A communication quality determination method, electronic device, and program product

Abstract

Embodiments of the present disclosure provide a communication quality determination method, an electronic device and a program product; the communication quality determination method comprises: obtaining communication quality evaluation data of a target device and a target base station; the communication quality evaluation data comprises: running characteristic data, running environment data and communication quality index data; determining an entropy value of each kind of communication quality evaluation data; determining a weight of each kind of communication quality evaluation data according to the entropy value of each kind of communication quality evaluation data; and determining a communication quality evaluation result according to each kind of communication quality evaluation data and the corresponding weight. Through the embodiment scheme, the communication quality between a mobile carrier and a base station can be accurately evaluated in a complex and dynamically changing environment.

Description

Technical Field

[0001] This disclosure relates to the field of communication technology, and in particular to a method for determining communication quality, an electronic device, and a program product. Background Technology

[0002] Traditional methods for assessing the quality of high-speed mobile communications often rely on system logs and scattered performance data, which are clearly insufficient if the mobile vehicle operates in a complex and dynamically changing environment. These limitations severely impact the accuracy of communication quality assessment. Summary of the Invention

[0003] This disclosure provides a method for determining communication quality, an electronic device, and a program product.

[0004] In a first aspect, embodiments of this disclosure provide a method for determining communication quality, comprising:

[0005] Acquire communication quality assessment data between the target device and the target base station; the communication quality assessment data includes: operating characteristic data, operating environment data, and communication quality index data;

[0006] Determine the entropy value for each type of communication quality assessment data;

[0007] The weight of each communication quality assessment data is determined based on the entropy value of each type of communication quality assessment data;

[0008] The communication quality assessment result is determined based on each of the communication quality assessment data and the corresponding weight.

[0009] Secondly, embodiments of this disclosure also provide an electronic device, including:

[0010] One or more processors;

[0011] A memory having stored one or more programs, which, when executed by one or more processors, enable the one or more processors to implement the communication quality determination method.

[0012] One or more input / output (I / O) interfaces are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.

[0013] Thirdly, this disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the communication quality determination method.

[0014] This embodiment of the scheme obtains communication quality assessment data between the target device and the target base station. The communication quality assessment data includes: operational characteristic data, operational environment data, and communication quality index data. The scheme determines the entropy value of each type of communication quality assessment data; determines the weight of each type of communication quality assessment data based on its entropy value; and determines the communication quality assessment result based on each type of communication quality assessment data and its corresponding weight. This embodiment of the scheme enables accurate assessment of the communication quality between the mobile vehicle and the base station in complex and dynamically changing environments. Attached Figure Description

[0015] In the accompanying drawings of the embodiments disclosed herein:

[0016] Figure 1 This is a schematic flowchart of a communication quality determination method provided in an embodiment of the present disclosure;

[0017] Figure 2 A schematic flowchart of a method for obtaining the entropy value of each type of communication quality assessment data by performing entropy value calculation based on each type of communication quality assessment data, provided in an embodiment of this disclosure;

[0018] Figure 3 A schematic flowchart of a method for obtaining the weight of each type of communication quality assessment data by performing weight calculation based on the entropy value of each type of communication quality assessment data according to an embodiment of this disclosure;

[0019] Figure 4 A block diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions of this disclosure, the communication-sensing data processing method and computer-readable storage medium provided in the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0021] The present disclosure will be described more fully below with reference to the accompanying drawings; however, the embodiments shown may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of the disclosure.

[0022] The accompanying drawings of the embodiments disclosed herein are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the detailed embodiments to explain this disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the description of the detailed embodiments with reference to the accompanying drawings.

[0023] This disclosure may be described with reference to plan and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances.

[0024] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0025] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. The term "and / or" as used in this disclosure includes any and all combinations of one or more of the associated enumerated entries. The singular forms "a" and "the" as used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. The terms "comprising," "made of," etc., as used in this disclosure specify the presence of the stated feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0026] Unless otherwise specified, all terms used in this disclosure (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this disclosure.

[0027] With the continuous advancement of communication technology, the demand for internet access during flight is becoming increasingly strong. While traditional satellite communication methods are mature, their limitations in bandwidth, latency, and cost make them unsuitable for modern aviation communication needs. Against this backdrop, a new type of mobile cellular technology, which involves setting up air-to-air base stations along flight routes, has emerged, enabling aircraft to establish direct communication connections with ground base stations. This technology is also applicable to wireless communication for various mobile vehicles such as high-speed trains, coastal vessels, low-altitude flying cars, and drones.

[0028] However, achieving stable and reliable air-to-ground network services faces numerous challenges. Aircraft characteristics are constantly changing, with uncertain flight paths that typically fluctuate within a 2-kilometer radius. During flight, the aircraft continuously adjusts its attitude, including pitch, roll, and yaw. Its altitude varies greatly, from the ground to tens of thousands of meters above the ground, and its speed is usually maintained at 800-1000 kilometers per hour. These characteristics significantly impact the stability and continuity of air-to-ground communication.

[0029] Natural environmental factors also play a significant role in communication quality, with significant differences in weather conditions: communication signals are relatively stable on sunny days; on rainy days, raindrops scatter and absorb the signal, leading to signal attenuation; the impact of snow is similar to, and may be more severe than, that of rain; and thunderstorms can directly cause communication outages. Furthermore, low-visibility fog and haze affect signal propagation; strong winds not only interfere with signals but also alter aircraft attitude, indirectly affecting communication quality; thick clouds reflect and scatter communication signals, also reducing quality. In addition, various environmental variables, such as ground obstructions and altitude, interact and comprehensively affect the stability and reliability of air-to-ground communication systems.

[0030] Furthermore, flight testing is costly, and it is extremely difficult to conduct comprehensive simulations in complex scenarios involving a combination of factors.

[0031] Nearshore navigation faces similar challenges. The marine environment is complex and changeable. Waves, fog, and strong winds not only interfere with the navigation of ships, but also have an adverse effect on communication equipment and signal transmission.

[0032] Against this backdrop, accurately assessing and comparing the performance of wireless communication quality across different flight routes has become crucial for improving overall communication quality. Traditional methods often rely on complex system logs and scattered performance data, which are not only inefficient in assessment but also fail to provide intuitive evidence for operational decisions. Especially in application scenarios where moving vehicles such as high-speed trains, ships, and drones are subject to environmental interference, the lack of unified performance evaluation standards makes it impossible to intuitively and accurately grasp communication quality.

[0033] This embodiment of the scheme obtains communication quality assessment data between the target device and the target base station. The communication quality assessment data includes: operational characteristic data, operational environment data, and communication quality index data. The scheme determines the entropy value of each type of communication quality assessment data; determines the weight of each type of communication quality assessment data based on its entropy value; and determines the communication quality assessment result based on each type of communication quality assessment data and its corresponding weight. This embodiment of the scheme enables accurate assessment of the communication quality between the mobile vehicle and the base station in complex and dynamically changing environments.

[0034] The solutions disclosed herein can be used for communication quality assessment of any mobile vehicle, especially high-speed mobile vehicles. For example, they can be used for communication quality assessment of mobile vehicles such as airplanes, high-speed trains, offshore vessels, low-altitude flying cars, and drones during operation, especially during operation in harsh environments.

[0035] The solutions disclosed herein can be combined with other technologies:

[0036] 1) Integration with satellite base station communication:

[0037] Leveraging the wide coverage of satellite base stations, communication quality assessments are conducted on global shipping routes and maritime areas, providing more comprehensive communication support for transnational and transoceanic aviation and maritime activities. This assessment model and performance mechanism are used to analyze the performance of satellite networks, providing a fundamental evaluation tool for improving the quality and efficiency of satellite communications.

[0038] 2) Integration with Internet of Things (IoT) technology:

[0039] Intelligent Transportation Systems (ITS): In air and maritime transportation, ITS connects aircraft, ships, and other vehicles with ground infrastructure and other modes of transport via the Internet of Things (IoT). By assessing communication quality, ITS ensures stable data transmission between devices, enabling intelligent traffic management such as automatic obstacle avoidance and collaborative scheduling.

[0040] 3) Integration with artificial intelligence technology:

[0041] Predictive maintenance: Utilizes artificial intelligence algorithms to analyze communication quality assessment data and results, predicts potential equipment failures and performance issues, and performs maintenance and upkeep in advance to reduce the impact of equipment failures on communication and operations;

[0042] Intelligent Decision Support: Combining the data analysis and decision-making capabilities of artificial intelligence, based on communication quality assessment results and other relevant data (such as weather, flight traffic, marine environment, etc.), it provides intelligent decision support for airlines, marine operators, etc., such as flight plan adjustments, route selection, and resource allocation.

[0043] The embodiments of this disclosure will be described in detail below.

[0044] This disclosure provides a method for determining communication quality, such as... Figure 1 As shown, it includes steps S11-S14:

[0045] S11. Obtain communication quality assessment data between the target device and the target base station.

[0046] In this embodiment of the disclosure, the target device may include, but is not limited to, mobile vehicles, such as, but not limited to, aircraft, high-speed trains, offshore vessels, low-altitude flying cars, drones, and other equipment.

[0047] In this embodiment of the disclosure, the target base station may refer to a ground base station that communicates with these mobile vehicles.

[0048] In this embodiment of the disclosure, the communication quality assessment data may include, but is not limited to: operating characteristic data, operating environment data, and communication quality index data.

[0049] In this embodiment, considering that single or limited operational data samples from mobile vehicles are insufficient to reflect long-term signal quality trends; unforeseen factors such as weather result in significant data dispersion, making it impossible to accurately assess overall performance; and the differences in communication signal reception capabilities between different mobile vehicle models at different stages of the same operational route are difficult to quantify, these limitations severely impact the accurate assessment of communication quality. Therefore, to comprehensively evaluate the communication quality of the target device, communication quality assessment data can be collected from multiple aspects, including the target device's operational characteristics, various operating environments, and the communication quality indicators of the target base station, thereby improving the accuracy of the target device's communication quality assessment.

[0050] In this embodiment of the disclosure, the operational characteristic data may include, but is not limited to, at least one of the following: communication signal stability during operation, communication delay duration, communication signal interference, communication signal strength, and operational stage.

[0051] In this embodiment of the disclosure, the operating environment data may include, but is not limited to, at least one of the following: visibility, precipitation, wind speed, cloud thickness, etc.

[0052] In this embodiment of the disclosure, the quality indicator data may include, but is not limited to, at least one of the following: signal strength, signal-to-noise ratio, packet loss rate, communication delay, etc.

[0053] In this embodiment of the disclosure, when the communication quality assessment data includes operational characteristic data and operational environment data, obtaining the communication quality assessment data between the target device and the target base station may include:

[0054] Acquire operational characteristic data and operational environment data of the target device at different stages of its operational route during at least one operation.

[0055] In this embodiment of the disclosure, the following uses air-to-ground communication during aircraft flight as an example to illustrate the solution of this embodiment.

[0056] The ATG (Air-to-Group) air-to-ground interconnection system consists of two main parts: the airborne communication system and the ground base station. The airborne communication system mainly includes airborne antennas, airborne stations (i.e., airborne CPEs), and airborne WiFi APs (Wireless Access Points). The airborne CPEs, installed in the equipment bay, enable data backhaul based on 5G NR (5G New Radio, the fifth-generation wireless technology). By connecting to the ground-based 5G NR base station, they provide wireless backhaul to the onboard WiFi AP system, allowing WiFi (Wireless Fidelity) users within the cabin to access broadband internet.

[0057] In this embodiment of the disclosure, throughout the entire ATG solution, the airborne CPE typically collects operational characteristic data (or performance key performance indicator (KPI) data) every second. These KPIs are determined based on a network KPI evaluation system, primarily reflecting user perception and network performance. They typically include dimensions such as network health, accessibility, persistence, mobility, network coverage and interference, network load, network air interface performance, and network awareness, with each dimension characterized by several indicators. Additionally, operational characteristic information such as aircraft GPS location, flight attitude (pitch, roll angle), and environmental situational awareness KPI data are continuously reported to the network management system in real-time from the moment the airborne CPE connects to the network.

[0058] In this embodiment of the disclosure, the airborne CPE can automatically aggregate all operational characteristic data and operational environment data within a specific flight phase (such as takeoff, level flight, and landing) or geographical segment.

[0059] In this embodiment of the disclosure, for example, operational characteristic data and operational environment data of an aircraft during one or more flights can be collected. For a single flight, the flight trajectory can be divided into different flight stages, such as takeoff, landing, and flight altitudes of 3000 meters, 5000 meters, 8000 meters, 10,000 meters, and 12,000 kilometers. Data for each flight stage can be collected separately, and combined with flight variables, the communication quality of each flight stage can be evaluated.

[0060] In this embodiment of the disclosure, Table 1 and below Figure 2 The data includes five flights per week for the same aircraft on the same route. Table 1 shows an example of the data during takeoff, and Table 2 shows an example of the data during landing.

[0061] Table 1

[0062]

[0063] Table 2

[0064]

[0065] In this embodiment of the disclosure, when the communication quality assessment data includes communication quality index data, obtaining the communication quality assessment data between the target device and the target base station may include:

[0066] Obtain communication quality index data of at least one target base station and target device at different communication distances.

[0067] In this embodiment of the disclosure, the communication distance can refer to the distance in any direction.

[0068] In this embodiment of the disclosure, in order to evaluate the performance of the target base station at different altitudes, communication quality index data of the same target base station at different altitudes (e.g., 3000 meters, 5000 meters, 8000 meters, 10,000 meters, 10,000 meters, 10,000 meters) can be extracted.

[0069] In this embodiment of the disclosure, as shown in Table 3, there are examples of communication quality index data at distances of 3000 meters, 5000 meters, 8000 meters, 10,000 meters, and 10,000 meters.

[0070] Table 3

[0071] Flight altitude Signal strength (dBm) Signal-to-noise ratio (dB) Packet loss rate (%) Communication delay (ms) 3000 meters -80 20 2 50 5000 meters -85 18 5 60 8000 meters -90 15 10 70 10,000 meters -75 25 1 40 12,000 meters -95 12 15 80

[0072] S12. Determine the entropy value for each type of communication quality assessment data.

[0073] In this embodiment of the disclosure, based on the multiple communication quality assessment data obtained in the above steps, the relationship between the communication quality assessment data and the communication quality assessment results can be abstracted into a mapping function F in advance. This function F can take one or more of the following as inputs: multi-dimensional input space, such as operational characteristic data, operational environment data, and communication quality index data, and then map them to the assessment result space (e.g., a scoring space) to generate a comprehensive assessment result (e.g., a score) that reflects the communication quality. The specific relationship can be expressed as follows:

[0074] Score = F(X1, X2, ..., X...) M ;Y1, Y2, ..., Y K );

[0075] Where: X1, X2, ..., X M The target equipment is represented by M input factors (such as signal stability, communication delay, signal interference, signal strength, altitude, speed, weather, visibility, etc.) that reflect its own operating characteristics and the influence of the natural environment during operation.

[0076] Y1, Y2, ..., Y K This represents K communication quality metrics (such as signal strength, packet loss rate, signal-to-noise ratio, communication delay, etc.).

[0077] Score represents a comprehensive rating of communication quality.

[0078] In this embodiment of the disclosure, the above-mentioned abstract relationship will be described in detail below:

[0079] 1) Description of abstract relationships:

[0080] Input factors: Input factors X and Y together constitute a sample point. Data from N operating routes can constitute a set of sample points.

[0081] Mapping function F: F can be a mapping function from the input space to the scoring space, with the goal of integrating multiple factors and indicators to generate a score reflecting communication quality. Through the calculation of F, a performance score can be calculated for each flight or each route, and ranking and evaluation can be performed based on the score.

[0082] Score: The score is a scalar value used to quantify the overall performance of communication quality.

[0083] 2) Characteristics of the relationship:

[0084] Multi-factor: This model has the feature of comprehensive consideration of multiple factors, including operational characteristics, natural environmental factors, and signal communication quality indicators.

[0085] Scalability: F's design supports the flexible addition or deletion of input factors and output indicators, and allows for timely adjustments to the evaluation model based on actual needs and newly discovered influencing factors.

[0086] In the embodiments of this disclosure, the solutions of the embodiments of this disclosure are described in detail below.

[0087] In this embodiment of the disclosure, determining the entropy value of each type of communication quality assessment data may include:

[0088] Entropy values ​​are calculated for each type of communication quality assessment data to obtain the entropy value for each type of communication quality assessment data.

[0089] In the embodiments disclosed herein, such as Figure 2 As shown, the entropy value of each type of communication quality assessment data is obtained by performing entropy calculation based on each type of communication quality assessment data, which may include steps S21-S23:

[0090] S21. Standardize each communication quality assessment data in each type of communication quality assessment data to obtain standardized data.

[0091] In this embodiment of the disclosure, the entropy calculation scheme of this embodiment is described below based on the data in Table 1.

[0092] In this embodiment of the disclosure, the data in each column of Table 1 can first be standardized. For example, the Z-score standardization method can be used for standardization:

[0093]

[0094] Where Z is the standardization result, x is the original data of each column, μ is the mean of each column, and σ is the standard deviation of each column.

[0095] In this embodiment of the disclosure, the significance of Z-score standardized data lies in transforming data with different characteristics to the same scale, eliminating the influence of dimensions, and making the comparison between data with different characteristics more fair and intuitive.

[0096] In this embodiment of the disclosure, the standardized data shown in Table 4 can be obtained by standardizing the data in Table 1.

[0097] Table 4

[0098]

[0099] S22. Determine the probability value corresponding to each communication quality assessment data based on standardized data.

[0100] In this embodiment of the disclosure, the standardized data in Table 4 above can be converted into a probabilistic form to determine the probability value corresponding to each communication quality assessment data.

[0101] In this embodiment of the disclosure, the probability value corresponding to each communication quality assessment data can be calculated using the following probability calculation formula:

[0102]

[0103] Among them, P ij Z represents the probability value in the i-th row and j-th column. ij It is standardized data obtained after standardization processing. This represents the summation of all columns in the i-th row, i.e., the sum of all standardized data in the i-th row. The above formula calculates the relative probability of each data point in that row. This method converts standardized data into probabilistic form for further analysis.

[0104] S23. Calculate the entropy value based on the probability value of each communication quality assessment data to obtain the entropy value of each type of communication quality assessment data.

[0105] In this embodiment of the disclosure, the entropy value of each type of communication quality assessment data can be calculated based on the following entropy value calculation formula:

[0106]

[0107] in, m is the number of rows, log(P) ij ) represents the probability value P ij Take the natural logarithm, P ij This is an element in the probability matrix (or probability matrix) calculated in the above steps, representing the probability value in the i-th row and j-th column. It is used to measure the uncertainty or entropy of information. By taking the logarithm of the probability value, the probability value can be converted into a measure of information content, which helps in calculating the entropy value.

[0108] In this embodiment of the disclosure, the uncertainty of a series of data (i.e., a kind of communication quality assessment data) can be understood through the entropy value. The higher the entropy value, the greater the uncertainty.

[0109] In this embodiment of the disclosure, based on the foregoing embodiments, the entropy value of each type of communication quality assessment data is determined as follows:

[0110] E1=0.98, E2=0.95, E3=0.92, E4=0.93, E5=0.91, E6=0.94, E7=0.96, E8=0.97.

[0111] S13. Determine the weight of each communication quality assessment data based on the entropy value of each type of communication quality assessment data.

[0112] In this embodiment of the disclosure, determining the weight of each communication quality assessment data based on the entropy value of each type of communication quality assessment data may include:

[0113] The weight of each communication quality assessment data is obtained by performing weight calculations based on the entropy value of each data type.

[0114] In the embodiments disclosed herein, such as Figure 3 As shown, the weight of each communication quality assessment data is obtained by performing weight calculation based on the entropy value of each data type, which may include steps S31-S32:

[0115] S31. Determine the information utility value of each type of communication quality assessment data based on the entropy value of each type of communication quality assessment data. In this embodiment of the disclosure, the formula for calculating the information utility value may include:

[0116] d j =1-E j ;

[0117] Among them, E j The entropy value is calculated to measure the uncertainty or disorder of information; the higher the entropy value, the greater the uncertainty and disorder of the corresponding column of data (i.e., the communication quality assessment data). j The information utility value reflects the usefulness of information in each column of data; the higher the information utility value, the more valuable the information in that column and the less uncertain it is.

[0118] In this embodiment of the disclosure, based on the foregoing embodiments, the information utility value of each type of communication quality assessment data is determined as follows:

[0119] d1=0.02, d2=0.05, d3=0.08, d4=0.07, d5=0.09, d6=0.06, d7=0.04, d8=0.03.

[0120] S32. Perform weight calculations based on information utility values ​​to obtain the weight of each type of communication quality assessment data.

[0121] In this embodiment of the disclosure, the obtained information utility values ​​can be normalized to obtain the weight corresponding to each column of data. The weight calculation formula may include:

[0122]

[0123] Among them, w j Let d be the weight of the j-th column. j Let be the information utility value of the j-th column. It is the sum of the information utility values ​​of all columns, that is, the sum of all information utility values ​​from column 1 to column n.

[0124] In this embodiment of the disclosure, the weight w of the j-th column j The information utility value of column j is equal to the sum of the information utility values ​​of all columns. This formula calculates the weight of each column by normalizing the information utility value of each column. The weight reflects the relative importance of each column in the overall analysis.

[0125] In this embodiment of the disclosure, based on the foregoing embodiments, the weights of each type of communication quality assessment data are determined as follows:

[0126] w1=0.07, w2=0.17, w3=0.27, w4=0.23, w5=0.26, w6=0.19, w7=0.12, w8=0.09.

[0127] S14. Determine the communication quality assessment result based on each type of communication quality assessment data and its corresponding weight.

[0128] In this embodiment of the disclosure, the communication quality assessment result may include, but is not limited to, a communication quality score.

[0129] In this embodiment of the disclosure, a communication quality score can be calculated based on each of the above-described communication quality assessment data and its corresponding weight.

[0130] In this embodiment of the disclosure, the following description of the communication quality score calculation method will still take the aforementioned embodiment of the aircraft takeoff phase as an example.

[0131] In this embodiment of the disclosure, the standardized data corresponding to each communication quality assessment data in Table 4 can be multiplied by the corresponding weight, and then summed to obtain the communication quality score.

[0132] In this embodiment of the disclosure, the communication quality score for each single flight during the aircraft takeoff phase is as follows:

[0133] 1. Communication quality score for Flight 1: 0.00×0.07+(-1.29)×0.17+0.00×0.27+0.50×0.23+0.50×0.26+(-0.50)×0.19+(-0.50)×0.12+0.50×0.09=-0.22;

[0134] 2. Flight 2's communication quality score: -1.00×0.07+1.29×0.17+1.00×0.27+1.50×0.23+(-1.00)×0.26+1.00×0.19+1.00×0.12+(-1.00)×0.09=0.45;

[0135] 3. Flight 3 communication quality score: 1.00×0.07+(-1.29)×0.17+(-1.00)×0.27+(-1.00)×0.23+1.00×0.26+(-1.50)×0.19+(-1.50)×0.12+(-1.50)×0.09=-0.65;

[0136] 4. Flight 4's communication quality score: -0.50×0.07+(-0.50)×0.17+0.50×0.27+0.00×0.23+0.00×0.26+0.00×0.19+0.00×0.12+0.00×0.09=0.00;

[0137] 5. Flight 5's communication quality score: 0.50×0.07+(-1.29)×0.17+0.00×0.27+(-0.50)×0.23+0.25×0.26+(-0.25)×0.19+(-0.50)×0.12+(-0.25)×0.09=-0.15.

[0138] In this embodiment of the disclosure, the communication quality assessment data of the aircraft landing phase shown in Table 2 can also be used to obtain the communication quality score for each flight during the aircraft landing phase, as shown in Table 5.

[0139] Table 5

[0140] Flight number Takeoff score Landing score 3000m score 5000m score 8000m score 10,000-meter score 12,000 km score Overall score Flight 1 -0.22 -0.14 -0.18 -0.10 0.05 0.12 0.08 -0.39 Flight 2 0.45 0.45 0.40 0.50 0.55 0.60 0.58 3.53 Flight 3 -0.65 -0.65 -0.60 -0.55 -0.50 -0.45 -0.40 -3.80 Flight 4 0 -0.05 -0.02 0.03 0.10 0.15 0.12 0.33 Flight 5 -0.15 -0.15 -0.10 -0.05 0.02 0.08 0.05 -0.30

[0141] In this embodiment of the disclosure, the flight process of the aircraft can be analyzed based on the communication quality scores during the takeoff and landing phases described above, for example:

[0142] 3000-meter score: During low-altitude flight, communication signal quality is significantly affected by ground base station coverage and the environment. This score is close to the scores during takeoff and landing. For example, Flight 1 scores -0.18 at 3000 meters, slightly higher than its takeoff score (-0.22).

[0143] 5000m score: Communication signal quality may improve with increasing altitude, but is still affected by the low-altitude environment; for example, Flight 2 scores 0.50 at 5000m, slightly higher than its score at 3000m (0.40);

[0144] 8000-meter score: Communication signal quality is generally better and less affected by ground interference when flying at medium to high altitudes. For example, Flight 3 scores -0.50 at 8000 meters, which is better than its low-altitude score (-0.60).

[0145] Score at 10,000 meters: Communication signal quality may be optimal at high altitudes, but it is still affected by weather and equipment performance. For example, Flight 4 scores 0.15 at 10,000 meters, which is better than its score at medium and low altitudes.

[0146] 12,000 km score: Communication signal quality may decrease slightly during ultra-high altitude flight due to atmospheric attenuation. For example, Flight 5 scores 0.05 at 12,000 km, slightly lower than its score at 10,000 km (0.08).

[0147] In this embodiment of the disclosure, by adopting the above-described scheme, it is possible to assess the communication quality of aircraft during flight for different aircraft types, routes, and operational phases.

[0148] In this embodiment of the disclosure, the communication quality assessment data of the same target base station at different altitudes shown in Table 3 can also be used to obtain the communication quality score of the same target base station at different altitudes, as shown in Table 6, which is the standardized data corresponding to the data in Table 3.

[0149] Table 6

[0150] Flight altitude signal strength Signal-to-noise ratio Packet loss rate Communication delay 3000 meters 0.625 0.615 0.857 0.75 5000 meters 0.375 0.462 0.571 0.5 8000 meters 0.125 0.231 0.143 0.25 10,000 meters 1 1 1 1 12,000 meters 0 0 0 0

[0151] In this embodiment of the disclosure, the communication quality score of the corresponding target base station can be obtained based on the standardized data in Table 6, as shown in Table 7.

[0152] Table 7

[0153] Flight altitude Overall score 3000 meters 0.612 5000 meters 0.437 8000 meters 0.133 10,000 meters 0.958 12,000 meters 0

[0154] In this embodiment of the disclosure, the target base station can be analyzed based on the communication quality scores at different altitudes, for example:

[0155] Load issue: The communication quality scores of the ground target base stations at 8,000 meters and 12,000 meters are low, which may be due to excessive load on the target base stations.

[0156] Interference issues: At an altitude of 12,000 meters, the packet loss rate and communication delay of the target base station on the ground are relatively high, which may indicate interference issues.

[0157] In this embodiment of the disclosure, based on the above scheme, it is known that when the communication quality assessment data includes: operating characteristic data and operating environment data, the communication quality assessment result is the communication quality assessment result of the target device in the preset operating stage of the operating route in at least one operation; the communication quality assessment data is the operating characteristic data and operating environment data of the preset operating stage.

[0158] In this disclosure, the following are embodiments of a method for obtaining communication quality assessment results of a target device.

[0159] In this embodiment of the disclosure, the operation phase may include any first operation phase; determining the communication quality assessment result corresponding to each operation phase of the target device based on communication quality assessment data includes:

[0160] Standardize each communication quality assessment data corresponding to the first operational phase to obtain the first standardized data;

[0161] A first probability value corresponding to each communication quality assessment data point is determined based on the first standardized data.

[0162] The first entropy value of each communication quality assessment data is determined based on the first probability value of each communication quality assessment data in each type of communication quality assessment data;

[0163] The first information utility value of each type of communication quality assessment data is determined based on the first entropy value;

[0164] The first weight of each type of communication quality assessment data is determined based on the first information utility value;

[0165] The communication quality assessment results for the first operating phase are determined based on the communication quality assessment data of the first operating phase and the corresponding first weight.

[0166] In the embodiments disclosed herein, any of the foregoing embodiments are applicable to the method embodiments for obtaining the communication quality assessment results of the target device, and will not be described again here.

[0167] In this embodiment of the disclosure, based on the above scheme, it can be seen that when the communication quality assessment data may include: communication quality index data, the communication quality assessment result is the communication quality assessment result when the preset target base station and the target device communicate at a preset communication distance; the communication quality assessment data is the communication quality index data corresponding to the preset communication distance.

[0168] In this disclosure, the following is an embodiment of a method for obtaining the communication quality assessment results of a target base station.

[0169] In this embodiment of the disclosure, the target base station may include any first target base station; different distances include any first distance; determining the communication quality assessment result of each target base station at different distances based on communication quality assessment data includes:

[0170] Given a first distance between the first base station and the target device, each communication quality assessment data corresponding to the first base station is standardized to obtain second standardized data.

[0171] A second probability value is determined for each communication quality assessment data point based on the second standardized data.

[0172] The second entropy value of each communication quality assessment data is determined based on the second probability value of each communication quality assessment data in each type of communication quality assessment data;

[0173] The second information utility value is determined based on the second entropy value to determine the entropy value of each type of communication quality assessment data;

[0174] A second weight is determined for each type of communication quality assessment data based on the second information utility value;

[0175] The second evaluation result for the first base station at the first distance is determined based on the communication quality assessment data corresponding to the first base station at the first distance and the corresponding second weight.

[0176] In the embodiments disclosed herein, any of the foregoing schemes are applicable to the method embodiments for obtaining the communication quality assessment results of the target base station, and will not be described again here.

[0177] In this embodiment, the above scheme achieves the determination of communication quality assessment results based on entropy and weights. In other embodiments, preset assessment rules can also be established based on experience to determine the communication quality assessment results. These assessment rules may include, but are not limited to, determining the communication quality assessment result for each type of communication quality assessment data based on its specific numerical value and empirical value. For example, taking signal strength as an example, a score of 5 is given when the signal strength is >80dBm; a score of 3 is given when it is between 60-80dBm; and a score of 1 is given when it is less than 60dBm. Similar assessment rules can also be used to determine communication quality scores for indicators such as signal-to-noise ratio, packet loss rate, and communication delay. Then, the comprehensive score for each flight communication can be calculated based on these assessment rules.

[0178] In this embodiment of the disclosure, the scheme for evaluating communication quality based on evaluation rules is transparent, has a single dimension, and is easy to implement.

[0179] In this embodiment of the disclosure, after determining the communication quality assessment result based on each type of communication quality assessment data and the corresponding weight, the method may further include:

[0180] The communication excellence rate and / or pass rate for any given operational phase are determined based on the communication quality assessment results for each evaluation corresponding to any operational phase of the target device and the number of evaluations for that given operational phase; and / or,

[0181] The communication excellence rate and / or pass rate of each target base station are determined based on the communication quality assessment results corresponding to each assessment and the number of assessments of each target base station.

[0182] In this embodiment of the disclosure, for example, based on the communication quality score (or comprehensive score) of the target base station described above, the excellent rate and the qualified rate of the target base station can be calculated:

[0183] Excellence rate: Assuming the excellence threshold is 0.8, the excellence rate = (number of flights with an overall score ≥ 0.8) / (total number of flights) × 100% = 20%.

[0184] Pass rate: Assuming the pass threshold is 0.5, the pass rate = (number of flights with an overall score ≥ 0.5) / (total number of flights) × 100% = 40%.

[0185] Average score of target base stations: 0.428.

[0186] In this embodiment of the disclosure, after obtaining the communication quality assessment results, communication excellence rate, and / or pass rate based on the scheme, the following information can be displayed on the user interface:

[0187] 1) Data and Results Display: The interface displays communication quality assessment data and results for each route, along with dynamic data such as the excellent rate and pass rate. Airlines and operators can easily view the overall network communication quality level and promptly monitor routes with low scores. Furthermore, for each route, detailed assessment data for each flight can be viewed, including flight characteristics, environmental factors, and specific values ​​for various communication performance indicators, helping operators comprehensively understand the factors affecting communication quality.

[0188] 2) Integrated Display of Feedback and Optimization Suggestions: A feedback mechanism integration interface may be set up. When the system assesses a decline in communication quality for a particular route, it can notify the operator or airline of the relevant situation and provide possible optimization suggestions, such as adjusting or increasing base station deployment or adjusting antenna parameters. After the operator takes optimization measures, the system can track the optimization effect in real time and display the new communication quality assessment data on the interface, forming a closed-loop optimization management process.

[0189] This disclosure embodiment includes at least the following advantages:

[0190] 1. Entropy-based methods are used to calculate communication quality scores for base station service quality and flight path quality, quantifying service quality and helping operators and equipment manufacturers quickly identify problems and provide optimization directions. Additionally, visual analysis methods such as heatmaps, combined with GIS (Geographic Information System) and GPS (Global Positioning System) information, can be used to statistically analyze KPI percentages, further pinpointing areas with poor coverage and proactively developing countermeasures. Furthermore, flight paths can be displayed using color or other visual codes to show communication quality, making anomalies immediately apparent.

[0191] 2. Optimize network coverage: The higher the excellence rate and pass rate, the better the network coverage. Airlines can use this to optimize flight routes and schedules, improve user experience and enhance their competitiveness in aviation communications.

[0192] 3. Optimize resource allocation: By analyzing the impact of different aircraft types' communication signal reception capabilities on communication quality on the same route, and by analyzing the operator's network load, resource allocation can be optimized.

[0193] 4. Achieving Intelligent Aircraft Optimization: Based on the aircraft's communication quality assessment results, a corresponding feedback mechanism can be established. When the communication quality of the flight path deteriorates, timely measures such as adjusting equipment parameters and optimizing flight paths can be taken, and the effects can be fed back to the system. This enables the formulation of automatic optimization strategies based on multi-dimensional communication quality assessment data and model prediction, predicting problem areas, automatically adjusting network resources, or recommending optimal flight parameters according to aircraft characteristics, continuously improving communication quality, and achieving intelligent aircraft optimization.

[0194] 5. The solutions disclosed in this embodiment are also applicable to the management of marine ranches. These ranches use feeding vessels with masts reaching a certain height to communicate with ground base stations on land, ensuring communication between the ground and the feeding vessels. The feeding vessels can transmit large amounts of data in real time, including feed amounts, marine environmental data, and aquaculture data. Traditional communication methods often cannot provide stable and high-speed connections in such environments, leading to data transmission interruptions or delays. Operators can establish a spatiotemporal heat map of marine communication quality through dynamic performance scores (i.e., communication quality scores) of continuous operating routes, optimize communication base station deployment schemes, and ensure stable communication connections in the vast coastal marine ranch areas.

[0195] 6. Offshore vessels (such as research vessels and fishing fleets) can optimize their navigation routes and operational schedules based on communication performance data, avoid areas of communication interference, ensure stable data transmission and smooth mission execution, reduce operational risks, and improve economic efficiency. The communication experience for maritime users (crew members, offshore platform staff, and leisure tourists) will also be greatly improved, enhancing the satisfaction and attractiveness of maritime activities.

[0196] This disclosure also provides an electronic device 100, such as... Figure 4 As shown, it includes:

[0197] One or more processors 101;

[0198] The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors 101 to implement the communication quality determination method.

[0199] One or more input / output (I / O) interfaces 103 are connected between the processor 101 and the memory 102 and configured to enable information interaction between the processor 101 and the memory 102.

[0200] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the communication quality determination method.

[0201] This disclosure also provides a computer program product, which includes a computer program that, when executed by a processor, implements the communication quality determination method.

[0202] In the embodiments disclosed herein, any of the aforementioned communication quality determination methods are applicable to the embodiments of the electronic device, storage medium, and program product, and will not be described in detail here.

[0203] Those skilled in the art will understand that all or some of the functional modules / units disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0204] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be executed by several physical components working together.

[0205] Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit (CPU), digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH) or other disk storage; read-only optical disc (CD-ROM), digital versatile disc (DVD) or other optical disc storage; magnetic cartridges, magnetic tapes, disk storage or other magnetic storage; and any other media that can be used to store desired information and can be accessed by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0206] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A method for determining communication quality, comprising: Obtain communication quality assessment data between the target device and the target base station; The communication quality assessment data includes: operational characteristic data, operational environment data, and communication quality index data; Determine the entropy value for each type of communication quality assessment data; The weight of each communication quality assessment data is determined based on the entropy value of each type of communication quality assessment data; The communication quality assessment result is determined based on each of the communication quality assessment data and the corresponding weight.

2. The communication quality determination method according to claim 1, wherein, The communication quality assessment data includes: the operational characteristic data and the operational environment data; The acquisition of communication quality assessment data between the target device and the target base station includes: Acquire the operational characteristic data and operational environment data of the target device at different stages of its operational route during at least one operation.

3. The communication quality determination method according to claim 1, wherein, The communication quality assessment data includes: communication quality index data; The acquisition of communication quality assessment data between the target device and the target base station includes: Obtain communication quality index data of at least one of the target base stations and the target device at different communication distances.

4. The communication quality determination method according to claim 1, wherein, Determining the entropy value for each type of communication quality assessment data includes: Entropy values ​​are calculated based on each type of communication quality assessment data to obtain the entropy value of each type of communication quality assessment data.

5. The communication quality determination method according to claim 4, wherein, The step of performing entropy calculation based on each type of communication quality assessment data to obtain the entropy value of each type of communication quality assessment data includes: Each of the communication quality assessment data in each type of communication quality assessment data is standardized to obtain standardized data; Based on the standardized data, determine the probability value corresponding to each of the communication quality assessment data; Entropy values ​​are calculated based on the probability values ​​of each communication quality assessment data to obtain the entropy value for each type of communication quality assessment data.

6. The communication quality determination method according to claim 1, wherein, Determining the weight of each communication quality assessment data based on the entropy value of each type of communication quality assessment data includes: The weight of each communication quality assessment data is obtained by performing weight calculation based on the entropy value of each type of communication quality assessment data.

7. The communication quality determination method according to claim 6, wherein, The step of performing weight calculations based on the entropy value of each type of communication quality assessment data to obtain the weight of each type of communication quality assessment data includes: The information utility value of each communication quality assessment data is determined based on the entropy value of each of the communication quality assessment data. The weights of each type of communication quality assessment data are obtained by performing weight calculations based on the information utility values.

8. The communication quality determination method according to any one of claims 4-7, wherein, The communication quality assessment data includes: operational characteristic data and operational environment data; The communication quality assessment result is the communication quality assessment result of the target device in the preset operation phase of the operation route in at least one operation. The communication quality assessment data consists of the operating characteristic data and the operating environment data for the preset operating phase.

9. The communication quality determination method according to any one of claims 4-7, wherein, The communication quality assessment data includes: communication quality index data; The communication quality assessment result is the communication quality assessment result when the target base station and the target device communicate at a preset communication distance. The communication quality assessment data is the communication quality index data corresponding to the preset communication distance.

10. The communication quality determination method according to claim 1, wherein, After determining the communication quality assessment result based on each type of communication quality assessment data and the corresponding weight, the method further includes: The communication excellence rate and / or pass rate for any given operating stage are determined based on the communication quality assessment results for each evaluation corresponding to any operating stage of the target device and the number of evaluations corresponding to that operating stage; and / or, The communication excellence rate and / or pass rate of each target base station are determined based on the communication quality assessment results corresponding to each assessment of each target base station and the number of assessments of the target base station.

11. An electronic device, comprising: One or more processors; A memory having stored one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the communication quality determination method according to any one of claims 1-10; One or more input / output (I / O) interfaces are connected between the processor and the memory and configured to enable information exchange between the processor and the memory.

12. A computer program product comprising a computer program that, when executed by a processor, implements the communication quality determination method according to any one of claims 1-10.

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