Multi-mode dynamic positioning and tracking method and system for recyclable packaging appliance

Abstract

The invention relates to the technical field of intelligent logistics, in particular to a multi-mode dynamic positioning and tracking method and system for recyclable packaging utensils. The method comprises the following steps that a microcontroller unit is used for controlling a multi-mode communication unit to conduct multi-mode monitoring signal scanning, multi-mode cooperative positioning is conducted according to multi-mode communication signal data, and multi-target positioning mode optimization is conducted according to the multi-mode cooperative positioning data. Therefore, the positioning mode pre-switching decision of the recyclable packaging appliance is realized. The invention provides a dynamic switching and cooperative positioning mechanism, the optimal positioning mode is automatically selected according to environmental conditions, the positioning signal strength and cost are evaluated in real time, the technical combination is dynamically adjusted, and the continuity and accuracy of positioning data are ensured. According to the invention, the tracking reliability of the recyclable packaging appliance in a complex scene is greatly improved, the communication energy consumption is reduced, and the method is suitable for global supply chain management.

Description

Technical Field

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

[0002] As an important part of the modern supply chain, the efficient management and tracking of recyclable packaging equipment has become a key factor in optimizing the logistics system. Recyclable packaging equipment can effectively reduce resource waste, reduce 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 equipment has become a key issue that needs to be solved urgently. However, traditional recyclable packaging equipment tracking mainly relies on a single technical means, such as RFID tags, barcode scanning or simple GPS positioning system. For example, although GPS has high positioning accuracy in open outdoor environments, its positioning performance will be significantly reduced or even impossible indoors or in areas with severe signal obstruction. Although 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 environmental interference, making it difficult to meet the needs 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 cannot cover the various complex environments that recyclable packaging equipment experiences during the circulation of the supply chain, such as indoor warehouses, outdoor transport vehicles, city 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 equipment, long-term operation and low power consumption are crucial, so it is necessary to reduce the power consumption of the equipment while ensuring positioning accuracy. Traditional positioning solutions often use a single positioning technology. For example, they always rely on GPS for positioning. Even in indoor environments or environments with poor network signals, GPS positioning is still attempted, resulting in positioning failure and reduced accuracy. At the same time, it also consumes a lot of electricity, making it difficult to cover all links of recyclable packaging equipment in the entire supply chain, which can easily lead 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 containers to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above-mentioned purpose, a multi-mode dynamic positioning and tracking method for recyclable packaging utensils is provided, which is applied to the recyclable packaging utensils. The recyclable packaging utensils 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 recyclable packaging utensils comprises the following steps: Step S1: using a microcontroller unit to control a multi-mode communication unit to scan a multi-mode monitoring signal to obtain multi-mode communication signal data; using a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data; Step S2: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; performing multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; Step S3: 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; Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to achieve a pre-switching decision on the positioning mode of the recyclable packaging container.

[0005] Preferably, step S1 comprises the following steps: Step S11: using the microcontroller unit to configure the low power timer to obtain timer configuration parameters; Step S12: periodically interrupt the microcontroller unit through the timer configuration parameters to obtain a wake-up interrupt signal; Step S13: activating the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up state signal; Step S14: using the wake-up state signal to perform multi-mode power supply control on the multi-mode communication unit to generate multi-mode power supply state data; Step S15: performing a multi-mode monitoring signal scan based on the multi-mode power supply status data to obtain multi-mode communication signal data; Step S16: Use a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data.

[0006] 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, and step S15 includes the following steps: Step S151: sending a beacon scan instruction to the BLE communication component based on the multi-mode power supply status data, and then performing a complete beacon scan to obtain complete BLE scan data; Step S152: parse the complete BLE scan data by signal strength and beacon ID to obtain a BLE signal strength list; Step S153: performing WiFi AP scanning on the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scanning data; Step S154: parse the initial WiFi scan data for signal strength and SSID to obtain a WiFi signal list; Step S155: activating 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 the 4G base station information; Step S157: starting the GPS communication component based on the multi-mode power supply status data, and then performing GPS data collection to obtain GPS raw data; Step S158: Analyze the number of satellites and HDOP value of the GPS raw data to generate GPS signal data.

[0007] Preferably, step S2 comprises the following steps: 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: performing a whitelist comparison on the WiFi signal list in the multi-mode communication signal data through a preset WiFi whitelist, and then performing an AP effectiveness evaluation to obtain a WiFi confidence level; Step S24: performing signal strength evaluation according to the 4G base station information in the multi-mode communication signal data to obtain a 4G signal strength score; Step S25: performing GPS positioning accuracy evaluation based on the GPS signal data in the multi-mode communication signal data to obtain a GPS signal quality score; Step S26: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; 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 real-time positioning scene tag data to generate multi-mode collaborative positioning data.

[0008] Preferably, step S26 comprises the following steps: Step S261: Perform indoor / outdoor environment judgment based on the BLE indoor matching degree and generate indoor / outdoor environment judgment data; Step S262: querying 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; Step S263: geographically matching the base station coverage data using preset geographical fence information to obtain a geographical location matching degree; Step S264: performing motion state recognition based on the tool motion monitoring data to generate tool motion state data; Step S265: determine the region type based on the geographic location match and obtain a region type label; Step S266: classifying the motion mode according to the motion state data of the device to obtain a motion mode label; Step S267: Integrate the positioning scene label based on the indoor / outdoor environment judgment data, the area type label and the motion mode label to obtain real-time positioning scene label data.

[0009] Preferably, step S27 includes the following steps: Step S271: configuring the positioning mode availability weight 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; Step S272: Prioritize the positioning modes according to the initial communication positioning mode weight coefficients to obtain a priority score for each mode; Step S273: performing decision tree multi-mode combination reasoning based on the real-time positioning scene label data through the priority score of each mode to obtain priority positioning mode combination data; Step S274: Perform collaborative positioning processing on each mode according to the priority positioning mode combination data to generate multi-mode collaborative positioning data.

[0010] Preferably, the step S274 includes: Activate the current collaborative positioning component according to the priority positioning mode combination data to obtain the collaborative positioning component activation state data; Extract multi-mode positioning data according to the activation state data of the collaborative positioning component to obtain the positioning input data of each mode; Based on the positioning input data of each mode, parallel positioning calculation of each mode is performed to obtain independent positioning result data of each mode; The confidence of the positioning result is evaluated on the independent positioning result data of each mode through the weight coefficient of the initial communication positioning mode, and the positioning confidence of each mode is obtained; The positioning reliability of each mode is used to perform weighted collaborative positioning calculation on the independent positioning result data of each mode to generate multi-mode collaborative positioning data.

[0011] Preferably, step S3 comprises the following steps: Step S31: extracting the timestamp of the multi-mode collaborative positioning data to obtain the current positioning time data; Step S32: performing historical positioning data retrieval on the current positioning time data through a preset historical retrieval time window to obtain historical trajectory data; Step S33: extracting the motion characteristics of the device using a low-power inertial measurement unit to obtain motion characteristic parameters; Step S34: performing motion pattern recognition on the motion feature parameters through the historical trajectory data to generate motion pattern recognition data; Step S35: predicting the motion position of the multi-mode collaborative positioning data at the next moment through the motion mode recognition data to obtain the predicted position coordinates; Step S36: Acquire the recyclable packaging equipment operation demand data; perform short-term trajectory tracking on the predicted position coordinates through the recyclable packaging equipment operation demand data to obtain positioning tracking trajectory prediction data.

[0012] Preferably, step S4 comprises the following steps: Step S41: performing future trajectory point analysis based on the positioning and tracking trajectory prediction data to generate future positioning trajectory point data; Step S42: Perform future environment scene analysis based on the real-time positioning scene label data and the future positioning trajectory point data to generate future scene sequence data; Step S43: Evaluate the availability of each positioning mode according to the future scene sequence data to obtain a future availability score for each mode; Step S44: Obtaining energy consumption data per unit time of each positioning component and communication cost data per unit data volume of each positioning component; Step S45: Optimize the multi-objective positioning modes 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 container.

[0013] Preferably, the present invention further provides a multi-mode dynamic positioning and tracking system for recyclable packaging utensils, which executes the multi-mode dynamic positioning and tracking method for recyclable packaging utensils as described above, and the multi-mode dynamic positioning and tracking system for recyclable packaging utensils comprises: The multi-source data acquisition module is used to 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; and use the low-power inertial measurement unit to perform periodic motion sampling to obtain the device motion monitoring data; The collaborative positioning processing module is used to perform positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; The trajectory prediction and tracking module is used to predict the motion position based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning and tracking trajectory prediction data; The multi-mode dynamic positioning module is used to optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data, so as to realize the pre-switching decision of the positioning mode for recyclable packaging equipment.

[0014] 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 capability avoids the delay and accuracy reduction problems of the traditional system when switching the positioning mode, and ensures the continuity and accuracy of the positioning information. In addition, by optimizing the positioning mode, the system can also achieve refined management of power consumption. For example, when the device is stationary, high-power 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 utensils of the present invention can automatically select the optimal positioning mode according to the specific environment (such as indoor / outdoor, regional network coverage) of the recyclable packaging utensils 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, ensures the continuity and accuracy of the positioning data, and thus realizes the continuous and accurate positioning of recyclable packaging utensils in the supply chain. It effectively solves the problems of traditional single tracking technology, which is prone to positioning blind spots, reduced accuracy, and excessive energy consumption when facing complex scenarios such as indoor and outdoor switching and network signal changes, resulting in packaging equipment tracking interruptions and increased management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic flow chart of the steps of the multi-mode dynamic positioning and tracking method for recyclable packaging equipment of the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG. Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can 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.

[0018] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. 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.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a multi-mode dynamic positioning and tracking method for recyclable packaging utensils, which is applied to the recyclable packaging utensils. The recyclable packaging utensils 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 recyclable packaging utensils comprises the following steps: Step S1: using a microcontroller unit to control a multi-mode communication unit to scan a multi-mode monitoring signal to obtain multi-mode communication signal data; using a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data; Step S2: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; performing multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; Step S3: 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; Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to achieve a pre-switching decision on the positioning mode of the recyclable packaging container.

[0020] In an embodiment of the present invention, the multi-mode dynamic positioning and tracking method for recyclable packaging containers comprises the following steps: Step S1: using a microcontroller unit to control a multi-mode communication unit to scan a multi-mode monitoring signal to obtain multi-mode communication signal data; using a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data; In an embodiment of the present invention, a microcontroller unit (MCU) with an integrated 32-bit ARM Cortex-M0+ core is selected, and the MCU has a built-in 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 register. Enable the timer peripheral clock (write 1 to the corresponding bit of the clock control register), configure the prescaler (for example, the division coefficient is 32, write 31 to the prescaler register), configure the automatic reload register (for example, 1 second cycle, write 1023), and enable the update interrupt (write 1 to the corresponding bit of the interrupt enable register). When the timer counter reaches the automatic reload value, an update event is generated to trigger an interrupt. In the interrupt service routine (ISR), the interrupt flag bit is cleared (write 1 to the corresponding bit of the interrupt status register), and the global wake-up flag variable is set 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 sleeping, set the wake-up status indicator variable to 1, and clear the global wake-up flag variable. When the wake-up status indicator variable is 1, trigger the power supply control of the multi-mode communication unit. According to the preset strategy (for example, NB-IoT first, fail to switch to LoRa, and finally GNSS), control the power supply of each module through GPIO. For example, first set the NB-IoT module power control pin to a 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 the signal. For example, when "NB-IoT is powered", 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 WiFi APs, obtain 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, it sends configuration instructions to the IMU through the I2C interface to set the sampling rate (for example, 10Hz), range, and low power mode. The IMU collects acceleration and angular velocity data at a set sampling rate, and the MCU periodically reads the FIFO data through I2C and stores it as device motion monitoring data.

[0021] Step S2: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; performing multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; In an embodiment of the present invention, the motion state of the tool is calculated using the three-axis acceleration data in the tool motion monitoring data, such as stationary, uniform motion or accelerated motion. Through the preset threshold judgment, the motion state of the tool is converted into a motion label, such as "stationary", "slow-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 scene label data. For example, when the GPS signal is available and the tool is in a stationary state, the real-time positioning scene label is "outdoor stationary"; when the GPS signal is not available and the tool is in a low-speed moving state, the real-time positioning scene label is "indoor low-speed movement". According to the real-time positioning scene label data, a suitable positioning algorithm is selected for multi-mode collaborative positioning. For example, when the real-time positioning scene label is "outdoor stationary", GPS positioning is used first; when the real-time positioning scene label is "indoor low-speed movement", Bluetooth beacon positioning is used in combination with inertial navigation for positioning. The final positioning result (latitude and longitude or indoor coordinates) is used as multi-mode collaborative positioning data.

[0022] Step S3: 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; In an embodiment of the present invention, a Kalman filter algorithm is used to process multi-mode collaborative positioning data to predict the future position of the device. The state vector of the Kalman filter algorithm contains the position and velocity information of the device, and the measurement vector is the multi-mode collaborative positioning data. Based on the current position, velocity and acceleration of the device (from the device motion monitoring data), the position of the device at the next moment is predicted. Multiple predicted position points are connected to form a short-term trajectory, that is, positioning and tracking trajectory prediction data. For example, the position of the device in the next 5 seconds is predicted, and these 5 predicted position points are stored in the designated memory area of ​​the microcontroller unit to constitute positioning and tracking trajectory prediction data.

[0023] Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to achieve a pre-switching decision on the positioning mode of the recyclable packaging container.

[0024] In an embodiment of the present invention, the positioning tracking trajectory prediction data is analyzed to determine the future movement trend of the device. For example, if the predicted trajectory shows that the device 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 pre-switching decision of the positioning mode is made. For example, when it is predicted that the device is about to enter an indoor environment, Bluetooth scanning is turned on in advance so that it can be seamlessly switched to Bluetooth positioning mode after the GPS signal is lost. The result of the pre-switching decision (for example, turning on Bluetooth scanning) is used to control 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.

[0025] Preferably, step S1 comprises the following steps: Step S11: using the microcontroller unit to configure the low power consumption timer to obtain timer configuration parameters; Step S12: periodically interrupt the microcontroller unit through the timer configuration parameters to obtain a wake-up interrupt signal; Step S13: activating the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up state signal; Step S14: using the wake-up state signal to perform multi-mode power supply control on the multi-mode communication unit to generate multi-mode power supply state data; Step S15: performing a multi-mode monitoring signal scan based on the multi-mode power supply status data to obtain multi-mode communication signal data; Step S16: Use a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data.

[0026] 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. The low-power timer module is independent of the system main clock and uses an external 32.768kHz crystal oscillator as the clock source. Configuration is performed by directly operating the register of the MCU. First, the low-power timer peripheral clock is enabled by writing "1" to the corresponding bit of a specific clock control register. Then, the prescaler is configured to divide the 32.768kHz clock source. For example, the prescaler coefficient is set to 32, the clock frequency is reduced to 1.024kHz, and the value 31 is written to the prescaler register (because the prescaler value on the hardware is the written value + 1). Next, the auto-reload register (ARR) is configured to determine the timing period. For example, if a timing period of 1 second is required, 1023 is written to the ARR register (because the auto-reload value is also the written value + 1, and under a 1.024kHz clock, 1024 clock cycles are 1 second). Finally, the timer update interrupt is enabled by writing "1" to the corresponding bit of the interrupt enable register. When the timer counter value reaches the value (1023) set in the auto-reload register (ARR), the hardware automatically clears the counter value and generates an update event. Since the update interrupt has been 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, the interrupt flag bit is first cleared by writing "1" to the corresponding bit of the interrupt status register. Then, a global wake-up flag variable is set to "1" to indicate that a wake-up interrupt has occurred. This global wake-up flag variable is a Boolean variable defined in the MCU memory. Finally, the interrupt service routine is executed and the program returns to the interrupted program point to continue execution. After initialization, the microcontroller unit (MCU) enters a low-power sleep mode, such as stop mode (STOP Mode), at which time the CPU stops running and most peripherals stop working to reduce power consumption. In the main loop program of the MCU, the global wake-up flag variable generated in step S12 is checked at the beginning of each loop. If the variable is "0", it means that no wake-up interrupt occurs, and the MCU continues to execute low-power wait instructions (such as the WFI instruction of the ARM Cortex-M0+ core) and remains in sleep mode. If the variable is "1", it means that a wake-up interrupt occurs, the MCU exits the low-power wait state, and continues to execute the subsequent code of the main loop. At the same time, the global wake-up flag variable is cleared by writing "0" to it. In the main loop, set a wake-up status indicator variable to "1" to indicate that the MCU is in the wake-up state. The 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 NB-IoT module, LoRa module and GNSS module.Each module has an independent power control pin. The microcontroller unit (MCU) is connected to these power control pins through general input and output (GPIO) pins. The wake-up state signal (the wake-up state indicator variable is "1") generated in step S13 triggers the MCU to execute the multi-mode communication unit power control logic. According to the preset communication strategy, for example, NB-IoT is used for communication first, and if it fails, LoRa is tried, and finally GNSS is used to obtain location information for power control. First, the MCU turns on the NB-IoT module power by setting the GPIO connected to the NB-IoT module power control pin to a high level (for example, 3.3V). Then, the multi-mode power supply status data is updated to "NB-IoT power supply". If subsequent communication fails, the MCU pulls the GPIO low (0V), turns off the NB-IoT module power, and then sets the GPIO connected to the LoRa module power control pin to a high level, turns on the LoRa module power, and updates the multi-mode power supply status data to "LoRa power supply". The microcontroller unit (MCU) controls the corresponding communication module to perform signal scanning. If the multi-mode power supply status data is "NB-IoT power supply", the MCU sends an AT command 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 the surrounding NB-IoT base station signals according to the command, and returns the searched signal strength, cell ID and other information to the MCU through the UART interface. The MCU stores the received data in the memory as NB-IoT communication signal data. If the multi-mode power supply status data is "LoRa power supply", the MCU sends a configuration command to the LoRa module through the SPI interface to set the parameters of the LoRa module, such as the spreading factor, bandwidth, and coding rate, and starts the receiving mode. The LoRa module receives the LoRa signal in the surrounding environment that meets the set parameters, and returns the received signal strength, signal-to-noise ratio and other information to the MCU through the SPI interface. The MCU stores these data as LoRa communication signal data. Similarly, if the GNSS module is used, the MCU sends a command 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 instructions to the IMU through the I2C interface to set the range, sampling rate (for example, 10Hz) and low power mode 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 to temporarily store the collected data. The MCU periodically reads the data in the FIFO buffer of the IMU through the I2C interface. For example, the FIFO buffer is read once per second.The data read include the acceleration values ​​of three axes (X, Y, Z) and the angular velocity values ​​of three axes. These data are represented in binary complement form as the equipment motion monitoring data.

[0027] 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, and step S15 includes the following steps: Step S151: sending a beacon scan instruction to the BLE communication component based on the multi-mode power supply status data, and then performing a complete beacon scan to obtain complete BLE scan data; Step S152: parse the complete BLE scan data by signal strength and beacon ID to obtain a BLE signal strength list; Step S153: performing WiFi AP scanning on the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scanning data; Step S154: parse the initial WiFi scan data for signal strength and SSID to obtain a WiFi signal list; Step S155: activating 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 the 4G base station information; Step S157: starting the GPS communication component based on the multi-mode power supply status data, and then performing GPS data collection to obtain GPS raw data; Step S158: Analyze the number of satellites and HDOP value of the GPS raw data to generate GPS signal data.

[0028] In an embodiment of the present invention, a microcontroller unit (MCU) is connected to a BLE communication component via a UART interface. First, the MCU sends a predefined beacon scan instruction via the UART. The instruction is a specific byte sequence, such as "0x01 0x020x03 0x04" (the specific instruction is defined in the data manual of the BLE chip). The instruction instructs the BLE chip to start scanning the BLE beacon broadcast in the surrounding environment. After receiving the instruction, the BLE chip starts its internal RF receiving circuit and listens for BLE beacon broadcast packets on three broadcast channels (channels 37, 38, and 39) in the 2.4GHz ISM band. The BLE chip stays on each channel for a period of time (e.g., 100 milliseconds) to capture all broadcast packets sent within the time window. Each captured broadcast packet contains raw data, such as the type of the broadcast packet, the transmission power, the MAC address, and the payload data. The BLE chip sends all captured raw broadcast packet data back to the MCU via the UART interface in a predefined format (e.g., a length byte is added before each broadcast packet data). The MCU first parses the header information of the advertising packet according to the BLE protocol specification, and extracts the type of the advertising packet (determines whether it is a connectable advertising packet or an unconnectable advertising packet) and the transmission power. Then, the received signal strength indication (RSSI) value is extracted from the raw data. The RSSI value is usually a negative integer in dBm, indicating the received signal strength. Then, according to the different types of advertising packets, the corresponding beacon ID is parsed. The MCU is connected to the WiFi communication component through the 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" (refer to the data manual of the WiFi chip for specific instructions). The instruction instructs the WiFi chip to scan the WiFi access points (APs) in the surrounding environment. After receiving the instruction, the WiFi chip starts its internal RF receiving circuit and scans for available WiFi networks on the 2.4GHz and / or 5GHz frequency bands. The WiFi chip will stay on each channel for a period of time in turn to receive and decode the Beacon frame of the 802.11 protocol. Beacon frames are broadcast periodically by WiFi APs and contain information such as the AP's SSID, MAC address, encryption method, signal strength, etc. The WiFi chip returns the information of all scanned APs to the MCU through the SPI interface in a predefined format (for example, each piece of AP information is separated by a comma, and fields are separated by 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). Typically, each piece of AP information contains multiple fields, such as SSID (Service Set Identifier, i.e., WiFi name), BSSID (MAC address of the AP), RSSI (Received Signal Strength Indicator), encryption method, etc.The MCU extracts the SSID and RSSI value of each AP through the string splitting function, such as splitting by commas and semicolons. The MCU first controls the GPIO connected to the power control pin of the 4GCat1 module to output a high level to power the 4G Cat1 module. Then, the MCU sends a series of AT commands to the 4G Cat1 module through the UART interface to initialize and activate the module. These AT commands include: AT (test whether the AT command is working properly), AT+CPIN? (check whether the SIM card is ready), AT+CREG? (check the network registration status), etc. (For specific commands and sequences, refer to the data sheet of the 4G Cat1 module). After sending the necessary AT commands, the MCU receives the response of the 4G Cat1 module through the UART interface. The MCU controls the GPIO connected to the power control pin of the GPS module to output a high level (for example, 3.3V) to power the GPS module. Then, the MCU sends a startup command to the GPS module through the UART interface (for specific commands and baud rate, refer to the data sheet of the GPS module). The command sets the output frequency of the GPS module to 1Hz. After receiving the startup command, the GPS module starts its internal RF receiving circuit and starts searching and tracking GPS satellite signals. The GPS module receives navigation messages from multiple GPS satellites, which contain satellite ephemeris data, time information, etc. The MCU reads NMEA 0183 sentences line by line and determines the sentence type based on the sentence identifier (such as "GPGGA", "GPGGA", "GPRMC"). For the GPGGA sentence, the MCU extracts the 7th field (the number of satellites) and the 8th field (HDOP, horizontal dilution of precision) through a string splitting function (for example, splitting by commas). The number of satellites indicates the number of GPS satellites currently used for positioning, usually an integer. The HDOP value indicates the horizontal positioning accuracy. The smaller the value, the higher the accuracy. It is usually a floating point number. For the GPRMC sentence, the MCU can extract information such as UTC time, positioning status (valid / invalid), and parse the number of satellites, HDOP value, and positioning status.

[0029] Preferably, step S2 comprises the following steps: 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: performing a whitelist comparison on the WiFi signal list in the multi-mode communication signal data through a preset WiFi whitelist, and then performing an AP effectiveness evaluation to obtain a WiFi confidence level; Step S24: performing signal strength evaluation according to the 4G base station information in the multi-mode communication signal data to obtain a 4G signal strength score; Step S25: performing GPS positioning accuracy evaluation based on the GPS signal data in the multi-mode communication signal data to obtain a GPS signal quality score; Step S26: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; 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 real-time positioning scene tag data to generate multi-mode collaborative positioning data.

[0030] In an embodiment of the present invention, an indoor beacon database is pre-stored in a Flash memory or an external memory (such as an EEPROM, an SD card) of a microcontroller unit (MCU). 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. These three IDs are used as key values ​​to search in the indoor beacon database. If a fully matching record is found in the database (that is, the UUID, Major ID, and Minor ID are the same), the beacon deployment location coordinates and other information in the record are extracted as a matching beacon. The information of all the matching beacons found (including beacon IDs 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, its corresponding RSSI value is obtained from the BLE signal strength list. A matching score is calculated based on the RSSI value and the deployment location recorded by the beacon in the database. The calculation formula for the matching 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). The matching scores of all matching beacons are averaged to obtain an average matching score. The average matching score is multiplied by a weight coefficient (such as 100) to adjust the score range to between 0-100 as the BLE indoor matching degree. A WiFi whitelist is pre-set and stored in the Flash memory of the MCU. The whitelist contains a series of SSIDs (WiFi names) of WiFi access points (APs) that are allowed to connect. After obtaining the WiFi signal list in step S154, the MCU traverses each WiFi AP information in the list. For each AP information, its SSID is extracted. The extracted SSID is compared one by one with the SSID in the WiFi whitelist. If a matching SSID is found in the whitelist, the AP is considered valid. The number of APs that appear in the whitelist in the WiFi signal list is counted and recorded as Valid_AP_Count. Then, the AP effectiveness is evaluated. Calculate the ratio of the number of valid APs to the number of all APs in the WiFi signal list: Ratio = Valid_AP_Count / Total_AP_Count, where Total_AP_Count is the number of all APs in the WiFi signal list. Multiply the ratio by a weight coefficient (for example, 100) to get a value between 0 and 100 as the WiFi confidence. If the WiFi signal list is empty, or the whitelist is empty, or no matching AP is found, the WiFi confidence is set to 0. The MCU extracts the RSRP (reference signal received power) value of the current serving cell. The RSRP value is usually a negative integer in dBm. According to the size of the RSRP value, the signal strength is evaluated. Set several thresholds, such as -80dBm, -90dBm, and -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. Evaluate the GPS positioning accuracy based on the HDOP value and the number of satellites. Set the HDOP threshold and the satellite number threshold, for example, the HDOP threshold is 2.0 and the satellite number 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", it means that the positioning is invalid, and the GPS signal quality score is 0. The motion state of the instrument is calculated based on the acceleration data collected by the IMU (Inertial Measurement Unit). For example, by calculating the variance of the acceleration data over a period of time, if the variance is greater than a certain threshold (for example, 0.1m / s²), it is determined that the instrument is in motion; if the variance is less than the threshold, it is determined that the instrument is stationary. Then, based on the positioning state in the GPS signal data, it is determined whether it is in a valid GPS positioning scenario. If the GPS signal quality score is 0, it means that the GPS positioning is invalid; otherwise, it means that the GPS positioning is valid. Combine the motion state and GPS positioning state, as well as the BLE indoor matching degree and WiFi confidence, to generate a positioning scene label. For example: if the GPS positioning is valid and the device is in motion, the scene label is "outdoor motion"; if the GPS positioning is valid and the device is stationary, the scene label is "outdoor stationary"; if the GPS positioning is invalid, the BLE indoor matching degree is greater than 70 and the device is stationary, the scene label is "indoor stationary"; if the GPS positioning is invalid, the WiFi confidence is greater than 70 and the device is in motion, the scene label is "indoor motion"; if the GPS, BLE and WiFi signals are all weak, the scene label is "weak signal". The generated scene label (string) is stored in the MCU memory as real-time positioning scene label data. According to the real-time positioning scene label data, select the appropriate positioning algorithm and data source for multi-mode collaborative positioning. For example, if the real-time positioning scene label is "outdoor stationary", GPS positioning data is used first; if the real-time positioning scene label is "indoor low-speed movement", BLE indoor positioning data is used in combination with inertial navigation data for positioning. The positioning results of different data sources are fused, for example, by using a weighted average method to assign different weights based on the real-time positioning scene label data and the quality scores of each data source. The final fused positioning result is the multi-mode collaborative positioning data, including longitude and latitude or indoor coordinate system coordinates.

[0031] Preferably, step S26 comprises the following steps: Step S261: Perform indoor / outdoor environment judgment based on the BLE indoor matching degree and generate indoor / outdoor environment judgment data; Step S262: querying 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; Step S263: geographically matching the base station coverage data using preset geographical fence information to obtain a geographical location matching degree; Step S264: performing motion state recognition based on the tool motion monitoring data to generate tool motion state data; Step S265: determine the region type based on the geographic location match and obtain a region type label; Step S266: classifying the motion mode according to the motion state data of the device to obtain a motion mode label; Step S267: Integrate the positioning scene label based on the indoor / outdoor environment judgment data, the area type label and the motion mode label to obtain real-time positioning scene label data.

[0032] In an embodiment of the present invention, a threshold value is set, for example, 70. If the BLE indoor matching degree is greater than or equal to 70, the current environment is judged to be an indoor environment. If the BLE indoor matching degree is less than 70, the current environment is judged to be an outdoor environment. The judgment result is stored as a Boolean variable, for example, "1" represents an indoor environment, "0" represents an outdoor environment, or the string "Indoor" represents indoors, and "Outdoor" represents outdoors. The Boolean variable or string is used as 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 therefrom. These four parameters are combined into a unique base station identifier, for example, "460-00-12345-67890" (where 460 is MCC, 00 is MNC, 12345 is LAC, and 67890 is Cell ID). Using the base station identifier, a pre-built base station database is queried. The database stores the identifiers of known base stations and their corresponding geographical coverage information. The geographic coverage of the base station can be represented by a circular area, including the latitude and longitude coordinates of the center point and the radius (unit: meter), or by a polygonal area, including the latitude and longitude coordinates of multiple vertices. If a matching base station identifier is found in the database, the geographic coverage information of the base station is extracted. If there is no matching base station identifier in the database, a default coverage 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 larger value (for example, 5000 meters). The queried or default base station coverage information (center point latitude and longitude and radius, or polygon vertex coordinates) is stored in the MCU memory as base station coverage data. A series of geo-fences are pre-set, each geo-fence represents a specific area, such as a warehouse, factory, logistics center, etc. Each geo-fence is represented by a polygonal area, including the latitude and longitude coordinates of multiple vertices, and is associated with an area type label (for example, "warehouse", "factory", "logistics center"). The geo-fence information is stored in the MCU's Flash memory. Based on the base station coverage data obtained in step S262, determine the relationship between the coverage and the preset geo-fence. If the base station coverage data is represented by a circular area, calculate the shortest distance from the center of the circle to each geo-fence polygon. If the distance is less than the radius of the circle, it is considered that the base station coverage intersects with the geo-fence. If the base station coverage data is represented by a polygon, determine whether the two polygons intersect (GIS algorithms such as the ray method can be used). Count the number of geo-fences that intersect with the base station coverage, and calculate the ratio of the number of intersecting geo-fences to the number of all geo-fences as the geographic location match.If no intersecting geofences are found, the geolocation match 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 get the total acceleration value: a=sqrt(ax²+ay²+az²), where ax, ay, and az are the acceleration values ​​of the three axes. Calculate the average value A_avg of all the total acceleration values. Calculate the sum of the squares of the differences between all the total acceleration values ​​and the average value, and then divide it by the number of data points to get the acceleration variance Var_a. Set an acceleration variance threshold, such as 0.1m² / s. 4 . If Var_a is greater than the threshold, the device is judged to be in motion; if Var_a is less than the threshold, the device is judged to be in a stationary state. The judgment result is stored as a Boolean variable, for example, "1" indicates the motion state, "0" indicates the stationary state, or the string "Moving" indicates motion, and "Static" indicates stationary. If the geographic location match is 0, it means that no intersecting geographic fences are found, and the area type label is set to "unknown area". If the geographic location match is greater than 0, the area type labels of all intersecting geographic fences are checked. The area type labels with the most occurrences are counted. If there is only one area type label with the most occurrences, the label is used as the final area type label. If there are multiple area type labels with the most occurrences, one of them is selected (for example, selected in a predefined priority order, or randomly selected) as the final area type label, for example (string, such as "warehouse", "factory", "logistics center", "unknown area"). If the device motion state data indicates that the device is in a stationary state, the motion mode label is set to "stationary". If the device motion state data indicates that the device is in motion, the IMU data is further analyzed. For example, the integral of the angular velocity data over a period of time (e.g., 5 seconds) can be calculated to obtain the angle change. If the angle change exceeds a certain threshold (e.g., 90 degrees), it is determined that the device has rotated, and the motion mode label is set to "rotation". If the angle change is small, but the acceleration data is continuously large, it is determined that the device is in linear motion, and the motion mode label is set to "linear motion". If both the acceleration and angular velocity data show periodic changes, it means that the device is vibrating or swaying, and the motion mode label is set to "vibration". Based on the indoor / outdoor environment judgment data, the area type label, and the motion mode label, these three pieces of information are combined 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", then the real-time positioning scene label data is "indoor-warehouse-stationary".

[0033] Preferably, step S27 includes the following steps: Step S271: configuring the positioning mode availability weight 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; Step S272: Prioritize the positioning modes according to the initial communication positioning mode weight coefficients to obtain a priority score for each mode; Step S273: performing decision tree multi-mode combination reasoning based on the real-time positioning scene label data through the priority score of each mode to obtain priority positioning mode combination data; Step S274: Perform collaborative positioning processing on each mode according to the priority positioning mode combination data to generate multi-mode collaborative positioning data.

[0034] In the embodiment 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 signal is good). For example: BLE indoor positioning: Since the accuracy is higher in an indoor environment where beacons are pre-deployed, the initial weight coefficient is set to 0.4. WiFi positioning: The accuracy is greatly affected by the AP deployment density and environment, and the initial weight coefficient is set to 0.2. 4G positioning: The accuracy is lower, mainly used to provide rough location or auxiliary positioning, and the initial weight coefficient is set to 0.1. GPS positioning: The accuracy is higher in an open outdoor 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 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, set the GPS weight coefficient to 0. Store the adjusted weight coefficients of each mode (BLE, WiFi, 4G, GPS) in the MCU memory as the initial communication positioning mode weight coefficient, and the sum of the weight coefficients is 1. If all weight coefficients are 0, set a default weight coefficient, such as 1 for GPS and 0 for others. Sort each positioning mode (BLE, WiFi, 4G, GPS) from large to small according to the weight coefficient. According to the sorting result, assign a priority score to each mode. If the weight coefficient is sorted as: BLE (0.4) > GPS (0.3) > WiFi (0.2) > 4G (0.1), the priority score is: BLE (4 points) > GPS (3 points) > WiFi (2 points) > 4G (1 point). If the weight coefficient is sorted as: GPS (0.6) > 4G (0.4) > BLE (0) > WiFi (0), 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 will still be sorted, but its priority is lower than the mode with a non-zero weight. Build a decision tree model 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: Root node: Real-time positioning scene label data = "indoor-warehouse-stationary"? Yes: Go to the BLE positioning branch.

[0035] BLE priority score > 1? Yes: Priority positioning mode combination data = {BLE}.

[0036] No: Prioritize positioning mode combination data = {} (indicates that all modes are unavailable).

[0037] No: Go to the next level of judgment.

[0038] Next layer node: Real-time positioning scene label data = "outdoor-unknown area-straight line motion"? Yes: Go to the GPS positioning branch.

[0039] GPS priority score > 1? Yes: Priority positioning mode combined data = {GPS} No: Prioritize positioning mode combination data = {} No: Go to the next level of judgment.

[0040] Next layer node: Real-time positioning scene label data = "Indoor-Factory-Rotation"? Yes: Go to the WiFi Positioning branch.

[0041] WiFi priority score > 1? Yes: Prioritize positioning mode combination data = {WiFi}.

[0042] No: Prioritize positioning mode combination data = {}.

[0043] No: Go to the 4G positioning branch. Since it is a fallback option, select 4G positioning as long as 4G is available.

[0044] 4G priority score > 1? Yes: Priority positioning mode combined data = {4G} No: Prioritize positioning mode combination data = {}.

[0045] The priority positioning mode combination data (a set containing one or more positioning modes, such as {BLE}, {GPS}, {WiFi, 4G}, or an empty set {}) obtained by decision tree inference is used. When the priority positioning mode combination data is {BLE}, the three-side positioning algorithm or the weighted centroid positioning algorithm is used to calculate the estimated position coordinates (x, y) of the device. When the priority positioning mode combination data is {GPS}, the longitude and latitude coordinates in the GPS raw data are directly used as the positioning result. If higher accuracy is required, the GPS raw data can be differentially processed. When the priority positioning mode combination data is {WiFi}, the WiFi fingerprint positioning algorithm is used to match the WiFi signal list in the fingerprint library to calculate the estimated position of the device. If the fingerprint library is not built, no positioning calculation is performed. When the priority positioning mode combination data is {4G}, the base station triangulation positioning algorithm performs positioning calculations to obtain rough position coordinates. When the priority positioning mode combination data is a combination of multiple modes, the corresponding positioning components are activated according to the priority positioning mode combination data. For example, if the combination data indicates the use of GPS and BLE, the GPS receiver and BLE scanning module are activated. After activation, each component starts to collect data independently. For example, the GPS receiver receives satellite signals and calculates location information, and the BLE scanning module scans surrounding beacons and obtains signal strength. Data collection of each component is carried out in parallel to improve efficiency. A Kalman filter or other type of filter is used to smooth the GPS signal and eliminate positioning jitter. Based on the weight coefficient of the initial communication positioning mode, each positioning data is fused and the final position coordinates are weighted and calculated as multi-mode collaborative positioning data.

[0046] Preferably, the step S274 includes: Activate the current collaborative positioning component according to the priority positioning mode combination data to obtain the collaborative positioning component activation state data; Extract multi-mode positioning data according to the activation state data of the collaborative positioning component to obtain the positioning input data of each mode; Based on the positioning input data of each mode, parallel positioning calculation of each mode is performed to obtain independent positioning result data of each mode; The confidence of the positioning result is evaluated on the independent positioning result data of each mode through the weight coefficient of the initial communication positioning mode, and the positioning confidence of each mode is obtained; The positioning reliability of each mode is used to perform weighted collaborative positioning calculation on the independent positioning result data of each mode to generate multi-mode collaborative positioning data.

[0047] In an embodiment of the present invention, based on the priority positioning mode combination data (such as {BLE}, {GPS, 4G}, {}, etc.), the power supply and working state of the corresponding communication component are controlled. If the priority positioning mode combination data is an empty set {}, no positioning component is activated, and the collaborative positioning component activation state data is empty or set to "none". If the priority positioning mode combination data includes BLE, the BLE module power is turned on by setting the GPIO connected to the BLE module power control pin to a high level, and ensure 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 priority positioning mode combination data includes GPS, the GPS module power is turned on (if it was previously turned off), and ensure that the GPS module is in a state of searching for satellite signals. If the priority positioning mode combination data includes WiFi, the WiFi module power is turned on, and ensure that the WiFi module is in a state of scanning APs. If the priority positioning mode combination data includes 4G, the 4G module power is turned on. If the activation status data of the collaborative positioning component contains "BLE", the IDs (UUID, Major, Minor) and RSSI values ​​of all matching beacons are extracted from the matching beacon list 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 SSID and RSSI values ​​of all scanned APs are extracted from the WiFi signal list 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 according to the 4G base station information as 4G positioning input data. Based on the positioning input data of each mode, independent positioning calculations are performed respectively to obtain their own independent positioning results. Based on the initial communication positioning mode weight coefficient and the independent positioning result data of each mode obtained in the previous step, the confidence of the positioning result of each mode is calculated. If the independent positioning result of a mode is empty (indicating that the positioning of the mode fails), its confidence is 0. If the independent positioning result of a mode is not empty, its confidence is equal to the calculated adjusted weight coefficient of the mode. For example, if BLE positioning is successful and the adjusted weight coefficient of BLE is 0.4, the BLE positioning confidence is 0.4; if GPS positioning fails, the GPS positioning confidence is 0. Convert the latitude and longitude coordinates of each mode to plane coordinates in meters (for example, using UTM projection). Then, for each coordinate component (x coordinate and y coordinate), calculate the weighted average to obtain the final position coordinate.

[0048] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes: Step S31: extracting the timestamp of the multi-mode collaborative positioning data to obtain the current positioning time data; In the embodiment of the present invention, the internal real-time clock (RTC) module of the microcontroller unit (MCU) is used to obtain the time. The RTC module is usually driven by an independent crystal oscillator and can continue to time even when the MCU main clock is turned off. The current time is read from the RTC module, which usually includes information such as year, month, day, hour, minute, and second.

[0049] Step S32: performing historical positioning data retrieval on the current positioning time data through a preset historical retrieval time window to obtain historical trajectory data; In an embodiment of the present invention, a historical retrieval time window is pre-set, such as 1 hour, 24 hours or 7 days. The 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, the start time of the historical retrieval is calculated. The historical positioning data is read 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 a timestamp and positioning coordinates previously stored. The timestamps in the historical positioning data are compared with the start time and current time of the historical retrieval one by one. If the timestamp of a piece of historical positioning data is between the start time and the current time, the data is extracted. All the extracted historical positioning data are arranged in the order of timestamps to form an array or a linked list as historical trajectory data.

[0050] Step S33: extracting the motion characteristics of the device using a low-power inertial measurement unit to obtain motion characteristic parameters; In an embodiment of the present invention, more detailed motion characteristic parameters are extracted based on the motion monitoring data of the equipment, and the acceleration and angular velocity data in the most recent period of time (for example, 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 combined acceleration value: a=sqrt(ax²+ay²+az²), where ax, ay, and az are the acceleration values ​​of the three axes, respectively. Calculate the average value A_avg and variance Var_a of all combined acceleration values. For the angular velocity data at each time point, calculate the square root of the sum of the squares of the three-axis angular velocities to obtain the combined angular velocity value: ω=sqrt(ωx²+ωy²+ωz²), where ωx, ωy, and ωz are the angular velocity values ​​of the three axes, respectively. Calculate the average value Ω_avg and variance Var_ω of all combined angular velocity values. Find the axis with the largest variance among the three acceleration axes 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, acceleration main direction, and inclination angle change values ​​are used as motion feature parameters.

[0051] Step S34: performing motion pattern recognition on the motion feature parameters through the historical trajectory data to generate motion pattern recognition data; In an embodiment of the present invention, a motion pattern recognition model is established. The motion pattern can be divided into several typical types, such as: stationary, linear motion, turning, uphill and downhill, bumpy, etc. If the average acceleration A_avg and the acceleration variance Var_a are both lower than a certain threshold, and the average angular velocity Ω_avg and the angular velocity variance Var_ω are also lower than a certain threshold, it is judged as a 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 change in inclination), it is judged 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 a significant angle change is detected by the change in inclination, it is judged as a turn. If the main direction of acceleration has a significant angle with the vertical direction (judged by gravity acceleration), and the angle lasts for a period of time, it is judged as uphill or downhill. It can be judged whether it is uphill or downhill according to the change in inclination. If the acceleration variance Var_a is large and the acceleration changes frequently, it is judged as bumpy.

[0052] Step S35: predicting the motion position of the multi-mode collaborative positioning data at the next moment through the motion mode recognition data to obtain the predicted position coordinates; In an embodiment of the present invention, the longitude and latitude coordinates at the current moment are obtained from the multi-mode collaborative positioning data, and the distance between two adjacent positioning points in the most recent period (for example, the most recent 5 seconds) is calculated from the historical trajectory data, and divided by the time interval to obtain a series of instantaneous speeds. The average of these instantaneous speeds is obtained 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 uniformly accelerated linear motion for a short period of time. According to the current position, current speed V_current, average acceleration A_avg and prediction time interval Δt (for example, 1 second), the kinematic formula is used to predict the position at the next moment: the direction of motion needs to be determined. Since it is a linear motion, it can be assumed that the direction of motion is the same as the direction of the average speed in the most recent period, or the same as the main direction of acceleration. The direction is converted into an azimuth θ (unit: radian) relative to the true north direction. If the motion mode is "turning", the position at the next moment is predicted based on the current position, angular velocity data and the average speed in the most recent period. Since turning motion is more complicated, a simplified model can be used, for example, assuming that the turning radius remains unchanged, and calculating the position after turning based on the angular velocity and speed. For these two modes, since the motion is more complicated and the position change is not obvious in a short time, a similar processing method as the static mode can be used, that is, the predicted position is the same as the current position, or a short-distance prediction is made based on the average speed.

[0053] Step S36: Acquire the recyclable packaging equipment operation demand data; perform short-term trajectory tracking on the predicted position coordinates through the recyclable packaging equipment operation demand data to obtain positioning tracking trajectory prediction data.

[0054] In an embodiment of the present invention, the data is obtained from a remote server through a communication module (such as 4G, WiFi). The operation demand data includes: target position: the latitude and longitude coordinates of the target position that the device needs to reach; estimated arrival time: the time when the device is expected to arrive at the target position; operation type: the type of operation that the device needs to perform, such as "loading", "unloading", "transportation", etc.; route planning information: if there is a pre-planned route, it contains the coordinates of key points on the route. If the operation demand data contains the target position, the distance between the predicted position and the target position is calculated. If the distance is less than a certain threshold (such as 10 meters), it is considered that the device is close to the target position and the prediction can be stopped. If the operation type is "loading" or "unloading", the predicted position may remain unchanged or change within a small range. If the operation type is "transportation", a more accurate prediction can be made in combination with the route planning information (if any). Arrange multiple predicted position coordinates (such as predicting one position per second) in a future period of time (such as the next 1 minute) in chronological order to form an array or linked list. The array or linked list and information such as the operation type currently used for prediction are used as positioning tracking trajectory prediction data.

[0055] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes: Step S41: performing future trajectory point analysis based on the positioning and tracking trajectory prediction data to generate future positioning trajectory point data; In an embodiment of the present invention, the longitude and latitude coordinates of each predicted position are extracted from the positioning tracking trajectory prediction data. If the longitude and latitude coordinates of the predicted position 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 position coordinates can be smoothed. For example, a sliding average filter can be used to average several consecutive predicted position coordinates to obtain a smoother trajectory. According to actual needs, key trajectory points are screened out. For example, only points on the trajectory whose distance change exceeds a certain threshold are retained, or only points within a specific time interval are retained. Stored in chronological order in an array or linked list as future positioning trajectory point data.

[0056] Step S42: Perform future environment scene analysis based on the real-time positioning scene label data and the future positioning trajectory point data to generate future scene sequence data; In the embodiment of the present invention, the real-time positioning scene label data at the current moment is used as the initial value of the future scene sequence. Each track point in the future positioning track point data is traversed, and the scene in which the track point is located is predicted according to the position of the track point and the current scene. If the current scene is indoors, and the future track point is still in a known indoor area (for example, determined according to a pre-stored indoor map or geo-fence information), it is predicted that the track point is still in an indoor scene. If the current scene is outdoor, and the future track point is still in an outdoor area, it is predicted that the track point is still in an outdoor scene. If the current scene is indoors, but the future track point moves to an outdoor area, it is predicted that the track point is in an outdoor scene. If the current scene is outdoor, but the future track point moves to an indoor area, it is predicted that the track point is in an indoor scene. If the future track point is located in a known geo-fence (for example, a warehouse, a factory, a logistics center, etc.), it is predicted that the track point is in a corresponding area type. If the future track point is not in any known geo-fence, it is predicted that the track point is in an "unknown area". The predicted scene (including indoor / outdoor, area type, motion mode) of each future track point and its corresponding timestamp are combined into a data pair. Arrange all data pairs in chronological order to form an array or linked list as future scene sequence data.

[0057] Step S43: Evaluate the availability of each positioning mode according to the future scene sequence data to obtain a future availability score for each mode; In the embodiment 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 static and motion are distinguished in the score table, the corresponding value in the table is directly used; if there is no distinction, it can be +1 based on the initial score, but not more than 4). If there is a description of "static" in the scene and the positioning mode is GPS, the GPS availability score 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 subtracted from the original basis to obtain the future availability score data of each mode.

[0058] Step S44: Obtaining energy consumption data per unit time of each positioning component and communication cost data per unit data volume of each positioning component; In an embodiment of the present invention, the datasheet of each positioning component (BLE, WiFi, 4GCat1, GPS module) is consulted. The datasheet usually provides information such as typical operating current and operating voltage. 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 (for example, the cost of maintaining a local server). For 4GCat1, the communication cost mainly depends on the operator's traffic package fee. You can query the operator's tariff standard to get the cost per MB of data.

[0059] Step S45: Optimize the multi-objective positioning modes 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 container.

[0060] In an embodiment of the present invention, the future availability score 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 are comprehensively considered to perform multi-objective optimization and select the optimal positioning mode combination. For example, a weighted sum method is used to multiply the availability score, energy consumption, and communication cost of each positioning mode by a preset weight coefficient and then add them together to obtain a comprehensive score of the mode. The mode with the highest comprehensive score is selected as the main positioning mode, and a pre-switching decision is made based on the future scene sequence data. For example, each time point in the future availability score data of each mode is traversed, and for each time point, a multi-objective optimization problem is constructed. 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 condition is: at least one positioning mode is turned on, and the sum of the availability scores of the turned-on positioning modes must be greater than a threshold (for example, 5, indicating that a certain positioning accuracy is required). The multi-objective optimization problem is solved using a greedy algorithm, and then a mode with a high availability score, low energy consumption, and low communication cost is preferentially selected. Sort the modes from high to low according to the availability score, and try to turn on each mode in turn until the constraint condition is met. Generate a positioning mode pre-switching decision based on the optimal positioning mode combination at each time point. For example, if the decision result is to switch the positioning mode from GPS to BLE at a certain time point in the future, a switching instruction is issued some time in advance (for example, 1 second in advance) to turn off the GPS module and turn on the BLE module. Combine the positioning mode pre-switching decision at each time point (for example, "turn on BLE, turn off GPS, WiFi, 4G") and its corresponding timestamp into a data pair. Arrange all data pairs in chronological order to form an array or linked list as the final positioning mode pre-switching decision data.

[0061] Preferably, the present invention further provides a multi-mode dynamic positioning and tracking system for recyclable packaging utensils, which executes the multi-mode dynamic positioning and tracking method for recyclable packaging utensils as described above, and the multi-mode dynamic positioning and tracking system for recyclable packaging utensils comprises: The multi-source data acquisition module is used to 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; and use the low-power inertial measurement unit to perform periodic motion sampling to obtain the device motion monitoring data; The collaborative positioning processing module is used to perform positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; The trajectory prediction and tracking module is used to predict the motion position based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning and tracking trajectory prediction data; The multi-mode dynamic positioning module is used to optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data, so as to realize the pre-switching decision of the positioning mode for recyclable packaging equipment.

[0062] The present application is to integrate multiple communication components such as BLE, WiFi, 4G Cat1 and GPS, combined with a low-power inertial measurement unit, to automatically select the optimal positioning mode according to different environmental conditions. In indoor environments, the BLE and WiFi signal strengths are high, and the system can give priority to using these signals for high-precision indoor positioning; in open outdoor areas, the GPS signal is stable and accurate, and the system switches to the GPS positioning mode to achieve long-distance, high-precision tracking. This multi-mode collaborative positioning mechanism overcomes the limitations of a single technology in a specific scenario and significantly improves the accuracy and reliability of positioning. For example, in an indoor environment where the GPS signal is blocked, the system can automatically switch to WiFi or BLE mode, use indoor base stations or beacons for positioning, and ensure the continuity of positioning information; while in a wide outdoor area, it can flexibly switch to 4G Cat1 or GPS mode to achieve long-distance, high-precision tracking. This multi-mode signal acquisition capability greatly expands the coverage of the positioning system, reduces the probability of positioning blind spots, and ensures the real-time visibility of recyclable packaging equipment throughout the supply chain. Even if the device switches between different environments, it can always maintain tracking status to avoid information gaps caused by signal loss. By evaluating the positioning signal strength and cost in real time and dynamically adjusting the technology combination, the continuity and accuracy of positioning data are ensured while significantly reducing communication energy consumption. The system can automatically select the optimal positioning mode based on 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 long-term operating costs, making it suitable for global supply chain management.

[0063] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0064] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A multi-mode dynamic positioning and tracking method for recyclable packaging equipment, characterized in that: Applied to recyclable packaging utensils, the recyclable packaging utensils 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 with the low-power inertial measurement unit and the multi-mode communication unit, and the multi-mode dynamic positioning and tracking method of the recyclable packaging utensils comprises the following steps: Step S1: using a microcontroller unit to control a multi-mode communication unit to scan a multi-mode monitoring signal to obtain multi-mode communication signal data; using a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data; Step S2: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; performing multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; Step S3: 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; Step S4: Optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data to achieve a pre-switching decision on the positioning mode of the recyclable packaging container.

2. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using the microcontroller unit to configure the low power consumption timer to obtain timer configuration parameters; Step S12: periodically interrupt the microcontroller unit through the timer configuration parameters to obtain a wake-up interrupt signal; Step S13: activating the main loop of the microcontroller unit based on the wake-up interrupt signal to obtain a wake-up state signal; Step S14: using the wake-up state signal to perform multi-mode power supply control on the multi-mode communication unit to generate multi-mode power supply state data; Step S15: performing a multi-mode monitoring signal scan based on the multi-mode power supply status data to obtain multi-mode communication signal data; Step S16: Use a low-power inertial measurement unit to perform periodic motion sampling to obtain device motion monitoring data.

3. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 2 is characterized in that: The multi-mode communication signal data includes a BLE signal strength list, a WiFi signal list, 4G base station information and GPS signal data, and step S15 includes the following steps: Step S151: sending a beacon scan instruction to the BLE communication component based on the multi-mode power supply status data, and then performing a complete beacon scan to obtain complete BLE scan data; Step S152: parse the complete BLE scan data by signal strength and beacon ID to obtain a BLE signal strength list; Step S153: performing WiFi AP scanning on the WiFi communication component based on the multi-mode power supply status data to obtain initial WiFi scanning data; Step S154: parse the initial WiFi scan data for signal strength and SSID to obtain a WiFi signal list; Step S155: activating 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 the 4G base station information; Step S157: starting the GPS communication component based on the multi-mode power supply status data, and then performing GPS data collection to obtain GPS raw data; Step S158: Analyze the number of satellites and HDOP value of the GPS raw data to generate GPS signal data.

4. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 3 is characterized in that: Step S2 includes the following steps: 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: performing a whitelist comparison on the WiFi signal list in the multi-mode communication signal data through a preset WiFi whitelist, and then performing an AP effectiveness evaluation to obtain a WiFi confidence level; Step S24: performing signal strength evaluation according to the 4G base station information in the multi-mode communication signal data to obtain a 4G signal strength score; Step S25: performing GPS positioning accuracy evaluation based on the GPS signal data in the multi-mode communication signal data to obtain a GPS signal quality score; Step S26: performing positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; 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 real-time positioning scene tag data to generate multi-mode collaborative positioning data.

5. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 4 is characterized in that: Step S26 includes the following steps: Step S261: Perform indoor / outdoor environment judgment based on the BLE indoor matching degree and generate indoor / outdoor environment judgment data; Step S262: querying 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; Step S263: geographically matching the base station coverage data using preset geographical fence information to obtain a geographical location matching degree; Step S264: performing motion state recognition based on the tool motion monitoring data to generate tool motion state data; Step S265: determine the region type based on the geographic location match and obtain a region type label; Step S266: classifying the motion mode according to the motion state data of the device to obtain a motion mode label; Step S267: Integrate the positioning scene label based on the indoor / outdoor environment judgment data, the area type label and the motion mode label to obtain real-time positioning scene label data.

6. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 4, characterized in that: Step S27 includes the following steps: Step S271: configuring the positioning mode availability weight 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; Step S272: Prioritize the positioning modes according to the initial communication positioning mode weight coefficients to obtain a priority score for each mode; Step S273: performing decision tree multi-mode combination reasoning based on the real-time positioning scene label data through the priority score of each mode to obtain priority positioning mode combination data; Step S274: Perform collaborative positioning processing on each mode according to the priority positioning mode combination data to generate multi-mode collaborative positioning data.

7. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 6, characterized in that: The step S274 includes: Activate the current collaborative positioning component according to the priority positioning mode combination data to obtain the collaborative positioning component activation state data; Extract multi-mode positioning data according to the activation state data of the collaborative positioning component to obtain the positioning input data of each mode; Based on the positioning input data of each mode, parallel positioning calculation of each mode is performed to obtain independent positioning result data of each mode; The confidence of the positioning result is evaluated on the independent positioning result data of each mode through the weight coefficient of the initial communication positioning mode, and the positioning confidence of each mode is obtained; The positioning reliability of each mode is used to perform weighted collaborative positioning calculation on the independent positioning result data of each mode to generate multi-mode collaborative positioning data.

8. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting the timestamp of the multi-mode collaborative positioning data to obtain the current positioning time data; Step S32: performing historical positioning data retrieval on the current positioning time data through a preset historical retrieval time window to obtain historical trajectory data; Step S33: extracting the motion characteristics of the device using a low-power inertial measurement unit to obtain motion characteristic parameters; Step S34: performing motion pattern recognition on the motion feature parameters through the historical trajectory data to generate motion pattern recognition data; Step S35: predicting the motion position of the multi-mode collaborative positioning data at the next moment through the motion mode recognition data to obtain the predicted position coordinates; Step S36: Acquire the recyclable packaging equipment operation demand data; perform short-term trajectory tracking on the predicted position coordinates through the recyclable packaging equipment operation demand data to obtain positioning tracking trajectory prediction data.

9. The multi-mode dynamic positioning and tracking method for recyclable packaging containers according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing future trajectory point analysis based on the positioning and tracking trajectory prediction data to generate future positioning trajectory point data; Step S42: Perform future environment scene analysis based on the real-time positioning scene label data and the future positioning trajectory point data to generate future scene sequence data; Step S43: Evaluate the availability of each positioning mode according to the future scene sequence data to obtain a future availability score for each mode; Step S44: Obtaining energy consumption data per unit time of each positioning component and communication cost data per unit data volume of each positioning component; Step S45: Optimize the multi-objective positioning modes 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 container.

10. A multi-mode dynamic positioning and tracking system for recyclable packaging containers, applied to the multi-mode dynamic positioning and tracking method for recyclable packaging containers according to any one of claims 1 to 9, characterized in that: The multi-mode dynamic location tracking system for returnable packaging includes: The multi-source data acquisition module is used to 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; and use the low-power inertial measurement unit to perform periodic motion sampling to obtain the device motion monitoring data; The collaborative positioning processing module is used to perform positioning scene label processing according to the multi-mode communication signal data and the device motion monitoring data to obtain real-time positioning scene label data; perform multi-mode collaborative positioning on the multi-mode communication signal data through the real-time positioning scene label data to generate multi-mode collaborative positioning data; The trajectory prediction and tracking module is used to predict the motion position based on the multi-mode collaborative positioning data, and then perform short-term trajectory tracking to obtain positioning and tracking trajectory prediction data; The multi-mode dynamic positioning module is used to optimize the multi-target positioning mode according to the positioning tracking trajectory prediction data, so as to realize the pre-switching decision of the positioning mode for recyclable packaging equipment.

Images