Communication capacity assessment method, device and equipment of spatial network and storage medium

Abstract

The invention provides a communication capacity evaluation method and device of a spatial network, equipment and a storage medium, and the method comprises the steps: obtaining communication capacity indexes of the spatial network, the communication capacity indexes comprise a first type of indexes and a second type of indexes, the first type of indexes comprise a first number of network indexes, and the second type of indexes comprise a second number of network indexes; the first type of indexes comprise a first number of user indexes, the second type of indexes comprise a second number of user indexes, a first weight of the first type of indexes and a second weight of the second type of indexes are determined according to an importance comparison result between the first type of indexes and the second type of indexes, index weights are determined, and the index weights comprise a network index weight corresponding to each network index and a network index weight corresponding to each network index. And determining a capacity evaluation result according to the network index, the user index, the first weight, the second weight and the index weight, and / or the user index weight corresponding to each user index, through the capacity evaluation method and apparatus, the influence of subjective factors of the user can be effectively reduced, and the accuracy of the obtained capacity evaluation result is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of business support technology, and in particular to a method, device, equipment and storage medium for evaluating the communication capacity of a space network. Background Art

[0002] Space network, with satellite constellations as its core, is a system that acquires, transmits and processes all kinds of information in real time. As an important part of the space communication network, satellite constellation design and communication performance evaluation play a decisive role in the scheme selection, optimization design and system decision-making of space communication networking. As an important part of the space communication network, satellite constellation design and communication performance evaluation play a decisive role in the scheme selection, optimization design and system decision-making of space communication networking. Communication capacity is an important evaluation indicator of the carrying capacity of satellite constellations. Research on satellite network communication capacity can provide a theoretical basis for the construction and deployment of high-quality satellite constellations. It can also optimize the satellite constellation capacity through further analysis, so that the actually deployed satellite network can reach or approach the theoretical analysis value of the satellite constellation capacity.

[0003] In the related art, performance evaluation is greatly influenced by the user's subjective factors.

[0004] In this way, the accuracy of the communication capacity assessment results cannot be guaranteed. Summary of the invention

[0005] The present disclosure aims to solve one of the technical problems in the related art at least to some extent.

[0006] To this end, the purpose of the present disclosure is to propose a communication capacity assessment method, device, communication equipment, readable storage medium and computer program product for a space network, which can effectively reduce the influence of user subjective factors and improve the accuracy of the obtained capacity assessment results.

[0007] The communication capacity evaluation method proposed in the first aspect of the present disclosure includes:

[0008] Acquire a communication capacity indicator of a space network, wherein the communication capacity indicator includes a first type of indicator and a second type of indicator, the first type of indicator includes a first number of network indicators, the second type of indicator includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1;

[0009] Determine, according to the comparison result of the importance between the first category of indicators and the second category of indicators, a first weight corresponding to the first category of indicators and a second weight corresponding to the second category of indicators;

[0010] Determine an indicator weight, wherein the indicator weight includes: a network indicator weight corresponding to each of the network indicators, and / or a user indicator weight corresponding to each of the user indicators, the network indicator weight is related to an importance comparison result between the first number of network indicators, and the user indicator weight is related to an importance comparison result between the second number of user indicators;

[0011] A capacity assessment result is determined according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight.

[0012] In a possible implementation manner of the embodiment of the present disclosure, the obtaining of the communication capacity indicator of the space network includes:

[0013] Obtain a target throughput, a target link capacity, a target number of channels of the space network, and a target number of users served by the space network, wherein the target throughput, the target link capacity, and the target number of channels are collectively used as the first category indicators, and the target number of users is used as the second category indicator.

[0014] In a possible implementation of the embodiment of the present disclosure, determining, according to the comparison result of the importance between the first category of indicators and the second category of indicators, a first weight corresponding to the first category of indicators and a second weight corresponding to the second category of indicators includes:

[0015] Constructing a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, each of which corresponds to an importance comparison result between at least two of the first category indicators and the second category indicators;

[0016] According to the first judgment matrix, the first weight corresponding to the first type of indicator and the second weight corresponding to the second type of indicator are determined respectively.

[0017] In a possible implementation of the embodiment of the present disclosure, determining the indicator weight includes:

[0018] Constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, and each second element corresponds to an importance comparison result between at least two of the first number of network indicators;

[0019] According to the second judgment matrix, the first number of network indicator weights are determined, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels.

[0020] In a possible implementation of the embodiment of the present disclosure, determining the indicator weight includes:

[0021] Constructing a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, and each of the third elements corresponds to an importance comparison result between at least two of the second number of user indicators;

[0022] The second number of user indicator weights is determined according to the third judgment matrix, wherein the second number of user indicator weights includes: a sixth weight corresponding to the number of target users.

[0023] In a possible implementation of the embodiment of the present disclosure, the target throughput is determined based on the following method:

[0024] Obtaining an initial throughput, a reference throughput, and a required minimum throughput of the spatial network;

[0025] Determine a comparison result between the initial throughput and the required minimum throughput;

[0026] The initial throughput is normalized according to the comparison result, the reference throughput and the required minimum throughput to obtain the target throughput.

[0027] In a possible implementation of the embodiment of the present disclosure, the target link capacity is determined based on the following method:

[0028] Determining an initial link capacity and a link capacity threshold of the spatial network;

[0029] Determining a comparison result between the initial link capacity and the link capacity threshold;

[0030] The initial link capacity is normalized according to the comparison result and the link capacity threshold to obtain the target link capacity.

[0031] In a possible implementation of the embodiment of the present disclosure, the target number of channels is determined based on the following method:

[0032] Obtaining an initial number of channels and a channel number threshold of the spatial network;

[0033] Determine a comparison result between the initial number of channels and the channel number threshold;

[0034] The initial number of channels is normalized according to the comparison result and the channel number threshold to obtain the target number of channels.

[0035] In a possible implementation of the embodiment of the present disclosure, the number of target users is determined based on the following method:

[0036] Determining an initial number of users of the spatial network, a reference number of users that can be simultaneously served, and a maximum number of users that can be supported;

[0037] Determining a comparison result between the initial number of users and the reference number of users;

[0038] The initial number of users is normalized according to the comparison result, the reference number of users, and the maximum number of users to obtain the target number of users.

[0039] In a possible implementation manner of the embodiment of the present disclosure, determining the first weight corresponding to the first type of indicator and the second weight corresponding to the second type of indicator respectively according to the first judgment matrix includes:

[0040] Determine a first target feature vector of the first judgment matrix, wherein the first target feature vector includes a plurality of fourth elements, each of the fourth elements represents a first ranking weight, and the first ranking weight corresponds to one of the first category indicator and the second category indicator;

[0041] Performing a consistency check on the first judgment matrix to obtain a first check result;

[0042] If the first test result indicates that the first judgment matrix satisfies the consistency test index, the first sorting weight is used as the weight of one of the first category indicators and the second category indicators, wherein the weight of one of the first category indicators and the second category indicators is the first weight or the second weight.

[0043] In a possible implementation manner of the embodiment of the present disclosure, performing a consistency check on the first judgment matrix to obtain a first check result includes:

[0044] Determining a first consistency index corresponding to the first judgment matrix, wherein the first consistency index is related to the number of rows or columns and the maximum eigenvalue of the first judgment matrix;

[0045] Determining a first random consistency indicator corresponding to the first judgment matrix, wherein the first random consistency indicator is related to the matrix order of the first judgment matrix;

[0046] Determining a first consistency ratio of the first judgment matrix according to the first consistency indicator and the first random consistency indicator;

[0047] A first comparison result between the first consistency ratio and a first preset ratio is determined, and the first comparison result is used as the first inspection result.

[0048] In a possible implementation of the embodiment of the present disclosure, wherein:

[0049] If the first consistency ratio is less than the first preset ratio, determining that the first inspection result indicates that the first judgment matrix satisfies the consistency inspection indicator;

[0050] If the first consistency ratio is greater than or equal to the first preset ratio, it is determined that the first inspection result indicates that the first judgment matrix does not meet the consistency inspection indicator.

[0051] In a possible implementation manner of the embodiment of the present disclosure, determining the first target eigenvector of the first judgment matrix includes:

[0052] Determine the maximum eigenvalue of the first judgment matrix, and determine a first initial eigenvector corresponding to the maximum eigenvalue of the first judgment matrix;

[0053] The first initial feature vector is normalized to obtain the first target feature vector.

[0054] In a possible implementation manner of the embodiment of the present disclosure, determining the first number of network indicator weights according to the second judgment matrix includes:

[0055] Determine a second target feature vector of the second judgment matrix, wherein the second target feature vector includes a plurality of fifth elements, each of the fifth elements represents a second sorting weight, and the second sorting weight corresponds to one of the first number of network indicators;

[0056] Performing a consistency check on the second judgment matrix to obtain a second check result;

[0057] If the second test result indicates that the second judgment matrix satisfies the consistency test index, the second sorting weight is used as the weight of one of the first number of network indicators, wherein the weight of one of the first number of network indicators is the third weight or the fourth weight or the fifth weight.

[0058] In a possible implementation manner of the embodiment of the present disclosure, performing a consistency check on the second judgment matrix to obtain a second check result includes:

[0059] Determine a second consistency index corresponding to the second judgment matrix, wherein the second consistency index is related to the number of rows or columns and the maximum eigenvalue of the second judgment matrix;

[0060] Determining a second random consistency indicator corresponding to the second judgment matrix, wherein the second random consistency indicator is related to the matrix order of the second judgment matrix;

[0061] Determining a second consistency ratio of the second judgment matrix according to the second consistency index and the second random consistency index;

[0062] A second comparison result between the second consistency ratio and a second preset ratio is determined, and the second comparison result is used as the second inspection result.

[0063] In a possible implementation of the embodiment of the present disclosure, wherein:

[0064] If the second consistency ratio is less than the second preset ratio, determining that the second inspection result indicates that the second judgment matrix satisfies the consistency inspection indicator;

[0065] If the second consistency ratio is greater than or equal to the second preset ratio, it is determined that the second inspection result indicates that the second judgment matrix does not meet the consistency inspection indicator.

[0066] In a possible implementation manner of the embodiment of the present disclosure, determining the second target eigenvector of the second judgment matrix includes:

[0067] Determine the maximum eigenvalue of the second judgment matrix, and determine a second initial eigenvector corresponding to the maximum eigenvalue of the second judgment matrix;

[0068] The second initial feature vector is normalized to obtain the second target feature vector.

[0069] In a possible implementation manner of the embodiment of the present disclosure, determining the second number of user indicator weights according to the third judgment matrix includes:

[0070] Determine a third target feature vector of the third judgment matrix, wherein the third target feature vector correspondingly includes a plurality of sixth elements, each of the sixth elements represents a third sorting weight, and the third sorting weight corresponds to one of the second number of user indicators;

[0071] Performing a consistency check on the third judgment matrix to obtain a third check result;

[0072] If the third test result indicates that the third judgment matrix satisfies the consistency test index, the third sorting weight is used as the weight of one of the second number of user indicators, wherein the weight of one of the second number of user indicators is the sixth weight.

[0073] In a possible implementation manner of the embodiment of the present disclosure, performing a consistency check on the third judgment matrix to obtain a third check result includes:

[0074] Determine a third consistency index corresponding to the third judgment matrix, wherein the third consistency index is related to the number of rows or columns and the maximum eigenvalue of the third judgment matrix;

[0075] Determining a third random consistency index corresponding to the third judgment matrix, wherein the third random consistency index is related to the matrix order of the third judgment matrix;

[0076] Determining a third consistency ratio of the third judgment matrix according to the third consistency index and the third random consistency index;

[0077] A third comparison result between the third consistency ratio and a third preset ratio is determined, and the third comparison result is used as the third inspection result.

[0078] In a possible implementation of the embodiment of the present disclosure, wherein:

[0079] If the third consistency ratio is less than the third preset ratio, determining that the third test result indicates that the third judgment matrix satisfies the consistency test indicator;

[0080] If the third consistency ratio is greater than or equal to the third preset ratio, it is determined that the third test result indicates that the third judgment matrix does not meet the consistency test index.

[0081] In a possible implementation manner of the embodiment of the present disclosure, determining the third target eigenvector of the third judgment matrix includes:

[0082] Determine the maximum eigenvalue of the third judgment matrix, and determine a third initial eigenvector corresponding to the maximum eigenvalue of the third judgment matrix;

[0083] The third initial feature vector is normalized to obtain the third target feature vector.

[0084] In a possible implementation manner of the embodiment of the present disclosure, determining a capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight includes:

[0085] Performing weighted summation according to the network indicator and the network indicator weight to obtain a first sum value;

[0086] Performing a weighted sum according to the user indicator and the user indicator weight to obtain a second sum value;

[0087] A weighted sum is performed according to the first sum value, the second sum value, the first weight value, and the second weight value to obtain a third sum value, and the third sum value is used as the capacity assessment result.

[0088] The communication capacity evaluation device of a space network proposed in the second aspect of the present disclosure includes:

[0089] An acquisition module, configured to acquire a communication capacity indicator of a space network, wherein the communication capacity indicator includes a first type of indicator and a second type of indicator, the first type of indicator includes a first number of network indicators, the second type of indicator includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1;

[0090] A first determination module, configured to determine a first weight corresponding to the first category indicator and a second weight corresponding to the second category indicator according to a comparison result of the importance between the first category indicator and the second category indicator;

[0091] A second determination module is used to determine an indicator weight, wherein the indicator weight includes: a network indicator weight corresponding to each of the network indicators, and / or a user indicator weight corresponding to each of the user indicators, the network indicator weight is related to the importance comparison result between the first number of network indicators, and the user indicator weight is related to the importance comparison result between the second number of user indicators;

[0092] The third determination module is used to determine a capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight and the indicator weight.

[0093] In a possible implementation of the embodiment of the present disclosure, the acquisition module is specifically used to:

[0094] Obtain a target throughput, a target link capacity, a target number of channels of the space network, and a target number of users served by the space network, wherein the target throughput, the target link capacity, and the target number of channels are collectively used as the first category indicators, and the target number of users is used as the second category indicator.

[0095] In a possible implementation manner of the embodiment of the present disclosure, the first determining module is specifically configured to:

[0096] Constructing a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, each of which corresponds to an importance comparison result between at least two of the first category indicators and the second category indicators;

[0097] According to the first judgment matrix, the first weight corresponding to the first type of indicator and the second weight corresponding to the second type of indicator are determined respectively.

[0098] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is specifically configured to:

[0099] Constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, and each second element corresponds to an importance comparison result between at least two of the first number of network indicators;

[0100] According to the second judgment matrix, the first number of network indicator weights are determined, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels.

[0101] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is specifically configured to:

[0102] Constructing a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, and each of the third elements corresponds to an importance comparison result between at least two of the second number of user indicators;

[0103] The second number of user indicator weights is determined according to the third judgment matrix, wherein the second number of user indicator weights includes: a sixth weight corresponding to the number of target users.

[0104] In a possible implementation of the embodiment of the present disclosure, the target throughput is determined based on the following method:

[0105] Obtaining an initial throughput, a reference throughput, and a required minimum throughput of the spatial network;

[0106] Determine a comparison result between the initial throughput and the required minimum throughput;

[0107] The initial throughput is normalized according to the comparison result, the reference throughput and the required minimum throughput to obtain the target throughput.

[0108] In a possible implementation of the embodiment of the present disclosure, the target link capacity is determined based on the following method:

[0109] Determining an initial link capacity and a link capacity threshold of the spatial network;

[0110] Determining a comparison result between the initial link capacity and the link capacity threshold;

[0111] The initial link capacity is normalized according to the comparison result and the link capacity threshold to obtain the target link capacity.

[0112] In a possible implementation of the embodiment of the present disclosure, the target number of channels is determined based on the following method:

[0113] Obtaining an initial number of channels and a channel number threshold of the spatial network;

[0114] Determine a comparison result between the initial number of channels and the channel number threshold;

[0115] The initial number of channels is normalized according to the comparison result and the channel number threshold to obtain the target number of channels.

[0116] In a possible implementation of the embodiment of the present disclosure, the number of target users is determined based on the following method:

[0117] Determining an initial number of users of the spatial network, a reference number of users that can be simultaneously served, and a maximum number of users that can be supported;

[0118] Determining a comparison result between the initial number of users and the reference number of users;

[0119] The initial number of users is normalized according to the comparison result, the reference number of users, and the maximum number of users to obtain the target number of users.

[0120] In a possible implementation manner of the embodiment of the present disclosure, the first determining module is further configured to:

[0121] Determine a first target feature vector of the first judgment matrix, wherein the first target feature vector includes a plurality of fourth elements, each of the fourth elements represents a first ranking weight, and the first ranking weight corresponds to one of the first category indicator and the second category indicator;

[0122] Performing a consistency check on the first judgment matrix to obtain a first check result;

[0123] If the first test result indicates that the first judgment matrix satisfies the consistency test index, the first sorting weight is used as the weight of one of the first category indicators and the second category indicators, wherein the weight of one of the first category indicators and the second category indicators is the first weight or the second weight.

[0124] In a possible implementation manner of the embodiment of the present disclosure, the first determining module is further configured to:

[0125] Determining a first consistency index corresponding to the first judgment matrix, wherein the first consistency index is related to the number of rows or columns and the maximum eigenvalue of the first judgment matrix;

[0126] Determining a first random consistency indicator corresponding to the first judgment matrix, wherein the first random consistency indicator is related to the matrix order of the first judgment matrix;

[0127] Determining a first consistency ratio of the first judgment matrix according to the first consistency indicator and the first random consistency indicator;

[0128] A first comparison result between the first consistency ratio and a first preset ratio is determined, and the first comparison result is used as the first inspection result.

[0129] In a possible implementation of the embodiment of the present disclosure, wherein:

[0130] If the first consistency ratio is less than the first preset ratio, determining that the first inspection result indicates that the first judgment matrix satisfies the consistency inspection indicator;

[0131] If the first consistency ratio is greater than or equal to the first preset ratio, it is determined that the first inspection result indicates that the first judgment matrix does not meet the consistency inspection indicator.

[0132] In a possible implementation manner of the embodiment of the present disclosure, the first determining module is further configured to:

[0133] Determine the maximum eigenvalue of the first judgment matrix, and determine a first initial eigenvector corresponding to the maximum eigenvalue of the first judgment matrix;

[0134] The first initial feature vector is normalized to obtain the first target feature vector.

[0135] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0136] Determine a second target feature vector of the second judgment matrix, wherein the second target feature vector includes a plurality of fifth elements, each of the fifth elements represents a second sorting weight, and the second sorting weight corresponds to one of the first number of network indicators;

[0137] Performing a consistency check on the second judgment matrix to obtain a second check result;

[0138] If the second test result indicates that the second judgment matrix satisfies the consistency test index, the second sorting weight is used as the weight of one of the first number of network indicators, wherein the weight of one of the first number of network indicators is the third weight or the fourth weight or the fifth weight.

[0139] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0140] Determine a second consistency index corresponding to the second judgment matrix, wherein the second consistency index is related to the number of rows or columns and the maximum eigenvalue of the second judgment matrix;

[0141] Determining a second random consistency indicator corresponding to the second judgment matrix, wherein the second random consistency indicator is related to the matrix order of the second judgment matrix;

[0142] Determining a second consistency ratio of the second judgment matrix according to the second consistency index and the second random consistency index;

[0143] A second comparison result between the second consistency ratio and a second preset ratio is determined, and the second comparison result is used as the second inspection result.

[0144] In a possible implementation of the embodiment of the present disclosure, wherein:

[0145] If the second consistency ratio is less than the second preset ratio, determining that the second inspection result indicates that the second judgment matrix satisfies the consistency inspection indicator;

[0146] If the second consistency ratio is greater than or equal to the second preset ratio, it is determined that the second inspection result indicates that the second judgment matrix does not meet the consistency inspection indicator.

[0147] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0148] Determine the maximum eigenvalue of the second judgment matrix, and determine a second initial eigenvector corresponding to the maximum eigenvalue of the second judgment matrix;

[0149] The second initial feature vector is normalized to obtain the second target feature vector.

[0150] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0151] Determine a third target feature vector of the third judgment matrix, wherein the third target feature vector correspondingly includes a plurality of sixth elements, each of the sixth elements represents a third sorting weight, and the third sorting weight corresponds to one of the second number of user indicators;

[0152] Performing a consistency check on the third judgment matrix to obtain a third check result;

[0153] If the third test result indicates that the third judgment matrix satisfies the consistency test index, the third sorting weight is used as the weight of one of the second number of user indicators, wherein the weight of one of the second number of user indicators is the sixth weight.

[0154] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0155] Determine a third consistency index corresponding to the third judgment matrix, wherein the third consistency index is related to the number of rows or columns and the maximum eigenvalue of the third judgment matrix;

[0156] Determining a third random consistency index corresponding to the third judgment matrix, wherein the third random consistency index is related to the matrix order of the third judgment matrix;

[0157] Determining a third consistency ratio of the third judgment matrix according to the third consistency index and the third random consistency index;

[0158] A third comparison result between the third consistency ratio and a third preset ratio is determined, and the third comparison result is used as the third inspection result.

[0159] In a possible implementation of the embodiment of the present disclosure, wherein:

[0160] If the third consistency ratio is less than the third preset ratio, determining that the third test result indicates that the third judgment matrix satisfies the consistency test indicator;

[0161] If the third consistency ratio is greater than or equal to the third preset ratio, it is determined that the third test result indicates that the third judgment matrix does not meet the consistency test index.

[0162] In a possible implementation manner of the embodiment of the present disclosure, the second determining module is further configured to:

[0163] Determine the maximum eigenvalue of the third judgment matrix, and determine a third initial eigenvector corresponding to the maximum eigenvalue of the third judgment matrix;

[0164] The third initial feature vector is normalized to obtain the third target feature vector.

[0165] In a possible implementation manner of the embodiment of the present disclosure, the third determining module is specifically configured to:

[0166] Performing weighted summation according to the network indicator and the network indicator weight to obtain a first sum value;

[0167] Performing a weighted sum according to the user indicator and the user indicator weight to obtain a second sum value;

[0168] A weighted sum is performed according to the first sum value, the second sum value, the first weight value, and the second weight value to obtain a third sum value, and the third sum value is used as the capacity assessment result.

[0169] The communication device proposed in the embodiment of the third aspect of the present disclosure includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the communication capacity assessment method for the space network proposed in the embodiment of the first aspect of the present disclosure is implemented.

[0170] The fourth aspect embodiment of the present disclosure proposes a non-temporary computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the communication capacity evaluation method of the space network proposed in the first aspect embodiment of the present disclosure is implemented.

[0171] The fifth aspect embodiment of the present disclosure proposes a computer program product. When the instructions in the computer program product are executed by a processor, the steps of the communication capacity assessment method of the space network proposed in the first aspect embodiment of the present disclosure are executed.

[0172] The communication capacity evaluation method, device, communication equipment, storage medium and computer program product of a space network proposed in the present disclosure obtain the communication capacity index of the space network, wherein the communication capacity index includes a first type of index and a second type of index, the first type of index includes a first number of network indexes, the second type of index includes a second number of user indexes, the first number and the second number are integers greater than or equal to 1, and according to the importance comparison result between the first type of index and the second type of index, determine the first weight corresponding to the first type of index and the second weight corresponding to the second type of index, and determine the index weight, wherein the index weight includes: a network index weight corresponding to each network index, and / or a user index weight corresponding to each user index, the network index weight is related to the importance comparison result between the first number of network indicators, and the user index weight is related to the importance comparison result between the second number of user indicators, and the capacity evaluation result is determined according to the network indicator, the user indicator, the first weight, the second weight and the index weight, thereby effectively reducing the influence of user subjective factors and improving the accuracy of the obtained capacity evaluation result.

[0173] Additional aspects and advantages of the present disclosure will be given in part in the following description and in part will be obvious from the following description or learned through practice of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0174] The above and / or additional aspects and advantages of the present disclosure will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0175] Figure 1 A schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure;

[0176] Figure 2 It is a flow chart of a method for evaluating the communication capacity of a space network provided by an embodiment of the present disclosure;

[0177] Figure 3 It is a flow chart of another method for evaluating the communication capacity of a space network provided by an embodiment of the present disclosure;

[0178] Figure 4 is a schematic diagram of a comprehensive evaluation scheme for space network communication capacity proposed in the present disclosure;

[0179] Figure 5 It is a flow chart of another method for evaluating the communication capacity of a space network provided by an embodiment of the present disclosure;

[0180] Figure 6 is a flow chart of space network communication capacity performance evaluation proposed in the present disclosure;

[0181] Figure 7It is a structural schematic diagram of a communication capacity evaluation device for a space network provided by an embodiment of the present disclosure;

[0182] Figure 8 A block diagram of an exemplary communication device suitable for implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0183] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present disclosure, and are not to be construed as limitations of the present disclosure. On the contrary, the embodiments of the present disclosure include all changes, modifications, and equivalents that fall within the spirit and connotation of the appended claims.

[0184] In order to better understand the communication capacity evaluation method of a space network disclosed in an embodiment of the present disclosure, the communication system to which the embodiment of the present disclosure is applicable is first described below.

[0185] See also Figure 1 , Figure 1 The following is a schematic diagram of the architecture of a communication system provided by an embodiment of the present disclosure. The communication system may include but is not limited to a satellite and a terminal device. Figure 1 The number and form of the devices shown are for illustrative purposes only and do not constitute a limitation on the embodiments of the present disclosure. In actual applications, two or more satellites and two or more terminal devices may be included. Figure 1 The communication system shown includes a satellite 101 and a terminal device 102 as an example.

[0186] The satellite 101 in the embodiment of the present disclosure is an entity for transmitting or receiving signals. The embodiment of the present disclosure does not limit the specific technology and specific device form used by the satellite.

[0187] The terminal device 102 in the embodiment of the present disclosure is an entity on the user side for receiving or transmitting signals, such as a mobile phone. The terminal device may also be referred to as a terminal device (terminal), user equipment (UE), mobile station (MS), mobile terminal device (MT), etc. The terminal device may be a car with communication function, a smart car, a mobile phone, a wearable device, a tablet computer (Pad), a computer with wireless transceiver function, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in self-driving, a wireless terminal device in remote medical surgery, a wireless terminal device in smart grid (smart grid), a wireless terminal device in transportation safety (transportation safety), a wireless terminal device in smart city (smart city), a wireless terminal device in smart home (smart home), etc. The embodiment of the present disclosure does not limit the specific technology and specific device form adopted by the terminal device.

[0188] It can be understood that the communication system described in the embodiment of the present disclosure is for the purpose of more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not constitute a limitation on the technical solution provided by the embodiment of the present disclosure. A person skilled in the art can know that with the evolution of the system architecture and the emergence of new business scenarios, the technical solution provided by the embodiment of the present disclosure is also applicable to similar technical problems.

[0189] The communication capacity evaluation method and device of the space network provided by the present disclosure are described in detail below with reference to the accompanying drawings. Figure 2 It is a flow chart of a method for evaluating the communication capacity of a space network provided by an embodiment of the present disclosure.

[0190] like Figure 2 As shown, the method may include but is not limited to the following steps:

[0191] S201: Obtain a communication capacity indicator of a spatial network, wherein the communication capacity indicator includes a first category of indicators and a second category of indicators, the first category of indicators includes a first number of network indicators, the second category of indicators includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1.

[0192] The space network refers to a communication network based on a space environment, which is used for communication and data transmission between spacecraft, satellites, drones, etc. in the universe or in Earth orbit. In the embodiments of the present disclosure, the space network may refer to a satellite constellation, or may refer to any other possible space network device, without limitation.

[0193] The communication capacity index may refer to an index related to the communication capacity of the space network. The first type of index may refer to an index related to the communication capacity on the network side. The second type of index may refer to an index related to the communication capacity on the user side.

[0194] In the disclosed embodiment, when the communication capacity index of the space network is obtained, reliable data support can be provided for the subsequent communication capacity evaluation process of the space network.

[0195] S202: Determine a first weight corresponding to the first category indicator and a second weight corresponding to the second category indicator according to a comparison result of the importance between the first category indicator and the second category indicator.

[0196] The first weight and the second weight can be used to indicate the weight information of the first type of indicator and the second type of indicator in the communication capacity evaluation process respectively. The sum of the first weight and the second weight can be 1.

[0197] That is to say, in the embodiment of the present disclosure, after obtaining the communication capacity index of the space network, the first weight corresponding to the first category indicator and the second weight corresponding to the second category indicator can be determined according to the importance comparison result between the first category indicator and the second category indicator, thereby providing the weight description information corresponding to the first category indicator and the second category indicator for the subsequent communication capacity evaluation process.

[0198] S203: Determine indicator weights, wherein the indicator weights include: network indicator weights corresponding to each network indicator, and / or user indicator weights corresponding to each user indicator, the network indicator weights are related to the importance comparison results between the first number of network indicators, and the user indicator weights are related to the importance comparison results between the second number of user indicators.

[0199] The indicator weight can be used to describe the weight of the network indicator or user indicator in the indicator set of the corresponding type. The sum of all network indicator weights can be 1, and the sum of all user indicator weights can also be 1.

[0200] In the embodiment of the present disclosure, when determining the indicator weight, weight information corresponding to each indicator weight may be provided for the communication capacity evaluation process.

[0201] S204: Determine a capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight.

[0202] Among them, the capacity evaluation result can be a comprehensive evaluation result of the space network communication capacity obtained after comprehensive analysis based on various indicators and the weights corresponding to each indicator in the implementation of the present disclosure.

[0203] In the embodiment of the present disclosure, when determining the capacity assessment result based on the network indicators, user indicators, the first weight, the second weight and the indicator weight, the network indicators, user indicators, the first weight, the second weight and the indicator weight can be input into a pre-trained communication capacity assessment model to obtain the corresponding capacity assessment result. Alternatively, the capacity assessment result can be determined based on the network indicators, user indicators, the first weight, the second weight and the indicator weight based on a method combining numbers and shapes, and there is no limitation on this.

[0204] In this embodiment, by obtaining the communication capacity index of the spatial network, wherein the communication capacity index includes a first type of index and a second type of index, the first type of index includes a first number of network indexes, the second type of index includes a second number of user indexes, the first number and the second number are integers greater than or equal to 1, according to the importance comparison result between the first type of index and the second type of index, a first weight corresponding to the first type of index and a second weight corresponding to the second type of index are determined, and the index weight is determined, wherein the index weight includes: a network index weight corresponding to each network indicator, and / or a user index weight corresponding to each user indicator, the network index weight is related to the importance comparison result between the first number of network indicators, and the user index weight is related to the importance comparison result between the second number of user indicators, and the capacity assessment result is determined according to the network indicator, the user indicator, the first weight, the second weight and the index weight, thereby effectively reducing the influence of user subjective factors and improving the accuracy of the obtained capacity assessment result.

[0205] Figure 3 It is a flow chart of another method for evaluating the communication capacity of a space network provided in an embodiment of the present disclosure.

[0206] like Figure 3 As shown, the method may include but is not limited to the following steps:

[0207] S301: Obtain a target throughput, a target link capacity, a target number of channels, and a target number of users served by the space network, wherein the target throughput, the target link capacity, and the target number of channels are collectively used as first-category indicators, and the target number of users is used as a second-category indicator.

[0208] Among them, the target throughput can be used to indicate the maximum amount of data that can be transmitted in a space network per unit time.

[0209] Optionally, in some embodiments, when obtaining the target throughput of the spatial network, the initial throughput, reference throughput and required minimum throughput of the spatial network can be obtained, and a comparison result between the initial throughput and the required minimum throughput can be determined. The initial throughput is normalized according to the comparison result, the reference throughput and the required minimum throughput to obtain the target throughput. Thus, the normalization effect of the initial throughput can be effectively improved in combination with the comparison result between the initial throughput and the required minimum throughput, thereby ensuring the practicality of the obtained target throughput.

[0210] The reference throughput may refer to the throughput that a space network can theoretically achieve.

[0211] Among them, the target link capacity can be used to indicate the satellite constellation capacity. When the link capacity is larger, the corresponding system capacity is larger.

[0212] Optionally, in some embodiments, when obtaining the target link capacity of the spatial network, the initial link capacity and the link capacity threshold of the spatial network may be determined, a comparison result between the initial link capacity and the link capacity threshold may be determined, and the initial link capacity may be normalized according to the comparison result and the link capacity threshold to obtain the target link capacity. Thus, the initial link capacity may be normalized quickly and accurately in combination with the comparison result between the initial link capacity and the link capacity threshold, thereby ensuring the accuracy of the indication of the obtained target link capacity.

[0213] The link capacity threshold may refer to a limit value pre-set for the spatial network. When the capacity of the link reaches or exceeds this limit value, certain controls or operations will be triggered.

[0214] The target number of channels refers to the number of channels that the space network can provide.

[0215] Optionally, in some embodiments, when obtaining the target channel number of the spatial network, the initial channel number and a channel number threshold of the spatial network can be obtained, a comparison result between the initial channel number and the channel number threshold can be determined, and the initial channel number can be normalized according to the comparison result and the channel number threshold to obtain the target channel number. Thus, the comparison result between the initial channel number and the channel number threshold can be combined to provide a reliable execution basis for the normalization process of the initial channel number, which can effectively improve the reliability of the obtained target channel number.

[0216] The channel quantity threshold may be a limiting value used to determine the minimum number of channels required for the spatial network.

[0217] Among them, the target number of users refers to the number of users that the space network can serve at the same time.

[0218] Optionally, in some embodiments, when obtaining the target number of users of the spatial network, the initial number of users of the spatial network, the reference number of users that can be served simultaneously, and the maximum number of users that can be supported can be determined, and the comparison result between the initial number of users and the reference number of users can be determined. The initial number of users is normalized according to the comparison result, the reference number of users, and the maximum number of users to obtain the target number of users. Thus, the initial number of users can be normalized in combination with the reference number of users that can be served simultaneously and the maximum number of users that can be supported by the spatial network, thereby effectively improving the applicability of the normalization process.

[0219] The reference number of users may refer to the number of users that the spatial network needs to ensure simultaneous service.

[0220] Among them, the initial throughput, initial link capacity, initial number of channels and initial number of users refer to the unprocessed throughput, link capacity, number of channels and number of users obtained for the above-mentioned spatial network in the embodiments of the present disclosure.

[0221] In the embodiment of the present disclosure, when obtaining the initial throughput, initial link capacity, initial number of channels and initial number of users served by the space network, corresponding utility functions may be constructed respectively, and then the initial throughput, initial link capacity, initial number of channels and initial number of users served by the space network are obtained respectively according to each utility function. Figure 4 As shown, Figure 4 It is a schematic diagram of a comprehensive evaluation scheme for space network communication capacity proposed in the present disclosure.

[0222] For example, the satellite throughput utility function is constructed as follows:

[0223] Given a satellite spot beam bandwidth B beam , the number of spot beams N beams , the satellite spectrum transmission efficiency η, the throughput of a single satellite can be obtained as follows:

[0224] C sat =B beam ·N beams ·k·η (1)

[0225] Among them, k represents the frequency reuse factor between beams, and η represents the spectral efficiency of the satellite. The frequency reuse factor k refers to the minimum number of cells using different frequencies. η is usually defined as the ratio of the net bit rate (excluding the useful bit rate of the error correction code) to the channel bandwidth. It describes the amount of data that can be transmitted in a given bandwidth and is measured in bit / s / Hz (bps / Hz). At the same time, the spectral efficiency η of the satellite is mainly affected by the modulation and coding method, as shown below:

[0226] η=M·FEC / (1+α) (2)

[0227] Where M is the modulation coefficient, α is the roll-off factor of the modulator, and FEC is the efficiency coefficient of the error correction code used.

[0228] Therefore, by calculating the throughput of a single satellite, we can calculate the throughput of the satellite according to the number of satellites N. sat The throughput of the entire satellite constellation is obtained as follows:

[0229] C=C sat ·N sat (3)

[0230] For example, the link capacity utility function construction process is as follows:

[0231] Free space path loss (FSPL), free space refers to vacuum or air, where there are no obstacles that may hinder the propagation of electromagnetic waves. FSPL refers to the intensity loss of electromagnetic wave signals when they pass through free space within the line of sight from the transmitting antenna to the receiving antenna. FSPL is proportional to the distance between the transmitter and the receiver and the square of the frequency of the radio signal. For satellite communications, FSPL is also the main cause of signal attenuation.

[0232] As the distance increases, the area over which the signal spreads becomes larger and larger, and the signal attenuation becomes greater and greater. As shown below:

[0233]

[0234] Where f is the signal frequency, λ 1 is the wavelength, d is the distance from the transmitter to the receiver, and c is the speed of light in a vacuum.

[0235] Cloud and rain loss L o , when the signal propagates in the atmosphere, the high concentration of water vapor in the clouds will cause signal loss, and the signal loss will vary with factors such as cloud height and density, rainfall rate, etc., as shown below:

[0236] L o =L c ·L r ·L a (5)

[0237] Where L c is the atmospheric attenuation, L r is the rainfall attenuation, L a Indicates other losses.

[0238] Link capacity is affected by link bandwidth and frequency efficiency, so it is directly defined as the product of link bandwidth and frequency efficiency. T , Transmitting antenna gain G T , free space propagation loss L FSPL 、Antenna receiving gain G r and system noise P N , the signal-to-interference-to-noise ratio (SINR) of the link received signal can be obtained:

[0239]

[0240] Among them, the system noise is the thermal noise that has the main impact on the system and belongs to Gaussian white noise, which can be calculated as:

[0241] P N =KTB beam (7)

[0242] Where K = 1.38 × 10 -23 is the Boltzmann constant, and T is the absolute temperature.

[0243] If the spot beam width B is known beam The link capacity can be calculated based on the Shannon formula, as shown below:

[0244]

[0245] in, It is the ratio of signal power to interference and noise power converted from dB. beams represents the number of spot beams, and k represents the frequency reuse factor.

[0246] For example, the channel quantity utility function is constructed as follows:

[0247] The maximum number of channels that a satellite network can theoretically accommodate is as follows:

[0248] N S =N Z ·N sat ·N beam (9)

[0249] Among them, N beam Indicates the number of beams of a single satellite, N sat Indicates the number of satellites, N Z Indicates the number of channels in a spot beam, N Z As shown below:

[0250]

[0251] Among them, E b / L i E is the ratio of the energy per bit to the interference plus noise power spectral density under a specified bit error performance. b / N 0 represents the ratio of energy per bit to thermal noise spectral density. In formula (10), α is the average voice activation rate, and B beam is the spread spectrum bandwidth, r is the data rate, and β is the total interference coefficient.

[0252] For example, the process of constructing the user quantity utility function is as follows:

[0253] To calculate the number of users that a satellite constellation can serve, we must first calculate the satellite's coverage of the ground, based on the satellite's minimum elevation angle ε min , satellite orbit height h, earth radius R earth The angle between the center of the earth and the center of the satellite coverage area and the angle between the center of the earth and a point on the edge of the coverage area can be calculated, that is, the satellite coverage angle β max , the expression is as follows:

[0254]

[0255] After obtaining the satellite coverage angle, the average number of satellites that a user at latitude λ can connect to can be calculated: visible , as shown below:

[0256]

[0257] Where p = max{-ρ,λ-β max}, q=max{-ρ,λ+β max},N visible represents the specific number of satellites that a user can connect to at a specific latitude λ, and ρ represents the inclination of the orbital plane with respect to the equatorial plane.

[0258] According to the half-power beam width φ HPBW , the coverage area A of a single cell within the coverage range of a single satellite can be calculated cell , as shown below:

[0259]

[0260] Calculate the number of cells within the coverage of a single satellite, taking into account that a cell may be covered by multiple satellites at the same time. Therefore, by dividing by the number of visible satellites for a single user to exclude the influence of other satellite coverage, further calculate the number of cells within the coverage of a single satellite As shown below:

[0261]

[0262] The satellite coverage S can be calculatedsat , as shown below:

[0263]

[0264] If the satellite spot beam bandwidth B is known beam , the number of spot beams N beams , the satellite spectrum transmission efficiency η, the throughput of a single satellite can be obtained as follows:

[0265] C sat =B beam ·N beams ·k·η (16)

[0266] The user demand model is used to measure the number of potential satellite communication users and communication capacity. The satellite capacity utilization rate D is obtained based on the surface population distribution model. u , user demand rate r (bps), then the number of users N that the entire constellation can serve can be obtained u , as shown below:

[0267]

[0268] in, N represents the capacity that a single satellite can provide per square kilometer. sat represents the number of satellites, and S represents the ground coverage area of ​​the constellation. Its values ​​are as follows:

[0269]

[0270] Among them, S ter Represents the surface area of ​​the Earth.

[0271] For example, in the embodiment of the present disclosure, when obtaining the reference throughput and the required minimum throughput of the satellite constellation, determining the comparison result between the initial throughput and the required minimum throughput, and normalizing the initial throughput according to the comparison result, the reference throughput, and the required minimum throughput to obtain the target throughput, a satellite throughput normalization function may be constructed as shown below:

[0272] Satellite throughput refers to the maximum amount of data that can be transmitted per unit time by a satellite constellation. Assume that the theoretically achievable throughput of a satellite constellation (i.e., the reference throughput mentioned above) is C th , the actual satellite throughput (i.e. the initial throughput mentioned above) is C, and the minimum throughput requirement of the constellation (i.e. the minimum throughput required above) is C req , in order to ensure the quality of user service, the following method can be used to normalize the indicators:

[0273]

[0274] For example, in the embodiment of the present disclosure, when determining the link capacity threshold of the satellite constellation, determining the comparison result between the initial link capacity and the link capacity threshold, and normalizing the initial link capacity according to the comparison result and the link capacity threshold to obtain the target link capacity, a link capacity normalization function may be constructed as shown below:

[0275] Link capacity indicator, link capacity is also a key indicator reflecting the capacity of the satellite constellation. The larger the link capacity, the larger the system capacity. Assume that the link capacity value (i.e. the initial link capacity mentioned above) is R, the link capacity threshold (i.e. the link capacity threshold mentioned above) is R, and its normalized value (i.e. the target link capacity mentioned above) is U R It can be expressed as

[0276]

[0277] For example, in the embodiment of the present disclosure, when obtaining the channel number threshold of the satellite constellation, determining the comparison result between the initial channel number and the channel number threshold, and normalizing the initial channel number according to the comparison result and the channel number threshold to obtain the target channel number, a channel number normalization function may be constructed as shown below:

[0278] Assume that the number of channels that the satellite constellation can provide (i.e. the initial number of channels mentioned above) is N S , the channel number threshold is Its normalized value (i.e. the target channel number mentioned above) It can be expressed as

[0279]

[0280] For example, in the embodiments of the present disclosure, when determining the number of reference users that can be simultaneously served by the satellite constellation and the maximum number of users that can be supported, determining the comparison result between the initial number of users and the reference number of users, and normalizing the initial number of users according to the comparison result, the number of reference users, and the maximum number of users to obtain the target number of users, a user number normalization function may be constructed as shown below:

[0281] The number of users is measured by the number of users that the satellite constellation can serve at the same time. Assume that the actual number of users (i.e. the initial number of users mentioned above) is N u , the number of users to be served simultaneously (i.e. the number of reference users mentioned above) must be at least n u The maximum number of users expected by the constellation is N max , its normalized value (i.e. the number of target users mentioned above) It can be expressed as

[0282]

[0283] It is understandable that the units of various indicators of satellite communication capacity are different, resulting in large differences in the physical properties and numerical magnitudes of various indicators. Therefore, when using AHP for comprehensive performance evaluation, it is necessary to first standardize and normalize the various indicators of satellite communication capacity, that is, to eliminate the influence of the indicator dimension and index variation range through mathematical transformation, and then convert the indicators with different properties and dimensions into normalized values ​​that can be comprehensively compared. In the embodiment of the present disclosure, when the initial throughput is normalized to obtain the target throughput, and the initial link capacity is normalized to obtain the target link capacity, the initial number of channels is normalized to obtain the target number of channels, and the initial number of users is normalized to obtain the target number of users, the practicability of the obtained target throughput, target link capacity, target number of channels and target number of users served by the satellite constellation in the evaluation process can be effectively improved.

[0284] That is to say, in the embodiments of the present disclosure, the target throughput, target link capacity, target number of channels and target number of users served by the space network can be obtained, among which the target throughput, target link capacity and target number of channels are collectively used as the first type of indicators, and the target number of users is used as the second type of indicators. This can effectively improve the comprehensiveness of the obtained communication capacity indicators to ensure the practicality of the communication capacity indicators in the capacity assessment process.

[0285] S302: Determine a first weight corresponding to the first category indicator and a second weight corresponding to the second category indicator according to a comparison result of the importance between the first category indicator and the second category indicator.

[0286] S303: Determine indicator weights, wherein the indicator weights include: network indicator weights corresponding to each network indicator, and / or user indicator weights corresponding to each user indicator, the network indicator weights are related to the importance comparison results between the first number of network indicators, and the user indicator weights are related to the importance comparison results between the second number of user indicators.

[0287] The description of S302 and S303 can be specifically referred to the above embodiment, which will not be repeated here.

[0288] S304: Perform weighted summation according to the network indicator and the network indicator weight to obtain a first sum value.

[0289] In the disclosed embodiment, when a weighted sum is performed based on network indicators and network indicator weights to obtain a first sum, the obtained first sum can provide comprehensive indicator data corresponding to the first type of indicators during the communication capacity evaluation process.

[0290] S305: Perform weighted summation according to the user index and the user index weight to obtain a second sum value.

[0291] In the disclosed embodiment, when a weighted sum is performed based on the user index and the user index weight to obtain a second sum, the obtained second sum can provide comprehensive index data corresponding to the second type of index in the communication capacity evaluation process.

[0292] S306: Perform weighted summation according to the first sum, the second sum, the first weight, and the second weight to obtain a third sum, and use the third sum as the capacity assessment result.

[0293] That is to say, in the embodiment of the present disclosure, after obtaining each indicator and determining the weight corresponding to each indicator, a weighted sum can be performed according to the network indicator and the network indicator weight to obtain a first sum, a weighted sum can be performed according to the user indicator and the user indicator weight to obtain a second sum, a weighted sum can be performed according to the first sum, the second sum, the first weight and the second weight to obtain a third sum, and the third sum can be used as the capacity assessment result. As a result, the reliability and rationality of the capacity assessment process can be effectively improved, and the accuracy and confidence of the obtained capacity assessment results can be improved.

[0294] In this embodiment, by obtaining the target throughput, target link capacity, target number of channels and target number of users served by the space network, the target throughput, target link capacity and target number of channels are used as the first type of indicators, and the target number of users is used as the second type of indicators, thereby effectively improving the comprehensiveness of the communication capacity indicators obtained to ensure the practicality of the communication capacity indicators in the capacity assessment process. By performing weighted summation according to network indicators and network indicator weights to obtain a first sum value, performing weighted summation according to user indicators and user indicator weights to obtain a second sum value, performing weighted summation according to the first sum value, the second sum value, the first weight value and the second weight value to obtain a third sum value, and using the third sum value as the capacity assessment result, thereby effectively improving the reliability and rationality of the capacity assessment process and improving the accuracy of the obtained capacity assessment result.

[0295] Figure 5 It is a flow chart of another method for evaluating the communication capacity of a space network provided in an embodiment of the present disclosure.

[0296] like Figure 5 As shown, the method may include but is not limited to the following steps:

[0297] S501: Obtain a target throughput, a target link capacity, a target number of channels, and a target number of users served by the space network, wherein the target throughput, the target link capacity, and the target number of channels are collectively used as first-category indicators, and the target number of users is used as a second-category indicator.

[0298] For the description of S501, please refer to the above embodiment, which will not be repeated here.

[0299] S502: Construct a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, and each first element corresponds to an importance comparison result between at least two of the first category indicators and the second category indicators.

[0300] Among them, the judgment matrix is ​​a tool for comparing and evaluating the relative importance of multiple elements. It is used in decision analysis, hierarchical analysis and other fields.

[0301] The matrix is ​​in the form of a square matrix, where each element represents the comparison between two different elements. Usually, these comparison results are expressed in the form of numerical values ​​and are based on expert opinions or subjective judgments. The comparison results can be relative weights, relative importance, priorities, etc.

[0302] For example, the matrix construction process usually includes the following steps:

[0303] Identify the elements that need to be compared;

[0304] Use some scale (e.g. 1-9) to compare two elements based on their relative importance.

[0305] Construct a square matrix and fill the comparison results into the corresponding positions.

[0306] The constructed judgment matrix can be analyzed through a series of methods, such as calculating eigenvectors, maximum eigenvalues, consistency ratios, etc., to obtain the weight or priority ranking of each element.

[0307] Among them, the first judgment matrix refers to the judgment matrix constructed based on the relative importance between the first category indicators and the second category indicators.

[0308] In the embodiment of the present disclosure, when the first judgment matrix is ​​constructed, a reliable tool support can be provided for the subsequent weight allocation process between the first category indicators and the second category indicators.

[0309] S503: According to the first judgment matrix, determine the first weight corresponding to the first type of indicator and the second weight corresponding to the second type of indicator respectively.

[0310] Optionally, in some embodiments, when determining the first weight corresponding to the first category of indicators and the second weight corresponding to the second category of indicators respectively according to the first judgment matrix, the first target characteristic vector of the first judgment matrix can be determined, wherein the first target characteristic vector includes multiple fourth elements, each fourth element represents a first sorting weight, and the first sorting weight corresponds to one of the first category of indicators and the second category of indicators. A consistency check is performed on the first judgment matrix to obtain a first check result. If the first check result indicates that the first judgment matrix satisfies the consistency check indicator, the first sorting weight is used as the weight of one of the first category of indicators and the second category of indicators, wherein the weight of one of the first category of indicators and the second category of indicators is the first weight or the second weight. Thus, the reliability of the process of determining the first weight and the second weight can be effectively improved.

[0311] The first target feature vector may refer to a feature vector obtained after feature extraction is performed on the first judgment matrix.

[0312] The first sorting weight may refer to the weights of the first category indicators and the second category indicators determined based on the first target feature vector.

[0313] It is understandable that in the judgment matrix, each factor needs to be compared with other factors in pairs to obtain a weight value. The comparison results between factors must meet certain conditions, such as transitivity, symmetry, and reflectivity, otherwise inconsistency will occur, causing the judgment matrix to lose credibility and reliability. Consistency testing refers to the process of testing the rationality and accuracy of the judgment matrix when using the judgment matrix for decision analysis and multi-criteria decision-making. The consistency test aims to check whether the comparison results in the judgment matrix have an inherent logical relationship and stability, so as to avoid affecting the accuracy and reliability of the decision results due to subjective bias and inconsistency.

[0314] The first test result refers to the result obtained by performing a consistency test on the first judgment matrix.

[0315] Optionally, in some embodiments, when performing a consistency check on the first judgment matrix to obtain a first check result, a first consistency index corresponding to the first judgment matrix can be determined, wherein the first consistency index is related to the number of rows or columns and the maximum eigenvalue of the first judgment matrix; a first random consistency index corresponding to the first judgment matrix can be determined, wherein the first random consistency index is related to the matrix order of the first judgment matrix; a first consistency ratio of the first judgment matrix can be determined based on the first consistency index and the first random consistency index; a first comparison result between the first consistency ratio and the first preset ratio can be determined; and the first comparison result can be used as the first check result, thereby effectively improving the applicability and reliability of the first judgment matrix in the consistency check process.

[0316] Among them, the consistency index (Consistency Index) and the random consistency index (Random Consistency Index) are indicators used to evaluate and measure the consistency degree of the judgment matrix in the judgment matrix analysis.

[0317] For example, the consistency index (CI) is a numerical value that indicates the degree of inconsistency of the comparison results within the judgment matrix. Its calculation is based on the eigenvalue and eigenvector of the judgment matrix. The calculation formula of the consistency index is as follows:

[0318] CI=(λ max -n) / (n-1)

[0319] Among them, λ max represents the maximum eigenvalue of the judgment matrix, and n represents the dimension or order of the judgment matrix.

[0320] The random consistency index (RCI) is a random reference value used to compare the consistency index. It is obtained by comparing the consistency index of the matrix with random consistency. The smaller the value of the random consistency index, the higher the consistency of the judgment matrix. Usually, according to the dimension or order of the judgment matrix, a pre-calculated random consistency index table can be searched to obtain the corresponding random consistency index value.

[0321] The first consistency index and the first random consistency index refer to the consistency index and the random consistency index corresponding to the first judgment matrix.

[0322] The consistency ratio may refer to the ratio of the consistency index to the random consistency index, and the first consistency ratio refers to the ratio between the first consistency index and the first random consistency index.

[0323] The first preset ratio may refer to a ratio threshold configured in advance for the first consistency ratio, which may be used to determine whether the first judgment matrix passes the consistency test. For example, the first preset ratio may be set to 0.1, or may be any other possible value, without limitation.

[0324] Optionally, in some embodiments, if the first consistency ratio is less than the first preset ratio, it is determined that the first test result indicates that the first judgment matrix satisfies the consistency test index; if the first consistency ratio is greater than or equal to the first preset ratio, it is determined that the first test result indicates that the first judgment matrix does not satisfy the consistency test index. Thus, it is possible to accurately and quickly determine whether the first judgment matrix satisfies the consistency test index based on the comparison result between the first consistency ratio and the first preset ratio.

[0325] Optionally, in some embodiments, when determining the first target eigenvector of the first judgment matrix, the maximum eigenroot of the first judgment matrix can be determined, and the first initial eigenvector corresponding to the maximum eigenroot of the first judgment matrix can be determined, and the first initial eigenvector can be normalized to obtain the first target eigenvector, thereby effectively improving the practicality of the first target eigenvector.

[0326] That is to say, in the embodiment of the present disclosure, a first judgment matrix can be constructed, wherein the first judgment matrix includes multiple first elements, each first element corresponds to the importance comparison result between at least two of the first category indicators and the second category indicators, and according to the first judgment matrix, the first weights corresponding to the first category indicators and the second weights corresponding to the second category indicators are respectively determined, thereby effectively improving the accuracy of the indications corresponding to the obtained first weights and the second weights.

[0327] S504: constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, and each second element corresponds to and represents an importance comparison result between at least two of the first number of network indicators.

[0328] The second judgment matrix refers to a judgment matrix constructed based on the relative importance of the target throughput, the target link capacity, and the target number of channels.

[0329] In the embodiment of the present disclosure, when the second judgment matrix is ​​constructed, reliable tool support can be provided for the subsequent weight allocation process among the target throughput, the target link capacity and the target number of channels.

[0330] S505: Determine a first number of network indicator weights according to the second judgment matrix, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels.

[0331] Optionally, in some embodiments, when determining the weights of the first number of network indicators according to the second judgment matrix, the second target eigenvector of the second judgment matrix can be determined, wherein the second target eigenvector corresponds to multiple fifth elements, each fifth element represents a second sorting weight, and the second sorting weight corresponds to one of the first number of network indicators. A consistency check is performed on the second judgment matrix to obtain a second check result. If the second check result indicates that the second judgment matrix satisfies the consistency check indicator, the second sorting weight is used as the weight of one of the first number of network indicators, wherein the weight of one of the first number of network indicators is the third weight, the fourth weight, or the fifth weight. Thus, the reliability of the obtained third weight, the fourth weight, and the fifth weight can be effectively improved.

[0332] The second target feature vector may refer to a feature vector obtained after feature extraction is performed on the second judgment matrix.

[0333] The second ranking weight may refer to the weight of the target throughput, the target link capacity and the target channel quantity determined based on the second target feature vector.

[0334] The second test result refers to the result obtained by performing a consistency test on the second judgment matrix.

[0335] Optionally, in some embodiments, when performing a consistency check on the second judgment matrix to obtain a second check result, a second consistency index corresponding to the second judgment matrix can be determined, wherein the second consistency index is related to the number of rows or columns and the maximum eigenvalue of the second judgment matrix; a second random consistency index corresponding to the second judgment matrix can be determined, wherein the second random consistency index is related to the matrix order of the second judgment matrix; a second consistency ratio of the second judgment matrix can be determined based on the second consistency index and the second random consistency index; a second comparison result between the second consistency ratio and the second preset ratio can be determined; and the second comparison result can be used as the second check result, thereby effectively improving the applicability and reliability of the second judgment matrix in the consistency check process.

[0336] The second consistency index and the second random consistency index refer to the consistency index and the random consistency index corresponding to the second judgment matrix.

[0337] The second consistency ratio refers to the ratio between the second consistency index and the second random consistency index.

[0338] The second preset ratio may refer to a ratio threshold configured in advance for the second consistency ratio, which may be used to determine whether the second judgment matrix passes the consistency test. For example, the second preset ratio may be set to 0.1, or may be any other possible value, without limitation.

[0339] In the embodiment of the present disclosure, the first preset ratio and the second preset ratio may be the same ratio value, or may be different ratio values, and there is no limitation on this.

[0340] Optionally, in some embodiments, if the second consistency ratio is less than the second preset ratio, it is determined that the second test result indicates that the second judgment matrix satisfies the consistency test index; if the second consistency ratio is greater than or equal to the second preset ratio, it is determined that the second test result indicates that the second judgment matrix does not satisfy the consistency test index. Thus, whether the second judgment matrix satisfies the consistency test index can be accurately and quickly determined based on the comparison result between the second consistency ratio and the second preset ratio.

[0341] Optionally, in some embodiments, when determining the second target eigenvector of the second judgment matrix, the maximum eigenroot of the second judgment matrix can be determined, and the second initial eigenvector corresponding to the maximum eigenroot of the second judgment matrix can be determined, and the second initial eigenvector can be normalized to obtain the second target eigenvector, thereby effectively improving the practicality of the second target eigenvector.

[0342] That is to say, in the embodiment of the present disclosure, a second judgment matrix can be constructed, wherein the second judgment matrix includes multiple second elements, each second element corresponds to the importance comparison result between at least two of the first number of network indicators, and the first number of network indicator weights are determined according to the second judgment matrix, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels, thereby effectively improving the reliability of the process of determining the third weight, the fourth weight, and the fifth weight.

[0343] S506: construct a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, and each third element corresponds to an importance comparison result between at least two of the second number of user indicators.

[0344] The third judgment matrix refers to a judgment matrix constructed based on the relative importance of the second number of user indicators.

[0345] S507: Determine a second number of user indicator weights according to the third judgment matrix, wherein the second number of user indicator weights includes: a sixth weight corresponding to the number of target users.

[0346] Optionally, in some embodiments, when determining the weights of the second number of user indicators based on the third judgment matrix, the third target feature vector of the third judgment matrix can be determined, wherein the third target feature vector corresponds to multiple sixth elements, each sixth element represents a third sorting weight, and the third sorting weight corresponds to one of the second number of user indicators. The third judgment matrix is ​​subjected to a consistency check to obtain a third check result. If the third check result indicates that the third judgment matrix satisfies the consistency check indicator, the third sorting weight is used as the weight of one of the second number of user indicators, wherein the weight of one of the second number of user indicators is the sixth weight. Thus, the reliability of the obtained sixth weight can be effectively improved.

[0347] The third target feature vector may refer to a feature vector obtained after feature extraction is performed on the third judgment matrix.

[0348] The third ranking weight may refer to the weight of the number of target users determined based on the third target feature vector.

[0349] The third test result refers to the result obtained by performing a consistency test on the third judgment matrix.

[0350] Optionally, in some embodiments, when performing a consistency check on the third judgment matrix to obtain a third check result, a third consistency index corresponding to the third judgment matrix can be determined, wherein the third consistency index is related to the number of rows or columns and the maximum eigenvalue of the third judgment matrix; a third random consistency index corresponding to the third judgment matrix can be determined, wherein the third random consistency index is related to the matrix order of the third judgment matrix; a third consistency ratio of the third judgment matrix can be determined based on the third consistency index and the third random consistency index; a third comparison result between the third consistency ratio and the third preset ratio can be determined; and the third comparison result can be used as the third check result, thereby effectively improving the practicality of the obtained third check result.

[0351] The third consistency index and the third random consistency index refer to the consistency index and the random consistency index corresponding to the third judgment matrix.

[0352] The third consistency ratio refers to the ratio between the third consistency index and the third random consistency index.

[0353] The third preset ratio may refer to a ratio threshold configured in advance for the third consistency ratio, which may be used to determine whether the third judgment matrix passes the consistency test. For example, the third preset ratio may be set to 0.1, or may be any other possible value, without limitation.

[0354] In the embodiment of the present disclosure, the first preset ratio, the second preset ratio and the third preset ratio may be the same ratio value, or may be different ratio values, and there is no limitation on this.

[0355] Optionally, in some embodiments, if the third consistency ratio is less than the third preset ratio, it is determined that the third test result indicates that the third judgment matrix satisfies the consistency test index; if the third consistency ratio is greater than or equal to the third preset ratio, it is determined that the third test result indicates that the third judgment matrix does not satisfy the consistency test index. Thus, whether the third judgment matrix satisfies the consistency test index can be accurately and quickly determined based on the comparison result between the third consistency ratio and the third preset ratio.

[0356] Optionally, in some embodiments, when determining the third target eigenvector of the third judgment matrix, the maximum eigenroot of the third judgment matrix can be determined, and a third initial eigenvector corresponding to the maximum eigenroot of the third judgment matrix can be determined, and the third initial eigenvector can be normalized to obtain the third target eigenvector, thereby effectively improving the practicality of the third target eigenvector.

[0357] That is to say, in the embodiment of the present disclosure, a third judgment matrix can be constructed, wherein the third judgment matrix includes multiple third elements, each third element corresponds to the importance comparison result between at least two of the second number of user indicators, and the second number of user indicator weights are determined according to the third judgment matrix, wherein the second number of user indicator weights include: a sixth weight corresponding to the number of target users, thereby effectively improving the reliability of the sixth weight determination process.

[0358] S508: Determine a capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight.

[0359] For the description of S508, please refer to the above embodiment for details, which will not be repeated here.

[0360] In this embodiment, by constructing a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, each of which corresponds to the comparison result of the importance between at least two of the first category indicators and the second category indicators, according to the first judgment matrix, the first weight corresponding to the first category indicator and the second weight corresponding to the second category indicator are respectively determined, thereby effectively improving the indication accuracy corresponding to the obtained first weight and the second weight. By determining the first target eigenvector of the first judgment matrix, wherein the first target eigenvector includes a plurality of fourth elements, each of which represents a first sorting weight, and the first sorting weight corresponds to one of the first category indicators and the second category indicators, the first judgment matrix is ​​subjected to a consistency check to obtain a first check result, and if the first check result indicates that the first judgment matrix satisfies the consistency check indicator, the first sorting weight is used as the weight of one of the first category indicators and the second category indicators, wherein the weight of one of the first category indicators and the second category indicators is the first weight or the second weight, thereby effectively improving the reliability of the first weight and the second weight determination process. Determine a first consistency index corresponding to the first judgment matrix, wherein the first consistency index is related to the number of rows or columns and the maximum eigenvalue of the first judgment matrix, determine a first random consistency index corresponding to the first judgment matrix, wherein the first random consistency index is related to the matrix order of the first judgment matrix, determine a first consistency ratio of the first judgment matrix according to the first consistency index and the first random consistency index, determine a first comparison result between the first consistency ratio and the first preset ratio, and use the first comparison result as the first test result, thereby effectively improving the applicability and reliability of the consistency test process of the first judgment matrix. If the first consistency ratio is less than the first preset ratio, determine that the first test result indicates that the first judgment matrix meets the consistency test index, and if the first consistency ratio is greater than or equal to the first preset ratio, determine that the first test result indicates that the first judgment matrix does not meet the consistency test index, thereby, it is possible to accurately and quickly determine whether the first judgment matrix meets the consistency test index based on the comparison result between the first consistency ratio and the first preset ratio. Determine the maximum eigenvalue of the first judgment matrix, determine the first initial eigenvalue corresponding to the maximum eigenvalue of the first judgment matrix, and normalize the first initial eigenvalue to obtain the first target eigenvalue, thereby effectively improving the practicality of the first target eigenvalue. By constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, each second element corresponds to the importance comparison result between at least two of the first number of network indicators, and according to the second judgment matrix, determine the first number of network indicator weights, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels, thereby effectively improving the reliability of the third weight, the fourth weight, and the fifth weight determination process.Each fifth element represents a second sorting weight, and the second sorting weight corresponds to one of the first number of network indicators. The second judgment matrix is ​​subjected to a consistency check to obtain a second test result. If the second test result indicates that the second judgment matrix satisfies the consistency check indicator, the second sorting weight is used as the weight of one of the first number of network indicators, wherein the weight of one of the first number of network indicators is the third weight, the fourth weight, or the fifth weight, thereby effectively improving the reliability of the obtained third weight, the fourth weight, and the fifth weight. A second consistency indicator corresponding to the second judgment matrix is ​​determined, wherein the second consistency indicator is related to the number of rows or columns and the maximum eigenvalue of the second judgment matrix, a second random consistency indicator corresponding to the second judgment matrix is ​​determined, wherein the second random consistency indicator is related to the matrix order of the second judgment matrix, a second consistency ratio of the second judgment matrix is ​​determined according to the second consistency indicator and the second random consistency indicator, a second comparison result between the second consistency ratio and the second preset ratio is determined, and the second comparison result is used as the second test result, thereby effectively improving the applicability and reliability of the second judgment matrix in the consistency check process. If the second consistency ratio is less than the second preset ratio, it is determined that the second test result indicates that the second judgment matrix meets the consistency test index. If the second consistency ratio is greater than or equal to the second preset ratio, it is determined that the second test result indicates that the second judgment matrix does not meet the consistency test index. Thus, it is possible to accurately and quickly determine whether the second judgment matrix meets the consistency test index based on the comparison result between the second consistency ratio and the second preset ratio. Determine the maximum eigenvalue of the second judgment matrix, determine the second initial eigenvector corresponding to the maximum eigenvalue of the second judgment matrix, and normalize the second initial eigenvector to obtain the second target eigenvector, thereby effectively improving the practicality of the second target eigenvector. By constructing a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, each third element corresponds to the importance comparison result between at least two of the second number of user indicators, and according to the third judgment matrix, determine the second number of user indicator weights, wherein the second number of user indicator weights include: a sixth weight corresponding to the number of target users, thereby effectively improving the reliability of the sixth weight determination process. Determine a third target eigenvector of the third judgment matrix, wherein the third target eigenvector corresponds to a plurality of sixth elements, each sixth element represents a third sorting weight, and the third sorting weight corresponds to one of the second number of user indicators. Perform a consistency check on the third judgment matrix to obtain a third test result. If the third test result indicates that the third judgment matrix satisfies the consistency check indicator, the third sorting weight is used as the weight of one of the second number of user indicators, wherein the weight of one of the second number of user indicators is the sixth weight. Thus, the reliability of the obtained sixth weight can be effectively improved.Determine a third consistency index corresponding to the third judgment matrix, wherein the third consistency index is related to the number of rows or columns and the maximum eigenvalue of the third judgment matrix, determine a third random consistency index corresponding to the third judgment matrix, wherein the third random consistency index is related to the matrix order of the third judgment matrix, determine a third consistency ratio of the third judgment matrix according to the third consistency index and the third random consistency index, determine a third comparison result between the third consistency ratio and the third preset ratio, and use the third comparison result as the third test result, thereby effectively improving the practicality of the obtained third test result. If the third consistency ratio is less than the third preset ratio, then determine that the third test result indicates that the third judgment matrix meets the consistency test index, if the third consistency ratio is greater than or equal to the third preset ratio, then determine that the third test result indicates that the third judgment matrix does not meet the consistency test index, thereby, based on the comparison result between the third consistency ratio and the third preset ratio, accurately and quickly determine whether the third judgment matrix meets the consistency test index. The maximum eigenvalue of the third judgment matrix is ​​determined, and the third initial eigenvalue corresponding to the maximum eigenvalue of the third judgment matrix is ​​determined. The third initial eigenvalue is normalized to obtain a third target eigenvalue, thereby effectively improving the practicality of the third target eigenvalue.

[0361] For example, Figure 6 As shown, Figure 6 According to the space network communication capacity performance evaluation flow chart proposed in the present disclosure, after normalizing the satellite capacity indicators, the analytic hierarchy process AHP can be applied to determine the weight of each capacity indicator. AHP includes four basic steps: establishing a ladder hierarchy model, constructing a judgment matrix, hierarchical single sorting and consistency test, and total sorting:

[0362] 1) Construct a hierarchical model, the total communication capacity target of the low-orbit satellite constellation is C Q The first-level indicator domain includes the normalized satellite throughput U C , link capacity U R , Number of channels and the number of users The judgment matrix of the first-level indicator domain is recorded as A;

[0363] 2) The judgment matrix is ​​a matrix that expresses the relative importance of each factor in each level relative to the upper level factors. The method of constructing the judgment matrix in AHP is the consistent matrix method, that is, not comparing all factors together, but comparing them two by two to minimize the difficulty of comparing factors of different natures, so as to improve accuracy. For example, the judgment matrix can be constructed using the scaling method, as shown in Table 1, which is an example table of scaling method values ​​proposed in the present disclosure:

[0364] Table 1

[0365]

[0366] The judgment matrix is ​​constructed by using the scaling method. All indicators are compared pairwise according to the above table to obtain the judgment matrix A of the first-level indicator domain, where element a ij The comparison result of the importance of factor i and factor j is:

[0367]

[0368] 3) Hierarchical single sorting and one-time test, the maximum characteristic root λ of the n-order judgment matrix max The eigenvector of is normalized and recorded as W. The elements of W are the ranking weights of the relative importance of the factors at the same level under a factor at the previous level. This process is called hierarchical single sorting. Whether the hierarchical single sorting can be confirmed requires a consistency test, that is, to determine the allowable range of inconsistency for A. The consistency index is defined as:

[0369]

[0370] CI = 0 indicates complete consistency; CI close to 0 indicates satisfactory consistency; the larger the CI, the more serious the inconsistency. In order to measure the size of CI, the random consistency index RI is introduced, which is related to the order of the judgment matrix. Generally, the larger the matrix order, the greater the possibility of random deviation from consistency. The corresponding relationship is shown in Table 2 below. Table 2 is an example table of standard values ​​of the average random consistency index RI proposed in this disclosure.

[0371] Table 2

[0372]

[0373] Define the consistency ratio:

[0374]

[0375] It is generally believed that when the consistency ratio CR < 0.1, the inconsistency of A is within the allowable range and has satisfactory consistency. After passing the consistency test, its normalized eigenvector can be used as the weight vector. ij Adjust it and reconstruct the judgment matrix A.

[0376] 4) Hierarchical total ranking and consistency test, calculate the relative importance of all factors at a certain level to the highest level (total goal G), called hierarchical total ranking, this process is carried out from the highest level to the lowest level. 1 ,F 2 ,...,F n The ranking of the total goal G is:

[0377] W=(w 1 ,w 2 ,...,w i ,...,w n ),i=1,2,...,n (26)

[0378] The jth factor of the second layer has an effect on the ith factor of the upper layer F i The hierarchical order of is:

[0379] W'=(w i1 ,w i2 ,...,w ij ,...,w ini ),j=1,2,...,n i (27)

[0380] Where n i is the number of second-level indicators under the first-level ith indicator. Then the total hierarchical ranking of the second layer (i.e., the weight of the second-level jth factor to the total goal) is:

[0381] W j =w i w ij (28)

[0382] The consistency ratio of the total hierarchical order is:

[0383]

[0384] When CR < 0.1, the hierarchical total ranking is considered to have passed the consistency test. The hierarchical total ranking has satisfactory consistency, otherwise it is necessary to readjust the element values ​​of the judgment matrix with high consistency ratio. At this point, the final decision is made based on the hierarchical total ranking of the lowest level (decision-making level).

[0385] After obtaining the weights of each indicator, the weighted sum of the results of each communication capacity performance indicator is performed to obtain the following comprehensive evaluation results of satellite constellation capacity:

[0386] C Q =ω 1 (ω 11 U C +ω 12 U R +ω 13 U Ns )+ω 2 ω 21 U Nu (30)

[0387] Among them, C Q Represents the comprehensive index of satellite constellation capacity, the sum of the weights of each index ω 1+ω 2 =1,ω 11 +ω 12 +ω 13 =1,ω 21 =1.

[0388] Figure 7 It is a structural diagram of a communication capacity evaluation device for a space network provided in an embodiment of the present disclosure.

[0389] like Figure 7 As shown, the communication capacity evaluation device 70 of the space network includes:

[0390] An acquisition module 701 is used to acquire a communication capacity indicator of a space network, wherein the communication capacity indicator includes a first type of indicator and a second type of indicator, the first type of indicator includes a first number of network indicators, the second type of indicator includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1;

[0391] A first determination module 702, configured to determine a first weight corresponding to the first category indicator and a second weight corresponding to the second category indicator according to a comparison result of the importance between the first category indicator and the second category indicator;

[0392] A second determination module 703 is used to determine an indicator weight, wherein the indicator weight includes: a network indicator weight corresponding to each network indicator, and / or a user indicator weight corresponding to each user indicator, the network indicator weight is related to the importance comparison result between the first number of network indicators, and the user indicator weight is related to the importance comparison result between the second number of user indicators;

[0393] The third determination module 704 is used to determine the capacity evaluation result according to the network indicator, the user indicator, the first weight, the second weight and the indicator weight.

[0394] In a possible implementation of the embodiment of the present disclosure, the acquisition module 701 is specifically configured to:

[0395] The target throughput, target link capacity, target number of channels and target number of users served by the space network are obtained, wherein the target throughput, target link capacity and target number of channels are collectively used as the first category of indicators, and the target number of users is used as the second category of indicators.

[0396] In a possible implementation of the embodiment of the present disclosure, the first determining module 702 is specifically configured to:

[0397] Constructing a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, each of which corresponds to an importance comparison result between at least two of the first category indicators and the second category indicators;

[0398] According to the first judgment matrix, a first weight corresponding to the first type of indicator and a second weight corresponding to the second type of indicator are determined respectively.

[0399] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is specifically configured to:

[0400] Constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, and each second element corresponds to and represents an importance comparison result between at least two of the first number of network indicators;

[0401] According to the second judgment matrix, a first number of network indicator weights are determined, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels.

[0402] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is specifically configured to:

[0403] Constructing a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, and each third element corresponds to and represents an importance comparison result between at least two of the second number of user indicators;

[0404] According to the third judgment matrix, a second number of user indicator weights are determined, wherein the second number of user indicator weights include: a sixth weight corresponding to the number of target users.

[0405] In a possible implementation of the embodiment of the present disclosure, the target throughput is determined based on the following method:

[0406] Obtaining the initial throughput, reference throughput and required minimum throughput of the spatial network;

[0407] Determine the comparison between the initial throughput and the required minimum throughput;

[0408] The initial throughput is normalized according to the comparison result, the reference throughput and the required minimum throughput to obtain the target throughput.

[0409] In a possible implementation of the embodiment of the present disclosure, the target link capacity is determined based on the following method:

[0410] Determine an initial link capacity and a link capacity threshold of the space network;

[0411] Determine a comparison result between an initial link capacity and a link capacity threshold;

[0412] The initial link capacity is normalized according to the comparison result and the link capacity threshold to obtain the target link capacity.

[0413] In a possible implementation of the embodiment of the present disclosure, the target number of channels is determined based on the following method:

[0414] Obtaining the initial number of channels and a channel number threshold of the spatial network;

[0415] Determine a comparison result between an initial number of channels and a threshold number of channels;

[0416] The initial number of channels is normalized according to the comparison result and the channel number threshold to obtain the target number of channels.

[0417] In a possible implementation of the embodiment of the present disclosure, the number of target users is determined based on the following method:

[0418] Determine the initial number of users of the space network, the reference number of users that can be served simultaneously, and the maximum number of users that can be supported;

[0419] Determine the comparison between the initial number of users and the reference number of users;

[0420] The initial number of users is normalized according to the comparison results, the number of reference users, and the maximum number of users to obtain the target number of users.

[0421] In a possible implementation of the embodiment of the present disclosure, the first determining module 702 is further configured to:

[0422] Determine a first target feature vector of the first judgment matrix, wherein the first target feature vector includes a plurality of fourth elements, each fourth element represents a first ranking weight, and the first ranking weight corresponds to one of the first category indicator and the second category indicator;

[0423] Performing a consistency check on the first judgment matrix to obtain a first test result;

[0424] If the first test result indicates that the first judgment matrix satisfies the consistency test index, the first sorting weight is used as the weight of one of the first category index and the second category index, wherein the weight of one of the first category index and the second category index is the first weight or the second weight.

[0425] In a possible implementation of the embodiment of the present disclosure, the first determining module 702 is further configured to:

[0426] Determine a first consistency index corresponding to the first judgment matrix, wherein the first consistency index is related to the number of rows or columns and the maximum eigenvalue of the first judgment matrix;

[0427] Determine a first random consistency indicator corresponding to the first judgment matrix, wherein the first random consistency indicator is related to the matrix order of the first judgment matrix;

[0428] Determining a first consistency ratio of a first judgment matrix according to the first consistency index and the first random consistency index;

[0429] A first comparison result between the first consistency ratio and the first preset ratio is determined, and the first comparison result is used as a first inspection result.

[0430] In a possible implementation of the embodiment of the present disclosure, wherein:

[0431] If the first consistency ratio is less than the first preset ratio, determining that the first test result indicates that the first judgment matrix satisfies the consistency test indicator;

[0432] If the first consistency ratio is greater than or equal to the first preset ratio, it is determined that the first test result indicates that the first judgment matrix does not meet the consistency test index.

[0433] In a possible implementation of the embodiment of the present disclosure, the first determining module 702 is further configured to:

[0434] Determine the maximum eigenvalue of the first judgment matrix, and determine the first initial eigenvector corresponding to the maximum eigenvalue of the first judgment matrix;

[0435] The first initial eigenvector is normalized to obtain a first target eigenvector.

[0436] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0437] Determine a second target feature vector of the second judgment matrix, wherein the second target feature vector includes a plurality of fifth elements, each fifth element represents a second sorting weight, and the second sorting weight corresponds to one of the first number of network indicators;

[0438] Performing a consistency check on the second judgment matrix to obtain a second test result;

[0439] If the second test result indicates that the second judgment matrix satisfies the consistency test index, the second sorting weight is used as the weight of one of the first number of network indicators, wherein the weight of one of the first number of network indicators is the third weight, the fourth weight, or the fifth weight.

[0440] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0441] Determine a second consistency index corresponding to the second judgment matrix, wherein the second consistency index is related to the number of rows or columns and the maximum eigenvalue of the second judgment matrix;

[0442] Determining a second random consistency indicator corresponding to the second judgment matrix, wherein the second random consistency indicator is related to the matrix order of the second judgment matrix;

[0443] Determining a second consistency ratio of the second judgment matrix according to the second consistency index and the second random consistency index;

[0444] A second comparison result between the second consistency ratio and the second preset ratio is determined, and the second comparison result is used as the second inspection result.

[0445] In a possible implementation of the embodiment of the present disclosure, wherein:

[0446] If the second consistency ratio is less than the second preset ratio, determining that the second test result indicates that the second judgment matrix satisfies the consistency test indicator;

[0447] If the second consistency ratio is greater than or equal to the second preset ratio, it is determined that the second test result indicates that the second judgment matrix does not meet the consistency test index.

[0448] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0449] Determine the maximum eigenvalue of the second judgment matrix, and determine the second initial eigenvector corresponding to the maximum eigenvalue of the second judgment matrix;

[0450] The second initial eigenvector is normalized to obtain a second target eigenvector.

[0451] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0452] Determine a third target feature vector of the third judgment matrix, wherein the third target feature vector correspondingly includes a plurality of sixth elements, each sixth element represents a third sorting weight, and the third sorting weight corresponds to one of the second number of user indicators;

[0453] Performing a consistency test on the third judgment matrix to obtain a third test result;

[0454] If the third test result indicates that the third judgment matrix satisfies the consistency test index, the third sorting weight is used as the weight of one of the second number of user indicators, wherein the weight of one of the second number of user indicators is the sixth weight.

[0455] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0456] Determine a third consistency index corresponding to the third judgment matrix, wherein the third consistency index is related to the number of rows or columns and the maximum eigenvalue of the third judgment matrix;

[0457] Determine a third random consistency index corresponding to the third judgment matrix, wherein the third random consistency index is related to the matrix order of the third judgment matrix;

[0458] Determining a third consistency ratio of a third judgment matrix according to the third consistency index and the third random consistency index;

[0459] A third comparison result between the third consistency ratio and the third preset ratio is determined, and the third comparison result is used as the third inspection result.

[0460] In a possible implementation of the embodiment of the present disclosure, wherein:

[0461] If the third consistency ratio is less than the third preset ratio, determining that the third test result indicates that the third judgment matrix satisfies the consistency test indicator;

[0462] If the third consistency ratio is greater than or equal to the third preset ratio, it is determined that the third inspection result indicates that the third judgment matrix does not meet the consistency inspection index.

[0463] In a possible implementation of the embodiment of the present disclosure, the second determining module 703 is further configured to:

[0464] Determine the maximum eigenvalue of the third judgment matrix, and determine the third initial eigenvector corresponding to the maximum eigenvalue of the third judgment matrix;

[0465] The third initial eigenvector is normalized to obtain a third target eigenvector.

[0466] In a possible implementation of the embodiment of the present disclosure, the third determining module 704 is specifically configured to:

[0467] Performing weighted summation according to the network indicator and the network indicator weight to obtain a first sum value;

[0468] Performing weighted summation according to the user index and the user index weight to obtain a second sum value;

[0469] A weighted sum is performed according to the first sum value, the second sum value, the first weight value and the second weight value to obtain a third sum value, and the third sum value is used as the capacity assessment result.

[0470] It should be noted that the aforementioned explanation of the communication capacity evaluation method for a space network is also applicable to the communication capacity evaluation device for a space network of this embodiment, and will not be repeated here.

[0471] In this embodiment, by obtaining the communication capacity index of the spatial network, wherein the communication capacity index includes a first type of index and a second type of index, the first type of index includes a first number of network indexes, the second type of index includes a second number of user indexes, the first number and the second number are integers greater than or equal to 1, according to the importance comparison result between the first type of index and the second type of index, a first weight corresponding to the first type of index and a second weight corresponding to the second type of index are determined, and the index weight is determined, wherein the index weight includes: a network index weight corresponding to each network indicator, and / or a user index weight corresponding to each user indicator, the network index weight is related to the importance comparison result between the first number of network indicators, and the user index weight is related to the importance comparison result between the second number of user indicators, and the capacity assessment result is determined according to the network indicator, the user indicator, the first weight, the second weight and the index weight, thereby effectively reducing the influence of user subjective factors and improving the accuracy of the obtained capacity assessment result.

[0472] Figure 8 A block diagram of an exemplary communication device suitable for implementing embodiments of the present disclosure is shown. Figure 8 The communication device 12 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present disclosure. Figure 8 As shown, the communication device 12 is in the form of a general purpose computing device. Components of the communication device 12 may include, but are not limited to, one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0473] The bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnection (PCI) bus.

[0474] The communication device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the communication device 12, including volatile and non-volatile media, removable and non-removable media.

[0475] The memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The communication device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 Not shown, often called a "hard drive").

[0476] although Figure 8 Not shown in the figure, a disk drive for reading and writing a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing a removable non-volatile optical disk (e.g., a compact disc read only memory (hereinafter referred to as: CD-ROM), a digital versatile disc read only memory (hereinafter referred to as: DVD-ROM) or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0477] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0478] The communication device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a human body to interact with the communication device 12, and / or any device that enables the communication device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the communication device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the communication device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the communication device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0479] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the communication capacity evaluation method of the space network mentioned in the above embodiment.

[0480] In order to implement the above embodiments, the present disclosure also proposes a non-temporary computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the communication capacity evaluation method of the space network proposed in the above embodiments of the present disclosure is implemented.

[0481] In order to implement the above embodiments, the present disclosure further proposes a computer program product. When the instruction processor in the computer program product is executed, the communication capacity evaluation method of the space network proposed in the above embodiments of the present disclosure is executed.

[0482] It should be noted that, in the description of the present disclosure, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present disclosure, unless otherwise specified, the meaning of "plurality" is two or more.

[0483] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present disclosure includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present disclosure belong.

[0484] It should be understood that the various parts of the present disclosure can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0485] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0486] In addition, each functional unit in each embodiment of the present disclosure may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0487] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0488] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0489] Although the embodiments of the present disclosure have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present disclosure. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present disclosure.

Claims

1. A method for evaluating the communication capacity of a space network, characterized in that: The method comprises: Acquire a communication capacity indicator of a space network, wherein the communication capacity indicator includes a first type of indicator and a second type of indicator, the first type of indicator includes a first number of network indicators, the second type of indicator includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1; Determine, according to the comparison result of the importance between the first category of indicators and the second category of indicators, a first weight corresponding to the first category of indicators and a second weight corresponding to the second category of indicators; Determine an indicator weight, wherein the indicator weight includes: a network indicator weight corresponding to each of the network indicators, and / or a user indicator weight corresponding to each of the user indicators, the network indicator weight is related to an importance comparison result between the first number of network indicators, and the user indicator weight is related to an importance comparison result between the second number of user indicators; A capacity assessment result is determined according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight.

2. The method according to claim 1, characterized in that The obtaining of the communication capacity index of the space network includes: Obtain a target throughput, a target link capacity, a target number of channels of the space network, and a target number of users served by the space network, wherein the target throughput, the target link capacity, and the target number of channels are collectively used as the first category indicators, and the target number of users is used as the second category indicator.

3. The method according to claim 1, characterized in that The determining, according to the comparison result of the importance between the first category of indicators and the second category of indicators, a first weight corresponding to the first category of indicators and a second weight corresponding to the second category of indicators comprises: Constructing a first judgment matrix, wherein the first judgment matrix includes a plurality of first elements, each of which corresponds to an importance comparison result between at least two of the first category indicators and the second category indicators; According to the first judgment matrix, the first weight corresponding to the first type of indicator and the second weight corresponding to the second type of indicator are determined respectively.

4. The method according to claim 2, characterized in that The determining of the indicator weights includes: Constructing a second judgment matrix, wherein the second judgment matrix includes a plurality of second elements, and each second element corresponds to an importance comparison result between at least two of the first number of network indicators; According to the second judgment matrix, the first number of network indicator weights are determined, wherein the first number of network indicator weights include: a third weight corresponding to the target throughput, a fourth weight corresponding to the target link capacity, and a fifth weight corresponding to the target number of channels.

5. The method according to claim 2, characterized in that The determining of the indicator weights includes: Constructing a third judgment matrix, wherein the third judgment matrix includes a plurality of third elements, and each of the third elements corresponds to an importance comparison result between at least two of the second number of user indicators; The second number of user indicator weights is determined according to the third judgment matrix, wherein the second number of user indicator weights includes: a sixth weight corresponding to the number of target users.

6. The method according to claim 1, characterized in that The determining of the capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight, and the indicator weight includes: Performing weighted summation according to the network indicator and the network indicator weight to obtain a first sum value; Performing a weighted sum according to the user indicator and the user indicator weight to obtain a second sum value; A weighted sum is performed according to the first sum value, the second sum value, the first weight value, and the second weight value to obtain a third sum value, and the third sum value is used as the capacity assessment result.

7. A communication capacity evaluation device for a space network, characterized in that: The device comprises: An acquisition module, configured to acquire a communication capacity indicator of a space network, wherein the communication capacity indicator includes a first type of indicator and a second type of indicator, the first type of indicator includes a first number of network indicators, the second type of indicator includes a second number of user indicators, and the first number and the second number are integers greater than or equal to 1; A first determination module, configured to determine a first weight corresponding to the first category indicator and a second weight corresponding to the second category indicator according to a comparison result of the importance between the first category indicator and the second category indicator; A second determination module is used to determine an indicator weight, wherein the indicator weight includes: a network indicator weight corresponding to each of the network indicators, and / or a user indicator weight corresponding to each of the user indicators, the network indicator weight is related to the importance comparison result between the first number of network indicators, and the user indicator weight is related to the importance comparison result between the second number of user indicators; The third determination module is used to determine a capacity assessment result according to the network indicator, the user indicator, the first weight, the second weight and the indicator weight.

8. A communication device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: in, The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 6.

Images