Edge-cloud collaborative positioning method and system based on composite sensor

Abstract

This invention belongs to the field of satellite positioning chip technology, and particularly relates to a composite sensor-based edge-cloud collaborative positioning method and system. This method first uses a positioning chip to obtain the positioning task status, battery state, and signal strength. Using a configured storage-transmission control model, it determines the transmission of locally stored batch data, enabling edge-cloud data transmission. Secondly, based on the current positioning task information, a computational complexity model is used to determine the task's computational complexity. Thirdly, an edge-cloud collaborative positioning model is configured to determine edge-cloud collaborative positioning based on the positioning task's computational complexity, the chip's battery state, and signal strength. Finally, based on the determination result, the method chooses to perform positioning calculations at the edge or collaboratively allocate computing resources between the edge and the cloud to obtain high-precision positioning information. By optimizing data transmission and computing resource allocation, the method significantly improves the positioning system's response speed and energy efficiency, making it suitable for positioning applications in various complex environments.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite positioning chips, and in particular relates to an edge-cloud collaborative positioning method and system based on a composite sensor. Background Art

[0002] Traditional positioning devices have significant deficiencies in coverage, real-time positioning capabilities, and performance in weak signal areas. These devices typically have narrow coverage and are unable to provide comprehensive positioning services in remote or weak signal areas, resulting in extremely low cost-effectiveness in various application scenarios. For example, in areas such as outdoor adventures, agricultural monitoring, and intelligent transportation, traditional positioning devices often fail to meet actual needs, especially in areas with poor signal coverage, where their positioning accuracy and reliability are significantly reduced. Furthermore, traditional positioning devices also have shortcomings in data transmission and processing. The high power consumption caused by real-time data transmission shortens device battery life, and the need for frequent charging increases maintenance costs. At the same time, data processing capabilities are limited, making it difficult to meet the big data processing and analysis needs of modern IoT and smart applications. Existing solutions often rely on single sensors and simple data processing methods, lacking the ability to comprehensively analyze and intelligently process multi-source data. Therefore, positioning accuracy and reliability in complex environments are difficult to guarantee, limiting their widespread application in high-precision positioning and smart applications.

[0003] For example, the Chinese patent application with publication number CN117761732A discloses a satellite navigation positioning and orientation chip and a signal processing method. The satellite navigation positioning device includes: a first-level functional chip, a second-level functional chip, a third-level functional chip and a substrate; the first-level functional chip is arranged on the substrate, and is used to realize full-frequency satellite navigation positioning and orientation; the second-level functional chip is arranged above the first-level functional chip, and is used to run a satellite navigation positioning program; the third-level functional chip is arranged above the second-level functional chip, and is used to store the satellite navigation positioning program.

[0004] The above existing technologies have the following problems: traditional positioning devices generally have narrow coverage, cannot be positioned in real time, and cannot provide full coverage in remote or signal-weak areas. To this end, the present invention provides an edge-cloud collaborative positioning method and system based on composite sensors. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention proposes an edge-cloud collaborative positioning method and system based on a composite sensor. The method first obtains positioning task status data, battery status, and signal strength data through a positioning chip and stores them locally. At the same time, the configured storage-transmission control model is used to perform transmission judgment on the locally stored batch data, and edge-cloud data transmission is realized through a full-network module. Secondly, based on the current positioning task information, the computational complexity of the task is determined through a computational complexity model. Thirdly, an edge-cloud collaborative positioning model is configured, the edge positioning sub-model is built into the chip, and the collaborative positioning model is deployed to the cloud. Based on the computational complexity of the positioning task, the chip battery status, and the signal strength, an edge-cloud collaborative positioning judgment is made. Finally, based on the judgment result, it is chosen to perform positioning calculations at the edge or to collaboratively allocate computing resources between the edge and the cloud to obtain high-precision positioning information. By optimizing data transmission and computing resource allocation, this method significantly improves the response speed and energy efficiency of the positioning system and is suitable for positioning applications in various complex environments.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The edge-cloud collaborative positioning method based on composite sensors includes the following steps:

[0008] S1. Obtain the positioning task status data, battery status, and signal strength data of the positioning chip and store them locally on the chip. At the same time, the configured storage-transmission control model is used to determine the local storage batch data transmission, and the configured full-network module is used to perform edge-to-cloud data transmission.

[0009] S2. Obtain the current positioning task information and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model;

[0010] S3. Configure the edge-cloud collaborative positioning model, embed the edge positioning sub-model in the edge-cloud collaborative positioning model into the chip, deploy the collaborative positioning sub-model to the cloud, and make edge-cloud collaborative positioning decisions based on the computational complexity of the corresponding positioning task, the chip's power state, and the signal strength.

[0011] S4. Perform edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the judgment result to obtain the positioning information of the corresponding positioning task.

[0012] Specifically, the steps for constructing the storage-transmission regulation model in S1 include:

[0013] S101, obtaining the positioning task type and result time point, the positioning acquisition data packet size and timestamp corresponding to each positioning task, the current signal strength, the data transmission rate per unit time, and the chip SOC state at the current moment, and preprocessing the obtained data;

[0014] S102, configure the priority model, and obtain the positioning task processing priority according to the positioning task type, result time point, and positioning acquisition data packet timestamp. And sort the obtained task priorities in descending order; where d i represents the positioning task processing priority corresponding to the i-th positioning task, T e Indicates the result time point corresponding to the i-th positioning task, T c Indicates the timestamp of the positioning data packet corresponding to the i-th positioning task;

[0015] S103, calculating the network signal evaluation index corresponding to each moment through a support vector machine based on the current signal strength, the data transmission rate per unit time, and the network packet loss rate per unit time period;

[0016] S104: Use the acquired network signal evaluation index and chip SOC state to construct a signal transmission-power consumption curve, and obtain the network signal evaluation index value corresponding to the lowest signal transmission power consumption value as the data batch transmission signal index threshold value [q1…q k ], where q k Indicates the data batch transmission signal indicator threshold in the k-th time period.

[0017] Specifically, the steps of constructing the storage-transmission regulation model in S1 also include:

[0018] S105: Obtain the size of the positioning data packet for the corresponding type of positioning task, the required computing resources, the remaining available computing resources of the chip at the corresponding time, the computational complexity of each task, and the computational delay of the local positioning task, and input the obtained data into the computational complexity model constructed by the support vector machine for training to obtain a trained computational complexity model;

[0019] S106. Deploy the trained computational complexity model to the local database of the chip, input the positioning acquisition data packet size, required computing resource data, and the remaining available computing resources of the chip at the current moment for each type of positioning task corresponding to each priority obtained in S102 into the computational complexity model to obtain the computational complexity and computational delay of the corresponding positioning task.

[0020] Specifically, the steps of constructing the storage-transmission regulation model in S1 also include:

[0021] S107: Construct the current input state of the storage-transmission control model based on the acquired positioning task processing priority, positioning acquisition data packet size, current signal strength, unit time data transmission rate and current chip SOC state. Among them soc tIndicates the current storage capacity of the positioning chip. represents the computational complexity of the i-th positioning task at the current moment, represents the computational delay of the i-th positioning task at the current moment, q t Indicates the network signal evaluation index corresponding to the current moment, It represents the positioning calculation accuracy of the jth local positioning task at the current moment, and soc0 represents the minimum working power storage capacity threshold corresponding to the positioning chip;

[0022] S108: Setting trigger information for executing an action at the current moment according to the current input state, including:

[0023] At the current moment q t =q k And soc t >soc0, the first execution action trigger information is triggered;

[0024] In the state of triggering the first execution action trigger information, the corresponding lower-level execution action trigger information includes:

[0025] S1081, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered;

[0026] S1082, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the accuracy is less than the positioning calculation threshold, the third execution action trigger information is triggered.

[0027] Specifically, setting the trigger information for executing the action at the current moment also includes:

[0028] S1083, when d i Greater than or equal to the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or is greater than the configured computational latency threshold and When the value is less than the positioning calculation accuracy threshold, the fourth execution action triggering information is triggered;

[0029] S1084, when d i Less than the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the fifth execution action trigger information is triggered;

[0030] S1085, when d i Less than the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or When it is less than the configured calculation delay threshold, the sixth execution action trigger information is triggered;

[0031] S1086. Set a maximum throughput threshold corresponding to the network signal evaluation indicator. When the size of the positioning data packet set to be uploaded for the positioning task at the current moment is equal to the maximum throughput threshold corresponding to the network signal evaluation indicator, upload the corresponding batch task to the cloud and perform positioning calculation by calling the collaborative positioning sub-model.

[0032] Specifically, setting the trigger information for executing the action at the current moment also includes:

[0033] At the current moment q t ≠q k And soc t >soc0, the seventh execution action trigger information is triggered;

[0034] When the seventh execution action triggering information is triggered, the corresponding lower-level execution action triggering information includes:

[0035] S1087, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered;

[0036] S1088, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is less than the positioning calculation accuracy threshold, the eighth execution action triggering information is triggered;

[0037] S1089, when d i Less than or equal to the configured task processing priority threshold, if When the positioning accuracy threshold is greater than or equal to the positioning accuracy threshold, the fifth execution action trigger information is triggered. When the value is less than the positioning calculation accuracy threshold, the eighth execution action triggering information is triggered;

[0038] S1090, when the current moment soc t When ≤soc0, the second execution action trigger information is triggered and a low battery warning is issued.

[0039] Specifically, the calculation steps of performing edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the determination result in S4 include:

[0040] S401: Real-time monitoring and collection of positioning data packets corresponding to the positioning task, performing initial positioning calculations through the deployed edge positioning sub-model, and directly feeding back the corresponding results to the corresponding user if the corresponding positioning accuracy meets the positioning calculation accuracy threshold;

[0041] S402. When the corresponding positioning accuracy does not meet the positioning calculation accuracy threshold and the corresponding data is uploaded to the cloud, the collaborative positioning sub-model is called to perform secondary calculation based on the positioning acquisition data packet of the corresponding positioning task and the initial positioning calculation result of the edge positioning sub-model to obtain the secondary positioning task calculation result.

[0042] The edge-cloud collaborative positioning system based on composite sensors includes: a transmission discrimination module, a complexity module and a discrimination calculation module;

[0043] The transmission discrimination module is used for data acquisition and data transmission discrimination; the transmission discrimination module includes a data acquisition unit and a transmission discrimination unit;

[0044] A data acquisition unit is used to obtain the positioning task status data, battery status and signal strength data of the positioning chip and perform pre-processing and local chip storage;

[0045] The transmission identification unit is used to identify local storage batch data transmission through the configured storage-transmission control model, and to perform edge-to-cloud data transmission through the configured full-network module;

[0046] The complexity module is used to obtain the current positioning task information and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model.

[0047] Specifically, the discriminant computing module includes a deployment unit, a computational discrimination unit, and an edge-cloud computing unit;

[0048] A deployment unit, configured to build the edge positioning sub-model in the configured edge-cloud co-positioning model into the chip and deploy the co-positioning sub-model to the cloud;

[0049] The calculation and judgment unit is used to perform edge positioning calculation and edge-cloud collaborative positioning calculation judgment based on the calculation complexity of the corresponding positioning task, the chip power storage state and the signal strength, and obtain the judgment result;

[0050] The edge-cloud computing unit is used to perform edge positioning calculation or edge-cloud collaborative positioning calculation based on the judgment results of the calculation judgment unit and the corresponding collected positioning task status data, through the deployed edge positioning sub-model or collaborative positioning sub-model, to obtain the positioning result of the corresponding positioning task.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] In response to the shortcomings of the existing technology, the present invention uses the status data of the locally stored positioning chip and the configuration storage-transmission control model to perform data transmission judgment and batch data transmission of the full network module in real time according to the chip status, further reducing the chip's power loss under low power and weak signal conditions, and improving the chip's endurance and adaptability to different transmission networks; secondly, by utilizing the computational complexity model and the edge-cloud collaborative positioning model, the method can dynamically adjust the resource allocation of data calculation according to task requirements and environmental conditions, significantly improving the response speed and energy efficiency of the positioning system; especially in remote or signal-weak areas, through the edge positioning sub-model, computational complexity and locally stored positioning data in the edge-cloud collaborative positioning model, real-time low-complexity and low-power positioning services can be achieved, and support can be provided for subsequent in-depth analysis of large amounts of data; in addition, when the signal is good, the locally stored data can be quickly uploaded to the cloud for high-precision positioning, thereby improving the accuracy of positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the edge-cloud collaborative positioning method based on composite sensors according to Example 1 of the present invention;

[0054] Figure 2 This is a structural diagram of the chip body according to embodiment 1 of the present invention;

[0055] Figure 3 This is a module diagram of the edge-cloud collaborative positioning system based on composite sensors in Example 2 of the present invention. DETAILED DESCRIPTION

[0056] Example 1

[0057] See also Figure 1 The present invention provides an embodiment of an edge-cloud collaborative positioning method based on a composite sensor, comprising the following steps:

[0058] S1. Obtain the positioning task status data, battery status, and signal strength data of the positioning chip and store them locally on the chip. At the same time, the configured storage-transmission control model is used to determine the local storage batch data transmission, and the configured full-network module is used to perform edge-to-cloud data transmission.

[0059] Furthermore, the positioning chip of this embodiment is as follows Figure 2 As shown;

[0060] Furthermore, in this embodiment, the positioning chip housing is equipped with an FPC antenna, a Beidou III positioning antenna, and a CAT1 communication antenna for collecting and transmitting positioning data. By configuring different antennas, the positioning chip can support global navigation satellite system positioning and has the ability to track 64 channels simultaneously on a single frequency.

[0061] Furthermore, this embodiment adopts the NMEA 4.1 protocol when performing data transmission to ensure compatibility with other devices and achieve seamless docking and data exchange;

[0062] Furthermore, the full network access module in this embodiment is a communication module configured on the chip body, which can adapt to the network environment of different operators and ensure the communication connection of the device worldwide;

[0063] S2. Obtain current positioning task information, and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model; further, in this embodiment, the computational complexity model is constructed by a linear function;

[0064] S3. Configure the edge-cloud collaborative positioning model, embed the edge positioning sub-model in the edge-cloud collaborative positioning model into the chip, deploy the collaborative positioning sub-model to the cloud, and make edge-cloud collaborative positioning decisions based on the computational complexity of the corresponding positioning task, the chip's power state, and the signal strength.

[0065] S4. Perform edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the judgment result to obtain the positioning information of the corresponding positioning task.

[0066] This process significantly improves the performance and efficiency of the positioning system by optimizing data management and computing resource allocation. First, by acquiring and locally storing the positioning chip's positioning task status data, battery status, and signal strength data, the system intelligently determines when to initiate batch data transmissions, reducing unnecessary network transmissions, lowering energy consumption, and improving data transmission reliability. Second, a configured computational complexity model (based on linear functions) accurately assesses the computational complexity of each positioning task, providing a scientific basis for subsequent task allocation. Furthermore, the edge-cloud collaborative positioning model integrates the edge positioning sub-model into the chip and deploys the collaborative positioning sub-model to the cloud. This model intelligently determines the optimal task allocation based on computational complexity, chip battery status, and signal strength. This flexible task allocation mechanism not only reduces the computational burden on individual devices but also extends battery life. Furthermore, with support for all-network modules, the system can adapt to the network environments of different operators, ensuring stable communication connections worldwide.

[0067] Furthermore, the steps of constructing the storage-transmission control model in S1 in this embodiment include:

[0068] S101, obtaining the positioning task type and result time point, the positioning acquisition data packet size and timestamp corresponding to each positioning task, the current signal strength, the data transmission rate per unit time, and the chip SOC state at the current moment, and preprocessing the obtained data;

[0069] S102, configure the priority model, and obtain the positioning task processing priority according to the positioning task type, result time point, and positioning acquisition data packet timestamp. And sort the obtained task priorities in descending order; where d i represents the positioning task processing priority corresponding to the i-th positioning task, T e Indicates the result time point corresponding to the i-th positioning task, T c Indicates the timestamp of the positioning data packet corresponding to the i-th positioning task;

[0070] S103, calculating the network signal evaluation index corresponding to each moment through a support vector machine based on the current signal strength, the data transmission rate per unit time, and the network packet loss rate per unit time period;

[0071] S104: Use the acquired network signal evaluation index and chip SOC state to construct a signal transmission-power consumption curve, and obtain the network signal evaluation index value corresponding to the lowest signal transmission power consumption value as the data batch transmission signal index threshold value [q1…q k ], where q k Indicates the data batch transmission signal indicator threshold in the kth time period;

[0072] S105: Obtain the size of the positioning data packet for the corresponding type of positioning task, the required computing resources, the remaining available computing resources of the chip at the corresponding time, the computational complexity of each task, and the computational delay of the local positioning task, and input the obtained data into the computational complexity model constructed by the support vector machine for training to obtain a trained computational complexity model;

[0073] S106: Deploy the trained computational complexity model to the local database of the chip, input the positioning acquisition data packet size, required computing resource amount data, and the remaining available computing resources of the chip at the current moment for each priority type positioning task obtained in S102 into the computational complexity model, and obtain the computational complexity and computational delay of the corresponding positioning task;

[0074] S107: Construct the current input state of the storage-transmission control model based on the acquired positioning task processing priority, positioning acquisition data packet size, current signal strength, unit time data transmission rate and current chip SOC state. Among them soc t Indicates the current storage capacity of the positioning chip. represents the computational complexity of the i-th positioning task at the current moment, represents the computational delay of the i-th positioning task at the current moment, q t Indicates the network signal evaluation index corresponding to the current moment, It represents the positioning calculation accuracy of the jth local positioning task at the current moment, and soc0 represents the minimum working power storage capacity threshold corresponding to the positioning chip;

[0075] S108: Setting trigger information for executing an action at the current moment according to the current input state, including:

[0076] At the current moment q t =q k And soc t >soc0, the first execution action trigger information is triggered;

[0077] In the state of triggering the first execution action trigger information, the corresponding lower-level execution action trigger information includes:

[0078] S1081, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When it is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered, that is, the i-th positioning task data is processed in the local chip;

[0079] S1082, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the accuracy is less than the positioning calculation threshold, the third execution action trigger information is triggered;

[0080] That is, for d i For tasks with a processing priority greater than or equal to the configured threshold, the edge positioning sub-model is used to perform initial positioning calculations and feedback the initial positioning results to the corresponding user. At the same time, the positioning data packet information and initial positioning results corresponding to the i-th positioning task are uploaded to the cloud, and the collaborative positioning sub-model is called to perform secondary positioning calculations.

[0081] S1083, when d i Greater than or equal to the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or is greater than the configured computational latency threshold and When it is less than the positioning calculation accuracy threshold, the fourth execution action trigger information is triggered; that is, the i-th positioning task is uploaded to the cloud for positioning calculation;

[0082] S1084, when d i Less than the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the fifth execution action trigger information is triggered;

[0083] That is, repeatedly triggering the second execution action triggering information, and performing local positioning calculation according to the task processing priority order of all positioning tasks for performing local positioning calculation;

[0084] S1085, when d i Less than the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or When the delay is less than the configured calculation delay threshold, the sixth execution action trigger information is triggered. That is, the i-th positioning task is directly uploaded to the cloud for positioning calculation;

[0085] S1086. Set a maximum throughput threshold corresponding to the network signal evaluation indicator. When the size of the positioning data packet set to be uploaded for the positioning task at the current moment is equal to the maximum throughput threshold corresponding to the network signal evaluation indicator, upload the corresponding batch task to the cloud and perform positioning calculation by calling the collaborative positioning sub-model.

[0086] At the current moment q t ≠q k And soc t >soc0, the seventh execution action trigger information is triggered;

[0087] When the seventh execution action triggering information is triggered, the corresponding lower-level execution action triggering information includes:

[0088] S1087, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered;

[0089] S1088, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is less than the positioning calculation accuracy threshold, the eighth execution action triggering information is triggered;

[0090] That is, the initial positioning calculation is first performed using the edge positioning sub-model. Then, the signal transmission-power consumption curve is used to obtain the feasible transmission interval of the local network signal evaluation index within the corresponding time period. Using the network signal evaluation index corresponding to the feasible transmission interval and the maximum data throughput of the corresponding signal index, a batch transmission fitting function is fitted using a support vector machine. This batch transmission fitting function is then integrated into the storage-transmission control model. The network signal evaluation index and the size of the positioning data collection data set to be uploaded for the positioning task at the corresponding moment are monitored in real time. When the size of the positioning data collection data set to be uploaded for the positioning task equals the maximum throughput corresponding to the network signal evaluation index, the batch positioning task is uploaded and the positioning calculation is performed by calling the collaborative positioning sub-model. Furthermore, in this embodiment, the maximum data throughput of the corresponding signal index is obtained using the Iperf tool; Iperf is a widely used network performance testing tool that can measure the bandwidth performance of TCP and UDP, thereby obtaining the network throughput. To use it, the corresponding command must be run on both the sending and receiving ends. By setting different parameters such as transmission time and packet size, the throughput under different conditions is tested to determine the maximum throughput.

[0091] Furthermore, in this embodiment, the feasible transmission interval of the local network signal evaluation index is an interval in which the network signal evaluation index value corresponding to data transmission can be constructed;

[0092] S1089, when d i Less than or equal to the configured task processing priority threshold, if When the positioning accuracy threshold is greater than or equal to the positioning accuracy threshold, the fifth execution action trigger information is triggered. When the value is less than the positioning calculation accuracy threshold, the eighth execution action trigger information is triggered, and the process of S1086 is repeated to upload batch data;

[0093] S1090, when the current moment soc t When ≤soc0, the second execution action trigger information is triggered and a low battery warning is issued;

[0094] S1091: Constructing a current-time execution action based on the current-time execution action trigger information, and inputting the current-time input state, the current-time execution action trigger information, and the current-time execution action into the storage-transmission control model constructed by the SAC model for training, obtaining a trained storage-transmission control model, and deploying the trained model to the chip body storage database interface for data batch transmission judgment and transmission execution;

[0095] This process significantly improves the performance and efficiency of the positioning system through refined data management and intelligent task allocation. First, by acquiring positioning task type, result time, packet size and timestamp, signal strength, data transmission rate, and chip SOC status, and performing preprocessing, the system accurately determines data priority and processing order. A configured priority model sorts tasks based on task type and timestamp, ensuring that high-priority tasks are processed promptly. A support vector machine (SVM) calculates network signal evaluation metrics and, combined with chip SOC status, constructs a signal transmission-power consumption curve to determine the optimal transmission timing, thereby reducing energy consumption and improving transmission efficiency. Using a trained computational complexity model, the system accurately assesses the computational complexity and latency of each task, enabling intelligent task allocation. The edge-cloud collaborative positioning model dynamically determines whether tasks should be processed at the edge or in the cloud based on task priority, computational complexity, latency, and accuracy, optimizing resource utilization. Furthermore, by real-time monitoring of network signal evaluation metrics and packet size, the system can batch upload tasks at the optimal time, further improving data transmission efficiency and reliability. A low-battery warning mechanism ensures stable system operation.

[0096] Furthermore, in this embodiment, the calculation steps of performing edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the determination result include:

[0097] S401: Real-time monitoring and collection of positioning data packets corresponding to the positioning task, performing initial positioning calculations through the deployed edge positioning sub-model, and directly feeding back the corresponding results to the corresponding user if the corresponding positioning accuracy meets the positioning calculation accuracy threshold;

[0098] S402. When the corresponding positioning accuracy does not meet the positioning calculation accuracy threshold and the corresponding data is uploaded to the cloud, the collaborative positioning sub-model is called to perform secondary calculation based on the positioning acquisition data packet of the corresponding positioning task and the initial positioning calculation result of the edge positioning sub-model to obtain the secondary positioning task calculation result.

[0099] This process significantly improves the positioning system's response speed and efficiency by monitoring the collected data packets of the positioning task in real time and using the edge positioning sub-model to perform initial positioning calculations. When the edge computing positioning accuracy meets the threshold, the results are directly fed back to the user, reducing data transmission delays and cloud computing resource usage. If the edge computing accuracy is insufficient, the data is uploaded to the cloud and the collaborative positioning sub-model is called for secondary calculations to ensure high-precision final positioning results. This edge-cloud collaborative computing mechanism not only improves positioning accuracy, but also optimizes resource allocation and reduces energy consumption.

[0100] Example 2

[0101] See also Figure 3 ,Another embodiment provided by the present invention: an edge-cloud collaborative positioning system based on a composite sensor, comprising: a transmission discrimination module, a complexity module, and a discrimination calculation module;

[0102] The transmission discrimination module is used for data acquisition and data transmission discrimination; the transmission discrimination module includes a data acquisition unit and a transmission discrimination unit;

[0103] A data acquisition unit is used to obtain the positioning task status data, battery status and signal strength data of the positioning chip and perform pre-processing and local chip storage;

[0104] The transmission identification unit is used to identify local storage batch data transmission through the configured storage-transmission control model, and to perform edge-to-cloud data transmission through the configured full-network module;

[0105] The complexity module is used to obtain the current positioning task information and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model;

[0106] The discriminant computing module is used for model deployment and edge-cloud computing judgment of positioning tasks. The discriminant computing module includes a deployment unit, a computational discrimination unit, and an edge-cloud computing unit.

[0107] The deployment unit is used to build the edge positioning sub-model in the configured edge-cloud collaborative positioning model into the chip and deploy the collaborative positioning sub-model to the cloud. The calculation and judgment unit is used to perform edge positioning calculation and edge-cloud collaborative positioning calculation judgment based on the computational complexity of the corresponding positioning task, the chip power storage state and the signal strength, and obtain the judgment result.

[0108] The edge-cloud computing unit is used to perform edge positioning calculation or edge-cloud collaborative positioning calculation based on the judgment results of the calculation judgment unit and the corresponding collected positioning task status data, through the deployed edge positioning sub-model or collaborative positioning sub-model, to obtain the positioning result of the corresponding positioning task.

[0109] Example 3

[0110] Another embodiment provided by the present invention: an edge-cloud collaborative positioning device based on a composite sensor, comprising: a chip body, an FPC antenna on the chip body, a Beidou III positioning antenna and a CAT1 communication antenna, and a chip SOC module configured on the chip body, an SD card slot, a mechanical switch, a Type C interface, and an LA780EG; the FPC antenna, a Beidou III positioning antenna and a CAT1 communication antenna are all in a ceramic antenna module;

[0111] The chip body first uses FPC (flexible printed circuit) soft board and PCB (printed circuit board), and minimizes the circuit board layout to reduce redundant components. Secondly, the chip body uses SMT (surface mount technology) to solder all components, reducing the number of cables and connectors in the assembly process and reducing the risk of failure.

[0112] The chip SOC (system on chip) module uses a vertical stacking method to superimpose power, positioning, and CAT1 communication on a single module, making the chip body highly integrated, reducing components and lowering power consumption. CAT1 is a communication standard in LTE networks, providing a lower data transmission rate and suitable for IoT applications with low bandwidth requirements.

[0113] The chip body adopts BGA (ball grid array) packaging and automotive-grade design, which optimizes the heat dissipation, stress and reliability of the chip after it is reduced in size; the power supply part of the chip body adopts a 3.7V gallium arsenide flexible battery, which can work for a long time and provide partial charging for battery life.

[0114] The combined application of the above-mentioned chip components not only enables the chip body to achieve high integration and miniaturization, but also improves reliability and performance; for example, FPC and SMT reduce redundant devices and cables, reducing the risk of failure; the vertical stacking design of the SOC module further reduces the number of devices and reduces power consumption; BGA packaging and automotive-grade design optimize heat dissipation and stress management.

[0115] Example 4

[0116] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements an edge-cloud collaborative positioning method based on a composite sensor when executing the computer program.

[0117] A computer-readable storage medium stores computer instructions, which, when executed, execute an edge-cloud collaborative positioning method based on a composite sensor.

[0118] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are protected by the present invention.

[0119] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. The edge-cloud collaborative positioning method based on composite sensors is characterized by the following steps: include: S1. Obtain the positioning task status data, battery status, and signal strength data of the positioning chip and store them locally on the chip. At the same time, the configured storage-transmission control model is used to determine the local storage batch data transmission, and the configured full-network module is used to perform edge-to-cloud data transmission. S2. Obtain the current positioning task information and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model; S3. Configure the edge-cloud collaborative positioning model, embed the edge positioning sub-model in the edge-cloud collaborative positioning model into the chip, deploy the collaborative positioning sub-model to the cloud, and make edge-cloud collaborative positioning decisions based on the computational complexity of the corresponding positioning task, the chip's power state, and the signal strength. S4. Perform edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the determination result to obtain positioning information corresponding to the positioning task; The calculation step of performing edge positioning calculation or edge-cloud collaborative allocation positioning calculation on the positioning task according to the determination result in S4 includes: S401: Real-time monitoring and collection of positioning data packets corresponding to the positioning task, performing initial positioning calculations through the deployed edge positioning sub-model, and directly feeding back the corresponding results to the corresponding user if the corresponding positioning accuracy meets the positioning calculation accuracy threshold; S402. When the corresponding positioning accuracy does not meet the positioning calculation accuracy threshold and the corresponding data is uploaded to the cloud, the collaborative positioning sub-model is called to perform secondary calculation based on the positioning acquisition data packet of the corresponding positioning task and the initial positioning calculation result of the edge positioning sub-model to obtain the secondary positioning task calculation result.

2. The edge-cloud collaborative positioning method based on composite sensors according to claim 1, characterized in that: The steps for constructing the storage-transmission regulation model in S1 include: S101, obtaining the positioning task type and result time point, the positioning acquisition data packet size and timestamp corresponding to each positioning task, the current signal strength, the data transmission rate per unit time, and the chip SOC state at the current moment, and preprocessing the obtained data; S102, configure the priority model, and obtain the positioning task processing priority according to the positioning task type, result time point, and positioning acquisition data packet timestamp. And sort the obtained task priorities in descending order; where d i represents the positioning task processing priority corresponding to the i-th positioning task, T e Indicates the result time point corresponding to the i-th positioning task, T c Indicates the timestamp of the positioning data packet corresponding to the i-th positioning task; S103, calculating the network signal evaluation index corresponding to each moment through a support vector machine based on the current signal strength, the data transmission rate per unit time, and the network packet loss rate per unit time period; S104: Use the acquired network signal evaluation index and chip SOC state to construct a signal transmission-power consumption curve, and obtain the network signal evaluation index value corresponding to the lowest signal transmission power consumption value as the data batch transmission signal index threshold value [q1…q k ], where q k Indicates the data batch transmission signal indicator threshold in the k-th time period.

3. The edge-cloud collaborative positioning method based on composite sensors according to claim 2, characterized in that: The step of constructing the storage-transmission regulation model in S1 further includes: S105: Obtain the size of the positioning data packet for the corresponding type of positioning task, the required computing resources, the remaining available computing resources of the chip at the corresponding time, the computational complexity of each task, and the computational delay of the local positioning task, and input the obtained data into the computational complexity model constructed by the support vector machine for training to obtain a trained computational complexity model; S106. Deploy the trained computational complexity model to the local database of the chip, input the positioning acquisition data packet size, required computing resource data, and the remaining available computing resources of the chip at the current moment for each type of positioning task corresponding to each priority obtained in S102 into the computational complexity model to obtain the computational complexity and computational delay of the corresponding positioning task.

4. The edge-cloud collaborative positioning method based on composite sensors according to claim 3, characterized in that: The step of constructing the storage-transmission regulation model in S1 further includes: S107: Construct the current input state of the storage-transmission control model based on the acquired positioning task processing priority, positioning acquisition data packet size, current signal strength, unit time data transmission rate and current chip SOC state. Among them soc t Indicates the current storage capacity of the positioning chip. represents the computational complexity of the i-th positioning task at the current moment, represents the computational delay of the i-th positioning task at the current moment, q t Indicates the network signal evaluation index corresponding to the current moment, It represents the positioning calculation accuracy of the jth local positioning task at the current moment, and soc0 represents the minimum working power storage capacity threshold corresponding to the positioning chip; S108: Setting trigger information for executing an action at the current moment according to the current input state, including: At the current moment q t =q k And soc t >soc0, the first execution action trigger information is triggered; In the state of triggering the first execution action trigger information, the corresponding lower-level execution action trigger information includes: S1081, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When it is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered; that is, the i-th positioning task data is processed in the local chip; S1082, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the positioning accuracy is less than the threshold, the third execution action trigger information is triggered; that is, for d i When the task processing priority is greater than or equal to the configured threshold, the edge positioning sub-model is used to perform initial positioning calculations and feedback the initial positioning results to the corresponding user. At the same time, the positioning data packet information and initial positioning results corresponding to the i-th positioning task are uploaded to the cloud, and the collaborative positioning sub-model is called to perform secondary positioning calculations.

5. The edge-cloud collaborative positioning method based on composite sensors according to claim 4, characterized in that: The setting of trigger information for executing an action at the current moment also includes: S1083, when d i Greater than or equal to the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or is greater than the configured computational latency threshold and When it is less than the positioning calculation accuracy threshold, the fourth execution action trigger information is triggered; that is, the i-th positioning task is uploaded to the cloud for positioning calculation; S1084, when d i Less than the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the fifth execution action trigger information is triggered; that is, the second execution action trigger information is repeatedly triggered, and the local positioning calculation is performed according to the task processing priority of all positioning tasks for local positioning calculation; S1085, when d i Less than the configured task processing priority threshold, Greater than or equal to the local executable maximum complexity calculation threshold or When the delay is less than the configured calculation delay threshold, the sixth execution action trigger information is triggered; that is, the i-th positioning task is directly uploaded to the cloud for positioning calculation; S1086. Set the maximum throughput threshold corresponding to each network signal evaluation indicator. When the size of the positioning acquisition data packet set to be uploaded for the positioning task at the current moment is equal to the maximum throughput threshold corresponding to the network signal evaluation indicator, upload the corresponding batch task to the cloud and perform positioning calculation by calling the collaborative positioning sub-model.

6. The edge-cloud collaborative positioning method based on composite sensors according to claim 5, characterized in that: The setting of trigger information for executing an action at the current moment also includes: At the current moment q t ≠q k And soc t >soc0, the seventh execution action trigger information is triggered; When the seventh execution action triggering information is triggered, the corresponding lower-level execution action triggering information includes: S1087, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is greater than or equal to the positioning calculation accuracy threshold, the second execution action trigger information is triggered; S1088, when d i Greater than or equal to the configured task processing priority threshold, Less than the local executable maximum complexity calculation threshold or Less than the configured computational latency threshold and When the value is less than the positioning calculation accuracy threshold, the eighth execution action triggering information is triggered; That is, first, the initial positioning calculation is performed through the edge positioning sub-model, and then the signal transmission-power consumption curve is used to obtain the feasible transmission interval of the local network signal evaluation index in the corresponding time period, and the network signal evaluation index corresponding to the feasible transmission interval and the maximum data throughput of the corresponding signal index are used to fit the batch transmission fitting function through the support vector machine, and the batch transmission fitting function is built into the storage-transmission control model, and the network signal evaluation index and the size of the positioning acquisition data packet set to be uploaded for the positioning task at the corresponding moment are monitored in real time. When the size of the positioning acquisition data packet set to be uploaded for the positioning task is equal to the maximum throughput corresponding to the network signal evaluation index, the batch positioning task is uploaded, and the positioning calculation is performed by calling the collaborative positioning sub-model; the feasible transmission interval of the local network signal evaluation index is the interval constructed by the network signal evaluation index value corresponding to the data transmission; S1089, when d i Less than or equal to the configured task processing priority threshold, if When the positioning accuracy threshold is greater than or equal to the positioning accuracy threshold, the fifth execution action trigger information is triggered. When the value is less than the positioning calculation accuracy threshold, the eighth execution action triggering information is triggered; S1090, when the current moment soc t When ≤soc0, the second execution action trigger information is triggered and a low battery warning is issued.

7. An edge-cloud collaborative positioning system based on a composite sensor, which is used to implement the edge-cloud collaborative positioning method based on a composite sensor according to any one of claims 1 to 6, characterized in that: include: Transmission discrimination module, complexity module and discrimination calculation module; The transmission identification module is used for data acquisition and data transmission identification; The transmission discrimination module includes a data acquisition unit and a transmission discrimination unit; The data acquisition unit is used to acquire the positioning task status data, power storage status and signal strength data of the positioning chip and perform pre-processing and local chip storage; The transmission discrimination unit is used to perform local storage batch data transmission discrimination through the configured storage-transmission control model, and to perform edge-to-cloud data transmission through the configured full-network module; The complexity module is used to obtain the current positioning task information and obtain the computational complexity of the corresponding positioning task through the configured computational complexity model.

8. The edge-cloud collaborative positioning system based on composite sensors according to claim 7, characterized in that: The discrimination calculation module includes a deployment unit, a computation discrimination unit, and an edge-cloud computing unit; The deployment unit is configured to build the edge positioning sub-model in the configured edge-cloud collaborative positioning model into the chip and deploy the collaborative positioning sub-model to the cloud; The calculation and judgment unit is used to perform edge positioning calculation and edge-cloud collaborative positioning calculation judgment based on the calculation complexity of the corresponding positioning task, the chip power storage state and the signal strength, and obtain the judgment result; The edge-cloud computing unit is used to perform edge positioning calculation or edge-cloud collaborative positioning calculation based on the judgment result of the calculation judgment unit and the corresponding collected positioning task status data, through the deployed edge positioning sub-model or collaborative positioning sub-model, to obtain the positioning result of the corresponding positioning task.

9. A computer-readable storage medium, characterized in that Computer instructions are stored thereon, and when the computer instructions are executed, the edge-cloud collaborative positioning method based on composite sensors as described in any one of claims 1 to 6 is executed.

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