Antenna adjustment method and device, electronic equipment and storage medium

Abstract

The invention discloses an antenna adjustment method and device, electronic equipment and a storage medium. Comprising the following steps: acquiring first key index data of at least one base station of a target grid, and judging an adjustment state of each base station based on the first key index data of the at least one base station so as to determine a to-be-adjusted base station; acquiring base station associated feature data of a base station to be adjusted, determining parameter adjustment data based on a mapping relationship between the base station associated feature data and a parameter adjustment model, the base station associated feature data including a scene type and a network operation state, and the parameter adjustment data including azimuth angle adjustment data, downward inclination angle adjustment data and transmitting power adjustment data; and performing compliance verification on the parameter adjustment data and the to-be-adjusted base station based on an intelligent contract deployed in a preset alliance chain, and performing parameter adjustment operation on an antenna of the to-be-adjusted base station based on the parameter adjustment data under the condition that the parameter adjustment data and the to-be-adjusted base station are successfully verified. And the accuracy and compliance of antenna adjustment are improved.

Description

Technical Field

[0001] This invention relates to the field of base station antenna technology, and in particular to an antenna adjustment method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the large-scale deployment of 5G networks, operators are widely adopting a 5G base station co-construction and sharing model to reduce construction costs and improve resource utilization. Under this model, different operators divide the construction work by region and allow other parties to use the existing base station resources. Sometimes, the sharing party needs to adjust the antenna parameters of a base station cell built by the contractor.

[0003] In the context of 5G network co-construction and sharing, base station parameter adjustment has become a crucial means of ensuring user experience and network performance. Currently, there are two strategies for adjusting base station antennas: one is to adjust them according to the needs of the operator without notifying other operators, for example, if the signal is poor in a certain area, the antenna is adjusted directly without notifying other operators. The other strategy is that whoever builds the network adjusts it; in this case, the sharing operator needs to apply to the construction contractor, who is responsible for the adjustment. However, the existing adjustment methods are chaotic. Adjusting antennas according to each operator's own needs without notifying the other party can lead to inaccurate and subjective understanding of the adjusted antenna parameters by both parties, resulting in slow adjustment efficiency. It may also lead to the construction contractor being unmotivated, careless, or untimely in adjusting the antennas, resulting in inaccurate antenna adjustments and poor compliance. Summary of the Invention

[0004] This invention provides an antenna adjustment method, apparatus, electronic device, and storage medium to solve the problems of inaccurate and non-compliant antenna alignment.

[0005] According to one aspect of the present invention, an antenna adjustment method is provided, comprising: Obtain first key indicator data of at least one base station in the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The base station-related feature data of the base station to be adjusted is obtained, and the parameter adjustment data is determined based on the mapping relationship between the base station-related feature data and the parameter adjustment model. The base station-related feature data includes the scenario type and network operation status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The system uses a smart contract deployed on a pre-defined consortium blockchain to verify the compliance of the parameter adjustment data and the base station to be adjusted. If the parameter adjustment data and the base station to be adjusted are successfully verified, the system performs parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0006] Optionally, the first key indicator data includes reference signal received power, channel quality data, and load assessment data. The adjustment status of each base station is determined based on the first key indicator data of at least one base station, and the base station to be adjusted is determined based on the adjustment status of each base station. This includes: calculating the average reference signal received power, average channel quality, and average load assessment based on the first key indicator data of at least one base station; for each base station, calculating a first ratio of the base station's reference signal received power to the average reference signal received power, calculating a second ratio of the base station's channel quality data to the average channel quality, and calculating a third ratio of the base station's load assessment data to the average load assessment; performing a weighted summation of the first, second, and third ratios to obtain a weighted result; if the weighted result is greater than or equal to a preset threshold, the base station's adjustment status is determined to be adjustable, and the base station is identified as the base station to be adjusted; if the weighted result is less than the preset threshold, the base station's adjustment status is determined to be normal operation.

[0007] Optionally, the mapping relationship of the parameter adjustment model includes scenario type, parameter adjustment data, and corresponding indicator adjustment results; determining the parameter adjustment data based on the mapping relationship between base station associated feature data and parameter adjustment model includes: retrieving the mapping relationship of the preset parameter adjustment model, matching the scenario type and network operation status in the base station associated feature data with the mapping relationship to obtain the matching result; and determining the parameter adjustment data corresponding to the highest indicator adjustment result in the matching result as the parameter adjustment data.

[0008] Optionally, the mapping relationship of the preset parameter adjustment model is constructed, including: obtaining base station adjustment-related data within a preset historical time period, clustering the base station adjustment-related data with scenario type features and network operation status features before adjustment as the core clustering dimensions, obtaining clustering results, and constructing the mapping relationship of the preset parameter adjustment model based on the clustering results.

[0009] Optionally, the smart contract includes data integrity verification, data compliance verification, and base station legality verification. Data integrity verification checks the field integrity and transmission consistency of the parameter adjustment data; data compliance verification checks whether the parameter adjustment data conforms to the parameter threshold specifications of the network planning; and base station legality verification checks the network access qualifications and adjustment permissions of the base station to be adjusted. Based on the smart contract deployed on the pre-defined consortium blockchain, compliance verification of the parameter adjustment data and the base station to be adjusted is performed, including: transmitting the parameter adjustment data and the base station basic information corresponding to the base station to be adjusted to the authorized node of the pre-defined consortium blockchain; and verifying the parameter adjustment data and the base station to be adjusted based on the data integrity verification, data compliance verification, and base station legality verification in the smart contract deployed on the pre-defined consortium blockchain, so that the pre-defined consortium blockchain returns the verification result.

[0010] Optionally, the parameter adjustment operation is performed on the antenna of the base station to be adjusted based on the parameter adjustment data, including: using the parameter adjustment data as initial adjustment data, performing the parameter adjustment operation on the antenna of the base station to be adjusted based on the initial adjustment data, and determining the second key indicator of the antenna after performing the parameter adjustment operation; judging whether the antenna meets the preset adjustment end condition based on the second key indicator; if the antenna does not meet the preset adjustment end condition, adjusting one or more items in the parameter adjustment data step by step according to the preset step size; adjusting the antenna based on the adjusted parameter adjustment data until the antenna meets the preset adjustment end condition.

[0011] Optionally, the method further includes: after performing parameter adjustment operations on the antenna of the base station to be adjusted based on parameter adjustment data, obtaining adjustment records, transmitting the adjustment records to a preset consortium blockchain, and storing the adjustment records.

[0012] According to another aspect of the present invention, an antenna adjustment device is provided, comprising: The base station to be adjusted determination module is used to obtain the first key indicator data of at least one base station in the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The parameter adjustment data determination module is used to obtain the base station associated feature data of the base station to be adjusted, and determine the parameter adjustment data based on the mapping relationship between the base station associated feature data and the parameter adjustment model. The base station associated feature data includes the scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The antenna parameter adjustment module is used to verify the compliance of parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a pre-defined consortium blockchain. If the parameter adjustment data and the base station to be adjusted are successfully verified, the module performs parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory that is communicatively connected to at least one processor; wherein, The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the antenna adjustment method of any embodiment of the present invention.

[0014] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the antenna adjustment method of any embodiment of the present invention.

[0015] The technical solution of this invention involves acquiring first key indicator data of at least one base station in a target grid, determining the adjustment status of each base station based on the first key indicator data of at least one base station, and identifying the base station to be adjusted based on the adjustment status of each base station; acquiring base station-related feature data of the base station to be adjusted, and determining parameter adjustment data based on the mapping relationship between the base station-related feature data and the parameter adjustment model, wherein the base station-related feature data includes scenario type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data, and transmit power adjustment data; verifying the compliance of the parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a preset consortium blockchain, and performing parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data if the parameter adjustment data and the base station to be adjusted are successfully verified. This solution achieves precise location of base stations to be adjusted through a process of indicator collection, status determination, and base station screening, avoiding the inefficiency of indiscriminate screening. The parameter adjustment scheme output by combining the mapping relationship between base station associated feature data and parameter adjustment model can adapt to the needs of base stations in different scenarios and operating states, improving the scientific nature and adaptability of parameter adjustment. At the same time, relying on the automated compliance verification of consortium blockchain smart contracts, it not only ensures the compliance and security of adjustment operations, but also achieves full data traceability by leveraging the tamper-proof characteristics of consortium blockchain, significantly reducing the cost of manual intervention, improving the overall efficiency and standardization of base station communication optimization work, and enhancing the accuracy and compliance of antenna adjustment.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of an antenna adjustment method provided in Embodiment 1 of the present invention; Figure 2 This is a flowchart of an antenna adjustment method provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an antenna adjustment device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the antenna adjustment method of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] Example 1 Figure 1 This is a flowchart of an antenna adjustment method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where the antenna of a base station needs to be adjusted. The method can be executed by an antenna adjustment device, which can be implemented in hardware and / or software. The antenna adjustment device can be configured in electronic devices such as computers and servers. Figure 1 As shown, the method includes: S110. Obtain the first key indicator data of at least one base station of the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station.

[0022] Specifically, the target grid can be understood as a pre-defined regional unit with clear geographical boundaries or communication service range for achieving refined base station communication optimization. Its division can be determined by factors such as user density, service requirements, and geographical environment. It serves as the basic spatial carrier for base station management and performance optimization. For example, the target area is divided according to a preset grid size, resulting in multiple grids, each of which can serve as a target grid. For instance, the grid size could be 500*500. The first key indicator data can be understood as a set of core parameters reflecting the base station's communication operation status and service quality, including but not limited to reference signal received power, channel quality data, and load assessment data. It is the core basis for determining whether a base station needs adjustment. The adjustment status can be understood as the hierarchical result determined for each base station's current operating status based on the first key indicator data, through comparison with preset thresholds or analysis using a performance evaluation model. This can be set as an adjustable status or a normal operating status, intuitively reflecting whether the base station needs adjustment. The base stations to be adjusted can be understood as those selected from all base stations within the target grid based on their adjustment status, which have performance defects and require intervention through parameter optimization, equipment maintenance, or location adjustment. These are the specific targets for subsequent communication optimization work.

[0023] Specifically, for a pre-defined target grid, the first key indicator data of at least one base station deployed within the target grid is collected. Then, based on a preset indicator threshold range or performance evaluation model, the collected first key indicator data of each base station is quantitatively analyzed to determine the current adjustment status of each base station, which is used to determine whether the base station needs to be adjusted. For example, if the adjustment status of a base station is adjustable, then the base station is determined to be a base station to be adjusted. Finally, based on the adjustment status of each base station, the base stations with the adjustment status of adjustable are selected and determined to be base stations to be adjusted.

[0024] In this embodiment, by focusing on the base station cluster of the target grid and using core key indicators as the basis for analysis, it is possible to accurately locate base stations with performance shortcomings within the grid, avoiding the inefficient operation of indiscriminately checking all base stations. At the same time, based on quantified indicator data and clear status classification standards, the base stations to be adjusted are judged, which greatly improves the objectivity and scientific nature of base station adjustment decisions, effectively reduces the subjective error of manual judgment, and provides accurate target guidance for subsequent base station parameter optimization, equipment upgrades or location adjustments, ensuring the efficient improvement of the communication performance of the target grid.

[0025] Optionally, the first key indicator data includes reference signal received power, channel quality data, and load assessment data. The adjustment status of each base station is determined based on the first key indicator data of at least one base station, and the base station to be adjusted is determined based on the adjustment status of each base station. This includes: calculating the average reference signal received power, average channel quality, and average load assessment based on the first key indicator data of at least one base station; for each base station, calculating a first ratio of the base station's reference signal received power to the average reference signal received power, calculating a second ratio of the base station's channel quality data to the average channel quality, and calculating a third ratio of the base station's load assessment data to the average load assessment; performing a weighted summation of the first, second, and third ratios to obtain a weighted result; if the weighted result is greater than or equal to a preset threshold, the base station's adjustment status is determined to be adjustable, and the base station is identified as the base station to be adjusted; if the weighted result is less than the preset threshold, the base station's adjustment status is determined to be normal operation.

[0026] Specifically, the reference signal received power can be understood as a core parameter measuring the strength of the base station reference signal received by the terminal device. It directly reflects the coverage capability of the base station signal and the connection stability between the terminal and the base station. The value is positively correlated with the signal reception effect of the terminal and is a basic indicator for evaluating the signal coverage quality of the base station. Channel quality data can be understood as a parameter characterizing the signal transmission link quality between the base station and the terminal. It can intuitively reflect the signal loss, interference, and effective transmission capability during transmission and is an important basis for judging whether the communication link is stable and reliable. Load assessment data can be understood as an indicator used to measure the current resource occupancy of the base station. It can accurately reflect the operating load status of the base station and is the core data for judging whether the base station is overloaded or idle. The preset threshold can be understood as a critical value pre-set based on the communication service requirements of the target grid, industry technical standards, historical operating data of the base station, and the characteristics of the weighted summation model. It is a quantitative judgment basis for classifying the adjustment state of the base station. Its value must take into account both the grid communication quality target and the actual operating conditions of the base station to ensure the accuracy and rationality of the base station adjustment state judgment.

[0027] Specifically, the composition of the first key indicator data is first clarified, including reference signal received power (reflecting the signal connection strength between the terminal and the base station), channel quality data (reflecting signal transmission quality), and load assessment data (measuring base station resource utilization). Then, for at least one base station within the target grid, the above three types of indicator data are collected, and the average reference signal received power, average channel quality data, and average load assessment data are calculated as three grid-level benchmarks. Next, for each base station within the grid, the first ratio of its own reference signal received power to the corresponding average, the second ratio of its channel quality data to the corresponding average, and the third ratio of its load assessment data to the corresponding average are calculated sequentially. Then, based on the weights of the three indicators' impact on base station performance, these three ratios are weighted and summed to obtain a weighted result. Finally, this weighted result is compared with a preset threshold. If the weighted result is greater than or equal to the preset threshold, the base station is determined to be in an adjustable state and identified as a base station to be adjusted. If the weighted result is less than the preset threshold, the base station is determined to be in a normal operating state. The expression for determining the weighted result is as follows: ; in, The proportion of base stations whose reference signal received power is greater than -105 dBm; : The current channel quality data of the i-th base station, which reflects the signal transmission quality; The load assessment data for the i-th base station can be based on, specifically, PDCP throughput * average PRB utilization * number of users; , , These represent the average values ​​of the corresponding indicators for all base stations within the same grid; , , These are adjustable weighting coefficients, which can be adjusted according to the actual situation, for example, 0.3, 0.4, and 0.3 respectively.

[0028] In this embodiment, by refining the first key indicator data into three core parameters—reference signal received power, channel quality data, and load assessment data—a multi-dimensional and accurate characterization of the base station's operating status is achieved. Simultaneously, the average value at the grid level is introduced as a comparison benchmark, and a weighted summation quantitative calculation method is used to determine the base station's adjustment status. This avoids the one-sidedness of single-indicator evaluation and eliminates the differences in basic parameters between different base stations through ratio calculation, improving the fairness and scientific rigor of status determination. It can quickly and accurately identify base stations that need optimization, providing a clear and reliable target guide for subsequent base station communication optimization work.

[0029] S120. Obtain the base station association feature data of the base station to be adjusted, and determine the parameter adjustment data based on the mapping relationship between the base station association feature data and the parameter adjustment model. The base station association feature data includes the scenario type and network operation status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data.

[0030] Specifically, base station associated feature data can be understood as a set of core information characterizing the operating environment and conditions of the base station to be adjusted. This includes scenario type and network operating status. Scenario type reflects the geographical environment attributes of the base station, such as residential area, commercial area, and urban fringe area. Network operating status reflects real-time operating parameters such as the base station's current service load, user access status, and neighboring cell interference level, serving as a key input for subsequent parameter adjustment decisions. The mapping relationship of the parameter adjustment model can be understood as a one-to-one or many-to-one quantitative correlation built between the base station associated feature data and the parameter adjustment data after training on a large amount of historical base station optimization data. This relationship can accurately output an appropriate parameter adjustment scheme based on the specific input scenario type and network operating status. Specifically, parameter adjustment data can be understood as a set of data generated based on the mapping relationship of the parameter adjustment model, used to guide the hardware parameter optimization of the base station to be adjusted. The core data includes azimuth adjustment data, downtilt adjustment data, and transmit power adjustment data. Azimuth adjustment data refers to the adjustment value of the horizontal rotation angle of the base station antenna, which optimizes the horizontal coverage range of the base station signal and reduces interference from neighboring cells. Downtilt adjustment data refers to the adjustment value of the vertical tilt angle of the base station antenna, used to precisely control the vertical coverage area of ​​the base station signal and avoid resource waste caused by signal over-coverage. Transmit power adjustment data refers to the increase or decrease in the base station signal transmit power, which can directly change the transmission distance and coverage strength of the base station signal, thereby adapting to the signal coverage requirements of different scenarios.

[0031] Specifically, after screening the base stations to be adjusted within the target grid, the base station-related feature data of the base stations to be adjusted is first collected. This type of data specifically covers the scenario type of the base station and the current network operation status, such as user access volume, service type distribution, and neighboring cell interference level. Then, a pre-trained parameter adjustment model is called. Based on the mapping relationship between the base station-related feature data and parameter adjustment data already constructed in the model, parameter adjustment data that is suitable for the characteristics of the current base station to be adjusted is matched. This parameter adjustment data specifically includes three core types of data that directly guide the optimization of base station hardware parameters: azimuth angle adjustment data, downtilt angle adjustment data, and transmit power adjustment data.

[0032] In this embodiment, by introducing two types of associated feature data—base station scenario type and network operation status—precise matching of parameter adjustment data can be achieved, avoiding the blindness of traditional indiscriminate parameter adjustment. At the same time, the parameter adjustment data clearly focuses on the three core parameters that have the most significant impact on base station signal coverage and communication quality: azimuth angle, downtilt angle, and transmit power. These parameters can be directly used to guide on-site optimization operations of base stations, greatly improving the operability and effectiveness of base station adjustments. In addition, the parameter matching method based on preset mapping relationships can simplify the adjustment decision-making process, reduce the cost of manual intervention, and achieve efficient advancement of base station optimization work.

[0033] Optionally, the mapping relationship of the parameter adjustment model includes scenario type, parameter adjustment data, and corresponding indicator adjustment results; determining the parameter adjustment data based on the mapping relationship between base station associated feature data and parameter adjustment model includes: retrieving the mapping relationship of the preset parameter adjustment model, matching the scenario type and network operation status in the base station associated feature data with the mapping relationship to obtain the matching result; and determining the parameter adjustment data corresponding to the highest indicator adjustment result in the matching result as the parameter adjustment data.

[0034] Specifically, after identifying the base station to be adjusted and obtaining its associated feature data, which includes scenario type and network operation status, the mapping relationship already constructed in the preset parameter adjustment model is first retrieved. This mapping relationship pre-integrates different scenario types, corresponding parameter adjustment data, and the indicator adjustment results that can be achieved after implementing the parameter adjustment data. Then, the scenario type and network operation status of the base station to be adjusted are precisely matched with the data dimensions in the mapping relationship to obtain multiple sets of matching results containing parameter adjustment data and corresponding indicator adjustment results. Finally, the set with the best indicator adjustment results is selected from these matching results, and the parameter adjustment data corresponding to this set is determined as the final parameter adjustment data used to guide base station optimization.

[0035] In this embodiment, the mapping relationship of the parameter adjustment model introduces the key dimension of indicator adjustment result, constructing a complete correlation link between scenario type, parameter adjustment data, and indicator adjustment result, rather than relying solely on scenario type for parameter matching. At the same time, by selecting the parameter adjustment data corresponding to the highest indicator adjustment result, it can be ensured that the output parameter adjustment scheme is the optimal solution for the current scenario and operating state, effectively avoiding the problem of insufficient parameter adaptability caused by single-dimensional matching, significantly improving the accuracy and effectiveness of base station parameter adjustment, thereby ensuring a significant improvement in communication indicators after base station optimization and reducing the cost of repeated parameter debugging.

[0036] Optionally, the mapping relationship of the preset parameter adjustment model is constructed, including: obtaining base station adjustment-related data within a preset historical time period, clustering the base station adjustment-related data with scenario type features and network operation status features before adjustment as the core clustering dimensions, obtaining clustering results, and constructing the mapping relationship of the preset parameter adjustment model based on the clustering results.

[0037] Specifically, firstly, all base station adjustment-related data generated during base station optimization within a preset historical time period is collected. This data covers core information such as the scene type characteristics of the base station, the network operation status characteristics before adjustment, the implemented parameter adjustment data, and the corresponding indicator adjustment results. Then, using scene type characteristics and network operation status characteristics before adjustment as two core clustering dimensions, K-means and other clustering algorithms are used to group and cluster the collected base station adjustment-related data. Base station adjustment data with the same or similar scene type and network operation status characteristics before adjustment are divided into the same cluster. Finally, based on the obtained clustering results, the corresponding parameter adjustment data and indicator adjustment results within each cluster are extracted to construct the correspondence between scene type characteristics, network operation status characteristics before adjustment, parameter adjustment data, and indicator adjustment results, thereby forming the mapping relationship of the preset parameter adjustment model.

[0038] In this embodiment, by introducing a clustering algorithm and classifying historical data using scenario type and network operating status before adjustment as the core dimensions, complex base station adjustment data can be sorted into clusters with clear feature associations. This avoids the subjectivity and inefficiency of manually sorting historical data. At the same time, the mapping relationship constructed based on the clustering results can accurately match the optimal parameter adjustment scheme under different scenarios and operating states, ensuring that the mapping relationship has strong operability and adaptability. This provides scientific and reliable data support for subsequent intelligent adjustment of base station parameters, and greatly improves the decision-making efficiency and accuracy of the parameter adjustment model.

[0039] S130. Based on the smart contract deployed on the preset consortium blockchain, the parameter adjustment data and the base station to be adjusted are verified for compliance. If the parameter adjustment data and the base station to be adjusted are successfully verified, the parameter adjustment operation is performed on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0040] Specifically, the smart contract of the pre-set consortium blockchain can be understood as a programmed protocol with automatic execution capability deployed on a consortium blockchain jointly maintained by multiple authorized entities (such as telecommunications operators, network management agencies, base station operation and maintenance units, etc.). The smart contract is pre-written with compliance verification rules, operation permission requirements and data interaction standards related to base station parameter adjustment. Without third-party intervention, it can automatically verify and execute based on the input base station information to be adjusted and parameter adjustment data according to the pre-set logic. At the same time, the decentralized and tamper-proof characteristics of the consortium blockchain can ensure that the execution process and verification results of the smart contract are traceable and cannot be forged throughout the entire process, providing secure and reliable technical support for base station parameter adjustment operations. Compliance verification can be understood as a multi-dimensional verification process that relies on the verification rules written in the pre-set consortium blockchain smart contract to verify the input parameter adjustment data and the base station information to be adjusted. Specifically, it includes verifying whether the parameter adjustment data conforms to the technical standards of the communications industry, whether it is compatible with the hardware specifications and operating conditions of the base station to be adjusted, whether the base station to be adjusted is within the optimization range of the target grid, whether it has legal adjustment authority, and whether the adjustment operation meets the requirements of network operation security specifications. Only when all verification items pass will the smart contract allow subsequent base station antenna parameter adjustment operations to be carried out based on the parameter adjustment data.

[0041] Specifically, after determining the base station to be adjusted and its corresponding parameter adjustment data, the identity information, parameter adjustment data, and adjustment basis of the base station to be adjusted are first uploaded to a smart contract deployed on a pre-defined consortium blockchain. Relying on the decentralized and tamper-proof technical characteristics of the consortium blockchain, the smart contract automatically verifies the rationality of the parameter adjustment data (such as whether it conforms to industry technical standards and whether it is adapted to the target grid communication requirements), the legality of the base station to be adjusted (such as whether it falls within the target grid optimization scope and whether it has adjustment authority), and the compliance of the adjustment operation (such as whether it conforms to the base station operation security specifications) according to the pre-defined compliance verification rules. After the smart contract outputs the result that both the parameter adjustment data and the base station to be adjusted have been successfully verified, the azimuth angle, downtilt angle, and transmit power of the antenna of the corresponding base station to be adjusted are then adjusted based on the verified parameter adjustment data.

[0042] In this embodiment, by leveraging the automated verification mechanism of consortium blockchain smart contracts, the subjectivity and inefficiency of manual compliance review can be eliminated. At the same time, the immutability of the consortium blockchain ensures that the adjustment data and base station information are traceable throughout the process, effectively avoiding the risk of data tampering during parameter adjustment and ensuring the compliance and security of the adjustment operation. In addition, the real-time verification and execution capabilities of smart contracts can significantly shorten the process cycle from parameter determination to antenna adjustment, improve the overall efficiency of base station optimization work, and provide reliable technical support for the rapid improvement of the target grid communication quality.

[0043] Optionally, the parameter adjustment operation is performed on the antenna of the base station to be adjusted based on the parameter adjustment data, including: using the parameter adjustment data as initial adjustment data, performing the parameter adjustment operation on the antenna of the base station to be adjusted based on the initial adjustment data, and determining the second key indicator of the antenna after performing the parameter adjustment operation; judging whether the antenna meets the preset adjustment end condition based on the second key indicator; if the antenna does not meet the preset adjustment end condition, adjusting one or more items in the parameter adjustment data step by step according to the preset step size; adjusting the antenna based on the adjusted parameter adjustment data until the antenna meets the preset adjustment end condition.

[0044] The second key indicator can be understood as a set of core parameters used to measure the effectiveness of antenna adjustment and base station performance after parameter adjustment of the base station's antenna. It maintains the same dimension as the first key indicator and typically includes reference signal received power, channel quality data, and load assessment data. It directly reflects the degree to which parameter adjustment improves base station signal coverage, communication transmission quality, and resource utilization, serving as a direct quantitative basis for determining whether antenna parameter adjustment has achieved its expected goals. The preset adjustment termination condition can be understood as a pre-set standard threshold or target requirement used to determine whether antenna parameter adjustment can be terminated. This condition is usually formulated based on the communication service requirements of the target grid, industry technical specifications, and the expected effects of base station optimization. Specifically, it can be reflected in the second key indicator reaching a preset optimal range, the improvement of the indicator compared to before adjustment meeting a set proportion, or the base station's communication quality meeting user service requirements. Only when the second key indicator meets the preset adjustment termination condition will the iterative adjustment process of antenna parameters terminate, thereby ensuring that base station optimization achieves the expected performance standards.

[0045] Specifically, after obtaining parameter adjustment data verified by the consortium blockchain smart contract, this parameter adjustment data is first used as initial adjustment data. Based on this, the antenna of the base station to be adjusted is adjusted for parameters such as azimuth angle, downtilt angle, and transmit power. At the same time, the second key indicator of the antenna after this adjustment is collected (covering optimization effect verification indicators such as reference signal received power, channel quality, and load assessment). Then, the second key indicator is compared with the preset adjustment end conditions (such as the indicator reaching the industry standard threshold, meeting the target grid communication quality requirements, etc.) to determine whether the antenna parameter adjustment has achieved the expected effect. If the preset adjustment end conditions are not met, one or more parameters in the parameter adjustment data are fine-tuned step by step according to the preset step size. Then, a new round of adjustment is carried out on the antenna based on the fine-tuned parameter adjustment data, and the above indicator collection and condition judgment process is repeated until the second key indicator meets the preset adjustment end conditions.

[0046] In this embodiment, by setting up an operation process that combines initial adjustment with iterative fine-tuning, the accuracy of the adjustment direction is ensured by relying on the parameter adjustment data output by the model in the early stage, and the deviation between the theoretical data of the model and the actual operating conditions of the base station is made up by the step-by-step fine-tuning method. This avoids the problem of insufficient parameter adaptation caused by one-time adjustment. At the same time, the second key indicator is used as the core judgment criterion for the adjustment effect, which can realize the precise optimization of the base station antenna parameters, ensuring that the adjusted base station fully meets the communication quality requirements of the target grid, and greatly improving the reliability and final effect of base station optimization.

[0047] Optionally, the method further includes: after performing parameter adjustment operations on the antenna of the base station to be adjusted based on parameter adjustment data, obtaining adjustment records, transmitting the adjustment records to a preset consortium blockchain, and storing the adjustment records.

[0048] Specifically, after completing the iterative parameter adjustment operation of the base station antenna to be adjusted based on the parameter adjustment data, and after the second key indicator meets the preset adjustment termination condition, the complete adjustment record of this base station optimization is first collected. This record includes the identity information of the base station to be adjusted, the initial parameter data, the parameter fine-tuning step size and parameter values ​​at each stage during the iterative adjustment process, the change data of the second key indicator, the finally determined optimal parameter adjustment data, and the core information such as the execution time and execution subject of the adjustment operation. Then, the above adjustment record is transmitted to the preset consortium blockchain. Relying on the decentralized and tamper-proof technical characteristics of the consortium blockchain, the adjustment record is distributed and stored to ensure the integrity and security of the record. It can be used to optimize the smart contract of the preset consortium blockchain in real time.

[0049] For example, to ensure that every antenna parameter adjustment operation has a reliable and tamper-proof record for easy post-event auditing, effect evaluation, and accountability, the execution status can be collected during the adjustment process. This includes the operation result (success / failure), execution parameter values ​​(antenna transmit power adjustment data ΔTxPower, antenna downtilt angle adjustment data ΔTilt, antenna azimuth adjustment ΔAzimuth), execution timestamp, and execution node or operator information. The execution status and key data are then encapsulated into a transaction record. The transaction's legality is verified by a smart contract (e.g., whether it passes a security threshold check), and after consensus confirmation by multiple nodes, it is submitted to the consortium blockchain and written into a block, forming an immutable and traceable operation log. This means that every parameter adjustment execution and its status are generated as a reliable record through smart contracts and the consortium blockchain mechanism, ensuring transparency, traceability, and the reliability of historical data.

[0050] In this embodiment, by collecting and storing the entire process information of base station parameter adjustment on the blockchain, not only is the entire adjustment operation traceable, facilitating subsequent review and analysis of base station optimization effects, iterative optimization of technical solutions, and definition of related responsibilities, but the immutability of the consortium blockchain also effectively avoids the risk of adjustment records being tampered with or lost, ensuring the authenticity and credibility of the data. At the same time, the complete adjustment records can also provide reusable reference for base station optimization work in similar scenarios, further improving the overall efficiency and standardization level of base station communication optimization.

[0051] Optionally, after adjusting the antenna of the base station to be adjusted, the incentive allocation model between the sharing party and the contractor is invoked to combine the benefits brought by the current antenna adjustment with the operating costs paid by the contractor, and allocate the total amount of the preset reward pool proportionally. The expression corresponding to the incentive allocation model is as follows: ; ; in, This represents the reward for the sharing party. This represents a reward for the contractor. This indicates the benefits brought about by this adjustment, such as the estimated improvement after system optimization, converted into monetary value. This indicates the operational costs incurred by the contractor to complete this adjustment. This indicates the proportion of the shared benefits contributed by the parties in the overall value. The larger the value, the greater the benefits from the adjustment, and the more rewards the sharing party receives. The larger the value, the higher the contractor's costs, which dilutes the share of the shared resources, resulting in less reward for the shared party. This represents the preset total amount of the reward pool. In one specific embodiment, The calculation is based on the changes in KPIs (key performance indicators) before and after parameter adjustments. First, the differences in key KPIs before and after the adjustment are statistically analyzed, such as the increase in average throughput, the reduction in congestion time, and the decrease in user complaints. Then, the changes in KPIs are converted into uniform benefit values ​​through preset benefit conversion rules, such as conversion based on revenue per unit of traffic or value per unit of time. This refers to the cumulative benefits obtained within the observation period. The cost model is determined by the contractor's prior declaration or the system's pre-set cost model, including labor operation costs, system operation times, and possible on-site maintenance costs; it can be standardized by parameter adjustment type + adjustment times + whether on-site operation is involved, avoiding manual calculation for each order. It is pre-set by the operator within a statistical period and can be determined based on the annual or monthly network optimization budget; as a fixed parameter in the smart contract, it is used to constrain the overall incentive expenditure limit and prevent disorderly or excessive incentives. Based on actual effect evaluation, From the standardized cost model, The budget is preset from the operations side, and all three can be automatically determined through rules and uniformly verified by the system or contract.

[0052] The technical solution of this embodiment involves acquiring first key indicator data of at least one base station in the target grid, determining the adjustment status of each base station based on the first key indicator data of at least one base station, and identifying the base station to be adjusted based on the adjustment status of each base station; acquiring base station-related feature data of the base station to be adjusted, and determining parameter adjustment data based on the mapping relationship between the base station-related feature data and the parameter adjustment model, wherein the base station-related feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data, and transmit power adjustment data; verifying the compliance of the parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a preset consortium blockchain, and performing parameter adjustment operation on the antenna of the base station to be adjusted based on the parameter adjustment data if the parameter adjustment data and the base station to be adjusted are successfully verified. This solution achieves precise location of base stations to be adjusted through a process of indicator collection, status determination, and base station screening, avoiding the inefficiency of indiscriminate screening. The parameter adjustment scheme output by combining the mapping relationship between base station associated feature data and parameter adjustment model can adapt to the needs of base stations in different scenarios and operating states, improving the scientific nature and adaptability of parameter adjustment. At the same time, relying on the automated compliance verification of consortium blockchain smart contracts, it not only ensures the compliance and security of adjustment operations, but also achieves full data traceability by leveraging the tamper-proof characteristics of consortium blockchain, significantly reducing the cost of manual intervention and improving the overall efficiency and standardization level of base station communication optimization work.

[0053] Example 2 Figure 2This is a flowchart of an antenna adjustment method provided in Embodiment 2 of the present invention. This embodiment is a further optimization of the method described in the previous embodiment. Optionally, the smart contract includes data integrity verification, data compliance verification, and base station legality verification. Data integrity verification is used to check the field integrity and transmission consistency of the parameter adjustment data; data compliance verification is used to check whether the parameter adjustment data conforms to the parameter threshold specifications of the network planning; and base station legality verification is used to check the network access qualifications and adjustment permissions of the base station to be adjusted. The parameter adjustment data and the basic base station information corresponding to the base station to be adjusted are transmitted to the authorized node of the preset consortium blockchain. Based on the data integrity verification, data compliance verification, and base station legality verification deployed in the smart contract of the preset consortium blockchain, the parameter adjustment data and the base station to be adjusted are verified so that the preset consortium blockchain returns the verification result. Figure 2 As shown, the method includes: S210. Obtain the first key indicator data of at least one base station of the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station.

[0054] S220. Obtain the base station association feature data of the base station to be adjusted, and determine the parameter adjustment data based on the mapping relationship between the base station association feature data and the parameter adjustment model. The base station association feature data includes the scenario type and network operation status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data.

[0055] S230. Transmit the parameter adjustment data and the base station basic information corresponding to the base station to be adjusted to the authorized node of the preset consortium blockchain. Verify the parameter adjustment data and the base station to be adjusted based on the data integrity verification, data compliance verification and base station legality verification in the smart contract deployed in the preset consortium blockchain, so that the preset consortium blockchain returns the verification result.

[0056] Among them, the base station basic information corresponding to the base station to be adjusted is a core set of information used to uniquely identify the base station and clarify its basic attributes and affiliation. It mainly includes the base station's unique number, the regional identifier of the target grid to which it belongs, the specific geographical deployment location (such as latitude and longitude coordinates), the hardware equipment model (such as antenna type and transmitter specifications), the network access attributes (such as the communication frequency band and neighboring base station information), and the operation and maintenance responsibility entity. This information is not only the core basis for the alliance chain smart contract to carry out the legality verification of the base station, but also provides a basic hardware adaptation reference for subsequent parameter adjustment operations, ensuring that the parameter adjustment data matches the actual hardware conditions of the base station and avoiding adjustment failures due to hardware incompatibility.

[0057] Specifically, after determining the base station to be adjusted and the corresponding parameter adjustment data, the parameter adjustment data and the basic information of the base station to be adjusted (including the base station number, grid to which it belongs, hardware model, deployment location, and other core identification information) are first transmitted to the authorized node of the pre-set consortium blockchain to ensure that the data is only interacted between authorized participants. Then, the smart contract deployed on the consortium blockchain is invoked, and the smart contract executes three verifications in sequence according to the pre-set verification logic: data integrity verification (checking whether the parameter adjustment data and the base station's basic information are complete and without missing parts, and whether the format is uniform and standardized), data compliance verification (checking whether the parameter adjustment data conforms to the technical standards of the communications industry and whether it is compatible with the hardware specifications and operating conditions of the base station to be adjusted), and base station legality verification (verifying whether the base station to be adjusted belongs to the optimization scope of the target grid and whether it has legal adjustment authority). After all the verification processes are completed, the pre-set consortium blockchain returns an overall verification report containing the results of each verification item.

[0058] In this embodiment, by limiting the data transmission scope to authorized nodes of the consortium blockchain, the transmission security of core base station data and the controllability of access permissions can be effectively guaranteed, avoiding the leakage of sensitive information. At the same time, the multi-dimensional layered verification executed by the smart contract significantly improves the comprehensiveness and accuracy of the verification results compared to traditional single-dimensional manual verification. In addition, relying on the immutable nature of the consortium blockchain, the verification process and results can be traced throughout, providing reliable technical support for the compliance of subsequent base station adjustment operations, effectively avoiding the risk of illegal adjustments, and improving the standardization and security of base station optimization work.

[0059] S240. If the parameter adjustment data and the base station to be adjusted are successfully verified, perform parameter adjustment operation on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0060] The technical solution of this embodiment involves acquiring first key indicator data of at least one base station in the target grid, determining the adjustment status of each base station based on the first key indicator data of at least one base station, and identifying the base station to be adjusted based on the adjustment status of each base station; acquiring base station association feature data of the base station to be adjusted, and determining parameter adjustment data based on the mapping relationship between the base station association feature data and the parameter adjustment model, wherein the base station association feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data, and transmit power adjustment data; transmitting the parameter adjustment data and the base station basic information corresponding to the base station to be adjusted to the authorized node of the preset consortium blockchain, verifying the parameter adjustment data and the base station to be adjusted based on the data integrity verification, data compliance verification, and base station legality verification in the smart contract deployed in the preset consortium blockchain, so that the preset consortium blockchain returns the verification result; if the parameter adjustment data and the base station to be adjusted are successfully verified, performing parameter adjustment operation on the antenna of the base station to be adjusted based on the parameter adjustment data. This solution achieves precise location of base stations to be adjusted through a quantitative judgment method of "indicator collection - ratio weighting - threshold comparison," avoiding the inefficiency of indiscriminate screening. The parameter adjustment scheme output by combining the mapping relationship between base station associated feature data and parameter adjustment model can adapt to the needs of base stations in different scenarios and operating states, improving the scientificity and adaptability of parameter adjustment. At the same time, relying on the data transmission mechanism of authorized nodes of the consortium blockchain and the multi-dimensional layered verification of smart contracts, it not only ensures the transmission security of core base station data and controllable access permissions, but also ensures the compliance and legality of adjustment operations. With the immutability of the consortium blockchain, the verification process and results can be fully traceable, greatly reducing the cost of manual intervention and the risk of illegal adjustments, and improving the overall efficiency and standardization of base station communication optimization.

[0061] Example 3 Figure 3 This is a schematic diagram of an antenna adjustment device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The base station to be adjusted determination module 310 is used to obtain the first key indicator data of at least one base station of the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The parameter adjustment data determination module 320 is used to acquire base station associated feature data of the base station to be adjusted, and determine parameter adjustment data based on the mapping relationship between the base station associated feature data and the parameter adjustment model. The base station associated feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The antenna parameter adjustment module 330 is used to verify the compliance of parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a preset consortium blockchain. If the parameter adjustment data and the base station to be adjusted are successfully verified, the parameter adjustment operation is performed on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0062] The technical solution of this embodiment involves obtaining first key indicator data of at least one base station in the target grid through a base station determination module, judging the adjustment status of each base station based on the first key indicator data of at least one base station, and determining the base station to be adjusted based on the adjustment status of each base station; a parameter adjustment data determination module obtains base station association feature data of the base station to be adjusted, and determines parameter adjustment data based on the mapping relationship between the base station association feature data and the parameter adjustment model, wherein the base station association feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data, and transmit power adjustment data; and an antenna parameter adjustment module performs compliance verification of the parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a preset consortium blockchain. If the parameter adjustment data and the base station to be adjusted are successfully verified, parameter adjustment operation is performed on the antenna of the base station to be adjusted based on the parameter adjustment data. This solution achieves precise location of base stations to be adjusted through a process of indicator collection, status determination, and base station screening, avoiding the inefficiency of indiscriminate screening. The parameter adjustment scheme output by combining the mapping relationship between base station associated feature data and parameter adjustment model can adapt to the needs of base stations in different scenarios and operating states, improving the scientific nature and adaptability of parameter adjustment. At the same time, relying on the automated compliance verification of consortium blockchain smart contracts, it not only ensures the compliance and security of adjustment operations, but also achieves full data traceability by leveraging the tamper-proof characteristics of consortium blockchain, significantly reducing the cost of manual intervention and improving the overall efficiency and standardization level of base station communication optimization work.

[0063] Based on the above embodiments, optionally, the first key indicator data includes reference signal received power, channel quality data, and load assessment data; the base station to be adjusted determination module 310 is specifically used to calculate the average reference signal received power, the average channel quality, and the average load assessment based on the first key indicator data of at least one base station; for each base station, calculate a first ratio of the base station's reference signal received power to the average reference signal received power, calculate a second ratio of the base station's channel quality data to the average channel quality, and calculate a third ratio of the base station's load assessment data to the average load assessment; perform weighted summation on the first ratio, the second ratio, and the third ratio to obtain a weighted result; if the weighted result is greater than or equal to a preset threshold, then determine that the base station's adjustment state is an adjustable state and determine the base station as a base station to be adjusted; if the weighted result is less than the preset threshold, then determine that the base station's adjustment state is a normal operating state.

[0064] Optionally, the mapping relationship of the parameter adjustment model includes scenario type, parameter adjustment data and corresponding indicator adjustment results; the parameter adjustment data determination module 320 is specifically used to retrieve the mapping relationship of the preset parameter adjustment model, match the scenario type and network operation status in the base station associated feature data with the mapping relationship to obtain the matching result; and determine the parameter adjustment data corresponding to the highest indicator adjustment result in the matching result as the parameter adjustment data.

[0065] Optionally, the parameter adjustment data determination module 320 is specifically used to obtain base station adjustment-related data within a preset historical time period, and to perform clustering processing on the base station adjustment-related data with scene type characteristics and network operation status characteristics before adjustment as the core clustering dimensions to obtain clustering results, and to construct the mapping relationship of the preset parameter adjustment model based on the clustering results.

[0066] Optionally, the smart contract includes data integrity verification, data compliance verification, and base station legality verification. Data integrity verification checks the field integrity and transmission consistency of the parameter adjustment data; data compliance verification checks whether the parameter adjustment data conforms to the parameter threshold specifications of the network planning; and base station legality verification checks the network access qualifications and adjustment permissions of the base station to be adjusted. The antenna parameter adjustment module 330 is specifically used to transmit the parameter adjustment data and the base station basic information corresponding to the base station to be adjusted to the authorized node of the preset consortium blockchain. Based on the data integrity verification, data compliance verification, and base station legality verification in the smart contract deployed on the preset consortium blockchain, the parameter adjustment data and the base station to be adjusted are verified so that the preset consortium blockchain returns the verification result.

[0067] Optionally, the antenna parameter adjustment module 330 is specifically used to take the parameter adjustment data as initial adjustment data, perform parameter adjustment operation on the antenna of the base station to be adjusted based on the initial adjustment data, and determine the second key indicator of the antenna after the parameter adjustment operation is performed; determine whether the antenna meets the preset adjustment end condition based on the second key indicator; if the antenna does not meet the preset adjustment end condition, adjust one or more of the parameter adjustment data step by step according to the preset step size, and adjust the antenna based on the adjusted parameter adjustment data until the antenna meets the preset adjustment end condition.

[0068] Optionally, the device is also used to obtain adjustment records after performing parameter adjustment operations on the antenna of the base station to be adjusted based on parameter adjustment data, transmit the adjustment records to a preset consortium blockchain, and store the adjustment records.

[0069] The antenna adjustment device provided in the embodiments of the present invention can execute the antenna adjustment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0070] Example 4 Figure 4 This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0071] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0072] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0073] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as antenna adjustment methods.

[0074] In some embodiments, the antenna adjustment method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the antenna adjustment method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the antenna adjustment method by any other suitable means (e.g., by means of firmware).

[0075] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0076] Computer programs for implementing the antenna adjustment method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0077] Example 5 Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute an antenna adjustment method, the method comprising: Obtain first key indicator data of at least one base station in the target grid, determine the adjustment status of each base station based on the first key indicator data of at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The base station-related feature data of the base station to be adjusted is obtained, and the parameter adjustment data is determined based on the mapping relationship between the base station-related feature data and the parameter adjustment model. The base station-related feature data includes the scenario type and network operation status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The system uses a smart contract deployed on a pre-defined consortium blockchain to verify the compliance of the parameter adjustment data and the base station to be adjusted. If the parameter adjustment data and the base station to be adjusted are successfully verified, the system performs parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data.

[0078] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0079] To provide interaction with an object, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the object; and a keyboard and pointing device (e.g., a mouse or trackball) through which the object provides input to the electronic device. Other types of devices can also be used to provide interaction with the object; for example, feedback provided to the object can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the object can be received in any form (including sound input, voice input, or tactile input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., a computer with a graphical user interface or web browser through which an item can interact with the implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0081] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0082] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. 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 substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An antenna adjustment method, characterized in that, include: Obtain first key indicator data of at least one base station of the target grid, determine the adjustment status of each base station based on the first key indicator data of the at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The base station association feature data of the base station to be adjusted is obtained, and the parameter adjustment data is determined based on the mapping relationship between the base station association feature data and the parameter adjustment model. The base station association feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The parameter adjustment data and the base station to be adjusted are verified for compliance based on a smart contract deployed on a pre-defined consortium blockchain. If the parameter adjustment data and the base station to be adjusted are successfully verified, the antenna of the base station to be adjusted is adjusted based on the parameter adjustment data.

2. The method according to claim 1, characterized in that, The first key performance indicator data includes reference signal received power, channel quality data, and load assessment data; The step of determining the adjustment status of each base station based on the first key indicator data of the at least one base station, and determining the base station to be adjusted based on the adjustment status of each base station, includes: Calculate the average reference signal received power, average channel quality, and average load assessment based on the first key indicator data of the at least one base station; For each base station, calculate a first ratio of the base station's reference signal received power to the average reference signal received power, calculate a second ratio of the base station's channel quality data to the average channel quality data, and calculate a third ratio of the base station's load assessment data to the average load assessment data. The first ratio, the second ratio, and the third ratio are weighted and summed to obtain a weighted result. If the weighted result is greater than or equal to a preset threshold, the base station is determined to be in an adjustable state and is identified as a base station to be adjusted. If the weighted result is less than the preset threshold, the base station is determined to be in a normal operating state.

3. The method according to claim 1, characterized in that, The mapping relationship of the parameter adjustment model includes scenario type, parameter adjustment data, and corresponding indicator adjustment results; The determination of parameter adjustment data based on the mapping relationship between the base station associated feature data and the parameter adjustment model includes: The mapping relationship of the model is adjusted by retrieving preset parameters, and the scene type and network operation status in the base station associated feature data are matched with the mapping relationship to obtain the matching result; The parameter adjustment data corresponding to the highest indicator adjustment result in the matching results is determined as the parameter adjustment data.

4. The method according to claim 3, characterized in that, Constructing the mapping relationship of the preset parameter adjustment model includes: Obtain base station adjustment-related data within a preset historical time period, and perform clustering processing on the base station adjustment-related data with scene type features and network operation status features before adjustment as the core clustering dimensions to obtain clustering results. Based on the clustering results, construct the mapping relationship of the preset parameter adjustment model.

5. The method according to claim 1, characterized in that, The smart contract includes data integrity verification, data compliance verification, and base station legality verification; wherein, the data integrity verification is used to verify the field integrity and transmission consistency of the parameter adjustment data, the data compliance verification is used to verify whether the parameter adjustment data conforms to the parameter threshold specifications of the network planning, and the base station legality verification is used to verify the network access qualifications and adjustment authority of the base station to be adjusted. The compliance verification of the parameter adjustment data and the base station to be adjusted based on the smart contract deployed on the preset consortium blockchain includes: The parameter adjustment data and the base station basic information corresponding to the base station to be adjusted are transmitted to the authorized node of the preset consortium blockchain. The parameter adjustment data and the base station to be adjusted are verified based on the data integrity verification, data compliance verification and base station legality verification in the smart contract deployed in the preset consortium blockchain, so that the preset consortium blockchain returns the verification result.

6. The method according to claim 1, characterized in that, The step of performing parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data includes: The parameter adjustment data is used as initial adjustment data. Based on the initial adjustment data, parameter adjustment operation is performed on the antenna of the base station to be adjusted, and the second key indicator of the antenna after the parameter adjustment operation is performed is determined. Based on the second key indicator, it is determined whether the antenna meets the preset adjustment end condition. If the antenna does not meet the preset adjustment end condition, one or more of the parameter adjustment data are adjusted one by one according to the preset step size. The antenna is then adjusted based on the adjusted parameter adjustment data until the antenna meets the preset adjustment end condition.

7. The method according to claim 1, characterized in that, The method also includes: After performing parameter adjustment operations on the antenna of the base station to be adjusted based on the parameter adjustment data, the adjustment record is obtained, the adjustment record is transmitted to the preset consortium blockchain, and the adjustment record is stored.

8. An antenna adjustment device, characterized in that, include: The base station to be adjusted determination module is used to acquire first key indicator data of at least one base station of the target grid, determine the adjustment status of each base station based on the first key indicator data of the at least one base station, and determine the base station to be adjusted based on the adjustment status of each base station. The parameter adjustment data determination module is used to acquire base station association feature data of the base station to be adjusted, and determine parameter adjustment data based on the mapping relationship between the base station association feature data and the parameter adjustment model. The base station association feature data includes scene type and network operating status, and the parameter adjustment data includes azimuth adjustment data, downtilt adjustment data and transmit power adjustment data. The antenna parameter adjustment module is used to perform compliance verification on the parameter adjustment data and the base station to be adjusted based on a smart contract deployed on a preset consortium blockchain. If the parameter adjustment data and the base station to be adjusted are successfully verified, the module performs parameter adjustment operation on the antenna of the base station to be adjusted based on the parameter adjustment data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the antenna adjustment method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the antenna adjustment method according to any one of claims 1-7.

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