Space-based Internet of Things data scheduling method and device

Abstract

The present application discloses a method and device for scheduling space-based Internet of Things data. The method comprises: obtaining Internet of Things communication data collected by the space-based Internet of Things; classifying the Internet of Things communication data to obtain first-category communication data corresponding to the collection-type space-based Internet of Things, second-category communication data corresponding to the perception-type space-based Internet of Things, and third-category communication data corresponding to the monitoring-type space-based Internet of Things; and scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel for the third-category communication data. The present application solves the technical problem of low efficiency in communication data scheduling within the space-based Internet of Things in related technologies.

Description

Technical Field

[0001] The present application relates to the field of data scheduling technology, and more specifically, to a space-based Internet of Things data scheduling method and device. Background Art

[0002] The Space-Based Internet of Things (SIoT) is an interconnected network based on space-based communication networks. While supporting existing terrestrial IoT applications, it integrates space-based sensing, monitoring, and time synchronization services to achieve intelligent identification, positioning, tracking, monitoring, and management of targets. Therefore, the SIoT effectively complements and expands the capabilities of the terrestrial IoT. On the one hand, the SIoT can be widely applied in areas difficult to reach by terrestrial networks, such as offshore islands, oil pipelines, power grid monitoring, and logistics and transportation data collection, thus expanding the management scope of existing IoT systems. On the other hand, the migration of sensor equipment from ground deployment to space, such as various remote sensing, meteorological, and aviation / maritime surveillance sensors, further expands and enhances the capabilities of IoT systems. However, the current efficiency of communication data scheduling within the SIoT remains low.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a space-based Internet of Things data scheduling method and device to at least solve the technical problem of low communication data scheduling efficiency within the space-based Internet of Things in the related art.

[0005] According to one aspect of an embodiment of the present application, a space-based Internet of Things data scheduling method is provided, comprising: acquiring Internet of Things communication data collected by the space-based Internet of Things; classifying the Internet of Things communication data to obtain first-category communication data corresponding to a collection-type space-based Internet of Things, second-category communication data corresponding to a perception-type space-based Internet of Things, and third-category communication data corresponding to a monitoring-type space-based Internet of Things; and scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, respectively. The scheduling strategy model includes at least: a first scheduling sub-model for determining a communication time for the first-category communication data, a second scheduling sub-model for determining a target satellite for processing the second-category communication data, and a third scheduling sub-model for determining a communication channel for the third-category communication data.

[0006] Optionally, the Internet of Things communication data is classified to obtain first-category communication data corresponding to the collection-type space-based Internet of Things, second-category communication data corresponding to the perception-type space-based Internet of Things, and third-category communication data corresponding to the monitoring-type space-based Internet of Things, respectively, including: using the k-nearest neighbor classification method to classify the Internet of Things communication data; determining the first-category communication data corresponding to the collection-type space-based Internet of Things in the Internet of Things communication data, wherein the collection-type space-based Internet of Things is used to manage each ground sensing node; determining the second-category communication data corresponding to the perception-type space-based Internet of Things in the Internet of Things communication data, wherein the perception-type space-based Internet of Things is used to manage each space-based sensing satellite node, and the space-based sensing satellite node has light sensing capability, and / or electrical sensing capability, and / or atmospheric sensing capability; determining the third-category communication data corresponding to the monitoring-type space-based Internet of Things in the Internet of Things communication data, wherein the monitoring-type space-based Internet of Things is used to manage each space-based monitoring satellite node, and the space-based monitoring satellite node has aviation surveillance sensing capability, and / or maritime surveillance sensing capability.

[0007] Optionally, the first scheduling sub-model is a Markov transition probability matrix model, which is used to predict the data transmission status at the next moment based on the data transmission status at the current moment, wherein the data transmission status includes at least one of the following: transmission signal gain, transmission delay; scheduling the first type of communication data according to the scheduling strategy model, including: determining the target communication time according to the first scheduling sub-model, and transmitting the first type of communication data within the collection-type space-based Internet of Things at the target communication time, wherein the transmission signal gain at the target communication time is greater than the signal gain threshold, or the transmission delay at the target communication time is less than the delay threshold.

[0008] Optionally, the second scheduling sub-model is a business computing power quantification model, which is used to calculate the business computing power of each information processing satellite based on a preset mapping function and the chip configuration information of each information processing satellite in the perception-type space-based Internet of Things, wherein the business computing power includes at least: logical operation capability, parallel computing capability and neural network acceleration capability; scheduling the second type of communication data according to the scheduling strategy model, including: determining the target business computing power required to process the second type of communication data, and determining the target information processing satellite that matches the target business computing power based on the second scheduling sub-model, and transmitting the second type of communication data to the target information processing satellite for processing.

[0009] Optionally, the third scheduling sub-model is a channel impulse response calculation model, which is used to calculate the impulse response vector of each transmission channel based on the three-dimensional characteristics of time, space, and frequency of each transmission channel within the monitoring-type space-based Internet of Things; scheduling the third type of communication data according to the scheduling strategy model, including: determining the target transmission channel according to the third scheduling sub-model, and transmitting the third type of communication data within the monitoring-type space-based Internet of Things through the target transmission channel, wherein the impulse response vector of the target transmission channel is less than the impulse response threshold.

[0010] Optionally, the scheduling strategy model also includes a satellite scheduling model, which is used to establish a communication link between two satellites that meet the communication connection conditions; the third type of communication data is scheduled according to the scheduling strategy model, including: in a monitoring-type space-based Internet of Things, when the target object moves out of the monitoring range of the first monitoring satellite that monitors the target object, determining a second monitoring satellite that corresponds to the position of the target object and meets the communication connection conditions with the first monitoring satellite, establishing a communication link between the first monitoring satellite and the second monitoring satellite through the satellite scheduling model, transmitting the third type of communication data in the first monitoring satellite to the second monitoring satellite, and the second monitoring satellite continuing to monitor the target object.

[0011] Optionally, communication and data transmission are performed between satellites in the space-based Internet of Things by means of tracking beams.

[0012] According to another aspect of an embodiment of the present application, a space-based Internet of Things data scheduling device is also provided, including: an acquisition module for acquiring Internet of Things communication data collected by the space-based Internet of Things; a classification module for classifying the Internet of Things communication data, and obtaining first-category communication data corresponding to the acquisition-type space-based Internet of Things, second-category communication data corresponding to the perception-type space-based Internet of Things, and third-category communication data corresponding to the monitoring-type space-based Internet of Things; a scheduling module for scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored program, wherein the device where the non-volatile storage medium is located executes the above-mentioned space-based Internet of Things data scheduling method by running the program.

[0014] According to another aspect of an embodiment of the present application, an electronic device is further provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned space-based Internet of Things data scheduling method through the computer program.

[0015] In an embodiment of the present application, IoT communication data collected by a space-based IoT is obtained; the IoT communication data is classified to obtain first-category communication data corresponding to a collection-type space-based IoT, second-category communication data corresponding to a perception-type space-based IoT, and third-category communication data corresponding to a monitoring-type space-based IoT; and the first-category communication data, the second-category communication data, and the third-category communication data are scheduled according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data, thereby realizing aerial scheduling of a space-ground integrated space-based IoT through the three types of communication data, thereby solving the technical problem of low efficiency in communication data scheduling within the space-based IoT in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 This is a flowchart of an optional space-based Internet of Things data scheduling method according to an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an optional framework of a remote sensing application perception-type space-based Internet of Things according to an embodiment of the present application;

[0019] Figure 3 This is a schematic diagram of a framework of an optional surveillance-type space-based Internet of Things application according to an embodiment of the present application;

[0020] Figure 4 This is a flowchart of an optional surveillance-type space-based Internet of Things application according to an embodiment of the present application;

[0021] Figure 5 This is a structural diagram of an optional space-based Internet of Things data scheduling device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification, claims, and drawings of the present application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0024] In order to better understand the embodiments of the present application, some nouns or terms that appear in the description of the embodiments of the present application are first translated and explained as follows:

[0025] k-nearest neighbor classification method: An instance-based classification learning method that does not require a complex training process to build a classification model and can be used for categorizable attributes or for the classification of continuous attributes. The k-nearest neighbor classification method has been applied in fields such as fraud detection, customer response prediction, and collaborative filtering (CF). The basic idea of ​​the k-nearest neighbor classification method is: given a sample x of an undetermined category, a search is performed in the sample space to find the k samples closest to the undetermined category sample. The category to which the sample to be classified belongs is determined by the category to which the majority of the samples among the k neighbors belong. The main problem of the k-nearest neighbor classification method is to determine the appropriate sample set, distance function, combination function, and k value. For various types of attributes, the distance function can refer to the sample similarity measurement formula in cluster analysis, and the combination function can use simple unweighted voting or weighted voting methods.

[0026] Example 1

[0027] According to an embodiment of the present application, a method for scheduling space-based Internet of Things data is provided. It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] Figure 1 FIG. 1 is a flow chart of an optional space-based Internet of Things data scheduling method according to an embodiment of the present application, such as Figure 1 As shown, the method includes at least steps S102-S106, wherein:

[0029] Step S102: Obtain IoT communication data collected by the space-based IoT.

[0030] In the technical solution provided in step S102 of the present invention, the space-based Internet of Things (SIoT) is achieved by launching several satellites into space, using them as base stations to form a network, thereby forming a launch center in space and providing IoT services to users on the ground. Therefore, the data scheduling system obtains IoT communication data collected by the SIoT.

[0031] In step S104, the IoT communication data is classified to obtain first-category communication data corresponding to the acquisition-type space-based IoT, second-category communication data corresponding to the perception-type space-based IoT, and third-category communication data corresponding to the monitoring-type space-based IoT.

[0032] In the technical solution provided in step S104 of the present invention, the data scheduling system classifies the IoT communication data collected from the space-based IoT. According to different sensing objects, the IoT communication data can be divided into a first category of communication data corresponding to the collection-type space-based IoT, a second category of communication data corresponding to the perception-type space-based IoT, and a third category of communication data corresponding to the monitoring-type space-based IoT.

[0033] Step S106, schedule the first category of communication data, the second category of communication data, and the third category of communication data respectively according to the scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first category of communication data, a second scheduling sub-model for determining the target satellite for processing the second category of communication data, and a third scheduling sub-model for determining the communication channel of the third category of communication data.

[0034] In the technical solution provided in the above step S106 of the present invention, the data scheduling system schedules the first type of communication data, the second type of communication data, and the third type of communication data respectively through a scheduling strategy model. This can solve the problems of a large number of collisions in space of the perception information of the collection-type space-based Internet of Things, resulting in data loss, the lack of reasonable scheduling of air resources in the perception-type space-based Internet of Things, and the use of tracking wave technology in the monitoring-type space-based Internet of Things, resulting in poor edge coverage signals.

[0035] In the technical solution provided in steps S102-S106 of the present application, IoT communication data collected by a space-based IoT is obtained; the IoT communication data is classified to obtain first-category communication data corresponding to a collection-type space-based IoT, second-category communication data corresponding to a perception-type space-based IoT, and third-category communication data corresponding to a monitoring-type space-based IoT; and the first-category communication data, second-category communication data, and third-category communication data are scheduled according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data, thereby realizing aerial scheduling of a space-ground integrated space-based IoT through the three types of communication data, thereby solving the technical problem of low efficiency in communication data scheduling within the space-based IoT in related technologies.

[0036] The above method in this embodiment will be further introduced below.

[0037] As an optional implementation, in the technical solution provided in the above step S104 of the present invention, the method includes: classifying the Internet of Things communication data using the k-nearest neighbor classification method; determining the first type of communication data corresponding to the collection-type space-based Internet of Things in the Internet of Things communication data, wherein the collection-type space-based Internet of Things is used to manage each ground sensing node; determining the second type of communication data corresponding to the perception-type space-based Internet of Things in the Internet of Things communication data, wherein the perception-type space-based Internet of Things is used to manage each space-based sensing satellite node, and the space-based sensing satellite node has light sensing capability, and / or electrical sensing capability, and / or atmospheric sensing capability; determining the third type of communication data corresponding to the monitoring-type space-based Internet of Things in the Internet of Things communication data, wherein the monitoring-type space-based Internet of Things is used to manage each space-based monitoring satellite node, and the space-based monitoring satellite node has aviation surveillance sensing capability, and / or maritime surveillance sensing capability.

[0038] In this embodiment, the k-nearest neighbor classification method is used to classify IoT communication data and determine three data sets, thereby discovering possible hidden sensing objects. Specifically, the k-nearest neighbor classification method counts the categories of k neighbors x and classifies x into the category with the largest count. Therefore, its calculation formula is as follows:

[0039]

[0040] Where η represents the counting function, if x i ∈C j , then η(x i ∈C j )=1; otherwise, η(x i ∈C j)=0. When the category counts are the same, a category is randomly selected for x. The combination function uses a weighted voting method, and its calculation formula is as follows:

[0041]

[0042] The weight is generally defined as wi = 1 / d(x, x l ) 2 , and d(x, x i ) represents the relationship between sample x and its neighbor x i distance.

[0043] Therefore, the k-nearest neighbor classification method makes predictions based on local data and is relatively sensitive to noise. Furthermore, the choice of k value is data-dependent. Excessively large k values ​​can reduce the impact of noise, but this can result in a large number of neighboring samples for undetermined classes, potentially leading to misclassification. Excessively small k values ​​can lead to voting failures or the influence of noise. Therefore, a good k value can be determined through various heuristic techniques. By finding the nearest neighbor samples of a particular sample, it is possible to calculate the distances between all pairs of samples. To effectively discover nearest neighbors, clustering algorithms can be used to classify the training set. If the centers of two clusters are far apart, samples in the corresponding clusters are generally unlikely to be neighbors. Simply calculating the distances between samples in adjacent clusters allows us to find the nearest neighbors of a particular sample.

[0044] Furthermore, the collection-based space-based IoT corresponds to the terrestrial mobile IoT in that it manages various ground-based sensor nodes and requires support for a large number of ground access nodes. Furthermore, the collection-based space-based IoT has a low transmission rate, and single access information is short-lived, contains small amounts of information, and occupies minimal satellite resources. Satellites provide transparent forwarding or onboard processing to forward sensor information.

[0045] The perception-based space-based Internet of Things (IoT) manages various space-based perception satellite nodes capable of optical photography, electronic signal sensing, and atmospheric information perception. These IoT features a small number of access nodes, high transmission rates, large amounts of information per access, and relatively regular storage and forwarding. Furthermore, the perception sensors in this IoT can be integrated with satellite nodes in a space-based transmission network, or independently designed and connected to the space-based transmission network via intersatellite links. The space-based transmission network satellites forward perception information through transparent forwarding or onboard processing.

[0046] The surveillance-type space-based Internet of Things (IoT) manages space-based data collection satellite nodes with aviation and maritime surveillance capabilities. It tracks and monitors target objects through signal recognition and analysis. It features numerous access nodes, low transmission rates, significant regional variations, and periodic patterns. Transmission protocols generally adhere to international standards. Furthermore, the surveillance sensor payloads of the IoT are typically deployed on low-orbit satellites, such as Inmarsat, Iridium, and Orbcomm satellites, along with satellite communication payloads. The satellites utilize onboard processing to forward sensor information, occupying only satellite resources from the satellite to the ground gateway.

[0047] As an optional implementation, in the technical solution provided in the above step S106 of the present invention, the method includes: the first scheduling sub-model is a Markov transition probability matrix model, which is used to predict the data transmission status at the next moment based on the data transmission status at the current moment, wherein the data transmission status includes at least one of the following: transmission signal gain, transmission delay; scheduling the first type of communication data according to the scheduling strategy model, including: determining the target communication time according to the first scheduling sub-model, and transmitting the first type of communication data within the collection-type space-based Internet of Things at the target communication time, wherein the transmission signal gain at the target communication time is greater than the signal gain threshold, or the transmission delay at the target communication time is less than the delay threshold.

[0048] In this embodiment, since each sensor node in a collection-based space-based IoT is primarily deployed on the ground, it can access the satellite communication network directly or indirectly via a satellite access gateway. Therefore, all sensor nodes can serve as access users of the satellite communication network, and sensor information and management information for the sensor nodes exist as satellite communication services. Furthermore, the collection-based space-based IoT is a star-shaped network, with all data transmitted via satellite to a sensor data service center. However, the massive number of sensor nodes requires direct access to the satellite communication network, and the transmitted sensor information is mostly sent in bursts. Therefore, when single occupancy times are short and bursty, a Markov transition probability matrix model is triggered, assisting the operation and maintenance management system in developing appropriate access control and resource allocation strategies. This adapts to the efficient transmission requirements of multiple users and short bursts, reduces resource waste caused by collisions and conflicts during sensor node information transmission, and ensures the timeliness and integrity of transmission.

[0049] Specifically, by analyzing the abnormality of the current air-to-ground data transmission between the satellite and the ground, the probability of the next air-to-ground data transmission abnormality is predicted, thereby helping the ground and satellite to adjust the signal reception strategy in a timely manner. The calculation formula of the Markov transition probability matrix model is as follows:

[0050] X k+1 =Xk +P

[0051] Among them, X k represents the state vector of the trend analysis and prediction object at time t=k, P represents the transition probability matrix; X k+1 Represents the state vector of the trend analysis and prediction object at time t = k + 1. A rectangular set is generated by accessing historical log data of ground communications stored on the air infrastructure. The rectangular set includes: user terminal UE, satellite node name, transmission delay (ms), and transmission signal gain (dB).

[0052] As an optional implementation, in the technical solution provided in the above-mentioned step S106 of the present invention, the method includes: the second scheduling sub-model is a business computing power quantization model, which is used to calculate the business computing power of each information processing satellite based on a preset mapping function and the chip configuration information of each information processing satellite in the perception-type space-based Internet of Things, wherein the business computing power includes at least: logical operation capability, parallel computing capability and neural network acceleration capability; scheduling the second type of communication data according to the scheduling strategy model, including: determining the target business computing power required to process the second type of communication data, and determining the target information processing satellite that matches the target business computing power based on the second scheduling sub-model, and transmitting the second type of communication data to the target information processing satellite for processing.

[0053] In this embodiment, the terrestrial IoT contains a large number of video sensors, electronic spectrum sensors, meteorological / oceanographic sensors, and other sensors. These sensors are widely used in scenarios such as urban management, security control, radio monitoring, and weather forecasting, but they can only manage a limited local area. Wide-area information collection requires the deployment of a large number of devices. Leveraging the wide coverage of space-based satellite nodes, sensors can be mounted on satellite platforms to build a perception-based space-based IoT, significantly reducing the number of deployed devices. Furthermore, in perception-based space-based IoTs, high-orbit satellites transmit sufficient idle resources to low-orbit satellites via tracking wave signals, but lack of proper resource scheduling results in inefficient satellite transmission tasks and slow perception. A task computing quantification model, also known as a business computing quantification model, is constructed. This model uniformly quantifies computing power based on the different computing power requirements assigned to high-orbit satellites. This uniform quantification of computing power is the basis for computing power scheduling and utilization. Computing power can be categorized into logical computing power, parallel computing power, and neural network acceleration power, depending on the algorithms being run and the types of data computations involved.

[0054] Specifically, for different computing types, chips from different manufacturers have different designs. In this case, a unified measurement of heterogeneous computing power is needed, so that the computing power provided by different chips can be mapped to a unified dimension through a measurement function. In the embodiment of this application, it is assumed that there are n logic operation chips, m parallel computing chips, and p neural network acceleration chips. Therefore, the computing power requirements of the business are described as:

[0055]

[0056] in, Indicates logical operation capability; Indicates parallel computing capability; Indicates the acceleration capability of the neural network; C br represents the total computing power requirement; f(x) represents the mapping table function, α, β, γ represent the mapping ratio function; q represents the redundant calculation example. Taking parallel computing capacity as an example, the above calculation formula can be understood as follows: assuming that there are three different types of parallel computing chip resources b1, b2, and b3, then f(b i ) represents the mapping function of the parallel computing capability that can be provided by the j-th parallel computing chip b, and q2 represents the redundant computing power of parallel computing.

[0057] As an optional implementation, in the technical solution provided in the above-mentioned step S106 of the present invention, the method includes: the third scheduling sub-model is a channel impulse response calculation model, which is used to calculate the impulse response vector of each transmission channel based on the three-dimensional characteristics of time, space, and frequency of each transmission channel in the monitoring-type space-based Internet of Things; scheduling the third type of communication data according to the scheduling strategy model, including: determining the target transmission channel according to the third scheduling sub-model, and transmitting the third type of communication data in the monitoring-type space-based Internet of Things through the target transmission channel, wherein the impulse response vector of the target transmission channel is less than the impulse response threshold.

[0058] In this embodiment, the impulse response vector of each transmission channel is calculated by monitoring the time, space, and frequency three-dimensional characteristics of each transmission channel in the space-based Internet of Things. Therefore, the specific formula of the channel impulse response calculation model is as follows:

[0059]

[0060] Where L(t) represents the number of paths from the transmitter to the receiver; τ l (t) represents the relative delay of the Lth path relative to the first path; represents the phase offset of the first path, τ l (t) and The two together reflect the frequency selectivity; h l(t) represents the attenuation factor of the first path, which is a parameter belonging to the category of time selectivity; Indicates the airspace azimuth; represents the corresponding signal intensity, which reflects the spatial selectivity.

[0061] In practical applications, spatial selectivity can usually be ignored. Therefore, for frequency-selective fading channels, assuming that the channel impulse response remains constant in OFDM (Orthogonal Frequency Division Multiplexing) and setting the start time t = 0, the channel impulse response calculation model can be simplified to:

[0062]

[0063] Thus, the impulse response vector of each transmission channel can be obtained as h = [h1, h2, ..., h N ] T .

[0064] Secondly, the target transmission channel with the minimum impulse vector is calculated based on the channel impulse response calculation model of compressed sensing. First, the single measurement vector model is set as:

[0065] y=Φ x

[0066] Where x∈R n Represents a known vector, so Φ∈R nx represents the known measurement matrix.

[0067] Assuming that the above system of equations is underdetermined, it has infinite solutions. Compressed sensing theory proves that when the sparsity of x S<<n, where S represents the number of non-zero values ​​in n, then the problem of solving x will be transformed into an optimization problem, that is, finding the sparsest solution is to satisfy the minimum L0 norm problem, which is expressed as:

[0068] Min||x||0,y=Φ x

[0069] Here, ||x||0 represents the L0 norm.

[0070] Therefore, the solution to the impulse response vector of the target transmission channel can be converted into a minimization problem, and its calculation formula can be written as:

[0071] Min||h||0,y=Θh+v

[0072] As an optional implementation, in the technical solution provided in the above step S106 of the present invention, the scheduling strategy model also includes a satellite scheduling model, which is used to establish a communication link between two satellites that meet the communication connection conditions; the third type of communication data is scheduled according to the scheduling strategy model, including: in a monitoring-type space-based Internet of Things, when the target object moves out of the monitoring range of the first monitoring satellite that monitors the target object, determining a second monitoring satellite corresponding to the position of the target object and meeting the communication connection conditions with the first monitoring satellite, establishing a communication link between the first monitoring satellite and the second monitoring satellite through the satellite scheduling model, transmitting the third type of communication data in the first monitoring satellite to the second monitoring satellite, and the second monitoring satellite continuing to monitor the target object.

[0073] In this embodiment, the problem of poor signal coverage at the edge of the monitoring satellite's monitoring range caused by tracking wave technology is addressed by introducing a satellite scheduling model to solve the problem of poor signal or loss of coverage due to satellite movement causing the user's relative position to move to the edge of the monitoring satellite's monitoring range. The satellite scheduling model includes an inter-satellite handover procedure and a signal enhancement procedure. Specifically, the inter-satellite handover procedure establishes a communication link between a first monitoring satellite and a second monitoring satellite that meet preset conditions through a handover procedure. The conditions that their positions must meet are as follows:

[0074]

[0075] Where h represents the satellite altitude, Hp represents the distance between the communication link and the Earth's surface (i.e., clearance), and Re represents the Earth's radius. The minimum clearance corresponds to the maximum inter-satellite geocentric angle alpha(max). When alpha < alpha(max), a communication link can be established between the first and second surveillance satellites; otherwise, a communication link cannot be established.

[0076] Optionally, communication and data transmission are performed between satellites in the space-based Internet of Things by means of tracking beams.

[0077] Specifically, Figure 2This is a schematic diagram of the framework of an optional remote sensing application perception-type space-based Internet of Things according to an embodiment of the present application. The perception-type space-based Internet of Things is constructed based on space-based sensors and satellite communication networks, and is mainly composed of high- and low-orbit communication satellites (responsible for information transmission), high- and low-orbit remote sensing satellites (equipped with various sensor payloads such as optical remote sensing, electronic signal sensing, and environmental sensing), a satellite network management center for perception data management, a remote sensing data service center, and a remote sensing satellite management center. Since low-orbit remote sensing satellites move at high speeds relative to communication satellites, in order to ensure the complete transmission of perception information, when transmitting through high-orbit communication satellites, a tracking beam method is used to achieve continuous communication with the remote sensing satellite for a period of time; when transmitting through low-orbit communication satellites, while using the tracking beam method, it is also necessary to master the switching control when the remote sensing satellite accesses different communication satellites to ensure the continuity of transmission.

[0078] Figure 3 This is a schematic diagram of the framework of an optional surveillance-type space-based Internet of Things application according to an embodiment of the present application, wherein the surveillance-type space-based Internet of Things is generally also a star network architecture. For the Automatic Dependent Surveillance-Broadcast (ADS-B) system, the Automatic Identification System (AIS), etc., the wide-area coverage capability of the surveillance-type space-based Internet of Things is utilized to enhance the surveillance capability of the ADS-B system and AIS. In the ground-based AIS and ADS-B systems, the maximum theoretical monitoring ranges are less than 40km and 370km respectively, while when using satellite-based monitoring at an altitude of 1000km, the maximum theoretical monitoring range can be extended to 3700km. However, due to the influence of indicators such as the ground response terminal capability, latency and coverage, low-orbit communication satellites are generally used as space-based collection platforms, and the monitoring information is transmitted to the ground data operation and maintenance control center for processing.

[0079] Further, Figure 4This is a flowchart of an optional surveillance-type space-based Internet of Things application according to an embodiment of the present application. In the ADS-B system, aircraft can use airborne equipment such as the Global Positioning System (GPS) data transceiver and antenna to generate accurate flight information, and broadcast their own flight information (such as speed, latitude, altitude, weather, etc.) to other aircraft and ground control stations through a data link at a fixed frequency, so that aircraft and ground control stations within their communication range can know their accurate flight navigation information. AIS uses Self-Organized Time Division Multiple Access (SOTDMA) to automatically broadcast and receive dynamic and static information of ships to achieve identification, monitoring and communication. Therefore, the main purpose of AIS is to exchange information such as the position, route and speed between ships, identify, locate, navigate and avoid collisions of ships, etc. The surveillance-type space-based Internet of Things mainly occupies satellite link resources from satellites to the ground data operation and maintenance control center, so that all information must be aggregated to the operation and maintenance control center. However, due to the small amount of monitoring information per unit time, the timeliness is at the second level and is always changing dynamically. Therefore, providing it with fixed satellite resources for transmission guarantee is not cost-effective. For this reason, an on-demand guarantee strategy is generally adopted, such as sharing satellite channels with other services, setting a priority for such services, etc., to ensure that the transmission is completed within a certain time. Furthermore, in order to realize the transmission and distribution of data collected by satellite-based surveillance payloads such as ADS-B systems and AIS, low-orbit satellites need to have on-board switching and processing capabilities, carry routing and switching payloads, interact with surveillance payloads to collect data, and exchange and forward the collected data to the ground data operation and maintenance control center for processing.

[0080] In the above steps, the Internet of Things communication data is accurately classified by the k-nearest neighbor classification method to obtain three types of communication data sets, namely the first type of communication data, the second type of communication data and the third type of communication data, so as to discover possible hidden perception objects; a scheduling strategy model is constructed to solve the various drawbacks of perception objects in the space-based Internet of Things, and the first type of communication data corresponding to the collection type space-based Internet of Things, the second type of communication data corresponding to the perception type space-based Internet of Things and the third type of communication data corresponding to the monitoring type space-based Internet of Things are reasonably scheduled through the scheduling strategy model, thereby solving the technical problem of low communication data scheduling efficiency in the space-based Internet of Things in related technologies.

[0081] Example 2

[0082] According to an embodiment of the present application, a space-based Internet of Things data scheduling device for implementing the space-based Internet of Things data scheduling method in Example 1 is also provided. Figure 5is a structural diagram of an optional space-based Internet of Things data scheduling device according to an embodiment of the present application, such as Figure 5 As shown, the space-based Internet of Things data scheduling device includes at least an acquisition module 51, a classification module 52 and a scheduling module 53, wherein:

[0083] The acquisition module 51 is used to obtain the Internet of Things communication data collected by the space-based Internet of Things.

[0084] The space-based Internet of Things (IoT) is achieved by launching a number of satellites into space, using them as base stations to form a network, thereby forming a launch center in space and providing IoT services to users on the ground. Therefore, the acquisition module 51 can acquire IoT communication data collected by the space-based IoT.

[0085] The classification module 52 is used to classify the IoT communication data to obtain the first type of communication data corresponding to the collection type space-based IoT, the second type of communication data corresponding to the perception type space-based IoT, and the third type of communication data corresponding to the monitoring type space-based IoT.

[0086] Specifically, the classification module 52 classifies the IoT communication data collected from the space-based IoT. According to different sensing objects, the IoT communication data can be divided into the first category of communication data corresponding to the collection type space-based IoT, the second category of communication data corresponding to the perception type space-based IoT, and the third category of communication data corresponding to the monitoring type space-based IoT.

[0087] As an optional implementation, the classification module 52 can use the k-nearest neighbor classification method to classify the Internet of Things communication data; determine the first category of communication data in the Internet of Things communication data corresponding to the collection-type space-based Internet of Things, wherein the collection-type space-based Internet of Things is used to manage each ground sensing node; determine the second category of communication data in the Internet of Things communication data corresponding to the perception-type space-based Internet of Things, wherein the perception-type space-based Internet of Things is used to manage each space-based sensing satellite node, and the space-based sensing satellite node has light sensing capability, and / or electrical sensing capability, and / or atmospheric sensing capability; determine the third category of communication data in the Internet of Things communication data corresponding to the monitoring-type space-based Internet of Things, wherein the monitoring-type space-based Internet of Things is used to manage each space-based monitoring satellite node, and the space-based monitoring satellite node has aviation monitoring sensing capability, and / or maritime monitoring sensing capability.

[0088] Specifically, the k-nearest neighbor classification method counts the categories of the k nearest neighbors x and classifies x into the category with the largest count. Therefore, its calculation formula is as follows:

[0089]

[0090] Where η represents the counting function, if x i ∈C j , then η(x i∈C j )=1; otherwise, η(x i ∈C j )=0. When the category counts are the same, a category is randomly selected for x. The combination function uses a weighted voting method, and its calculation formula is as follows:

[0091]

[0092] The weight is generally defined as wi = 1 / d(x, x i ) 2 , and d(x, x i ) represents the relationship between sample x and its neighbor x i distance.

[0093] Therefore, the k-nearest neighbor classification method makes predictions based on local data and is relatively sensitive to noise. Furthermore, the choice of k value is data-dependent. Excessively large k values ​​can reduce the impact of noise, but this can result in a large number of neighboring samples for undetermined classes, potentially leading to misclassification. Excessively small k values ​​can lead to voting failures or the influence of noise. Therefore, a good k value can be determined through various heuristic techniques. By finding the nearest neighbor samples of a particular sample, it is possible to calculate the distances between all pairs of samples. To effectively discover nearest neighbors, clustering algorithms can be used to classify the training set. If the centers of two clusters are far apart, samples in the corresponding clusters are generally unlikely to be neighbors. Simply calculating the distances between samples in adjacent clusters allows us to find the nearest neighbors of a particular sample.

[0094] Furthermore, the collection-based space-based IoT corresponds to the terrestrial mobile IoT in that it manages various ground-based sensor nodes and requires support for a large number of ground access nodes. Furthermore, the collection-based space-based IoT has a low transmission rate, and single access information is short-lived, contains small amounts of information, and occupies minimal satellite resources. Satellites provide transparent forwarding or onboard processing to forward sensor information.

[0095] The perception-based space-based Internet of Things (IoT) manages various space-based perception satellite nodes capable of optical photography, electronic signal sensing, and atmospheric information perception. These IoT features a small number of access nodes, high transmission rates, large amounts of information per access, and relatively regular storage and forwarding. Furthermore, the perception sensors in this IoT can be integrated with satellite nodes in a space-based transmission network, or independently designed and connected to the space-based transmission network via intersatellite links. The space-based transmission network satellites forward perception information through transparent forwarding or onboard processing.

[0096] The surveillance-type space-based Internet of Things (IoT) manages space-based data collection satellite nodes with aviation and maritime surveillance capabilities. It tracks and monitors target objects through signal recognition and analysis. It features numerous access nodes, low transmission rates, significant regional variations, and periodic patterns. Transmission protocols generally adhere to international standards. Furthermore, the surveillance sensor payloads of the IoT are typically deployed on low-orbit satellites, such as Inmarsat, Iridium, and Orbcomm satellites, along with satellite communication payloads. The satellites utilize onboard processing to forward sensor information, occupying only satellite resources from the satellite to the ground gateway.

[0097] The scheduling module 53 is used to schedule the first category of communication data, the second category of communication data and the third category of communication data respectively according to the scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first category of communication data, a second scheduling sub-model for determining the target satellite for processing the second category of communication data, and a third scheduling sub-model for determining the communication channel of the third category of communication data.

[0098] Specifically, the scheduling module 53 schedules the first type of communication data, the second type of communication data and the third type of communication data respectively through the scheduling strategy model, which can solve the problems of a large number of collisions in space of the perception information of the collection-type space-based Internet of Things, resulting in data loss, the lack of reasonable scheduling of air resources in the perception-type space-based Internet of Things, and the use of tracking wave technology in the monitoring-type space-based Internet of Things, resulting in poor edge coverage signals.

[0099] Optionally, the first scheduling sub-model in the scheduling module 53 is a Markov transition probability matrix model, which is used to predict the data transmission status at the next moment based on the data transmission status at the current moment, wherein the data transmission status includes at least one of the following: transmission signal gain, transmission delay; scheduling the first type of communication data according to the scheduling strategy model, including: determining the target communication time according to the first scheduling sub-model, and transmitting the first type of communication data within the collection-type space-based Internet of Things at the target communication time, wherein the transmission signal gain at the target communication time is greater than the signal gain threshold, or the transmission delay at the target communication time is less than the delay threshold.

[0100] Optionally, the second scheduling sub-model in the scheduling module 53 is a business computing power quantification model, which is used to calculate the business computing power of each information processing satellite based on a preset mapping function and the chip configuration information of each information processing satellite in the perception-type space-based Internet of Things, wherein the business computing power includes at least: logical operation capability, parallel computing capability and neural network acceleration capability; scheduling the second type of communication data according to the scheduling strategy model, including: determining the target business computing power required to process the second type of communication data, and determining the target information processing satellite that matches the target business computing power based on the second scheduling sub-model, and transmitting the second type of communication data to the target information processing satellite for processing.

[0101] Optionally, the third scheduling sub-model in the scheduling module 53 is a channel impulse response calculation model, which is used to calculate the impulse response vector of each transmission channel based on the time, space, and frequency three-dimensional characteristics of each transmission channel in the monitoring-type space-based Internet of Things; scheduling the third type of communication data according to the scheduling strategy model, including: determining the target transmission channel according to the third scheduling sub-model, and transmitting the third type of communication data in the monitoring-type space-based Internet of Things through the target transmission channel, wherein the impulse response vector of the target transmission channel is less than the impulse response threshold.

[0102] As an optional implementation, optionally, the scheduling module 53 also includes a satellite scheduling model for establishing a communication link between two satellites that meet the communication connection conditions; scheduling the third type of communication data according to the scheduling strategy model, including: in a monitoring-type space-based Internet of Things, when the target object moves out of the monitoring range of the first monitoring satellite that monitors the target object, determining a second monitoring satellite that corresponds to the position of the target object and meets the communication connection conditions with the first monitoring satellite, establishing a communication link between the first monitoring satellite and the second monitoring satellite through the satellite scheduling model, transmitting the third type of communication data in the first monitoring satellite to the second monitoring satellite, and the second monitoring satellite continuing to monitor the target object.

[0103] Optionally, communication and data transmission are performed between satellites in the space-based Internet of Things by means of tracking beams.

[0104] It should be noted that the modules in the space-based Internet of Things data scheduling device in the embodiment of the present application correspond one-to-one to the implementation steps of the space-based Internet of Things data scheduling method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be repeated here.

[0105] Example 3

[0106] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored program, wherein the device where the non-volatile storage medium is located executes the space-based Internet of Things data scheduling method in Example 1 by running the program.

[0107] Optionally, the device where the non-volatile storage medium is located implements the following steps by running the program: obtaining Internet of Things communication data collected by the space-based Internet of Things; classifying the Internet of Things communication data to obtain first-category communication data corresponding to the collection-type space-based Internet of Things, second-category communication data corresponding to the perception-type space-based Internet of Things, and third-category communication data corresponding to the monitoring-type space-based Internet of Things; scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data.

[0108] According to an embodiment of the present application, a processor is also provided, which is used to run a program, wherein the space-based Internet of Things data scheduling method in Example 1 is executed when the program is running.

[0109] Optionally, when the program is running, the following steps are executed: obtaining IoT communication data collected by the space-based IoT; classifying the IoT communication data to obtain first-category communication data corresponding to the collection-type space-based IoT, second-category communication data corresponding to the perception-type space-based IoT, and third-category communication data corresponding to the monitoring-type space-based IoT; and scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, respectively, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data.

[0110] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the space-based Internet of Things data scheduling method in Example 1 through the computer program.

[0111] Optionally, the processor is configured to implement the following steps through a computer program: obtaining Internet of Things communication data collected by the space-based Internet of Things; classifying the Internet of Things communication data to obtain first-category communication data corresponding to the collection-type space-based Internet of Things, second-category communication data corresponding to the perception-type space-based Internet of Things, and third-category communication data corresponding to the monitoring-type space-based Internet of Things; and scheduling the first-category communication data, the second-category communication data, and the third-category communication data according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first-category communication data, a second scheduling sub-model for determining the target satellite for processing the second-category communication data, and a third scheduling sub-model for determining the communication channel of the third-category communication data.

[0112] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0113] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0115] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0118] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A space-based Internet of Things data scheduling method, characterized in that: include: Obtain IoT communication data collected by space-based IoT; Classifying the IoT communication data to obtain first-category communication data corresponding to a collection-type space-based IoT, second-category communication data corresponding to a perception-type space-based IoT, and third-category communication data corresponding to a monitoring-type space-based IoT; The first category of communication data, the second category of communication data, and the third category of communication data are respectively scheduled according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first category of communication data, a second scheduling sub-model for determining the target satellite for processing the second category of communication data, and a third scheduling sub-model for determining the communication channel of the third category of communication data; wherein the second scheduling sub-model is a business computing power quantization model, which is used to calculate the business computing power of each information processing satellite in the perception-type space-based Internet of Things according to a preset mapping function and the chip configuration information of each information processing satellite, wherein the business computing power includes at least: logical operation capability, parallel computing capability, and neural network acceleration capability; The second type of communication data is scheduled according to the scheduling strategy model, including: determining the target business computing power required to process the second type of communication data, and determining the target information processing satellite that matches the target business computing power according to the second scheduling sub-model, and transmitting the second type of communication data to the target information processing satellite for processing.

2. The method according to claim 1, characterized in that The IoT communication data is classified to obtain first-category communication data corresponding to the acquisition-type space-based IoT, second-category communication data corresponding to the perception-type space-based IoT, and third-category communication data corresponding to the monitoring-type space-based IoT, including: Classifying the IoT communication data using a k-nearest neighbor classification method; Determining the first type of communication data corresponding to the acquisition-type space-based Internet of Things in the Internet of Things communication data, wherein the acquisition-type space-based Internet of Things is used to manage each ground sensing node; Determining the second type of communication data corresponding to the perception-type space-based Internet of Things in the Internet of Things communication data, wherein the perception-type space-based Internet of Things is used to manage each space-based perception satellite node, and the space-based perception satellite node has light perception capability, and / or electrical perception capability, and / or atmospheric perception capability; Determine the third type of communication data in the Internet of Things communication data corresponding to the surveillance-type space-based Internet of Things, wherein the surveillance-type space-based Internet of Things is used to manage each space-based surveillance satellite node, and the space-based surveillance satellite node has aviation surveillance perception capability and / or maritime surveillance perception capability.

3. The method according to claim 1, characterized in that The first scheduling sub-model is a Markov transition probability matrix model, which is used to predict the data transmission state at the next moment based on the data transmission state at the current moment, wherein the data transmission state includes at least one of the following: transmission signal gain and transmission delay; Scheduling the first type of communication data according to the scheduling strategy model includes: determining a target communication time according to the first scheduling sub-model, and transmitting the first type of communication data within the collection-type space-based Internet of Things at the target communication time, wherein the transmission signal gain at the target communication time is greater than a signal gain threshold, or the transmission delay at the target communication time is less than a delay threshold.

4. The method according to claim 1, wherein The third scheduling sub-model is a channel impulse response calculation model, which is used to calculate the impulse response vector of each transmission channel in the surveillance-type space-based Internet of Things based on the time, space, and frequency three-dimensional characteristics of each transmission channel; Scheduling the third type of communication data according to the scheduling strategy model includes: determining a target transmission channel according to the third scheduling sub-model, and transmitting the third type of communication data within the monitoring-type space-based Internet of Things through the target transmission channel, wherein the impulse response vector of the target transmission channel is less than an impulse response threshold.

5. The method according to claim 1, wherein The scheduling strategy model also includes a satellite scheduling model for establishing a communication link between two satellites that meet the communication connection conditions; The third type of communication data is scheduled according to the scheduling strategy model, including: in the monitoring type space-based Internet of Things, when the target object moves out of the monitoring range of the first monitoring satellite that monitors the target object, determining a second monitoring satellite corresponding to the position of the target object and satisfying the communication connection conditions with the first monitoring satellite, establishing a communication link between the first monitoring satellite and the second monitoring satellite through the satellite scheduling model, transmitting the third type of communication data in the first monitoring satellite to the second monitoring satellite, and having the second monitoring satellite continue to monitor the target object.

6. The method according to any one of claims 1 to 5, characterized in that The satellites in the space-based Internet of Things communicate and transmit data by means of tracking beams.

7. A space-based Internet of Things data scheduling device, characterized in that: include: The acquisition module is used to obtain the IoT communication data collected by the space-based IoT; a classification module, configured to classify the IoT communication data to obtain first-category communication data corresponding to a collection-type space-based IoT, second-category communication data corresponding to a perception-type space-based IoT, and third-category communication data corresponding to a monitoring-type space-based IoT; a scheduling module, configured to respectively schedule the first category of communication data, the second category of communication data, and the third category of communication data according to a scheduling strategy model, wherein the scheduling strategy model includes at least: a first scheduling sub-model for determining the communication time of the first category of communication data, a second scheduling sub-model for determining the target satellite for processing the second category of communication data, and a third scheduling sub-model for determining the communication channel for the third category of communication data, wherein the second scheduling sub-model is a business computing power quantization model, configured to calculate the business computing power of each information processing satellite in the perception-type space-based Internet of Things according to a preset mapping function and the chip configuration information of each information processing satellite, wherein the business computing power includes at least: logical operation capability, parallel computing capability, and neural network acceleration capability; The second type of communication data is scheduled according to the scheduling strategy model, including: determining the target business computing power required to process the second type of communication data, and determining the target information processing satellite that matches the target business computing power according to the second scheduling sub-model, and transmitting the second type of communication data to the target information processing satellite for processing.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein the device where the non-volatile storage medium is located executes the space-based Internet of Things data scheduling method according to any one of claims 1 to 6 by running the program.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the space-based Internet of Things data scheduling method according to any one of claims 1 to 6 through the computer program.

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