Signal amplification method, device, equipment, medium and program product

Abstract

The invention discloses a signal amplification method, device and equipment, a medium and a program product. The method comprises the following steps: analyzing a received base station signal to obtain a tracking area code (TAC) value of the base station signal; determining whether the TAC value of the base station signal is in a white list; when the TAC value of the base station signal is in the white list, determining whether the working power of the base station signal does not belong to a preset power range; under the condition that the working power of the base station signal does not belong to a preset power range, performing power adjustment on the base station signal; and carrying out amplification processing on the adjusted base station signal. By adopting the method provided by the embodiment of the invention, the system security can be improved, and the flexibility and efficiency of network coverage can be improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of wireless communication, and particularly relates to a signal amplification method, device, equipment, medium and program product. BACKGROUND

[0002] In the related art, the network coverage depth in some scenarios (for example, closed scenarios) is usually improved by using a micro-power amplifier. For example, a signal of an outdoor base station can be received by a main antenna placed outdoors, and then the signal is introduced into a micro-power amplifier in a scenario with insufficient depth coverage through a radio frequency cable, and indoor signal coverage is performed through the output end of the micro-power amplifier. However, the current micro-power amplifier usually performs signal amplification and cannot perform signal security protection, and the security is poor. SUMMARY

[0003] The present disclosure discloses a signal amplification method, device, equipment, medium and program product. The technical scheme of the present disclosure is as follows:

[0004] In a first aspect, the present disclosure discloses a signal amplification method, comprising:

[0005] parsing a received base station signal to obtain a tracking area code (TAC) value of the base station signal;

[0006] determining whether the TAC value of the base station signal is in a white list;

[0007] in a case where the TAC value of the base station signal is in the white list, determining whether the working power of the base station signal is not in a preset power range;

[0008] in a case where the working power of the base station signal is not in the preset power range, performing power adjustment on the base station signal;

[0009] performing amplification processing on the adjusted base station signal.

[0010] In a possible implementation, the determination of whether the TAC value of the base station signal is in the white list comprises:

[0011] comparing the TAC value of the base station signal with TAC values in the white list to determine whether the TAC value of the base station signal is contained in the TAC values in the white list;

[0012] if the TAC value of the base station signal is contained in the TAC values in the white list, outputting a first code; the first code is used to indicate that the TAC value of the base station signal is contained in the TAC values in the white list;

[0013] output a second code if the TAC value of the base station signal is not included in the TAC values in the whitelist; the first code is used to indicate that the TAC value of the base station signal is not in the TAC values in the whitelist.

[0014] In a possible implementation, the method further includes:

[0015] output the base station signal to a preset device if the TAC value of the base station signal is not included in the TAC values in the whitelist.

[0016] save the base station signal in the whitelist if a first preset instruction input by a user through the preset device is received; the first preset instruction is used to indicate that the base station signal passes the security verification.

[0017] add the base station signal in a blacklist if a second preset instruction input by a user through the preset device is received; the second preset instruction is used to indicate that the base station signal fails to pass the security verification.

[0018] In a possible implementation, the determining, in the case that the TAC value of the base station signal is in the whitelist, whether the working power of the base station signal is not in a preset power range includes:

[0019] determining whether a user signal quality and a user quantity of a current use scenario satisfy a preset condition in the case that the TAC value of the base station signal is in the whitelist.

[0020] acquiring data information of the current use scenario in the case that the user signal quality and the user quantity do not satisfy the preset condition.

[0021] determining a maximum power, an optimal power and a minimum power corresponding to the current use scenario based on the data information of the current use scenario through a preset prediction model.

[0022] determining the preset power range based on the maximum power and the minimum power.

[0023] determining whether the working power of the base station signal is not in the preset power range.

[0024] In a possible implementation, the preset prediction model is obtained by training training sample data based on gradient boosting decision tree (GBDT) and long short-term memory (LSTM).

[0025] In a possible implementation, the power adjustment of the base station signal in the case that the working power of the base station signal is not in the preset power range includes:

[0026] In a case where the working power of the base station signal does not belong to the preset power range, the optimal power is acquired.

[0027] The working power of the base station signal is adjusted to the optimal power.

[0028] In a second aspect, the present disclosure discloses a signal amplification device, comprising:

[0029] A signal receiving module is configured to parse a received base station signal to obtain a tracking area code (TAC) value of the base station signal.

[0030] A TAC determining module is configured to determine whether the TAC value of the base station signal is in a white list.

[0031] A power determining module is configured to determine whether the working power of the base station signal does not belong to a preset power range in a case where the TAC value of the base station signal is in the white list.

[0032] A power adjusting module is configured to perform power adjustment on the base station signal in a case where the working power of the base station signal does not belong to the preset power range.

[0033] A signal amplification module is configured to perform amplification processing on the adjusted base station signal.

[0034] In a third aspect, the present disclosure discloses an electronic device, comprising:

[0035] A processor;

[0036] A memory for storing instructions executable by the processor;

[0037] The processor is configured to execute the instructions to implement the method of the first aspect.

[0038] In a fourth aspect, the present disclosure discloses a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the method of the first aspect.

[0039] In a fifth aspect, the present disclosure discloses a computer program product comprising computer programs / instructions, wherein the computer programs / instructions are executed by a processor to implement the method of the first aspect.

[0040] The technical solutions disclosed by the present disclosure at least bring the following beneficial effects:

[0041] In the technical solution of the present disclosure, the tracking area code (TAC) value of the received base station signal is obtained by analyzing the base station signal; it is determined whether the TAC value of the base station signal is in the white list; in the case that the TAC value of the base station signal is in the white list, it is determined whether the working power of the base station signal is not in the preset power range; in the case that the working power of the base station signal is not in the preset power range, the power of the base station signal is adjusted; and the adjusted base station signal is amplified. In this way, on the one hand, by combining the TAC value and the white list, only the base station signal in the white list is amplified, so that the illegal 5G base station signal can be effectively isolated, the security protection function of the base station signal is realized, and the system security is improved. On the other hand, dynamic power adjustment can also be realized, so that the utilization rate of network resources can be improved, the dependence on additional equipment can be reduced, and the flexibility and efficiency of network coverage can be improved.

[0042] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings incorporated in the specification and forming a part of it illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure, and do not constitute an undue limitation on the present disclosure.

[0044] Figure 1 A flowchart of a signal amplification method provided by an embodiment of the present disclosure;

[0045] Figure 2 A functional module schematic diagram of a power amplifier provided by an embodiment of the present disclosure;

[0046] Figure 3 A flowchart of a signal anomaly detection process provided by an embodiment of the present disclosure;

[0047] Figure 4 A signal decoding and database comparison module provided by an embodiment of the present disclosure;

[0048] Figure 5 A power adjustment flowchart provided by an embodiment of the present disclosure;

[0049] Figure 6 A device power prediction calculation schematic diagram provided by an embodiment of the present disclosure;

[0050] Figure 7 An MQTT platform data collection and model training schematic diagram provided by an embodiment of the present disclosure;

[0051] Figure 8ais a schematic diagram of an LSTM model structure provided by an embodiment of the present disclosure.

[0052] Figure 8b is a schematic diagram of an LSTM model structure provided by an embodiment of the present disclosure.

[0053] Figure 9 is a schematic diagram of a power adjustment scheme provided by an embodiment of the present disclosure.

[0054] Figure 10 is a schematic diagram of a user signal quality detection algorithm provided by an embodiment of the present disclosure.

[0055] Figure 11 is a schematic diagram of a power amplifier working scenario and working mechanism provided by an embodiment of the present disclosure.

[0056] Figure 12 is a schematic diagram of a signal amplification device provided by an embodiment of the present disclosure.

[0057] Figure 13 is a schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] In order for those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below in conjunction with the accompanying drawings.

[0059] It should be noted that the terms "first", "second", and the like in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present disclosure described herein can be implemented in an order other than that illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0060] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data for analysis, stored data, displayed data, etc.) and signals involved in the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0061] The acquisition, storage, use, processing, etc. of data in the technical solutions of the present disclosure all comply with the relevant provisions of national laws and regulations.

[0062] It should be noted that in the embodiments of the present disclosure, some software, components, models, etc. in the industry have solutions, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present disclosure, but it does not mean that the applicant has or will necessarily use the solution.

[0063] In the related art, with the development of mobile network technology and the popularity of 5G signals, the demand for network deep coverage in scenarios such as closed machine rooms and basements where signals are difficult to penetrate is increasing. Currently, a micro-power amplifier is usually used, which receives signals from an outdoor base station through a main antenna placed outdoors, and then introduces the signals into a micro-power amplifier in a deep coverage scenario through a radio frequency cable. The indoor signal coverage is performed through the output end of the micro-power amplifier, the coverage range of the 5G signal is enhanced, and it is ensured that users in these special areas can also enjoy stable and high-speed network services, greatly improving user experience and work efficiency. However, the current micro-power amplifier has at least the following deficiencies:

[0064] 1. No safety protection function is set when amplifying base station signals.

[0065] 2. Only fixed power can be used for signal amplification, resulting in a very fixed coverage range of the signal. If a larger coverage area is to be achieved, multiple or different types of 5G signal micro-power amplifiers must be installed, and the flexibility is limited. When facing temporary physical changes in the network environment, the traditional approach often requires additional equipment to deal with, which not only increases the cost. When facing changes in the number of users using at different times, the traditional 5G signal micro-power amplifier cannot accurately adjust according to the time sequence characteristics of user usage time, such as fewer people using the underground warehouse at night, more people using the subway during rush hour, etc. This fixed power method that does not consider time and usage scenarios to some extent causes unnecessary resource waste and low resource utilization.

[0066] Therefore, the present application provides a signal amplification method, device, equipment, medium and program product, which can identify TAC (Tracking Area Code, Tracking Area Code) information through decoding and compare it with the TAC whitelist in the built-in database to solve the problem of unprotected intrusion detection. The power amplifier decodes the received 5G signal and identifies the TAC information, matches the identified TAC information with the whitelist TAC list, and does not amplify the TAC base station signal that is not in the list, thereby effectively isolating illegal 5G base station signals and improving system security.

[0067] On the other hand, this proposal introduces an AI-based dynamic power adjustment technology. This technology allows 5G signal micro-power amplifiers to dynamically adjust their transmission power within a preset range to meet coverage requirements in different network scenarios. By collecting and analyzing network environment data in real time (including usage scenarios, usage time, number of users, number of devices in the area, signal quality, etc.), it can intelligently predict and adjust transmission power, thereby ensuring the stability and reliability of network signals. In this way, dynamic power adjustment not only improves the utilization rate of network resources but also reduces reliance on additional equipment, making network coverage more flexible and efficient in different time periods and scenarios.

[0068] The technical solutions provided by the embodiments of this disclosure are described in detail below with reference to the accompanying drawings.

[0069] Figure 1 This is a flowchart illustrating a signal amplification method provided in an embodiment of the present disclosure. This signal amplification method can be applied to a signal amplification system, including a micropower amplifier and a cloud platform. A functional module diagram of the power amplifier can be shown below. Figure 2 As shown, refer to Figure 2 The micropower amplifier includes an indoor antenna, an outdoor antenna, a power amplification module, a power adjustment module, an MCU (Microcontroller Unit) module, a power detection module, a signal decoding and database comparison module, and a user signal quality detection module. The power amplification module is controlled by the MCU module through the power adjustment module, and it connects to both the indoor and outdoor antennas to achieve full-duplex power amplification. As a specific example, the power amplification module has an uplink and downlink automatic control gain range greater than 30dB, with an uplink amplifier gain of 60±3dB and an uplink output power of 13±2dBm. The downlink amplifier gain is 65±3dB, and the downlink output power is 20±2dBm, meeting a wide range of output power adjustment requirements. The user signal quality detection module receives the uplink input coupling signal, connects to the MCU module, and sends power adjustment commands. The power detection module receives both uplink and downlink input / output coupling signals, connects to the MCU module, and detects power intensity information. The MCU module receives the power adjustment commands sent by the user signal quality detection module, transmits data to the cloud, and controls the power amplification module through the power adjustment module. The signal decoding and database comparison module can perform signal filtering. Users can manage the whitelist in the legitimate TAC database in real time through the MQTT (Message Queuing Telemetry Transport) management platform, and automatically filter illegal signals based on this list. As a specific example, the specific examples of the signal frequencies and powers in this proposal are shown in Table 1. The coupling coefficient of each coupling node can be set to 0.1.

[0070] Table 1

[0071] Node signals Signal operating frequency f Signal power P uplink input signal S ui ]] 703 MHZ - 733 MHz and 2515 MHz - 2615 MHz - 47 ± 2 dBm uplink output signal S uo ]] 703 MHZ - 733 MHz and 2515 MHz - 2615 MHz 13 ± 2 dBm Downlink input signal S di ]] 758 MHz - 788 MHz and 2515 MHz - 2615 MHz - 45 ± 2 dBm downstream output signal S do ]]> 758 MHz - 788 MHz and 2515 MHz - 2615 MHz 20 ± 2 dBm uplink input coupled signal S uic ]] 703 MHZ - 733 MHz and 2515 MHz - 2615 MHz - 57 ± 2 dBm uplink output-coupled signal S uoc ]] 703 MHZ - 733 MHz and 2515 MHz - 2615 MHz 3 ± 2 dBm Downlink input coupled signal S dic ]] 758 MHz - 788 MHz and 2515 MHz - 2615 MHz - 55 ± 2 dBm Downgoing output-coupled signal S do ]] 758 MHz - 788 MHz and 2515 MHz - 2615 MHz 10 ± 2 dBm

[0072] The following will be described in conjunction with the accompanying Figure 1 The signal amplification process of the present disclosure will be described. As shown in the figure, the signal amplification method can include the following steps: Figure 1

[0073] S101, parsing the received base station signal to obtain the tracking area code (TAC) value of the base station signal.

[0074] In an embodiment of the present disclosure, when performing signal amplification, the base station signal can be received first, which can be a downlink signal for example. Then, the base station signal can be parsed, for example, by a signal decoding and database comparison module, to obtain the TAC value of the base station signal.

[0075] S102, determining whether the TAC value of the base station signal is in the white list.

[0076] In an embodiment of the present disclosure, after the TAC value of the base station signal is parsed, a pre-set white list can be obtained, which can include multiple TAC values. For example, the TAC database can be loaded by the signal decoding and database comparison module, and the white list can include a data structure (such as a dictionary or a list) of multiple TACs. Then, the obtained TAC value of the base station signal can be compared with the TAC values in the white list to determine whether the TAC value of the base station signal is in the white list.

[0077] S103, in the case that the TAC value of the base station signal is in the white list, determining whether the working power of the base station signal is not in the pre-set power range.

[0078] In an embodiment of the present disclosure, if the TAC value of the base station signal is in the white list, it can be considered that the base station signal is a legal signal and is safe. It can be understood that the pre-set power range can be the range of the working power of the base station signal that can meet the current scene requirements; since different scenes can have different requirements for the working power of the base station signal, the pre-set power ranges corresponding to different scenes can be the same or different.

[0079] S104, in the case that the working power of the base station signal is not in the pre-set power range, performing power adjustment on the base station signal.

[0080] ​In the embodiments of the present disclosure, if the working power of the base station signal belongs to the preset power range, power adjustment is not needed. On the contrary, if the working power of the base station signal does not belong to the preset power range, power adjustment is needed. It can be understood that the power adjustment can be adjusting the working power of the base station signal, for example, the working power can be increased or decreased according to the scene requirement, so that the adjusted working power can meet the scene requirement.

[0081] S105, amplifying the adjusted base station signal.

[0082] In the embodiments of the present disclosure, after the power adjustment of the base station signal, the adjusted base station signal can be amplified. For example, the power-adjusted base station signal can be amplified by the power amplification module of the micro-power amplifier, so as to cover the network in the scene area by the power-amplified base station signal, and meet the network demand in the scene.

[0083] In the embodiments of the present disclosure, by analyzing the received base station signal, the tracking area code (TAC) value of the base station signal is obtained; it is determined whether the TAC value of the base station signal is in the white list; in the case that the TAC value of the base station signal is in the white list, it is determined whether the working power of the base station signal does not belong to the preset power range; in the case that the working power of the base station signal does not belong to the preset power range, the power of the base station signal is adjusted; and the adjusted base station signal is amplified. In this way, on the one hand, by decoding and identifying the TAC value of the base station signal compared with the built-in TAC white list, the TAC base station signal not in the list is not amplified, so as to realize the safety detection of the signal, solve the problem of unguarded intrusion detection, effectively isolate the illegal 5G base station signal, and improve the system security. On the other hand, through the dynamic power adjustment of the signal, the coverage demand in different network scenes can be coped with, so as to ensure the stability and reliability of the network signal, improve the utilization rate of the network resource, reduce the dependence on the additional equipment, and improve the flexibility and efficiency of the network coverage.

[0084] It can be understood that the working power of a single or multiple micro-power amplifiers can also be adjusted within a certain range, which is more suitable

[0085] In some possible implementation manners, determining whether the TAC value of the base station signal is in the white list comprises:

[0086] Comparing the TAC value of the base station signal with the TAC values in the white list, to determine whether the TAC value of the base station signal is contained in the TAC values in the white list;

[0087] If the TAC value of the base station signal is contained in the TAC values in the white list, a first code is output; the first code is used to indicate that the TAC value of the base station signal is contained in the TAC values in the white list;

[0088] If the TAC value of the base station signal is not contained in the TAC values in the white list, a second code is output; the first code is used to indicate that the TAC value of the base station signal is not contained in the TAC values in the white list.

[0089] In the embodiments of the present disclosure, the comparison result of the TAC value of the base station signal and the white list can be indicated by a code. For example, the TAC value of the base station signal can be compared with the TAC values in the white list to determine whether the TAC value of the base station signal is contained in the TAC values in the white list. If the TAC value of the base station signal is contained in the TAC values in the white list, a first code can be output, for example, the first code can be a passed code, which is used to indicate that the base station signal is legal and safe. Conversely, if the TAC value of the base station signal is not contained in the TAC values in the white list, a second code can be output, for example, the second code can be an unpassed code, which is used to indicate that the base station signal is not legal and safe. In this way, whether the base station signal is a legal signal can be indicated by a code, so that the indication of whether the base station signal is a legal signal is more direct and clear.

[0090] In some possible implementation manners, the signal amplification method further includes:

[0091] If the TAC value of the base station signal is not contained in the TAC values in the white list, the base station signal is output to a preset device;

[0092] If a first preset instruction input by a user through the preset device is received, the base station signal is saved in the white list; the first preset instruction is used to indicate that the base station signal passes the security verification;

[0093] If a second preset instruction input by a user through the preset device is received, the base station signal is added to a black list; the second preset instruction is used to indicate that the base station signal fails to pass the security verification.

[0094] In the embodiments of the present disclosure, the whitelist can also be updated according to the base station signal. For example, if the TAC value of the base station signal is not included in the TAC values in the whitelist, the base station signal value can be output to a preset device, which can be a terminal device such as a mobile phone or a tablet of a manager. The manager determines whether the base station signal is legal and whether it passes the security verification. If the manager determines that the base station signal passes the security verification, a first preset instruction indicating that the base station signal passes the security verification is input through the preset device, and if the first preset instruction is received, the base station signal can be saved in the whitelist. On the contrary, if the manager determines that the base station signal does not pass the security verification, a second preset instruction indicating that the base station signal does not pass the security verification is input through the preset device, and if the second preset instruction is received, the base station signal can be saved in the blacklist. It can be understood that the whitelist and the blacklist can also be updated to the local devices in the same region at present.

[0095] In some possible implementations, in the case that the TAC value of the base station signal is in the whitelist, it is determined whether the working power of the base station signal is not in the preset power range, comprising:

[0096] In the case that the TAC value of the base station signal is in the whitelist, it is determined whether the user signal quality and the number of users in the current use scenario satisfy the preset condition;

[0097] In the case that the user signal quality and the number of users do not satisfy the preset condition, data information of the current use scenario is acquired;

[0098] The maximum power, the best power and the minimum power corresponding to the current use scenario are determined based on the data information of the current use scenario through a preset prediction model;

[0099] The preset power range is determined based on the maximum power and the minimum power;

[0100] It is determined whether the working power of the base station signal is not in the preset power range.

[0101] In embodiments of the present disclosure, if the TAC value of the base station signal is in the whitelist, the user signal quality and the number of users of the current use scenario can be acquired, and it is determined whether the user signal quality and the number of users of the current use scenario satisfy a preset condition. The preset condition may, for example, be that the user signal quality is poor or the number of users is too large. The user signal quality may, for example, be less than a certain value, and the number of users may, for example, be greater than or equal to a certain number. If the user signal quality and the number of users do not satisfy the preset condition, that is, the user signal quality is poor or the number of users is too large, data information of the current use scenario can be acquired. The data information of the current use scenario may, for example, include characteristic data of the device, and specifically includes: the current number of users, the number of devices in the same region, the range of the coverage area, the current weather, whether it is a holiday, the use scenario, and the current time, etc. The number of users has a direct impact on the power, and when the number of users is large, the device should increase the power of the 5G model. The number of devices in the same region and the range of the area determine the average size of the working power of each device. The current weather has a certain impact on the propagation of the 5G model. Then, the data information of the current use scenario can be input into a preset prediction model for inference processing to determine the maximum power, the optimal power, and the minimum power corresponding to the current use scenario. The preset prediction model is obtained by training the training sample data based on GBDT (Gradient Boosting Decision Tree) and LSTM (Long Short Term Memory Network). The maximum power and the minimum power can be the range of the working power of the base station signal suitable for the current use scenario predicted by the preset prediction model, and the optimal power can be the working power of the base station signal most suitable for the current scenario. Subsequently, the preset power range can be determined based on the maximum power and the minimum power, and it is determined whether the working power of the base station signal belongs to the preset power range. It can be understood that the preset prediction model can be set in the cloud.

[0102] In further possible implementations, in the case that the working power of the base station signal does not belong to the preset power range, the power of the base station signal is adjusted, including:

[0103] In the case that the working power of the base station signal does not belong to the preset power range, the optimal power is acquired;

[0104] The working power of the base station signal is adjusted to the optimal power.

[0105] In embodiments of the present disclosure, if the working power of the base station signal does not belong to the preset power range, the optimal power output by the preset prediction model can be acquired, and the optimal power signal is output to the power adjustment module for power adjustment. The working signal of the base station signal is adjusted to the optimal power, and then the base station signal adjusted to the optimal power is input to the power amplification module of the micro-power amplifier for amplification.

[0106] To make the signal amplification method provided by the embodiments of the present disclosure clearer, the following will be described in conjunction with the drawings.

[0107] In related technologies, 5G power amplifiers have certain limitations in security and cannot effectively identify illegal signals. The signal amplification method provided by the embodiments of the present disclosure can identify the TAC of the signal to be amplified through the introduction of wireless signal decoding based on the TAC-based intrusion detection, and compare it with the whitelist in the database to build an efficient signal security filtering mechanism. The power amplifier can identify and filter illegal signals, improving the system security. At the same time, the introduction of the white / black list enhances the flexibility and adaptability of the system, and the administrator can independently configure to realize specific signal power amplification. Moreover, the traditional 5G micro-power amplifier power adjustment mode is fixed and cannot be adjusted in real time according to the network environment, and the flexibility is insufficient. The intelligent dynamic power adjustment based on AI (Artificial Intelligence, Artificial Intelligence) neural network adopts AI neural network algorithms such as LSTM and GBDT, and realizes intelligent dynamic power adjustment based on signal quality and timing. This dynamic adjustment mechanism not only improves the resource utilization rate, but also reduces the equipment operation cost.

[0108] At the same time, the traditional 5G micro-power amplifier has limitations in networking and remote management, and it is difficult to realize cluster management. The 5G power amplifier networking technology based on MQTT (Message Queue Telemetry Transport) solves the limitations of traditional 5G power amplifiers in networking and remote management, and it is difficult to realize cluster management. The signal amplification method provided by the embodiments of the present disclosure adopts the lightweight MQTT protocol, and realizes the networking and remote monitoring of the 5G power amplifier cluster. The publish / subscribe mode of the MQTT protocol enables device administrators to easily remotely monitor and manage device clusters. When a single point failure occurs in the cluster, the system can quickly locate the fault point and type, and calculate the fault impact range, thereby improving the reliability and maintenance efficiency of the system. In addition, the lightweight characteristics of the MQTT protocol make the network transmission overhead small, and it is suitable for use in unstable or low-bandwidth network environments. By introducing the MQTT technology, the problem of the traditional 5G micro-power amplifier being unable to be remotely managed is solved. The management efficiency of the cluster equipment is greatly improved, and the applicability of the traditional 5G micro-power amplifier to different scenarios is enhanced.

[0109] For example, the signal amplification method provided by the embodiments of the present disclosure can realize a self-adaptive uplink / downlink micro-power amplifier with TAC decoding and power adjustment, which combines Figure 2The power amplifier includes an indoor antenna, an outdoor antenna, a power amplification module, a power adjustment module, an MCU module, a power detection module, a signal decoding and database comparison module, and a user signal quality detection module. Specific examples of signal frequencies and power levels can be found in Table 1. The power amplification module is controlled by the MCU module through the power adjustment module and is connected to the indoor / outdoor antenna to achieve full-duplex power amplification. The power amplification module has an uplink and downlink automatic control gain range greater than 30dB, with an uplink amplifier gain of 60±3dB and an uplink output power of 13±2dBm. The downlink amplifier gain is 65±3dB and the downlink output power is 20±2dBm, meeting a wide range of output power adjustment requirements. The user signal quality detection module receives the uplink input coupling signal, connects to the MCU module, and sends power adjustment commands. The power detection module receives both uplink and downlink input / output coupling signals, connects to the MCU module, and detects power intensity information. The MCU module receives commands from the user signal quality detection module, transmits data to the cloud, and controls the power amplification module through the power adjustment module. Signal filtering is performed by the signal decoding and database comparison module. Users can manage the whitelist in the legitimate TAC code database in real time through the MQTT management platform, and automatically filter illegal signals based on this list.

[0110] Based on the aforementioned low-power amplifier and cloud computing, the signal amplification method provided in this disclosure embodiment may include the following processing:

[0111] Step 1: Abnormal signal detection.

[0112] First, the signal decoding and database comparison module incorporates a signal demodulation module with a built-in SIM card. Upon receiving a base station signal, it performs anomaly detection. If no anomalies are found, a feedback signal is sent to the MCU module. The MCU module receives a power adjustment command and adjusts the power of the received signal accordingly to obtain a gain signal (the amplified base station signal with a certain gain), which is then transmitted through the indoor antenna. Anomaly detection of the base station signal primarily involves matching the TAC value of the demodulated 5G base station signal with the existing network-compliant TAC list (whitelist) stored in the signal decoding and database comparison module. The implementation process can be found in [link to relevant documentation]. Figure 3 If the TAC value of the 5G base station signal is not on the database whitelist, the base station signal is deemed to pose a security risk. The MCU module issues a command not to adjust the power and simultaneously reports the relevant information to the management platform. After receiving the relevant information, the administrator can verify the insecure base station. If the verification is valid (e.g., the TAC was adjusted normally or a legally added TAC), the database built into the MCU can be updated, allowing the 5G base station signal to be amplified and introduced into the target coverage area normally.

[0113] When the outdoor antenna receives the outdoor base station signal, the downlink input signal enters the signal decoding and database comparison module, and after filtering out illegal signals, the legal signals are input to the power amplification module, and then the signal is sent to the user through the indoor antenna, so as to realize abnormal signal detection and interception. The signal decoding and database comparison module can be seen from Figure 4 , the downlink input signal is detected and separated by demodulation and MMSE (minimum mean square error) estimation signal separation algorithm, and each separated signal is decoded and compared with the legal TAC table (white list) in the database. If the TAC comparison passes, the legal signal is modulated and input to the subsequent power amplification module; if the signal comparison does not pass, the illegal signal is not sent.

[0114] As an example, the specific implementation of step one can be as follows:

[0115] (1) Decoding: The signal decoding and database comparison module built-in signal demodulation module completes the analysis of the downlink signal, and obtains the TAC value of the base station signal by demodulation.

[0116] (2) Comparison: The signal decoding and database comparison module loads the TAC database white list, which contains the data structure (such as dictionary or list) of all TACs, and compares the TAC value of the demodulated signal with the white list in the database.

[0117] (3) Filtering: If the TAC is in the database, return the pass code "passed". If the TAC is not in the database, return the unpassed code "unpassed", and the 5G signal is added to the blacklist database.

[0118] (4) Push: Push the unpassed 5G signal identification to the cloud server, and the server pushes the illegal signal to the device administrator. If the device administrator confirms that it is passed, the 5G signal will be added to the whitelist database. If the device administrator confirms that it is an illegal signal, the 5G signal will be added to the blacklist database.

[0119] (5) Synchronization: Synchronize the white / black list to the local devices in the same area.

[0120] (6) Output: Output the legal power amplifier signal for the next step of adaptive power calculation.

[0121] Step two: adaptive power regulation.

[0122] Step two introduces an adaptive power adjustment module, receives the output signal of step one, and completes user signal record data analysis, preliminarily judges whether the transmission power needs to be adjusted, if it needs to be adjusted, uploads the current use scene, use time, user quantity, regional device number and signal quality data to the cloud end MQTT platform, returns to the amplifier after the cloud end artificial intelligence model calculates the adjustment scheme, and executes the power adjustment action, which can be seen from Figure 5 .

[0123] The specific implementation of the adaptive power adjustment module of this step can be as follows:

[0124] (1) Data collection and upload: when the indoor antenna receives the user signal, a small part of the coupled signal enters the user signal quality detection module, if the user signal quality is detected to be poor or the user is too much, the module sends the user information to the cloud end through the MCU module.

[0125] (2) Power calculation based on AI model: according to the data information uploaded by the device, the inference calculation of the cloud end preset prediction model is completed, and the calculation result is returned to the device as the power adjustment instruction information, such as the micro power amplifier.

[0126] As an example, the process of preset prediction model calculation can be seen from Figure 6 .

[0127] After the cloud server receives the data information uploaded by the device (such as the micro power amplifier), the data packet is parsed to obtain the characteristic data of the device, including: the current number of users, the number of devices in the same area, the coverage area, the current weather, whether it is a holiday, the use scene and the current time. The number of users will have a direct impact on the power, when the number of users is large, the device should increase the power of the 5G model. The number of devices in the same area and the area range determine the average size of the working power of each device. The current weather will have a certain impact on the propagation of the 5G model. After collecting the characteristic data of the current number of users, the number of devices in the same area and the coverage area of the device to the cloud, the GBDT machine learning algorithm is constructed to realize the modeling of the above characteristics. When inferring, the device characteristics parsed will be converted into a feature vector, and the feature vector will output the maximum power (MAX), the best power (OPT) and the minimum power (MIN) after the trained model infers. If the current device working power is between MIN and MAX, no adjustment instruction is sent, if it is not in this range, the best power (OPT) instruction is sent to the terminal device.

[0128] The training process of the preset prediction model can be as follows Figure 7As shown, the dynamic adjustment module involves a preset prediction model. The training process of this preset prediction model is executed in the cloud, and then updated to the 5G power amplifier MCU module to achieve inference on the terminal device. The process includes eight detailed steps to complete the model training and update: ① Collecting user data from the 5G power amplifier to the cloud, specifically including information such as the number of connected users, amplification power, and signal quality. ② Training the AI ​​model (i.e., the preset prediction model) using the collected data. ③ Storing the model. ④ Updating the latest model to the cloud server. ⑤ Sending a message to the device administrator (…). Figure 7 (The instruction publisher client in the middle). ⑥ Obtain the instruction from the device administrator, that is, whether to update the latest model to the MCU module of the terminal 5G power amplifier device. ⑦ and ⑧ are the processes of publishing the latest power prediction model to the terminal 5G power amplifier through the "message publish / subscribe module".

[0129] The AI ​​model used in this embodiment is a Long Short-Term Memory (LSTM) network, the structure of which can be found in [reference needed]. Figure 8a , 8b The AI ​​model primarily uses Long Short-Term Memory (LSTM) networks, which excel at processing time-series data. This model can capture long-term dependencies in the sequence data and is suitable for dynamically changing features such as user usage time, number of users, and signal quality. By analyzing this input data, LSTM networks can generate corresponding power schemes to meet the needs of different time periods. It also predicts data trends or values ​​over a future period by analyzing past observations. LSTM, as a powerful neural network model, demonstrates superior performance in handling such problems. LSTM can effectively handle long-term dependencies in time-series data and simultaneously consider both forward and backward information in the sequence, thereby improving the accuracy and reliability of predictions. Therefore, LSTM has become one of the commonly used models in the prediction field. Figure 8a , 8b The function for the preset prediction model can be as follows:

[0130] i t =σ(W xixt +W hi h t-1 +W ci c t-1 +b i ) ①

[0131] f t = σ ( W xf x t +W hf h t-1 +W cf c t-1 +bf ) ②

[0132]

[0133] o t = σ (W xo x t +W ho h t-1 +W co c t +b o ) ⑤

[0134] h t = o t · tanh (c t ) ⑥

[0135] where x t is the input of time step t, h t -1 is the hidden state of the previous time step, c t -1 is the cell state of the previous time step, i t , f t , c^t, c t , o t are the input gate, the forget gate, the cell state, the output gate and the hidden state respectively, W and b are model parameters, σ is the sigmoid function, and tanh is the hyperbolic tangent function.

[0136] Based on Figure 8a , 8b the long short-term memory network layer structure is built to build a time series power decision model. First, the user data is loaded, the user data is preprocessed, and the feature vector for model training is formed. The feature vector is input into the LSTM layer, and the output matrix of the LSTM layer is input into the linear transformation layer. After processing by the fully connected layer, the time series prediction power is output.

[0137] (3) Perform power adaptive adjustment.

[0138] Get the cloud model calculation result, and execute the power adjustment instruction through the power adjustment module. The power adjustment scheme is shown in Figure 9 . The user signal quality detection module is as follows Figure 10As shown, the uplink input coupled signal undergoes RF front-end processing such as filtering, amplification, and mixing to reach the intermediate frequency (IF) signal. After clock synchronization and preprocessing, the IF signal is demodulated. The MMSE (Multi-Mean Separation and Optimization) estimation signal separation algorithm is used to detect and separate each user signal. The signal-to-noise ratio (SNR), EVM (Expansion Vector Error), and BER (Bit Error Probability) of each user signal are used to determine the user signal quality. (However, due to the channel inconsistency between uplink and downlink signals, uplink signal quality cannot directly measure downlink signal quality. A feasible solution is to add relevant parameters of the received signal quality to the uplink signal encoding transmitted by the user for feedback.) According to the server scheme, the MCU module sends a command to increase or decrease the power amplifier module gain. For example, the logic for determining whether to adjust the transmit power can be as follows:

[0139]

[0140] In the above formula ①, S do Describe the downlink signal, f(S) do The sum of all user signal qualities is represented by , where a0 represents the influence shadow. Formula ② averageQuality-S do This represents the average signal quality. Formula ③ sumSuaredDifferencesS do This represents the variance calculated from the 5G downlink signal. If the variance is higher than a predetermined value and the average value is lower than a predetermined value, the transmission power is increased. If the variance is lower than a predetermined value and the average value is higher than a predetermined value, the transmission power is decreased. If both the variance and the average value are within a predetermined range, no adjustment to the transmission power is required.

[0141] The signal amplification method provided in this embodiment will be described below using specific application scenarios. For example, suppose in scenario a, within a building, the amplifier in room 1 has a large number of users and a poor signal; the amplifier in room 2 has a better signal. The algorithm can then automatically adjust, increasing the power of the amplifier in room 1 and appropriately decreasing the power of the amplifier in room 2. In scenario b, within a building, room 1 has a large number of users during the day, while room 2 has a large number of users at night. The algorithm can then automatically adjust, increasing the power in room 1 from 8:00 AM to 6:00 PM and increasing the power in room 2 from 6:00 PM to 8:00 AM.

[0142] Next, combined Figure 11 The specific working scenarios and mechanisms of the signal amplification method provided in the embodiments of this disclosure are described. For example... Figure 11As shown, in the underground mall scenario, due to its large area range, it is impossible to cover the A / B / C three areas of the underground mall with one device, so the device administrator deployed 3 5G power amplifiers in the mall. One day, the crowd in the C area of the underground mall experienced a large-scale signal quality decline, and the mall monitoring center and the administrator's mobile phone quickly received the 5G device fault information in the C area, so the personnel organized to repair the device. At the same time, the MQTT cloud platform power prediction model identified that the C area device may fail or exceed the adjustable power upper limit, so the MQTT cloud platform issued an instruction to increase the 5G amplifier power in the A / B area. Because the C area is densely populated, if waiting for personnel to complete the repair will cause a large number of customers to fail to collect and pay, the MQTT cloud platform temporarily enhances the 5G signal of the C area by issuing an instruction to the A / B area amplifier, filling the time gap of repair.

[0143] In the underground parking lot scenario, the user's demand for signal will have obvious time period related trends. For example Figure 12 , in a mall parking lot, 3 5G power amplifier devices are set up, respectively located in the parking lot A area, the parking lot entrance gate and the parking lot exit gate. Because the mall business hours are from 9:00 am to 9:00 pm. So at 9:30-10:30 on Saturday, there is a peak of traffic flow at the entrance gate of the mall parking lot, and customers need to scan the code to enter, causing a certain delay of the 5G signal. The AI neural network (LSTM, GBDT) intelligent dynamic power regulation technology of the present application solves this problem. The time series model on the MQTT cloud platform learns from historical data which time period will have a user traffic peak and which time period has less user usage, so the MQTT cloud platform has already issued an instruction to the terminal device of the 5G power amplifier before the peak occurs. The device has reasonably adjusted the working power before the user traffic peak arrives.

[0144] In the intrusion detection scenario, assume that the 5G power amplifier device in the underground mall B area receives an illegal signal. The traditional 5G power amplifier amplifies the illegal signal without selection, and the crowd in the underground mall B area may unknowingly access the illegal signal, resulting in a large amount of privacy data loss and causing a major security hazard. The innovative 5G power amplifier proposed in the present application decodes the TAC code of the signal, and then compares it with the database white / black list to find that it is an illegal signal, so the illegal signal is filtered out and not amplified. Improve the security and stability of the device.

[0145] In a networking scenario, the 5G signal micro-power amplifier in the present proposal builds a series of interactive management capabilities of cloud, edge and terminal, such as power monitoring, fault handling, power increasing, power decreasing, fault early warning, fault range prediction, traffic peak prediction, illegal signal monitoring, whitelist adding, deleting and modifying, and device disconnection alarm. Different from the traditional 5G signal micro-power amplifier, the 5G signal micro-power amplifier in the present proposal is improved by using the MQTT protocol architecture for networking, thereby improving the interaction capability of the device and greatly reducing the management and maintenance cost.

[0146] The specific implementation and technical effects of each step of the embodiment are similar to those of the method embodiment described above, and will not be described here again.

[0147] Based on the same inventive concept, the present disclosure also provides a signal amplification device. As shown in Figure 12 The signal amplification device 1200 includes:

[0148] A TAC determination module 1210 is configured to determine whether the TAC value of the base station signal is in a whitelist.

[0149] A power determination module 1220 is configured to determine whether the working power of the base station signal is not within a preset power range, if the TAC value of the base station signal is in the whitelist.

[0150] A power adjustment module 1230 is configured to adjust the power of the base station signal, if the working power of the base station signal is not within the preset power range.

[0151] A signal amplification module 1240 is configured to amplify the adjusted base station signal.

[0152] In a possible implementation, the TAC determination module 1210 is configured to:

[0153] compare the TAC value of the base station signal with the TAC values in the whitelist, to determine whether the TAC value of the base station signal is contained in the TAC values in the whitelist;

[0154] output a first code if the TAC value of the base station signal is contained in the TAC values in the whitelist; the first code is used to indicate that the TAC value of the base station signal is contained in the TAC values in the whitelist.

[0155] output a second code if the TAC value of the base station signal is not contained in the TAC values in the whitelist; the first code is used to indicate that the TAC value of the base station signal is not contained in the TAC values in the whitelist.

[0156] In a possible implementation, the signal amplification device 1200 further includes:

[0157] a signal output module, configured to output the base station signal to a preset device if a TAC value of the base station signal is not included in the TAC values in the whitelist;

[0158] a whitelist updating module, configured to save the base station signal in the whitelist if a first preset instruction input by a user through the preset device is received; the first preset instruction is used to indicate that the base station signal passes the security verification;

[0159] a blacklist updating module, configured to add the base station signal in a blacklist if a second preset instruction input by the user through the preset device is received; the second preset instruction is used to indicate that the base station signal does not pass the security verification.

[0160] In a possible implementation, the power determination module 1220 is configured to:

[0161] determine whether a user signal quality and a user quantity of a current use scenario satisfy a preset condition in a case where a TAC value of the base station signal is in the whitelist;

[0162] obtain data information of the current use scenario in a case where the user signal quality and the user quantity do not satisfy the preset condition;

[0163] determine a maximum power, an optimal power and a minimum power corresponding to the current use scenario based on the data information of the current use scenario through a preset prediction model;

[0164] determine the preset power range based on the maximum power and the minimum power;

[0165] determine whether the working power of the base station signal is not in the preset power range.

[0166] In a possible implementation, the preset prediction model is obtained by training training sample data based on gradient boosting decision tree (GBDT) and long short-term memory (LSTM).

[0167] In a possible implementation, the power adjustment module 1230 includes:

[0168] obtain the optimal power in a case where the working power of the base station signal is not in the preset power range;

[0169] adjust the working power of the base station signal to the optimal power.

[0170] The specific implementation manners and technical effects of the apparatus provided in the embodiments of the present disclosure are similar to those of the above method embodiments, and will not be described here again.

[0171] According to embodiments of the present disclosure, the present disclosure also discloses an electronic device, a computer-readable storage medium, and a computer program product.

[0172] Figure 13 A schematic block diagram of an example electronic device 1300 that can be used to implement embodiments of the present disclosure is shown. The electronic device 1300 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0173] As shown in Figure 13 The electronic device 1300 includes a computing unit 1301 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 1302 or a computer program loaded from a storage unit 1308 into a random access memory (RAM) 1303. Various programs and data required for the operation of the device 1300 can also be stored in the RAM 1303. The computing unit 1301, the ROM 1302, and the RAM 1303 are connected to each other through a bus 1304. An input / output (I / O) interface 1305 is also connected to the bus 1304.

[0174] Various components in the electronic device 1300 are connected to the I / O interface 1305, including an input unit 1306, such as a keyboard, a mouse, etc.; an output unit 1307, such as various types of displays, a speaker, etc.; a storage unit 1308, such as a magnetic disk, an optical disk, etc.; and a communication unit 1309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1309 allows the electronic device 1300 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0175] The computing unit 1301 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 1301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1301 performs various methods and processes described above, such as the signal amplification method. For example, in some embodiments, the signal amplification method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 1308. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 1300 via the ROM 1302 and / or the communication unit 1309. When the computer program is loaded onto the RAM 1303 and executed by the computing unit 1301, one or more steps of the signal amplification method described above can be performed. Alternatively, in other embodiments, the computing unit 1301 can be configured to perform the signal amplification method by any other suitable means, such as by means of firmware.

[0176] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0177] Program code of a computer program product for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, implements the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0178] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can be a machine-readable signal medium or a machine-readable storage medium. A computer-readable storage medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a computer-readable storage medium can include a wired or wireless electrical connection, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0179] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0180] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0181] The computer system can include clients and servers. This relationship can be. remote, where each server is stored on a remote computer from a client. The clients and the servers can be connected through a communication network. The relationship can be a client-server relationship over a network. Servers can be cloud servers, also known as cloud computing servers or cloud hosts, which are a host product in the cloud computing service system to solve the defects of large management difficulty and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS"). The server can also be a server of a distributed system or a server combined with a blockchain.

[0182] It should be understood that the steps shown above can be reordered, added to, or deleted from. For example, the steps described in the present disclosure can be executed in parallel, in sequence, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, and the present disclosure is not limited herein.

[0183] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A signal amplification method, characterized in that, include: The received base station signal is parsed to obtain the Tracking Area Code (TAC) value of the base station signal; Determine whether the TAC value of the base station signal is in the whitelist; If the TAC value of the base station signal is in the whitelist, determine whether the operating power of the base station signal is outside the preset power range; When the operating power of the base station signal is not within the preset power range, the power of the base station signal is adjusted. The adjusted base station signal is then amplified.

2. The signal amplification method according to claim 1, characterized in that, Determining whether the TAC value of the base station signal is in the whitelist includes: The TAC value of the base station signal is compared with the TAC value in the whitelist to determine whether the TAC value of the base station signal is included in the TAC value in the whitelist. If the TAC value of the base station signal is included in the TAC values ​​in the whitelist, then a first code is output; the first code is used to indicate that the TAC value of the base station signal is included in the TAC values ​​in the whitelist. If the TAC value of the base station signal is not included in the TAC values ​​in the whitelist, then a second code is output; the first code is used to indicate that the TAC value of the base station signal is not included in the TAC values ​​in the whitelist.

3. The signal amplification method according to claim 2, characterized in that, The method further includes: If the TAC value of the base station signal is not included in the TAC values ​​in the whitelist, the base station signal will be output to a preset device; If a first preset instruction is received from a user via the preset device, the base station signal is saved to the whitelist; the first preset instruction is used to indicate that the base station signal passes security verification. If a second preset instruction is received from the user through the preset device, the base station signal is added to the blacklist; the second preset instruction is used to indicate that the base station signal has not passed security verification.

4. The signal amplification method according to claim 1, characterized in that, When the TAC value of the base station signal is in the whitelist, determining whether the operating power of the base station signal is outside the preset power range includes: If the TAC value of the base station signal is in the whitelist, determine whether the user signal quality and number of users in the current usage scenario meet the preset conditions; If the user signal quality and number of users do not meet the preset conditions, obtain data information of the current usage scenario; Based on the data information of the current usage scenario, the maximum power, optimal power, and minimum power corresponding to the current usage scenario are determined by a preset prediction model. The preset power range is determined based on the maximum power and the minimum power; Determine whether the operating power of the base station signal is outside the preset power range.

5. The signal amplification method according to claim 4, characterized in that, The preset prediction model is obtained by training the training sample data based on the gradient boosting decision tree (GBDT) and the long short-term memory network (LSTM).

6. The signal amplification method according to claim 4, characterized in that, The step of adjusting the power of the base station signal when its operating power is not within a preset power range includes: When the operating power of the base station signal does not fall within a preset power range, the optimal power is obtained; Adjust the operating power of the base station signal to the optimal power.

7. A signal amplification device, characterized in that, include: The signal receiving module is used to parse the received base station signal and obtain the Tracking Area Code (TAC) value of the base station signal; The TAC determination module is used to determine whether the TAC value of the base station signal is in the whitelist; The power determination module is used to determine whether the operating power of the base station signal is outside the preset power range when the TAC value of the base station signal is in the whitelist. A power adjustment module is used to adjust the power of the base station signal when the operating power of the base station signal is not within a preset power range; The signal amplification module is used to amplify the adjusted base station signal.

8. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the signal amplification method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the signal amplification method according to any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the signal amplification method according to any one of claims 1-6.

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