TWI863080B - Dynamic locating correction method and system - Google Patents

Abstract

A dynamic locating correction system includes: a computing device; a correction moving vehicle installing a first and a second precision location module communicating with the computing device, the first and the second precision location module respectively generating a first and a second precision coordinate data to the computing device; and a plurality of client devices installing a plurality of second precision location modules communicating with the computing device, the plurality of second precision location modules generating a plurality of second precision coordinate data to the computing device. In training, the computing device trains a dynamic learning unit based on the first and the second precision coordinate data from the correction moving vehicle. In coordinate data correction, the dynamic learning unit corrects the second precision coordinate data from the plurality of second precision location modules of the plurality of client devices and sends a plurality of corrected second precision coordinate data to the second precision location modules of the plurality of client devices; or the computing device sends a dynamic learning model parameter data of the dynamic learning unit to the second precision location modules of the plurality of client devices and the second precision location modules of the plurality of client devices correct the second precision coordinate data.

Description

Dynamic positioning correction method and system

The present invention relates to a dynamic positioning correction method and system.

In the automated operation management of container yards, controlling operations and improving efficiency are the management goals of container yards. Positioning and tracking of container trucks in container yards is an important link. The container yards of general ports are large in area, and there may be up to 5,000 vehicles entering and leaving the yard every day. During peak hours, there may be up to 500 vehicles operating in the container yard at the same time. When performing operations such as handing over containers, picking up containers, and moving containers in the container yard, the management unit must track the location of container trucks in real time, make schedules in advance, and improve operation efficiency. However, due to the high metal walls formed by the stacking of multiple layers of containers, it causes great interference to the Global Positioning System (GPS) signal commonly used for positioning.

In addition, in the automated operation management of the counter, how to build an image positioning system is also a big challenge. Because it is not easy to set up a camera on site, and it is greatly affected by the outdoor environment (day, night, rainy days, strong light, etc.), and the construction cost is high.

As for current positioning technology, high-precision GPS technology (such as real-time kinematic technology (RTK)) can achieve high-precision positioning, but its unit price is high. When it is used in large-scale application scenarios (such as container yards), the system construction and maintenance costs are extremely high.

As for cheaper positioning technologies, such as those based on radio waves, although their construction costs are low, they still need to overcome the following problems: metal interference, multipath effects, errors in the positioning module itself, climate and environmental influences, and time-varying properties.

Therefore, a dynamic positioning correction method and system that is easy to build, has low maintenance costs, and can overcome radio wave interference is needed.

The purpose of this invention is to use deep learning to perform dynamic positioning correction, overcome the interference of metal container stacking and weather on GPS signals, improve GPS positioning accuracy, and achieve the goal of low-cost, high-precision, large-area outdoor positioning tracking.

The present invention provides a dynamic positioning correction system, comprising: a computing device; a correction mobile vehicle, equipped with a first precision positioning module and a second precision positioning module, the first precision positioning module and the second precision positioning module communicate with the computing device, the first precision positioning module generates a first precision coordinate data for the computing device, and the second precision positioning module generates a second precision coordinate data for the computing device; and a plurality of client devices, equipped with a plurality of second precision positioning modules, the second precision positioning modules communicate with the computing device, the second precision positioning modules generate a plurality of second precision coordinate data for the computing device, wherein during training, the computing device is based on the first precision positioning module and the second precision positioning module. The first precision coordinate data and the second precision coordinate data transmitted by the calibration mobile vehicle are used to train a dynamic learning unit of the computing device; when the coordinate data is calibrated, the dynamic learning unit calibrates the second precision coordinate data transmitted by the second precision positioning modules installed on the client devices and transmits a plurality of corrected second precision coordinate data to the second precision positioning modules of the client devices, or the computing device transmits a dynamic learning model parameter data of the dynamic learning unit to the second precision positioning modules installed on the client devices, and the second precision positioning modules installed on the client devices calibrate the second precision coordinate data.

Another embodiment of the present invention is to provide a dynamic positioning calibration method, comprising: generating a first precision coordinate data to a computing device by a first precision positioning module, the first precision positioning module being installed on a calibration mobile vehicle; generating a second precision coordinate data to the computing device by a second precision positioning module installed on the calibration mobile vehicle; training a dynamic learning unit of the computing device according to the first precision coordinate data and the second precision coordinate data transmitted by the calibration mobile vehicle; and during the coordinate data calibration, The dynamic learning unit calibrates a plurality of second-precision coordinate data transmitted from a plurality of second-precision positioning modules installed in a plurality of client devices and transmits a plurality of calibrated second-precision coordinate data to the second-precision positioning modules of the client devices, or the computing device transmits a dynamic learning model parameter data of the dynamic learning unit to the second-precision positioning modules installed in the client devices, and the second-precision positioning modules installed in the client devices calibrate the second-precision coordinate data.

The dynamic positioning correction system of the present invention requires low infrastructure costs and is easy to build. Moreover, the present invention can bring high efficiency and uninterrupted services through the integration of network automation systems, which can help container ports achieve the goals of reducing labor costs, shortening operation time, increasing operation safety, reducing accidents and improving management efficiency. The present invention can pave the way for future self-driving trailer technology: vehicle positioning can become the basis for automatic reporting, data exchange, work order issuance and other operation functions, achieving the goal of automation of operations in the container yard.

In order to better understand the above and other aspects of the present invention, the following is a specific example and a detailed description with the attached drawings as follows:

100: Dynamic positioning correction system

110: High-precision positioning module

105: Calibrate moving vehicles

120: Low-precision positioning module

115: Client device

130: Computing device

131: Dynamic Learning Unit

133: Output data unit

135: Computational performance judgment unit

137: Storage unit

D: Low-precision coordinate data

P: parameter data

D”: Corrected coordinate data

300: Positioning module

310: Positioning unit

320: Communication unit

330: Processing unit

340: Storage unit

341:Track database

401:GPS satellite

403:Industrial Computer

405: Raspberry Pi motherboard

Figure 1 shows a block diagram of a dynamic positioning correction system according to an embodiment of the present invention.

Figure 2A shows the training phase of the dynamic positioning correction system according to an embodiment of the present invention.

Figures 2B and 2C show schematic block diagrams of the operation of a dynamic positioning correction system according to an embodiment of the present invention.

Figure 3 shows a functional block diagram of a positioning module according to an embodiment of the present invention.

Figure 4 shows a schematic diagram of the operation of a dynamic positioning correction system according to an embodiment of the present invention.

Figure 5 shows a flow chart of a dynamic positioning correction method according to an embodiment of the present invention.

The technical terms in this manual refer to the customary terms in this technical field. If this manual explains or defines some terms, the interpretation of these terms shall be based on the explanation or definition in this manual. Each embodiment disclosed in this disclosure has one or more technical features. Under the premise of possible implementation, a person with ordinary knowledge in this technical field can selectively implement some or all of the technical features in any embodiment, or selectively combine some or all of the technical features in these embodiments.

Please refer to FIG. 1, which shows a block diagram of a dynamic positioning correction system according to an embodiment of the present invention. As shown in FIG. 1, a dynamic positioning correction system 100 according to an embodiment of the present invention includes: a correction mobile vehicle 105 equipped with a high-precision positioning module 110 and a low-precision positioning module 120, a plurality of client devices 115 (each client device 115 is equipped with a respective low-precision positioning module 120), and a computing device 130. The computing device 130 includes: a dynamic learning unit 131, an output data unit 133, a computing performance judgment unit 135, and a storage unit 137.

Among them, the dynamic learning unit 131, the output data unit 133, and the computing performance judgment unit 135 can be implemented by using, for example, a chip, a circuit block in a chip, a firmware circuit, a circuit board containing several electronic components and wires, or a storage medium storing multiple sets of program codes. The storage unit 137 is, for example but not limited to, a hard disk, an optical disk, a solid state drive, etc.

The high-precision positioning module 110 is wired or wirelessly communicated with the computing device 130. The high-precision positioning module 110 can provide high-precision coordinate data (such as but not limited to, real-time kinematic (RTK) coordinate signals or image coordinate signals) to the computing device 130. The high-precision positioning module 110 is such as but not limited to, RTK, camera or laser detection and ranging (Light Detection And Ranging, LiDAR) and the like. Hereinafter, the high-precision coordinate data may also be referred to as the first-precision coordinate data. The low-precision positioning module 120 is based on the GPS signal of hardware such as smartphones, and uses the signal of the high-precision positioning module 110 of hardware such as RTK, cameras or LiDAR as a benchmark fact to train the AI dynamic learning unit to generate corrected coordinate data, improve the accuracy of GPS signals, and achieve the goal of low-cost, high-precision, large-area outdoor positioning.

The low-precision positioning modules 120 communicate with the computing device 130. The low-precision positioning modules 120 are, for example but not limited to, the GPS signal receiving circuit of a smart phone. The low-precision positioning modules 120 can generate low-precision coordinate data (for example but not limited to, general GPS coordinate information) for the computing device 130. The low-precision coordinate data generated in real time by the low-precision positioning module 120 includes, for example but not limited to, a hardware unique identification code, low-precision coordinates, and low-precision coordinate generation time. The hardware unique identification code can be used to identify which client device 115 generated the low-precision coordinate data. In the following, the low-precision coordinate data may also be referred to as second-precision coordinate data.

In one embodiment of the present invention, the positioning error of the low-precision coordinate data provided by the low-precision positioning module 120 may be several meters, and the positioning error belongs to the meter level; and the positioning error of the high-precision coordinate data provided by the high-precision positioning module 110 may be only a few centimeters, and the positioning error belongs to the centimeter level.

For example but not limited to, the client devices 115 are smart phones of truck drivers.

In one embodiment of the present invention, the computing device 130 is installed on a cloud server, and the high-precision positioning module 110 is in wireless communication with the computing device 130. In another embodiment of the present invention, the computing device 130 is installed on a calibration mobile vehicle 105, and the high-precision positioning module 110 is in wired communication with the computing device 130.

During the training phase, the dynamic learning unit 131 can execute training of the AI dynamic learning model based on the high-precision coordinate data and low-precision coordinate data provided by the high-precision positioning module 110 and the low-precision positioning module 120 installed on the same calibration mobile vehicle 105. The AI dynamic learning model can be, for example but not limited to, an AI calculation model such as a long short-term memory (LSTM), a recurrent neural network (RNN), or a gated recurrent unit (GRU).

That is, during the training phase, the dynamic learning unit 131 can use the high-precision coordinate data provided by the high-precision positioning module 110 of the calibration mobile vehicle 105 as a reference value, and then calibrate the low-precision coordinate data provided by the low-precision positioning module 120 installed on the calibration mobile vehicle 105, thereby training the AI dynamic learning model.

In the coordinate correction stage, the dynamic learning unit 131 can correct the real-time low-precision coordinate data transmitted by the low-precision positioning modules 120 installed in the client device 115 into corrected coordinate data.

The output data unit 133 can output the dynamic learning model and/or the corrected coordinate data of the dynamic learning unit 131 to the client devices 115.

When the computing performance determination unit 135 determines that the computing performance value of the client device 115 is greater than a threshold value (indicating that the computing power of the client device 115 is sufficient to automatically calibrate its own low-precision coordinate data to the calibrated coordinate data), the data output unit 133 outputs the parameter data of the AI dynamic learning model to the client device 115. After receiving the parameter data of the AI dynamic learning model, the client device 115 can use it to correct its own AI dynamic learning model, wherein the AI dynamic learning model of the client device 115 is, for example, a mobile phone application installed on the client device 115.

On the contrary, when the computing performance determination unit 135 determines that the computing performance value of the client device 115 is less than the threshold value (indicating that the computing capability of the client device 115 is insufficient to calibrate its own low-precision coordinate data into the corrected coordinate data), the data output unit 133 outputs the corrected coordinate data to the client device 115. After receiving the corrected coordinate data, the client device 115 can use the corrected coordinate data as its own coordinate signal to improve positioning accuracy.

The storage unit 137 can store the individual unique identification codes of the low-precision positioning modules 120, the low-precision coordinate data of the low-precision positioning modules 120 and the low-precision coordinate generation time information, the corrected coordinate data of the low-precision positioning modules 120, etc.

FIG. 2A shows the training phase of the dynamic positioning correction system according to an embodiment of the present invention. In an embodiment of the present invention, during the training phase, the dynamic learning unit 131 can use the high-precision coordinate data provided by the high-precision positioning module 110 of the correction mobile vehicle 105 as a reference value, and then correct the low-precision coordinate data provided by the low-precision positioning module 120 installed on the correction mobile vehicle 105, thereby training the AI dynamic learning model.

During the training phase, a calibration mobile vehicle equipped with a high-precision positioning module 110 and a low-precision positioning module 120 circulates around the store. The high-precision positioning module 110 and the low-precision positioning module 120 of the calibration mobile vehicle 105 each continuously generate high-precision coordinate data and low-precision coordinate data, and transmit them to the computing device 130. The storage unit 137 of the computing device 130 saves the high-precision trajectory (composed of high-precision coordinate data) and the low-precision trajectory (composed of low-precision coordinate data).

The trajectory data comparison module (not shown) in the computing device 130 retrieves the high-precision coordinate data and low-precision coordinate data generated by the high-precision positioning module 110 and the low-precision positioning module 120 of the calibrated mobile vehicle 105 from the storage unit 137 within the same start and end time period, thereby ensuring that the retrieved high-precision trajectory and low-precision trajectory are in the same start and end time period. The dynamic learning unit 131 performs AI model training based on the high-precision trajectory and the low-precision trajectory. When the training is completed, the computing device 130 updates the new AI model to the storage unit 137, and the computing device 130 uniformly transmits the new AI model to the client device 115 with threshold calculation capabilities in the storage unit 137. The computing device 130 will not transmit the new AI model to the client device 115 that does not have the threshold computing capability.

That is to say, in an embodiment of the present case, during the training phase, the low-precision coordinate data provided by the low-precision positioning module 120 of the client device 115 will not be used to train the AI dynamic learning model.

The trained dynamic learning unit 131 can generate corrected coordinate data to correct low-precision coordinate data, improve the accuracy of low-precision coordinate data, and achieve the goal of low-cost, high-precision, large-area outdoor positioning.

FIG. 2B and FIG. 2C show schematic block diagrams of the operation of a dynamic positioning correction system according to an embodiment of the present invention. In FIG. 2B and FIG. 2C, the low-precision positioning modules 120 generate low-precision coordinate data D to the computing device 130. The computing device 130 stores the low-precision coordinate data D in the storage unit 137 for use or query in other application fields, etc.

In FIG. 2B , when the computing performance determination unit 135 determines that the computing performance value of the client device 115 is greater than the threshold value (indicating that the computing capability of the client device 115 is sufficient to automatically calibrate its own low-precision coordinate data to the calibrated coordinate data), the data output unit 133 outputs the parameter data P of the AI dynamic learning model to the client device 115. After receiving the parameter data P of the AI dynamic learning model, the client device 115 can use it to correct its own AI dynamic learning model, wherein the AI dynamic learning model of the client device 115 is, for example, a mobile phone application installed on the client device 115.

In FIG. 2C, when the computing performance determination unit 135 determines that the computing performance value of the client device 115 is less than the threshold value (indicating that the computing capability of the client device 115 is insufficient to calibrate its own low-precision coordinate data to the corrected coordinate data), the data output unit 133 outputs the corrected coordinate data D" to the client device 115. After receiving the corrected coordinate data D", the client device 115 can use the corrected coordinate data as its own coordinate signal to improve positioning accuracy.

In FIG. 2B and FIG. 2C, the dynamic learning unit 131 receives the low-precision coordinate data transmitted by the low-precision positioning modules 120 in real time, calculates the corrected coordinate data and its trajectory according to the trajectory of each low-precision coordinate data, and saves all the corrected coordinate data and its trajectory in the storage unit 137.

In Figures 2B and 2C, the computing device 130 provides a trajectory query interface. Any device can access the query interface through the Internet to obtain the calibrated coordinate data. The computing device 130 can be a processor or a high-level computing device, etc.

In one embodiment of the present invention, when the low-precision positioning modules 120 of the client device 115 register with the system of the computing device 130, the hardware specifications (e.g., central processing unit (CPU) specifications, memory usage, etc.) of the client device 115 are transmitted to the computing performance determination unit 135. The computing performance determination unit 135 calculates the computing performance values of the client devices 115 according to the hardware specifications and other information of the client devices 115.

In one embodiment of the present invention, during the coordinate correction stage, low-precision coordinate data from any number of low-precision positioning modules can be continuously received simultaneously, and then the dynamic learning unit 131 reads the historical uncorrected low-precision coordinate data for each low-precision positioning module and outputs the corresponding corrected coordinate data.

In one embodiment of the present invention, when the container yard is surrounded by stacked containers, the dynamic positioning correction system 100 can overcome the interference of the external metal environment on the radio waves and is not affected by the weather.

In one embodiment of the present invention, the dynamic positioning correction system 100 can simultaneously and continuously receive high-precision coordinate data from a high-precision positioning module from any direction (because the high-precision positioning module 110 is installed on a correction mobile vehicle, and this correction mobile vehicle 105 can also be a container truck that can move freely in the container yard) to train the dynamic learning unit.

In one embodiment of the present invention, the dynamic positioning correction system 100 uses deep learning to perform dynamic positioning correction, overcome the interference of metal container stacking and weather on GPS signals, improve GPS positioning accuracy, and achieve the goal of low-cost, high-precision, large-area outdoor positioning tracking.

The relevant details of the dynamic learning unit 131 of an embodiment of the present invention will now be described. Here, a neural network is used as an example to implement the dynamic learning unit 131, but it should be noted that the present invention is not limited to this. The neural network includes an input layer, a hidden layer, and an output layer.

For example, you can select multiple neurons in the input layer, and one neuron is responsible for calculating one mobile phone GPS coordinate. Assume that one GPS coordinate is received every 0.1 seconds, and 10 GPS coordinates can be received in 1 second. One GPS coordinate includes longitude and latitude coordinates, so 20 longitude and latitude coordinates are received in 1 second. For the received longitude and latitude coordinates, these neurons perform iterative operations. As for the output layer, the current corrected coordinate data or the next corrected coordinate data can be predicted.

The following describes an application scenario of an embodiment of the present invention. For example, but not limited to, there are currently 500 container trucks in the container yard, and one container truck (calibration mobile vehicle) is equipped with a high-precision positioning module (the driver's mobile phone of this container truck can also optionally be equipped with a low-precision positioning module). This calibration mobile vehicle will move in the container yard to allow the dynamic learning unit to dynamically learn and train the AI dynamic learning model. If the AI dynamic learning model has reached stability, dynamic learning is no longer required. The dynamic learning unit can also be placed on the calibration mobile vehicle, or the dynamic learning unit is not installed on the calibration mobile vehicle but is installed on a cloud server (computing device), all of which are within the spirit and scope of the present invention. As for the drivers' mobile phones of other vehicles in the container yard, there are applications installed. The applications can receive the corrected coordinate data (calculated by the computing device 130 and sent to the driver's mobile phone, the computing power of the driver's mobile phone is insufficient), or the application can perform the coordinate correction calculation by itself (the computing power of the driver's mobile phone is sufficient).

FIG. 3 shows a functional block diagram of a positioning module 300 according to an embodiment of the present invention. As shown in FIG. 3, the positioning module 300 according to an embodiment of the present invention includes a positioning unit 310, a communication unit 320, a processing unit 330 and a storage unit 340. The positioning module 300 of FIG. 3 can be used to implement a high-precision positioning module 110.

The positioning unit 310 can generate high-precision coordinate data. The positioning unit 310 is a hardware circuit.

The communication unit 320 can communicate with other devices, such as the computing device 130, to transmit the high-precision coordinate data generated by the positioning unit 310 to the computing device 130. The communication unit 320 is a hardware circuit.

The processing unit 330 can be a processor to perform a variety of processing operations, such as but not limited to, track point synchronization comparison, positioning displacement vector calculation, positioning correction, etc.

The storage unit 340 may be a memory, and the memory includes a track database 341, and the track database 341 may store track-related data.

The processing unit 330 can be implemented, for example, by using a chip, a circuit block in a chip, a firmware circuit, a circuit board containing a plurality of electronic components and wires, or a storage medium storing a plurality of sets of program codes. The storage unit 340 can be, for example but not limited to, a hard disk, an optical disk, a solid state drive, etc.

FIG. 4 shows an operation diagram of a dynamic positioning correction system according to an embodiment of the present invention. Please refer to FIG. 3 at the same time. As shown in FIG. 4, GPS satellite 401 can transmit GPS satellite signals to positioning unit 310 (for example, but not limited to, a ROVER interface card) of high-precision positioning module 110. In FIG. 4, processing unit 330 of high-precision positioning module 110 can include industrial computer 403 or Raspberry Pi motherboard 405. The processing unit 330 receives data (high-precision coordinate data or low-precision coordinate data) from the positioning unit 310 via a USB cable (RS232 protocol), and transmits the high-precision coordinate data before and after the solution to the dynamic learning unit 130 via the communication unit 320 (for example, but not limited to, a 4G/5G Subscriber Identity Module (SIM) card or a high-speed mobile network card).

In addition, the track database 341 of the high-precision positioning module 110 includes a ROVER application. The ROVER application enables the high-precision positioning module 110 to transmit high-precision coordinate data to the dynamic learning unit.

The track database 343 of the low-precision positioning module 120 includes a smartphone application. The smartphone application enables the smartphone to transmit low-precision coordinate data to the dynamic learning unit 131.

In addition, in FIG. 4 , the client device (such as a smart phone) 115 can install relevant applications to transmit low-precision coordinate data to the dynamic learning unit 131 via a 4G/5G SIM card.

FIG. 5 shows a flow chart of a dynamic positioning calibration method according to an embodiment of the present invention. In step 510, a first precision positioning module generates a first precision coordinate data to a computing device, and the first precision positioning module is installed on a calibration mobile vehicle. In step 520, a second precision positioning module installed on the calibration mobile vehicle generates a second precision coordinate data to the computing device. In step 530, a dynamic learning unit of the computing device is trained according to the first precision coordinate data and the second precision coordinate data transmitted from the calibration mobile vehicle. In step 540, when the coordinate data is corrected, the dynamic learning unit corrects the plurality of second-precision coordinate data transmitted from the plurality of second-precision positioning modules installed in the plurality of client devices and transmits the plurality of corrected second-precision coordinate data to the second-precision positioning modules of the client devices, or the computing device transmits a dynamic learning model parameter data of the dynamic learning unit to the second-precision positioning modules installed in the client devices, and the second-precision positioning modules installed in the client devices correct the second-precision coordinate data.

In one embodiment of the present invention, a smartphone is used as a carrier. In addition to positioning, it can also achieve convenient communication, add new functions and facilitate maintenance, such as locker reservation, communication with station management personnel, etc.

The infrastructure cost required for the dynamic positioning correction system of an embodiment of the present invention is low and easy to build. In contrast, current technology generally uses cameras for vehicle tracking and comparison, which requires the installation of a large number of cameras, is difficult to set up on site, and image recognition is easily affected by the external environment.

The dynamic positioning correction system of the embodiment of the present invention can provide high efficiency and uninterrupted services through an automated system integrated with the network, and can help container ports achieve the goals of reducing labor costs, shortening operation time, increasing operation safety, reducing accidents and improving management efficiency.

Furthermore, the dynamic positioning correction system of the embodiment of the present invention can pave the way for future self-driving trailer technology: vehicle positioning can become the basis for automatic reporting, data exchange, work order issuance and other operational functions, achieving the goal of automated operations in the container yard.

In summary, although the present invention has been disclosed as above by the embodiments, it is not intended to limit the present invention. Those with common knowledge in the technical field to which the present invention belongs can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of the patent application attached hereto.

510-540: Steps

Claims (8)

A dynamic positioning correction system includes: a computing device; a correction mobile vehicle, equipped with a first precision positioning module and a second precision positioning module, the first precision positioning module and the second precision positioning module communicate with the computing device, the first precision positioning module generates a first precision coordinate data for the computing device, and the second precision positioning module generates a second precision coordinate data for the computing device; and a plurality of client devices, equipped with a plurality of second precision positioning modules, the second precision positioning modules communicate with the computing device, and the second precision positioning modules generate a plurality of second precision coordinate data for the computing device, wherein during training, The computing device trains a dynamic learning unit of the computing device according to the first precision coordinate data and the second precision coordinate data transmitted by the calibration mobile vehicle; when the coordinate data is calibrated, the dynamic learning unit calibrates the second precision coordinate data transmitted by the second precision positioning modules installed on the client devices and transmits a plurality of corrected second precision coordinate data to the second precision positioning modules of the client devices, or the computing device transmits a dynamic learning model parameter data of the dynamic learning unit to the second precision positioning modules installed on the client devices and the second precision positioning modules installed on the client devices The computing device further comprises: an output data unit, which outputs the dynamic learning model parameter data of the dynamic learning unit and/or the corrected second precision coordinate data to the second precision positioning modules installed in the client devices; an computing performance judgment unit, when the computing performance judgment unit judges that a computing performance value of the client device is greater than a threshold value, the data output unit outputs the dynamic learning model parameter data to the second precision positioning module of the client device, or when the computing performance judgment unit judges that the computing performance value of the client device is less than the threshold value, the data output unit outputs the dynamic learning model parameter data to the second precision positioning module of the client device. When the threshold value is reached, the data output unit outputs the corrected second precision coordinate data to the second precision positioning module of the client device; and a storage unit stores a plurality of unique identification codes of the second precision positioning modules installed in the client devices, the second precision coordinate data and a plurality of second precision coordinate generation time information, and the corrected second precision coordinate data, wherein the dynamic learning unit, the output data unit and the computing performance judgment unit are implemented by using a chip, a circuit block in a chip, a firmware circuit, a circuit board containing a plurality of electronic components and wires, or a storage medium storing a plurality of sets of program codes. The dynamic positioning correction system as described in claim 1, wherein the first precision coordinate data is a real-time dynamic coordinate signal or an image coordinate signal; the second precision coordinate data is a global positioning system coordinate signal; and the computing device is installed on a cloud server or the correction mobile vehicle. The dynamic positioning correction system as described in claim 1, wherein the computing device includes a trajectory query interface, and the trajectory query interface is accessed through the Internet to obtain the corrected second-precision coordinate data; and when the client device is registered in the computing device, the client device transmits a hardware specification information to the computing performance determination unit, and the computing performance determination unit calculates a computing performance value of the client device according to the hardware specification information. The dynamic positioning correction system as described in claim 1, wherein the first precision positioning module includes: a positioning unit, generating the first precision coordinate data; a communication unit, communicating with the computing device, to transmit the first precision coordinate data generated by the positioning unit to the computing device; a processing unit, for executing track point synchronization comparison, positioning displacement vector calculation, and positioning correction; and a storage unit, including a track database, the track database storing track-related data, wherein the processing unit is implemented by using a chip, a circuit block in a chip, a firmware circuit, a circuit board containing a plurality of electronic components and wires, or a storage medium storing a plurality of sets of program codes. A dynamic positioning correction method includes: generating first precision coordinate data from a first precision positioning module to a computing device, the first precision positioning module being installed on a calibration mobile vehicle; generating second precision coordinate data from a second precision positioning module installed on the calibration mobile vehicle to the computing device; training a dynamic learning unit of the computing device according to the first precision coordinate data and the second precision coordinate data transmitted from the calibration mobile vehicle; and during coordinate data correction, the dynamic learning unit corrects the second precision coordinate data transmitted from the second precision positioning modules installed on the plurality of client devices and transmits the corrected second precision coordinate data to the second precision positioning modules of the client devices, or the computing device transmits the dynamic learning unit The second precision positioning modules installed on the client devices are provided with a dynamic learning model parameter data, and the second precision positioning modules installed on the client devices correct the second precision coordinate data, wherein when it is determined that a computing performance value of the client device is greater than a threshold value, the dynamic learning model parameter data is output to the second precision positioning module of the client device; when it is determined that When the computing performance value of the client device is less than the threshold value, the corrected second precision coordinate data is output to the second precision positioning module of the client device; and a plurality of unique identification codes of the second precision positioning modules installed in the client devices, the second precision coordinate data and a plurality of second precision coordinate generation time information, and the corrected second precision coordinate data are stored. The dynamic positioning correction method as described in claim 5, wherein, the first precision coordinate data is a real-time dynamic coordinate signal or an image coordinate signal; the second precision coordinate data is a global positioning system coordinate signal, and the second precision coordinate data includes: a hardware unique identification code, a second precision coordinate and a second precision coordinate generation time; and the computing device is installed on a cloud server or the calibration mobile vehicle. The dynamic positioning correction method as described in claim 5, wherein the computing device includes a trajectory query interface, and the trajectory query interface is accessed through the Internet to obtain the corrected second-precision coordinate data; and when the client device is registered in the computing device, the client device transmits a hardware specification information to the computing device to calculate a computing performance value of the client device according to the hardware specification information. The dynamic positioning correction method as described in claim 5, wherein the first precision positioning module: generates the first precision coordinate data; transmits the generated first precision coordinate data to the computing device; performs track point synchronization comparison, positioning displacement vector calculation, positioning correction; and stores track-related data in a track database.

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