Distribution network satellite-ground fusion-based common inductance computing resource allocation method and system

Abstract

The invention discloses a distribution network satellite-ground fusion-based communication computing resource allocation method and system. The method comprises the steps of constructing a satellite-ground fusion distribution network architecture comprising a satellite, a ground base station, a communication terminal, a computing terminal and a sensing terminal; modeling is carried out according to signal data extracted from corresponding links of a communication terminal, a computing terminal and a sensing terminal in the satellite-ground fusion power distribution network architecture, and the transmission rate of the signal data in a corresponding channel of each terminal is calculated according to a modeling result; with the goal of maximizing the reachable total transmission rate of all communication terminals, constructing a joint non-convex optimization function comprising transmitting beamforming, receiving beamforming, transmitting power, a sensing signal covariance matrix and a common inductance calculation optimization variable of computing resources; processing the joint non-convex optimization function, and formulating and executing a target general inductance calculation resource allocation strategy according to a processing result; according to the invention, diversified terminal requirements can be met, and the distribution network reliability is improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless technology of power grids, and in particular to a method and system for configuring synaesthesia computing resources based on satellite-ground fusion of distribution networks. Background Art

[0002] With the development of new power systems, a large number of distributed renewable energy sources are connected to the distribution network, which puts forward more stringent requirements on the intelligence and flexibility of the distribution network hierarchical control. However, the independent operation mode of communication, perception and computing in the current distribution network system seriously restricts the collaborative linkage ability of software and hardware resources, making it difficult for the system to meet the urgent needs of the distribution network hierarchical control business in terms of high-precision information perception, real-time data transmission and efficient computing.

[0003] There have been some studies on distribution network synaptic resource allocation methods, but these methods are restricted by a single ground network and cannot provide reliable support for distribution network services in remote areas where renewable energy is widely distributed, resulting in a waste of resources.

[0004] It can be seen that how to optimize the configuration strategy of distribution network communication computing resources and improve the coverage of power communication services has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a method and system for configuring synergistic computing resources based on satellite-ground fusion of distribution network, so as to solve the problem of how to achieve deep integration of communication, perception and computing functions and coordinated configuration of multi-domain resources through the established satellite-ground fusion distribution network architecture, so as to improve resource utilization and reliability of distribution network operation.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a method for configuring synaesthesia computing resources based on satellite-ground fusion of distribution network, including: Acquire interactive signal data according to a communication terminal in a pre-built satellite-ground integrated power distribution network architecture, perform modeling based on the interactive signal data, and calculate the transmission rate of the interactive signal data in a channel corresponding to the communication terminal according to the modeling result; the satellite-ground integrated power distribution network architecture includes a satellite, a ground base station, a communication terminal, a computing terminal, and a sensing terminal; Taking the maximum achievable total transmission rate of each communication terminal as the goal, a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resource synaesthesia optimization variables is constructed according to the satellite-ground integrated power distribution network architecture; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals; The joint non-convex optimization function is decomposed into two sub-functions and subjected to alternating iterative processing, and a target synaesthesia computing resource allocation strategy is formulated and executed according to the processing results.

[0007] Further, the calculating, according to the modeling result, the transmission rate of the interactive signal data in the corresponding channel of the communication terminal includes: Modeling is performed based on the first interactive signal data extracted from the corresponding link of the communication terminal to obtain an uplink communication model, a downlink communication model and a satellite direct connection communication model; According to the uplink communication model, the downlink communication model and the satellite direct connection communication model, a corresponding communication signal-to-interference-and-noise ratio is acquired; Substituting the communication signal to interference and noise ratio into a preset rate calculation formula, the transmission rate of each communication terminal is obtained.

[0008] Furthermore, the process of acquiring the communication signal-to-interference-and-noise ratio includes: Acquire a first mixed signal received by a downlink communication terminal in the communication terminal, a second mixed signal received by a ground base station, and a third mixed signal received by a satellite; The communication signal to interference noise ratio corresponding to each communication terminal is calculated based on the target signal power and the interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal and the third mixed signal.

[0009] Furthermore, the constraints of the joint non-convex optimization function include: Base station total transmit power constraints, transmit power constraints of each communication terminal and each computing terminal, perceived signal-to-interference-and-noise ratio constraints, total computing resource constraints, task processing delay constraints, receive beamforming power constraints, and backhaul link rate constraints.

[0010] Furthermore, decomposing the joint non-convex optimization function into two sub-functions and performing alternating iterative processing includes: Decomposing the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed; An alternating iterative algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function.

[0011] Furthermore, the adopting of an alternating iterative algorithm in combination with a Gaussian randomization process to solve the first sub-function and the second sub-function includes: The non-convex constraint corresponding to the first sub-function is converted into a convex constraint by introducing an auxiliary variable, and the converted first sub-function is solved by using a semidefinite programming method combined with a Gaussian randomization process; The second sub-function is decomposed into a plurality of generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by an eigenvalue decomposition method.

[0012] Furthermore, after solving the first sub-function and the second sub-function by adopting an alternating iterative algorithm in combination with a Gaussian randomization process, the method further includes: Inputting the optimal solution consisting of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function to obtain the target achievable total transmission rate; The target achievable total transmission rate is iterated by using an alternating iterative algorithm until a convergence condition is met, and a target solution consisting of target synaesthesia optimization variables is obtained.

[0013] Furthermore, the first sub-function includes the following formula: In the formula, Downlink communication terminal The transmission rate; Uplink communication terminal The transmission rate; Satellite direct communication terminal The transmission rate; is the set of optimization variables corresponding to the first sub-function.

[0014] Furthermore, the second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

[0015] Another embodiment of the present invention provides a synaesthesia computing resource configuration system based on distribution network satellite-ground integration, including: A terminal model building module is used to obtain interactive signal data according to a communication terminal in a pre-built satellite-ground integrated power distribution network architecture, perform modeling based on the interactive signal data, and calculate the transmission rate of the interactive signal data in a channel corresponding to the communication terminal according to the modeling result; the satellite-ground integrated power distribution network architecture includes a satellite, a ground base station, a communication terminal, a computing terminal, and a sensing terminal; A joint optimization function construction module is used to construct a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resource synaesthesia optimization variables with the goal of maximizing the achievable total transmission rate of each communication terminal according to the satellite-ground integrated power distribution network architecture; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals; The synaesthesia resource configuration module is used to decompose the joint non-convex optimization function into two sub-functions and perform alternating iterative processing. According to the processing results, a target synaesthesia resource configuration strategy is formulated and executed. The terminal model construction module is specifically used to: Modeling is performed based on the first interactive signal data extracted from the corresponding link of the communication terminal to obtain an uplink communication model, a downlink communication model and a satellite direct connection communication model; According to the uplink communication model, the downlink communication model and the satellite direct connection communication model, a corresponding communication signal-to-interference-and-noise ratio is acquired; Substituting the communication signal to interference and noise ratio into a preset rate calculation formula, the transmission rate of each communication terminal is obtained.

[0016] Furthermore, the process of acquiring the communication signal-to-interference-and-noise ratio includes: Acquire a first mixed signal received by a downlink communication terminal in the communication terminal, a second mixed signal received by a ground base station, and a third mixed signal received by a satellite; The communication signal to interference noise ratio corresponding to each communication terminal is calculated based on the target signal power and the interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal and the third mixed signal.

[0017] Furthermore, the constraints of the joint non-convex optimization function include: Base station total transmit power constraints, transmit power constraints of each communication terminal and each computing terminal, perceived signal-to-interference-and-noise ratio constraints, total computing resource constraints, task processing delay constraints, receive beamforming power constraints, and backhaul link rate constraints.

[0018] Furthermore, the synaesthesia computing resource configuration module is specifically used to: Decomposing the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed; An alternating iterative algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function.

[0019] Furthermore, the adopting of an alternating iterative algorithm in combination with a Gaussian randomization process to solve the first sub-function and the second sub-function includes: The non-convex constraint corresponding to the first sub-function is converted into a convex constraint by introducing an auxiliary variable, and the converted first sub-function is solved by using a semidefinite programming method combined with a Gaussian randomization process; The second sub-function is decomposed into a plurality of generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by an eigenvalue decomposition method.

[0020] Furthermore, after solving the first sub-function and the second sub-function by adopting an alternating iterative algorithm in combination with a Gaussian randomization process, the method further includes: Inputting the optimal solution consisting of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function to obtain the target achievable total transmission rate; The target achievable total transmission rate is iterated by using an alternating iterative algorithm until a preset convergence condition is met, and a target solution consisting of target synaesthesia optimization variables is obtained.

[0021] Furthermore, the first sub-function includes the following formula: In the formula, Downlink communication terminal The transmission rate; Uplink communication terminal The transmission rate; Satellite direct communication terminal The transmission rate; is the set of optimization variables corresponding to the first sub-function.

[0022] Furthermore, the second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

[0023] Another embodiment of the present invention provides a computer device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the synaesthesia resource configuration method based on distribution network satellite-ground fusion as described above.

[0024] Yet another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the method for configuring synaesthesia resources based on distribution network satellite-ground fusion as described above is implemented.

[0025] Compared with the prior art, the embodiments of the present invention have the following advantages: The embodiment of the present invention fully considers the differences in terminal requirements by constructing a satellite-ground fusion network architecture covering multiple terminals, and realizes the deep integration of tele-sensing and computing functions; with the goal of maximizing the reachability and rate of all communication terminals, the transmit / receive beamforming vector, transmit power, perception signal covariance and computing resources are jointly optimized, and their corresponding designs are formulated as non-convex optimization problems. The sub-function solution method is adopted, which can alleviate the interference of heterogeneous networks, realize the reasonable allocation of spectrum resources, and improve the utilization efficiency of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flow chart of a method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion in one embodiment of the present invention; Figure 2 It is a schematic diagram of the satellite-ground integrated distribution network architecture in one embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of a synaesthesia resource configuration system based on distribution network satellite-ground fusion in one embodiment of the present invention; Figure 4 It is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] In the description of this application, the terms "first", "second", "third", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0029] In the description of the present application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0030] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood by specific circumstances.

[0031] An embodiment of the present invention provides a method for configuring synaesthesia computing resources based on the integration of distribution network satellite and ground. For details, please refer to Figure 1 , Figure 1 The figure shows a schematic flow chart of a method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion in one embodiment of the present invention, which includes the following steps: S1. Acquire interactive signal data according to a communication terminal in a pre-constructed satellite-ground integrated distribution network architecture, perform modeling based on the interactive signal data, and calculate the transmission rate of the interactive signal data in a channel corresponding to the communication terminal according to the modeling result.

[0032] Build a satellite-ground integrated power distribution network architecture including satellites, ground base stations, communication terminals, computing terminals and sensing terminals.

[0033] First, the embodiment of the present invention will construct a satellite-ground integrated power distribution network architecture, which integrates satellites, ground base stations, communication terminals, computing terminals and sensing terminals. The specific structure is as follows Figure 2 As shown, the communication terminal includes a plurality of uplink communication terminals, a downlink communication terminal and a satellite direct connection terminal. The sensing terminal includes a plurality of sensing targets to be detected.

[0034] In this architecture, satellites can communicate directly with ground terminals such as uplink communication terminals, downlink communication terminals, computing terminals, and satellite direct communication terminals, while assisting ground base stations to transmit relevant data back to the gateway. While communicating with ground communication terminals, ground base stations can sense malicious attackers in the environment and provide task offloading services for computing devices.

[0035] In some embodiments of the present invention, the linear antenna array at the ground base station may include Transmitter units and The transmitting array is mainly used to send integrated signals, communicate with the downlink communication terminal, and sense the location of the target in the environment. The receiving array is used to receive signals sent by the uplink communication terminal, calculate the computing tasks of the terminal, and sense the echo signal.

[0036] The ground base station data can be directly transmitted to the gateway via optical fiber, or transmitted to the satellite via a backhaul link, and then to the gateway. In this embodiment, in order to enhance the communication guarantee of the entire area, the architecture includes a number of satellite direct connection terminals that can be directly connected to the satellite to directly transmit data back to the gateway via the satellite. In the following content of the embodiment of the present invention, the ground base station is referred to as the base station.

[0037] As an example, this embodiment uses , , , , They represent the collection of uplink communication terminals, downlink communication terminals, computing terminals, satellite direct communication terminals and sensing targets respectively. And the total bandwidth of the architecture is set to ,in For ground communications and perception, Used for satellite-to-earth communications.

[0038] In the satellite-ground integrated power distribution network architecture, the first interactive signal data extracted from the corresponding link of the communication terminal includes all signals received by the downlink communication terminal, all signals sent by the uplink communication terminal to the base station, and all signals sent by the satellite direct connection terminal received by the satellite. The second interactive signal data extracted from the corresponding link of the computing terminal includes all signals sent by the computing terminal to the base station. The third interactive signal data extracted from the corresponding link of the perception terminal includes all echo detection signals sent by the perception terminal to the base station.

[0039] Based on this, in this embodiment, the interactive signal data extracted from the corresponding links of various terminals are modeled to obtain the uplink communication model, downlink communication model, satellite direct communication model, perception model and computing task offloading model. It can be understood that in this embodiment, Prior to this, the first choice is to model the transmission signals of all transmitting nodes in the network architecture. It is known that the signals sent by ground base stations consist of two parts: one is the integrated signal, which includes the signals of all downlink communication terminals and target perception signals; the other is the signal sent to the satellite.

[0040] In this embodiment, the synaesthesia integrated signal is expressed by the following formula: In the formula, It is sent by the base station to the downlink communication terminal. signal, satisfying , represents the expectation operation, Represents modulo operation; It is allocated by the base station to the downlink communication terminal. The beamforming vector of represents the complex domain, is the perception signal, and its covariance matrix is , represents the conjugate transpose.

[0041] The signal sent to the satellite is expressed as , the beamforming vector used is .

[0042] Uplink communication terminal The signal sent is represented by ,satisfy , the transmission power is .

[0043] The signal sent by the computing terminal is expressed as ,satisfy , the transmission power is .

[0044] The signal sent by the satellite direct connection terminal is expressed as ,satisfy , the transmission power is .

[0045] After modeling all the transmitted signals, the embodiment of the present invention will calculate the communication signal-to-noise ratio and data transmission rate corresponding to each terminal according to the modeling results. A specific example is provided below to describe the above process in detail: (1) Downlink communication model Set the base station to the downlink communication terminal The channel is , Uplink communication terminal To the downlink communication terminal The channel is , computing terminal To the downlink communication terminal The channel is , then the downlink communication terminal The first mixed signal received is sent by the base station to the downlink communication terminal The sum of the six parts is the signal sent by the base station, the accumulated signal sent by the base station to other downlink communication terminals, the perception signal sent by the base station, the signal sent by the uplink communication terminal, the signal sent by the computing terminal, and the noise.

[0046] The first mixed signal It is expressed as: In the formula, is the Gaussian white noise of the downlink communication terminal, The mean is 0 and the variance is The complex Gaussian distribution of .

[0047] Extracting the downlink communication terminal from the first mixed signal The received target signal power and interference signal power are calculated, and the ratio of the target signal power to the sum of multiple interference signal powers is obtained to obtain the downlink communication terminal Communication signal-to-interference-to-noise ratio , expressed by the following formula: It can be seen that the interference signal includes the accumulated signal sent by the base station to other downlink communication terminals, the perception signal sent by the base station, the signal sent by the uplink communication terminal, the signal sent by the computing terminal and the noise.

[0048] The obtained downlink communication terminal Communication signal-to-interference-to-noise ratio Substitute into the preset rate calculation formula, preferably, in this embodiment, substitute into the Shannon formula to calculate the data transmission rate of the downlink communication terminal , expressed as: (2) Perception Model After the sensing signal emitted by the base station reaches multiple targets, the reflected echo reaches the base station, and the base station then detects the target based on the echo. The direction angle of the target relative to the base station is , then the base station is The launch steering vector on and receive steering vector Respectively expressed as: The echo detection signal received at the base station It is expressed as: in, represents the complex channel coefficient.

[0049] Assume that the channels from the uplink communication terminal and the computing terminal to the base station are expressed as , Since the second mixed signal received at the base station includes the signal sent by the uplink communication terminal, the signal sent by the computing terminal, the echo detection signal and the noise, the second mixed signal It is expressed as: in, is the Gaussian white noise vector at the base station receiving array, is the covariance matrix.

[0050] Extract the first The target signal power and interference signal power of each target are calculated, and the communication signal-to-interference-noise ratio is obtained by calculating the ratio of the target signal power to the sum of multiple interference signal powers. , expressed by the following formula: In the formula, , All are From the receiving beamforming vector of the target echo signal, it can be seen that the interference signal includes the signal from the uplink communication terminal, the signal from the computing terminal, the echo detection signal of other targets and noise.

[0051] (3) Satellite direct communication model Since satellite-to-ground communication is not in the same frequency band as ground communication, satellite-to-ground communication is basically not interfered by ground signals. The signals received by the satellite only include the signals transmitted by the satellite direct connection terminal and the signals transmitted by the base station to the satellite. The channel to the satellite is , the channel from the base station to the satellite is , the third mixed signal received by the satellite is the sum of the satellite direct terminal signal, the base station to satellite signal and the noise. Then the third mixed signal It is expressed as: in, is the Gaussian white noise at the satellite receiver.

[0052] It is worth noting that in order to alleviate signal interference, in the embodiment of the present invention, satellite-to-ground communication adopts non-orthogonal multiple access (NOMA) technology, and the satellite receiver performs serial interference cancellation (SIC) to reduce interference between different signals. Specifically, assuming that the channel gains are ranked according to , and the satellite receiver decodes in descending order of channel gain, that is, the signal transmitted by the node with stronger channel gain is decoded first and removed from the received signal, and the remaining signal transmitted by the node with weaker channel gain is directly regarded as interference. Based on this, this embodiment calculates the terminal received by the satellite The ratio of the target signal power to the sum of the corresponding interference signal power is obtained by the terminal Communication signal-to-interference-to-noise ratio : According to Shannon's formula, the data transmission rate is: Similarly, calculate the signal-to-interference-to-noise ratio of the signal received by the satellite from the base station for: The data transfer rates are: (4) Uplink communication model According to the second mixed signal received by the base station The expression of the base station receiving the uplink communication terminal The ratio of the target signal power to the sum of multiple interference signal powers is obtained to obtain the uplink communication terminal The communication signal-to-interference-to-noise ratio is: in, It is From the receive beamforming vector of an uplink communication terminal, it can be seen that the interference signal includes the signals sent by other uplink communication terminals, the signal sent by the computing terminal, the echo detection signal and noise.

[0053] Therefore, according to Shannon's formula, the uplink communication terminal The data transfer rate is: (5) Computational task offloading model Set up terminal The computational tasks that need to be offloaded to the base station for processing are expressed as ,in Indicates the task data size, Indicates the number of CPU clock cycles required to calculate a unit of data. Indicates the maximum tolerable processing delay of the task. Computing resources are , represents the CPU frequency, in clock cycles / second. The processing time cost of offloading the task to the ground base station is calculated as: in, Indicates the transmission rate.

[0054] According to the second mixed signal received by the base station The expression of the base station receiving the data from the computing terminal The ratio of the target signal power to the sum of multiple interference signal powers is obtained by the terminal The communication signal-to-interference-to-noise ratio is: in, It is Calculate the receiving beamforming vector of the terminal.

[0055] The calculation terminal The data transfer rate is: According to the embodiments of the present invention, signal modeling is performed for uplink communication, downlink communication, perception target, satellite direct communication, and computing task offloading end respectively, and data transmission rate and signal-to-interference-and-noise ratio are derived to provide support for differentiated terminal needs and lay a solid foundation for the rational allocation of spectrum resources.

[0056] S2. Taking the maximum achievable total transmission rate of each communication terminal as the goal, a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and synaesthesia optimization variables of computing resources is constructed according to the satellite-ground integrated power distribution network architecture; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals.

[0057] In this embodiment, the resource allocation process will consider establishing a non-convex optimization problem, i.e., a non-convex optimization function, of the joint synaesthesia optimization variable, and convert it into a convex problem in the process of solving this non-convex problem. The convex optimization problem has a unique global optimal solution. After converting the non-convex problem into a convex form, it can ensure that the algorithm converges to the global optimal solution and avoids falling into the local optimal trap.

[0058] Based on this, in an embodiment of the present invention, according to the transmission rates of all uplink communications, downlink communications and satellite direct communication terminals calculated previously, with the maximum achievable total transmission rate of all these communication terminals as the goal, an objective function is established, that is, a joint non-convex optimization function combining multiple synaesthesia optimization variables is constructed, and the total transmission power constraint of the base station, the transmission power constraint of each communication terminal and each computing terminal, the perceived signal-to-interference-noise ratio constraint, the total amount of computing resources constraint, the task processing delay constraint, the receive beamforming power constraint and the return link rate constraint are used as constraints. Specifically, the embodiment of the present invention represents the transmit beamforming vector as: , , the receive beamforming vector is expressed as: , , , the transmission power is expressed as: , , , the covariance matrix of the perception signal is expressed as: , the computing resources allocated by the base station to the computing terminal are expressed as: .

[0059] Then the joint non-convex optimization function is constructed as: in is the set of variables optimized by synaesthesia. is the matrix trace operation, Indicates that the total transmit power of the base station is lower than the maximum power budget , , , They are the maximum transmission power limits for uplink communication terminals, computing terminals and satellite direct connection terminals, Indicates that the perceived signal-to-interference-to-noise ratio cannot be lower than the threshold , Indicates that the total computing resources allocated by the base station to each computing terminal does not exceed , Indicates that the delay of the computing task does not exceed , , , is the power normalization limit of the receive beamforming vector, Indicates that the return link rate between the base station and the satellite is between and between.

[0060] S3, decomposing the joint non-convex optimization function into two sub-functions and performing alternating iterative processing, formulating and executing a target synaesthesia computing resource allocation strategy according to the processing results.

[0061] In order to solve the constructed joint non-convex optimization function, the embodiment of the present invention decomposes it into two sub-function problems. By designing a non-convex objective function and a constraint condition convex approximation method, the two non-convex sub-function problems are transformed into a convex function problem that can be directly solved.

[0062] Specifically, the joint non-convex optimization function is decomposed into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that the receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that the transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed. The first sub-function (SP1) is expressed as: It can be seen that is the transmit beamforming vector, transmit power and computing resource set corresponding to the first sub-function.

[0063] The second sub-function (SP2) is expressed as: It can be seen that, in the formula, It is the vector set of receive beamforming.

[0064] Furthermore, in the embodiment of the present invention, an alternating iterative algorithm is used in combination with a Gaussian randomization process to solve the first sub-function and the second sub-function.

[0065] The first is the solution process of the first sub-function (SP1). Since it is a non-convex optimization problem, the non-convex constraints corresponding to the first sub-function are converted into convex constraints by introducing auxiliary variables, and the semidefinite programming method combined with the Gaussian randomization process is used to solve the converted first sub-function.

[0066] Specifically, the transformed first sub-function (SP3) is expressed as: In order to eliminate the quadratic constraint, define , and satisfies , ,definition , and satisfies , , then constraint C1 can be rewritten as: Constraint C5 can be rewritten as: in, .constraint , , , Still non-convex constraints, we will perform convex approximation on them one by one, including the following steps: (1) Convex approximation of C7 Defining auxiliary variables , constraint C7 can be approximately transformed into the following constraint: in, Constraints C7.2, C7.4, and C7.5 are convex constraints, while C7.1 and C7.3 are non-convex constraints. To handle the non-convexity of C7.1, an auxiliary variable is introduced , converting C7.1 into: Introducing auxiliary variables , constraint C7.6 can be further transformed into: C7.8 is a convex constraint. C7.7 and C7.9 have the same form and can be written as a standard second-order cone convex constraint, expressed as: In order to deal with the non-convexity of C7.3, the arithmetic geometric mean (AGM) inequality is introduced, that is, ,in , if and only if According to the AGM inequality, C7.3 can be approximately transformed into the following convex constraint: In summary, the original constraint C7 is approximately converted to C7.2, C7.3', C7.4, C7.5, C7.7', C7.8, and C7.9'.

[0067] (2) Convex approximation of C11 C11 can be equivalently converted to: Among them, C11.1 can be written as the following convex constraint: C11.2 can be written as the following convex constraint: In summary, the original constraint C11 is converted to C11.1' and C11.2'.

[0068] (3) Convex approximation of C12 Introducing auxiliary variables , constraint C12 can be equivalently converted to: Among them, C12.1 is a convex constraint, and an auxiliary variable is introduced , constraint C12.2 can be equivalently converted to: According to the AGM inequality, C12.3 can be approximately transformed into a convex constraint: In summary, the original constraint C12 is approximately converted to C12.1, C12.3', C12.4, and C12.5.

[0069] (4) Convex approximation of C13 Introducing auxiliary variables , constraint C13 can be equivalently converted to: in, C13.1 is a convex constraint, and an auxiliary variable is introduced , constraint C13.2 can be equivalently converted to: According to the AGM inequality, C13.3 can be approximately transformed into the following convex constraint: In summary, the original constraint C13 is approximately converted to C13.1, C13.3', C13.4, and C13.5.

[0070] (5) Convex processing of C14 Introducing auxiliary variables , constraint C14 can be equivalently converted to: Among them, C14.1 is a convex constraint, and an auxiliary variable is introduced. , constraint C14.2 can be equivalently converted to: According to the AGM inequality, C14.3 can be approximately transformed into the following convex constraint: In summary, the original constraint C14 is approximately converted to C14.1, C14.3', C14.4, and C14.5.

[0071] After processing (1) to (5) above, the non-convex problem (SP3) can be approximated as the following convex optimization function problem SP4, which is specifically expressed as: In an embodiment of the present invention, the rank 1 constraint C17 is ignored, so that the function problem (SP4) becomes a semidefinite programming problem. Preferably, the MatlabCVX toolbox can be used to directly solve it. After the optimal solution is obtained, the high-rank solution can be converted into a feasible rank 1 solution through a Gaussian randomization process.

[0072] Furthermore, the second sub-function is solved. In this embodiment, the second sub-function is decomposed into a plurality of generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by eigenvalue decomposition method.

[0073] Exemplarily, the second sub-function (SP2) can be decomposed into Optimizing variables for synaesthesia The sub-optimization problem (SP2.1) of About optimization variables sub-optimization problem (SP2.2), and About optimization variables The sub-optimization problem (SP2.3) of . Then the problem (SP2.1) can be expressed as: in, ; , represents the interference plus noise covariance matrix.

[0074] The problem (SP2.2) can be expressed as: in, ; , represents the interference noise covariance matrix.

[0075] The problem (SP2.3) can be expressed as: in, ; , represents the interference plus noise covariance matrix.

[0076] Problems (SP2.1), (SP2.2), and (SP2.3) are all generalized Rayleigh entropy problems, and their optimal solutions are expressed as follows: After respectively solving the two decomposed sub-functions, the embodiment of the present invention inputs the optimal solution composed of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function, so as to obtain the target achievable total transmission rate, and adopts an alternating iterative algorithm to iterate the target achievable total transmission rate until the convergence condition is met, so as to obtain the target solution composed of the target synaesthesia optimization variables.

[0077] The embodiment of the present invention will provide a specific implementation step to further describe the above content to obtain the transmit beamforming vector , , receive beamforming vector , , , transmit power , , , the covariance matrix of the sensor signal , the computing resources allocated by the base station to the computing terminal The optimal solution is the target solution, which includes the following steps (the following sub-problems are sub-functions, and the main function is the joint optimization non-convex function): Let iteration count , initialize the receive beamforming vector , , , convergence threshold , maximum number of iterations and all communication terminals reachable and rate ; According to the current , , Solving subproblem (SP1), we get , , , , , , decomposed using a Gaussian randomization process , Get the beamforming vector , ; wherein the problem (SP1) refers to the sub-problem of joint optimization of transmit beamforming, transmit power and computing resources; According to the current , , , , , , Solving subproblem (SP2), we get , , ; wherein the problem (SP2) refers to the receive beamforming optimization subfunction; Substitute all currently obtained optimization variables into the objective function of the main problem (P1) to obtain the corresponding reachable sum rate of all communication terminals ; Calculate the current convergence value ; judge Is it equal to the maximum number of iterations? or Is it less than the convergence threshold? If so, the algorithm ends and the current transmit beamforming vector is output. , , receive beamforming vector , , , transmit power , , , the covariance matrix of the sensor signal , the computing resources allocated by the base station to the computing terminal As the optimal solution; otherwise, , and go to step 2.

[0078] It should be understood that after obtaining the optimal solution, the optimal telemetry resource allocation plan for transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resources is formulated based on the optimal solution, so as to meet the needs of differentiated terminals and provide reliable support for distribution network services in remote areas.

[0079] In some embodiments of the present invention, the synaesthesia computing resource configuration strategy may include the following specific examples: Example 1: For scenarios where multiple ground base stations or satellites need to collaborate to complete wide-area sensing tasks (such as environmental monitoring), and where satellites need to sense malicious attackers in the environment and provide security for ground terminals, the following strategies can be implemented: According to the location and characteristics of the attacker, the perception signal covariance matrix is ​​optimized so that the perception signal covers the area where the attacker is located (covariance matrix optimization: design the perception signal covariance matrix to make the multi-station transmission signals orthogonal and reduce mutual interference).

[0080] Example 2: For scenarios where satellites and ground base stations collaborate to provide communication services for multiple ground terminals (such as acquisition and control terminals), the following policies can be configured: According to the communication needs of the terminal (such as data volume and real-time requirements), the transmit beamforming vector is dynamically adjusted to align the beam direction with the terminal with high demand. When allocating transmit power, terminals with poor channel quality are given priority, and their transmit power is increased to compensate for channel loss and ensure communication quality.

[0081] In summary, the embodiment of the present invention constructs a satellite-ground integrated power distribution network architecture, covering satellites, ground base stations, communication terminals, computing terminals and perception detection targets. On this basis, signal modeling is performed for uplink communication, downlink communication, target perception, satellite direct communication, and computing task offloading, and the signal-to-interference-noise ratio expression is derived; secondly, considering the power consumption limit and service quality requirements in the network, the constraint incentives are reasonably planned, and the collaborative configuration problem of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resources is constructed with the goal of maximizing the reachability and rate of all communication terminals; finally, by designing a non-convex objective function and a convex approximation method of constraint conditions, the resource joint optimization problem is transformed into two convex optimization sub-problems, thereby proposing a solution algorithm based on alternating iteration, which provides efficient support for the hierarchical regulation of distribution networks.

[0082] An embodiment of the present invention provides a synaesthesia computing resource configuration system based on distribution network satellite-ground fusion. For details, see Figure 3 , Figure 3 The figure shows a schematic diagram of the structure of a synaesthesia resource configuration system based on distribution network satellite-ground fusion in one embodiment of the present invention, including: The terminal model building module M1 is used to obtain interactive signal data from the communication terminal in the pre-built satellite-ground integrated power distribution network architecture, perform modeling based on the interactive signal data, and calculate the transmission rate of the interactive signal data in the corresponding channel of the communication terminal according to the modeling result; the satellite-ground integrated power distribution network architecture includes satellites, ground base stations, communication terminals, computing terminals and sensing terminals; The joint optimization function construction module M2 is used to construct a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resources based on the satellite-ground integrated power distribution network architecture, with the maximum achievable total transmission rate of each communication terminal as the goal; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals; The synaesthesia resource configuration module M3 is used to decompose the joint non-convex optimization function into two sub-functions and perform alternating iterative processing, formulate a target synaesthesia resource configuration strategy according to the processing results and execute it.

[0083] As an example, this embodiment uses a terminal model to construct a module, which is specifically used to obtain the transmission rate of each communication terminal, specifically: Modeling is performed based on the first interactive signal data extracted from the corresponding link of the communication terminal to obtain an uplink communication model, a downlink communication model and a satellite direct connection communication model; According to the uplink communication model, the downlink communication model and the satellite direct connection communication model, a corresponding communication signal-to-interference-and-noise ratio is acquired; Substituting the communication signal to interference and noise ratio into a preset rate calculation formula, the transmission rate of each communication terminal is obtained.

[0084] Furthermore, the process of obtaining the communication signal-to-interference-and-noise ratio includes the following steps: Acquire a first mixed signal received by a downlink communication terminal in the communication terminal, a second mixed signal received by a ground base station, and a third mixed signal received by a satellite; The communication signal to interference noise ratio corresponding to each communication terminal is calculated based on the target signal power and the interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal and the third mixed signal.

[0085] In this embodiment, the constraints of the joint non-convex optimization function are configured to include: total base station transmit power constraint, transmit power constraints of each communication terminal and each computing terminal, perceived signal-to-interference-and-noise ratio constraint, total computing resource constraint, task processing delay constraint, receive beamforming power constraint and backhaul link rate constraint.

[0086] In this embodiment, the synaesthesia resource configuration module is specifically used to: decompose the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that the receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that the transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed.

[0087] Preferably, this embodiment adopts an alternating iterative algorithm combined with a Gaussian randomization process to solve the first sub-function and the second sub-function. Specifically: the non-convex constraint corresponding to the first sub-function is converted into a convex constraint by introducing auxiliary variables, and the semidefinite programming method is used in combination with a Gaussian randomization process to solve the converted first sub-function; the second sub-function is decomposed into multiple generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by the eigenvalue decomposition method.

[0088] Furthermore, after solving the first sub-function and the second sub-function by using an alternating iterative algorithm in combination with a Gaussian randomization process, this embodiment inputs the optimal solution consisting of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function to obtain a target achievable total transmission rate. The target achievable total transmission rate is iterated by using an alternating iterative algorithm until a preset convergence condition is met, and a target solution consisting of target synaesthesia optimization variables is obtained.

[0089] The first sub-function includes the following formula: In the formula, Downlink communication terminal The transmission rate; Uplink communication terminal The transmission rate; Satellite direct communication terminal The transmission rate; is the set of optimization variables corresponding to the first sub-function.

[0090] The second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

[0091] like Figure 4 As shown, the embodiment of the present invention further provides a computer device, Figure 4 A structural block diagram of a preferred embodiment of a computer device provided by the present invention, wherein the computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned synaesthesia resource configuration method based on distribution network satellite-ground fusion when executing the computer program.

[0092] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2, ...), and the one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0093] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor. The processor is the control center of the terminal device, and various parts of the terminal device are connected using various interfaces and lines.

[0094] The memory mainly includes a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function, etc., and the data storage area can store related data, etc. In addition, the memory can be a high-speed random access memory, or a non-volatile memory, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, and a flash card (Flash Card), etc., or the memory can also be other volatile solid-state storage devices.

[0095] It should be noted that the above terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Figure 4The structural block diagram is only an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or combine certain components, or different components. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0096] Accordingly, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the steps in the method in the above embodiment, for example Figure 1 The above-mentioned embodiments only express several implementation modes of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for configuring synaesthesia computing resources based on satellite-ground fusion of distribution network, characterized in that: include: Acquire interactive signal data according to a communication terminal in a pre-built satellite-ground integrated distribution network architecture, perform modeling based on the interactive signal data, and calculate a transmission rate of the interactive signal data in a channel corresponding to the communication terminal according to the modeling result; The satellite-ground integrated power distribution network architecture includes satellites, ground base stations, communication terminals, computing terminals and sensing terminals; Taking the maximum achievable total transmission rate of each communication terminal as the goal, a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resource synaesthesia optimization variables is constructed according to the satellite-ground integrated power distribution network architecture; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals; The joint non-convex optimization function is decomposed into two sub-functions and subjected to alternating iterative processing, and a target synaesthesia computing resource allocation strategy is formulated and executed according to the processing results.

2. The method for configuring synaesthesia computing resources based on satellite-ground fusion of distribution network as claimed in claim 1, characterized in that: The calculating, according to the modeling result, the transmission rate of the interactive signal data in the channel corresponding to the communication terminal includes: Modeling is performed based on the first interactive signal data extracted from the corresponding link of the communication terminal to obtain an uplink communication model, a downlink communication model and a satellite direct connection communication model; According to the uplink communication model, the downlink communication model and the satellite direct communication model, a corresponding communication signal-to-interference-and-noise ratio is acquired; Substituting the communication signal to interference and noise ratio into a preset rate calculation formula, the transmission rate of each communication terminal is obtained.

3. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 2, characterized in that: The process of acquiring the communication signal to interference and noise ratio includes: Acquire a first mixed signal received by a downlink communication terminal in the communication terminal, a second mixed signal received by a ground base station, and a third mixed signal received by a satellite; The communication signal to interference noise ratio corresponding to each communication terminal is calculated based on the target signal power and the interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal and the third mixed signal.

4. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 1, characterized in that: The constraints of the joint non-convex optimization function include: Base station total transmit power constraints, transmit power constraints of each communication terminal and each computing terminal, perceived signal-to-interference-and-noise ratio constraints, total computing resource constraints, task processing delay constraints, receive beamforming power constraints, and backhaul link rate constraints.

5. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 1, characterized in that: Decomposing the joint non-convex optimization function into two sub-functions and performing alternating iterative processing includes: Decomposing the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed; An alternating iterative algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function.

6. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 5, characterized in that: The adopting of an alternating iterative algorithm combined with a Gaussian randomization process to solve the first sub-function and the second sub-function comprises: The non-convex constraint corresponding to the first sub-function is converted into a convex constraint by introducing an auxiliary variable, and the converted first sub-function is solved by using a semidefinite programming method combined with a Gaussian randomization process; The second sub-function is decomposed into a plurality of generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by an eigenvalue decomposition method.

7. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 5, characterized in that: After solving the first sub-function and the second sub-function by adopting an alternating iterative algorithm combined with a Gaussian randomization process, the method further includes: Inputting the optimal solution consisting of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function to obtain the target achievable total transmission rate; The target achievable total transmission rate is iterated by using an alternating iterative algorithm until a preset convergence condition is met, and a target solution consisting of target synaesthesia optimization variables is obtained.

8. The method for configuring synaesthesia computing resources based on distribution network satellite-ground integration as claimed in claim 5, characterized in that: The first sub-function includes the following formula: In the formula, Downlink communication terminal The transmission rate; Uplink communication terminal The transmission rate; Satellite direct communication terminal The transmission rate; is the set of optimization variables corresponding to the first sub-function.

9. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 8, characterized in that: The second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

10. A synaesthesia computing resource configuration system based on distribution network satellite-ground integration, characterized in that: include: A terminal model building module, used to obtain interactive signal data according to a communication terminal in a pre-built satellite-ground integrated distribution network architecture, perform modeling based on the interactive signal data, and calculate the transmission rate of the interactive signal data in a channel corresponding to the communication terminal according to the modeling result; The satellite-ground integrated power distribution network architecture includes satellites, ground base stations, communication terminals, computing terminals and sensing terminals; A joint optimization function construction module is used to construct a joint non-convex optimization function consisting of transmit beamforming, receive beamforming, transmit power, perception signal covariance matrix and computing resource synaesthesia optimization variables according to the satellite-ground integrated power distribution network architecture, with the maximum achievable total transmission rate of each communication terminal as the goal; wherein the achievable total transmission rate is obtained by processing the transmission rate of each of the communication terminals; The synaesthesia resource configuration module is used to decompose the joint non-convex optimization function into two sub-functions and perform alternating iterative processing, formulate a target synaesthesia resource configuration strategy according to the processing results and execute it.

11. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 10, characterized in that: The terminal model construction module is specifically used for: Modeling is performed based on the first interactive signal data extracted from the corresponding link of the communication terminal to obtain an uplink communication model, a downlink communication model and a satellite direct connection communication model; According to the uplink communication model, the downlink communication model and the satellite direct communication model, a corresponding communication signal-to-interference-and-noise ratio is acquired; Substituting the communication signal to interference and noise ratio into a preset rate calculation formula, the transmission rate of each communication terminal is obtained.

12. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 11, characterized in that: The process of acquiring the communication signal to interference and noise ratio includes: Acquire a first mixed signal received by a downlink communication terminal in the communication terminal, a second mixed signal received by a ground base station, and a third mixed signal received by a satellite; The communication signal to interference noise ratio corresponding to each communication terminal is calculated based on the target signal power and the interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal and the third mixed signal.

13. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 10, characterized in that: The constraints of the joint non-convex optimization function include: Base station total transmit power constraints, transmit power constraints of each communication terminal and each computing terminal, perceived signal-to-interference-and-noise ratio constraints, total computing resource constraints, task processing delay constraints, receive beamforming power constraints, and backhaul link rate constraints.

14. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 10, characterized in that: The synaesthesia computing resource configuration module is specifically used to: Decomposing the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power and computing resources under the condition that receive beamforming parameters are fixed, and a second sub-function for optimizing receive beamforming under the condition that transmit beamforming, transmit power, perception signal covariance matrix and computing resource parameters are fixed; An alternating iterative algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function.

15. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 14, characterized in that: The adopting of an alternating iterative algorithm combined with a Gaussian randomization process to solve the first sub-function and the second sub-function comprises: The non-convex constraint corresponding to the first sub-function is converted into a convex constraint by introducing an auxiliary variable, and the converted first sub-function is solved by using a semidefinite programming method combined with a Gaussian randomization process; The second sub-function is decomposed into a plurality of generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by an eigenvalue decomposition method.

16. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 14, characterized in that: After solving the first sub-function and the second sub-function by adopting an alternating iterative algorithm combined with a Gaussian randomization process, the method further includes: Inputting the optimal solution consisting of each optimization variable obtained by respectively solving the first sub-function and the second sub-function into the joint non-convex optimization function to obtain the target achievable total transmission rate; The target achievable total transmission rate is iterated by using an alternating iterative algorithm until a preset convergence condition is met, and a target solution consisting of target synaesthesia optimization variables is obtained.

17. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 14, characterized in that: The first sub-function includes the following formula: In the formula, Downlink communication terminal The transmission rate; Uplink communication terminal The transmission rate; Satellite direct communication terminal The transmission rate; is the set of optimization variables corresponding to the first sub-function.

18. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 17, characterized in that: The second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

19. A computer device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the synaesthesia resource configuration method based on distribution network satellite-ground integration as described in any one of claims 1 to 9.

20. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein when the device where the computer-readable storage medium is located executes the computer program, the method for configuring synaesthesia resources based on distribution network satellite-ground integration as described in any one of claims 1 to 9 is implemented.

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