning. For example, when the real-time positioning scenario label is "outdoor stationary", GPS positioning is preferentially used; when the real-time positioning scenario label is "indoor low-speed movement", Bluetooth beacon positioning combined with inertial navigation is used for positioning. The finally obtained positioning result (latitude and longitude or indoor coordinates) is used as multi-mode collaborative positioning data.

[0088] Step S3: Perform motion position prediction based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning tracking trajectory prediction data;

[0089] In an embodiment of the present invention, the Kalman filter algorithm is used to process the multi-mode collaborative positioning data to predict the future position of the appliance. The state vector of the Kalman filter algorithm includes the position and velocity information of the appliance, and the measurement vector is the multi-mode collaborative positioning data. According to the current position, velocity, and acceleration of the appliance (from the appliance motion monitoring data), the position of the appliance at the next moment is predicted. Connecting multiple predicted position points forms a short-term trajectory, that is, the positioning tracking trajectory prediction data. For example, predict the position of the appliance within the next 5 seconds, and store these 5 predicted position points in a specified memory area of the microcontroller unit to form the positioning tracking trajectory prediction data.

[0090] Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to achieve a positioning mode pre-switching decision for the recyclable packaging appliance.

[0091] In an embodiment of the present invention, the analysis and positioning tracking trajectory prediction data is analyzed to judge the future movement trend of the appliance. For example, if the predicted trajectory indicates that the appliance is about to enter an indoor environment, it is predicted that the GPS signal will be lost. According to the prediction result, the multi-target positioning mode is optimized, that is, a positioning mode pre-switching decision is made. For example, when it is predicted that the appliance is about to enter an indoor environment, Bluetooth scanning is started in advance so that it can seamlessly switch to the Bluetooth positioning mode after the GPS signal is lost. The result of the pre-switching decision (for example, starting Bluetooth scanning) controls the corresponding hardware module through the GPIO of the microcontroller unit. For example, the power control pin of the Bluetooth module is set to a high level to turn on the Bluetooth module.

[0092] Preferably, step S1 includes the following steps:

[0093] Step S11: Configure the low-power timer using the microcontroller unit to obtain timer configuration parameters;

[0094] Step S12: Trigger a periodic interrupt for the microcontroller unit through the timer configuration parameters to obtain a wake-up interrupt signal;

[0095] Step S13: Activate the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up status signal;

[0096] Step S14: Control the multi-mode power supply of the multi-mode communication unit using the wake-up status signal to generate multi-mode power supply status data;

[0097] Step S15: Scan the multi-mode monitoring signal based on the multi-mode power supply status data to obtain multi-mode communication signal data;

[0098] Step S16: Perform periodic motion sampling using the low-power inertial measurement unit to obtain appliance motion monitoring data.

[0099] In an embodiment of the present invention, a 32-bit ARM Cortex-M0+ core microcontroller unit (MCU) with a built-in low-power timer module is selected. This low-power timer module is independent of the system main clock and uses an external 32.768 kHz crystal oscillator as the clock source. It is configured by directly operating the registers of the MCU. First, enable the low-power timer peripheral clock by writing "1" to the corresponding bit of a specific clock control register. Then, configure the prescaler to divide the 32.768 kHz clock source. For example, set the prescaler coefficient to 32 to reduce the clock frequency to 1.024 kHz, and write the value 31 to the prescaler register (because the hardware prescaler value is the written value + 1). Next, configure the auto-reload register (ARR) to determine the timing period. For example, if a 1-second timing period is required, write 1023 to the ARR register (because the auto-reload value is also the written value + 1, and at a 1.024 kHz clock, 1024 clock cycles is 1 second). Finally, enable the timer update interrupt by writing "1" to the corresponding bit of the interrupt enable register. When the value of the timer counter reaches the value set in the auto-reload register (ARR) (1023), the hardware automatically clears the counter value and generates an update event. Since the update interrupt was enabled in step S11, this update event triggers the interrupt controller. The interrupt controller jumps to the entry address of the corresponding interrupt service routine (ISR) according to the preset interrupt vector table. In the interrupt service routine, first clear the interrupt flag bit by writing "1" to the corresponding bit of the interrupt status register. Then, set a global wake-up flag variable to "1", indicating that a wake-up interrupt has occurred. This global wake-up flag variable is a boolean variable defined in the MCU memory. Finally, after the interrupt service routine is executed, the program returns to the interrupted program point to continue execution. The microcontroller unit (MCU) enters the low-power sleep mode, such as the STOP Mode, after initialization. At this time, the CPU stops running and most peripherals stop working to reduce power consumption. In the main loop program of the MCU, check the global wake-up flag variable generated in step S12 at the beginning of each loop. If the variable is "0", it means that no wake-up interrupt has occurred, and the MCU continues to execute the low-power wait instruction (such as the WFI instruction of the ARM Cortex-M0+ core) and remains in the sleep mode. If the variable is "1", it means that a wake-up interrupt has occurred, and the MCU exits the low-power wait state and continues to execute the subsequent code of the main loop. At the same time, clear the global wake-up flag variable by writing "0" to it. In the main loop, set a wake-up status indicator variable to "1", indicating that the MCU is in the wake-up state. This wake-up status indicator variable is also a boolean variable defined in the MCU memory. The multi-mode communication unit includes multiple communication modules, such as an NB-IoT module, a LoRa module, and a GNSS module.Each module has an independent power control pin. The microcontroller unit (MCU) is connected to these power control pins through general-purpose input / output (GPIO) pins. The wake-up state signal generated in step S13 (the wake-up state indication variable is "1") triggers the MCU to execute the power supply control logic for the multi-mode communication unit. According to the preset communication strategy, for example, preferentially use NB-IoT for communication, if it fails, try LoRa, and finally use GNSS to obtain location information for power control. First, the MCU turns on the power of the NB-IoT module by setting the GPIO connected to the power control pin of the NB-IoT module to high level (e.g., 3.3V). Then, update the multi-mode power supply state data to "NB-IoT powered". If subsequent communication fails, the MCU pulls this GPIO low (0V) to turn off the power of the NB-IoT module, then sets the GPIO connected to the power control pin of the LoRa module to high level to turn on the power of the LoRa module, and updates the multi-mode power supply state data to "LoRa powered". The microcontroller unit (MCU) controls the corresponding communication module to perform signal scanning. If the multi-mode power supply state data is "NB-IoT powered", the MCU sends AT commands to the NB-IoT module through the UART interface to configure the NB-IoT module to enter the signal search mode. The NB-IoT module searches for surrounding NB-IoT base station signals according to the commands and returns information such as the signal strength and cell ID found through the UART interface to the MCU. The MCU stores the received data in memory as NB-IoT communication signal data. If the multi-mode power supply state data is "LoRa powered", the MCU sends configuration commands to the LoRa module through the SPI interface to set parameters such as the spreading factor, bandwidth, and coding rate of the LoRa module, and starts the receiving mode. The LoRa module receives LoRa signals that meet the set parameters in the surrounding environment and returns information such as the received signal strength and signal-to-noise ratio through the SPI interface to the MCU. The MCU stores this data as LoRa communication signal data. Similarly, if the GNSS module is used, the MCU sends commands through the UART interface to control the GNSS module to search for satellite signals and obtain positioning data, which is stored as GNSS communication signal data. The IMU is connected to the microcontroller unit (MCU) through the I2C interface. After the MCU is awakened, it sends configuration commands to the IMU through the I2C interface to set the measurement ranges, sampling rates (e.g., 10Hz), and low-power modes of the accelerometer and gyroscope. After the configuration is completed, the IMU periodically collects acceleration and angular velocity data at the set sampling rate. The IMU has a FIFO (First In First Out) buffer inside for temporarily storing the collected data. The MCU periodically reads the data in the FIFO buffer of the IMU through the I2C interface. For example, read the FIFO buffer once per second.The data read includes the acceleration values in three axes (X, Y, Z) and the angular velocity values in three axes. These data are represented in two's complement form and serve as the appliance motion monitoring data.

[0100] Preferably, the multi-mode communication signal data includes a BLE signal strength list, a WiFi signal list, 4G base station information, and GPS signal data. Step S15 includes the following steps:

[0101] Step S151: Send a beacon scan instruction to the BLE communication component based on the multi-mode power supply status data, and then perform a complete beacon scan to obtain complete BLE scan data;

[0102] Step S152: Analyze the signal strength and beacon ID of the complete BLE scan data to obtain the BLE signal strength list;

[0103] Step S153: Perform a WiFi AP scan on the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scan data;

[0104] Step S154: Analyze the signal strength and SSID of the initial WiFi scan data to obtain the WiFi signal list;

[0105] Step S155: Activate the 4G Cat1 communication component based on the multi-mode power supply status data to obtain a 4G module activation signal;

[0106] Step S156: Query the base station information according to the 4G module activation signal to obtain the 4G base station information;

[0107] Step S157: Start the GPS communication component based on the multi-mode power supply status data, and then collect GPS data to obtain the GPS raw data;

[0108] Step S158: Analyze the number of satellites and HDOP value of the GPS raw data to generate GPS signal data.

[0109] In an embodiment of the present invention, a microcontroller unit (MCU) is connected to a BLE communication component through a UART interface. First, the MCU sends a predefined beacon scanning instruction through the UART. This instruction is a specific byte sequence, such as "0x01 0x02 0x03 0x04" (the specific instruction is defined according to the data sheet of the BLE chip). This instruction instructs the BLE chip to start scanning for BLE beacon broadcasts in the surrounding environment. After receiving this instruction, the BLE chip activates its internal radio frequency receiving circuit and listens for BLE beacon broadcast packets on three broadcast channels (channels 37, 38, and 39) in the 2.4 GHz ISM band. The BLE chip stays on each channel for a period of time (e.g., 100 milliseconds) and captures all broadcast packets sent within this time window. Each captured broadcast packet contains raw data, such as the type of the broadcast packet, transmission power, MAC address, and payload data, etc. The BLE chip sends all the captured raw broadcast packet data back to the MCU through the UART interface in a predefined format (e.g., adding a length byte before each broadcast packet data). The MCU first parses the header information of the broadcast packet according to the BLE protocol specification, extracts the type of the broadcast packet (to determine whether it is a connectable broadcast packet or a non-connectable broadcast packet) and the transmission power from it. Then, the received signal strength indication (RSSI) value is extracted from the raw data. The RSSI value is usually a negative integer, with the unit of dBm, indicating the signal strength received. Next, according to different types of broadcast packets, the corresponding beacon ID is parsed. The MCU is connected to a WiFi communication component through an SPI interface. The MCU first sends a predefined WiFi AP scanning instruction to the WiFi chip through the SPI interface. This instruction is usually a specific command code, such as "AT+CWLAP" (the specific instruction refers to the data sheet of the WiFi chip). This instruction instructs the WiFi chip to scan for WiFi access points (APs) in the surrounding environment. After receiving the instruction, the WiFi chip activates its internal radio frequency receiving circuit and scans for available WiFi networks in the 2.4 GHz and / or 5 GHz bands. The WiFi chip stays on each channel for a period of time in sequence, receives and decodes the Beacon frames of the 802.11 protocol. The Beacon frames are periodically broadcast by the WiFi AP and contain information such as the SSID, MAC address, encryption method, signal strength, etc. of the AP. The WiFi chip returns the information of all scanned APs to the MCU through the SPI interface in a predefined format (e.g., separating each AP information with a comma and separating fields with a semicolon). The MCU parses the string in a predefined format (consistent with the format of the data returned by the WiFi chip in step S153). Usually, each AP information contains multiple fields, such as SSID (service set identifier, i.e., the WiFi name), BSSID (MAC address of the AP), RSSI (received signal strength indication), encryption method, etc.The MCU extracts the SSID and RSSI values of each AP through a string splitting function, such as splitting by commas and semicolons. First, the MCU supplies power to the 4G Cat1 module by controlling the GPIO connected to the power control pin of the 4G Cat1 module to output a high level. Then, the MCU sends a series of AT commands to the 4G Cat1 module through the UART interface for module initialization and activation. These AT commands include: AT (testing whether the AT command works properly), AT+CPIN? (checking whether the SIM card is ready), AT+CREG? (checking the network registration status), etc. (For the specific commands and sequence, refer to the data manual of the 4G Cat1 module). After sending the necessary AT commands, the MCU receives the response from the 4G Cat1 module through the UART interface. The MCU supplies power to the GPS module by controlling the GPIO connected to the power control pin of the GPS module to output a high level (such as 3.3V). Then, the MCU sends a start command to the GPS module through the UART interface (for the specific command and baud rate, refer to the data manual of the GPS module), and this command sets the output frequency of the GPS module to 1Hz. After receiving the start command, the GPS module starts its internal radio frequency receiving circuit and begins to search for and track GPS satellite signals. The GPS module receives navigation messages from multiple GPS satellites, which contain satellite ephemeris data, time information, etc. The MCU reads the NMEA 0183 statements line by line and determines the statement type according to the identifiers of the statements (such as "GPGGA", "GPGGA", "GPRMC"). For the GPGGA statement, the MCU extracts the 7th field (number of satellites) and the 8th field (HDOP, horizontal dilution of precision) through a string splitting function (such as splitting by commas). The number of satellites represents the number of GPS satellites currently used for positioning, usually an integer. The HDOP value represents the horizontal positioning accuracy. The smaller the value, the higher the accuracy, usually a floating-point number. For the GPRMC statement, the MCU can extract information such as UTC time and positioning status (valid / invalid), and parse out information such as the number of satellites, HDOP value, and positioning status.

[0110] Preferably, step S2 includes the following steps:

[0111] Step S21: Query the indoor beacon database according to the BLE signal strength list in the multi-mode communication signal data to obtain a list of matching beacons;

[0112] Step S22: Calculate the BLE indoor matching degree according to the list of matching beacons to generate the BLE indoor matching degree;

[0113] Step S23: Compare the WiFi signal list in the multi-mode communication signal data with the preset WiFi white list, and then evaluate the AP validity to obtain the WiFi confidence level;

[0114] Step S24: Evaluate the signal strength based on the 4G base station information in the multi-mode communication signal data to obtain the 4G signal strength score;

[0115] Step S25: Evaluate the GPS positioning accuracy based on the GPS signal data in the multi-mode communication signal data to obtain the GPS signal quality score;

[0116] Step S26: Process the positioning scenario tags according to the multi-mode communication signal data and the appliance motion monitoring data to obtain the real-time positioning scenario tag data;

[0117] Step S27: Perform multi-mode collaborative positioning on the BLE indoor matching degree, WiFi confidence, 4G signal strength score, and GPS signal quality score through the real-time positioning scenario tag data to generate multi-mode collaborative positioning data.

[0118] In the embodiment of the present invention, the indoor beacon database is pre-stored in the Flash memory of the microcontroller unit (MCU) or an external memory (such as EEPROM, SD card). After obtaining the BLE signal strength list, the MCU traverses each beacon information in the list. For each beacon information, its UUID, Major ID, and Minor ID are extracted. Using these three IDs as key values, a search is performed in the indoor beacon database. If a completely matching record (i.e., the UUID, Major ID, and Minor ID are all the same) is found in the database, the information such as the beacon deployment location coordinates in the record is extracted as a matching beacon. The information of all the found matching beacons (including the beacon ID and location coordinates) is stored in an array to form a matching beacon list. If the list is empty, the BLE indoor matching degree is set to 0. If the list is not empty, the MCU traverses each matching beacon in the matching beacon list. For each matching beacon, the corresponding RSSI value is obtained from the BLE signal strength list. According to the RSSI value and the deployment location of the beacon recorded in the database, a matching degree score is calculated. The calculation formula of the matching degree score can be: Score = 1 / (1 + |RSSI - Calibrated_RSSI| (Distance_Factor), where Calibrated_RSSI is the calibrated RSSI value measured by the beacon at the deployment location (pre-stored in the database), and Distance_Factor is a distance factor used to adjust the relationship between the RSSI value and the distance (adjusted according to the actual environment, such as 0.1). Calculate the average of the matching scores of all matching beacons to obtain the average matching score. Multiply this average matching score by a weight coefficient (such as 100) to adjust the score range to between 0 and 100, which is used as the BLE indoor matching degree. Preset a WiFi whitelist and store it in the Flash memory of the MCU. This whitelist contains a series of SSIDs (WiFi names) of WiFi access points (APs) allowed to connect. After obtaining the WiFi signal list in step S154, the MCU traverses each WiFi AP information in this list. For each AP information, extract its SSID. Compare the extracted SSID with the SSIDs in the WiFi whitelist one by one. If a matching SSID is found in the whitelist, then this AP is considered valid. Count the number of APs that appear in the whitelist in the WiFi signal list, denoted as Valid_AP_Count. Then, perform an AP validity evaluation. Calculate the ratio of the number of valid APs to the total number of APs in the WiFi signal list: Ratio = Valid_AP_Count / Total_AP_Count, where Total_AP_Count is the total number of APs in the WiFi signal list. Multiply this ratio by a weight coefficient (such as 100) to obtain a value between 0 and 100, which is used as the WiFi confidence level. If the WiFi signal list is empty, or the whitelist is empty, or no matching APs are found, then the WiFi confidence level is set to 0. The MCU extracts the RSRP (Reference Signal Received Power) value of the current serving cell from it. The RSRP value is usually a negative integer with the unit of dBm. According to the magnitude of the RSRP value, perform a signal strength evaluation. Set several thresholds, such as -80dBm, -90dBm, -100dBm. If the RSRP value is greater than -80dBm, the 4G signal strength score is 100; if the RSRP value is between -80dBm and -90dBm, the score is 80; if it is between -90dBm and -100dBm, the score is 60; if it is less than -100dBm, the score is 40. If the RSRP value cannot be obtained from the 4G base station information, the 4G signal strength score is 0. According to the HDOP value and the number of satellites, perform a GPS positioning accuracy evaluation. Set the HDOP threshold and the number of satellite thresholds, such as the HDOP threshold is 2.0 and the number of satellite threshold is 6.If the HDOP value is less than 2.0 and the number of satellites is greater than 6, the GPS signal quality score is 100; if the HDOP value is less than 2.0 but the number of satellites is less than 6, the score is 80; if the HDOP value is between 2.0 and 5.0 and the number of satellites is greater than 6, the score is 60; if the HDOP value is greater than 5.0 or the number of satellites is less than 4, the score is 40. If the Status field in the GPS signal data is "Invalid", indicating invalid positioning, the GPS signal quality score is 0. According to the acceleration data collected by the IMU (Inertial Measurement Unit), calculate the motion state of the appliance. For example, by calculating the variance of the acceleration data over a period of time, if the variance is greater than a certain threshold (e.g., 0.1 m / s²), it is determined that the appliance is in a moving state; if the variance is less than the threshold, it is determined that the appliance is in a stationary state. Then, according to the positioning state in the GPS signal data, determine whether it is in a GPS valid positioning scenario. If the GPS signal quality score is 0, it means that GPS positioning is invalid; otherwise, it means that GPS positioning is valid. Combine the motion state and GPS positioning state, as well as the BLE indoor matching degree and WiFi confidence level, to generate a positioning scenario label. For example: if GPS positioning is valid and the appliance is in a moving state, the scenario label is "Outdoor moving"; if GPS positioning is valid and the appliance is in a stationary state, the scenario label is "Outdoor stationary"; if GPS positioning is invalid, the BLE indoor matching degree is greater than 70 and the appliance is in a stationary state, the scenario label is "Indoor stationary"; if GPS positioning is invalid, the WiFi confidence level is greater than 70 and the appliance is in a moving state, the scenario label is "Indoor moving"; if the GPS, BLE, and WiFi signals are all weak, the scenario label is "Weak signal". Store the generated scenario label (string) in the MCU memory as the real-time positioning scenario label data. According to the real-time positioning scenario label data, select an appropriate positioning algorithm and data source for multi-mode collaborative positioning. For example, if the real-time positioning scenario label is "Outdoor stationary", give priority to using GPS positioning data; if the real-time positioning scenario label is "Indoor low-speed movement", use BLE indoor positioning data combined with inertial navigation data for positioning. Fuse the positioning results of different data sources. For example, adopt the method of weighted average, and assign different weights according to the real-time positioning scenario label data and the quality scores of each data source. The finally obtained fused positioning result is the multi-mode collaborative positioning data, which includes longitude and latitude or indoor coordinate system coordinates.

[0119] Preferably, step S26 includes the following steps:

[0120] Step S261: Judge the indoor / outdoor environment according to the BLE indoor matching degree, and generate indoor / outdoor environment judgment data;

[0121] Step S262: Query the base station coverage area according to the 4G base station information in the multi-mode communication signal data to obtain the base station coverage area data;

[0122] Step S263: Perform geographical location matching on the base station coverage area data through the preset geographical fence information to obtain the geographical location matching degree;

[0123] Step S264: Identify the motion state based on the appliance motion monitoring data to generate the appliance motion state data;

[0124] Step S265: Judge the area type according to the geographical location matching degree to obtain the area type label;

[0125] Step S266: Classify the motion mode according to the appliance motion state data to obtain the motion mode label;

[0126] Step S267: Integrate the positioning scenario labels based on the indoor / outdoor environment judgment data, area type label, and motion mode label to obtain the real-time positioning scenario label data.

[0127] In an embodiment of the present invention, a threshold is set, for example, 70. If the BLE indoor matching degree is greater than or equal to 70, it is determined that the current environment is an indoor environment. If the BLE indoor matching degree is less than 70, it is determined that the current environment is an outdoor environment. The judgment result is stored as a boolean variable. For example, "1" represents the indoor environment, "0" represents the outdoor environment, or the strings "Indoor" represent indoor and "Outdoor" represent outdoor. This boolean variable or string is used as the indoor / outdoor environment judgment data. The MCC (Mobile Country Code), MNC (Mobile Network Code), LAC (Location Area Code), and Cell ID (Cell ID) of the current serving cell are extracted from it. These four parameters are combined into a unique base station identifier, for example, "460-00-12345-67890" (where 460 is the MCC, 00 is the MNC, 12345 is the LAC, and 67890 is the Cell ID). Using this base station identifier, a pre-constructed base station database is queried. This database stores the identifiers of known base stations and their corresponding geographical coverage range information. The geographical coverage range of a base station can be represented by a circular area, including the longitude and latitude coordinates of the center point and the radius (unit: meter), or can be represented by a polygon area, including the longitude and latitude coordinates of multiple vertices. If a matching base station identifier is found in the database, the geographical coverage range information of this base station is extracted. If no matching base station identifier is found in the database, a default coverage range is used. For example, the center point is set to the location of the MCU (which can be a fixed default location or the last valid GPS location read when there is no GPS signal), and the radius is set to a relatively large value (for example, 5000 meters). The queried or default base station coverage range information (longitude and latitude of the center point and radius, or polygon vertex coordinates) is stored in the MCU memory as the base station coverage range data. A series of geographical fences are preset, and each geographical fence represents a specific area, such as a warehouse, a factory, a logistics center, etc. Each geographical fence is represented by a polygon area, including the longitude and latitude coordinates of multiple vertices, and is associated with an area type label (such as "warehouse", "factory", "logistics center"). The geographical fence information is stored in the Flash memory of the MCU. Based on the base station coverage range data obtained in step S262, the relationship between this coverage range and the preset geographical fences is judged. If the base station coverage range data is represented by a circular area, the shortest distance from the center of the circle to each geographical fence polygon is calculated. If this distance is less than the radius of the circle, it is considered that the base station coverage range intersects with this geographical fence. If the base station coverage range data is represented by a polygon, it is judged whether the two polygons intersect (GIS algorithms can be used, such as the ray method). The number of geographical fences that intersect with the base station coverage range is counted, and the ratio of the number of intersecting geographical fences to the total number of all geographical fences is calculated as the geographical location matching degree.If no intersecting geofences are found, the geographical location matching degree is set to 0. For the acceleration data at each time point, calculate the square root of the sum of the squares of the three-axis accelerations to obtain the resultant acceleration value: a = sqrt(ax² + ay² + az²), where ax, ay, and az are the acceleration values in the three axes respectively. Calculate the average value A_avg of all resultant acceleration values. Calculate the sum of the squares of the differences between all resultant acceleration values and the average value, and then divide by the number of data points to obtain the acceleration variance Var_a. Set an acceleration variance threshold, for example, 0.1 m² / s. 4 If Var_a is greater than the threshold, it is determined that the appliance is in a moving state; if Var_a is less than the threshold, it is determined that the appliance is in a stationary state. Store the judgment result as a boolean variable. For example, use "1" to represent the moving state and "0" to represent the stationary state, or use the string "Moving" to represent movement and "Static" to represent stationary. If the geographical location matching degree is 0, indicating that no intersecting geofences are found, the area type label is set to "unknown area". If the geographical location matching degree is greater than 0, view the area type labels of all intersecting geofences. Count the area type label that appears most frequently. If there is only one area type label that appears most frequently, use this label as the final area type label. If there are multiple area type labels that appear most frequently, select one of them (for example, select according to a predefined priority order or randomly) as the final area type label, such as (string, for example, "warehouse", "factory", "logistics center", "unknown area"). If the appliance motion state data indicates that the appliance is in a stationary state, the motion mode label is set to "stationary". If the appliance motion state data indicates that the appliance is in a moving state, further analyze the IMU data. For example, the integral of the angular velocity data over a period of time (such as 5 seconds) can be calculated to obtain the angular change amount. If the angular change amount exceeds a certain threshold (such as 90 degrees), it is determined that the appliance has rotated and the motion mode label is set to "rotation". If the angular change amount is small but the acceleration data remains large, it is determined that the appliance is moving in a straight line and the motion mode label is set to "linear motion". If both the acceleration and angular velocity data show periodic changes, it indicates that the appliance is vibrating or swaying and the motion mode label is set to "vibration". According to the indoor / outdoor environment judgment data, area type label, and motion mode label, combine these three pieces of information into a string according to certain rules. For example: If the indoor / outdoor environment judgment data is "indoor", the area type label is "warehouse", and the motion mode label is "stationary", the real-time positioning scenario label data is "indoor-warehouse-stationary".

[0128] Preferably, step S27 includes the following steps:

[0129] Step S271: Configure the availability weights of the positioning modes based on the BLE indoor matching degree, WiFi confidence, 4G signal strength score, and GPS signal quality score to obtain the initial communication positioning mode weight coefficients;

[0130] Step S272: Sort the positioning mode priorities according to the initial communication positioning mode weight coefficients to obtain the priority scores of each mode;

[0131] Step S273: Perform decision tree multi-mode combination reasoning based on the real-time positioning scenario tag data through the priority scores of each mode to obtain the priority positioning mode combination data;

[0132] Step S274: Perform collaborative positioning processing for each mode according to the priority positioning mode combination data to generate multi-mode collaborative positioning data.

[0133] In the embodiments of the present invention, an initial weight coefficient is configured for each positioning mode (BLE, WiFi, 4G, GPS). These weight coefficients reflect the positioning accuracy and reliability of each mode under ideal conditions (i.e., when all modes are available and the signals are good). For example: BLE indoor positioning: Since the accuracy is relatively high in an indoor environment with pre-deployed beacons, the initial weight coefficient is set to 0.4. WiFi positioning: The accuracy is greatly affected by the AP deployment density and the environment, and the initial weight coefficient is set to 0.2. 4G positioning: The accuracy is relatively low and is mainly used to provide a rough position or assist in positioning, and the initial weight coefficient is set to 0.1. GPS positioning: The accuracy is relatively high in an outdoor open environment, but the signal is poor in an indoor or blocked environment, and the initial weight coefficient is set to 0.3. Then, the initial weight coefficient is adjusted according to the actual signal quality of each mode. For example: If the BLE indoor matching degree is less than 30, the weight coefficient of BLE is adjusted to 0. If the WiFi confidence level is less than 30, the weight coefficient of WiFi is adjusted to 0. If the 4G signal strength score is less than 40, the weight coefficient of 4G is adjusted to 0. If the GPS signal quality score is less than 40, the weight coefficient of GPS is set to 0. The adjusted weight coefficients of each mode (BLE, WiFi, 4G, GPS) are stored in the MCU memory as the initial communication positioning mode weight coefficients, and the sum of the weight coefficients is 1. If all the weight coefficients are 0, a default weight coefficient is set. For example, GPS is 1 and the others are 0. Each positioning mode (BLE, WiFi, 4G, GPS) is sorted in descending order according to the weight coefficient. According to the sorting result, a priority score is assigned to each mode. If the weight coefficient sorting is: BLE(0.4)>GPS(0.3)>WiFi(0.2)>4G(0.1), then the priority score is: BLE(4 points)>GPS(3 points)>WiFi(2 points)>4G(1 point). If the weight coefficient sorting is: GPS(0.6)>4G(0.4)>BLE(0)>WiFi(0), then the priority score is: GPS(4 points)>4G(3 points)>BLE(2 points)>WiFi(1 point). Note that even if the weight is 0, it still participates in the sorting, but its priority is lower than that of the mode with a non-zero weight. A decision tree model is constructed to select the optimal positioning mode combination. Each node of the decision tree represents a judgment condition, each branch represents a judgment result, and the leaf node represents the final positioning mode combination. For example:

[0134] Root node: Is the real-time positioning scenario label data = "Indoor - Warehouse - Stationary"?

[0135] Yes: Enter the BLE positioning branch.

[0136] Is the BLE priority score > 1?

[0137] Yes: Preferred positioning mode combination data = {BLE}.

[0138] No: Preferred positioning mode combination data = {} (indicating that all modes are unavailable).

[0139] No: Proceed to the next level of judgment.

[0140] Next-level node: Is the real-time positioning scenario tag data = "Outdoor - Unknown area - Linear motion"?

[0141] Yes: Enter the GPS positioning branch.

[0142] Is the GPS priority score > 1?

[0143] Yes: Preferred positioning mode combination data = {GPS}

[0144] No: Preferred positioning mode combination data = {}

[0145] No: Proceed to the next level of judgment.

[0146] Next-level node: Is the real-time positioning scenario tag data = "Indoor - Factory - Rotation"?

[0147] Yes: Enter the WiFi positioning branch.

[0148] Is the WiFi priority score > 1?

[0149] Yes: Preferred positioning mode combination data = {WiFi}.

[0150] No: Preferred positioning mode combination data = {}.

[0151] No: Enter the 4G positioning branch. Since it is a fallback option, 4G positioning is selected as long as 4G is available.

[0152] Is the 4G priority score > 1?

[0153] Yes: Preferred positioning mode combination data = {4G}

[0154] No: Preferred positioning mode combination data = {}.

[0155] Combine the priority positioning mode combination data obtained by decision tree reasoning (a set containing one or more positioning modes, such as {BLE}, {GPS}, {WiFi, 4G}, or an empty set {}). When the priority positioning mode combination data is {BLE}, use the trilateration algorithm or the weighted centroid positioning algorithm to calculate the estimated position coordinates (x, y) of the appliance. When the priority positioning mode combination data is {GPS}, directly use the longitude and latitude coordinates in the GPS raw data as the positioning result. If higher accuracy is required, differential processing can be performed on the GPS raw data. When the priority positioning mode combination data is {WiFi}, use the WiFi fingerprint positioning algorithm to match in the fingerprint database according to the WiFi signal list and calculate the estimated position of the appliance. If the fingerprint database is not constructed, no positioning calculation is performed. When the priority positioning mode combination data is {4G}, use the base station triangulation algorithm for positioning calculation to obtain rough position coordinates. When the priority positioning mode combination data is a combination of multiple modes, activate the corresponding positioning components according to the priority positioning mode combination data. For example, if the combined data indicates the use of GPS and BLE, activate the GPS receiver and the BLE scanning module. After activation, each component starts to collect data independently. For example, the GPS receiver receives satellite signals and calculates position information, and the BLE scanning module scans the surrounding beacons and obtains the signal strength. The data collection of each component is carried out in parallel to improve efficiency. Use a Kalman filter or other types of filters to smooth the GPS signal and eliminate positioning jitter. Based on the initial communication positioning mode weight coefficients, fuse the various positioning data, and calculate the final position coordinates by weighting as the multi-mode collaborative positioning data.

[0156] Preferably, the step S274 includes:

[0157] Activate the current collaborative positioning components according to the priority positioning mode combination data to obtain the collaborative positioning component activation status data;

[0158] Extract the multi-mode positioning data according to the collaborative positioning component activation status data to obtain the positioning input data for each mode;

[0159] Perform parallel positioning calculations for each mode based on the positioning input data for each mode to obtain the independent positioning result data for each mode;

[0160] Evaluate the confidence of the positioning results of the independent positioning result data for each mode through the initial communication positioning mode weight coefficients to obtain the positioning confidence for each mode;

[0161] Use the positioning confidence for each mode to perform weighted collaborative positioning calculations on the independent positioning result data for each mode to generate multi-mode collaborative positioning data.

[0162] In the embodiments of the present invention, based on the prioritized positioning mode combined data (such as {BLE}, {GPS, 4G}, {}, etc.), the power supply and working states of the corresponding communication components are controlled. If the prioritized positioning mode combined data is an empty set {}, no positioning components are activated, and the collaborative positioning component activation status data is empty or set to "none". If the prioritized positioning mode combined data contains BLE, the GPIO connected to the power control pin of the BLE module is set to high level to turn on the power of the BLE module, and it is ensured that the BLE module is in a state where it can receive beacon broadcasts (which should have been configured in the previous steps). If the prioritized positioning mode combined data contains GPS, the power of the GPS module is turned on (if it was previously turned off), and it is ensured that the GPS module is in a state of searching for satellite signals. If the prioritized positioning mode combined data contains WiFi, the power of the WiFi module is turned on, and it is ensured that the WiFi module is in a state of scanning APs. If the prioritized positioning mode combined data contains 4G, the power of the 4G module is turned on. If the collaborative positioning component activation status data contains "BLE", the IDs (UUID, Major, Minor) and RSSI values of all matching beacons are extracted from the list of matching beacons obtained in step S21 as BLE positioning input data. If the activation status data contains "GPS", the latest NMEA 0183 statements (such as GPGGA and GPRMC statements) are extracted from the GPS raw data stored in step S157 as GPS positioning input data. If the activation status data contains "WiFi", the SSIDs and RSSI values of all scanned APs are extracted from the list of WiFi signals obtained in step S154 as WiFi positioning input data. If the activation status data contains "4G", the MCC, MNC, LAC, CellID, and RSRP values of the current serving cell are extracted from the 4G base station information as 4G positioning input data. Independent positioning calculations are performed respectively based on the positioning input data of each mode to obtain their respective independent positioning results. Based on the initial communication positioning mode weight coefficients and the independent positioning result data of each mode obtained in the previous step, the confidence levels of the positioning results of each mode are calculated. If the independent positioning result of a certain mode is empty (indicating that the positioning of this mode fails), its confidence level is 0. If the independent positioning result of a certain mode is not empty, its confidence level is equal to the adjusted weight coefficient of this mode calculated. For example, if the BLE positioning is successful and the adjusted weight coefficient of BLE is 0.4, the BLE positioning confidence level is 0.4; if the GPS positioning fails, the GPS positioning confidence level is 0. The longitude and latitude coordinates of each mode are converted into plane coordinates in meters (for example, using UTM projection). Then, for each coordinate component (x coordinate and y coordinate), the weighted average value is calculated to obtain the final position coordinates.

[0163] As an example of the present invention, refer toFigure 2 As shown, Figure 1 is a detailed implementation step flow diagram of step S3 in

[0164] Step S31: Extract the timestamp from the multi-mode collaborative positioning data to obtain the current positioning time data;

[0165] In the embodiment of the present invention, it is obtained through the internal real-time clock (RTC) module of the microcontroller unit (MCU). The RTC module is usually driven by an independent crystal oscillator and can continue to count even when the MCU main clock is turned off. Reading the current time from the RTC module usually includes information such as year, month, day, hour, minute, and second.

[0166] Step S32: Retrieve the historical positioning data from the current positioning time data through a preset historical retrieval time window to obtain the historical trajectory data;

[0167] In the embodiment of the present invention, a historical retrieval time window is preset, such as 1 hour, 24 hours, or 7 days. This time window represents the range of historical data to be retrieved. The length of the time window can be configured according to application requirements and stored in the Flash memory of the MCU. Based on the current positioning time data (timestamp) obtained in step S31, calculate the start time of historical retrieval. Read the historical positioning data from the memory of the MCU or an external memory (such as EEPROM, SD card). The historical positioning data is a complete positioning record containing timestamps and positioning coordinates stored previously. Compare the timestamps in the historical positioning data with the start time and the current time of historical retrieval one by one. If the timestamp of a certain historical positioning data is between the start time and the current time, extract this data. Arrange all the extracted historical positioning data in the order of timestamps to form an array or linked list as the historical trajectory data.

[0168] Step S33: Use the low-power inertial measurement unit to extract the appliance motion characteristics to obtain the motion characteristic parameters;

[0169] In the embodiments of the present invention, more detailed motion feature parameters are extracted based on the appliance motion monitoring data, and the acceleration and angular velocity data within a recent period of time (e.g., the most recent 5 seconds) are read from the memory. For the acceleration data at each time point, the square root of the sum of the squares of the three-axis accelerations is calculated to obtain the resultant acceleration value: a = sqrt(ax² + ay² + az²), where ax, ay, and az are the acceleration values of the three axes respectively. The average value A_avg and variance Var_a of all the resultant acceleration values are calculated. For the angular velocity data at each time point, the square root of the sum of the squares of the three-axis angular velocities is calculated to obtain the resultant angular velocity value: ω = sqrt(ωx² + ωy² + ωz²), where ωx, ωy, and ωz are the angular velocity values of the three axes respectively. The average value Ω_avg and variance Var_ω of all the resultant angular velocity values are calculated. The axis with the largest variance among the three acceleration axes is found as the main direction of acceleration. The calculated average acceleration A_avg, acceleration variance Var_a, average angular velocity Ω_avg, angular velocity variance Var_ω, main direction of acceleration, and inclination change value are used as the motion feature parameters.

[0170] Step S34: Perform motion pattern recognition on the motion feature parameters through the historical trajectory data to generate motion pattern recognition data;

[0171] In the embodiments of the present invention, a motion pattern recognition model is established. The motion patterns can be divided into several typical types, such as: stationary, linear motion, turning, uphill / downhill, bumpy, etc. If both the average acceleration A_avg and the acceleration variance Var_a are lower than a certain threshold, and both the average angular velocity Ω_avg and the angular velocity variance Var_ω are also lower than a certain threshold, it is determined as the stationary mode. If the average acceleration A_avg is greater than a certain threshold, the acceleration variance Var_a is small, and the angular velocity change is small (judged by the inclination change), it is determined as linear motion. If the average angular velocity Ω_avg is greater than a certain threshold, the angular velocity variance Var_ω is also large, or an obvious angle change is detected through the inclination change, it is determined as turning. If there is an obvious angle between the main direction of acceleration and the vertical direction (judged by the gravitational acceleration), and this angle lasts for a period of time, it is determined as uphill / downhill. It can be judged whether it is uphill or downhill according to the inclination change. If the acceleration variance Var_a is large and the acceleration changes frequently, it is determined as bumpy.

[0172] Step S35: Perform prediction of the motion position at the next moment on the multi-mode collaborative positioning data through the motion pattern recognition data to obtain the predicted position coordinates;

[0173] In an embodiment of the present invention, the latitude and longitude coordinates at the current moment are obtained from the multi-mode collaborative positioning data. From the historical trajectory data, the distances between adjacent two positioning points within a recent period of time (e.g., the recent 5 seconds) are calculated and divided by the time interval to obtain a series of instantaneous speeds. The average value of these instantaneous speeds is calculated to obtain the average speed V_current at the current moment. If the motion mode recognition data is "stationary", it is considered that the position at the next moment is the same as the position at the current moment. If the motion mode recognition data is "linear motion", it is assumed that the object maintains a uniformly accelerated linear motion within a short period of time. According to the current position, the current speed V_current, the average acceleration A_avg, and the prediction time interval Δt (e.g., 1 second), the position at the next moment is predicted using the kinematic formula: It is necessary to determine the motion direction. Since it is linear motion, it can be assumed that the motion direction is the same as the average speed direction in the recent period of time, or the same as the main direction of the acceleration. This direction is converted into an azimuth angle θ (unit: radian) relative to the due north direction. If the motion mode is "turning", the position at the next moment is predicted according to the current position, the angular velocity data, and the average speed in the recent period of time. Since the turning motion is relatively complex, a simplified model can be used, for example, assuming that the turning radius remains unchanged, and the position after turning is calculated based on the angular velocity and the speed. For these two modes, since the motion is relatively complex and the position change is not obvious within a short period of time, a processing method similar to the stationary mode can be adopted, that is, the predicted position is the same as the current position, or a short-distance prediction is made based on the average speed.

[0174] Step S36: Obtain the operation requirement data of the reusable packaging appliance; perform short-term trajectory tracking on the predicted position coordinates through the operation requirement data of the reusable packaging appliance to obtain the positioning tracking trajectory prediction data.

[0175] In the embodiments of the present invention, it is obtained from a remote server through a communication module (such as 4G, WiFi). The job requirement data includes: target location: the longitude and latitude coordinates of the target location where the appliance needs to arrive; estimated arrival time: the time when the appliance is expected to arrive at the target location; job type: the type of job that the appliance needs to perform, such as "loading", "unloading", "transportation", etc.; route planning information: if there is a pre-planned route, it includes the coordinates of key points on the route. If the target location is included in the job requirement data, the distance between the predicted location and the target location is calculated. If the distance is less than a certain threshold (such as 10 meters), it is considered that the appliance has approached the target location and the prediction can be stopped. If the job type is "loading" or "unloading", the predicted location may remain unchanged or vary within a small range. If the job type is "transportation", more accurate prediction can be made in combination with the route planning information (if any). Arrange the coordinates of multiple predicted locations within a future period of time (such as the next 1 minute) (for example, predicting a location every second) in chronological order to form an array or linked list. Together with the array or linked list and information such as the job type currently used for prediction, it is used as positioning tracking trajectory prediction data.

[0176] As an example of the present invention, refer to Figure 3 shown in Figure 1 is a schematic diagram of the detailed implementation steps of step S4 in

[0177] Step S41: Analyze future trajectory points based on the positioning tracking trajectory prediction data to generate future positioning trajectory point data;

[0178] In the embodiments of the present invention, the longitude and latitude coordinates of each predicted location are extracted from the positioning tracking trajectory prediction data. If the longitude and latitude coordinates of the predicted location are not in the WGS-84 coordinate system, they are converted to the WGS-84 coordinate system. If there is jitter or noise in the predicted trajectory, the predicted location coordinates can be smoothed. For example, a moving average filter can be used to average several consecutive predicted location coordinates to obtain a smoother trajectory. According to actual requirements, key trajectory points are screened. For example, only the points on the trajectory where the distance change exceeds a certain threshold are retained, or only the points within a specific time interval are retained. They are stored in an array or linked list in chronological order as future positioning trajectory point data.

[0179] Step S42: Perform future environmental scene analysis based on the real-time positioning scene label data and the future positioning trajectory point data to generate future scene sequence data;

[0180] In the embodiments of the present invention, the real-time positioning scene tag data at the current moment is used as the initial value of the future scene sequence. Each trajectory point in the future positioning trajectory point data is traversed, and the scene where the trajectory point is located is predicted according to the position of the trajectory point and the current scene. If the current scene is indoor and the future trajectory point is still within the known indoor area (for example, judged according to the pre-stored indoor map or geofence information), it is predicted that the trajectory point is still in the indoor scene. If the current scene is outdoor and the future trajectory point is still in the outdoor area, it is predicted that the trajectory point is still in the outdoor scene. If the current scene is indoor, but the future trajectory point moves to the outdoor area, it is predicted that the trajectory point is in the outdoor scene. If the current scene is outdoor, but the future trajectory point moves to the indoor area, it is predicted that the trajectory point is in the indoor scene. If the future trajectory point is within the known geofence (such as a warehouse, factory, logistics center, etc.), it is predicted that the trajectory point is in the corresponding area type. If the future trajectory point is not within any known geofence, it is predicted that the trajectory point is in the "unknown area". The predicted scene (including indoor / outdoor, area type, motion mode) of each future trajectory point is combined with its corresponding timestamp to form a data pair. All data pairs are arranged in chronological order to form an array or linked list as the future scene sequence data.

[0181] Step S43: Evaluate the availability of each positioning mode according to the future scene sequence data to obtain the future availability scores of each mode;

[0182] In the embodiments of the present invention, each future scene in the future scene sequence data is traversed. For each future scene, the availability of each positioning mode is evaluated according to the characteristics of the scene (indoor / outdoor, area type, motion mode). According to the "indoor / outdoor" and "area type" information of the future scene, the corresponding initial score is found in the availability score table. If there is a description of "motion" in the scene and the positioning mode is GPS, the availability score of GPS remains unchanged or increases (if the table distinguishes between stationary and moving, directly use the corresponding value in the table; if not, it can be +1 on the basis of the initial score, but not exceeding 4). If there is a description of "stationary" in the scene and the positioning mode is GPS, the availability score of GPS may need to be reduced. If there is a description of "rotation" in the scene and the positioning mode is BLE or WiFi, its availability needs to be reduced (because rotation may cause signal instability). It can be -1 on the original basis to obtain the future availability score data of each mode.

[0183] Step S44: Obtain the unit time energy consumption data of each positioning component and the communication cost data per unit data volume of each positioning component;

[0184] In the embodiments of the present invention, the data manuals (Datasheets) of each positioning component (BLE, WiFi, 4G Cat1, GPS module) are consulted. Information such as typical operating current and operating voltage is usually provided in the data manuals. Based on this information, the energy consumption per unit time can be calculated. For BLE and WiFi, since operator fees are usually not involved, the communication cost can be considered to be 0, or a very low fixed value (e.g., the cost of maintaining a local server). For 4G Cat1, the communication cost mainly depends on the operator's data package fees. The operator's tariff standard can be queried to obtain the cost per MB of data.

[0185] Step S45: Based on the future availability scores of each mode, the energy consumption data per unit time of each positioning component, and the communication cost data per unit data volume of each positioning component, perform multi-objective positioning mode optimization to make a pre-switching decision on the positioning mode of the reusable packaging appliance.

[0186] In the embodiments of the present invention, considering comprehensively the future availability scores of each mode, the energy consumption data per unit time of each positioning component, and the communication cost data per unit data volume of each positioning component, perform multi-objective optimization to select the optimal combination of positioning modes. For example, using the weighted sum method, multiply the availability score, energy consumption, and communication cost of each positioning mode by a preset weight coefficient and then sum them up to obtain the comprehensive score of the mode. Select the mode with the highest comprehensive score as the main positioning mode, and make a pre-switching decision based on the future scenario sequence data. For example, traverse each time point in the future availability score data of each mode. For each time point, construct a multi-objective optimization problem. The decision variable of this problem is the switch state of each positioning mode (BLE, WiFi, 4G, GPS) (0 means off, 1 means on). The constraint conditions are: at least one positioning mode is turned on, and the sum of the availability scores of the turned-on positioning modes should be greater than a threshold (e.g., 5, indicating a certain positioning accuracy). Use the greedy algorithm to solve the multi-objective optimization problem, and then preferentially select the mode with a high availability score, low energy consumption, and low communication cost. Sort the modes in descending order of availability score and try to turn on each mode in turn until the constraint conditions are met. Generate a pre-switching decision on the positioning mode according to the optimal combination of positioning modes obtained for each time point. For example, if at a certain future time point, the decision result is to switch the positioning mode from GPS to BLE, then send a switching instruction in advance for a period of time (e.g., 1 second in advance), turn off the GPS module, and turn on the BLE module. Combine the pre-switching decision on the positioning mode for each time point (e.g., "turn on BLE, turn off GPS, WiFi, 4G") with its corresponding timestamp into a data pair. Arrange all the data pairs in chronological order to form an array or linked list as the final pre-switching decision data on the positioning mode.

[0187] Preferably, the present invention further provides a multi-mode dynamic positioning and tracking system for recyclable packaging appliances, which executes the multi-mode dynamic positioning and tracking method for recyclable packaging appliances as described above. The multi-mode dynamic positioning and tracking system for recyclable packaging appliances includes:

[0188] A multi-source data acquisition module, configured to use a microcontroller unit to control a multi-mode communication unit to scan multi-mode monitoring signals, so as to obtain multi-mode communication signal data; use a low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data;

[0189] A collaborative positioning processing module, configured to perform positioning scenario label processing according to the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data;

[0190] A trajectory prediction and tracking module, configured to perform motion position prediction according to the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning tracking trajectory prediction data;

[0191] A multi-mode dynamic positioning module, configured to optimize multi-target positioning modes according to the positioning tracking trajectory prediction data, so as to implement a positioning mode pre-switching decision for recyclable packaging appliances.

[0192] This application aims to automatically select the optimal positioning mode according to different environmental conditions by integrating multiple communication components such as BLE, WiFi, 4G Cat1, and GPS, and combining with a low-power inertial measurement unit. In an indoor environment, where the BLE and WiFi signal strengths are relatively high, the system can preferentially utilize these signals for high-precision indoor positioning. In an outdoor open area, where the GPS signal is stable and accurate, the system switches to the GPS positioning mode to achieve long-distance and high-precision tracking. This multi-mode collaborative positioning mechanism overcomes the limitations of a single technology in specific scenarios and significantly improves the accuracy and reliability of positioning. For example, in an indoor environment where GPS signals are blocked, the system can automatically switch to the WiFi or BLE mode and use indoor base stations or beacons for positioning to ensure the continuity of positioning information. In an outdoor vast area, it can flexibly switch to the 4G Cat1 or GPS mode to achieve long-distance and high-precision tracking. This multi-mode signal acquisition ability greatly expands the coverage range of the positioning system, reduces the probability of the appearance of positioning blind spots, and ensures the real-time visibility of reusable packaging appliances throughout the supply chain circulation process. Even when the appliances switch between different environments, they can always maintain the tracking state, avoiding information breaks caused by signal loss. By real-time evaluating the positioning signal strength and cost, dynamically adjusting the technology combination, while ensuring the continuity and accuracy of positioning data, it significantly reduces communication energy consumption. The system can automatically select the optimal positioning mode according to environmental conditions and positioning requirements, and make pre-switching decisions when necessary, thereby further reducing energy consumption and communication costs. This dynamic optimization mechanism not only improves the energy efficiency of the system but also reduces the long-term operating costs, and is applicable to global supply chain management.

[0193] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0194] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A multi-mode dynamic positioning and tracking method for a recyclable packaging appliance, characterized in that, Applied to recyclable packaging appliances, the recyclable packaging appliances are embedded with a microcontroller unit, a low-power inertial measurement unit, and a multi-mode communication unit. The multi-mode communication unit includes a BLE communication component, a WiFi communication component, a 4G Cat1 communication component, and a GPS communication component. The microcontroller unit is electrically connected to the low-power inertial measurement unit and the multi-mode communication unit. The multi-mode dynamic positioning and tracking method of the recyclable packaging appliance includes the following steps: Step S1: Use the microcontroller unit to control the multi-mode communication unit to perform multi-mode monitoring signal scanning to obtain multi-mode communication signal data; use the low-power inertial measurement unit to perform periodic motion sampling to obtain appliance motion monitoring data; Step S2: Perform positioning scenario label processing based on the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data. Among them, Step S2 is specifically: Step S21: Query the indoor beacon database according to the BLE signal strength list in the multi-mode communication signal data to obtain a matching beacon list; Step S22: Calculate the BLE indoor matching degree according to the matching beacon list to generate the BLE indoor matching degree; Step S23: Compare the WiFi signal list in the multi-mode communication signal data with the preset WiFi white list, and then perform AP validity evaluation to obtain the WiFi confidence level; Step S24: Evaluate the signal strength according to the 4G base station information in the multi-mode communication signal data to obtain the 4G signal strength score; Step S25: Evaluate the GPS positioning accuracy according to the GPS signal data in the multi-mode communication signal data to obtain the GPS signal quality score; Step S26: Perform positioning scenario label processing based on the multi-mode communication signal data and the appliance motion monitoring data to obtain real-time positioning scenario label data; Step S27: Perform multi-mode collaborative positioning on the BLE indoor matching degree, the WiFi confidence level, the 4G signal strength score, and the GPS signal quality score through the real-time positioning scenario label data to generate multi-mode collaborative positioning data. Among them, Step S27 is specifically: Step S271: Configure the positioning mode availability weight according to the BLE indoor matching degree, the WiFi confidence level, the 4G signal strength score, and the GPS signal quality score to obtain the initial communication positioning mode weight coefficient; Step S272: Sort the positioning mode priorities according to the initial communication positioning mode weight coefficient to obtain the priority scores of each mode; Step S273: Perform decision tree multi-mode combination reasoning based on the real-time positioning scenario label data through the priority scores of each mode to obtain the priority positioning mode combination data; Step S274: Perform multi-mode collaborative positioning processing according to the priority positioning mode combination data to generate multi-mode collaborative positioning data. Among them, Step S274 is specifically: Activate the current collaborative positioning component according to the priority positioning mode combination data to obtain the collaborative positioning component activation status data; Extract multi-mode positioning data according to the activation status data of the co-location component to obtain positioning input data for each mode; Perform parallel positioning calculations for each mode based on the positioning input data for each mode to obtain independent positioning result data for each mode; Evaluate the confidence of the positioning results for each mode by using the initial communication positioning mode weight coefficients for the independent positioning result data of each mode to obtain the positioning confidence for each mode; Perform weighted collaborative positioning calculations on the independent positioning result data of each mode by using the positioning confidence for each mode to generate multi-mode collaborative positioning data; Step S3: Predict the movement position based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning and tracking trajectory prediction data; Step S4: Optimize the multi-target positioning mode according to the positioning and tracking trajectory prediction data to realize the pre-switching decision of the positioning mode for the recyclable packaging appliance.

2. The multi-mode dynamic positioning and tracking method for the recyclable packaging appliance according to claim 1, wherein Step S1 includes the following steps: Step S11: Configure the low-power timer by using the microcontroller unit to obtain timer configuration parameters; Step S12: Trigger periodic interrupts for the microcontroller unit through the timer configuration parameters to obtain wake-up interrupt signals; Step S13: Activate the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up status signal; Step S14: Control the multi-mode power supply of the multi-mode communication unit by using the wake-up status signal to generate multi-mode power supply status data; Step S15: Scan the multi-mode monitoring signals based on the multi-mode power supply status data to obtain multi-mode communication signal data; Step S16: Perform periodic motion sampling by using the low-power inertial measurement unit to obtain appliance motion monitoring data.

3. The multi-mode dynamic positioning and tracking method for the recyclable packaging appliance according to claim 2, wherein The multi-mode communication signal data includes a BLE signal strength list, a WiFi signal list, 4G base station information, and GPS signal data. Step S15 includes the following steps: Step S151: Send a beacon scan instruction to the BLE communication component based on the multi-mode power supply status data, and then perform a complete beacon scan to obtain complete BLE scan data; Step S152: Analyze the signal strength and beacon ID of the complete BLE scan data to obtain a BLE signal strength list; Step S153: Scan the WiFi AP by the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scan data; Step S154: Analyze the signal strength and SSID of the initial WiFi scan data to obtain a WiFi signal list; Step S155: Activate the 4G Cat1 communication component based on the multi-mode power supply status data to obtain a 4G module activation signal; Step S156: Query the base station information according to the 4G module activation signal to obtain 4G base station information; Step S157: Start the GPS communication component based on the multi-mode power supply status data, and then collect GPS data to obtain raw GPS data; Step S158: Analyze the number of satellites and HDOP value of the raw GPS data to generate GPS signal data.

4. The multi-mode dynamic positioning and tracking method for the recyclable packaging appliance according to claim 1, wherein Step S26 includes the following steps: Step S261: Determine the indoor / outdoor environment according to the BLE indoor matching degree, and generate indoor / outdoor environment judgment data; Step S262: Query the base station coverage area according to the 4G base station information in the multi-mode communication signal data to obtain the base station coverage area data; Step S263: Perform geographical location matching on the base station coverage area data through the preset geographical fence information to obtain the geographical location matching degree; Step S264: Identify the motion state based on the appliance motion monitoring data to generate appliance motion state data; Step S265: Judge the area type according to the geographical location matching degree to obtain the area type label; Step S266: Classify the motion mode according to the appliance motion state data to obtain the motion mode label; Step S267: Integrate the positioning scenario labels based on the indoor / outdoor environment judgment data, area type label, and motion mode label to obtain the real-time positioning scenario label data.

5. The multi-mode dynamic positioning and tracking method of the recyclable packaging appliance according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Extract the timestamp from the multi-mode collaborative positioning data to obtain the current positioning time data; Step S32: Retrieve the historical positioning data through the preset historical retrieval time window for the current positioning time data to obtain the historical trajectory data; Step S33: Extract the appliance motion characteristics using the low-power inertial measurement unit to obtain the motion characteristic parameters; Step S34: Identify the motion mode for the motion characteristic parameters through the historical trajectory data to generate the motion mode identification data; Step S35: Predict the next moment's motion position for the multi-mode collaborative positioning data through the motion mode identification data to obtain the predicted position coordinates; Step S36: Obtain the operation requirement data of the reusable packaging appliance; perform short-term trajectory tracking on the predicted position coordinates through the operation requirement data of the reusable packaging appliance to obtain the positioning tracking trajectory prediction data.

6. The multi-mode dynamic positioning and tracking method of the recyclable packaging appliance according to claim 1, characterized in that Step S4 includes the following steps: Step S41: Analyze the future trajectory points according to the positioning tracking trajectory prediction data to generate the future positioning trajectory point data; Step S42: Analyze the future environment scenario based on the real-time positioning scenario label data and the future positioning trajectory point data to generate the future scenario sequence data; Step S43: Evaluate the availability of each positioning mode according to the future scenario sequence data to obtain the future availability scores of each mode; Step S44: Obtain the unit time energy consumption data of each positioning component and the communication cost data per unit data volume of each positioning component; Step S45: Optimize the multi-objective positioning mode based on the future availability scores of each mode, the unit time energy consumption data of each positioning component, and the communication cost data per unit data volume of each positioning component to realize the pre-switching decision of the positioning mode for the reusable packaging appliance.

7. A multi-mode dynamic positioning and tracking system for reusable packaging appliances, which is applied to the multi-mode dynamic positioning and tracking method for reusable packaging appliances described in any one of claims 1-6, and is characterized in that, The multi-mode dynamic positioning tracking system for the reusable packaging appliance includes: A multi-source data acquisition module, which is used to control the multi-mode communication unit by using the microcontroller unit to scan the multi-mode monitoring signals to obtain the multi-mode communication signal data; and perform periodic motion sampling by using the low-power inertial measurement unit to obtain the appliance motion monitoring data; The collaborative positioning processing module is used to perform positioning scenario label processing based on multi-mode communication signal data and appliance motion monitoring data to obtain real-time positioning scenario label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scenario label data to generate multi-mode collaborative positioning data; The trajectory prediction and tracking module is used to perform motion position prediction based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning tracking trajectory prediction data; The multi-mode dynamic positioning module is used to optimize the multi-target positioning mode based on the positioning tracking trajectory prediction data to achieve a pre-switching decision on the positioning mode for recyclable packaging appliances.

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