A Method and System for Resource Allocation of Communication, Sensing and Computing Based on Terrestrial-Satellite Integration in Distribution Networks

Abstract

The present invention discloses a method and system for communication, sensing, and computing resource allocation based on satellite-ground integration in a distribution network. The method includes: constructing a satellite-ground integrated distribution network architecture including satellites, ground base stations, communication terminals, computing terminals, and sensing terminals; respectively modeling based on the signal data extracted from the corresponding links of the communication terminals, computing terminals, and sensing terminals in the satellite-ground integrated distribution network architecture, and calculating the transmission rate of the signal data in the corresponding channels of each terminal according to the modeling results; aiming at maximizing the total achievable transmission rate of all communication terminals, constructing a joint non-convex optimization function composed of communication, sensing, and computing optimization variables including transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources; processing the joint non-convex optimization function, formulating a target communication, sensing, and computing resource allocation strategy according to the processing results and executing it; the present invention can meet the diverse terminal requirements and improve the reliability of the distribution network.

Description

Technical Field

[0001] The present invention relates to the field of power grid wireless technology, and in particular, to a communication, sensing, and computing resource allocation method and system based on satellite-ground integration of a distribution network. Background Art

[0002] With the development of a new power system, a large number of high-proportion distributed new energy sources are connected to the distribution network, posing more stringent requirements for the intelligence and flexibility of hierarchical control of the distribution network. However, the current operating mode in which communication, sensing, and computing in the distribution network system are independent of each other severely restricts the collaborative linkage ability of software and hardware resources, making it difficult for the system to meet the urgent needs of distribution network hierarchical control services in terms of high-precision information sensing, real-time data transmission, and efficient computing.

[0003] There have been some studies on the allocation method of communication, sensing, and computing resources in the distribution network, but these methods are restricted by a single terrestrial network and are difficult to provide reliable support for distribution network services in remote areas with widespread renewable energy, resulting in waste of resources.

[0004] Therefore, it can be seen that how to optimize the allocation strategy of communication, sensing, and computing resources in the distribution network and improve the coverage of power communication services has become a technical problem that needs to be urgently solved by those skilled in the art. Summary of the Invention

[0005] The present invention provides a communication, sensing, and computing resource allocation method and system based on satellite-ground integration of a distribution network to solve the problem of how to achieve deep integration of communication, sensing, and computing functions and collaborative allocation of multi-domain resources through the established satellite-ground integrated distribution network architecture, and improve resource utilization rate and reliability of distribution network operation.

[0006] To solve the above technical problems, an embodiment of the present invention provides a communication, sensing, and computing resource allocation method based on satellite-ground integration of a distribution network, including:

[0007] Obtaining interactive signal data according to a communication terminal in a pre-constructed satellite-ground integrated distribution network architecture, modeling based on the interactive signal data, and calculating 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 distribution network architecture includes a satellite, a ground base station, a communication terminal, a computing terminal, and a sensing terminal;

[0008] Taking the maximum achievable total transmission rate of each communication terminal as the goal, constructing a joint non-convex optimization function including transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources according to the satellite-ground integrated distribution network architecture; wherein, the achievable total transmission rate is obtained by processing the transmission rates of each communication terminal;

[0009] Decompose the joint non-convex optimization function into two sub-functions and perform alternating iterative processing, and formulate and execute a target communication and sensing resource allocation strategy according to the processing results.

[0010] Further, calculating the transmission rate of the interactive signal data in the corresponding channels of the communication terminals according to the modeling results includes:

[0011] Model based on the first interactive signal data extracted from the corresponding links of the communication terminals to obtain an uplink communication model, a downlink communication model, and a satellite direct connection communication model;

[0012] Obtain the corresponding communication signal-to-interference-plus-noise ratio according to the uplink communication model, the downlink communication model, and the satellite direct connection communication model;

[0013] Substitute the communication signal-to-interference-plus-noise ratio into a preset rate calculation formula to obtain the transmission rate of each communication terminal.

[0014] Further, the process of obtaining the communication signal-to-interference-plus-noise ratio includes:

[0015] Obtain the first mixed signal received by the downlink communication terminal in the communication terminal, the second mixed signal received by the ground base station, and the third mixed signal received by the satellite;

[0016] Calculate the corresponding communication signal-to-interference-plus-noise ratio of each communication terminal according to the target signal power and interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal, and the third mixed signal.

[0017] Further, the constraint conditions of the joint non-convex optimization function include:

[0018] Total base station transmission power constraint, transmission power constraints of each communication terminal and each computing terminal, sensing signal-to-interference-plus-noise ratio constraint, total computing resource constraint, task processing delay constraint, receive beamforming power constraint, and backhaul link rate constraint.

[0019] Further, decomposing the joint non-convex optimization function into two sub-functions and performing alternating iterative processing includes:

[0020] Decompose the joint non-convex optimization function into a first sub-function that optimizes the transmit beamforming, transmit power, and computing resources under the condition that the receive beamforming parameters are fixed, and a second sub-function that optimizes the receive beamforming under the condition that the transmit beamforming, transmit power, sensing signal covariance matrix, and computing resource parameters are fixed;

[0021] Solve the first sub-function and the second sub-function using an alternating iterative algorithm combined with a Gaussian randomization process.

[0022] Further, solving the first sub-function and the second sub-function by using the alternating iteration algorithm in combination with the Gaussian randomization process includes:

[0023] Converting the non-convex constraints corresponding to the first sub-function into convex constraints by introducing auxiliary variables, and solving the converted first sub-function by using the semi-definite programming method in combination with the Gaussian randomization process;

[0024] Decomposing the second sub-function into multiple generalized Rayleigh entropy problems, and solving each of the generalized Rayleigh entropy problems by using the eigenvalue decomposition method.

[0025] Further, after solving the first sub-function and the second sub-function by using the alternating iteration algorithm in combination with the Gaussian randomization process, it further includes:

[0026] Inputting the optimal solutions composed of each optimization variable obtained by separately 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;

[0027] Using the alternating iteration algorithm to iterate the target achievable total transmission rate until the convergence condition is satisfied, and obtaining the target solution composed of the target communication sensing optimization variables.

[0028] Further, the first sub-function includes the following formula:

[0029]

[0030] In the formula, is the transmission rate of the downlink communication terminal ; is the transmission rate of the uplink communication terminal ; is the transmission rate of the satellite direct communication terminal ; is the set of optimization variables corresponding to the first sub-function.

[0031] Further, the second sub-function includes the following formula:

[0032]

[0033] In the formula, is the set of vectors for receive beamforming.

[0034] Another embodiment of the present invention provides a communication sensing computing resource allocation system based on power distribution satellite-terrestrial integration, including:

[0035] The terminal model construction module is used to obtain interactive signal data according to the communication terminals in the pre-constructed satellite-ground integrated power distribution network architecture, model based on the interactive signal data, and calculate the transmission rate of the interactive signal data in the corresponding channels of the communication terminals; the satellite-ground integrated power distribution network architecture includes satellites, ground base stations, communication terminals, computing terminals, and sensing terminals;

[0036] The joint optimization function construction module is used to construct a joint non-convex optimization function composed of communication-sensing-computation optimization variables including transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources with the goal of maximizing the total achievable transmission rate of each communication terminal; wherein, the total achievable transmission rate is obtained by processing the transmission rates of each of the communication terminals;

[0037] The communication-sensing-computation resource allocation module is used to decompose the joint non-convex optimization function into two sub-functions and perform alternating iteration processing, and formulate and execute the target communication-sensing-computation resource allocation strategy according to the processing results. The terminal model construction module is specifically used for:

[0038] Model according to the first interactive signal data extracted from the corresponding links of the communication terminals to obtain an uplink communication model, a downlink communication model, and a satellite direct connection communication model;

[0039] Obtain the corresponding communication signal-to-interference-plus-noise ratio according to the uplink communication model, the downlink communication model, and the satellite direct connection communication model;

[0040] Substitute the communication signal-to-interference-plus-noise ratio into a preset rate calculation formula to obtain the transmission rates of each communication terminal.

[0041] Further, the process of obtaining the communication signal-to-interference-plus-noise ratio includes:

[0042] Obtain the first mixed signal received by the downlink communication terminal in the communication terminal, the second mixed signal received by the ground base station, and the third mixed signal received by the satellite;

[0043] Calculate the corresponding communication signal-to-interference-plus-noise ratio of each communication terminal according to the target signal power and interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal, and the third mixed signal.

[0044] Further, the constraint conditions of the joint non-convex optimization function include:

[0045] Total base station transmit power constraint, transmit power constraints of each communication terminal and each computing terminal, sensing signal-to-interference-plus-noise ratio constraint, total computing resource constraint, task processing delay constraint, receive beamforming power constraint, and backhaul link rate constraint.

[0046] Furthermore, the cross-sensing computing resource allocation module is specifically configured to:

[0047] 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, sensing signal covariance matrix, and computing resource parameters are fixed;

[0048] Solve the first sub-function and the second sub-function by using an alternating iteration algorithm combined with a Gaussian randomization process.

[0049] Furthermore, the step of solving the first sub-function and the second sub-function by using an alternating iteration algorithm combined with a Gaussian randomization process includes:

[0050] Convert the non-convex constraints corresponding to the first sub-function into convex constraints by introducing auxiliary variables, and solve the transformed first sub-function by using a semi-definite programming method combined with a Gaussian randomization process;

[0051] Decompose the second sub-function into multiple generalized Rayleigh entropy problems, and solve each generalized Rayleigh entropy problem by using an eigenvalue decomposition method.

[0052] Furthermore, after solving the first sub-function and the second sub-function by using an alternating iteration algorithm combined with a Gaussian randomization process, it further includes:

[0053] Input the optimal solutions composed of each optimization variable obtained by solving the first sub-function and the second sub-function respectively into the joint non-convex optimization function to obtain the target achievable total transmission rate;

[0054] Use an alternating iteration algorithm to iterate the target achievable total transmission rate until a preset convergence condition is met, and obtain the target solution composed of the target cross-sensing computing optimization variables.

[0055] Furthermore, the first sub-function includes the following formula:

[0056]

[0057] where is the transmission rate of the downlink communication terminal ; is the transmission rate of the uplink communication terminal ; is the transmission rate of the satellite direct communication terminal ; is the set of optimization variables corresponding to the first sub-function.

[0058] Further, the second sub-function includes the following formula:

[0059]

[0060] In the formula, is the vector set for receive beamforming.

[0061] 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. When the processor executes the computer program, the above-mentioned method for allocating communication, sensing, and computing resources based on satellite-terrestrial integration for power distribution network is implemented.

[0062] Another embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the device where the computer-readable storage medium is located executes the computer program, the above-mentioned method for allocating communication, sensing, and computing resources based on satellite-terrestrial integration for power distribution network is implemented.

[0063] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following:

[0064] By constructing a satellite-terrestrial integration network architecture covering multiple terminals, the embodiments of the present invention fully consider the differences in terminal requirements and achieve the deep integration of communication, sensing, and computing functions. With the goal of maximizing the achievable sum rate of all communication terminals, the transmit / receive beamforming vectors, transmit power, sensing signal covariance, and computing resources are jointly optimized, and their corresponding design is formulated as a non-convex optimization problem. By using the method of solving sub-functions, it is possible to mitigate the interference of heterogeneous networks, achieve reasonable allocation of spectrum resources, and improve the utilization efficiency of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 is a schematic flowchart of the method for allocating communication, sensing, and computing resources based on satellite-terrestrial integration for power distribution network in one embodiment of the present invention;

[0066] Figure 2 is a schematic diagram of the satellite-terrestrial integrated power distribution network architecture in one embodiment of the present invention;

[0067] Figure 3 is a schematic diagram of the system structure for allocating communication, sensing, and computing resources based on satellite-terrestrial integration for power distribution network in one embodiment of the present invention;

[0068] Figure 4 is a structural block diagram of a preferred embodiment of a computer device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0070] In the description of this application, the terms "first", "second", "third", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", "third", etc. may explicitly or implicitly include one or more of such features. In the description of this application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0071] In the description of this application, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "connected" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two components. The terms "vertical", "horizontal", "left", "right", "up", "down" and similar expressions used herein are only for the purpose of illustration, 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 of the present invention. The term "and / or" used herein includes any and all combinations of one or more of the 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.

[0072] In the description of this application, it should be noted that unless otherwise defined, all the technical and scientific terms used in the present invention have the same meanings as those commonly understood by those of ordinary skill in the technical field to which this technology belongs. The terms used in the description of the present invention in the specification 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 according to specific circumstances.

[0073] An embodiment of the present invention provides a method for allocating communication, sensing, and computing resources based on satellite-ground integration of a distribution network. Specifically, please refer to Figure 1 , Figure 1 which shows a schematic flowchart of a method for allocating communication, sensing, and computing resources based on satellite-ground integration of a distribution network in one of the embodiments of the present invention, including the following steps:

[0074] S1. Obtain the interactive signal data according to the communication terminals in the pre-constructed space-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 channels of the communication terminals according to the modeling results.

[0075] Construct a space-ground integrated power distribution network architecture including satellites, ground base stations, communication terminals, computing terminals, and sensing terminals.

[0076] First, the embodiments of the present invention will construct a space-ground integrated power distribution network architecture, which integrates satellites, ground base stations, communication terminals, computing terminals, and sensing terminals. The specific structure is as Figure 2 shown, where the communication terminals include several uplink communication terminals, downlink communication terminals, and satellite direct connection terminals. The sensing terminals include several sensing targets to be detected.

[0077] In this architecture, the satellite can directly communicate with ground terminals such as uplink communication terminals, downlink communication terminals, computing terminals, and satellite direct connection communication terminals, and at the same time assist the ground base station to transmit relevant data back to the gateway. While communicating with ground communication terminals, the ground base station can sense malicious attackers in the environment and provide task offloading services for computing devices.

[0078] In some embodiments of the present invention, the linear antenna array at the ground base station may include transmitting units and receiving units. The transmitting array is mainly used to send integrated communication and sensing signals, communicate with downlink communication terminals, and at the same time sense the positions of sensing targets in the environment. The receiving array is used to receive the signals sent by uplink communication terminals, the computing tasks of computing terminals, and sensing echo signals.

[0079] The data of the ground base station can be directly transmitted to the gateway through optical fibers, or transmitted to the satellite through the backhaul link and then to the gateway. In this embodiment, in order to enhance the communication guarantee of the entire area, several satellite direct connection terminals capable of directly connecting to the satellite are included in this architecture to directly transmit data back to the gateway through the satellite. In the following content of the embodiments of the present invention, the ground base station is simply referred to as the base station.

[0080] As an example, this embodiment uses , , , , to represent the sets of uplink communication terminals, downlink communication terminals, computing terminals, satellite direct connection communication terminals, and sensing targets respectively. And set the total bandwidth of this architecture to be , where is used for ground communication and sensing, and is used for space-ground communication.

[0081] In the satellite-terrestrial integrated power distribution network architecture, the first interaction signal data extracted from the corresponding link of the communication terminal includes all the signals received by the downlink communication terminal, all the signals sent by the uplink communication terminal to the base station, and all the signals sent by the satellite direct connection terminal received by the satellite. The second interaction signal data extracted from the corresponding link of the computing terminal includes all the signals sent by the computing terminal to the base station. According to the third interaction signal data extracted from the corresponding link of the sensing terminal, it includes all the echo detection signals sent by the sensing terminal to the base.

[0082] Based on this, in this embodiment, the interaction signal data extracted from the corresponding links of various terminals will be respectively modeled to obtain the uplink communication model, downlink communication model, satellite direct connection communication model, sensing model, and computing task offloading model. It can be understood that in this embodiment,

[0083] Prior to this, it is preferred to model the transmission signals of all transmission nodes in the network architecture. It is known that the signals sent by the ground base station include two parts. One is the integrated communication and sensing signal, which includes the signals of all downlink communication terminals and the target sensing signal. The other is the signal sent to the satellite.

[0084] In this embodiment, the integrated communication and sensing signal is represented by the following formula:

[0085]

[0086] In the formula, is the signal sent by the base station to the downlink communication terminal and satisfies , represents the expectation operation, represents the modulo operation; is the beamforming vector assigned by the base station to the downlink communication terminal , represents the complex domain, is the sensing signal, and its covariance matrix is , represents the conjugate transpose.

[0087] The signal sent to the satellite is then represented as , and the beamforming vector adopted is .

[0088] The signal sent by the uplink communication terminal is represented as and satisfies , and the transmission power is .

[0089] The signal sent by the computing terminal is represented as and satisfies , the transmission power is .

[0090] The signal sent by the satellite direct connection terminal is expressed as , satisfying , the transmission power is .

[0091] After modeling all the transmitted signals, the embodiments 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. The following provides a specific example to elaborate on the above process:

[0092] (1) Downlink communication model

[0093] Let the channel from the base station to the downlink communication terminal be , the channel from the uplink communication terminal to the downlink communication terminal be , calculate the channel from the terminal to the downlink communication terminal be , then the first mixed signal received by the downlink communication terminal is the sum of the signal sent by the base station to the downlink communication terminal , the accumulated signal sent by the base station to other downlink communication terminals, the sensing signal sent by the base station, the signal sent by the uplink communication terminal, the signal sent by the calculation terminal, and the noise.

[0094] Then the first mixed signal is expressed as:

[0095]

[0096] In the formula, is the Gaussian white noise of the downlink communication terminal, is a complex Gaussian distribution with a mean of 0 and a variance of .

[0097] Extract the target signal power and interference signal power received by the downlink communication terminal from the first mixed signal, calculate the ratio of the target signal power to the sum of multiple interference signal powers, and obtain the communication signal-to-interference-plus-noise ratio of the downlink communication terminal, which is expressed by the following formula:

[0098]

[0099] ​It can be seen that the interference signals include the accumulated signals sent by the base station to other downlink communication terminals, the sensing signals sent by the base station, the signals sent by the uplink communication terminals, the signals sent by the computing terminals, and noise.

[0100] Substitute the obtained downlink communication terminal 's communication signal-to-interference-plus-noise ratio into the preset rate calculation formula. Preferably, in this embodiment, it will be substituted into the Shannon formula to calculate the data transmission rate of the downlink communication terminal , which is expressed as:

[0101]

[0102] (2)Sensing model

[0103] After the sensing signal emitted by the base station reaches multiple targets, the reflected echo reaches the base station, and the base station then performs target detection based on the echo. Based on this, in this embodiment, it is assumed that the th target's direction angle relative to the base station is , then the transmit steering vector and the receive steering vector at the base station on are respectively expressed as:

[0104]

[0105]

[0106] The echo detection signal received at the base station is expressed as:

[0107]

[0108] Among them, represents the complex channel coefficient.

[0109] Let the channels from the uplink communication terminal and the computing terminal to the base station be respectively expressed as , . Since the second mixed signal received at the base station includes the signals sent by the uplink communication terminal, the signals sent by the computing terminal, the echo detection signal, and noise, the second mixed signal is expressed as:

[0110]

[0111] Among them, is the Gaussian white noise vector at the base station's receiving array, and is the covariance matrix.

[0112] Extract the The target signal power and interference signal power of a target are used to calculate the ratio of the target signal power to the sum of multiple interference signal powers to obtain the communication signal-to-interference-plus-noise ratio (SINR). It is represented by the following formula:

[0113]

[0114] In the formula, , are all the receive beamforming vectors of the th target echo signal. It can be seen that the interference signals include the signals sent by the uplink communication terminal, the signals sent by the calculation terminal, the echo detection signals of other targets, and noise.

[0115] (3) Satellite direct communication model

[0116] Since satellite-ground communication and ground communication are not in the same frequency band, satellite-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 terminals and the signals transmitted by the base station to the satellite. Assume that the channel from the satellite direct connection terminal to the satellite is , and 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 connection terminal signal, the base station-to-satellite signal, and noise. Then the third mixed signal is expressed as:

[0117]

[0118] Wherein, is the Gaussian white noise at the satellite receiver.

[0119] It should be noted that, in order to mitigate signal interference, in the embodiments of the present invention, non-orthogonal multiple access (NOMA) technology is adopted for satellite-ground communication, and the satellite receiver performs successive interference cancellation (SIC) to reduce the interference between different signals. Specifically, assume that the channel gains are sorted according to , and the satellite receiver decodes in descending order of channel gain, that is, first decodes the signal transmitted by the node with stronger channel gain and removes it from the received signal, and the signals transmitted by the nodes with weaker channel gain are directly regarded as interference. Based on this, in this embodiment, by calculating the ratio of the target signal power of the terminal received by the satellite to the sum of the corresponding interference signal powers, the communication SINR of the terminal is obtained:

[0120]

[0121] According to the Shannon formula, the data transmission rate is:

[0122]

[0123] Similarly, calculate the signal-to-interference-plus-noise ratio of the satellite receiving the signal from the base station as:

[0124]

[0125] The data transmission rate is:

[0126]

[0127] (4) Uplink communication model

[0128] According to the expression of the second mixed signal received by the base station , calculate the ratio of the target signal power of the uplink communication terminal received by the base station to the sum of the powers of multiple interference signals, and obtain the communication signal-to-interference-plus-noise ratio of the uplink communication terminal as:

[0129]

[0130] where is the receive beamforming vector of the th uplink communication terminal. It can be seen that the interference signals include the signals sent by other uplink communication terminals, the signals sent by the computing terminal, the echo detection signals, and the noise.

[0131] Therefore, according to the Shannon formula, the data transmission rate of the uplink communication terminal is:

[0132]

[0133] (5) Calculate the task offloading model

[0134] Let the computing task that the terminal needs to offload to the base station for processing be expressed as , where represents the task data size, represents the number of CPU clock cycles required to compute a unit of data, represents the maximum tolerable processing delay of this task. The computing resources allocated by the base station to the terminal are , representing the CPU frequency, in units of clock cycles per second. The processing time cost of offloading the task to the ground base station is calculated as:

[0135]

[0136] where represents the transmission rate.

[0137] According to the second mixed signal received by the base station expression, calculate the ratio of the target signal power received by the base station from the computing terminal to the sum of the powers of multiple interference signals, and obtain the communication signal-to-interference-plus-noise ratio (SINR) of the terminal as:

[0138]

[0139] wherein, is the receive beamforming vector of the th computing terminal.

[0140] Then, the data transmission rate of the computing terminal is:

[0141]

[0142] For the embodiments of the present invention, by respectively performing signal modeling on uplink communication, downlink communication, sensing targets, satellite direct communication, and computing task offloading terminals, the derivation of data transmission rate and signal-to-interference-plus-noise ratio is carried out to provide support for the differentiated requirements of terminals, laying a solid foundation for the reasonable allocation of spectrum resources.

[0143] S2. Taking the maximum achievable total transmission rate of each communication terminal as the goal, construct a joint non-convex optimization function including transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources according to the satellite-ground integrated power distribution network architecture; wherein, the achievable total transmission rate is obtained by processing the transmission rates of each of the communication terminals.

[0144] In this embodiment, the process of resource allocation will consider establishing a non-convex optimization problem, that is, a non-convex optimization function, of joint communication-sensing-computing optimization variables, and convert it into a convex problem during 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 avoid falling into the local optimal trap.

[0145] Based on this, in the embodiments of the present invention, according to the transmission rates of all uplink communication, downlink communication, and satellite direct communication terminals calculated previously, taking the maximum achievable total transmission rate of all these communication terminals as the goal, establish an objective function, that is, construct a joint non-convex optimization function of multiple communication-sensing-computing optimization variables, and use the total transmit power constraint of the base station, the transmit power constraints of each communication terminal and each computing terminal, the sensing signal-to-interference-plus-noise ratio constraint, the total computing resource constraint, the task processing delay constraint, the receive beamforming power constraint, and the backhaul link rate constraint as constraint conditions. Specifically, the embodiments of the present invention represent the transmit beamforming vector as: , , the received beamforming vector is expressed as: , , , the transmit power is expressed as: , , , the covariance matrix of the sensing signal is expressed as: , the computing resources allocated by the base station to the computing terminal are expressed as: .

[0146] Then the joint non-convex optimization function is constructed as:

[0147]

[0148]

[0149]

[0150] where is the set of optimization variables for communication and sensing computing, is the trace operation of the matrix, indicates that the total transmit power of the base station is lower than the maximum power budget , , , are the maximum transmit power limits of the uplink communication terminal, the computing terminal, and the satellite direct connection terminal respectively, indicates that the sensing signal-to-interference-plus-noise ratio cannot be lower than the threshold , indicates that the total computing resources allocated by the base station to each computing terminal do not exceed , indicates that the delay of the computing task does not exceed , , , are the power normalization constraints of the received beamforming vector, indicates that the backhaul link rate between the base station and the satellite is between and .

[0151] S3. Decompose the joint non-convex optimization function into two sub-functions and perform alternating iteration processing, and formulate and execute the target communication and sensing computing resource allocation strategy according to the processing results.

[0152] To solve the constructed joint non-convex optimization function, the embodiment of the present invention decomposes it into two sub-function problems, and transforms the two non-convex sub-function problems into convex function problems that can be directly solved by designing non-convex objective functions and convex approximation methods for constraint conditions.

[0153] Specifically, the joint non-convex optimization function is decomposed into a first sub-function that optimizes the transmit beamforming, transmit power, and computing resources under the condition that the receive beamforming parameters are fixed, and a second sub-function that optimizes the receive beamforming under the condition that the transmit beamforming, transmit power, sensing signal covariance matrix, and computing resource parameters are fixed. Among them, the first sub-function (SP1) is expressed as:

[0154]

[0155]

[0156] It can be seen that is the set of transmit beamforming vectors, transmit power, and computing resources corresponding to the first sub-function.

[0157] The second sub-function (SP2) is expressed as:

[0158]

[0159]

[0160] It can be seen that in the formula, is the vector set of receive beamforming.

[0161] Furthermore, in the embodiments of the present invention, an alternating iteration algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function.

[0162] First is the solution process of the first sub-function (SP1). Since it is a non-convex optimization problem, therefore, by introducing auxiliary variables, the non-convex constraints corresponding to the first sub-function are transformed into convex constraints, and the transformed first sub-function is solved by using the semi-definite programming method combined with the Gaussian randomization process.

[0163] Specifically, the transformed first sub-function (SP3) is expressed as:

[0164]

[0165]

[0166] To eliminate the quadratic constraint condition, define , and satisfy , , define , and satisfy , , then the constraint C1 can be rewritten as:

[0167]

[0168] The constraint C5 can be rewritten as:

[0169]

[0170]

[0171] Among them, Constraint , , , are still non-convex constraints. Next, convex approximation processing will be carried out on them one by one, including the following steps:

[0172] (1) Convex approximation processing of C7

[0173] Define auxiliary variables , and constraint C7 can be approximately transformed into the following constraints:

[0174]

[0175] Among them, . Constraints C7.2, C7.4, and C7.5 are convex constraints, and C7.1 and C7.3 are non-convex constraints. To handle the non-convexity of C7.1, introduce auxiliary variables , and transform C7.1 into:

[0176]

[0177] Introduce auxiliary variables , and constraint C7.6 can be further transformed into:

[0178]

[0179] C7.8 is a convex constraint, and C7.7 and C7.9 have the same form and can be written as a convex constraint of the standard second-order cone, expressed as:

[0180]

[0181] To handle the non-convexity of C7.3, introduce the arithmetic-geometric mean (AGM) inequality, that is , where , and the equal sign holds if and only if . According to the AGM inequality, C7.3 can be approximately converted into the following convex constraint:

[0182]

[0183] In summary, the original constraint C7 is approximately converted into C7.2, C7.3’, C7.4, C7.5, C7.7’, C7.8, and C7.9’.

[0184] (2)Convex approximation processing of C11

[0185] C11 can be equivalently transformed into:

[0186]

[0187] Among them, C11.1 can be written as the following convex constraint:

[0188]

[0189] C11.2 can be written as the following convex constraint:

[0190]

[0191] In summary, the original constraint C11 is transformed into C11.1’ and C11.2’.

[0192] (3)Convex approximation processing of C12

[0193] Introduce an auxiliary variable , and the constraint C12 can be equivalently transformed into:

[0194]

[0195]

[0196] Among them, C12.1 is a convex constraint. Introduce an auxiliary variable , and the constraint C12.2 can be equivalently transformed into:

[0197]

[0198]

[0199] According to the AGM inequality, C12.3 can be approximately transformed into a convex constraint:

[0200]

[0201] In summary, the original constraint C12 is approximately transformed into C12.1, C12.3’, C12.4, and C12.5.

[0202] (4)Convex approximation processing of C13

[0203] Introduce an auxiliary variable , and the constraint C13 can be equivalently transformed into:

[0204]

[0205]

[0206] Among them, C13.1 is a convex constraint, and auxiliary variables are introduced. , constraint C13.2 can be equivalently transformed into:

[0207]

[0208]

[0209]

[0210] According to the AGM inequality, C13.3 can be approximately transformed into the following convex constraint:

[0211]

[0212] In summary, the original constraint C13 is approximately transformed into C13.1, C13.3’, C13.4, and C13.5.

[0213] (5) Convex processing of C14

[0214] Auxiliary variables are introduced , constraint C14 can be equivalently transformed into:

[0215]

[0216] where C14.1 is a convex constraint, and auxiliary variables are introduced , constraint C14.2 can be equivalently transformed into:

[0217]

[0218] According to the AGM inequality, C14.3 can be approximately transformed into the following convex constraint:

[0219]

[0220] In summary, the original constraint C14 is approximately transformed into C14.1, C14.3’, C14.4, and C14.5.

[0221] After the above processing of (1) to (5), the non-convex problem (SP3) can be approximated as the following convex optimization function problem SP4, which is specifically expressed as:

[0222]

[0223]

[0224] In the embodiments of the present invention, the rank-1 constraint C17 will be ignored, making the function problem (SP4) a semidefinite programming problem. Preferably, the Matlab CVX toolbox can be used to directly solve it, and after obtaining the optimal solution, the high-rank solution can be transformed into a feasible rank-1 solution through the Gaussian randomization process.

[0225] Further, it is the solution of the second sub-function. In this embodiment, 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.

[0226] Exemplarily, the second sub-function (SP2) can be decomposed into sub-optimization problems (SP2.1) regarding the communication-sensing optimization variable , sub-optimization problems (SP2.2) regarding the optimization variable , and sub-optimization problems (SP2.3) regarding the optimization variable . Then the problem (SP2.1) can be expressed as:

[0227]

[0228]

[0229] where ;

[0230] , representing the interference plus noise covariance matrix.

[0231] The problem (SP2.2) can be expressed as:

[0232]

[0233]

[0234] where ;

[0235] , representing the interference noise covariance matrix.

[0236] The problem (SP2.3) can be expressed as:

[0237]

[0238]

[0239] where ;

[0240] , representing the interference plus noise covariance matrix.

[0241] The problems (SP2.1), (SP2.2), and (SP2.3) are all generalized Rayleigh entropy problems, and their optimal solutions are respectively expressed as follows:

[0242]

[0243]

[0244]

[0245] After separately solving the two decomposed sub-functions, the embodiment of the present invention inputs the optimal solutions composed of each optimization variable obtained by separately solving the first sub-function and the second sub-function into the joint non-convex optimization function, and then the target achievable total transmission rate can be obtained. Then, the alternating iteration algorithm is used to iterate the target achievable total transmission rate until the convergence condition is satisfied, and the target solution composed of the target communication-sensing optimization variables is obtained.

[0246] The embodiment of the present invention will provide a specific implementation step to refine the above content to obtain the transmit beamforming vector 、 , the receive beamforming vector 、 、 , the transmit power 、 、 , the sensing signal covariance matrix , and the computing resources allocated by the base station to the computing terminal. The optimal solution, that is, the target solution, includes the following steps (the following sub-problems are sub-functions, and the main function is the joint optimization non-convex function):

[0247] Let the iteration count , initialize the receive beamforming vector 、 、 , the convergence threshold , the maximum number of iterations and the achievable sum rate of all communication terminals;

[0248] According to the current 、 、 , solve the sub-problem (SP1) to obtain 、 、 、 、 、 , and use the Gaussian randomization process to decompose 、 to obtain the beamforming vectors 、 ; where the problem (SP1) refers to the joint optimization sub-problem of transmit beamforming, transmit power, and computing resources; ​

[0249] According to the current , , , , , , Solve the sub-problem (SP2) to obtain , , ; where the problem (SP2) refers to the receive beamforming optimization sub-function;

[0250] Substitute all the currently obtained optimization variables into the objective function of the main problem (P1) to obtain the corresponding achievable sum rate of all communication terminals ;

[0251] Calculate the current convergence value ;

[0252] Judge Whether it is equal to the maximum number of iterations Or Whether it is less than the convergence threshold , if so, end the algorithm and output the current transmit beamforming vector , , receive beamforming vector , , , transmit power , , , sensing signal covariance matrix , computing resources allocated by the base station to the computing terminal as the optimal solution; otherwise, let , and transfer to step 2.

[0253] It should be understood that after obtaining the optimal solution, an optimal communication, sensing, and computing resource allocation scheme for transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources is formulated according to the optimal solution, so as to meet the differentiated terminal requirements and provide reliable support for the power distribution service in remote areas.

[0254] In some embodiments of the present invention, the communication, sensing, and computing resource allocation strategy may include the following specific examples:

[0255] Example 1, for scenarios where multiple ground base stations or satellites need to cooperate to complete a wide-area sensing task (such as environmental monitoring), and the satellite needs to sense malicious attackers in the environment to provide security protection for ground terminals, the following strategy can be executed:

[0256] Optimize the sensing signal covariance matrix according to the location and characteristics of the attacker, so that the sensing signal covers the area where the attacker is located (covariance matrix optimization: design the sensing signal covariance matrix to make the multi-station transmitted signals orthogonal and reduce mutual interference).

[0257] Example 2. For the scenario where satellites and ground base stations cooperate to provide communication services for multiple ground terminals (such as acquisition terminals and control terminals), the following strategies can be executed for configuration:

[0258] Dynamically adjust the transmit beamforming vector according to the communication requirements of the terminal (such as data volume and real-time requirements), so that the beam direction is aligned with the terminal with high demand. When allocating transmit power, give priority to the terminals with poor channel quality, and compensate for the channel loss by increasing their transmit power to ensure communication quality.

[0259] In summary, in the embodiment of the present invention, a space-ground integrated power distribution network architecture is constructed, covering satellites, ground base stations, communication terminals, computing terminals, and sensing detection targets. On this basis, signal modeling is performed on uplink communication, downlink communication, target sensing, satellite direct communication, and computing task offloading respectively, and the signal-to-interference-plus-noise ratio expression is derived. Secondly, considering factors such as power consumption limitations and quality of service requirements in the network, reasonable planning constraints and incentives are considered, and a collaborative configuration problem of transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources is constructed with the goal of maximizing the achievable sum rate of all communication terminals. Finally, by designing a non-convex objective function and a convex approximation method for constraints, the resource joint optimization problem is transformed into two convex optimization sub-problems, and a solution algorithm based on alternating iteration is proposed to provide efficient support for the hierarchical control service of the power distribution network.

[0260] An embodiment of the present invention provides a communication, sensing, and computing resource allocation system based on space-ground integration of a power distribution network. Specifically, please refer to Figure 3 , Figure 3 which shows a schematic structural diagram of a communication, sensing, and computing resource allocation system based on space-ground integration of a power distribution network in one embodiment of the present invention, including:

[0261] A terminal model construction module M1, configured to obtain interactive signal data from a communication terminal in a pre-constructed space-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 space-ground integrated power distribution network architecture includes satellites, ground base stations, communication terminals, computing terminals, and sensing terminals;

[0262] The joint optimization function construction module M2 is used to construct a joint non-convex optimization function consisting of communication-sensing-computation optimization variables including transmit beamforming, receive beamforming, transmit power, sensing signal covariance matrix, and computing resources with the goal of maximizing the total achievable transmission rate of each communication terminal; wherein, the total achievable transmission rate is obtained by processing the transmission rates of each of the communication terminals;

[0263] The communication-sensing-computation resource allocation module M3 is used to decompose the joint non-convex optimization function into two sub-functions and perform alternating iteration processing, and formulate and execute a target communication-sensing-computation resource allocation strategy according to the processing results.

[0264] Exemplarily, in this embodiment, the terminal model construction module is specifically used to obtain the transmission rates of each communication terminal, specifically:

[0265] Modeling based on the first interaction signal data extracted from the corresponding links of the communication terminals to obtain an uplink communication model, a downlink communication model, and a satellite direct connection communication model;

[0266] According to the uplink communication model, the downlink communication model, and the satellite direct connection communication model, obtain the corresponding communication signal-to-interference-plus-noise ratio;

[0267] Substitute the communication signal-to-interference-plus-noise ratio into a preset rate calculation formula to obtain the transmission rates of each communication terminal.

[0268] Further, for the process of obtaining the communication signal-to-interference-plus-noise ratio, it includes the following steps:

[0269] Obtain the first mixed signal received by the downlink communication terminal in the communication terminal, the second mixed signal received by the ground base station, and the third mixed signal received by the satellite;

[0270] According to the target signal power and interference signal power of each communication terminal respectively extracted from the first mixed signal, the second mixed signal, and the third mixed signal, calculate the corresponding communication signal-to-interference-plus-noise ratio of each communication terminal.

[0271] In this embodiment, the constraint conditions 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, sensing signal-to-interference-plus-noise ratio constraint, total computing resource constraint, task processing delay constraint, receive beamforming power constraint, and backhaul link rate constraint.

[0272] In this embodiment, the communication-sensing-computation resource allocation module is specifically configured to: decompose the joint non-convex optimization function into a first sub-function for optimizing transmit beamforming, transmit power, and computation 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, sensing signal covariance matrix, and computation resource parameters are fixed.

[0273] Preferably, in this embodiment, an alternating iteration algorithm combined with a Gaussian randomization process is used to solve the first sub-function and the second sub-function. Specifically: by introducing auxiliary variables, the non-convex constraints corresponding to the first sub-function are transformed into convex constraints, and the transformed first sub-function is solved by using the semi-definite programming method combined with the Gaussian randomization process; the second sub-function is decomposed into multiple generalized Rayleigh entropy problems, and each of the generalized Rayleigh entropy problems is solved by using the eigenvalue decomposition method.

[0274] Further, after using the alternating iteration algorithm combined with the Gaussian randomization process to solve the first sub-function and the second sub-function, in this embodiment, the optimal solutions composed of each optimization variable obtained by solving the first sub-function and the second sub-function are input into the joint non-convex optimization function to obtain the target achievable total transmission rate. The alternating iteration algorithm is used to iterate the target achievable total transmission rate until the preset convergence condition is satisfied, and the target solution composed of the target communication-sensing-computation optimization variables is obtained.

[0275] Among them, the first sub-function includes the following formula:

[0276]

[0277] In the formula, is the transmission rate of the downlink communication terminal ; is the transmission rate of the uplink communication terminal ; is the transmission rate of the satellite direct communication terminal ; is the set of optimization variables corresponding to the first sub-function.

[0278] The second sub-function includes the following formula:

[0279]

[0280] In the formula, is the vector set of receive beamforming.

[0281] As Figure 4 shown, an embodiment of the present invention also provides a computer device, Figure 4A structural block diagram of a preferred embodiment of a computer device provided by the present invention. The computer device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the method for configuring communication, sensing, and computing resources based on satellite-ground integration as described above is implemented.

[0282] Preferably, the computer program can be divided into one or more modules / units (such as computer program 1, computer program 2,...). 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 capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device.

[0283] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The processor is the control center of the terminal device and connects various parts of the terminal device through various interfaces and lines.

[0284] The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc., and the data storage area can store relevant 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 SmartMedia Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., or the memory can also be other volatile solid-state storage devices.

[0285] 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 can understand that Figure 4The block diagram is only an example of the terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components. Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0286] Correspondingly, an embodiment of the present invention provides a computer-readable storage medium. The computer-readable storage medium includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the steps in the methods in the above embodiments, for example Figure 1 the steps S1 to S3 described therein. The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it cannot be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for configuring synaesthesia computing resources based on satellite-ground fusion of distribution network, characterized in that: include: According to the communication terminal in the pre-built satellite-ground integrated distribution network architecture, the interactive signal data is obtained, and a model is built based on the interactive signal data. According to the modeling result, the transmission rate of the interactive signal data in the corresponding channel of the communication terminal is calculated, specifically: according to the first interactive signal data extracted from the corresponding link of the communication terminal, a model is built to obtain an uplink communication model, a downlink communication model and a satellite direct communication model; according to the uplink communication model, the downlink communication model and the satellite direct communication model, a corresponding communication signal to noise ratio is obtained; the communication signal to noise ratio is substituted into a preset rate calculation formula to obtain the transmission rate of each communication terminal; 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 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.

3. 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.

4. 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; The first sub-function and the second sub-function are solved by using an alternating iterative algorithm combined with a Gaussian randomization process.

5. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 4, characterized in that: 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 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.

6. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 4, 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.

7. The method for configuring synaesthesia computing resources based on distribution network satellite-ground fusion as claimed in claim 4, 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.

8. The method for configuring synaesthesia computing resources based on distribution network satellite-ground integration as claimed in claim 7, characterized in that: The second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

9. 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; Specifically used for: modeling according to 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 communication model; according to the uplink communication model, the downlink communication model and the satellite direct communication model, obtaining the corresponding communication signal-to-interference-to-noise ratio; substituting the communication signal-to-interference-to-noise ratio into a preset rate calculation formula to obtain the transmission rate of each communication terminal; 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 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.

10. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 9, 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.

11. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 9, 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.

12. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 9, 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; The first sub-function and the second sub-function are solved by using an alternating iterative algorithm combined with a Gaussian randomization process.

13. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 12, characterized in that: 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 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.

14. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 12, characterized in that: After the alternating iterative algorithm is combined with the Gaussian randomization process to solve the first sub-function and the second sub-function, 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.

15. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 12, 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.

16. The synaesthesia computing resource configuration system based on distribution network satellite-ground integration as claimed in claim 15, characterized in that: The second sub-function includes the following formula: In the formula, A set of vectors used to shape the receive beam.

17. 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 8.

18. 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 8 is implemented.

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