WO2023245589A1 - Procédé et appareil de détermination de modèle de positionnement - Google Patents

Procédé et appareil de détermination de modèle de positionnement Download PDF

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Publication number
WO2023245589A1
WO2023245589A1 PCT/CN2022/100933 CN2022100933W WO2023245589A1 WO 2023245589 A1 WO2023245589 A1 WO 2023245589A1 CN 2022100933 W CN2022100933 W CN 2022100933W WO 2023245589 A1 WO2023245589 A1 WO 2023245589A1
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WO
WIPO (PCT)
Prior art keywords
positioning
positioning model
terminal device
coordinate information
information
Prior art date
Application number
PCT/CN2022/100933
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English (en)
Chinese (zh)
Inventor
牟勤
Original Assignee
北京小米移动软件有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京小米移动软件有限公司 filed Critical 北京小米移动软件有限公司
Priority to PCT/CN2022/100933 priority Critical patent/WO2023245589A1/fr
Priority to CN202280002084.5A priority patent/CN117643078A/zh
Publication of WO2023245589A1 publication Critical patent/WO2023245589A1/fr

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W24/00Supervisory, monitoring or testing arrangements
    • H04W24/08Testing, supervising or monitoring using real traffic
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W4/00Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02Services making use of location information

Definitions

  • the present disclosure relates to the field of communication technology, and in particular, to a positioning model determination method, device, equipment and storage medium.
  • the present disclosure proposes a positioning model determination method, device, equipment and storage medium to monitor the performance of the AI positioning model, improve the accuracy of positioning model determination, and thereby improve the accuracy of terminal device location information determination.
  • An embodiment of the present disclosure provides a method for determining a positioning model.
  • the method is executed by a network side device.
  • the method includes:
  • the performance reference information of the AI positioning model currently used by the terminal device includes at least one of the following:
  • First coordinate information wherein the first coordinate information is information obtained by inferring the AI positioning model currently used by the terminal device;
  • determining a new positioning model or positioning mode for the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the method further includes:
  • Receive capability information sent by the terminal device where the capability information is used to indicate the capability of the terminal device to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • determining a new positioning model or positioning mode of the terminal device according to the first coordinate information includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • determining a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information includes at least one of the following:
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • a reporting configuration for coordinate information is sent to the terminal device.
  • receiving the second coordinate information sent by the terminal device includes:
  • the second coordinate information sent by the terminal device is received every first preset time period.
  • determining a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model currently used by the terminal device includes:
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • a switching configuration for the AI positioning model is sent to the terminal device.
  • Another aspect of the present disclosure provides a method for determining a positioning model.
  • the method is executed by a terminal device.
  • the method includes:
  • a new positioning model or positioning mode of the terminal device is determined, wherein the positioning model or the The positioning mode is used to determine the location information of the terminal device.
  • determining a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • determining a new positioning model or positioning mode of the terminal device based on the first coordinate information includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • determining a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information includes:
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • the network side receives The switching configuration for the AI positioning model sent by the device.
  • Another aspect of the present disclosure provides a positioning model determination method, which is characterized in that the method is executed by a terminal device, and the method includes:
  • second coordinate information is sent every first preset time period, wherein the second coordinate information is obtained based on a method different from the AI positioning model.
  • the method further includes:
  • the capability information includes at least one of the following:
  • a positioning model determination device which includes:
  • Determining module configured to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, wherein the positioning model or the positioning mode is used to determine the Location information of the terminal device.
  • a positioning model determination device which includes:
  • a determination module configured to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, wherein, the The positioning model or the positioning mode is used to determine the location information of the terminal device.
  • a positioning model determination device which includes:
  • a sending module configured to respond to the AI positioning model currently used by the terminal device and deploy it on the network side device, and send second coordinate information every first preset time period, wherein the second coordinate information is based on a different AI positioning model than the AI positioning model currently used by the terminal device. Obtained by positioning the model;
  • a receiving module configured to receive the reporting configuration sent by the network side device for the second coordinate information.
  • the device includes a processor and a memory.
  • a computer program is stored in the memory.
  • the processor executes the computer program stored in the memory so that the The device executes the method proposed in the embodiment of the above aspect.
  • the device includes a processor and a memory.
  • a computer program is stored in the memory.
  • the processor executes the computer program stored in the memory so that the The device performs the method proposed in the above embodiment.
  • the device includes a processor and a memory.
  • a computer program is stored in the memory.
  • the processor executes the computer program stored in the memory so that the The device performs the method proposed in the above embodiment.
  • a communication device provided by another embodiment of the present disclosure includes: a processor and an interface circuit
  • the interface circuit is used to receive code instructions and transmit them to the processor
  • the processor is configured to run the code instructions to perform the method proposed in the embodiment of one aspect.
  • a communication device provided by another embodiment of the present disclosure includes: a processor and an interface circuit
  • the interface circuit is used to receive code instructions and transmit them to the processor
  • the processor is configured to run the code instructions to perform the method proposed in another embodiment.
  • a communication device provided by another embodiment of the present disclosure includes: a processor and an interface circuit
  • the interface circuit is used to receive code instructions and transmit them to the processor
  • the processor is configured to run the code instructions to perform the method proposed in the embodiment of one aspect.
  • a computer-readable storage medium provided by an embodiment of another aspect of the present disclosure is used to store instructions. When the instructions are executed, the method proposed by the embodiment of the present disclosure is implemented.
  • a computer-readable storage medium provided by an embodiment of another aspect of the present disclosure is used to store instructions. When the instructions are executed, the method proposed by the embodiment of another aspect is implemented.
  • a computer-readable storage medium provided by an embodiment of another aspect of the present disclosure is used to store instructions. When the instructions are executed, the method proposed by the embodiment of another aspect is implemented.
  • a new positioning model or positioning mode of the terminal device is determined based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, where the positioning model or the mode is used Determine the location information of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the mismatch between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 1 is a schematic flow chart of a positioning model reasoning method provided by an embodiment of the present disclosure
  • Figure 2 is a schematic flowchart of a positioning model determination method provided by another embodiment of the present disclosure.
  • Figure 3 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 4 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 5 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 6 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 7 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 8 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 9 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 10 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 11 is a schematic flowchart of a positioning model determination method provided by another embodiment of the present disclosure.
  • Figure 12 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 13 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 14 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 15 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 16 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 17 is a schematic flowchart of a positioning model determination method provided by yet another embodiment of the present disclosure.
  • Figure 18 is a schematic structural diagram of a positioning model determination device provided by an embodiment of the present disclosure.
  • Figure 19 is a schematic structural diagram of a positioning model determination device provided by another embodiment of the present disclosure.
  • Figure 20 is a schematic structural diagram of a positioning model determination device provided by another embodiment of the present disclosure.
  • Figure 21 is a schematic structural diagram of a positioning model determination device provided by another embodiment of the present disclosure.
  • Figure 22 is a block diagram of a terminal device provided by an embodiment of the present disclosure.
  • Figure 23 is a block diagram of a network side device provided by an embodiment of the present disclosure.
  • first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, the information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other.
  • first information may also be called second information, and similarly, the second information may also be called first information.
  • the words "if” and “if” as used herein may be interpreted as “when” or “when” or “in response to determining.”
  • the consumer market includes shopping mall shopping guides, reverse car searches in parking lots, preventing family members from getting separated, self-guided tours in exhibition halls, etc.; vertical industries include people flow monitoring and analysis, smart warehousing and logistics, smart manufacturing, emergency rescue, personnel asset management and services Robotics and other industries.
  • 3GPP completed the first standard version of terminal equipment (UE) positioning based on New Radio (NR) signals in Rel-16, which mainly meets the horizontal positioning accuracy of less than 3 meters for 80% of users indoors and less than 3 meters outdoors.
  • the horizontal positioning accuracy of 80% of users is less than the positioning requirement of 10 meters.
  • 5G business puts forward higher positioning requirements.
  • the corresponding location service requirements are summarized in TS 22.261 and TR 22.804, as shown in Table 1 and Table 2. As shown in the table, high-precision positioning services are urgently needed in various scenarios and services.
  • multiple positioning methods are supported in the NR system, such as NR enhanced cell ID positioning method (E-CID), NR downlink time difference of arrival positioning method (DL-TDOA), NR Uplink Time Difference of Arrival Positioning Method (UL-TDOA), NR Multi-cell Round Trip Time Positioning Method (Multi-RTT), NR Downlink Historical Angle Positioning Method, and NR Uplink Angle of Arrival Positioning Method.
  • E-CID NR enhanced cell ID positioning method
  • DL-TDOA NR downlink time difference of arrival positioning method
  • UL-TDOA NR Uplink Time Difference of Arrival Positioning Method
  • Multi-RTT NR Multi-cell Round Trip Time Positioning Method
  • NR Downlink Historical Angle Positioning Method NR Uplink Angle of Arrival Positioning Method
  • FIG 1 is a schematic flow chart of a positioning model inference method provided by an embodiment of the present disclosure.
  • the AI-based positioning model AI-based positioning includes a training process and an inference process. During the training process, a data set is first constructed. The data set includes the measurement results of the terminal or network. The measurement results can be impulse response or RSRP, etc. The data set also includes the coordinate labels of the terminal. The model can be trained using the data set.
  • Model training is often performed on the network side, but after the AI model is trained, the trained AI model can be deployed on the network side or the terminal side for inference.
  • the input to the model during inference is still the measurement result.
  • the AI model will output the corresponding terminal position.
  • Figure 2 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 2, the method may include the following steps:
  • Step 201 Determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, where the positioning model or positioning mode is used to determine the location information of the terminal device.
  • the terminal device may be a device that provides voice and/or data connectivity to the user.
  • Terminal devices can communicate with one or more core networks via RAN (Radio Access Network).
  • Terminal devices can be IoT terminals, such as sensor devices, mobile phones (or "cellular" phones) and devices with The computer of the Internet of Things terminal, for example, can be a fixed, portable, pocket-sized, handheld, computer-built-in or vehicle-mounted device.
  • station STA
  • subscriber unit subscriber unit
  • subscriber station subscriber station
  • mobile station mobile station
  • remote station remote station
  • access terminal access terminal
  • user device user terminal
  • user agent useragent
  • the terminal device may also be a device of an unmanned aerial vehicle.
  • the terminal device may also be a vehicle-mounted device, for example, it may be a driving computer with wireless communication function, or a wireless terminal connected to an external driving computer.
  • the terminal device may also be a roadside device, for example, it may be a street light, a signal light or other roadside device with wireless communication function.
  • the terminal device refers to the terminal device that communicates with the location management function entity LMF.
  • the first is only used to indicate the terminal device that communicates with the LMF.
  • the terminal device does not Specifically refers to a certain fixed terminal equipment.
  • the performance reference information refers to reference parameters used to determine the performance of the AI positioning model.
  • This performance reference information is not specific to a fixed information. For example, when the first coordinate information included in the performance reference information changes, the performance reference information may also change accordingly.
  • the performance reference information of the AI positioning model currently used by the terminal device includes at least one of the following:
  • First coordinate information where the first coordinate information is information obtained by inferring the AI positioning model currently used by the terminal device;
  • a new positioning model or positioning mode for the terminal device is determined based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, including:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the method further includes:
  • the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • determining a new positioning model or positioning mode of the terminal device according to the first coordinate information includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • a new positioning model or positioning mode of the terminal device is determined, including at least one of the following:
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • a reporting configuration for coordinate information is sent to the terminal device.
  • receiving the second coordinate information sent by the terminal device includes:
  • the second coordinate information sent by the terminal device is received every first preset time period.
  • determining a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model currently used by the terminal device includes:
  • the AI positioning model In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, the AI positioning model is switched to a new AI positioning model;
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • a switching configuration for the AI positioning model is sent to the terminal device.
  • a new positioning model or positioning mode of the terminal device is determined based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, where the positioning model or the mode is used Determine the location information of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the mismatch between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced. situation, improving the accuracy of determining the location information of the terminal device.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 3 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 3, the method may include the following steps:
  • Step 301 Determine a new positioning model or positioning mode of the terminal device based on the first coordinate information.
  • the first coordinate information is information obtained by inferring the AI positioning model currently used by the terminal device.
  • the network side device can receive the first coordinate information sent by the terminal device.
  • the network side device can perform inference on the AI positioning model and obtain the first coordinate information.
  • the first coordinate information does not specifically refer to certain fixed information.
  • the first coordinate information may also change accordingly.
  • the first one in the first coordinate information is only used to indicate that the location information is obtained based on the AI positioning model currently used by the terminal device, and the first one is only used to distinguish it from the other coordinate information.
  • the positioning mode may be used to indicate at least one traditional positioning algorithm.
  • This positioning mode does not refer to a fixed positioning mode.
  • the positioning mode can also change accordingly.
  • a new positioning model or positioning mode of the terminal device is determined based on the first coordinate information.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • the embodiments of this disclosure specifically disclose a solution in which the performance reference information is the first coordinate information.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 4 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 4, the method may include the following steps:
  • Step 401 Receive capability information sent by the terminal device.
  • the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • the capability information does not specifically refer to certain fixed information.
  • the capability information can also change accordingly.
  • the network side device when the network side device receives the capability information sent by the terminal device, the network side device can send the positioning configuration to the terminal device to instruct the terminal device to obtain the location information.
  • the capability information sent by the terminal device is received, and the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the accuracy of monitoring the AI positioning model can be improved.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 5 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 5, the method may include the following steps:
  • Step 501 Receive the second coordinate information sent by the terminal device, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model;
  • Step 502 Determine a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information.
  • the second coordinate information refers to the coordinate information obtained by the terminal device, and the coordinate information refers to the coordinate information obtained by the terminal device based on a method different from the AI positioning model.
  • the second coordinate information does not specifically refer to certain fixed information. For example, when the acquisition method changes, the second coordinate information can also change accordingly.
  • the terminal device may obtain the second coordinate information based on Global Navigation Satellite System (GNSS), for example, and the terminal device may also obtain the second coordinate information based on Wireless Fidelity (Wireless Fidelity, WiFi). ) to obtain the second coordinate information.
  • GNSS Global Navigation Satellite System
  • WiFi Wireless Fidelity
  • the terminal device may also obtain the second coordinate information based on Bluetooth, for example.
  • the second coordinate information sent by the terminal device is received, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model; based on the first coordinate information and the third
  • the second coordinate information determines the new positioning model or positioning mode of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • the solution of determining the positioning model based on the first coordinate information and the second coordinate information is specifically explained. This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 6 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 6, the method may include the following steps:
  • Step 601 Receive the second coordinate information sent by the terminal device, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model;
  • Step 602 In response to the difference between the first coordinate information and the second coordinate information being greater than the first threshold, switch the AI positioning model to a new AI positioning model;
  • Step 603 In response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, switch the AI positioning model to the positioning mode.
  • the first coordinate information is information obtained by inferring the AI positioning model currently used by the terminal device.
  • the network side device in response to the difference between the first coordinate information and the second coordinate information being less than or equal to the first threshold, the network side device may not switch the AI positioning model.
  • the network side device in response to the difference between the first coordinate information and the second coordinate information being less than or equal to the second threshold, the network side device may not switch the AI positioning model.
  • the second coordinate information sent by the terminal device is received, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model; in response to the first coordinate information and If the difference between the second coordinate information is greater than the first threshold, the AI positioning model is switched to a new AI positioning model; in response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, the AI positioning model is switched for positioning mode.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • the solution of determining the positioning model based on the first coordinate information and the second coordinate information is specifically explained.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 7 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 7, the method may include the following steps:
  • Step 701 Receive the second coordinate information sent by the terminal device every first preset time period, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model;
  • Step 702 In response to the difference between the first coordinate information and the second coordinate information being greater than the first threshold, switch the AI positioning model to a new AI positioning model;
  • Step 703 In response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, switch the AI positioning model to the positioning mode;
  • Step 704 In response to the AI positioning model currently used by the terminal device being deployed on the network side device, send the reporting configuration for the coordinate information to the terminal device.
  • the first threshold does not specifically refer to a fixed threshold.
  • the first threshold can be modified. The first of the first thresholds is used only to distinguish it from the remaining thresholds.
  • the network side device can send reporting configurations for coordinate information to the terminal device.
  • the network side device may send the reporting configuration for the second coordinate information to the terminal device.
  • the second coordinate information sent by the terminal device is received, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model; in response to the first coordinate information and If the difference between the second coordinate information is greater than the first threshold, the AI positioning model is switched to a new AI positioning model; in response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, the AI positioning model is switched In positioning mode, in response to the AI positioning model currently used by the terminal device being deployed on the network side device, a reporting configuration for coordinate information is sent to the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • the solution of determining the positioning model based on the first coordinate information and the second coordinate information and the solution of sending the reported configuration to the terminal device are specifically explained, which can improve the accuracy of obtaining coordinate information and improve the positioning model. Determined accuracy.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 8 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 8, the method may include the following steps:
  • Step 801 Receive second coordinate information sent by the terminal device every first preset time period, where the second coordinate information is obtained by the terminal device based on a method different from the AI positioning model;
  • Step 802 Perform inference on the currently used AI positioning model to obtain the first coordinate information, where the AI positioning model is deployed on the network side device;
  • Step 803 In response to the difference between the first coordinate information and the second coordinate information being greater than the first threshold, switch the AI positioning model to a new AI positioning model;
  • Step 804 In response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, switch the AI positioning model to positioning mode.
  • the AI positioning model is deployed on a network-side device.
  • the network-side device can reason about the currently used AI positioning model and obtain the first coordinate information.
  • the preset duration does not specifically refer to a fixed duration.
  • the preset duration may be modified, for example, based on a duration modification instruction received by the network side device.
  • the second coordinate information sent by the terminal device is received every first preset time period, and the currently used AI positioning model is inferred to obtain the first coordinate information, where, The AI positioning model is deployed on the network side device.
  • the AI positioning model is switched to a new AI positioning model; in response to the difference between the first coordinate information and the second coordinate information If the difference in coordinate information is greater than the second threshold, the AI positioning model is switched to the positioning mode, and in response to the AI positioning model currently used by the terminal device being deployed on the network side device, a reporting configuration for the coordinate information is sent to the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced. matching situation to improve the accuracy of determining the location information of the terminal device.
  • the receiving method based on the first coordinate information and the second coordinate information can improve the accuracy of obtaining the coordinate information and improve the accuracy of positioning model determination.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 9 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 9, the method may include the following steps:
  • Step 901 In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, switch the AI positioning model to a new AI positioning model;
  • Step 902 In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the second performance threshold, switch the AI positioning model to the positioning mode.
  • the positioning application performance information may refer to, for example, application feedback information received after the AI positioning model is applied.
  • the AI positioning model in response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, the AI positioning model is switched to a new AI positioning model; in response to the terminal device If the positioning application performance information of the AI positioning model currently used by the device is greater than the second performance threshold, the AI positioning model is switched to positioning mode.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 10 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a network side device. As shown in Figure 10, the method may include the following steps:
  • Step 1001. In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, switch the AI positioning model to a new AI positioning model;
  • Step 1002 In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the second performance threshold, switch the AI positioning model to positioning mode;
  • Step 1003 In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, send the switching configuration for the AI positioning model to the terminal device.
  • the first performance threshold does not specifically refer to a fixed threshold.
  • the first of the first performance thresholds is used only to differentiate from other performance thresholds.
  • the first performance threshold refers to the performance threshold for whether to switch the AI positioning model to a new AI positioning model based on positioning application performance information.
  • the AI positioning model in response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, the AI positioning model is switched to a new AI positioning model; in response to the terminal device If the positioning application performance information of the AI positioning model currently used by the device is greater than the second performance threshold, the AI positioning model is switched to the positioning mode. In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, a switch for the AI positioning model is sent. Configure to the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • a solution for sending the switching configuration for the AI positioning model to the terminal device is specifically disclosed, which can improve the accuracy of the terminal device in determining coordinate information.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 11 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 11, the method may include the following steps:
  • Step 1101. In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, based on the performance reference information of the AI positioning model, determine a new positioning model or positioning mode of the terminal device, where the positioning model or positioning mode is used to determine the terminal device. location information.
  • determining a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • determining a new positioning model or positioning mode of the terminal device based on the first coordinate information includes:
  • a new positioning model or positioning mode of the terminal device is determined.
  • determining a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information includes:
  • the AI positioning model is switched to the positioning mode.
  • the method further includes:
  • a new positioning model or positioning mode of the terminal device is determined based on the performance reference information of the AI positioning model, where, The positioning model or positioning mode is used to determine the location information of the terminal device.
  • the positioning model or positioning mode is used to determine the location information of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the mismatch between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced. situation, improving the accuracy of determining the location information of the terminal device.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 12 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 12, the method may include the following steps:
  • Step 1201 In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, perform inference on the AI positioning model and obtain the first coordinate information;
  • Step 1202 Determine a new positioning model or positioning mode of the terminal device based on the first coordinate information.
  • the AI positioning model in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, the AI positioning model is inferred to obtain the first coordinate information; based on the first coordinate information, the terminal device is determined New positioning model or positioning mode.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 13 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 13, the method may include the following steps:
  • Step 1301 In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, perform inference on the AI positioning model and obtain the first coordinate information;
  • Step 1302. Second coordinate information obtained based on a method different from the AI positioning model
  • Step 1303 Determine a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information.
  • the terminal device in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, can determine a new positioning of the terminal device based on the acquired first coordinate information and the second coordinate information. model or positioning mode.
  • the AI positioning model in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, the AI positioning model is inferred to obtain the first coordinate information; the first coordinate information is obtained based on a method different from the AI positioning model. the second coordinate information; based on the first coordinate information and the second coordinate information, determine a new positioning model or positioning mode of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • a solution for determining a positioning model based on the first coordinate information and the second coordinate information is specifically disclosed.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 14 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 14, the method may include the following steps:
  • Step 1401 In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, perform inference on the AI positioning model and obtain the first coordinate information;
  • Step 1402. Second coordinate information obtained based on a method different from the AI positioning model
  • Step 1403 In response to the difference between the first coordinate information and the second coordinate information being greater than the first threshold, switch the AI positioning model to a new AI positioning model;
  • Step 1404 In response to the difference between the first coordinate information and the second coordinate information being greater than the second threshold, switch the AI positioning model to the positioning mode.
  • the first coordinate information is coordinate information obtained by the terminal device inferring the AI positioning model.
  • the second coordinate information refers to the location information obtained by the terminal device based on a method different from the AI positioning model. That is to say, the first coordinate information and the second coordinate information are obtained in different ways.
  • steps 1401 to 1404 are as described above and will not be described again here.
  • the AI positioning model in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, the AI positioning model is inferred to obtain the first coordinate information; the first coordinate information is obtained based on a method different from the AI positioning model. the second coordinate information; in response to the difference between the first coordinate information and the second coordinate information being greater than the first threshold, the AI positioning model is switched to a new AI positioning model; in response to the If the difference is greater than the second threshold, the AI positioning model is switched to positioning mode.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the inconsistency between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced.
  • the matching situation improves the accuracy of determining the location information of the terminal device.
  • a solution for determining a positioning model based on the first coordinate information and the second coordinate information and a solution for obtaining the first coordinate information and the second coordinate information are specifically disclosed. This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 15 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 15, the method may include the following steps:
  • Step 1501 In response to the AI positioning model currently used by the terminal device being deployed on the terminal device, and the performance reference information of the AI positioning model being the positioning application performance information of the AI positioning model currently used by the terminal device, receive the AI positioning information sent by the network side device. Switch configuration for positioning models.
  • step 1501 The detailed description of step 1501 is as mentioned above and will not be described again here.
  • the AI positioning model currently used by the terminal device is deployed on the terminal device, and the performance reference information of the AI positioning model is the positioning application performance information of the AI positioning model currently used by the terminal device.
  • receive the switching configuration for the AI positioning model sent by the network side device by monitoring the positioning application performance information of the AI positioning model, the switching configuration for the AI positioning model sent by the network side device can be received, thereby improving the accuracy of determining the new positioning model or positioning mode, and reducing the Improve the accuracy of determining the location information of the terminal device when the positioning model or positioning mode does not match the performance reference information of the AI positioning model.
  • a solution for determining a positioning model based on the first coordinate information and the second coordinate information and a solution for obtaining the first coordinate information and the second coordinate information are specifically disclosed.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 16 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 16, the method may include the following steps:
  • Step 1601 In response to the AI positioning model currently used by the terminal device being deployed on the network side device, send second coordinate information every first preset time period, where the second coordinate information is obtained based on a method different from the AI positioning model. ;
  • Step 1602 Receive the reporting configuration sent by the network side device for the second coordinate information.
  • the method further includes:
  • the capability information includes at least one of the following:
  • the terminal device can obtain a new positioning model or positioning mode of the terminal device sent by the network side device.
  • the network side device can receive the second coordinate information, and the network side device can infer the AI positioning model currently used by the terminal device to obtain the first coordinate information.
  • the network side device may determine a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information.
  • the network side device may send the reporting configuration for the second coordinate information to the terminal device.
  • the second coordinate information in response to the AI positioning model currently used by the terminal device being deployed on the network side device, the second coordinate information is sent every first preset time period, where the second coordinate information is Obtained based on a method different from the AI positioning model; receiving the reported configuration sent by the network side device for the second coordinate information.
  • the accuracy of positioning model determination can be improved.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 17 is a schematic flowchart of a positioning model determination method provided by an embodiment of the present disclosure. The method is executed by a terminal device. As shown in Figure 17, the method may include the following steps:
  • Step 1701 Send capability information to the network side device, where the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • capability information is sent to the network side device, where the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the accuracy of monitoring the AI positioning model can be improved.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • Figure 18 is a schematic structural diagram of a positioning model determination device provided by an embodiment of the present disclosure. As shown in Figure 18, the device 1800 may include:
  • the determination module 1801 is used to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, where the positioning model or positioning mode is used to determine the location information of the terminal device.
  • the determination module can determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device, where, The positioning model or positioning mode is used to determine the location information of the terminal device.
  • the positioning model or positioning mode is used to determine the location information of the terminal device.
  • by monitoring the performance reference information of the AI positioning model the accuracy of determining the new positioning model or positioning mode can be improved, and the mismatch between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced. situation, improving the accuracy of determining the location information of the terminal device.
  • the present disclosure provides a processing device for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • the performance reference information of the AI positioning model currently used by the terminal device includes at least one of the following:
  • First coordinate information where the first coordinate information is information obtained by inferring the AI positioning model currently used by the terminal device;
  • the determination module 1801 is configured to determine a new positioning model or positioning mode for the terminal device based on the performance reference information of the artificial intelligence AI positioning model currently used by the terminal device. Specifically, Used for:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the determining module 1801 is also used to:
  • the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • the determination module 1801 is configured to determine a new positioning model or positioning mode of the terminal device based on the first coordinate information, specifically for:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the determination module 1801 is configured to determine a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information, including at least one of the following:
  • the AI positioning model is switched to the positioning mode.
  • Figure 19 is a schematic structural diagram of a positioning model determination device provided by an embodiment of the present disclosure. As shown in Figure 19, the device 1800 also includes a sending module 1802. At:
  • the reporting configuration for the coordinate information is sent to the terminal device.
  • the determining module 1801 is used to receive the second coordinate information sent by the terminal device, specifically for:
  • the second coordinate information sent by the terminal device is received every first preset time period.
  • the determination module 1801 is configured to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model currently used by the terminal device, specifically for:
  • the AI positioning model In response to the positioning application performance information of the AI positioning model currently used by the terminal device being greater than the first performance threshold, the AI positioning model is switched to a new AI positioning model;
  • the AI positioning model is switched to the positioning mode.
  • the sending module 1802 is also used to:
  • a switching configuration for the AI positioning model is sent to the terminal device.
  • Figure 20 is a schematic structural diagram of a positioning model determination device provided by an embodiment of the present disclosure. As shown in Figure 20, the device 2000 may include:
  • the determination module 2001 is configured to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model in response to the AI positioning model currently used by the terminal device being deployed on the terminal device, where the positioning model or positioning mode is used Determine the location information of the terminal device.
  • the determination module can be deployed on the terminal device in response to the AI positioning model currently used by the terminal device, and determine the new positioning model of the terminal device based on the performance reference information of the AI positioning model.
  • a positioning model or positioning mode wherein the positioning model or positioning mode is used to determine the location information of the terminal device.
  • the accuracy of determining the new positioning model or positioning mode can be improved, and the mismatch between the positioning model or positioning mode and the performance reference information of the AI positioning model can be reduced. situation, improving the accuracy of determining the location information of the terminal device.
  • This disclosure provides a processing method for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • the determination module 2001 is configured to determine a new positioning model or positioning mode of the terminal device based on the performance reference information of the AI positioning model, specifically for:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the determination module 2001 is configured to determine a new positioning model or positioning mode of the terminal device based on the first coordinate information, specifically for:
  • a new positioning model or positioning mode of the terminal device is determined.
  • the determination module 2001 is configured to determine a new positioning model or positioning mode of the terminal device based on the first coordinate information and the second coordinate information, specifically for:
  • the AI positioning model is switched to the positioning mode.
  • the determining module 2001 is also used to:
  • Figure 21 is a schematic structural diagram of a positioning model determination device provided by an embodiment of the present disclosure. As shown in Figure 21, the device 2100 may include:
  • the sending module 2101 is configured to respond to the AI positioning model currently used by the terminal device being deployed on the network side device, and send second coordinate information every first preset time period, where the second coordinate information is based on a different AI positioning model. obtained by means of
  • the receiving module 2102 is configured to receive the reporting configuration sent by the network side device for the second coordinate information.
  • the sending module is deployed on the network side device in response to the AI positioning model currently used by the terminal device, and sends the second coordinate information every first preset time period,
  • the second coordinate information is obtained based on a method different from the AI positioning model;
  • the receiving module can receive the reported configuration sent by the network side device for the second coordinate information.
  • the present disclosure provides a processing device for the situation of "positioning model determination" to monitor the performance of the AI positioning model, improve the matching between the AI positioning model and the scene, improve the accuracy of positioning model determination, and thereby improve the terminal Accuracy of device location information determination.
  • the sending module 2101 is also used to:
  • the capability information is used to indicate the terminal device's ability to perform inference performance monitoring for the AI positioning model.
  • the capability information includes at least one of the following:
  • Figure 22 is a block diagram of a terminal device UE2200 provided by an embodiment of the present disclosure.
  • the UE2200 can be a mobile phone, a computer, a digital broadcast terminal device, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
  • UE 2200 may include at least one of the following components: a processing component 2202 , a memory 2204 , a power supply component 2206 , a multimedia component 2208 , an audio component 2210 , an input/output (I/O) interface 2212 , a sensor component 2214 , and a communication component. 2216.
  • Processing component 2202 generally controls the overall operations of UE 2200, such as operations associated with display, phone calls, data communications, camera operations, and recording operations.
  • the processing component 2202 may include at least one processor 2220 to execute instructions to complete all or part of the steps of the above method. Additionally, processing component 2202 may include at least one module that facilitates interaction between processing component 2202 and other components. For example, processing component 2202 may include a multimedia module to facilitate interaction between multimedia component 2208 and processing component 2202.
  • Memory 2204 is configured to store various types of data to support operations at UE 2200. Examples of this data include instructions for any application or method operating on the UE2200, contact data, phonebook data, messages, pictures, videos, etc.
  • Memory 2204 may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EEPROM), Programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EEPROM erasable programmable read-only memory
  • EPROM Programmable read-only memory
  • PROM programmable read-only memory
  • ROM read-only memory
  • magnetic memory flash memory, magnetic or optical disk.
  • Power supply component 2206 provides power to various components of UE 2200.
  • Power component 2206 may include a power management system, at least one power supply, and other components associated with generating, managing, and distributing power to UE 2200.
  • Multimedia component 2208 includes a screen that provides an output interface between the UE 2200 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes at least one touch sensor to sense touches, slides, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or sliding operation, but also detect the wake-up time and pressure related to the touch or sliding operation.
  • multimedia component 2208 includes a front-facing camera and/or a rear-facing camera. When the UE2200 is in an operating mode, such as shooting mode or video mode, the front camera and/or rear camera can receive external multimedia data.
  • Each front-facing camera and rear-facing camera can be a fixed optical lens system or have a focal length and optical zoom capabilities.
  • Audio component 2210 is configured to output and/or input audio signals.
  • audio component 2210 includes a microphone (MIC) configured to receive external audio signals when UE 2200 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 2204 or sent via communications component 2216.
  • audio component 2210 also includes a speaker for outputting audio signals.
  • the I/O interface 2212 provides an interface between the processing component 2202 and a peripheral interface module.
  • the peripheral interface module may be a keyboard, a click wheel, a button, etc. These buttons may include, but are not limited to: Home button, Volume buttons, Start button, and Lock button.
  • Sensor component 2214 includes at least one sensor for providing various aspects of status assessment for UE 2200 .
  • the sensor component 2214 can detect the on/off state of the device 2200, the relative positioning of components, such as the display and keypad of the UE 2200, the sensor component 2214 can also detect the position change of the UE 2200 or a component of the UE 2200, the user The presence or absence of contact with the UE2200, the orientation or acceleration/deceleration of the UE2200 and the temperature change of the UE2200.
  • Sensor assembly 2214 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact.
  • Sensor assembly 2214 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 2214 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • Communication component 2216 is configured to facilitate wired or wireless communication between UE 2200 and other devices.
  • UE2200 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, or a combination thereof.
  • the communication component 2216 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 2216 also includes a near field communication (NFC) module to facilitate short-range communications.
  • NFC near field communication
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • UE 2200 may be configured by at least one Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array ( FPGA), controller, microcontroller, microprocessor or other electronic component implementation for executing the above method.
  • ASIC Application Specific Integrated Circuit
  • DSP Digital Signal Processor
  • DSPD Digital Signal Processing Device
  • PLD Programmable Logic Device
  • FPGA Field Programmable Gate Array
  • controller microcontroller, microprocessor or other electronic component implementation for executing the above method.
  • Figure 23 is a block diagram of a network side device 2300 provided by an embodiment of the present disclosure.
  • the network side device 2300 may be provided as a network side device.
  • the network side device 2300 includes a processing component 2322 , which further includes at least one processor, and a memory resource represented by a memory 2332 for storing instructions, such as application programs, that can be executed by the processing component 2322 .
  • the application program stored in memory 2332 may include one or more modules, each corresponding to a set of instructions.
  • the processing component 2322 is configured to execute instructions to perform any of the foregoing methods applied to the network side device, for example, the method shown in FIG. 1 .
  • the network side device 2300 may also include a power supply component 2326 configured to perform power management of the network side device 2300, a wired or wireless network interface 2350 configured to connect the network side device 2300 to the network, and an input/output (I/O). O)Interface 2358.
  • the network side device 2300 may operate based on an operating system stored in the memory 2332, such as Windows Server TM, Mac OS X TM, Unix TM, Linux TM, Free BSD TM or similar.
  • the methods provided by the embodiments of the present disclosure are introduced from the perspectives of network side equipment and UE respectively.
  • the network side device and the UE may include a hardware structure and a software module to implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module.
  • a certain function among the above functions can be executed by a hardware structure, a software module, or a hardware structure plus a software module.
  • the methods provided by the embodiments of the present disclosure are introduced from the perspectives of network side equipment and UE respectively.
  • the network side device and the UE may include a hardware structure and a software module to implement the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module.
  • a certain function among the above functions can be executed by a hardware structure, a software module, or a hardware structure plus a software module.
  • the communication device may include a transceiver module and a processing module.
  • the transceiver module may include a sending module and/or a receiving module.
  • the sending module is used to implement the sending function
  • the receiving module is used to implement the receiving function.
  • the transceiving module may implement the sending function and/or the receiving function.
  • the communication device may be a terminal device (such as the terminal device in the foregoing method embodiment), a device in the terminal device, or a device that can be used in conjunction with the terminal device.
  • the communication device may be a network device, a device in a network device, or a device that can be used in conjunction with the network device.
  • the communication device may be a network device, or may be a terminal device (such as the terminal device in the foregoing method embodiment), or may be a chip, chip system, or processor that supports the network device to implement the above method, or may be a terminal device that supports A chip, chip system, or processor that implements the above method.
  • the device can be used to implement the method described in the above method embodiment. For details, please refer to the description in the above method embodiment.
  • a communications device may include one or more processors.
  • the processor may be a general-purpose processor or a special-purpose processor, etc.
  • it can be a baseband processor or a central processing unit.
  • the baseband processor can be used to process communication protocols and communication data
  • the central processor can be used to control and execute communication devices (such as network side equipment, baseband chips, terminal equipment, terminal equipment chips, DU or CU, etc.)
  • a computer program processes data for a computer program.
  • the communication device may also include one or more memories, on which a computer program may be stored, and the processor executes the computer program, so that the communication device performs the method described in the above method embodiment.
  • data may also be stored in the memory.
  • the communication device and the memory can be provided separately or integrated together.
  • the communication device may also include a transceiver and an antenna.
  • the transceiver can be called a transceiver unit, a transceiver, or a transceiver circuit, etc., and is used to implement transceiver functions.
  • the transceiver can include a receiver and a transmitter.
  • the receiver can be called a receiver or a receiving circuit, etc., and is used to implement the receiving function;
  • the transmitter can be called a transmitter or a transmitting circuit, etc., and is used to implement the transmitting function.
  • one or more interface circuits may also be included in the communication device.
  • Interface circuitry is used to receive code instructions and transmit them to the processor.
  • the processor executes the code instructions to cause the communication device to perform the method described in the above method embodiment.
  • the communication device is a network-side device: the processor is used to execute the method shown in any one of Figures 2 to 11.
  • the communication device is a terminal device (such as the terminal device in the foregoing method embodiment): the processor is configured to execute the method shown in any one of Figures 12 to 17.
  • a transceiver for implementing receiving and transmitting functions may be included in the processor.
  • the transceiver may be a transceiver circuit, an interface, or an interface circuit.
  • the transceiver circuits, interfaces or interface circuits used to implement the receiving and transmitting functions can be separate or integrated together.
  • the above-mentioned transceiver circuit, interface or interface circuit can be used for reading and writing codes/data, or the above-mentioned transceiver circuit, interface or interface circuit can be used for signal transmission or transfer.
  • the processor may store a computer program, and the computer program runs on the processor, which can cause the communication device to perform the method described in the above method embodiment.
  • the computer program may be embedded in the processor, in which case the processor may be implemented in hardware.
  • the communication device may include a circuit, and the circuit may implement the functions of sending or receiving or communicating in the foregoing method embodiments.
  • the processors and transceivers described in this disclosure may be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed signal ICs, application specific integrated circuits (ASICs), printed circuit boards ( printed circuit board (PCB), electronic equipment, etc.
  • the processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal oxide semiconductor (CMOS), n-type metal oxide-semiconductor (NMOS), P-type Metal oxide semiconductor (positive channel metal oxide semiconductor, PMOS), bipolar junction transistor (BJT), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.
  • CMOS complementary metal oxide semiconductor
  • NMOS n-type metal oxide-semiconductor
  • PMOS P-type Metal oxide semiconductor
  • BJT bipolar junction transistor
  • BiCMOS bipolar CMOS
  • SiGe silicon germanium
  • GaAs gallium arsenide
  • the communication device described in the above embodiments may be a network device or a terminal device (such as the terminal device in the foregoing method embodiment), but the scope of the communication device described in the present disclosure is not limited thereto, and the structure of the communication device may not be limited to limits.
  • the communication device may be a stand-alone device or may be part of a larger device.
  • the communication device may be:
  • the IC collection may also include storage components for storing data and computer programs;
  • the communication device may be a chip or a system on a chip
  • the chip includes a processor and an interface.
  • the number of processors may be one or more, and the number of interfaces may be multiple.
  • the chip also includes a memory for storing necessary computer programs and data.
  • the present disclosure also provides a readable storage medium on which instructions are stored, and when the instructions are executed by a computer, the functions of any of the above method embodiments are implemented.
  • the present disclosure also provides a computer program product, which, when executed by a computer, implements the functions of any of the above method embodiments.
  • the computer program product includes one or more computer programs.
  • the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
  • the computer program may be stored in or transferred from one computer-readable storage medium to another, for example, the computer program may be transferred from a website, computer, server, or data center Transmission to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more available media integrated therein.
  • the available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs (DVD)), or semiconductor media (e.g., solid state disks, SSD)) etc.
  • magnetic media e.g., floppy disks, hard disks, magnetic tapes
  • optical media e.g., high-density digital video discs (DVD)
  • DVD digital video discs
  • semiconductor media e.g., solid state disks, SSD
  • At least one in the present disclosure can also be described as one or more, and the plurality can be two, three, four or more, and the present disclosure is not limited.
  • the technical feature is distinguished by “first”, “second”, “third”, “A”, “B”, “C” and “D” etc.
  • the technical features described in “first”, “second”, “third”, “A”, “B”, “C” and “D” are in no particular order or order.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

La présente divulgation concerne un procédé et un appareil de détermination de modèle de positionnement, un dispositif et un support de stockage et se rapporte au domaine technique des communications. Le procédé consiste à : déterminer un nouveau modèle de positionnement ou un nouveau mode de positionnement d'un dispositif terminal sur la base d'informations de référence de performance d'un modèle de positionnement d'intelligence artificielle (IA) actuellement utilisé par le dispositif terminal, le modèle de positionnement ou le mode de positionnement étant utilisé pour déterminer les informations de position du dispositif terminal. La présente divulgation concerne un procédé de traitement pour le scénario de "détermination de modèle de positionnement", de façon à surveiller les performances du modèle de positionnement d'IA, à améliorer le degré de correspondance entre le modèle de positionnement d'IA et le réglage, à améliorer la précision de détermination de modèle de positionnement, et à augmenter la précision de détermination d'informations de position du dispositif terminal.
PCT/CN2022/100933 2022-06-23 2022-06-23 Procédé et appareil de détermination de modèle de positionnement WO2023245589A1 (fr)

Priority Applications (2)

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PCT/CN2022/100933 WO2023245589A1 (fr) 2022-06-23 2022-06-23 Procédé et appareil de détermination de modèle de positionnement
CN202280002084.5A CN117643078A (zh) 2022-06-23 2022-06-23 定位模型确定方法、装置

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110493720A (zh) * 2019-09-11 2019-11-22 深圳市名通科技股份有限公司 终端的定位方法、装置及存储介质
CN111989948A (zh) * 2020-07-17 2020-11-24 北京小米移动软件有限公司 定位测量数据上报方法、装置、终端及存储介质
CN112738726A (zh) * 2020-12-21 2021-04-30 深圳酷派技术有限公司 定位方法、装置、终端及存储介质
CN114189889A (zh) * 2021-12-03 2022-03-15 中国信息通信研究院 一种无线通信人工智能处理方法和设备

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110493720A (zh) * 2019-09-11 2019-11-22 深圳市名通科技股份有限公司 终端的定位方法、装置及存储介质
CN111989948A (zh) * 2020-07-17 2020-11-24 北京小米移动软件有限公司 定位测量数据上报方法、装置、终端及存储介质
CN112738726A (zh) * 2020-12-21 2021-04-30 深圳酷派技术有限公司 定位方法、装置、终端及存储介质
CN114189889A (zh) * 2021-12-03 2022-03-15 中国信息通信研究院 一种无线通信人工智能处理方法和设备

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