WO2025007727A1 - 数据处理方法、装置、设备及存储介质 - Google Patents

数据处理方法、装置、设备及存储介质 Download PDF

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Publication number
WO2025007727A1
WO2025007727A1 PCT/CN2024/099374 CN2024099374W WO2025007727A1 WO 2025007727 A1 WO2025007727 A1 WO 2025007727A1 CN 2024099374 W CN2024099374 W CN 2024099374W WO 2025007727 A1 WO2025007727 A1 WO 2025007727A1
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WIPO (PCT)
Prior art keywords
terminal
power
signal strength
antenna
network device
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PCT/CN2024/099374
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English (en)
French (fr)
Inventor
宋丹
汤凯
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China Mobile Communications Group Co Ltd
Research Institute of China Mobile Communication Co Ltd
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China Mobile Communications Group Co Ltd
Research Institute of China Mobile Communication Co Ltd
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Publication of WO2025007727A1 publication Critical patent/WO2025007727A1/zh
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/08—Testing, supervising or monitoring using real traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports

Definitions

  • the present disclosure relates to the field of wireless communication technology, and in particular to a data processing method, device, equipment and storage medium.
  • the existing network obtains the network deployment status by having many terminals participate in the test. That is, the terminal reports the received base station signal strength, such as the reference signal received power (RSRP) and its corresponding location information to the network.
  • the network analyzes the large amount of "public test" data obtained and provided by the terminal, and regards the area with weak RSRP as a weak coverage area, and may optimize the network for the weak coverage area.
  • the base station signal strength (such as RSRP) measured by the terminal may be inaccurate, which may lead to deviations in the judgment decision on whether network optimization is needed.
  • the embodiments of the present disclosure are intended to provide a data processing method, apparatus, device, and storage medium.
  • the acquiring a power compensation value of the terminal in an antenna mismatch state includes:
  • the acquiring a power compensation value of the terminal in an antenna mismatch state includes:
  • a power compensation value of the terminal in an antenna mismatch state is determined based on the uplink signal strength of the terminal, the first path loss, and the reflection coefficient.
  • a value is selected from the at least one value as a power compensation value of the terminal in an antenna mismatch state.
  • the method further includes:
  • the method further includes:
  • the power headroom value is less than a preset power headroom threshold and the second path loss is greater than a preset path loss threshold, determining that the area where the terminal is located is an uplink weak coverage area;
  • the method further includes:
  • the first information set includes a power headroom report sent by the terminal in a p-antenna mismatch state, and time information and/or location information of the terminal in the p-antenna mismatch state;
  • the power headroom report includes a power headroom value;
  • p is an integer greater than or equal to 1;
  • m power headroom values corresponding to the m antenna mismatch states from the first information set, wherein the m power headroom values are all less than a preset power headroom threshold, m is an integer greater than or equal to 1, and m is less than or equal to p;
  • the determining whether to perform a network optimization operation based on the time information and/or location information corresponding to the m power headroom values includes at least one of the following:
  • m weights are determined; after multiplying the m weights with the downlink signal strength of the network device obtained after compensation under the corresponding m antenna mismatch states, the first downlink signal strength is obtained by summing up; when the first downlink signal strength is less than a preset signal strength threshold, determining that the area where the terminal is located is an uplink weak coverage area; when it is determined that the area where the terminal is located is an uplink weak coverage area, performing a network optimization operation; and
  • the acquiring the power compensation value of the terminal in the antenna mismatch state includes: acquiring the power compensation value of the terminal in different antenna mismatch states;
  • the compensating the downlink signal strength of the network device based on the power compensation value of the terminal in the antenna mismatch state includes: compensating the downlink signal strength of the network device respectively based on the power compensation value of the terminal in different antenna mismatch states;
  • the method further comprises:
  • the method further includes:
  • AI/ML artificial intelligence/machine learning
  • the training data of the AI/ML model includes: the power compensation value of the terminal under different antenna mismatch states; or, the power compensation value of the terminal under different antenna mismatch states, and the time information and/or location information of the terminal under different antenna mismatch states.
  • the AI/ML model is trained by the network device, or trained by operation administration and maintenance (OAM), or trained by the terminal.
  • OAM operation administration and maintenance
  • the present disclosure provides a data processing method, which is applied to a terminal.
  • the method includes:
  • the power compensation value is used by the first device to compensate for the acquired downlink signal strength of the network device, so as to obtain the compensated downlink signal strength of the network device.
  • determining the power compensation value of the terminal in the antenna mismatch state includes:
  • the reflected power of the terminal at the antenna port is obtained through the power sensing module of the terminal; at least one numerical value is determined according to the power value of the reflected power, wherein any one of the at least one numerical value is less than or equal to the power value of the reflected power; and a numerical value is selected from the at least one numerical value as the power compensation value.
  • the method further includes:
  • the method further includes:
  • the time information and/or location information is used by the first device to determine whether to perform network optimization operations in combination with the downlink signal strength of the network device after compensation, or is used by the first device to train an AI/ML model in combination with the power compensation value of the terminal in an antenna mismatch state.
  • the present disclosure provides a data processing device, including:
  • An acquisition module used to acquire a power compensation value of a terminal in an antenna mismatch state; and to acquire a downlink signal strength of a network device;
  • the first processing module is used to compensate the downlink signal strength of the network device based on the power compensation value of the terminal in the antenna mismatch state to obtain the compensated downlink signal strength of the network device.
  • the present disclosure provides a data processing device, including:
  • a second processing module is used to determine a power compensation value of the terminal in an antenna mismatch state
  • the sending module is used to send the power compensation value of the terminal in the antenna mismatch state to the first device.
  • At least one embodiment of the present disclosure provides a device, comprising a processor and a memory for storing a computer program that can be run on the processor, wherein the processor is used to execute the steps of any one of the methods described above on the device side when running the computer program.
  • At least one embodiment of the present disclosure provides a terminal, comprising a processor and a memory for storing a computer program that can be run on the processor, wherein the processor is used to execute the steps of any one of the methods described above on the terminal side when running the computer program.
  • At least one embodiment of the present disclosure provides a storage medium having a computer program stored thereon, wherein the computer program implements the steps of any of the above methods when executed by a processor.
  • the power compensation value of the terminal in the antenna mismatch state is obtained; the downlink signal strength of the network device is obtained; based on the power compensation value of the terminal in the antenna mismatch state, the downlink signal strength of the network device is compensated to obtain the downlink signal strength of the network device after compensation.
  • FIG. 1 is a schematic diagram of improving terminal power loss through an antenna tuning module in the related art.
  • FIG. 2 is a first schematic diagram of an implementation flow of a data processing method provided in an embodiment of the present disclosure.
  • FIG. 3 is a second schematic diagram of the implementation flow of the data processing method provided in an embodiment of the present disclosure.
  • FIG4 is a schematic diagram of a system architecture for applying a data processing method provided in an embodiment of the present disclosure.
  • FIG. 5 is a first schematic diagram of a specific implementation flow of the data processing method provided in an embodiment of the present disclosure.
  • FIG. 6 is a second schematic diagram of a specific implementation flow of the data processing method provided in an embodiment of the present disclosure.
  • FIG. 7 is a first schematic diagram of the structure of a data processing device provided in an embodiment of the present disclosure.
  • FIG. 8 is a second schematic diagram of the composition structure of the data processing device provided in an embodiment of the present disclosure.
  • FIG. 9 is a schematic diagram of the composition structure of the device provided in an embodiment of the present disclosure.
  • FIG. 10 is a schematic diagram of the composition structure of the terminal according to an embodiment of the present disclosure.
  • the existing network currently obtains the network deployment situation through the terminal "crowd test" (many terminals participate in the test), that is, the terminal reports the received base station signal strength, such as the reference signal received power (RSRP, Reference Signal Receiver Power) and its corresponding location information to the network.
  • the network analyzes the large amount of "crowd test" data obtained and provided by the terminal, and regards the area with weak RSRP as a weak coverage area, and may need to optimize the network for the weak coverage area.
  • the path loss is calculated through the cell reference signal (CRS, Cell Reference Signal) sent by the base station and the base station downlink reference signal strength (such as RSRP) measured by the terminal, and then combined with the power headroom report (PHR, Power Headroom) reported by the terminal to determine whether there is weak coverage in the existing network.
  • CRS Cell Reference Signal
  • RSRP base station downlink reference signal strength
  • PHR Power Headroom report
  • the base station signal strength (such as RSRP) received by the terminal is not only related to the actual network coverage level of the existing network, but also closely related to the terminal receiving antenna efficiency and the terminal usage scenario (whether there are shielding materials around, etc.).
  • the base station signal strength (such as RSRP) measured by the terminal is generally mainly related to the terminal's receiving antenna efficiency.
  • the terminal UE1 measures the RSRP signal strength of base station A as RSRP1; when the terminal UE1 does not change its position (the network coverage of the terminal UE1 does not change), because the user's two-handed grip covers the working frequency f1, the antenna efficiency of the terminal at the working frequency f1 is reduced to Y% (Y ⁇ X), then the RSRP signal strength of base station A measured by the terminal UE1 at this time is RSRP2 ⁇ RSRP1. Therefore, the same terminal is at the same position in the cell, but the received base station RSRP signal strength is different.
  • the antenna mismatch of the terminal will lead to a decrease in antenna efficiency, and there are many reasons for the mismatch of the terminal antenna. For example, if there is an impedance mismatch between the antenna and the RF front-end chip, the terminal will suffer at least 1dB of signal strength loss; in different holding postures, the user's hand-held position may cover the terminal antenna, causing the area around the terminal antenna to become a non-free space, affecting signal transmission, which may lead to a signal strength loss of about 3dB. If the "public test" data reported by the terminal shows that the RSRP strength value of a certain cell or a certain cell is low, it may be decided that the network needs to be optimized for the cell or the cell.
  • the network side will make a negative evaluation of the existing network coverage level that is lower than the actual coverage level, which may lead to the operator making a decision to "need to optimize the network” to increase the number of base station deployments. It can be seen from this that the low signal strength of the base station received by the terminal due to the mismatch of the terminal's antenna will likely cause the operator to pay more unnecessary network planning and optimization (i.e. network planning and network optimization) costs.
  • the relevant technology provides an antenna tuning module, which can improve the signal strength loss caused by antenna mismatch to a certain extent.
  • the parameter control bit of the antenna tuning module in the related art is generally 2 to 3 bits.
  • open-loop control close-loop system without feedback
  • tuning is performed according to frequency band changes (different tuning parameters are selected according to the frequency band of the terminal).
  • the antenna tuning module can provide 4 to 8 types of antenna impedance matching networks.
  • the related art is limited by the inability to optimize according to actual scenarios and the limited accuracy of the tuning matching network, resulting in a very limited tuning effect. It can be seen that there is no method in the prior art that can completely eliminate terminal antenna mismatch, and thus it is impossible to solve the problem of terminal received signal strength loss caused by terminal antenna mismatch.
  • the disclosed embodiments can enable the network to accurately grasp the actual coverage of the existing network even when the terminal antenna mismatch cannot be eliminated, thereby avoiding unnecessary network planning and optimization cost expenditures for operators.
  • a power compensation value of a terminal in an antenna mismatch state is obtained; a downlink signal strength of a network device is obtained; and based on the power compensation value of the terminal in an antenna mismatch state, the downlink signal strength of the network device is compensated to obtain the downlink signal strength of the network device after compensation.
  • the downlink signal strength of the network device may refer to the downlink signal strength of the network device received by the terminal in an antenna mismatch state.
  • the first device includes but is not limited to a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station, the base station is not limited to a 5G wireless access network, a 6G wireless access network, a 7G wireless access network, etc.), a terminal, an OAM, etc.
  • the method includes steps 201 to 203.
  • step 201 a power compensation value of a terminal in an antenna mismatch state is obtained.
  • the antenna mismatch state may refer to energy reflection when the terminal antenna is mismatched, resulting in transmission energy loss.
  • the terminal antenna mismatch for example, if there is an impedance mismatch between the antenna and the RF front-end chip, the terminal may suffer a signal strength loss of more than 1dB; in different holding postures, the user's hand-held position may cover the terminal antenna, causing the area around the terminal antenna to become non-free space, affecting signal transmission, and causing a signal strength loss of about 3dB or more.
  • the antenna mismatch state may include one or more.
  • the downlink signal strength of the network device received by the terminal will be lost, that is, due to the problem of the terminal itself, the downlink signal strength of the network device received and perceived by the terminal is inconsistent with the actual signal strength of the network device at the location of the terminal, and there may be a large deviation.
  • a power compensation value of the terminal in the antenna mismatch state can be determined, and the power compensation value is used to compensate the downlink signal strength of the network device obtained by the terminal, so as to obtain an accurate and actual downlink signal strength of the network device at the location of the terminal.
  • the following describes a process for obtaining a power compensation value of a terminal in an antenna mismatch state.
  • the acquiring a power compensation value of the terminal in an antenna mismatch state includes:
  • a power compensation value of the terminal in an antenna mismatch state is determined based on the actual output power of the antenna of the terminal and the reflection coefficient.
  • they may include:
  • a power sensing module can be added inside the terminal, wherein the power sensing module can implement a mismatch sensing function, and through the power sensing module, the reflected power B at the antenna port can be obtained; the power sensing module can also obtain the input power A (also called incident power A) at the antenna port.
  • the reflection coefficient ⁇ of the antenna of the terminal in the antenna mismatch state is calculated:
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state
  • B represents the reflection power at the antenna port of the terminal
  • A represents the input power at the antenna port of the terminal.
  • the mismatch degree of the terminal antenna can be known through the reflection coefficient ⁇ . For example, if the reflection coefficient ⁇ is 0, it means that the terminal antenna is in a non-mismatched state. Otherwise, it means that the terminal antenna is in a mismatched state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or one of OAM, the first device can obtain the reflection coefficient corresponding to the antenna mismatch state sent by the terminal.
  • the actual output power of the antenna of the terminal may refer to the actual output power emitted by the terminal through the antenna when the terminal is in an antenna mismatch state.
  • the actual output power P UE transmitted by the terminal through the antenna when the terminal is in an antenna mismatch state can be known through the reflection coefficient ⁇ .
  • the actual output power P UE of the antenna of the terminal is calculated according to the following formula:
  • P UE represents the actual output power transmitted by the terminal through the antenna when the terminal is in the antenna mismatch state
  • B represents the reflected power at the antenna port
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or an OAM, the first device can obtain the reflection coefficient corresponding to the antenna mismatch state sent by the terminal and the reflection power at the antenna port of the terminal, and obtain the actual output power of the antenna of the terminal based on the reflection coefficient corresponding to the antenna mismatch state and the reflection power at the antenna port of the terminal.
  • the power compensation value of the terminal in the antenna mismatch state is calculated:
  • PC represents the power compensation value of the terminal in the antenna mismatch state
  • PUE represents the actual output power transmitted by the terminal through the antenna when the terminal is in the antenna mismatch state
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state.
  • the acquiring a power compensation value of the terminal in an antenna mismatch state includes:
  • a power compensation value of the terminal in an antenna mismatch state is determined based on the uplink signal strength of the terminal, the first path loss, and the reflection coefficient.
  • they may include:
  • a power sensing module can be added inside the terminal, wherein the power sensing module can implement a mismatch sensing function.
  • the reflected power B at the antenna port can be obtained.
  • the input power A also referred to as incident power A
  • the reflection coefficient ⁇ of the antenna of the terminal in the antenna mismatch state is calculated:
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state
  • B represents the reflection power at the antenna port of the terminal
  • A represents the input power at the antenna port of the terminal.
  • the mismatch degree of the terminal antenna can be known through the reflection coefficient ⁇ . For example, if the reflection coefficient ⁇ is 0, it means that the terminal antenna is in a non-mismatched state. Otherwise, it means that the terminal antenna is in a mismatched state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or one of OAM, the first device can obtain the reflection coefficient corresponding to the antenna mismatch state sent by the terminal.
  • the uplink signal strength of the terminal may refer to the uplink signal strength of the terminal received by the network device when the terminal is in an antenna mismatch state.
  • P gNB represents the uplink signal strength of the terminal received by the network device (such as a base station)
  • P UE represents the actual output power transmitted by the terminal through the antenna when the terminal is in an antenna mismatch state
  • L represents the first path loss between the terminal and the network device, in dB.
  • the actual output power P UE transmitted by the terminal through the antenna when the terminal is in the antenna mismatch state is calculated:
  • P UE represents the actual output power transmitted by the terminal through the antenna when the terminal is in an antenna mismatch state
  • B represents the reflected power at the antenna port of the terminal
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or an OAM, the first device can obtain the uplink signal strength of the terminal sent by the terminal.
  • the downlink reference signal strength sent by the network device to the terminal and the downlink reference signal strength measured by the terminal may be subtracted to obtain a first path loss between the terminal and the network device when the terminal is in an antenna mismatch state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or an OAM, the first device can obtain the first path loss between the terminal and the network device sent by the terminal.
  • a power compensation value of the terminal in an antenna mismatch state is acquired.
  • the power compensation value of the terminal in the antenna mismatch state is calculated:
  • PC represents the power compensation value of the terminal in the antenna mismatch state
  • PgNB represents the uplink signal strength of the terminal received by the network device (such as a base station)
  • L represents the first path loss between the terminal and the network device
  • ⁇ represents the reflection coefficient of the antenna of the terminal in the antenna mismatch state.
  • the acquiring a power compensation value of the terminal in an antenna mismatch state includes:
  • a value is selected from the at least one value as the power compensation value.
  • the power compensation value is not greater than the numerical value of the reflected power.
  • the terminal when the power compensation value is equal to the power value of the reflected power, the terminal will be compensated to a state where the terminal antenna is completely not mismatched (similar to the free space state). However, in actual use, it may not be necessary to compensate the terminal antenna to the free space state. Therefore, based on the power value of the reflected power, a value not greater than the power value of the reflected power can be determined as the power compensation value.
  • the power value of the reflected power is represented by B
  • the power compensation value is represented by Pc
  • step 202 the downlink signal strength of the network device is obtained.
  • the downlink signal strength of the network device may refer to the downlink signal strength of the network device received by the terminal in an antenna mismatch state.
  • the first device when the executor of the data processing method, that is, the first device, is a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), a network device (such as a base station), or one of OAM, the first device can obtain the downlink signal strength of the network device sent by the terminal.
  • step 203 based on the power compensation value of the terminal in the antenna mismatch state, the downlink signal strength of the network device is compensated to obtain the compensated downlink signal strength of the network device.
  • compensating the downlink signal strength of the network device may refer to summing the downlink signal strength value of the network device received by the terminal in the antenna mismatch state with the power compensation value of the terminal in the antenna mismatch state to obtain the compensated downlink signal strength value of the network device.
  • the power compensation value and the downlink signal strength of the network device have the same unit, which is dBm.
  • the method further comprises:
  • the area where the terminal is located is determined to be a downlink weak coverage area; when it is determined that the area where the terminal is located is a downlink weak coverage area, a network optimization operation is performed.
  • the downlink weak coverage area may refer to an area where the signal sent by the network device to the terminal is relatively weak, or may refer to an area where the signal strength transmitted by the network device is less than a certain threshold.
  • RSRPn_m+Pcm represents the downlink signal strength of the network device after compensation of terminal n
  • RSRPn_m represents the downlink signal strength of the network device obtained by terminal n in antenna mismatch state m
  • Pcm represents the power compensation value of terminal n in antenna mismatch state m. If RSRPn_m+Pcm is less than the preset signal strength threshold, it is determined that the area where the terminal is located is a downlink weak coverage area, and network optimization operations are performed.
  • the performing of the network optimization operation may refer to increasing the number of base stations in the area where the terminal is located, etc.
  • the method further comprises:
  • a network optimization operation is performed.
  • the power margin value is less than a preset power margin threshold and the second path loss is greater than a preset path loss threshold, it is determined that the area where the terminal is located is an uplink weak coverage area; when it is determined that the area where the terminal is located is an uplink weak coverage area, a network optimization operation is performed.
  • the uplink weak coverage area may refer to an area where the signal sent by the terminal to the network device is relatively weak, or may refer to an area where the signal strength transmitted by the terminal is less than a certain threshold.
  • the second path loss between the terminal and the network device may be obtained by subtracting the downlink reference signal strength sent by the network device to the terminal from the downlink signal strength of the network device after compensation.
  • the performing of the network optimization operation may refer to increasing the number of base stations in the area where the terminal is located, etc.
  • the method further comprises:
  • the first information set includes a power headroom report sent by the terminal in a p-antenna mismatch state, and time information and/or location information of the terminal in the p-antenna mismatch state;
  • the power headroom report includes a power headroom value;
  • p is an integer greater than or equal to 1;
  • m power headroom values corresponding to the m antenna mismatch states from the first information set, wherein the m power headroom values are all less than a preset power headroom threshold, m is an integer greater than or equal to 1, and m is less than or equal to p;
  • the determining whether to perform a network optimization operation based on time information and/or location information corresponding to the m power headroom values includes:
  • m weights are determined; and the m weights are compared with the time information corresponding to the m days.
  • the downlink signal strengths of the network devices after compensation obtained in the line mismatch state are multiplied, and the sum is obtained to obtain a first downlink signal strength; when the first downlink signal strength is less than a preset signal strength threshold, a network optimization operation is performed; and/or,
  • the area where the terminal is located is determined to be an uplink weak coverage area; when it is determined that the area where the terminal is located is an uplink weak coverage area, a network optimization operation is performed.
  • the area where the terminal is located is determined to be an uplink weak coverage area; when it is determined that the area where the terminal is located is an uplink weak coverage area, a network optimization operation is performed.
  • the time information may refer to the duration of the terminal being in the antenna mismatch state, or may refer to the moment when the terminal is in the antenna mismatch state.
  • T is used to represent the time information. If T is equal to 3 minutes, it means that the duration of the terminal being in the antenna mismatch state is 3 minutes. If T is equal to 8 o'clock, it means that the moment when the terminal is in the antenna mismatch state is 8 o'clock.
  • the location information may refer to geographical location information of the terminal when it is in an antenna mismatch state.
  • P represents the location information, and assuming that P is cell A in city A, it means that the geographical location of the terminal when it is in an antenna mismatch state is cell A in city A.
  • m power margin values corresponding to m antenna mismatch states are selected from the first information set, and the m power margin values are all less than a preset power margin threshold, which are represented by PHR1, PHR2, ..., PHRm respectively.
  • Time information corresponding to the m power margin values is selected from the first information set, which are represented by T1, T2, ..., Tm respectively.
  • Position information corresponding to the m power margin values is selected from the first information set, which are represented by P1, P2, ..., Pm respectively.
  • the downlink signal strength of the network device after compensation obtained by terminal n in the m antenna mismatch state is determined, and is represented by RSRPn_1+Pc1, RSRPn_2+Pc2, ..., RSRPn_m+Pcm respectively, where Pcm represents the power compensation value of terminal n in the antenna mismatch state m.
  • the duration corresponding to each antenna mismatch state is summed to obtain the total duration, and the duration corresponding to each antenna mismatch state is ratioed to the total duration to obtain the weight corresponding to each downlink signal strength, and the greater the corresponding duration, the greater the weight of the downlink signal strength.
  • the downlink signal strength of the network device after compensation of the terminal in the three antenna mismatch states is represented by RSRP1+Pc1, RSRP2+Pc2, and RSRP3+Pc3, respectively.
  • the corresponding durations are 1 minute, 2 minutes, and 3 minutes, respectively. The larger the duration, the greater the weight of the downlink signal strength.
  • the first downlink signal strength RSRP (RSRP1+Pc1) ⁇ (1/6)+(RSRP2+Pc2) ⁇ (2/6)+(RSRP3+Pc3) ⁇ (3/6), where 6 refers to the total duration of 6 minutes, that is, 1 minute, 2 minutes, and 3 minutes are summed to obtain 6 minutes, and the weights are 1/6, 2/6, and 3/6, respectively. If the first downlink signal strength value is less than the preset signal strength threshold, the network optimization operation is performed.
  • the duration corresponding to each antenna mismatch state is counted, the duration corresponding to each antenna mismatch state is summed to obtain the total duration, the duration corresponding to each antenna mismatch state is ratioed to the total duration, and the weight corresponding to each downlink signal strength is obtained, and the greater the corresponding duration, the greater the weight of the downlink signal strength.
  • the network optimization operation is performed.
  • the downlink signal strength of the network device after compensation of the terminal in the four antenna mismatch states is represented by RSRP1+Pc1, RSRP2+Pc2, RSRP3+Pc3, and RSRP4+Pc4, respectively, and the corresponding times are 8:00, 8:01, 8:02, and 8:03, respectively.
  • the time periods corresponding to (RSRP1+Pc1) and (RSRP3+Pc3) are 8:00 to 8:01 and 8:02 to 8:03, respectively, with a total duration of 2 minutes.
  • the time period corresponding to (RSRP2+Pc2) is 8:01 to 8:02, with a total duration of 1 minute.
  • the total duration corresponding to (RSRP4+Pc4) is 0 minute.
  • the downlink signal strength of the network device after compensation of the terminal in the three antenna mismatch states is represented by RSRP1+Pc1, RSRP2+Pc2, and RSRP3+Pc3, respectively.
  • the corresponding geographical locations are cell 1, cell 1, and Cell 2, if the RSRP1+Pc1 corresponding to cell 1 is less than the preset signal strength threshold, it is necessary to perform network optimization operations; if the RSRP2+Pc2 corresponding to cell 1 is less than the preset signal strength threshold, it is necessary to perform network optimization operations; if the RSRP3+Pc3 corresponding to cell 2 is less than the preset signal strength threshold, it is necessary to perform network optimization operations.
  • the time for performing the network optimization operation can also be indicated. For example, assuming that the indicated time for performing the network optimization operation is 8 o'clock in the evening, the network optimization operation will be performed at 8 o'clock in the evening.
  • the geographical location range in which the network optimization operations can be performed can also be indicated. For example, assuming that it is determined through judgment that cell 1 where the terminal is located needs to perform network optimization operations, but cell 1 does not belong to the geographical location range in which the network optimization operations can be performed, then cell 1 cannot perform the network optimization operation.
  • the terminal may be in the following two situations:
  • the terminal is still in an antenna mismatch state, wherein the antenna mismatch state may mean that when the antenna is mismatched, energy reflection may occur, resulting in transmission energy loss.
  • the reflection coefficient ⁇ of the antenna is not equal to 0, and the reflection power B at the antenna port is not equal to 0.
  • the terminal is in a non-antenna mismatch state, wherein the non-antenna mismatch state may mean that when the antenna is completely matched, lossless transmission of terminal power can be achieved.
  • the reflection coefficient ⁇ of the antenna is equal to 0, and the reflection power B at the antenna port is equal to 0.
  • the downlink signal strength of the network device after compensation is used to determine whether to perform network optimization operations.
  • the downlink signal strength of the network device after compensation is still used to determine whether to perform network optimization operations.
  • the mismatch state of the antenna of the terminal is alleviated to varying degrees.
  • the obtaining of the power compensation value of the terminal in an antenna mismatch state includes: obtaining the power compensation value of the terminal in different antenna mismatch states;
  • the compensating the downlink signal strength of the network device based on the power compensation value of the terminal in the antenna mismatch state includes: compensating the downlink signal strength of the network device respectively based on the power compensation value of the terminal in different antenna mismatch states;
  • the method further comprises:
  • the "same” may mean that the downlink signal strength of the network device after compensation under different antenna mismatch states is exactly the same, or the difference in the downlink signal strength of the network device after compensation under each two antenna mismatch states may also be considered the same within a preset range.
  • the method further comprises:
  • the training data of the AI/ML model includes: the power compensation value of the terminal under different antenna mismatch states; or, the power compensation value of the terminal under different antenna mismatch states, and the time information and/or location information of the terminal under different antenna mismatch states.
  • the AI/ML model is trained by the network device, or trained by the OAM, or trained by the terminal.
  • acquiring the AI/ML model includes: sending the training data to the OAM; and receiving the AI/ML model returned by the OAM.
  • acquiring the AI/ML model includes: sending the training data to the terminal; and receiving the AI/ML model returned by the terminal.
  • the present disclosure aims to enable the network to accurately grasp the actual coverage of the existing network through AI/ML model training, even when the terminal antenna mismatch cannot be eliminated, thereby avoiding unnecessary network planning and optimization costs for operators.
  • FIG. 3 is a schematic diagram of an implementation flow of a data processing method according to an embodiment of the present disclosure, which is applied to a terminal.
  • the method includes steps 301 to 302 .
  • step 301 a power compensation value of a terminal in an antenna mismatch state is determined.
  • step 302 a power compensation value of the terminal in an antenna mismatch state is sent to a first device; wherein the power compensation value is used by the first device to compensate for the acquired downlink signal strength of the network device, so as to obtain the compensated downlink signal strength of the network device.
  • the first device includes but is not limited to a server (deployed by an operator), a third-party server (deployed by a manufacturer other than an operator), network equipment (such as a base station, the base station includes but is not limited to a 5G wireless access network, a 6G wireless access network, a 7G wireless access network, etc.), a terminal, OAM, etc.
  • a server deployed by an operator
  • a third-party server deployed by a manufacturer other than an operator
  • network equipment such as a base station, the base station includes but is not limited to a 5G wireless access network, a 6G wireless access network, a 7G wireless access network, etc.
  • a terminal OAM, etc.
  • determining a power compensation value of the terminal in an antenna mismatch state includes:
  • the reflected power of the terminal at the antenna port is obtained through the power sensing module of the terminal; at least one numerical value is determined according to the power value of the reflected power, wherein any one of the at least one numerical value is less than or equal to the power value of the reflected power; and a numerical value is selected from the at least one numerical value as the power compensation value.
  • the method further comprises:
  • the power headroom value is used by the first device to determine whether to perform a network optimization operation in combination with the downlink signal strength of the network device after the compensation.
  • the first information set includes a power headroom report sent by the terminal in a p-antenna mismatch state, and time information and/or location information of the terminal in the p-antenna mismatch state;
  • the power headroom report includes a power headroom value;
  • p is an integer greater than or equal to 1;
  • the first information set is used by the terminal to determine whether to perform a network optimization operation.
  • the time information and/or location information is used by the first device to determine whether to perform network optimization operations in combination with the downlink signal strength of the network device after compensation, or is used by the first device to train an AI/ML model in combination with the power compensation value of the terminal in an antenna mismatch state.
  • the time information may refer to the duration of the terminal being in the antenna mismatch state, or may refer to the moment when the terminal is in the antenna mismatch state.
  • T is used to represent the time information. If T is equal to 3 minutes, it means that the duration of the terminal being in the antenna mismatch state is 3 minutes. If T is equal to 8 o'clock, it means that the moment when the terminal is in the antenna mismatch state is 8 o'clock.
  • the location information may refer to the geographical location information of the terminal when it is in an antenna mismatch state.
  • the location information assuming that P is cell A in city A, indicates that the geographical location of the terminal when in the antenna mismatch state is cell A in city A.
  • the method further comprises:
  • the training data of the AI/ML model includes: the power compensation value of the terminal under different antenna mismatch states; or, the power compensation value of the terminal under different antenna mismatch states, and the time information and/or location information of the terminal under different antenna mismatch states.
  • This public proposal aims to enable the network to accurately grasp the actual coverage of the existing network through AI/ML model training, even when the terminal antenna mismatch cannot be eliminated, thereby avoiding unnecessary network planning and optimization costs for operators.
  • FIG. 4 is a schematic diagram of the system architecture of the data processing method of the embodiment of the present disclosure.
  • the parameter configuration module, the power perception module, the time perception module, and the location perception module are located inside the terminal, and the power compensation calculation module, the mismatch data collection module (Mismatch Data Collection), and the network planning and optimization decision module (Actor) are located outside the terminal.
  • the power compensation calculation module may include a model training module and a model inference module.
  • Both the model training module and the model reasoning module can be deployed in network devices, such as base stations, including but not limited to gNBs in 5G wireless access networks, base stations in 6G wireless access networks, and 7G wireless access networks; or the model reasoning module can be deployed in network devices, such as base stations, while the model training module is deployed in OAM.
  • the network planning and optimization decision module can be deployed independently or in conjunction with OAM (integrated setting).
  • the mismatch data collection module is used to provide input data (Input data) for model training and model reasoning of the AI/ML model.
  • Input data including: reflection coefficient ⁇ and/or reflection power B from the power sensing module, duration sensing information T, location sensing information P, actual output power P UE of the antenna of the terminal, uplink signal strength P gNB of the terminal received by the network device, first path loss L between the terminal and the network device, power compensation value Pc of the terminal in the antenna mismatch state, and feedback data (Feedback, such as whether to decide to add new base stations and other network devices, the location of new base stations and other network devices, etc.) from the network planning and optimization decision module, and output data (Output, such as the downlink signal strength of the network device after compensation) from the power compensation calculation module.
  • These data can be used as raw data, and there is no need to prepare data for AI/ML algorithms (data cleaning, data formatting, data conversion, etc.).
  • Position perception information P can be a relatively rough position perception information, such as the physical cell identifier (PCI, Physical Cell Identifier) information of the cell perceived by the terminal.
  • PCI physical cell identifier
  • the model training module is responsible for the training, testing, and verification of AI/ML models and generates metrics for model performance.
  • the trained, tested, and verified AI/ML models are deployed to the model inference module through model deployment or update (Model Deployment/Update).
  • the model inference module is used to provide the inference output (prediction or decision) of the AI/ML model and feedback the results to the model training module.
  • the model performance feedback of the model inference module can be sent to the model training module through model performance feedback, so that the model training module can train and optimize the AI/ML model.
  • the process of feeding back the results to the model training module is an optional process.
  • a power compensation value of the terminal in an antenna mismatch state is obtained, and then based on the power compensation value of the terminal in the antenna mismatch state, the downlink signal strength of the network device is compensated to obtain the compensated downlink signal strength of the network device.
  • the inference output may be the downlink signal strength of the network device after compensation.
  • the first method uses the downlink signal strength of the network device after compensation, such as RSRPn_m+Pcm, where RSRPn_m+Pcm represents the downlink signal strength of the network device after compensation of terminal n, m represents the antenna mismatch state, RSRPn_m represents the downlink signal strength of the network device received by terminal n in the antenna mismatch state m, and Pcm represents the power compensation value of terminal n in the antenna mismatch state m.
  • RSRPn_m+Pcm represents the downlink signal strength of the network device after compensation of terminal n
  • m represents the antenna mismatch state
  • RSRPn_m represents the downlink signal strength of the network device received by terminal n in the antenna mismatch state m
  • Pcm represents the power compensation value of terminal n in the antenna mismatch state m.
  • the second method uses the second path loss between the terminal and the network device obtained by the downlink signal strength of the network device after the compensation and the power headroom value in the power headroom report sent by the terminal, such as [PL, PHR].
  • the third method uses the downlink signal strength of the network device after compensation and the power headroom value in the power headroom report sent by the terminal, as well as the time information and/or location information of the terminal in the antenna mismatch state, such as [RSRPn_m+Pcm, PHRm, Tm].
  • RSRPn_m+Pcm represents the downlink signal strength of the network device after compensation of terminal n
  • m represents the antenna mismatch state
  • PHRm represents the power headroom value corresponding to the antenna mismatch state m
  • Tm represents the time information of the terminal in the antenna mismatch state.
  • Network planning and optimization decision module which is used to perform corresponding network optimization operations after receiving the reasoning output (prediction or decision) of the module reasoning module.
  • performing network optimization operations may refer to increasing the number of network devices (such as base stations gNB) in the area where the terminal is located, etc.
  • the downlink signal strength of the network device after compensation can be used to determine whether to perform the network optimization operation.
  • the specific implementation process has been described in the previous text and will not be repeated here.
  • the specific implementation process has been described above and will not be repeated here.
  • the base station includes but is not limited to a base station in a 5G wireless access network, i.e., the next generation wireless access network (NG-RAN, NG Radio Access Network), a 6G wireless access network, a 7G wireless access network, etc.
  • NG-RAN next generation wireless access network
  • 6G wireless access network 6G wireless access network
  • 7G wireless access network etc.
  • the base station is represented by NG-RAN node n.
  • the method includes steps 501 to 505.
  • NG-RAN node n configures the UE and requests the UE to provide mismatch data.
  • the network side configures or sets parameters required for data transmission for the UE.
  • the mismatch data may include: the reflection coefficient ⁇ or the reflection power B of the antenna of the UE in the antenna mismatch state, the duration perception information T, and the position perception information P.
  • the mismatch data may also include: the reflection coefficient ⁇ and/or the reflection power B of the antenna of the UE in the antenna mismatch state, as well as the time information T of the UE in the antenna mismatch state and/or the position information P of the UE in the antenna mismatch state.
  • a power sensing module may be added inside the UE, and the reflected power B at the antenna port may be acquired through the power sensing module, and the input power A at the antenna port may be acquired through the power sensing module.
  • the reflection coefficient ⁇ of the UE antenna in the antenna mismatch state is calculated:
  • ⁇ represents the reflection coefficient of the antenna of the UE in the antenna mismatch state
  • B represents the reflected power at the antenna port
  • A represents the input power at the antenna port of the UE.
  • the mismatch degree of the UE antenna can be known through the reflection coefficient ⁇ . For example, if the reflection coefficient ⁇ is 0, it means that the terminal antenna is in a non-mismatched state, otherwise, it means that the terminal antenna is in a mismatched state.
  • the terminal can sense the "reflected power" through the power sensing module. When the reflected power is not 0, it is in the antenna mismatch state; or when the "reflection coefficient" obtained by further combining the reflected power and the incident power is not 0, it can also be judged to be in the antenna mismatch state.
  • a certain threshold range can be set according to actual conditions. When the reflected power exceeds a certain threshold or the reflection coefficient is greater than a certain threshold, it can be considered to be in an "antenna mismatch state".
  • step 503 the UE reports the mismatch data to the NG-RAN node n.
  • mismatch data can be used as input data for the model training module of the AI/ML model.
  • mismatch data can also be used as input data for the model reasoning module of the AI/ML model.
  • the AI/ML model is trained based on the mismatch data provided by the terminal, and the AI/ML model generates prediction information (Output) based on the accumulated information in the past, that is, the output of the downlink signal strength of the compensated network device that should be used at this time, such as RSRPn_m+Pcm, where RSRPn_m+Pcm represents the downlink signal strength of the compensated network device of terminal n, and m represents the antenna mismatch state.
  • n represents the identifier of different terminals
  • m represents the different mismatch states of the terminal, which can be used to represent the data identifier of different downlink signal strengths of the same base station received by the same terminal.
  • the terminal UE1 When training the model, it is assumed that the terminal UE1 is in a certain mismatch state S1. At this time, the downlink signal strength of base station A received by the terminal UE1 is RSRP1_1. At this time, the mismatch state of the terminal UE1 can be characterized by the reflected power B1 or the reflection coefficient ⁇ 1. Assuming that UE1 can be corrected to the ideal non-mismatched state S0, the downlink signal strength of base station A received by the terminal UE1 should be RSRP1_1+Pc1, where Pc1 is the power compensation value of the terminal in the antenna mismatch state S1.
  • the downlink signal strength of base station A received by the terminal UE1 is RSRP1_2.
  • the mismatch state of the terminal UE1 can be characterized by the reflected power B2 or the reflection coefficient ⁇ 2.
  • the downlink signal strength of base station A received by the terminal UE1 should be RSRP1_2+ Pc2, Pc2 is the power compensation value of the terminal in the antenna mismatch state S2.
  • the downlink signal strength RSRP1_1+Pc1 of base station A received by terminal UE1 after correction should be approximately equal to RSRP1_2+Pc2. It can be inferred that the network coverage situation of UE1 does not change significantly when it is in the above-mentioned mismatch states S1 and S2.
  • NG-RAN node n performs model reasoning based on the AI/ML model according to the mismatch data, and generates reasoning output and sends it to the network planning and optimization decision module.
  • the inference output may be the downlink signal strength of the network device after compensation.
  • the inference output can take the following forms:
  • the second method uses the second path loss between the terminal and the network device obtained by the downlink signal strength of the network device after the compensation and the power headroom value in the power headroom report sent by the terminal, such as [PL, PHR].
  • the third method uses the downlink signal strength of the network device after compensation and the power headroom value in the power headroom report sent by the terminal, as well as the time information and/or location information of the terminal in the antenna mismatch state, such as [RSRPn_m+Pcm, PHRm, Tm].
  • RSRPn_m+Pcm represents the downlink signal strength of the network device after compensation of terminal n
  • m represents the antenna mismatch state
  • PHRm represents the power headroom value corresponding to the antenna mismatch state m
  • Tm represents the time information of the terminal in the antenna mismatch state.
  • step 505 the network planning and optimization decision module determines whether to perform a network optimization operation.
  • performing network optimization operations may refer to increasing the number of network devices such as base stations gNB in the area where the terminal is located.
  • the downlink signal strength of the network device after compensation can be used to determine whether to perform the network optimization operation.
  • the specific implementation process has been described in the previous text and will not be repeated here.
  • the specific implementation process has been described above and will not be repeated here.
  • the network planning and optimization decision module can also consider making network planning and optimization decisions for the downlink signal strength that is likely to appear in the existing network, so as to ensure the speed experience of terminal users in the existing network.
  • RSRP downlink signal strength
  • AI/ML model training is introduced to help the network accurately grasp the actual coverage of the existing network, avoid unnecessary network planning and optimization costs for operators, and/or help operators make network planning and optimization decisions that help improve the speed experience of terminal users in the existing network.
  • AI/ML model training is performed through NG-RAN node n to help the network accurately grasp the actual coverage of the existing network, avoid unnecessary network planning and optimization costs for operators, and/or help operators make network planning and optimization decisions that help improve the speed experience of terminal users in the existing network.
  • the base station includes but is not limited to a base station in a 5G wireless access network, i.e., NG-RAN, a 6G wireless access network, a 7G wireless access network, etc.
  • NG-RAN 5G wireless access network
  • 6G wireless access network 6G wireless access network
  • 7G wireless access network etc.
  • the base station is represented by NG-RAN node n.
  • the method includes steps 601 to 605.
  • NG-RAN node n configures the UE and requests the UE to provide mismatch data.
  • step 603 the UE reports the collected mismatch data to the NG-RAN node n.
  • step 604 NG-RAN node n sends the mismatch data reported by the UE to OAM.
  • step 605 the OAM performs model training based on the AI/ML model according to the mismatch data.
  • OAM deploys/updates the AI/ML model to NG-RAN node n.
  • step 607 the UE reports mismatch data for model inference to the NG-RAN node n.
  • NG-RAN node n performs model inference based on the reported mismatch data and generates an inference model.
  • the processed output is sent to the network planning and optimization decision module.
  • NG-RAN node n can also send model performance feedback to OAM.
  • step 609 the network planning and optimization decision module determines whether to perform a network optimization operation.
  • performing network optimization operations may refer to increasing the number of network devices (such as base stations gNB) in the area where the terminal is located, etc.
  • the downlink signal strength of the network device after compensation can be used to determine whether to perform the network optimization operation.
  • the specific implementation process has been described in the previous text and will not be repeated here.
  • the specific implementation process has been described above and will not be repeated here.
  • AI/ML model training is introduced to help the network accurately grasp the actual coverage of the existing network, avoid unnecessary network planning and optimization costs for operators, and/or help operators make network planning and optimization decisions that help improve the speed experience of terminal users in the existing network.
  • AI/ML model training through OAM helps the network to accurately grasp the actual coverage of the existing network, avoid unnecessary network planning and optimization costs for operators, and/or help operators make network planning and optimization decisions that help improve the speed experience of terminal users in the existing network.
  • the embodiment of the present disclosure further provides a data processing device, which is arranged in the first device (including but not limited to, included in the first device, or integrated in the first device).
  • FIG. 7 is a schematic diagram of the composition structure of the data processing device of the embodiment of the present disclosure. As shown in FIG. 7, the device includes:
  • the acquisition module 71 is used to obtain the power compensation value of the terminal in the antenna mismatch state; obtain the downlink signal strength of the network device;
  • the first processing module 72 is configured to compensate the downlink signal strength of the network device based on the power compensation value of the terminal in the antenna mismatch state to obtain the compensated downlink signal strength of the network device.
  • the acquisition module 71 is used to:
  • a power compensation value of the terminal in an antenna mismatch state is determined based on the actual output power of the antenna of the terminal and the reflection coefficient.
  • the acquisition module 71 is used to:
  • a power compensation value of the terminal in an antenna mismatch state is determined based on the uplink signal strength of the terminal, the first path loss, and the reflection coefficient.
  • the acquisition module 71 is used to:
  • a value is selected from the at least one value as a power compensation value of the terminal in an antenna mismatch state.
  • the device is further used to:
  • the device is further used to:
  • the power headroom report includes a power headroom value
  • a network optimization operation is performed.
  • the device is further used to:
  • the first information set includes a power headroom report sent by the terminal in a p-antenna mismatch state, and time information and/or location information of the terminal in the p-antenna mismatch state;
  • the power headroom report includes a power headroom value;
  • p is an integer greater than or equal to 1;
  • m power headroom values corresponding to the m antenna mismatch states from the first information set; wherein the m power headroom values are all less than a preset power headroom threshold, m is an integer greater than or equal to 1, and m is less than or equal to p;
  • the device is further used to:
  • m weights are determined; after multiplying the m weights with the downlink signal strength of the network device obtained after compensation under the corresponding m antenna mismatch states, the first downlink signal strength is obtained by summing them; when the first downlink signal strength is less than a preset signal strength threshold, a network optimization operation is performed; and/or,
  • the acquisition module 71 is further used to acquire a power compensation value of the terminal under different antenna mismatch states
  • the first processing module 72 is also used to compensate the downlink signal strength of the network device based on the power compensation value of the terminal in different antenna mismatch states; compare the downlink signal strength of the network device after compensation under different antenna mismatch states to obtain a comparison result; when the comparison result indicates that the downlink signal strength of the network device after compensation under different antenna mismatch states is the same, it is determined that the network coverage of the area where the terminal is located has not changed.
  • the device is further used to:
  • the training data of the AI/ML model includes: the power compensation value of the terminal under different antenna mismatch states; or, the power compensation value of the terminal under different antenna mismatch states, and the time information and/or location information of the terminal under different antenna mismatch states.
  • the AI/ML model is trained by the network device, or trained by the OAM, or trained by the terminal.
  • the acquisition unit 71 can be implemented by a communication interface in a data processing device; the first processing unit 72 can be implemented by a processor in the data processing device.
  • the data processing device provided in the above embodiment performs data processing
  • only the division of the above program modules is used as an example.
  • the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above.
  • the data processing device provided in the above embodiment and the data processing method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.
  • the embodiment of the present disclosure also provides a data processing device, which is arranged in a terminal (including but not limited to, included in the terminal, or integrated in the terminal).
  • a data processing device which is arranged in a terminal (including but not limited to, included in the terminal, or integrated in the terminal).
  • FIG8 is a schematic diagram of the composition structure of the data processing device of the embodiment of the present disclosure. As shown in FIG8, the device includes:
  • a second processing module 81 configured to determine a power compensation value of the terminal in an antenna mismatch state
  • the sending module 82 is used to send the power compensation value of the terminal in the antenna mismatch state to the first device, wherein the power compensation value is used by the first device to compensate the acquired downlink signal strength of the network device to obtain the compensated downlink signal strength of the network device.
  • the second processing module 81 is used to:
  • the reflected power of the terminal at the antenna port is obtained through the power sensing module of the terminal; at least one numerical value is determined according to the power value of the reflected power, wherein any one of the at least one numerical value is less than or equal to the power value of the reflected power; and a numerical value is selected from the at least one numerical value as the power compensation value.
  • the device is further used to:
  • the power headroom value is used by the first device to determine whether to perform a network optimization operation in combination with the downlink signal strength of the network device after the compensation.
  • the device is further used for:
  • the time information and/or location information is used by the first device to determine whether to perform network optimization operations in combination with the downlink signal strength of the compensated network device, or is used by the first device to train an AI/ML model in combination with the power compensation value of the terminal in an antenna mismatch state.
  • the sending unit 82 can be implemented by a communication interface in a data processing device; the second processing unit 81 can be implemented by a processor in the data processing device.
  • the data processing device provided in the above embodiment performs data processing
  • only the division of the above program modules is used as an example.
  • the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above.
  • the data processing device provided in the above embodiment and the data processing method embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.
  • the present disclosure embodiment further provides a device (such as the first device in the above embodiment), as shown in FIG9 , including:
  • a first communication interface 91 capable of exchanging information with other devices
  • the first processor 92 is connected to the first communication interface 91 and is used to execute the method provided by one or more technical solutions of the device side when running a computer program.
  • the computer program is stored in the first memory 93.
  • bus system 94 is used to realize the connection and communication between these components.
  • bus system 94 also includes a power bus, a control bus and a status signal bus.
  • various buses are labeled as bus system 94 in FIG. 9.
  • the first memory 93 in the embodiment of the present disclosure is used to store various types of data to support the operation of the device 90. Examples of such data include: any computer program used to operate on the device 90.
  • the method disclosed in the above embodiment of the present disclosure can be applied to the first processor 92, or implemented by the first processor 92.
  • the first processor 92 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit in the first processor 92 or the instruction in the form of software.
  • the above first processor 92 may be a general processor, a digital data processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the first processor 92 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present disclosure.
  • the general processor may be a microprocessor or any conventional processor, etc.
  • the steps of the method disclosed in the embodiment of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in a storage medium, which is located in the first memory 93.
  • the first processor 92 reads the information in the first memory 93 and completes the steps of the above method in combination with its hardware.
  • the present disclosure also provides a terminal, as shown in FIG10 , including:
  • the second communication interface 101 is capable of exchanging information with other devices
  • the second processor 102 is connected to the second communication interface 101 and is used to execute the method provided by one or more technical solutions on the terminal side when running a computer program.
  • the computer program is stored in the second memory 103.
  • bus system 104 is used to realize the connection and communication between these components.
  • bus system 104 also includes a power bus, a control bus and a status signal bus.
  • various buses are marked as the bus system 104 in Figure 10.
  • the second memory 103 in the embodiment of the present disclosure is used to store various types of data to support the operation of the terminal 100. Examples of such data include: any computer program used to operate on the terminal 100.
  • the method disclosed in the above embodiment of the present disclosure can be applied to the second processor 102, or implemented by the second processor 102.
  • the second processor 102 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the second processor 102.
  • the above second processor 102 may be a general processor, a digital data processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.
  • the second processor 102 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present disclosure.
  • the general processor may be a microprocessor or any conventional processor, etc.
  • the steps of the method disclosed in the embodiment of the present disclosure can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute.
  • the software module can be located in a storage medium, which is located in the second memory 103.
  • the second processor 102 reads the information in the second memory 103 and completes the steps of the above method in combination with its hardware.
  • the device 90 and the terminal 100 can be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), field programmable gate array (FPGA), general processor, controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.
  • ASIC application specific integrated circuits
  • DSP digital signal processor
  • PLD programmable logic device
  • CPLD complex programmable logic device
  • FPGA field programmable gate array
  • general processor controller, microcontroller (MCU), microprocessor, or other electronic components to execute the aforementioned method.
  • MCU microcontroller
  • the memory (first memory 93, second memory 103) of the embodiment of the present disclosure can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories.
  • the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM);
  • the magnetic surface memory can be a disk memory or a tape memory.
  • the volatile memory can be a random access memory (RAM), which is used as an external cache.
  • RAM random access memory
  • many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM).
  • SRAM static random access memory
  • SSRAM synchronous static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDRSDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • DRRAM direct memory bus random access memory
  • the embodiment of the present disclosure further provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, for example, a memory storing a computer program, and the above-mentioned computer program can be executed by the first processor 92 of the device 90 to complete the steps described in the aforementioned device-side method.
  • the computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disk, or CD-ROM.
  • the storage medium can be a non-temporary computer-readable storage medium.

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Abstract

本申请公开了一种数据处理方法、装置、设备及存储介质。所述方法包括：获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。

Description

数据处理方法、装置、设备及存储介质
相关申请的交叉引用
本公开基于申请号为202310832172.4、申请日为2023年07月06日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本公开作为参考。
技术领域
本公开涉及无线通信技术领域，尤其涉及一种数据处理方法、装置、设备及存储介质。
背景技术
目前，现网通过众多终端参与测试的方式来获取网络部署情况，即，终端将接收到的基站信号强度，如参考信号接收功率(RSRP，Reference Signal Receiver Power)及其对应的位置信息，一同上报给网络，网络根据获取到的、终端提供的大量“众测”数据进行分析，将RSRP较弱的区域视为弱覆盖区，并可能针对该弱覆盖区进行网络优化。但是，在某些情况下，终端测量得到的基站信号强度(如RSRP)可能不准确，从而会导致是否需要进行网络优化的判断决策出现偏差。
发明内容
有鉴于此，本公开实施例期望提供一种数据处理方法、装置、设备及存储介质。
本公开的第一方面提供一种数据处理方法，应用于第一设备，所述方法包括：
获取终端在天线失配状态下的功率补偿值；
获取网络设备的下行信号强度；和
基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
此外，根据本公开的至少一个实施例，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述天线失配状态对应的反射系数；
获取所述终端的天线的实际输出功率；和
基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
此外，根据本公开的至少一个实施例，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述天线失配状态对应的反射系数；
获取所述终端的上行信号强度；
获取所述终端到所述网络设备之间的第一路损；和
基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
此外，根据本公开的至少一个实施例，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述终端在天线端口处的反射功率；
根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；和
从所述至少一个数值中选取一个数值作为所述终端在天线失配状态下的功率补偿值。
此外，根据本公开的至少一个实施例，所述方法还包括：
在所述补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为下行弱覆盖区域；和
当确定所述终端所在区域为下行弱覆盖区域时，执行网络优化操作。
此外，根据本公开的至少一个实施例，所述方法还包括：
获取所述终端发送的功率余量报告，其中所述功率余量报告包括功率余量值；
通过所述补偿后的所述网络设备的下行信号强度，获得所述终端到所述网络设备之间的第二路损；
在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；和
当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
此外，根据本公开的至少一个实施例，所述方法还包括：
获取第一信息集合，其中所述第一信息集合包括所述终端在p个天线失配状态下发送的功率余量报告，以及所述终端在所述p个天线失配状态下的时间信息和/或位置信息；所述功率余量报告包括功率余量值；p为大于或等于1的整数；
从所述第一信息集合中选取与m个天线失配状态对应的m个功率余量值，其中，m个功率余量值均小于预设功率余量阈值，m为大于或等于1的整数，m小于或等于p；
从所述第一信息集合中选取与所述m个功率余量值对应的时间信息和/或位置信息；和
基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作。
此外，根据本公开的至少一个实施例，所述基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作，包括以下中的至少一个：
基于与所述m个功率余量值对应的时间信息，确定m个权值；将所述m个权值与对应所述m个天线失配状态下得到的补偿后的所述网络设备的下行信号强度求乘积后，求和得到第一下行信号强度；在所述第一下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作；和
针对与所述m个功率余量值对应的位置信息中每个位置信息，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
此外，根据本公开的至少一个实施例，所述获取终端在天线失配状态下的功率补偿值，包括：获取所述终端在不同天线失配状态下的功率补偿值；
所述基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，包括：基于所述终端在不同天线失配状态下的功率补偿值，分别对所述网络设备的下行信号强度进行补偿；
所述方法还包括：
将在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度进行比较，得到比较结果；
当所述比较结果表征在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度相同时，确定所述终端所在区域的网络覆盖情况未发生变化。
此外，根据本公开的至少一个实施例，所述方法还包括：
获取人工智能/机器学习(AI/ML)模型，其中，所述AI/ML模型用于执行前述所述数据处理方法；
其中，所述AI/ML模型的训练数据包括：所述终端在不同天线失配状态下的功率补偿值；或者，所述终端在不同天线失配状态下的功率补偿值，以及所述终端在不同天线失配状态下的时间信息和/或位置信息。
此外，根据本公开的至少一个实施例，所述AI/ML模型由所述网络设备训练，或者由操作维护管理(OAM，Operation Administration Maintenance)训练，或者由所述终端训练。
本公开实施例提供一种数据处理方法，应用于终端，所述方法包括：
确定所述终端在天线失配状态下的功率补偿值；和
向第一设备发送所述终端在天线失配状态下的功率补偿值；
其中，所述功率补偿值用于所述第一设备对获取的网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
此外，根据本公开的至少一个实施例，所述确定所述终端在天线失配状态下的功率补偿值，包括：
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率，并获取所述终端在天线端口处的输入功率；
基于所述反射功率以及所述输入功率，获取所述终端在天线失配状态下的反射系数；
基于所述反射系数，执行以下操作之一：
获取所述终端的天线的实际输出功率；基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；或
获取所述终端的上行信号强度，获取所述终端到所述网络设备之间的第一路损；基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；
或者，
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率；根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；从所述至少一个数值中选取一个数值作为所述功率补偿值。
此外，根据本公开的至少一个实施例，所述方法还包括：
向所述第一设备发送功率余量报告，其中所述功率余量报告包括功率余量值；
其中，所述功率余量值用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。
此外，根据本公开的至少一个实施例，所述方法还包括：
获取所述终端在不同天线失配状态下的时间信息和/或位置信息；和
将所述时间信息和/或所述位置信息发送给所述第一设备；
其中，所述时间信息和/或位置信息用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度判断是否执行网络优化操作，或者用于所述第一设备结合所述终端在天线失配状态下的功率补偿值训练AI/ML模型。
本公开实施例提供一种数据处理装置，包括：
获取模块，用于获取终端在天线失配状态下的功率补偿值；和获取网络设备的下行信号强度；和
第一处理模块，用于基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，以得到补偿后的所述网络设备的下行信号强度。
本公开实施例提供一种数据处理装置，包括：
第二处理模块，用于确定所述终端在天线失配状态下的功率补偿值；和
发送模块，用于向第一设备发送所述终端在天线失配状态下的功率补偿值。
本公开的至少一个实施例提供一种设备，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器，其中，所述处理器用于运行所述计算机程序时，执行上述设备侧任一项所述方法的步骤。
本公开的至少一个实施例提供一种终端，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器，其中，所述处理器用于运行所述计算机程序时，执行上述终端侧任一项所述方法的步骤。
本公开的至少一个实施例提供一种存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述任一方法的步骤。
在本公开实施例提供的数据处理方法、装置、设备及存储介质中，获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。采用本公开实施例提供的技术方案，在终端处于天线失配状态的情况下，通过对所述网络设备的下行信号强度进行补偿，从而能够真实评估出终端所在位置处的网络设备的下行信号强度，后续可以为是否需要执行网络优化操作的判断决策提供便利。
附图说明
图1是相关技术中通过天线调谐模块改善终端功率损失的示意图。
图2是本公开实施例提供的数据处理方法的实现流程示意图一。
图3是本公开实施例提供的数据处理方法的实现流程示意图二。
图4是本公开实施例提供的数据处理方法应用的系统架构示意图。
图5是本公开实施例提供的数据处理方法的具体实现流程示意图一。
图6是本公开实施例提供的数据处理方法的具体实现流程示意图二。
图7是本公开实施例提供的数据处理装置的组成结构示意图一。
图8是本公开实施例提供的数据处理装置的组成结构示意图二。
图9是本公开实施例提供的设备的组成结构示意图。
图10是本公开实施例终端的组成结构示意图。
具体实施方式
在对本公开实施例的技术方案进行介绍之前，先对相关技术进行介绍。
相关技术中，现网目前通过终端“众测”(众多终端参与测试)的方式来获取网络部署情况，即，终端将接收到的基站信号强度，如参考信号接收功率(RSRP，Reference Signal Receiver Power)及其对应的位置信息，一同上报给网络，网络根据获取到的、终端提供的大量“众测”数据进行分析，将RSRP较弱的区域视为弱覆盖区，并可能需要针对该弱覆盖区进行网络优化。或者，通过基站下发的小区参考信号(CRS，Cell Reference Signal)、终端测量得到的基站下行参考信号强度(如RSRP)，计算出路径损耗(PL，Path Loss)，再结合终端上报的功率余量报告(PHR，Power Headroom)来判断现网是否存在弱覆盖的情况。具体地，若PL较小(例如：PL<100dB)，且PHR<0(即终端无法再继续抬升上行功率)，那么倾向于判断现网存在弱覆盖情况，需要进行增加站址等网络优化。
但是，如果终端测量得到的RSRP不准确，则将导致计算的PL不准确，进而导致是否需要进行网络优化的判断决策会出现偏差。终端接收到的基站信号强度(如RSRP)不仅与现网实际网络覆盖水平相关，还与终端接收天线效率以及终端使用场景(周围是否有屏蔽性材料等)息息相关。在终端使用场景一致的情况下，终端测量到的基站信号强度(如RSRP)一般主要与终端的接收天线效率强相关。例如：某终端UE1在工作频率f1的天线效率为X％时，该终端UE1测量基站A的RSRP信号强度为RSRP1；在终端UE1未改变位置的情况下(终端UE1所处的网络覆盖情况未改变)，由于用户双手握姿覆盖了工作频率f1，此时该终端在工作频率f1的天线效率降为Y％(Y<X)，则终端UE1此时测量到的基站A的RSRP信号强度RSRP2<RSRP1。因此，出现了同一终端处在小区的同一位置，接收到的基站RSRP信号强度却不同的情况。
一般地，终端的天线失配将导致天线效率下降，而导致终端天线失配的原因较多，例如：若天线与射频前端芯片间存在阻抗失配，那么将给终端带来至少1dB的信号强度损失；在不同的持握姿势下，可能会由于用户手持位置覆盖了终端天线，导致终端天线周围变为非自由空间，影响信号传输，可导致约3dB的信号强度损失。若终端上报的“众测”数据显示某小区或某片小区的RSRP强度值较低，可能得出需要针对该小区或该片小区进行网络优化的决策。因此，若现网中出现大量天线失配的终端，会导致网络侧对现网覆盖水平做出低于真实覆盖水平的偏负面性评价，进而可能导致运营商做出“需要进行网络优化”增加基站部署数量的决策。由此可见，终端的天线失配导致终端接收到的基站信号强度偏低，将很可能导致运营商付出更多不必要的网规网优(即网络规划网络优化)成本。
因此，准确掌握终端天线的失配情况，将有助于网络准确掌握现网的真实覆盖情况，避免运营商不必要的网规网优成本支出。为了改善天线失配带来的信号强度损失问题，相关技术中提供了天线调谐模块，可以在一定程度上改善天线失配带来的信号强度损失。
参见图1，图1是相关技术中通过天线调谐模块改善终端功率损失的示意图。如图1所示，相关技术中的天线调谐模块的参数控制位一般为2～3bit，通过预置参数，进行开环控制(无反馈闭环系统)，根据频段变化而调谐(根据终端工作的频段不同，选择不同的调谐参数)，天线调谐模块可提供4～8种天线阻抗匹配网络。但是，相关技术受限于无法根据实际场景进行优化、且调谐匹配网络精度有限，导致调谐效果很有限。可见，现有技术中尚没有能完全消除终端天线失配的方法，进而也无法解决由于终端天线失配带来的终端接收信号强度损失的问题。
本公开实施例可以实现在无法消除终端天线失配的情况下，仍旧能够使网络准确掌握现网的真实覆盖情况，避免运营商不必要的网规网优成本支出。
基于此，在本公开实施例中，获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
在本公开实施例中，所述网络设备的下行信号强度可以是指所述终端在天线失配状态下接收到的网络设备的下行信号强度。
参见图2，图2是本公开实施例提供的数据处理方法的实现流程示意图，应用于第一设备。所述第一设备包括但不限于为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站，所述基站包括但不限于5G无线接入网、6G无线接入网、7G无线接入网等)、终端、OAM等。所述方法包括步骤201至步骤203。
在步骤201，获取终端在天线失配状态下的功率补偿值。
作为示例，所述天线失配状态，可以是指当终端天线失配时，会出现能量反射，导致传输能量损失。其中，导致终端天线失配的原因较多，例如：若天线与射频前端芯片间存在阻抗失配，那么可给终端带来1dB以上的信号强度损失；在不同的持握姿势下，可能会由于用户手持位置覆盖了终端天线，导致终端天线周围变为非自由空间，影响信号传输，可导致约3dB以上的信号强度损失。
作为示例，所述天线失配状态可以包括一个或多个，例如，导致信号强度损失的握持姿势有多种，其中，一种持握姿势对应一个天线失配状态。
考虑到终端处于天线失配状态时会导致终端接收到的网络设备的下行信号强度出现损失，也就是说，由于终端自身的问题导致终端接收并感知到的网络设备的下行信号强度与终端所在位置处的网络设备的真实信号强度不一致，且可能出现较大偏差。本公开实施例，在终端处于天线失配状态时，为了得到准确的、真实的终端所在位置处的网络设备下行信号强度，可以确定终端在天线失配状态下的功率补偿值，利用所述功率补偿值，对终端获取的网络设备的下行信号强度进行补偿，以得到准确的、真实的终端所在位置处的网络设备的下行信号强度。
作为示例，所述终端在天线失配状态下的功率补偿值可以是指针对所述终端在天线失配状态下的下行信号接收功率进行补偿的功率补偿值。
下面对获取终端在天线失配状态下的功率补偿值的过程进行说明。
在一些实施例中，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述天线失配状态对应的反射系数；
获取所述终端的天线的实际输出功率；
基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
具体可以包括：
第一，获取所述天线失配状态对应的反射系数。
作为示例，可以在所述终端内部增加功率感知模块，其中，该功率感知模块可以实现失配感知功能，通过所述功率感知模块，获取天线端口处的反射功率B；该功率感知模块也可以获取天线端口处的输入功率A(也可称为入射功率A)。
按照下面公式，计算所述终端在天线失配状态下的天线的反射系数Γ：
其中，Γ表示所述终端在天线失配状态下的天线的反射系数，B表示所述终端的天线端口处的反射功率，A表示所述终端的天线端口处的输入功率。
这里，通过反射系数Γ可获知终端天线的失配程度，例如，反射系数Γ为0，表示终端天线处于非失配状态，否则，表示终端天线处于失配状态。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述天线失配状态对应的反射系数。
第二，获取所述终端的天线的实际输出功率。
作为示例，所述终端的天线的实际输出功率可以是指在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率。
通过反射系数Γ，可获知在所述终端处于天线失配状态时所述终端通过天线发射的实际输出功率PUE。
按照下面公式，计算所述终端的天线的实际输出功率PUE：
其中，PUE表示在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率，B表示天线端口处的反射功率，Γ表示所述终端在天线失配状态下的天线的反射系数。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述天线失配状态对应的反射系数以及所述终端的天线端口处的反射功率，基于所述天线失配状态对应的反射系数以及所述终端的天线端口处的反射功率，获得所述终端的天线的实际输出功率。
第三，基于所述终端的天线的实际输出功率以及所述反射系数，获取所述终端在天线失配状态下的功率补偿值。
按照下面公式，计算所述终端在天线失配状态下的功率补偿值：
其中，PC表示所述终端在天线失配状态下的功率补偿值，PUE表示在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率，Γ表示所述终端在天线失配状态下的天线的反射系数。
在一些实施例中，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述天线失配状态对应的反射系数；
获取所述终端的上行信号强度；
获取所述终端到所述网络设备之间的第一路损；
基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
具体可以包括：
第一，获取所述天线失配状态对应的反射系数。
作为示例，可以在所述终端内部增加功率感知模块，其中，该功率感知模块可以实现失配感知功能，通过所述功率感知模块，获取天线端口处的反射功率B，通过所述功率感知模块，也可以获取天线端口处的输入功率A(也可称为入射功率A)。
按照下面公式，计算所述终端在天线失配状态下的天线的反射系数Γ：
其中，Γ表示所述终端在天线失配状态下的天线的反射系数，B表示所述终端的天线端口处的反射功率，A表示所述终端的天线端口处的输入功率。
这里，通过反射系数Γ可获知终端天线的失配程度，例如，反射系数Γ为0，表示所述终端天线处于非失配状态，否则，表示所述终端天线处于失配状态。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述天线失配状态对应的反射系数。
第二，获取所述终端的上行信号强度。
作为示例，所述终端的上行信号强度，可以是指在所述终端处于天线失配状态时所述网络设备接收到的终端的上行信号强度。
按照下面公式，计算所述网络设备接收到的终端的上行信号强度：
PgNB＝PUE-L
其中，PgNB表示所述网络设备(如基站)接收到的终端的上行信号强度，PUE表示在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率，L表示所述终端到所述网络设备之间的第一路损，单位为dB。
按照下面公式，计算在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率PUE：
其中，PUE表示在所述终端处于天线失配状态时所述终端通过天线发射出的实际输出功率，B表示所述终端的天线端口处的反射功率，Γ表示所述终端在天线失配状态下的天线的反射系数。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述终端的上行信号强度。
第三，获取所述终端到所述网络设备之间的第一路损。
作为示例，可以将所述网络设备发送给所述终端的下行参考信号强度和所述终端测量得到的下行参考信号强度求差，得到在所述终端处于天线失配状态时所述终端到所述网络设备之间的第一路损。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述终端到所述网络设备之间的第一路损。
第四，基于所述终端的上行信号强度、所述第一路损以及所述反射系数，获取所述终端在天线失配状态下的功率补偿值。
按照下面公式，计算所述终端在天线失配状态下的功率补偿值：
其中，PC表示所述终端在天线失配状态下的功率补偿值，PgNB表示所述网络设备(如基站)接收到的终端的上行信号强度，L表示所述终端到所述网络设备之间的第一路损，Γ表示所述终端在天线失配状态下的天线的反射系数。
在一些实施例中，所述获取终端在天线失配状态下的功率补偿值，包括：
获取所述终端在天线端口处的反射功率；
根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；
从所述至少一个数值中选取一个数值作为所述功率补偿值。
也就是说，所述功率补偿值不大于所述反射功率的数值大小。
这里，当所述功率补偿值的取值等于所述反射功率的功率值时，终端将被补偿到终端天线完全不失配的状态(类似于自由空间状态)，但是在实际使用过程中，可能不一定需要将终端天线补偿到自由空间状态，因此，可以根据所述反射功率的功率值，确定一个不大于所述反射功率的功率值的数值作为所述功率补偿值。
这里，反射功率的功率值用B表示，所述功率补偿值用Pc表示，B与Pc之间的关系可以用功率补偿系数C来描述，其中，C＝Pc/B，C的取值可以由运营商来确定。当C＝1时，可将终端补偿到天线无损传输的理想状态(也即上述的自由空间状态)。
在步骤202，获取网络设备的下行信号强度。
作为示例，所述网络设备的下行信号强度，可以是指所述终端在天线失配状态下接收到的网络设备的下行信号强度。
这里，当所述数据处理方法的执行主体即所述第一设备为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站)、OAM中之一时，所述第一设备可以获取所述终端发送的所述网络设备的下行信号强度。
在步骤203，基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
作为示例，所述对所述网络设备的下行信号强度进行补偿，可以是指将所述终端在天线失配状态下接收的网络设备的下行信号强度值与所述终端在天线失配状态下的功率补偿值求和，得到补偿后的所述网络设备的下行信号强度值。
作为示例，所述功率补偿值和所述网络设备的下行信号强度的单位相同，均是dBm。
在一些实施例中，所述方法还包括：
在所述补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
作为示例，在所述补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为下行弱覆盖区域；当确定所述终端所在区域为下行弱覆盖区域时，执行网络优化操作。
作为示例，所述下行弱覆盖区域可以是指所述网络设备发送给所述终端的信号比较弱的区域，也可以是指所述网络设备发射的信号强度小于某个阈值的区域。
举例来说，假设用RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，其中，RSRPn_m表示终端n在天线失配状态m下获取的网络设备的下行信号强度，Pcm表示终端n在天线失配状态m下的功率补偿值，如果RSRPn_m+Pcm小于预设信号强度阈值，则确定所述终端所在区域为下行弱覆盖区域，执行网络优化操作。
作为示例，所述执行网络优化操作，可以是指在所述终端所处区域中增加基站数量等。
在一些实施例中，所述方法还包括：
获取所述终端发送的功率余量报告，其中所述功率余量报告包括功率余量值；
通过所述补偿后的所述网络设备的下行信号强度，获得所述终端到所述网络设备之间的第二路损；
在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，执行网络优化操作。
作为示例，在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
作为示例，所述上行弱覆盖区域可以是指所述终端发送给所述网络设备的信号比较弱的区域，也可以是指所述终端发射的信号强度小于某个阈值的区域。
作为示例，可以将所述网络设备发送给所述终端的下行参考信号强度与所述补偿后的所述网络设备的下行信号强度求差，获得所述终端到所述网络设备之间的第二路损。
举例来说，假设用PHR表示所述功率余量值，用PL表示所述第二路损，当PHR小于0且PL大于预设路损阈值时，执行网络优化操作。
作为示例，所述执行网络优化操作，可以是指在所述终端所处区域中增加基站数量等。
需要说明的是，在判断所述终端所在区域是否为上行弱覆盖区域时，先看PHR：如果PHR>0，就直接判定为非弱覆盖区域、不需要进行上行网络优化；如果PHR<0，先判断PHR<0的原因，如果不是由于终端所在小区的干扰指标达到一定程度而导致PHR<0，则进一步判断PL，如果PL大于预设路损阈值，可以判定所述终端所在区域为上行弱覆盖区域，执行网络优化操作。
在一些实施例中，所述方法还包括：
获取第一信息集合，其中所述第一信息集合包括所述终端在p个天线失配状态下发送的功率余量报告，以及所述终端在所述p个天线失配状态下的时间信息和/或位置信息；所述功率余量报告包括功率余量值；p为大于或等于1的整数；
从所述第一信息集合中选取与m个天线失配状态对应的m个功率余量值，其中，m个功率余量值均小于预设功率余量阈值，m为大于或等于1的整数，m小于或等于p；
从所述第一信息集合中选取与所述m个功率余量值对应的时间信息和/或位置信息；
基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作。
在一些实施例中，所述基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作，包括：
基于与所述m个功率余量值对应的时间信息，确定m个权值；将所述m个权值与对应所述m个天 线失配状态下得到的补偿后的所述网络设备的下行信号强度求乘积后，求和得到第一下行信号强度；在所述第一下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作；和/或，
针对与所述m个功率余量值对应的位置信息中每个位置信息，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
作为示例，在所述第一下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
作为示例，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
作为示例，所述时间信息可以是指所述终端处在天线失配状态的持续时长，或者，也可以是指所述终端处在天线失配状态的时刻。例如，用T表示所述时间信息，假设T等于3分钟，则表示所述终端处在天线失配状态的持续时长为3分钟。假设T等于8点，则表示所述终端处在天线失配状态的时刻为8点。
作为示例，所述位置信息可以是指所述终端处在天线失配状态时的地理位置信息。例如，用P表示所述位置信息，假设P为城市A的小区A，则表示所述终端处在天线失配状态时的地理位置为城市A的小区A。
首先，从所述第一信息集合中选取与m个天线失配状态对应的m个功率余量值，m个功率余量值均小于预设功率余量阈值，分别用PHR1，PHR2，…，PHRm表示，从所述第一信息集合中选取与所述m个功率余量值对应的时间信息，分别用T1，T2，…，Tm表示，从所述第一信息集合中选取与所述m个功率余量值对应的位置信息，分别用P1，P2，…，Pm表示。
然后，确定终端n在m个天线失配状态下得到的补偿后的所述网络设备的下行信号强度，分别用RSRPn_1+Pc1，RSRPn_2+Pc2，…，RSRPn_m+Pcm表示，其中，Pcm表示终端n在天线失配状态m下的功率补偿值。
最后，在所述时间信息表征所述终端处在天线失配状态的持续时长的情况下，将每个天线失配状态对应的时长求和，得到总时长，将每个天线失配状态对应的时长与总时长求比值，得到每个下行信号强度对应的权值，对应时长越大的下行信号强度的权值越大。将不同时长对应的补偿后下行信号强度值与对应权值求乘积后，求和得到第一下行信号强度，在所述第一下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
举例来说，天线失配状态有3个，终端在3个天线失配状态下的补偿后的网络设备的下行信号强度，分别用RSRP1+Pc1、RSRP2+Pc2、RSRP3+Pc3表示，对应的持续时长分别是1分钟，2分钟，3分钟，对应时长越大的下行信号强度的权值越大。将不同时长对应的下行信号强度值与对应权值求乘积后，求和得到第一下行信号强度，即第一下行信号强度RSRP＝(RSRP1+Pc1)×(1/6)+(RSRP2+Pc2)×(2/6)+(RSRP3+Pc3)×(3/6)，其中，6是指总时长6分钟，即将1分钟、2分钟、3分钟求和得到6分钟，权值分别为1/6、2/6、3/6。如果所述第一下行信号强度值小于预设信号强度阈值，则执行网络优化操作。
在所述时间信息表征的是所述终端处在天线失配状态的时刻的情况下，统计每个天线失配状态对应的时长，将每个天线失配状态对应的时长求和，得到总时长，将每个天线失配状态对应的时长与总时长求比值，得到每个下行信号强度对应的权值，对应时长越大的下行信号强度的权值越大。将不同时长对应的下行信号强度值与对应权值求乘积后，求和得到第一下行信号强度，在所述第一下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
举例来说，天线失配状态有4个，终端在4个天线失配状态下的补偿后的网络设备的下行信号强度，分别用RSRP1+Pc1、RSRP2+Pc2、RSRP3+Pc3、RSRP4+Pc4表示，对应的时刻分别是8：00，8:01，8:02，8:03，其中，(RSRP1+Pc1)和(RSRP3+Pc3)对应的时间段分别为8：00到8:01、8：02到8:03，总时长为2分钟，(RSRP2+Pc2)对应的时间段为8：01到8:02，总时长为1分钟，(RSRP4+Pc4)对应的总时长为0分钟，对应时长越大的下行信号强度的权值越大。将不同时长对应的下行信号强度值与对应权值求乘积后，求和得到第一下行信号强度，即第一下行信号强度RSRP＝(RSRP1+Pc1)×(1/3)+(RSRP2+Pc2)×(1/3)+(RSRP3+Pc3)×(1/3)+(RSRP4+Pc4)×(0/3)，其中，3是指总时长3分钟，即将2分钟、1分钟、0分钟求和得到3分钟，权值分别为1/3、1/3、1/3、0/3。如果所述第一下行信号强度值小于预设信号强度阈值，则执行网络优化操作。
针对与所述m个功率余量值对应的位置信息中每个位置信息，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
举例来说，天线失配状态有3个，终端在3个天线失配状态下的补偿后的网络设备的下行信号强度，分别用RSRP1+Pc1、RSRP2+Pc2、RSRP3+Pc3表示，对应的地理位置分别是城市A的小区1、小区1、 小区2，如果针对小区1对应的RSRP1+Pc1小于预设信号强度阈值，则需要执行网络优化操作；如果针对小区1对应的RSRP2+Pc2小于预设信号强度阈值，则需要执行网络优化操作；如果针对小区2对应的RSRP3+Pc3小于预设信号强度阈值，则需要执行网络优化操作。
需要说明的是，在执行网络优化操作时，还可以指示出执行网络优化操作的时间，例如，假设指示出的执行网络优化操作的时间为晚上8点，则在晚上8点时才执行网络优化操作。
需要说明的是，在执行网络优化操作时，还可以指示出能够执行网络优化操作的地理位置范围，例如，假设经过判断获知终端所在的小区1需要执行网络优化操作，但是小区1不属于能够执行网络优化操作的地理位置范围，则小区1不能够执行网络优化操作。
需要说明的是，在得到补偿后的所述网络设备的下行信号强度之后，所述终端可以处于以下两种情况：
第一种情况，所述终端仍处于天线失配状态，其中，天线失配状态，可以是指当天线失配时，会出现能量反射，导致传输能量损失。
作为示例，所述终端处于天线失配状态时，天线的反射系数Γ不等于0，天线端口处的反射功率B不等于0。
第二种情况，所述终端处于非天线失配状态，其中，非天线失配状态，可以是指当天线完全匹配时，能够实现终端功率的无损传输。
作为示例，所述终端处于非天线失配状态时，天线的反射系数Γ等于0，天线端口处的反射功率B等于0。
如此，在对大量终端执行网络优化操作时，例如，假设处于天线失配状态的终端总数量为100个，在对所述终端接收到的网络设备的下行信号强度进行补偿后，假设100个终端中的20个终端仍处于天线失配状态即天线的反射系数不为0，其余80个终端处于非天线失配状态即天线的反射系数为0，这种情况下，针对100个终端，采用补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。
或者，假设100个终端仍处于不同失配状态，针对100个终端，仍采用补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作，其中，针对100个终端，对所述终端接收到的网络设备的下行信号强度进行补偿后，所述终端的天线的失配状态都得到了不同程度的缓解。
在一些实施例中，所述获取终端在天线失配状态下的功率补偿值，包括：获取所述终端在不同天线失配状态下的功率补偿值；
所述基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，包括：基于所述终端在不同天线失配状态下的功率补偿值，分别对所述网络设备的下行信号强度进行补偿；
所述方法还包括：
将在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度进行比较，得到比较结果；
当所述比较结果表征在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度相同时，确定所述终端所在区域的网络覆盖情况未发生变化。
作为示例，所述“相同”可以是指在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度完全相同，或者，每两个天线失配状态下得到的补偿后的所述网络设备的下行信号强度的差值在预设范围内也可以视为相同。
在一些实施例中，所述方法还包括：
获取AI/ML模型，其中，所述AI/ML模型用于执行前述实施例所述数据处理方法；
其中，所述AI/ML模型的训练数据包括：所述终端在不同天线失配状态下的功率补偿值；或者，所述终端在不同天线失配状态下的功率补偿值，以及所述终端在不同天线失配状态下的时间信息和/或位置信息。
在一些实施例中，所述AI/ML模型由所述网络设备训练，或者由OAM训练，或者由所述终端训练。
在一些实施例中，所述获取AI/ML模型，包括：向所述OAM发送所述训练数据；接收所述OAM返回的所述AI/ML模型。
在一些实施例中，所述获取AI/ML模型，包括：向所述终端发送所述训练数据；接收所述终端返回的所述AI/ML模型。
本公开实施例中，具备以下优点：
(1)获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。如此，能够真实评估出终端所处位置处的网络设备的下行信号强度，后续也可以为执行网络优化操作提供便利。
(2)本公开旨在通过AI/ML模型训练，在无法消除终端天线失配的情况下，仍旧能够使网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出。
参见图3，图3是本公开实施例数据处理方法的实现流程示意图，应用于终端，所述方法包括步骤301至步骤302。
在步骤301，确定终端在天线失配状态下的功率补偿值。
在步骤302，向第一设备发送所述终端在天线失配状态下的功率补偿值；其中，所述功率补偿值用于所述第一设备对获取的网络设备的下行信号强度进行补偿，以便得到补偿后的所述网络设备的下行信号强度。
作为示例，所述第一设备包括但不限于为服务器(由运营商部署)、第三方服务器(由运营商之外的厂家部署)、网络设备(如基站，所述基站包括但不限于5G无线接入网、6G无线接入网、7G无线接入网等)、终端、OAM等。
在一些实施例中，所述确定所述终端在天线失配状态下的功率补偿值，包括：
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率，并获取所述终端在天线端口处的输入功率；基于所述反射功率以及所述输入功率，获取所述终端在天线失配状态下的反射系数；
基于所述反射系数，执行以下操作之一：
获取所述终端的天线的实际输出功率；基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；或
获取所述终端的上行信号强度，获取所述终端到所述网络设备之间的第一路损；基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；
或者，
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率；根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；从所述至少一个数值中选取一个数值作为所述功率补偿值。
需要说明的是，基于所述反射系数确定所述终端在天线失配状态下的功率补偿值的过程在上文已进行了描述，在此不再赘述。
在一些实施例中，所述方法还包括：
向所述第一设备发送功率余量报告，其中所述功率余量报告包括功率余量值；
其中，所述功率余量值用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。
作为示例，所述第一设备获取所述终端发送的功率余量报告；所述功率余量报告包括功率余量值；通过所述补偿后的所述网络设备的下行信号强度，获得所述终端到所述网络设备之间的第二路损；在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，确定所述终端所在区域为上行弱覆盖区域；当确定所述终端所在区域为上行弱覆盖区域时，执行网络优化操作。
作为示例，可以将所述网络设备发送给所述终端的下行参考信号强度与所述补偿后的所述网络设备的下行信号强度求差，获得所述终端到所述网络设备之间的第二路损。
举例来说，假设用PHR表示所述功率余量值，用PL表示所述第二路损，当PHR小于0且PL大于预设路损阈值时，执行网络优化操作。
作为示例，所述执行网络优化操作，可以是指在所述终端所处区域中增加基站数量等。
在一些实施例中，所述方法还包括：
向所述第一设备发送第一信息集合，其中所述第一信息集合包括所述终端在p个天线失配状态下发送的功率余量报告，以及所述终端在所述p个天线失配状态下的时间信息和/或位置信息；所述功率余量报告包括功率余量值；p为大于或等于1的整数；
其中，所述第一信息集合用于所述终端判断是否执行网络优化操作。
在一些实施例中，所述方法还包括：
获取所述终端在不同天线失配状态下的时间信息和/或位置信息；和
将所述时间信息和/或所述位置信息发送给所述第一设备；
其中，所述时间信息和/或位置信息用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度判断是否执行网络优化操作，或者用于所述第一设备结合所述终端在天线失配状态下的功率补偿值训练AI/ML模型。
作为示例，所述时间信息可以是指所述终端处在天线失配状态的持续时长，或者，也可以是指所述终端处在天线失配状态的时刻。例如，用T表示所述时间信息，假设T等于3分钟，则表示所述终端处在天线失配状态的持续时长为3分钟。假设T等于8点，则表示所述终端处在天线失配状态的时刻为8点。
作为示例，所述位置信息可以是指所述终端处在天线失配状态时的地理位置信息。例如，用P表示 所述位置信息，假设P为城市A的小区A，则表示所述终端处在天线失配状态时的地理位置为城市A的小区A。
在一些实施例中，所述方法还包括：
获取训练数据；
利用所述训练数据，训练所述AI/ML模型；和
将所述AI/ML模型发送给所述第一设备；
其中，所述AI/ML模型的训练数据包括：所述终端在不同天线失配状态下的功率补偿值；或者，所述终端在不同天线失配状态下的功率补偿值，以及所述终端在不同天线失配状态下的时间信息和/或位置信息。
本公开实施例中，具备以下优点：
(1)获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。如此，能够真实评估出终端所在位置处的网络设备的下行信号强度，后续也可以为执行网络优化操作执行便利。
(2)本公开提案旨在通过AI/ML模型训练，在无法消除终端天线失配的情况下，仍旧能够使网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出。
参见图4，图4是本公开实施例数据处理方法应用的系统架构示意图，如图4所示，以参数配置模块、功率感知模块、时间感知模块、位置感知模块位于终端内部，功率补偿计算模块、失配数据收集模块(Mismatch Data Collection)、网规网优决策模块(Actor)位于终端外部为例进行说明。其中，所述功率补偿计算模块可包括模型训练(Model Training)模块和模型推理(Model Inference)模块。
模型训练模块、模型推理模块都可以部署在网络设备，如基站，所述基站包括但不限于5G无线接入网中的gNB、6G无线接入网、7G无线接入网中的基站等；也可以是模型推理模块部署在网络设备，如基站，而模型训练模块部署在OAM。网规网优决策模块可以独立部署，也可以与OAM合设(集成设置)。
对图4中各模块介绍如下：
1)所述失配数据收集模块，用于为AI/ML模型的模型训练、模型推理提供输入数据(Input data)。
其中，
输入数据(Input data)，包括：来自功率感知模块处的反射系数Γ和/或反射功率B、时长感知信息T、位置感知信息P、所述终端的天线的实际输出功率PUE、所述网络设备接收到的终端的上行信号强度PgNB、所述终端到所述网络设备之间的第一路损L、所述终端在天线失配状态下的功率补偿值Pc，还可以包括来自网规网优决策模块的反馈数据(Feedback，如是否决定新增基站等网络设备、新增基站等网络设备的位置等信息)、以及来自功率补偿计算模块的输出数据(Output，如补偿后的所述网络设备的下行信号强度)。这些数据可以作为原始数据，不需要针对AI/ML算法进行数据准备(数据清洗、数据格式化、数据转换等)。
其中，时长感知信息T和位置感知信息P属于可选的上报信息，位置感知信息P可以是一个较为粗略的位置感知信息，例如：终端感知到的小区的物理小区标识符(PCI，Physical Cell Identifier)信息等。
2)功率补偿计算模块：
模型训练模块，用于负责AI/ML模型的训练、测试和验证确认，生成模型性能的度量标准。其中，通过模型部署或更新(Model Deployment/Update)将经过训练、测试和验证确认的AI/ML模型部署到模型推理模块。
模型推理模块，用于提供AI/ML模型的推理输出(预测或决策)，并向模型训练模块反馈结果。其中，通过模型性能反馈可将模型推理模块的模型性能反馈发到模型训练模块，供模型训练模块对AI/ML模型进行训练、优化使用。其中，向模型训练模块反馈结果这一过程是可选的过程。
具体地，基于所述输入数据，得到所述终端在天线失配状态下的功率补偿值，再基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
这里，所述推理输出可以是得到补偿后的所述网络设备的下行信号强度。
这里，所述推理输出可以采用以下几种形式：
第一种，采用补偿后的所述网络设备的下行信号强度，如RSRPn_m+Pcm，其中，RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，m表示天线失配状态，RSRPn_m表示终端n在天线失配状态m下接收到的网络设备的下行信号强度，Pcm表示终端n在天线失配状态m下的功率补偿值。
第二种，采用通过所述补偿后的所述网络设备的下行信号强度获得的所述终端到所述网络设备之间的第二路损以及所述终端发送的功率余量报告中的功率余量值，如[PL，PHR]。
第三种，采用补偿后的所述网络设备的下行信号强度以及所述终端发送的功率余量报告中的功率余量值，以及所述终端在天线失配状态下的时间信息和/或位置信息，如[RSRPn_m+Pcm，PHRm，Tm]。其中，RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，m表示天线失配状态，PHRm表示天线失配状态m对应的功率余量值，Tm表示所述终端在天线失配状态下的时间信息。
3)网规网优决策模块，用于在接收到模块推理模块的推理输出(预测或者决策)后，执行相应的网络优化操作。
这里，执行网络优化操作，可以是指在所述终端所处区域中增加网络设备(如基站gNB)的数量等。
这里，可以利用补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以基于通过所述补偿后的所述网络设备的下行信号强度获得的第二路损以及所述终端发送的功率余量报告，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以是基于所述功率余量值以及所述终端在天线失配状态下的时间信息和/或位置信息，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
参见图5，图5是本公开实施例数据处理方法的具体实现流程示意图，以模型训练模块和模型推理模块都位于基站、且对网络网优决策模块进行独立部署为例，基站包括但不限于5G无线接入网，即下一代无线接入网(NG-RAN，NG Radio Access Network)、6G无线接入网、7G无线接入网等中的基站，图5中基站用NG-RAN node n表示，如图5所示，所述方法包括步骤501至步骤505。
在步骤501，NG-RAN node n配置UE，请求UE提供失配数据。在一些实施例中，网络侧针对UE配置或设置了进行数据传输所需要的参数。
这里，所述失配数据，可以包括：UE在天线失配状态下天线的反射系数Γ或反射功率B、时长感知信息T、位置感知信息P。
这里，所述失配数据，也可以包括：UE在天线失配状态下天线的反射系数Γ和/或反射功率B，以及，UE在天线失配状态下的时间信息T和/或UE在天线失配状态下的位置信息P。
在步骤502，UE进行失配数据感知或失配数据的收集。
作为示例，可以在UE内部增加功率感知模块，通过所述功率感知模块获取天线端口处的反射功率B，通过所述功率感知模块，获取天线端口处的输入功率A。
按照下面公式，计算UE在天线失配状态下的天线的反射系数Γ：
其中，Γ表示UE在天线失配状态下的天线的反射系数，B表示天线端口处的反射功率，A表示UE的天线端口处的输入功率。
这里，通过反射系数Γ可获知UE天线的失配程度，例如，反射系数Γ为0，表示所述终端天线处于非失配状态，否则，表示所述终端天线处于失配状态。
终端通过功率感知模块可以感受到“反射功率”，当反射功率不为0时，即为处于天线失配状态；或者当进一步通过反射功率和入射功率得到的“反射系数”不为0时，也可以判断处于天线失配状态。
需要说明的是，不是只要反射功率不为0或反射系数不为0就都视为“天线失配状态”，在实际使用的过程中，也可以根据实际情况设定一定的阈值范围，当反射功率超过一定门限或反射系数大于某一阈值，即可视为处于“天线失配状态”。
在步骤503，UE向NG-RAN node n上报所述失配数据。
这里，所述失配数据可以作为AI/ML模型的模型训练模块的输入数据。
这里，所述失配数据也可以作为AI/ML模型的模型推理模块的输入数据。
这里，根据终端提供的失配数据进行基于AI/ML模型的模型训练，AI/ML模型根据既往累积信息生成预测信息(Output)，即输出此时应该采用的补偿后的网络设备的下行信号强度，如RSRPn_m+Pcm,其中，RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，m表示天线失配状态。也就是说，n代表不同终端的标识，m代表终端的不同失配状态，可以用于表示同一终端接收到的同一基站不同下行信号强度的数据标识。
在进行模型训练时，假设终端UE1处于某一失配状态S1，此时终端UE1接收到的基站A的下行信号强度为RSRP1_1，此时终端UE1的失配状态可用反射功率B1或反射系数Γ1来表征。假设UE1能够纠偏到理想未失配状态S0，那么终端UE1接收到的基站A的下行信号强度应为RSRP1_1+Pc1，Pc1是终端在天线失配状态S1的功率补偿值。假设终端UE1处于某一失配状态S2，此时终端UE1接收到的基站A的下行信号强度为RSRP1_2，此时终端UE1的失配状态可用反射功率B2或反射系数Γ2来表征，假设UE1能够纠偏到理想未失配状态S0，那么终端UE1接收到的基站A的下行信号强度应为RSRP1_2+ Pc2，Pc2是终端在天线失配状态S2的功率补偿值。
若UE1在处于上述失配状态S1和S2时所处位置并未发生变化，那么终端UE1接收到的基站A的纠偏后的下行信号强度RSRP1_1+Pc1应近似等于RSRP1_2+Pc2，即可推测出：UE1在处于上述失配状态S1和S2时所处的网络覆盖情况未发生明显变化。
在步骤504，NG-RAN node n根据所述失配数据，进行基于AI/ML模型的模型推理，并生成推理输出发送给网规网优决策模块。
这里，所述推理输出可以得到补偿后的所述网络设备的下行信号强度。
这里，所述推理输出可以采用以下几种形式：
第一种，采用补偿后的所述网络设备的下行信号强度，如RSRPn_m+Pcm，其中，RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，m表示天线失配状态，RSRPn_m表示终端n在天线失配状态m下接收到的网络设备的下行信号强度，Pcm表示终端n在天线失配状态m下的功率补偿值。
第二种，采用通过所述补偿后的所述网络设备的下行信号强度获得的所述终端到所述网络设备之间的第二路损以及所述终端发送的功率余量报告中的功率余量值，如[PL，PHR]。
第三种，采用补偿后的所述网络设备的下行信号强度以及所述终端发送的功率余量报告中的功率余量值，以及所述终端在天线失配状态下的时间信息和/或位置信息，如[RSRPn_m+Pcm，PHRm，Tm]。其中，RSRPn_m+Pcm表示终端n的补偿后的网络设备的下行信号强度，m表示天线失配状态，PHRm表示天线失配状态m对应的功率余量值，Tm表示所述终端在天线失配状态下的时间信息。
在步骤505，网规网优决策模块判断是否执行网络优化操作。
也就是说，对是否需要启动网规网优和/或如何进行网规网优进行分析，并输出网规网优必要性和/或实施方案的分析结果，还可以将分析结果反馈给NG-RAN node n。
这里，执行网络优化操作，可以是指在所述终端所处区域中增加网络设备如基站gNB的数量等。
这里，可以利用补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以基于通过所述补偿后的所述网络设备的下行信号强度获得的第二路损以及所述终端发送的功率余量报告，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以是基于所述功率余量值以及所述终端在天线失配状态下的时间信息和/或位置信息，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
需要说明的是，在结合所述终端在天线失配状态下的时间信息判断是否执行网络优化操作时，若终端接收到的某一下行信号强度(RSRP)的持续时长较长，即便是存在一定的终端天线失配情况，网规网优决策模块也可以考虑针对现网这一大概率出现的下行信号强度进行网规网优决策，以保障现网终端用户的速率体验。
这里，还可以再结合UE的型号信息，提供不同型号的终端在现网不同地点、不同环境下的终端天线失配情况信息，为运营商网优网规提供更多可供参考的信息，助力运营商更做出更准确的网优网规决策。
本示例中，具备以下优点：
(1)基于终端上报的失配数据，引入AI/ML模型训练，帮助网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出，和/或帮助运营商做出有助于提升现网终端用户速率体验的网规网优决策。
(2)通过NG-RAN node n进行AI/ML模型训练，帮助网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出，和/或帮助运营商做出有助于提升现网终端用户速率体验的网规网优决策。
参见图6，图6是本公开实施例数据处理方法的具体实现流程示意图，以模型训练模块和模型推理模块都位于基站、对网络网优决策模块进行独立部署为例，基站包括但不限于5G无线接入网，即NG-RAN、6G无线接入网、7G无线接入网等中的基站，图6中基站用NG-RAN node n表示，如图6所示，所述方法包括步骤601至步骤605。
在步骤601，NG-RAN node n配置UE，请求UE提供失配数据。
在步骤602，UE进行失配数据感知或失配数据收集。
这里，失配数据感知或者失配数据收集的实现过程在前文中已进行了描述，在此不再赘述。
在步骤603，UE向NG-RAN node n上报收集到的失配数据。
在步骤604，NG-RAN node n将UE上报的失配数据发给OAM。
在步骤605，OAM依据所述失配数据进行基于AI/ML模型的模型训练。
在步骤606，OAM向NG-RAN node n部署/更新AI/ML模型。
在步骤607，UE向NG-RAN node n上报用于模型推理的失配数据。
在步骤608，NG-RAN node n依据所述上报的失配数据进行基于AI/ML模型的模型推理，并生成推 理输出、将其发送给网规网优决策模块。
这里，NG-RAN node n还可以将模型性能反馈发给OAM。
在步骤609，网规网优决策模块判断是否执行网络优化操作。
也就是说，对是否需要启动网规网优和/或如何进行网规网优进行分析，并输出网规网优必要性和/或实施方案的分析结果，还可以将分析结果反馈给OAM。
这里，执行网络优化操作，可以是指在所述终端所处区域中增加网络设备(如基站gNB)的数量等。
这里，可以利用补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以基于通过所述补偿后的所述网络设备的下行信号强度获得的第二路损以及所述终端发送的功率余量报告，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
也可以是基于所述功率余量值以及所述终端在天线失配状态下的时间信息和/或位置信息，判断是否执行网络优化操作。具体实现过程在前文中已进行了描述，在此不再赘述。
本示例中，具备以下优点：
(1)基于终端上报的失配数据，引入AI/ML模型训练，帮助网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出，和/或帮助运营商做出有助于提升现网终端用户速率体验的网规网优决策。
(2)通过OAM进行AI/ML模型训练，帮助网络准确掌握现网真实覆盖情况，避免运营商不必要的网规网优成本支出，和/或帮助运营商做出有助于提升现网终端用户速率体验的网规网优决策。
为实现本公开实施例数据处理方法，本公开实施例还提供一种数据处理装置，设置在第一设备(包括但不限于，包括在第一设备中，或集成在第一设备中)。图7为本公开实施例数据处理装置的组成结构示意图，如图7所示，所述装置包括：
获取模块71，用于获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；
第一处理模块72，用于基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
在一些实施例中，所述获取模块71，用于：
获取所述天线失配状态对应的反射系数；
获取所述终端的天线的实际输出功率；和
基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
在一些实施例中，所述获取模块71，用于：
获取所述天线失配状态对应的反射系数；
获取所述终端的上行信号强度；
获取所述终端到所述网络设备之间的第一路损；和
基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
在一些实施例中，所述获取模块71，用于：
获取所述终端在天线端口处的反射功率；
根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；和
从所述至少一个数值中选取一个数值作为所述终端在天线失配状态下的功率补偿值。
在一些实施例中，所述装置还用于：
在所述补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
在一些实施例中，所述装置还用于：
获取所述终端发送的功率余量报告；所述功率余量报告包括功率余量值；
通过所述补偿后的所述网络设备的下行信号强度，获得所述终端到所述网络设备之间的第二路损；和
在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，执行网络优化操作。
在一些实施例中，所述装置还用于：
获取第一信息集合，其中所述第一信息集合包括所述终端在p个天线失配状态下发送的功率余量报告，以及所述终端在所述p个天线失配状态下的时间信息和/或位置信息；所述功率余量报告包括功率余量值；p为大于或等于1的整数；
从所述第一信息集合中选取与m个天线失配状态对应的m个功率余量值；其中，m个功率余量值均小于预设功率余量阈值，m为大于或等于1的整数，m小于或等于p；
从所述第一信息集合中选取与所述m个功率余量值对应的时间信息和/或位置信息；和
基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作。
在一些实施例中，所述装置还用于：
基于与所述m个功率余量值对应的时间信息，确定m个权值；将所述m个权值与对应所述m个天线失配状态下得到的补偿后的所述网络设备的下行信号强度求乘积后，求和得到第一下行信号强度；在所述第一下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作；和/或，
针对与所述m个功率余量值对应的位置信息中每个位置信息，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
在一些实施例中，所述获取模块71，还用于获取所述终端在不同天线失配状态下的功率补偿值；
所述第一处理模块72，还用于基于所述终端在不同天线失配状态下的功率补偿值，分别对所述网络设备的下行信号强度进行补偿；将在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度进行比较，得到比较结果；当所述比较结果表征在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度相同时，确定所述终端所在区域的网络覆盖情况未发生变化。
在一些实施例中，所述装置还用于：
获取AI/ML模型，其中，所述AI/ML模型用于执行所述数据处理方法；
其中，所述AI/ML模型的训练数据包括：所述终端在不同天线失配状态下的功率补偿值；或者，所述终端在不同天线失配状态下的功率补偿值，以及所述终端在不同天线失配状态下的时间信息和/或位置信息。
在一些实施例中，所述AI/ML模型由所述网络设备训练，或者由OAM训练，或者由所述终端训练。
实际应用时，所述获取单元71可以由数据处理装置中的通信接口实现；所述第一处理单元72可以由数据处理装置中的处理器实现。
需要说明的是：上述实施例提供的数据处理装置在进行数据处理时，仅以上述各程序模块的划分进行举例说明，实际应用中，可以根据需要而将上述处理分配由不同的程序模块完成，即将装置的内部结构划分成不同的程序模块，以完成以上描述的全部或者部分处理。另外，上述实施例提供的数据处理装置与数据处理方法实施例属于同一构思，其具体实现过程详见方法实施例，这里不再赘述。
为实现本公开实施例数据处理方法，本公开实施例还提供一种数据处理装置，设置在终端(包括但不限于，包括在终端中，或集成在终端中)。图8为本公开实施例数据处理装置的组成结构示意图，如图8所示，所述装置包括：
第二处理模块81，用于确定所述终端在天线失配状态下的功率补偿值；
发送模块82，用于向第一设备发送所述终端在天线失配状态下的功率补偿值，其中，所述功率补偿值用于所述第一设备对获取的网络设备的下行信号强度进行补偿，以得到补偿后的所述网络设备的下行信号强度。
在一些实施例中，所述第二处理模块81，用于：
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率，并获取所述终端在天线端口处的输入功率；基于所述反射功率以及所述输入功率，获取所述终端在天线失配状态下的反射系数；
基于所述反射系数，执行以下操作之一：
获取所述终端的天线的实际输出功率；基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；或
获取所述终端的上行信号强度，获取所述终端到所述网络设备之间的第一路损；基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；
或者，
通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率；根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；从所述至少一个数值中选取一个数值作为所述功率补偿值。
在一些实施例中，所述装置还用于：
向所述第一设备发送功率余量报告，其中所述功率余量报告包括功率余量值；
其中，所述功率余量值用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。
在一实施例中，所述装置还用于：
获取所述终端在不同天线失配状态下的时间信息和/或位置信息；和
将所述时间信息和/或所述位置信息发送给所述第一设备；
其中，所述时间信息和/或位置信息用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度判断是否执行网络优化操作，或者用于所述第一设备结合所述终端在天线失配状态下的功率补偿值训练AI/ML模型。
实际应用时，所述发送单元82可以由数据处理装置中的通信接口实现；所述第二处理单元81可以由数据处理装置中的处理器实现。
需要说明的是：上述实施例提供的数据处理装置在进行数据处理时，仅以上述各程序模块的划分进行举例说明，实际应用中，可以根据需要而将上述处理分配由不同的程序模块完成，即将装置的内部结构划分成不同的程序模块，以完成以上描述的全部或者部分处理。另外，上述实施例提供的数据处理装置与数据处理方法实施例属于同一构思，其具体实现过程详见方法实施例，这里不再赘述。
本公开实施例还提供了一种设备(如上述实施例中的第一设备)，如图9所示，包括：
第一通信接口91，能够与其它设备进行信息交互；
第一处理器92，与所述第一通信接口91连接，用于运行计算机程序时，执行上述设备侧一个或多个技术方案提供的方法。而所述计算机程序存储在第一存储器93上。
需要说明的是：所述第一处理器92和第一通信接口91的具体处理过程详见方法实施例，这里不再赘述。
当然，实际应用时，设备90中的各个组件通过总线系统94耦合在一起。可理解，总线系统94用于实现这些组件之间的连接通信。总线系统94除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图9中将各种总线都标为总线系统94。
本公开实施例中的第一存储器93用于存储各种类型的数据以支持设备90的操作。这些数据的示例包括：用于在设备90上操作的任何计算机程序。
上述本公开实施例揭示的方法可以应用于所述第一处理器92中，或者由所述第一处理器92实现。所述第一处理器92可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过所述第一处理器92中的硬件的集成逻辑电路或者软件形式的指令完成。上述的所述第一处理器92可以是通用处理器、数字数据处理器(DSP，Digital Signal Processor)，或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。所述第一处理器92可以实现或者执行本公开实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者任何常规的处理器等。结合本公开实施例所公开的方法的步骤，可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于存储介质中，该存储介质位于第一存储器93，所述第一处理器92读取第一存储器93中的信息，结合其硬件完成前述方法的步骤。
本公开实施例还提供了一种终端，如图10所示，包括：
第二通信接口101，能够与其它设备进行信息交互；
第二处理器102，与所述第二通信接口101连接，用于运行计算机程序时，执行上述终端侧一个或多个技术方案提供的方法。而所述计算机程序存储在第二存储器103上。
需要说明的是：所述第二处理器102和第二通信接口101的具体处理过程详见方法实施例，这里不再赘述。
当然，实际应用时，终端100中的各个组件通过总线系统104耦合在一起。可理解，总线系统104用于实现这些组件之间的连接通信。总线系统104除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但是为了清楚说明起见，在图10中将各种总线都标为总线系统104。
本公开实施例中的第二存储器103用于存储各种类型的数据以支持终端100的操作。这些数据的示例包括：用于在终端100上操作的任何计算机程序。
上述本公开实施例揭示的方法可以应用于所述第二处理器102中，或者由所述第二处理器102实现。所述第二处理器102可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过所述第二处理器102中的硬件的集成逻辑电路或者软件形式的指令完成。上述的所述第二处理器102可以是通用处理器、数字数据处理器(DSP，Digital Signal Processor)，或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。所述第二处理器102可以实现或者执行本公开实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者任何常规的处理器等。结合本公开实施例所公开的方法的步骤，可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于存储介质中，该存储介质位于第二存储器103，所述第二处理器102读取第二存储器103中的信息，结合其硬件完成前述方法的步骤。
在示例性实施例中，设备90、终端100可以被一个或多个应用专用集成电路(ASIC，Application Specific Integrated Circuit)、DSP、可编程逻辑器件(PLD，Programmable Logic Device)、复杂可编程逻辑器件(CPLD，Complex Programmable Logic Device)、现场可编程门阵列(FPGA，Field-Programmable Gate Array)、通用处理器、控制器、微控制器(MCU，Micro Controller Unit)、微处理器(Microprocessor)、或者其他电子元件实现，用于执行前述方法。
可以理解，本公开实施例的存储器(第一存储器93、第二存储器103)可以是易失性存储器或者非易失性存储器，也可包括易失性和非易失性存储器两者。其中，非易失性存储器可以是只读存储器(ROM，Read Only Memory)、可编程只读存储器(PROM，Programmable Read-Only Memory)、可擦除可编程只读存储器(EPROM，Erasable Programmable Read-Only Memory)、电可擦除可编程只读存储器(EEPROM，Electrically Erasable Programmable Read-Only Memory)、磁性随机存取存储器(FRAM，ferromagnetic random access memory)、快闪存储器(Flash Memory)、磁表面存储器、光盘、或只读光盘(CD-ROM，Compact Disc Read-Only Memory)；磁表面存储器可以是磁盘存储器或磁带存储器。易失性存储器可以是随机存取存储器(RAM，Random Access Memory)，其用作外部高速缓存。通过示例性但不是限制性说明，许多形式的RAM可用，例如静态随机存取存储器(SRAM，Static Random Access Memory)、同步静态随机存取存储器(SSRAM，Synchronous Static Random Access Memory)、动态随机存取存储器(DRAM，Dynamic Random Access Memory)、同步动态随机存取存储器(SDRAM，Synchronous Dynamic Random Access Memory)、双倍数据速率同步动态随机存取存储器(DDRSDRAM，Double Data Rate Synchronous Dynamic Random Access Memory)、增强型同步动态随机存取存储器(ESDRAM，Enhanced Synchronous Dynamic Random Access Memory)、同步连接动态随机存取存储器(SLDRAM，SyncLink Dynamic Random Access Memory)、直接内存总线随机存取存储器(DRRAM，Direct Rambus Random Access Memory)。本公开实施例描述的存储器旨在包括但不限于这些和任意其它适合类型的存储器。
在示例性实施例中，本公开实施例还提供了一种存储介质，即计算机存储介质，具体为计算机可读存储介质，例如包括存储计算机程序的存储器，上述计算机程序可由设备90的第一处理器92执行，以完成前述设备侧方法所述步骤。计算机可读存储介质可以是FRAM、ROM、PROM、EPROM、EEPROM、Flash Memory、磁表面存储器、光盘、或CD-ROM等存储器。在一示例中，该存储介质可以是非暂时性计算机可读存储介质。
需要说明的是：“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。
另外，本公开实施例所记载的技术方案之间，在不冲突的情况下，可以任意组合。
以上所述，仅为本公开的较佳实施例而已，并非用于限定本公开的保护范围。

Claims (21)

  1. 一种数据处理方法，应用于第一设备，所述方法包括：
    获取终端在天线失配状态下的功率补偿值；
    获取所述网络设备的下行信号强度；和
    基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
  2. 根据权利要求1所述的方法，其中，所述获取终端在天线失配状态下的功率补偿值，包括：
    获取所述天线失配状态对应的反射系数；
    获取所述终端的天线的实际输出功率；和
    基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
  3. 根据权利要求1所述的方法，其中，所述获取终端在天线失配状态下的功率补偿值，包括：
    获取所述天线失配状态对应的反射系数；
    获取所述终端的上行信号强度；
    获取所述终端到所述网络设备之间的第一路损；和
    基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值。
  4. 根据权利要求1所述的方法，其中，所述获取终端在天线失配状态下的功率补偿值，包括：
    获取所述终端在天线端口处的反射功率；
    根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；和
    从所述至少一个数值中选取一个数值作为所述终端在天线失配状态下的功率补偿值。
  5. 根据权利要求1所述的方法，还包括：
    在所述补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
  6. 根据权利要求1所述的方法，还包括：
    获取所述终端发送的功率余量报告，其中所述功率余量报告包括功率余量值；
    通过所述补偿后的所述网络设备的下行信号强度，获得所述终端到所述网络设备之间的第二路损；和
    在所述功率余量值小于预设功率余量阈值且所述第二路损大于预设路损阈值的情况下，执行网络优化操作。
  7. 根据权利要求1所述的方法，还包括：
    获取第一信息集合，其中所述第一信息集合包括所述终端在p个天线失配状态下发送的功率余量报告，以及所述终端在所述p个天线失配状态下的时间信息和/或位置信息；所述功率余量报告包括功率余量值；p为大于或等于1的整数；
    从所述第一信息集合中选取与m个天线失配状态对应的m个功率余量值，其中，m个功率余量值均小于预设功率余量阈值，m为大于或等于1的整数，m小于或等于p；
    从所述第一信息集合中选取与所述m个功率余量值对应的时间信息和/或位置信息；和
    基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作。
  8. 根据权利要求7所述的方法，其中，所述基于与所述m个功率余量值对应的时间信息和/或位置信息，判断是否执行网络优化操作，包括以下中的至少一个：
    基于与所述m个功率余量值对应的时间信息，确定m个权值；将所述m个权值与对应所述m个天线失配状态下得到的补偿后的所述网络设备的下行信号强度求乘积后，求和得到第一下行信号强度；在所述第一下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作；和
    针对与所述m个功率余量值对应的位置信息中每个位置信息，在相应位置信息对应的补偿后的所述网络设备的下行信号强度小于预设信号强度阈值的情况下，执行网络优化操作。
  9. 根据权利要求1所述的方法，其中，所述获取终端在天线失配状态下的功率补偿值，包括：获取所述终端在不同天线失配状态下的功率补偿值；
    所述基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，包括：基于所述终端在不同天线失配状态下的功率补偿值，分别对所述网络设备的下行信号强度进行补偿；
    所述方法还包括：
    将在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度进行比较，得到比较结果；
    当所述比较结果表征在不同天线失配状态下得到的补偿后的所述网络设备的下行信号强度相同时，确定所述终端所在区域的网络覆盖情况未发生变化。
  10. 根据权利要求1至9任一项所述的方法，还包括：
    获取人工智能/机器学习AI/ML模型，其中，所述AI/ML模型用于执行所述数据处理方法；
    其中，所述AI/ML模型的训练数据包括：
    所述终端在不同天线失配状态下的功率补偿值；或者，
    所述终端在不同天线失配状态下的功率补偿值，以及所述终端在不同天线失配状态下的时间信息和/或位置信息。
  11. 根据权利要求10所述的方法，其中，所述AI/ML模型由所述网络设备训练，或者由操作维护管理OAM训练，或者由所述终端训练。
  12. 一种数据处理方法，应用于终端，所述方法包括：
    确定所述终端在天线失配状态下的功率补偿值；和
    向第一设备发送所述终端在天线失配状态下的功率补偿值；
    其中，所述功率补偿值用于所述第一设备对获取的网络设备的下行信号强度进行补偿，以得到补偿后的所述网络设备的下行信号强度。
  13. 根据权利要求12所述的方法，其中，所述确定所述终端在天线失配状态下的功率补偿值，包括：
    通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率，并获取所述终端在天线端口处的输入功率；基于所述反射功率以及所述输入功率，获取所述终端在天线失配状态下的反射系数；
    基于所述反射系数，执行以下操作之一：
    获取所述终端的天线的实际输出功率；基于所述终端的天线的实际输出功率以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；或
    获取所述终端的上行信号强度，获取所述终端到所述网络设备之间的第一路损；基于所述终端的上行信号强度、所述第一路损以及所述反射系数，确定所述终端在天线失配状态下的功率补偿值；
    或者，
    通过所述终端的功率感知模块，获取所述终端在天线端口处的反射功率；根据所述反射功率的功率值，确定至少一个数值，其中，所述至少一个数值中任意一个数值均小于或等于所述反射功率的功率值；从所述至少一个数值中选取一个数值作为所述功率补偿值。
  14. 根据权利要求12所述的方法，还包括：
    向所述第一设备发送功率余量报告，其中所述功率余量报告包括功率余量值；
    其中，所述功率余量值用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度，判断是否执行网络优化操作。
  15. 根据权利要求12所述的方法，还包括：
    获取所述终端在不同天线失配状态下的时间信息和/或位置信息；和
    将所述时间信息和/或所述位置信息发送给所述第一设备；
    其中，所述时间信息和/或位置信息用于所述第一设备结合所述补偿后的所述网络设备的下行信号强度判断是否执行网络优化操作，或者用于所述第一设备结合所述终端在天线失配状态下的功率补偿值训练AI/ML模型。
  16. 一种数据处理装置，包括：
    获取模块，用于获取终端在天线失配状态下的功率补偿值；获取网络设备的下行信号强度；和
    第一处理模块，用于基于所述终端在天线失配状态下的功率补偿值，对所述网络设备的下行信号强度进行补偿，得到补偿后的所述网络设备的下行信号强度。
  17. 一种数据处理装置，包括：
    第二处理模块，用于确定所述终端在天线失配状态下的功率补偿值；和
    发送模块，用于向第一设备发送所述终端在天线失配状态下的功率补偿值。
  18. 一种设备，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器，其中，所述处理器用于运行所述计算机程序时，执行权利要求1至11任一项所述方法的步骤。
  19. 一种终端，包括处理器和用于存储能够在处理器上运行的计算机程序的存储器，其中，所述处理器用于运行所述计算机程序时，执行权利要求12至15任一项所述方法的步骤。
  20. 一种计算机可读存储介质，其上存储有计算机程序，其中，所述计算机程序被处理器执行时实现权利要求1至11任一项所述方法的步骤，或者，实现权利要求12至15任一项所述方法的步骤。
  21. 一种包括指令的计算机程序产品，其中所述指令被处理器执行时，使得所述处理器执行根据权利要求1至11中任一项所述的方法，或者权利要求12至15任一项所述的方法。
PCT/CN2024/099374 2023-07-06 2024-06-14 数据处理方法、装置、设备及存储介质 Ceased WO2025007727A1 (zh)

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