Sensing entity selection method, computer device and storage medium

Abstract

The invention discloses a sensing entity selection method, a computer device and a storage medium. The sensing entity selection method comprises the steps of determining communication paths according to first position information of a sensing target and second position information of a sensing entity, obtaining energy efficiency scores of the communication paths, selecting the sensing entity according to the energy efficiency scores and the like. According to the invention, the sensing entity can be selected from a plurality of sensing entities, and the selected sensing entity can meet the path requirement of communication with the sensing target, thereby guaranteeing the quality and efficiency of communication; in the process of selecting the sensing entities, screening is performed through the energy efficiency score, which is equivalent to optimizing the communication path by using the energy efficiency score, so that the selected sensing entities are used for communicating with the sensing target, and high energy efficiency of communication can be ensured; therefore, the sensing entity selection method in the embodiment can select the sensing entity according to a comprehensive and accurate standard, and ensures that the quality, efficiency, energy efficiency and the like of communication are at a high level. The method is widely applied to the technical field of communication.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for selecting a sensing entity, a computer device and a storage medium. Background Art

[0002] With the rapid development of the Internet of Things (IoT) and 5G networks, integrated sensing and communication (ISAC) technology is gradually maturing. In the ISAC system, the sensing entity (such as gNB, the next generation NodeB, which is used to provide wireless access and sensing functions) not only has to undertake communication tasks, but also has to perform target detection and sensor data collection. Generally speaking, in ISAC technology, multiple sensing entities are distributed in different locations. When the sensing target detects data, the data will be broadcasted, and the sensing entity will receive the data broadcast by the sensing target, and then further send it to the communication core network and other links.

[0003] In most cases, the data broadcast by a sensor target will be received by multiple sensor entities. However, although multiple sensor entities can receive data, the impact of receiving data by different sensor entities on the overall communication quality of ISAC is different. Therefore, among the multiple sensor entities that can receive the data broadcast by the sensor target, there are differences between optimal sensor entities and non-optimal sensor entities. If the optimal sensor entity is selected to receive the data broadcast by the sensor target and perform the next step such as data upload, it is beneficial to improve the energy efficiency of the system and improve the quality and efficiency of data collection.

[0004] However, due to the random distribution and dynamic mobility of sensing targets, it is difficult to select sensing entities, which is reflected in the lack of comprehensiveness and accuracy of the criteria for selecting sensing entities. Summary of the invention

[0005] In view of the fact that the current related technologies lack comprehensiveness and accuracy in the standards used to select sensing entities, resulting in technical problems such as low energy efficiency, quality and efficiency, the purpose of the present invention is to provide a method for selecting a sensing entity, a computer device and a storage medium.

[0006] In one aspect, an embodiment of the present invention includes a method for selecting a sensing entity, the method for selecting a sensing entity including the following steps:

[0007] Acquiring first position information of a sensing target;

[0008] Acquire second position information of each of the plurality of sensing entities;

[0009] For any of the sensing entities, determining a connection path between the sensing entity and the sensing target according to the first location information and the second location information;

[0010] Obtaining an energy efficiency score for each of the connected paths;

[0011] At least one of the sensing entities is selected according to the energy efficiency scores of the respective communication paths.

[0012] Further, for any of the sensing entities, determining a connection path between the sensing entity and the sensing target according to the first location information and the second location information includes:

[0013] Predicting a moving trajectory of the sensing target according to the first position information;

[0014] For any of the sensing entities, a connection path between the sensing entity and the sensing target is determined according to the movement trajectory and the second position information.

[0015] Further, determining a connection path between the sensing entity and the sensing target according to the movement trajectory and the second position information includes:

[0016] Determining at least one sampling point on the moving trajectory;

[0017] For any of the sampling points, using a shortest path algorithm, determining a corresponding shortest path according to the sampling point and the second position information;

[0018] Selecting the shortest path from among the shortest paths;

[0019] The shortest one is taken as the connecting path.

[0020] Furthermore, the obtaining of the energy efficiency score of each of the communication paths includes:

[0021] For any of the communication paths, obtaining distance information corresponding to the communication path, and total energy consumption information, total transmission data volume information, and renewable energy consumption information of the sensing entity corresponding to the communication path;

[0022] The energy efficiency score corresponding to the connectivity path is determined according to the distance information, the total energy consumption information, the total transmission data volume information and the renewable energy consumption information.

[0023] Furthermore, the energy efficiency score corresponding to the connectivity path is determined based on the distance information, the total energy consumption information, the total transmission data volume information, and the renewable energy consumption information, and the calculation formula used is:

[0024]

[0025] Among them, socre iis the energy efficiency score corresponding to the i-th connected path, P i is the distance information corresponding to the i-th connected path, E i is the total energy consumption information corresponding to the i-th sensing entity, B i is the total transmission data volume information corresponding to the i-th sensing entity, R i is the renewable energy consumption information corresponding to the i-th sensing entity, and ω1, ω2 and ω3 are weight coefficients.

[0026] Further, selecting at least one of the sensing entities according to the energy efficiency scores of the respective communication paths includes:

[0027] Detecting each of the connected paths in sequence according to the corresponding energy efficiency scores from high to low;

[0028] For the detected connection path, detecting data transmission availability between the corresponding sensing entity and the sensing target; the data transmission availability is data transmission available or data transmission unavailable;

[0029] The first sensing entity detected to be available for data transmission is selected.

[0030] Further, selecting at least one of the sensing entities according to the energy efficiency scores of the respective communication paths includes:

[0031] Set scoring thresholds;

[0032] Comparing each of the energy efficiency scores with the score threshold respectively;

[0033] The sensing entity corresponding to the energy efficiency score greater than the score threshold is selected.

[0034] Furthermore, the method for selecting the sensing entity further includes:

[0035] For each of the selected sensing entities, communication operating parameters are respectively allocated according to the corresponding energy efficiency score; wherein the communication operating parameters of the same sensing entity are positively correlated with the energy efficiency score;

[0036] The selected sensing entities establish a communication connection between the sensing target and the core network according to their respective communication working parameters.

[0037] On the other hand, an embodiment of the present invention further includes a computer device, including a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the method for selecting a sensing entity in the embodiment.

[0038] On the other hand, an embodiment of the present invention further includes a computer-readable storage medium, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to execute the method for selecting a sensing entity in the embodiment.

[0039] The beneficial effects of the present invention are as follows: the method for selecting a sensor entity in the embodiment can select at least one sensor entity from a plurality of sensor entities that can communicate with a sensor target, and the selected sensor entity can meet the path requirements for communicating with the sensor target, and can ensure the quality and efficiency of communication; and in the process of selecting the sensor entity, screening is performed through energy efficiency scores, which is equivalent to optimizing the communication path using the energy efficiency scores, so using the selected sensor entity to communicate with the sensor target can ensure high energy efficiency of the communication; therefore, the method for selecting the sensor entity in the embodiment can select the sensor entity with comprehensive and accurate standards, and ensure that the quality, efficiency and energy efficiency of communication are at a high level. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of the structure of a communication system to which the method for selecting a sensing entity can be applied in an embodiment;

[0041] Figure 2 A schematic diagram of the steps of the method for selecting a sensing entity in an embodiment;

[0042] Figure 3 is a flow chart of a method for selecting a sensing entity in an embodiment;

[0043] Figure 4 A schematic diagram of the principle of obtaining a connection path corresponding to a sensing entity in an embodiment;

[0044] Figure 5 Schematic diagram of the structure of the sensor entity selection system in the embodiment. DETAILED DESCRIPTION

[0045] Terminology explanation:

[0046] CNN: Convolutional Neural Network

[0047] KSP: K Shortest Paths

[0048] gNB: next Generation Node B

[0049] ISAC: Integrated Sensing and Communication

[0050] IoT: Internet of Things

[0051] AN:Access Network

[0052] RAN:Radio Access Network

[0053] UE:User Equipment

[0054] AMF: Access and Mobility Management Function

[0055] NWDAF: Network Data Analytics Function

[0056] PPSF: Prediction and Path Selection Function

[0057] Sensing target: generally refers to equipment such as vehicles, drones, and environmental sensors, which can be equipped with sensors to collect data and broadcast the collected data to nearby sensing entities;

[0058] Sensing entity: A device used to receive data sent by a sensing target, such as a gNB.

[0059] In this embodiment, the method for selecting a sensing entity can be applied to Figure 1 In the communication network system shown.

[0060] Reference Figure 1 The communication network system includes a sensor target, a communication core network, and n sensor entities, including sensor entity 1, sensor entity 2, sensor entity 3, ... sensor entity n. The sensor target detects sensor data through its sensor and sends the sensor data out, and sensor entity 1, sensor entity 2, sensor entity 3, ... sensor entity n, etc., can receive the sensor data. The n sensor entities are connected to the communication core network by wired or wireless means, and the sensor entity and the sensor target can be connected by wireless communication protocols such as Bluetooth, WiFi, and 5G.

[0061] In this embodiment, the task of executing the method for selecting a sensing entity is to select at least one sensing entity from n sensing entities that can receive sensing data to establish a communication connection with a sensing target, thereby receiving the sensing data.

[0062] In this embodiment, refer to Figure 2 ,The method for selecting the sensing entity includes the following steps:

[0063] S1. Obtaining the first position information of the sensing target;

[0064] S2. Obtaining second position information of each of the multiple sensing entities;

[0065] S3. For any sensing entity, determining a connection path between the sensing entity and the sensing target according to the first location information and the second location information;

[0066] S4. Obtaining the energy efficiency score of each connected path;

[0067] S5. Select at least one sensing entity according to the energy efficiency score of each connection path.

[0068] In this embodiment, the Figure 3 The process shown executes steps S1-S5.

[0069] In this embodiment, each step of the method for selecting a sensor entity, including steps S1-S5, etc., can be executed by the communication core network. The communication core network can call other devices to execute certain steps of the method for selecting a sensor entity.

[0070] In step S1, the communication core network can obtain the first position information of the sensing target through any sensing entity. Specifically, the sensing target can locate itself and obtain information indicating the position, such as longitude and latitude, as the first position information; in addition to the information indicating the position, the first position information can also include kinematic information obtained by processing the information indicating the position, such as speed, acceleration, and other information. The sensing target sends the first position information to any sensing entity, and the sensing entity forwards the first position information to the communication core network.

[0071] In step S2, each sensing entity locates itself and obtains the second position information. For example, the i-th sensing entity, i.e., sensing entity i, locates itself and obtains the second position information i. In this embodiment, the second position information is the same as the first position information, and in addition to the information indicating the position, it may also include information such as speed and acceleration. For example, the second position information i may indicate the position, speed and acceleration of the sensing entity i.

[0072] In step S3, after obtaining n pieces of second location information, namely, first location information and second location information 1, second location information 2, ... second location information n, the communication core network determines a connection path between the sensing entity and the sensing target according to the first location information and the second location information.

[0073] Specifically, the communication core network may execute a path planning algorithm to determine a connection path between the sensing entity and the sensing target. For example, for the sensing entity i, the communication core network executes a path planning algorithm based on the first location information and the second location information i, thereby calculating a connection path between the sensing entity i and the sensing target, that is, a connection path i.

[0074] Specifically, each sensing entity such as sensing entity 1, sensing entity 2, ..., sensing entity n, and the sensing target can be represented as vertices on a graph, and the coordinates of each vertex are the first position information of the sensing target or the second position information of the sensing entity. Figure 4 As shown in part (a) of FIG, by executing the shortest path planning algorithm such as the KSP algorithm, the Dijkstra algorithm, or the A* algorithm on the graph, the shortest path between each sensing entity and the sensing target can be calculated as a connected path. For example, by executing the shortest path planning algorithm such as the KSP algorithm, the Dijkstra algorithm, or the A* algorithm on the graph, the shortest path between the sensing entity i and the sensing target can be obtained as the connected path i corresponding to the sensing entity i.

[0075] In this embodiment, when executing step S3 and using the shortest path planning algorithm to calculate each connected path, after obtaining the graph represented by the first location information and each second location information, corresponding weights can also be assigned to the vertices in the graph. For example, for the vertex corresponding to the sensor entity i in the graph, that is, vertex i, the total energy consumption information E corresponding to the sensor entity i can be obtained. i , total transmission data volume information B i and / or renewable energy consumption informationR i The information is used as the weight of vertex i. Traverse i=1, 2...n, so as to assign corresponding weights to each vertex in the graph, and execute the shortest path planning algorithm such as KSP algorithm, Dijkstra algorithm or A* algorithm on the weighted graph to obtain various connected paths.

[0076] When executing step S3, i=1, 2, 3, ... n is traversed to obtain the corresponding connection path of each sensing entity.

[0077] In step S4, the communication core network determines the energy efficiency score of each connection path according to the working energy efficiency of the sensing entity and the sensing target connected to each connection path. For example, the communication core network determines the energy efficiency score corresponding to the connection path i, i.e., the energy efficiency score i, according to the working energy efficiency of the sensing entity i and the sensing target connected to the connection path i.

[0078] When executing step S4, i=1, 2, 3, ... n is traversed to obtain the energy efficiency score corresponding to each connection path.

[0079] In step S5, since there is a one-to-one correspondence between the connectivity path, the energy efficiency score and the sensing entity (for example, there is a connectivity path i between the sensing entity i and the sensing target, and the connectivity path i has an energy efficiency score i, so there is a one-to-one correspondence between the sensing entity i, the connectivity path i and the energy efficiency score i), at least one sensing entity can be selected from the n sensing entities according to the highest energy efficiency score or other criteria to complete the selection of the sensing entity.

[0080] In this embodiment, by executing steps S1-S5, at least one sensing entity can be selected from multiple sensing entities that can communicate with the sensing target, and the selected sensing entity can meet the path requirements for communicating with the sensing target, and can ensure the quality and efficiency of communication; and in the process of selecting the sensing entity, screening is performed through energy efficiency scores, which is equivalent to optimizing the communication path using energy efficiency scores. Therefore, using the selected sensing entity to communicate with the sensing target can ensure high energy efficiency of communication; therefore, executing steps S1-S5 can select the sensing entity with comprehensive and accurate standards, and ensure that the quality, efficiency and energy efficiency of communication are at a high level.

[0081] Reference Figure 3 , the AMF in the communication core network may obtain the first location information and the second location information; after obtaining the first location information and the second location information i (i=1, 2...n) and other data, the NWDAF in the communication core network may perform data preprocessing on the first location information and the second location information. Specifically, the data preprocessing may include the following steps:

[0082] Standardize the acquired data to ensure that the data is processed at the same scale.

[0083] Encode and select features of data and extract key features for model training and prediction.

[0084] Handle missing values ​​and use linear interpolation to fill in the missing parts of the data.

[0085] Handle outliers and replace abnormal values ​​in the data with the mean.

[0086] Reference Figure 3 In this embodiment, when executing step S3, that is, for any sensing entity, determining the connection path between the sensing entity and the sensing target according to the first location information and the second location information, the following steps may be specifically performed:

[0087] S301. Predicting the movement trajectory of the sensing target according to the first position information;

[0088] S302. For any sensing entity, determine a connection path between the sensing entity and the sensing target according to the movement trajectory and the second position information.

[0089] In step S301, the first position information indicates one or more positions where the sensing target has been located in history, and the trained artificial intelligence model can be run by the PPSF in the communication core network. The artificial intelligence model can be a convolutional neural network CNN, a recurrent neural network RNN, or a long short-term memory network LSTM, etc. The trained artificial intelligence model has the performance of predicting the movement trajectory of the sensing target in the future period of time based on the first position information.

[0090] In step S301, the first position information is input into the artificial intelligence model, which performs prediction processing and outputs a movement trajectory. The movement trajectory represents the trajectory formed by the predicted movement of the sensing target in a future period of time.

[0091] In this embodiment, when executing step S302, that is, determining the connection path between the sensing entity and the sensing target according to the movement trajectory and the second position information, the following steps may be specifically performed:

[0092] S30201. Determine at least one sampling point on the moving trajectory;

[0093] S30202. For any sampling point, use the shortest path algorithm to determine the corresponding shortest path according to the sampling point and the second position information;

[0094] S30203. Select the shortest path from among the shortest paths;

[0095] S30204. Take the shortest one as the connection path.

[0096] like Figure 4 As shown in part (b) of FIG. 1 , the connection path i between the sensing entity i and the sensing target is determined as an example for explanation.

[0097] In step S30201, at least one sampling point is determined on the moving trajectory corresponding to the sensing entity i, that is, the moving trajectory i, so as to obtain sampling points i1, i2, i3, i4, i5, etc. Specifically, each sampling point can be determined randomly, or the moving trajectory i can be divided into multiple segments, and the dividing point between two adjacent segments is used as a sampling point. Among them, the sampling point i1 coincides with the first position information.

[0098] Reference Figure 4Part (b) of the above method, by executing step S30201, is equivalent to expanding the first position information, which originally had only one point, into multiple sampling points. These sampling points can also be used as vertices of the graph together with the second position information i (i=1, 2...n), etc., so that when executing step S30202, the shortest path planning algorithm such as KSP algorithm, Dijkstra algorithm or A* algorithm is executed on this graph to obtain the shortest path between the sampling point i1 and the second position information i, that is, the shortest path i1. Similarly, the shortest path i2 between the sampling point i2 and the second position information i, the shortest path i3 between the sampling point i3 and the second position information i, the shortest path i4 between the sampling point i4 and the second position information i, and the shortest path i5 between the sampling point i5 and the second position information i can be obtained.

[0099] In steps S30203-S30204, for the shortest paths corresponding to the sensing entity i, namely the shortest path i1, the shortest path i2, the shortest path i3, the shortest path i4 and the shortest path i5, the shortest path is selected as the connecting path i corresponding to the sensing entity i, that is, the connecting path i = min{shortest path i1, shortest path i2, shortest path i3, shortest path i4, shortest path i5}.

[0100] When executing steps S30203-S30204, i=1, 2...n is traversed to obtain the connection path corresponding to each sensing entity.

[0101] In this embodiment, the principle of executing steps S30201-S30204 is: by determining at least one sampling point on the moving trajectory of the sensor target, the first position information of the sensor target can be expanded into multiple positions, and the shortest paths between these positions and the sensor entity are calculated respectively, and the shortest one is selected as the connection path of the sensor entity. The mobility of the sensor target, that is, the influence of the sensor target being able to reach different positions in the future, can be considered, which is conducive to more comprehensive selection of suitable sensor entities.

[0102] In this embodiment, when executing step S4, that is, the step of obtaining the energy efficiency score of each connection path, the following steps may be specifically performed:

[0103] S401. For any connected path, obtain the distance information corresponding to the connected path, as well as the total energy consumption information, the total transmission data volume information and the renewable energy consumption information of the sensing entity corresponding to the connected path;

[0104] S402. Determine the energy efficiency score corresponding to the connected path according to the distance information, the total energy consumption information, the total transmission data volume information and the renewable energy consumption information.

[0105] In step S401, taking the connected path i as an example, the connected path i itself corresponds to the distance information P i (Specifically, the distance information P i The meaning of may be the length of the connection path i, or the spatial distance between the first location information and the second location information i), and the sensing entity i corresponding to the connection path i has the total energy consumption information E i , total transmission data volume information B i and renewable energy consumption information R i And other parameters.

[0106] Among them, the total energy consumption information E i Indicates the total amount of energy consumed by the sensor entity i in the past period of time; the total amount of data transmitted B i Represents the total amount of data transmitted by sensor entity i in the past period of time (can be unlimited or limited to specific objects such as sensing targets); renewable energy consumption information R i It represents the amount of renewable energy (such as photovoltaic power generation, wind power generation, etc.) consumed by the sensor entity i during the past period of time.

[0107] When executing step S402, for the connected path i, its corresponding energy efficiency score can be calculated according to the following formula:

[0108]

[0109] Among them, socre i is the energy efficiency score corresponding to the ith connected path, ie, connected path i, and ω1, ω2, and ω3 are weight coefficients. In this embodiment, ω1=0.5, ω2=0.3, and ω3=0.2 can be taken.

[0110] In this embodiment, the calculated energy efficiency score socre i The energy efficiency level of sensor entity i in the process of transmitting data can be measured according to the total energy consumption and renewable energy consumption consumed by sensor entity i in order to transmit a certain amount of data, where the energy efficiency score socre i The higher it is, the higher the energy efficiency level of the sensing entity i.

[0111] When executing steps S401 - S402 , i=1, 2, ... n are traversed to obtain the energy efficiency score corresponding to each sensing entity.

[0112] In this embodiment, when executing step S5, that is, selecting at least one sensing entity according to the energy efficiency score of each connection path, the following steps may be specifically performed:

[0113] S501A. Detect each connected path in order from high to low according to the corresponding energy efficiency score;

[0114] S502A. For the detected connectivity path, detecting the data transmission availability between the corresponding sensing entity and the sensing target;

[0115] S503A. Select the first sensing entity detected to be available for data transmission.

[0116] Steps S501A-S503A are the first execution mode of step S5.

[0117] When step S501A is executed for the first time, the connection path with the highest energy efficiency score is read first. For example, assuming that energy efficiency score 3 is the highest among all energy efficiency scores, the connection path 3 corresponding to energy efficiency score 3 is detected. In step S502A, the data transmission availability between the sensing entity 3 corresponding to the connection path 3 and the sensing target is detected. Specifically, the AMF in the communication core network can call the sensor entity 3, so that the sensor entity 3 collects data from the sensor target, and the communication core network runs a timer to count. If the sensor entity 3 cannot receive any data from the sensor target after a certain period of time (for example, twice the maximum data transmission time), then the data transmission availability of the sensor entity 3 is "data transmission unavailable", and then return to execute step S501A, select the connection path with the second highest energy efficiency score (assuming it is connection path 5) to continue execution; if the sensor entity 3 receives data from the sensor target after a certain period of time, then the data transmission availability of the sensor entity 3 is "data transmission available"; if no sensor entity has been detected as "data transmission available" before, then the sensor entity 3 is the first sensor entity detected as "data transmission available", and when executing step S503A, the sensor entity 3 is selected to complete the selection of the sensor entity.

[0118] In this embodiment, by executing steps S501A-S503A, the sensor entity with available data transmission relative to the sensor target and the highest energy efficiency score can be selected. When the selected sensor entity is used for communication such as data collection with the sensor target, the availability and high energy efficiency of the communication can be guaranteed.

[0119] In this embodiment, based on the execution of steps S1-S5, the communication core network can call the selected sensor entity (for example, sensor entity 3) to communicate with the sensor target; specifically, the communication core network can send instructions to each sensor entity to control the sensor entity selected by steps S1-S5 (for example, sensor entity 3) to receive the sensor data broadcast by the sensor target and perform communication processes such as data forwarding, and control the unselected sensor entities (for example, other sensor entities except sensor entity 3) not to receive the sensor data broadcast by the sensor target, thereby ensuring that the quality, efficiency and energy efficiency of communication are at a high level.

[0120] In this embodiment, when executing step S5, that is, selecting at least one sensing entity according to the energy efficiency score of each connection path, the following steps may be specifically performed:

[0121] S501B. Set a scoring threshold;

[0122] S502B. Compare each energy efficiency score with the score threshold;

[0123] S503B. Select the sensor entities whose corresponding energy efficiency scores are greater than the score threshold.

[0124] Steps S501B-S503B are a second execution mode of step S5.

[0125] In step S501B, the set scoring threshold can be used to determine the size of an energy efficiency score. For example, if an energy efficiency score is greater than the scoring threshold, then the energy efficiency score can be determined to be large, and conversely, if an energy efficiency score is less than or equal to the scoring threshold, then the energy efficiency score can be determined to be small.

[0126] In this embodiment, by executing steps S501B-S503B, a sensing entity with a larger corresponding energy efficiency score can be selected for communicating with the sensing target.

[0127] In this embodiment, on the basis of executing steps S1-S5 and executing steps S501B-S503B when executing step S5, the method for selecting a sensing entity further includes the following steps:

[0128] S6. For each selected sensing entity, assign communication working parameters according to the corresponding energy efficiency score;

[0129] S7. Establish a communication connection between the sensing target and the core network with each selected sensing entity according to its own communication working parameters.

[0130] In step S6, the communication working parameters allocated may be communication time slots or total amount of single-round communication data, etc. Specifically, the greater the energy efficiency score of a sensor entity, the greater the communication time slot (or total amount of single-round communication data) allocated to the sensor entity by the communication core network.

[0131] S7. Establish a communication connection between the sensing target and the core network with each selected sensing entity according to its own communication working parameters.

[0132] For example, assume that through steps S501B-S503B, sensor entity 3 (with energy efficiency score 3), sensor entity 5 (with energy efficiency score 5), and sensor entity 6 (with energy efficiency score 6) are selected. Step S6 is executed to allocate communication time slot 3 (duration is proportional to energy efficiency score 3) to sensor entity 3, communication time slot 5 (duration is proportional to energy efficiency score 5) to sensor entity 5, and communication time slot 6 (duration is proportional to energy efficiency score 6) to sensor entity 6. In step S7, the communication core network calls sensor entity 3 to communicate with the sensor target for data collection and other communications within communication time slot 3; after communication time slot 3 passes, in communication time slot 5, calls sensor entity 5 to communicate with the sensor target for data collection and other communications; after communication time slot 5 passes, in communication time slot 6, calls sensor entity 6 to communicate with the sensor target for data collection and other communications. After communication time slot 6 passes, communication time slot 3 is started again, and this cycle repeats.

[0133] For example, assuming that through steps S501B-S503B, sensing entity 3 (with energy efficiency score 3), sensing entity 5 (with energy efficiency score 5), and sensing entity 6 (with energy efficiency score 6) are selected. Step S6 is executed to allocate a total amount of single-round communication data of 3 (proportional to the energy efficiency score 3) to sensing entity 3, a total amount of single-round communication data of 5 (proportional to the energy efficiency score 5) to sensing entity 5, and a total amount of single-round communication data of 6 (proportional to the energy efficiency score 6) to sensing entity 6. In step S7, the communication core network first calls the sensor entity 3 to perform data collection and other communications with the sensor target; after the amount of data transmitted by the sensor entity 3 in communication with the sensor target reaches the total amount of data in a single round of communication of 3, the communication core network calls the sensor entity 5 to perform data collection and other communications with the sensor target; after the amount of data transmitted by the sensor entity 5 in communication with the sensor target reaches the total amount of data in a single round of communication of 5, the communication core network calls the sensor entity 6 to perform data collection and other communications with the sensor target; after the amount of data transmitted by the sensor entity 6 in communication with the sensor target reaches the total amount of data in a single round of communication of 6, the communication core network again calls the sensor entity 3 to perform data collection and other communications with the sensor target, and the cycle repeats.

[0134] In this embodiment, the selected sensor entities, such as the sensor entity 3, the sensor entity 5 and the sensor entity 6, all send the sensor data to the communication core network after obtaining the sensor data broadcast by the sensor target.

[0135] In this embodiment, by executing steps S6-S7, the advantages of multiple sensing entities in energy efficiency scoring can be utilized, corresponding communication working parameters can be allocated, and the communication connection between the sensing target and the core network can be shared. This can ensure high energy efficiency of communication on the one hand, and reduce the burden on a single sensing entity, thereby improving the communication stability between the sensing target and the core network.

[0136] In this embodiment, you can use Figure 5 The sensing entity selection system shown is used to perform the sensing entity selection method. Figure 5 ,The selection system of the sensing entity includes data acquisition module, data preprocessing module, CNN prediction module, KSP path selection module, energy efficiency evaluation module, and result output module.

[0137] The functions of each module are as follows:

[0138] Data collection module: collects the location (longitude, latitude), speed, acceleration and time information of the sensing target and the sensing entity; the sensing entity collects the location, the total energy (E) consumed by the collected data and the renewable energy consumption (R);

[0139] Data preprocessing module: provides data preprocessing functions, including data standardization, feature encoding and selection, outlier processing, and missing value processing. Based on the historical movement data of the sensor target, missing values ​​and outliers are processed to obtain complete dimensional data. Normalized data is obtained through data standardization, and feature encoding and selection are performed on the normalized data to obtain key feature data; the distance (P) between the sensor target position and the sensor entity position and the amount of data transmitted (D) are calculated and saved for subsequent use;

[0140] CNN prediction module: provides the prediction function of the moving trajectory of the sensor target; uses convolutional neural network to extract and predict the input data features to generate the moving trajectory prediction results of the sensor target;

[0141] KSP path selection module: provides K shortest path selection function. Based on the sensor target movement trajectory predicted by CNN, the KSP algorithm is used to calculate the K shortest paths from the sensor target to multiple sensor entities, and each path is scored;

[0142] Energy efficiency evaluation module: Provides energy efficiency and renewable energy ratio evaluation functions. Evaluate the energy efficiency and renewable energy ratio of the selected path to ensure that the selected path is optimal in terms of energy efficiency and renewable energy utilization;

[0143] Result output module: Output path selection and evaluation results. Select the sensor entity with the highest score for data collection, and detect whether the corresponding sensor target confirms that this sensor entity is available. Set a timer to calculate whether data is received within twice the maximum transmission time. If data is received, the sensor entity is optimal; if data is not received, continue to select according to the score order until it is connected.

[0144] By running each module in the sensor entity selection system, each step in the sensor entity selection method can be executed, thereby achieving the technical effect of the sensor entity selection method.

[0145] A computer program that executes the method for selecting the sensing entity in this embodiment can be written and written into a computer device or a storage medium. When the computer program is read out and executed, the method for selecting the sensing entity in this embodiment is executed, thereby achieving the same technical effect as the method for selecting the sensing entity in the embodiment.

[0146] It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature, or it may be indirectly fixed or connected to the other feature. In addition, the descriptions of up, down, left, right, etc. used in the present disclosure are only relative to the relative positional relationship of the components of the present disclosure in the accompanying drawings. The singular forms of "a", "" and "the" used in the present disclosure are also intended to include the plural forms, unless the context clearly indicates other meanings. In addition, unless otherwise defined, all technical and scientific terms used in this embodiment have the same meaning as those generally understood by those skilled in the art. The terms used in the specification of this embodiment are only for describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in this embodiment includes any combination of one or more related listed items.

[0147] It should be understood that, although the term first, second, third etc. may be adopted to describe various elements in the present disclosure, these elements should not be limited to these terms. These terms are only used to distinguish the same type of elements from each other. For example, without departing from the scope of the present disclosure, the first element may also be referred to as the second element, and similarly, the second element may also be referred to as the first element. The use of any and all examples or exemplary language ("for example", "such as" etc.) provided by the present embodiment is only intended to better illustrate embodiments of the present invention, and unless otherwise required, the scope of the present invention will not be limited.

[0148] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed dedicated integrated circuit for this purpose.

[0149] In addition, the operations of the process described in this embodiment may be performed in any suitable order, unless otherwise indicated in this embodiment or otherwise clearly contradicted by the context. The process described in this embodiment (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as a code (e.g., executable instructions, one or more computer programs, or one or more applications) executed on one or more processors in common, by hardware or a combination thereof. A computer program includes a plurality of instructions that may be executed by one or more processors.

[0150] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the above steps in conjunction with a microprocessor or other data processor, the invention of this embodiment includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention also includes the computer itself.

[0151] The computer program can be applied to input data to perform the functions of the present embodiment, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0152] The above are only preferred embodiments of the present invention. The present invention is not limited to the above embodiments. As long as the technical effects of the present invention are achieved by the same means, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention. Within the scope of protection of the present invention, its technical solutions and / or implementation methods may have various modifications and changes.

Claims

1. A method for selecting a sensing entity, characterized in that: The method for selecting the sensing entity comprises: Acquiring first position information of a sensing target; Acquire second position information of each of the plurality of sensing entities; For any of the sensing entities, determining a connection path between the sensing entity and the sensing target according to the first location information and the second location information; Obtaining an energy efficiency score for each of the connected paths; At least one of the sensing entities is selected according to the energy efficiency scores of the respective communication paths.

2. The method for selecting a sensing entity according to claim 1, characterized in that: The step of determining, for any of the sensing entities, a connection path between the sensing entity and the sensing target according to the first location information and the second location information includes: Predicting a moving trajectory of the sensing target according to the first position information; For any of the sensing entities, a connection path between the sensing entity and the sensing target is determined according to the movement trajectory and the second position information.

3. The method for selecting a sensing entity according to claim 2, characterized in that: The determining, according to the movement trajectory and the second position information, a connection path between the sensing entity and the sensing target includes: Determining at least one sampling point on the moving trajectory; For any of the sampling points, using a shortest path algorithm, determining a corresponding shortest path according to the sampling point and the second position information; Selecting the shortest path from among the shortest paths; The shortest one is taken as the connecting path.

4. The method for selecting a sensing entity according to claim 1, characterized in that: The obtaining of the energy efficiency score of each of the communication paths comprises: For any of the communication paths, obtaining distance information corresponding to the communication path, and total energy consumption information, total transmission data volume information, and renewable energy consumption information of the sensing entity corresponding to the communication path; The energy efficiency score corresponding to the connectivity path is determined according to the distance information, the total energy consumption information, the total transmission data volume information and the renewable energy consumption information.

5. The method for selecting a sensing entity according to claim 4, characterized in that: The energy efficiency score corresponding to the connectivity path is determined according to the distance information, the total energy consumption information, the total transmission data volume information and the renewable energy consumption information, and the calculation formula used is: Among them, socre i is the energy efficiency score corresponding to the i-th connected path, P i is the distance information corresponding to the i-th connected path, E i is the total energy consumption information corresponding to the i-th sensing entity, B i is the total transmission data volume information corresponding to the i-th sensing entity, R i is the renewable energy consumption information corresponding to the i-th sensing entity, and ω1, ω2 and ω3 are weight coefficients.

6. The method for selecting a sensing entity according to any one of claims 1 to 5, characterized in that: The selecting at least one of the sensing entities according to the energy efficiency scores of the respective communication paths comprises: Detecting each of the connected paths in sequence according to the corresponding energy efficiency scores from high to low; For the detected connection path, detecting data transmission availability between the corresponding sensing entity and the sensing target; the data transmission availability is data transmission available or data transmission unavailable; The first sensing entity detected to be available for data transmission is selected.

7. The method for selecting a sensing entity according to any one of claims 1 to 5, characterized in that: The selecting at least one of the sensing entities according to the energy efficiency scores of the respective communication paths comprises: Set scoring thresholds; Comparing each of the energy efficiency scores with the score threshold respectively; The sensing entity corresponding to the energy efficiency score greater than the score threshold is selected.

8. The method for selecting a sensing entity according to claim 7, characterized in that: The method for selecting the sensing entity further comprises: For each of the selected sensing entities, communication operating parameters are respectively allocated according to the corresponding energy efficiency score; wherein the communication operating parameters of the same sensing entity are positively correlated with the energy efficiency score; The selected sensing entities establish a communication connection between the sensing target and the core network according to their respective communication working parameters.

9. A computer device, characterized in that: The device comprises a memory and a processor, wherein the memory is used to store at least one program, and the processor is used to load at least one program to execute the method for selecting a sensing entity according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to perform the method for selecting a sensing entity according to any one of claims 1 to 8 when executed by the processor.

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