Network coverage blind area detection method, communication node and storage medium

Abstract

The invention provides a network coverage blind area detection method, a communication node and a storage medium. The method comprises the following steps: sending a task confirmation message of network coverage blind area detection to target user equipment, and storing corresponding task transaction information to a block chain; and under the condition that blind area detection data of the target user equipment is received, determining a network coverage blind area according to the blind area detection data, issuing rewards to the target user equipment, and storing task completion information to a block chain.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, for example, to a network coverage blind spot detection method, a communication node and a storage medium. Background Art

[0002] In existing network planning and optimization processes, wireless network coverage data is primarily collected through drive tests conducted by operators, which is inefficient. Furthermore, signal coverage in scenarios such as campuses and factories remains a challenge. Due to factors such as buildings, terrain, signal attenuation, and inaccessible areas within non-public spaces and buildings, these areas often experience uneven signal coverage and blind spots. Because network blind spot detection relies on drive test data, operators struggle to penetrate private areas or collect comprehensive and accurate data across all areas, resulting in an inaccurate understanding of coverage conditions. The efficiency and reliability of blind spot detection needs to be improved. Summary of the Invention

[0003] The present application provides a network coverage blind spot detection method, a communication node and a storage medium.

[0004] The present invention provides a method for detecting a network coverage blind spot, including:

[0005] Send a task confirmation message for network coverage blind spot detection to the target user device and store the corresponding task transaction information in the blockchain;

[0006] Upon receiving the blind spot detection data of the target user device, the network coverage blind spot is determined according to the blind spot detection data, a reward is issued to the target user device, and task completion information is stored in the blockchain.

[0007] An embodiment of the present application further provides a communication node, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned network coverage blind spot detection method when executing the program.

[0008] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned network coverage blind spot detection method is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of a method for detecting a network coverage blind spot provided by an embodiment;

[0010] Figure 2 A schematic diagram of a network architecture for detecting network coverage blind spots provided by an embodiment

[0011] Figure 3Flowchart of another network coverage blind spot detection method provided by an embodiment

[0012] Figure 4 A schematic diagram of a user-side architecture for detecting network coverage blind spots provided by an embodiment

[0013] Figure 5 A schematic diagram of the interaction between the network side and the user side of a network coverage blind spot detection provided by an embodiment

[0014] Figure 6 A schematic structural diagram of a network coverage blind spot detection device provided by an embodiment;

[0015] Figure 7 A schematic structural diagram of another network coverage blind spot detection device provided by an embodiment;

[0016] Figure 8 A schematic diagram of the hardware structure of a communication node provided by one embodiment. DETAILED DESCRIPTION

[0017] The present application is described below in conjunction with the accompanying drawings and embodiments. It will be understood that the specific embodiments described herein are merely intended to explain the present application and are not intended to limit the present application. It should be noted that, unless there is a conflict, the embodiments and features within the embodiments of the present application may be combined with each other in any manner. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present application, not all structures.

[0018] Figure 1 The flowchart of a method for detecting a network coverage blind spot provided by an embodiment is applicable to network side nodes, such as base stations or access points (APs). Figure 1 As shown, the method provided in this embodiment includes step 110 and step 120.

[0019] In step 110, a task confirmation message for network coverage blind spot detection is sent to the target user equipment, and the corresponding task transaction information is stored in the blockchain.

[0020] In this embodiment, the user equipment is a user-side node. The user equipment can be moved to a designated location during use and has the function of detecting network coverage. The target user equipment primarily refers to the user equipment performing the network coverage blind spot detection task. The target user equipment can be selected or configured by the network-side node, determined through negotiation with the network-side node, or can be any user equipment connected to the network-side node. This embodiment does not specifically limit the method for determining the target user equipment.

[0021] The task confirmation message can be used to instruct the target user device to perform the corresponding network coverage blind spot detection task (hereinafter referred to as the task). The task confirmation message may include information such as the detection area, the detection target (network coverage parameters to be detected, such as coverage range, signal strength and / or data transmission rate, etc.), the detection time (task deadline), the detection method and / or the incentive method. It should be noted that there can be multiple target user devices, and the network side node can instruct multiple target user devices to perform different tasks respectively, thereby improving the detection efficiency and ensuring the comprehensiveness of the detection. In addition, by issuing certain rewards to the target user devices, the enthusiasm of the user-side nodes for detection can be increased, and the reliability of the detection results can be improved to a certain extent.

[0022] For task confirmation messages from different target user devices, the information can be the same, different, or overlapping. For example, the detection areas of target user devices A and B are different, while the detection areas of target user devices B and C partially overlap. Another example is that the detection methods of target user devices A and B are the same, while those of target user devices B and C are different. Another example is that the detection time and network coverage parameters to be detected for each user device can be the same. The process of the network node confirming the target user device and the corresponding task, sending the task confirmation message, and the target user device receiving the task confirmation message can be understood as the generation of a task transaction. The relevant task transaction information can be stored in the blockchain to record the transaction status of each task. Task transaction information may include the task identifier (ID), the corresponding target user device ID, and / or a summary of the task confirmation information.

[0023] In step 120, upon receiving the blind spot detection data of the target user device, the network coverage blind spot is determined based on the blind spot detection data, a reward is issued to the target user device, and task completion information is stored in the blockchain.

[0024] In this embodiment, the blind spot detection data may include information such as the detection area, the detection results of the network coverage parameters, the detection time and / or detection method of the blind spot detection data. The network coverage parameters include, for example, the location coordinates of the detection area and the corresponding reference signal receiving power (RSRP) and / or signal to interference plus noise ratio (SINR). After each target user device completes the corresponding task, it uploads the obtained blind spot detection data to the network side node. The network side node can determine the network coverage blind area based on the received blind spot detection data and issue a reward to the corresponding target user device. The content of the reward can be an amount, or it can be a discount coupon, exchange coupon, traffic data package and / or free call time. The content of the reward for different target user devices can be the same or different. In addition, the value of the reward for different target user devices can be the same (e.g., each target user device is rewarded 10 yuan) or different. For example, the value of the reward can be determined based on the time taken by the target user device to perform the corresponding task, whether the functions of the target user device are comprehensive, the historical detection status of the target user device and / or the credibility of the target user device.

[0025] Task completion information can be stored in the blockchain to record the completion of each task. Task completion information may include the task identifier (ID), the corresponding target user device ID, detection time, blind spot detection data, and / or reward information.

[0026] This embodiment provides a blockchain-based method for detecting blind spots in wireless network coverage involving user devices. Blockchain technology is used to record and verify various tasks and transaction information, ensuring the security, reliability, and tamper-proof nature of the entire process. By distributing tasks to target user devices, different target user devices can be deployed in areas such as parks and factories according to actual needs, fully utilizing the resources of user devices and thereby improving the efficiency and reliability of blind spot detection.

[0027] In one embodiment, before sending the task confirmation message to the target user equipment, the method further includes:

[0028] Step 101: Broadcast a task release message for network coverage blind spot detection and write the task release message into the smart contract of the blockchain; wherein the task release message carries at least one of the following: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria.

[0029] In this embodiment, a network node can broadcast a task release message to notify user devices within the network that network blind spot detection is required, thereby establishing a connection or engaging in negotiation with the user device. Based on this, user devices can also be screened to determine target user devices. Task release messages can be stored in a blockchain to record the initial configuration of each task. The publisher identifier can be the identifier of the network node; the task identifier can be a unique identifier used to identify a task; the task requirement quantity can be the number of target user devices or the number of tasks required for network blind spot detection; the detection target primarily refers to the network coverage parameters to be tested; the test plan can refer to the test time, location, equipment, and / or test method; the task deadline primarily refers to the validity period of the task or the time required for target user devices to execute the task; the incentive method can refer to the content, value, and / or calculation method of the reward; and the task acceptance criteria primarily refer to criteria for determining the validity of blind spot detection data, such as whether the blind spot detection data is uploaded within the task deadline, conforms to the required format, is within a preset range, and / or whether there are no conflicts between blind spot detection data from different target user devices.

[0030] In one embodiment, before sending the task confirmation message to the target user equipment, the method further includes:

[0031] Step 103: Receive an order request from at least one user device;

[0032] Step 105: Determine a target user equipment for performing the network coverage blind spot detection task from at least one user equipment based on the order receiving time and the order quantity;

[0033] In this embodiment, after receiving the task release message, the user device may send an order acceptance request to the network side node to indicate that the user device can perform the corresponding network coverage blind spot detection task. The network side node may receive an order acceptance request from one or more user devices. On this basis, it can determine the target user device from the user devices that sent the order acceptance request based on the order acceptance time and the order quantity. For example, for a single task, the network side node may determine the specified number of user devices that accepted the order the earliest after the broadcast task release message (the specified number is less than or equal to the order quantity) as the target user devices of the task; or determine the specified number of user devices that accepted the order within a set time period after the broadcast task release message (the specified number is less than or equal to the order quantity) as the target user devices of the task; or determine the user devices whose order acceptance time meets the set proportion of the order quantity as the target user devices of the task, etc.

[0034] In one embodiment, the order acceptance request carries at least one of the following: a user device identifier, a task identifier, an order acceptance time, and a task release message digest. The task identifier may indicate the task for which the user device is accepting the order, i.e., a task that the user device can accept or execute. The task release message digest may be understood as key information in the task release message, such as the publisher identifier and the task identifier.

[0035] It should be noted that the network side node can broadcast a task release message containing the corresponding task identifier for each task; a user device can send an order request for at least one task; for each task, the network side node can determine at least one target user device.

[0036] In one embodiment, upon receiving blind spot detection data of a target user equipment, determining a network coverage blind spot according to the blind spot detection data includes:

[0037] Step 122: When blind spot detection data of multiple target user equipments are received, compare the blind spot detection data of each target user equipment and evaluate the quality of the blind spot detection data of each target user equipment;

[0038] Step 124: Determine the blind spot detection data of the target user equipment that meets the standards based on the task acceptance criteria and the quality of the blind spot detection data of each target user equipment;

[0039] Step 126: Determine the network coverage blind area based on the qualified blind area detection data.

[0040] In this embodiment, the blind spot detection data of different target user devices may involve the same detection area. For the same detection area, the blind spot detection data of different target user devices may differ to a certain extent. For example, the blind spot detection data of target user device A may be more accurate than the blind spot detection data of target user device B. For another example, the blind spot detection data of target user device A may be collected more frequently than the blind spot detection data of target user device B. For another example, the blind spot detection data of target user device A may conflict with the blind spot detection data of target user device B.

[0041] By comparing the blind spot detection data of each target user device, the quality of the blind spot detection data can be evaluated based on the accuracy, collection frequency and / or functional format of the blind spot detection data of each target user device. In the case of conflicts in the blind spot detection data of different target user devices, the blind spot detection data with poor quality can be eliminated and the blind spot detection data with better quality can be retained.

[0042] Task acceptance criteria primarily refer to criteria for determining the validity of blind spot detection data, such as the target user device's complete functionality, the target user device's detection method meeting requirements, the blind spot detection data being uploaded within the task deadline, the blind spot detection data conforming to the required format, the detection target value being within a preset range, and / or the absence of conflicts between blind spot detection data from different target user devices. Based on the task acceptance criteria and the quality of the blind spot detection data from each target user device, qualified blind spot detection data can be determined. This qualified blind spot detection data has a high degree of credibility and can be used by network-side nodes to determine network coverage blind spots. For example, if the blind spot detection data from target user device A meets the standards and indicates that area a1 has no network coverage, then area a1 can be determined as a network coverage blind spot. Alternatively, if the blind spot detection data from both target user device A and target user device B meet the standards, and the detection target of target user device A's blind spot detection data indicates that area a1 has no network coverage, while the detection target of target user device B's blind spot detection data indicates that area b1 has no network coverage, then the overlapping area between areas a1 and b1 can be determined as a network coverage blind spot.

[0043] In one embodiment, issuing rewards to target user devices includes: issuing rewards to target user devices according to a first incentive method, the first incentive method is to issue rewards to corresponding target user devices based on the order of submission time of qualified blind spot detection data, and the rewards corresponding to each target user device decrease in sequence.

[0044] In this embodiment, the target user device may refer to a user device that performs a network coverage blind spot detection task and whose blind spot detection data meets the standards. For multiple target user devices, the network-side node issues corresponding rewards to each target user device, and the earlier the blind spot detection data is submitted, the higher the value of the reward for the corresponding target user device. For example, for three target user devices, rewards of 20 yuan, 15 yuan, and 10 yuan are issued respectively according to the submission time of the blind spot detection data; for another example, according to the submission time, the reward weight of the top 30% submitters is higher, the weight of 31%-80% remains unchanged, and the weight of the remaining rewards is reduced, etc. On this basis, the enthusiasm of the target user devices and the efficiency of task execution can be further improved.

[0045] It should be noted that the submission time reflects the speed or efficiency of the target device in performing the corresponding task. The submission time can refer to the absolute time when each target user device uploads the blind spot detection data. For example, for the same task, the network side node can send a task confirmation message to multiple target user devices at the same time, and issue rewards in a descending order according to the absolute time when each target user device uploads the blind spot detection data; the submission time can also be determined based on the time it takes for the target user device to perform the corresponding task (the time from receiving the task confirmation message to uploading the blind spot detection data, or the time from sending the task confirmation message to receiving the blind spot detection data by the network side node, etc.). In this case, the time when the network side node sends the task confirmation message to multiple target user devices may not be exactly the same, and certain differences are allowed. In one embodiment, issuing rewards to the target user device includes: issuing rewards to the target user device according to a second incentive method, the second incentive method is to issue rewards to the target user devices corresponding to the blind spot detection data that meets the standard, and the rewards corresponding to each target user device that meets the standard are equal.

[0046] In this embodiment, the target user device may refer to a user device that performs a network coverage blind spot detection task and whose blind spot detection data meets the standards. For multiple target user devices, the network side node issues corresponding rewards to each target user device, and the rewards corresponding to each target user device that meets the standards are equal. For example, for three target user devices, a reward of 10 yuan is issued according to the submission time of the blind spot detection data. In this case, the requirements for task execution efficiency and real-time performance are relatively low, and the computational load of the network side node is small. It is applicable to situations where there are a large number of network coverage blind spots to be detected, a large number of users within the network coverage blind spots to be detected, a large number of user devices receiving orders or target user devices, etc.

[0047] In one embodiment, before sending the task confirmation message to the target user equipment, the method further includes: allocating the network coverage blind spot detection task to the target user equipment according to the customer complaint area and / or potential blind spot;

[0048] The potential blind spots are determined by performing statistics, clustering and / or neural network prediction on the current network coverage data.

[0049] In this embodiment, a customer complaint area may refer to a network coverage blind spot reported by customers through complaints or reports. A potential blind spot may refer to a possible network coverage blind spot determined by a network node through analysis of current network coverage data. Based on the determination of target user devices, the network node may assign network coverage blind spot detection tasks to the target user devices. For example, if there are x1 customer complaint areas and / or potential blind spots and y1 target user devices, the network coverage blind spot detection task for x1 areas may be assigned to y1 target user devices. Ensure that all x1 areas are detected, and the areas detected by each target user device may overlap.

[0050] Potential blind spots are determined by statistics, clustering, and / or neural network prediction of current network coverage data. Current network coverage data may include the network signal quality of each area within the current and previous set time periods, whether customer complaints have been received, whether there is data service, and / or data service quality (which can be evaluated by interruption rate and / or data transmission rate, etc.). For example, based on statistics, areas where the network signal quality (the average signal quality within the set time period can be calculated) is lower than a threshold, the customer complaint rate is higher than a threshold, and / or the data service quality is lower than a threshold can be determined as potential blind spots; based on clustering, areas can be clustered according to network signal quality, customer complaint rate, and / or data service quality, and one or more areas with poor network signal quality, high customer complaint rate, and / or poor data service quality can be determined as potential blind spots; based on neural network prediction, current network coverage data can be input into a trained neural network model (the neural network model has the ability to predict whether one or more areas are potential blind spots through training), and potential blind spots can be determined based on the output of the neural network model.

[0051] In one embodiment, the network coverage blind spot detection task is assigned to the target user device based on the customer complaint area and / or potential blind spot, including: assigning the network coverage blind spot detection task to the target user device based on the geographical location, network topology and / or task coverage of the customer complaint area and / or potential blind spot.

[0052] In this embodiment, in the process of allocating network coverage blind spot detection tasks, the customer complaint areas and / or potential blind spots are first determined, and the network coverage blind spot detection tasks are allocated to target user devices based on the geographical location, network topology and / or task coverage of the customer complaint areas and / or potential blind spots, and it is necessary to ensure that all customer complaint areas and / or potential blind spots have corresponding target user devices for detection. Among them, the geographical location includes the latitude and longitude coordinate range or the street, community or park range; the network topology mainly refers to the physical layout of network equipment, such as the connection structure of various physical or virtual nodes of the wireless network, the physical wiring and / or the distance between nodes; the task coverage can be understood as the proportion of the corresponding area in the entire area to be detected. For example, the entire area to be detected includes area c1, area c2 and area c3. For any one of the areas, such as area c1, the higher the proportion of area c1 (area) in the entire area to be detected (area), it indicates that the area c1 has a greater probability, or there may be more areas within the area that are network coverage blind spots, then more target user devices can be allocated to the area for more comprehensive and reliable detection; and for areas with lower task coverage, such as area c2 and area C3, relatively fewer target user devices can be allocated to save resources.

[0053] In one embodiment, the method further comprises:

[0054] Step 130: Generate a blind spot data credibility map;

[0055] Step 140: Based on the blind spot data credibility graph, periodically establish a network coverage blind spot detection task for areas where the credibility is lower than a preset threshold.

[0056] In this embodiment, a blind spot data credibility map can be generated based on the quality of historical blind spot detection data, customer complaint areas, and / or potential blind spots. This blind spot data credibility map can be used to indicate whether each area is a blind spot, as well as the probability of each area being a blind spot. Based on this, network-side nodes, by maintaining the blind spot data credibility map, can promptly establish network coverage blind spot detection tasks when blind spot detection is needed. For example, network coverage blind spot detection tasks can be periodically established (e.g., weekly) for areas whose credibility falls below a preset threshold.

[0057] Figure 2 A schematic diagram of a network architecture for detecting a network coverage blind spot is provided in an embodiment. Figure 2 As shown in the figure, the blockchain basic module serves as the infrastructure of the entire architecture. It uses blockchain technology to record, verify, and transmit messages for various tasks and transaction information. The blockchain basic module ensures the security and reliability of the entire process, preventing tampering. The blockchain module includes the following functions:

[0058] Data storage: used to persistently store various important information such as task release messages, task transaction information, task completion information, etc. Data storage can use the underlying distributed ledger database of the blockchain or an independently designed distributed storage network.

[0059] Public key encryption: used for identity management and secure information transmission. Public key cryptography technology can be used to generate and identify independent public and private key pairs for each participating node, achieving secure data transmission throughout the entire process from publication to transaction.

[0060] Consensus mechanism: used to maintain the consistency of the system's participating nodes and reach consensus on transaction results to ensure the irreversibility of transactions.

[0061] Smart contracts: used to encode and deploy various business contracts, such as task distribution contracts, task incentive contracts, etc., and automatically execute related transactions and operations.

[0062] P2P network: infrastructure support for data exchange and transmission between participating nodes.

[0063] The blind spot calculation and management module analyzes and assesses existing network data, identifies potential blind spots and / or customer complaint areas, establishes and assigns blind spot detection tasks, and utilizes blind spot detection data from user-side nodes for blind spot calculation and credibility analysis. Furthermore, a blind spot data credibility graph is maintained to periodically dispatch tasks for monitoring and management of blind spots. This architecture helps improve network coverage and quality, and optimizes network performance. For example, the blind spot calculation and management module includes the following functions:

[0064] Potential blind spot calculation: Extract relevant data from the live network, including signal strength, data transmission rate, and other information. Using this extracted data and customer complaint areas, analyze and process the live network to identify potential blind spots. Algorithms used include, but are not limited to, statistical analysis, cluster analysis, and neural network prediction.

[0065] Task creation and assignment: After identifying potential blind spot areas, these areas are divided into specific inspection tasks based on a specific partitioning strategy. This partitioning strategy can take into account factors such as geographic location, network topology, and / or task coverage to rationally assign tasks and ensure comprehensive blind spot detection coverage. Generated inspection tasks include detailed information such as a unique task identifier (ID), task requirements, deadlines, and incentive mechanisms, facilitating subsequent task issuance and execution.

[0066] Detection data analysis and blind spot calculation: The blind spot calculation and management module receives blind spot detection data submitted by user devices, which includes network performance test data conducted by user devices at designated locations. The module performs credibility analysis on the blind spot detection data to evaluate the accuracy and credibility of the data. The calculation methods include but are not limited to algorithms based on correlation coefficients, algorithms based on verification, and / or algorithms based on artificial intelligence (AI). Based on reliable user road test data, the blind spot calculation and management module performs blind spot calculations to determine the network coverage blind spot areas actually tested.

[0067] For example, an algorithm based on correlation coefficients can be used to analyze the degree of correlation (or approximation) between key variables (such as the measured blind spot area, the location coordinates of boundary points, and / or the location coordinates of the center point) in the blind spot detection data submitted by each user device. The higher the degree of correlation, the more likely the key variables satisfy a linear relationship, and thus the higher the credibility of the corresponding blind spot detection data. Another example is a test-based algorithm (such as analysis of variance, t-test, or rank sum test algorithm) that can test the consistency of key variables in the blind spot detection data submitted by each user device and determine the size of the gap or fluctuation between key variables. The higher the consistency, the higher the credibility of the corresponding blind spot detection data. Another example is an AI-based algorithm (such as a machine learning algorithm or an AI model such as a neural network) that can use known sample detection data to train the AI model, enabling the AI model to predict credibility. The blind spot detection data submitted by each user device is then input into the trained AI model to predict and output the credibility of each blind spot detection data. In some scenarios, multiple algorithms can be combined to comprehensively determine the credibility of the blind spot detection data. On this basis, the network coverage blind spot area can be finally determined based on the blind spot detection data with a credibility higher than or equal to the set threshold.

[0068] Periodic Management of Blind Spot Data: The blind spot calculation and management module maintains a regional blind spot data credibility map, which records the credibility of blind spot data at different locations. This credibility map can be a geo-based heat map or other visual chart to illustrate the blind spot situation in different regions. Based on the blind spot data credibility map, the module periodically selects regions with lower credibility for task delivery.

[0069] The task transaction module uses blockchain-based smart contracts to automatically match demand and services, reducing manual confirmation and transaction costs. Contract execution results are publicly recorded on the blockchain, enhancing transaction transparency and traceability.

[0070] The aforementioned network architecture encourages active user participation through a comprehensive incentive mechanism: users earn rewards for measurement services, increasing their income stream. A bonus-sharing incentive system is also designed to increase participation in remote areas. For example, in hotspots, participation is high, the number of participants is large, and the number of people who ultimately meet the requirements is high, so each user ultimately receives a relatively small incentive. In remote areas, the number of orders is small, so each user receives a larger incentive. Having multiple users collaborate on measurement tasks improves the reliability of the data and allows cross-comparison to identify fraudulent users. These transparent incentive mechanisms effectively motivate users and ensure task quality. Ultimately, they can leverage a wider range of community resources to quickly and accurately collect coverage information. The resulting data is then analyzed by the blind spot calculation module, facilitating network coverage monitoring and optimization.

[0071] Figure 3 This is a flow chart of another network coverage blind spot detection method provided by an embodiment. The method can be applied to a communication node, which can be a user-side node such as a UE or a mobile terminal. Figure 3 As shown, the method provided in this embodiment includes step 210, step 220 and step 230.

[0072] In step 210, a task confirmation message for detecting a network coverage blind spot is received.

[0073] In step 220, the network coverage blind spot detection task is executed according to the task confirmation message to obtain blind spot detection data.

[0074] In step 230, the blind spot detection data is sent to a network-side node.

[0075] In this embodiment, after receiving the task confirmation message, the target user device can reach the designated detection area to detect network coverage based on the detection area, detection target (network coverage parameters to be detected), detection time (task deadline), detection method and / or incentive method, and obtain blind spot detection data. The blind spot detection data may include information such as the detection area, detection results of the detection target (network coverage parameters), detection time and / or detection method of the blind spot detection data. On this basis, users can actively participate in network coverage blind spot detection. Different target user devices can be deployed in areas such as parks and factory areas according to actual needs, fully utilizing the resources of user devices and thereby improving the efficiency and reliability of blind spot detection.

[0076] It should be noted that for technical details not fully described in this embodiment, reference can be made to any of the above embodiments.

[0077] In one embodiment, before receiving a task confirmation message for detecting a network coverage blind spot, the method further includes:

[0078] Step 201: upon receiving a task release message, sending an order request to a network-side node;

[0079] The task release message carries at least one of the following: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria;

[0080] The order acceptance request carries at least one of the following: user equipment identification, task identification, order acceptance time, and task release message summary.

[0081] In one embodiment, executing the network coverage blind spot detection task according to the task confirmation message includes: measuring the network performance corresponding to the detection target in the designated area according to the test plan within the task deadline according to the task confirmation message.

[0082] In this embodiment, the target user equipment can reach the designated detection area according to the task confirmation message, and detect the network coverage according to the test plan (test time, location, equipment and / or test method, etc.) within the corresponding task period to obtain blind spot detection data. The blind spot detection data may include information such as the detection area, the detection results of the detection target (network coverage parameters), the detection time and / or detection method of the blind spot detection data.

[0083] In one embodiment, the method further comprises:

[0084] Step 240: Receive a reward if the blind spot detection data passes the task acceptance criteria.

[0085] In this embodiment, the reward received by the target user device can be pre-set by the network side node, or determined according to the submission time of the blind spot detection data, for example, the earlier the blind spot detection data is submitted or the faster it is submitted, the higher the reward obtained by the corresponding target user device; it can also be determined according to the credibility of the blind spot detection data, for example, the higher the credibility of the blind spot detection data, the higher the reward obtained by the corresponding target user device; it can also be determined according to the type of blind spot detection task, for example, if the detection area corresponding to the blind spot detection task is a private area that is difficult to enter, or an area with a remote location or complex terrain that is more difficult to detect, then the reward obtained by the corresponding target user device is also higher; in some scenarios, the reward obtained by each target user device can also be determined based on the above-mentioned multiple factors.

[0086] Figure 4 This is a schematic diagram of a user-side architecture for detecting a network coverage blind spot provided by an embodiment. Figure 4As shown, network-side nodes (operators) can generate task requirements based on potential blind spots and use the broadcast message interface on the blockchain to broadcast network blind spot detection tasks to the entire network. The task release message includes: task publisher ID, task ID, task quantity, detection objectives (coverage, signal strength and / or data transmission rate, etc.), test plan (test time, location, equipment and / or test method, etc.), test recording method, task deadline, total task incentive amount and calculation method and / or task acceptance criteria, etc., and the above content is written into the blockchain smart contract.

[0087] For user-side nodes, after receiving the broadcast, the user device can choose to accept the task and participate in network blind spot detection. When accepting an order, the user device sends an order request to the network-side node, including its user ID, task ID, order acceptance time, and / or a summary of the received task confirmation message. The smart transaction contract determines the return status of the user request based on the order acceptance time and order quantity. If the number of user devices accepting orders exceeds the task requirements specified in the contract and broadcast message, the user device will be stopped from accepting orders. Users who successfully accept an order will receive a task confirmation message from the network-side node, including the task ID, user ID, and task confirmation message summary, and will automatically sign the task contract.

[0088] Blind spot detection and data upload: The user device that receives the order begins the task. According to the task requirements, network blind spot detection is performed at the designated time and location using the specified equipment and test methods. The user device collects and records network performance data as specified by the test objective. It then submits this data, such as location coordinates, RSRP, and / or SINR, to the network node, and returns an identity ID, task ID, upload time, and a digital summary of the receipt packet on the chain.

[0089] Cross-comparison and quality assessment: The network side node cross-compares and conducts quality assessment on the test results submitted by the user equipment to ensure the consistency, accuracy and credibility of the data, so as to ensure the reliability of the data. The consistent and reliable data is transmitted to the blind spot calculation module, and the ID of the qualified user is recorded at the same time. Among them, cross-comparison can be understood as comparing the blind spot detection data submitted by different user devices to determine their consistency or degree of difference. The cross-comparison can be achieved through algorithms based on correlation coefficients, algorithms based on inspections and / or algorithms based on AI. If the blind spot detection data submitted by different user devices for a certain detection area are highly consistent, then the detection area is more likely to be a network coverage blind spot area. To a certain extent, it can also be considered that the quality of the corresponding blind spot detection data is good or the credibility is high. Quality assessment can be understood as an assessment of the quality of the blind spot detection data submitted by each user device. For example, if the functions of the corresponding user device are relatively complete, the detection method of the corresponding user device meets the requirements, the upload time of the blind spot detection data is within the task deadline, the blind spot detection data meets the required format, the value of the detection target is within the preset range and / or there is no conflict with the blind spot detection data of other user devices (that is, the consistency is high), etc., then the quality of the corresponding blind spot detection data is high.

[0090] Reward Distribution and Task Information Uploaded on-chain: The smart contract calculates rewards based on the number of participants meeting the task requirements and the total amount according to previously broadcasted calculation rules. These rewards are then distributed through the smart contract. Incentive calculation methods include, but are not limited to, splitting all rewards equally among all participants, or decreasing rewards based on submission time, such as giving higher rewards to the top 30% of submitters, keeping the weights unchanged for those between 31% and 80%, and reducing the weights for the remaining participants. Rewards can be in the form of currency, points, phone credit, or other incentives. Task information, participant IDs, and test results will be uploaded to the blockchain to ensure transparency and immutability.

[0091] Figure 5 The following is a schematic diagram of the interaction between the network side and the user side of a network coverage blind spot detection provided by an embodiment. Figure 5 As shown, blockchain technology is used to record and verify each task and transaction information, ensuring the security and reliability of the entire process and preventing tampering. Figure 2 and Figure 4 The network-side architecture and user-side architecture shown in the figure, through the interaction between the network side and the user side, the network side distributes the task to the target user equipment. Different target user equipment can reach the designated area for blind spot detection according to actual needs, making full use of the resources of the user equipment, thereby improving the efficiency and reliability of blind spot detection.

[0092] The network coverage blind spot detection method of the present application is exemplified below through some embodiments.

[0093] Example 1

[0094] In this embodiment, tasks can be assigned based on the customer complaint area. The target area is a suburban scene, and the blind spot detection data of all user-side nodes meet the standards. The incentive method is to assign tasks in descending order of time.

[0095] The network-side node (operator) first publishes a task. The task release message includes detailed information such as the task ID, required quantity, detection targets, test plan, task deadline, total task incentive amount and calculation method, and task acceptance criteria. In this scenario, the bid is 200 yuan, and the required quantity is 5 (target user devices).

[0096] After receiving the task, a total of 10 user devices attempt to send an order acceptance request to the operator, including the user ID, task ID, and order acceptance time.

[0097] The smart contract selects five user devices as target user devices based on the order acceptance time and order acceptance capabilities, and sends a task confirmation message, which includes the task ID, user ID, and task confirmation message summary.

[0098] After receiving the mission confirmation message, the five target user devices conduct blind spot detection in the suburbs according to the mission requirements, recording the detection target data, upload time, and data summary. The target user devices then upload the blind spot detection data to the operator.

[0099] The operator performs cross-comparison and quality assessment on the received blind spot detection data. Based on the task acceptance criteria, in this embodiment, it is determined that all five target user equipments have met the task quality requirements.

[0100] The smart contract allocates rewards based on the number of target user devices that meet the requirements and the incentive method, in descending order of submission time. In this scenario, each target user device will receive rewards of 60 yuan, 50 yuan, 40 yuan, 35 yuan, and 15 yuan, respectively.

[0101] Example 2

[0102] In this embodiment, tasks can be assigned based on potential blind spots calculated from existing network data. The target area is a factory scene. Only when the blind spot detection data meets the standards can the incentive be evenly distributed according to the number of people who meet the standards.

[0103] The operator publishes a task, including detailed information such as the task ID, required quantity, detection targets, test plan, task deadline, total incentive amount and calculation method, and task acceptance criteria. In this scenario, the bid is 100 yuan and the required quantity is 5 (target user devices).

[0104] After receiving the task, a total of 5 user devices attempt to send an order acceptance request to the operator, including the user ID, task ID and order acceptance time.

[0105] The smart contract selects five user devices as target user devices based on the order acceptance time and order acceptance capabilities, and sends a task confirmation message, which includes the task ID, user ID, and task confirmation message summary.

[0106] After receiving the task confirmation message, the five target user devices conduct blind spot detection within the factory according to the task requirements, recording the detection target data, upload time, and data summary. The target user devices then upload the blind spot detection data to the operator.

[0107] The operator cross-checked and evaluated the quality of the received blind spot detection data and determined that four target user devices met the mission quality requirements based on the mission acceptance criteria.

[0108] The smart contract distributes the rewards evenly based on the number of target user devices that meet the target and the incentive method. In this scenario, the total amount is 100 yuan, and ultimately four target user devices meet the target. Each target user device will receive an incentive of 25 yuan.

[0109] The embodiment of the present application also provides a network coverage blind spot detection device. Figure 6 FIG. 1 is a schematic diagram of a network coverage blind spot detection device provided by an embodiment. Figure 6 As shown, the network coverage blind spot detection device includes:

[0110] The task confirmation module 310 is configured to send a task confirmation message for network coverage blind spot detection to the target user equipment and store the corresponding task transaction information in the blockchain;

[0111] The blind spot determination module 320 is configured to determine the network coverage blind spot based on the blind spot detection data received from the target user device, issue a reward to the target user device, and store the task completion information in the blockchain.

[0112] In one embodiment, before sending the task confirmation message to the target user equipment, the apparatus further includes:

[0113] A broadcast module, configured to broadcast a task release message for network coverage blind spot detection, and write the task release message into a smart contract of the blockchain;

[0114] The task release message carries at least one of the following: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria.

[0115] In one embodiment, before sending the task confirmation message to the target user equipment, the apparatus further includes:

[0116] An order receiving module, configured to receive an order receiving request from at least one user device;

[0117] A selection module is configured to determine a target user equipment for performing the network coverage blind spot detection task from the at least one user equipment based on the order receiving time and order quantity;

[0118] The order acceptance request carries at least one of the following: user equipment identification, task identification, order acceptance time, and task release message summary.

[0119] In one embodiment, the blind spot determination module includes:

[0120] a comparing unit configured to, upon receiving blind spot detection data of a plurality of target user equipments, compare the blind spot detection data of each target user equipment and evaluate the quality of the blind spot detection data of each target user equipment;

[0121] An acceptance unit is configured to determine the blind spot detection data of the target user equipment that meets the standards based on the task acceptance standard and the quality of the blind spot detection data of each target user equipment;

[0122] The blind spot determination unit is configured to determine the network coverage blind spot based on the qualified blind spot detection data.

[0123] In one embodiment, the blind spot determination module includes:

[0124] The first distribution module is configured to distribute rewards to the target user devices according to a first incentive method. The first incentive method refers to distributing rewards to the corresponding target user devices based on the order of submission time of the blind spot detection data that meets the standards, and the rewards corresponding to each target user device decrease in sequence.

[0125] In one embodiment, the blind spot determination module includes:

[0126] The second issuing module is configured to issue rewards to the target user devices according to a second incentive method. The second incentive method is to issue rewards to the target user devices corresponding to the blind spot detection data that meets the standards, and the rewards corresponding to the target user devices that meet the standards are equal.

[0127] In one embodiment, before sending the task confirmation message to the target user equipment, the apparatus further includes:

[0128] an allocation module configured to allocate network coverage blind spot detection tasks to target user devices based on customer complaint areas and / or potential blind spots;

[0129] The potential blind spots are determined by performing statistics, clustering and / or neural network prediction on current network coverage data.

[0130] In one embodiment, the allocation module is configured to:

[0131] Assign network coverage blind spot detection tasks to target user devices based on the geographical location of customer complaint areas and / or potential blind spots, network topology, and / or task coverage.

[0132] In one embodiment, the apparatus further comprises:

[0133] A generation module configured to generate a blind area data credibility map;

[0134] The task establishment module is configured to periodically establish network coverage blind spot detection tasks for areas where the credibility is lower than a preset threshold according to the blind spot data credibility map.

[0135] The network coverage blind spot detection device proposed in this embodiment and the network coverage blind spot detection method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be referred to any of the above embodiments, and this embodiment has the same beneficial effects as executing the network coverage blind spot detection method.

[0136] The embodiment of the present application also provides another network coverage blind spot detection device. Figure 7 This is a schematic diagram of the structure of another network coverage blind spot detection device provided by an embodiment. Figure 7 As shown, the network coverage blind spot detection device includes:

[0137] The task receiving module 410 is configured to receive a task confirmation message for detecting a network coverage blind spot;

[0138] An execution module 420 is configured to execute the network coverage blind spot detection task according to the task confirmation message and obtain blind spot detection data;

[0139] The sending module 430 is configured to send the blind spot detection data to a network-side node.

[0140] In one embodiment, before receiving the task confirmation message for detecting the network coverage blind spot, the apparatus further includes:

[0141] The request module is configured to send an order request to the network side node when receiving a task release message;

[0142] The task release message carries at least one of the following: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria;

[0143] The order acceptance request carries at least one of the following: user equipment identification, task identification, order acceptance time, and task release message summary.

[0144] In one embodiment, the execution module 420 is configured to: measure the network performance corresponding to the detection target in the designated area according to the test plan within the task deadline based on the task confirmation message.

[0145] In one embodiment, the apparatus comprises:

[0146] The reward receiving module is configured to receive a reward when the blind spot detection data passes the task acceptance criteria.

[0147] The network coverage blind spot detection device proposed in this embodiment and the network coverage blind spot detection method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be referred to any of the above embodiments, and this embodiment has the same beneficial effects as executing the network coverage blind spot detection method.

[0148] The embodiment of the present application also provides a communication node, Figure 8 A hardware structure diagram of a communication node provided in an embodiment is shown as follows: Figure 8 As shown, the communication node provided by the present application includes a processor 510 and a memory 520; the processor 510 in the communication node may be one or more, Figure 8 Take a processor 510 as an example; the memory 520 is configured to store one or more programs; the one or more programs are executed by the one or more processors 510, so that the one or more processors 510 implement the network coverage blind spot detection method as described in the embodiment of the present application.

[0149] The communication node further includes: a communication device 530 , an input device 540 and an output device 550 .

[0150] The processor 510, memory 520, communication device 530, input device 540 and output device 550 in the communication node may be connected via a bus or other means. Figure 8 The bus connection is taken as an example.

[0151] The input device 540 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the communication node. The output device 550 may include a display device such as a display screen.

[0152] The communication device 530 may include a receiver and a transmitter. The communication device 530 is configured to perform information transmission and reception communication according to the control of the processor 510.

[0153] The memory 520, as a computer-readable storage medium, can be configured to store software programs, computer executable programs, and modules, such as the program instructions / modules corresponding to the network coverage blind spot detection method described in the embodiments of the present application (for example, the task confirmation module 310 and the blind spot determination module 220 in the network coverage blind spot detection device). The memory 520 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created based on the use of the communication node, etc. In addition, the memory 520 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 520 may further include a memory remotely located relative to the processor 510, and these remote memories may be connected to the communication node via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0154] The present application also provides a storage medium storing a computer program, which, when executed by a processor, implements the network coverage blind spot detection method described in any of the embodiments of the present application. The method includes: sending a task confirmation message for network coverage blind spot detection to a target user device, and storing the corresponding task transaction information in a blockchain; upon receiving blind spot detection data from the target user device, determining the network coverage blind spot based on the blind spot detection data, issuing a reward to the target user device, and storing task completion information in a blockchain.

[0155] Alternatively, the method includes: receiving a task confirmation message for network coverage blind spot detection; executing the network coverage blind spot detection task according to the task confirmation message to obtain blind spot detection data; and sending the blind spot detection data to a network side node.

[0156] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media.Computer-readable media can be computer-readable signal media or computer-readable storage media.Computer-readable storage media can be, for example, but not limited to: electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above.More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM), flash memories, optical fibers, portable CD-ROMs, optical storage devices, magnetic storage devices, or any suitable combination of the above.Computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0157] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0158] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0159] The computer program code for performing the operations of the present application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and also conventional procedural programming languages such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0160] The above description is merely an exemplary embodiment of the present application and is not intended to limit the scope of protection of the present application.

[0161] It will be understood by those skilled in the art that the term user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processor, a portable web browser or a vehicle-mounted mobile station.

[0162] In general, various embodiments of the present application may be implemented in hardware or dedicated circuits, software, logic, or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device, although the present application is not limited thereto.

[0163] Embodiments of the present application may be implemented by executing computer program instructions by a data processor of a mobile device, for example, in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.

[0164] The block diagram of any logical flow in the drawings of this application may represent program steps, or may represent interconnected logical circuits, modules and functions, or may represent a combination of program steps and logical circuits, modules and functions. A computer program may be stored on a memory. The memory may be of any type suitable for the local technical environment and may be implemented using any suitable data storage technology, such as but not limited to read-only memory (ROM), random access memory (RAM), optical storage devices and systems (digital versatile discs (DVD) or compact disks (CD), etc.). Computer-readable media may include non-transitory storage media. The data processor may be of any type suitable for the local technical environment, such as but not limited to a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) and a processor based on a multi-core processor architecture.

[0165] The above description of exemplary embodiments of the present application has been provided by way of exemplary and non-limiting examples. However, various modifications and adaptations of the above embodiments will be apparent to those skilled in the art, when considered in conjunction with the accompanying drawings and the appended claims, without departing from the scope of the present application. Therefore, the proper scope of the present application will be determined by reference to the appended claims.

Claims

1. A network coverage blind spot detection method, applied to a network side node, characterized in that: include: Send a task confirmation message for network coverage blind spot detection to the target user device and store the corresponding task transaction information in the blockchain; Upon receiving the blind spot detection data of the target user device, the network coverage blind spot is determined according to the blind spot detection data, a reward is issued to the target user device, and task completion information is stored in the blockchain.

2. The method according to claim 1, characterized in that Before sending the task confirmation message to the target user device, the following steps are also included: Broadcasting a task release message for network coverage blind spot detection, and writing the task release message into the smart contract of the blockchain; The task release message carries the following: At least one of: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria.

3. The method according to claim 1, characterized in that Before sending the task confirmation message to the target user device, the following steps are also included: receiving an order acceptance request from at least one user device; Determining a target user equipment for performing the network coverage blind spot detection task from the at least one user equipment according to the order receiving time and order quantity; The order acceptance request carries at least one of the following: user equipment identification, task identification, order acceptance time, and task release message summary.

4. The method according to claim 1, wherein In a case where the blind spot detection data of the target user equipment is received, determining the network coverage blind spot according to the blind spot detection data includes: When receiving blind spot detection data of multiple target user equipments, comparing the blind spot detection data of each target user equipment and evaluating the quality of the blind spot detection data of each target user equipment; Determining the blind spot detection data of the target user equipment that meets the standards based on the task acceptance criteria and the quality of the blind spot detection data of each target user equipment; Determine network coverage blind spots based on qualified blind spot detection data.

5. The method according to claim 1, wherein Issuing a reward to the target user device, including: Rewards are issued to the target user devices according to a first incentive method. The first incentive method refers to issuing rewards to corresponding target user devices based on the order of submission time of the qualified blind spot detection data, and the rewards corresponding to each target user device decrease in sequence.

6. The method according to claim 1, characterized in that Issuing a reward to the target user device, including: The target user equipment is rewarded according to a second incentive method, wherein the second incentive method is to respectively reward the target user equipment corresponding to the blind spot detection data that meets the standards, and the rewards corresponding to the target user equipment that meets the standards are equal.

7. The method according to claim 1, characterized in that Before sending the task confirmation message to the target user device, the following steps are also included: Assign network coverage blind spot detection tasks to target user devices based on customer complaint areas and / or potential blind spots; The potential blind spots are determined by performing statistics, clustering and / or neural network prediction on current network coverage data.

8. The method according to claim 7, characterized in that Assign network coverage blind spot detection tasks to target user devices based on customer complaint areas and / or potential blind spots, including: Assign network coverage blind spot detection tasks to target user devices based on the geographical location of customer complaint areas and / or potential blind spots, network topology, and / or task coverage.

9. The method according to claim 1, characterized in that Also includes: Generate blind spot data credibility map; According to the blind spot data credibility map, a network coverage blind spot detection task is periodically established for areas where the credibility is lower than a preset threshold.

10. A network coverage blind area detection method, applied to a user equipment, characterized in that: include: Receive task confirmation message for network coverage blind spot detection; Execute the network coverage blind spot detection task according to the task confirmation message to obtain blind spot detection data; The blind spot detection data is sent to a network side node.

11. The method according to claim 10, characterized in that Before receiving the task confirmation message of network coverage blind spot detection, the following steps are also included: Upon receiving the task release message, an order request is sent to the network side node; The task release message carries at least one of the following: publisher identification, task identification, task requirement quantity, detection target, test plan, task deadline, incentive method, and task acceptance criteria; The order acceptance request carries at least one of the following: user equipment identification, task identification, order acceptance time, and task release message summary.

12. The method according to claim 11, characterized in that Executing a network coverage blind spot detection task according to the task confirmation message includes: According to the task confirmation message, the network performance corresponding to the detection target is measured for the designated area within the task period according to the test plan.

13. The method according to claim 10, characterized in that Also includes: If the blind spot detection data passes the task acceptance criteria, a reward is received.

14. A communication node, characterized in that include: memory, and one or more processors; The memory is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the network coverage blind area detection method according to any one of claims 1 to 13.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the network coverage blind area detection method according to any one of claims 1 to 13 is implemented.

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