CN118192303A - Semi-physical simulation method and system - Google Patents
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Abstract
The invention relates to the technical field of semi-physical simulation, and discloses a semi-physical simulation method and a semi-physical simulation system. The method solves the problems of low algorithm reliability, time consumption, financial consumption and the like in the prior art.
Description
Technical Field
The invention relates to the technical field of semi-physical simulation, in particular to a semi-physical simulation method and a semi-physical simulation system.
Background
The development direction of the modern industrial products is as follows: highly intelligent, integrated and coupled, and meanwhile, the standards related to product development and algorithm are also becoming stricter. Therefore, the development environment of the conventional pure digital simulation system is far from meeting the requirements. In order to increase the reliability of the simulation, the authenticity of the simulation system is increased, and a very high platform for improving the reliability is provided for algorithm development and product design. In view of the fact that the simulation technology can remarkably reduce development cost and shorten development period, improving algorithm reliability through improving simulation means has become a common consensus in the field of modern industry.
The semi-physical simulation technology combines the advantages of digital simulation and real hardware equipment, and can develop the research of the key technology hardware in loop verification technology in the field of system simulation, thereby breaking through the high-fidelity key technology semi-physical simulation verification technology.
Semi-physical simulation is a particularly important technology, and is to connect a part of real hardware devices and a part of digital simulation devices to form a complete communication network. In this way, the advantages of real hardware devices and digital simulation devices can be fully utilized. Real hardware devices may provide more realistic hardware performance and physical characteristics, while digital emulation devices may provide flexible configuration and large-scale analog capabilities.
Because the energy that unmanned aerial vehicle carried is limited, unmanned aerial vehicle's service range receives the realistic restriction that can't long-distance flight or long-time flight. Meanwhile, battery life of sensor nodes in wireless sensor networks is often limited, and it is difficult to periodically replace batteries in many cases. Thus, the method is applicable to a variety of applications. Frequent communication with the drone can cause the sensor nodes to quickly run out of energy. If the energy of the unmanned aerial vehicle's node is exhausted, the node cannot support the transmission of the data packet, and the data packet carried by the node is lost. Therefore, the energy-saving problem of the unmanned aerial vehicle wireless sensor network is studied to have great significance.
In the past period, a series of methods are proposed by a plurality of students for energy-saving route design, and the common method is to effectively detect the algorithm performance and the algorithm effectiveness of an energy-saving communication algorithm in a pure digital simulation system, but the communication environment is a huge and complex system, and in the pure digital simulation system, the simulation of complex environments such as communication delay, node failure, link congestion and the like is difficult. Along with the development of electronic information technology, simulation means are effectively developed in a period of time, and the reliability of a communication algorithm is improved by improving the simulation means, so that the method is a new research road.
The simulation difficulty of the traditional energy-saving ad hoc network routing algorithm is low, but the communication environment and network delay in the experiment are large and complex systems, and the algorithm performance and the algorithm effectiveness of the energy-saving communication algorithm are difficult to effectively detect through pure digital simulation, so that the algorithm has certain unreliability. Therefore, in order to improve the development quality and the authenticity of the design algorithm in the algorithm design process, development of an energy-saving communication algorithm for verifying hardware in a loop system is needed.
Currently, communication energy-saving algorithm verification generally builds an experimental platform in a digital simulation system or a real environment, but the former cannot fully simulate a key environment, and the latter requires a large amount of equipment and manpower.
In summary, the prior art has the following disadvantages:
The energy-saving communication algorithm based on pure digital simulation design has the problem of low algorithm reliability; the energy-saving communication algorithm designed based on the real environment has the problems of time consumption and financial consumption.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a semi-physical simulation method and a semi-physical simulation system, which solve the problems of low algorithm reliability, time consumption, financial consumption and the like in the prior art.
The invention solves the problems by adopting the following technical scheme:
A semi-physical simulation method adopts various factor metrics and verifies by a semi-physical simulation system, wherein the adopted factors comprise track metric values, and the calculation formula of the track metric values is as follows:
In the method, in the process of the invention, Is the track metric value,/>As a distance factor,/>Is a node density factor,/>For Euclidean distance between the neighboring node and the destination node,/>For Euclidean distance between source node and destination node,/>For the number of neighboring nodes owned by the current node,/>Is the number of nodes in the network.
As a preferred technical scheme, the factors adopted include energy consumption, assuming that the initial energy of the nodes isBoth node forwarding and receiving data follow a first order radio model when node/>The directional distance is/>Node/>Transmission/>Node/>, when bit informationThe energy consumption calculation formula is as follows:
In the method, in the process of the invention, For node/>Energy consumption of/>For node/>To node/>The number of bits of the transmitted bit information,For node/>To node/>Distance of/>For the running energy consumption coefficient of the electronic system,/>Is the free space propagation loss coefficient,/>For multipath fading propagation loss coefficient,/>Is an information transmission distance threshold.
As a preferred technical solution, a method for manufacturing a semiconductor device,When/>When the communication between the nodes adopts a free space model; when/>When the communication between the nodes adopts a multipath fading model.
As a preferable technical scheme, when the nodeAccept/>Node/>, when bit informationThe energy consumption is as follows:
In the method, in the process of the invention, For node/>Is not limited by the energy consumption of (a).
As a preferred technical solution, the factors used include the remaining energy, and the calculation formula is as follows:
In the method, in the process of the invention, For the remaining energy,/>For the transmitted indicator variable,/>As an accepted indicator variable, if the unmanned aerial vehicle sends data, then/>If unmanned aerial vehicle accepts data then/>If the unmanned aerial vehicle sends and receives dataIf the unmanned aerial vehicle does not send data/>If the unmanned aerial vehicle does not accept the data/>。
As a preferred technical solution, the factors used include utility functions, the calculation formula is as follows:
In the method, in the process of the invention, As utility function,/>Is the weight factor of the track metric value,/>Is a weight factor for the remaining energy.
As a preferable technical scheme, assuming that the node carries control information to be forwarded, the node firstly judges whether the target node is a neighboring node, and if the target node is a neighboring node, the node directly forwards data; otherwise, selecting the relay node according to the calculation formula of the utility function until the control information is transmitted to the destination node, and completing the data transmission.
A semi-physical simulation system is used for realizing the semi-physical simulation method and comprises a communication module, an externally hung task computer, a flight control computer, a real-time simulator, a reflective memory board card, semi-physical integrated software and a semi-physical simulation management system which are sequentially in communication connection.
As a preferable technical scheme, the real-time simulator runs an RTX real-time operating system, the flight control computer runs a VxWorks operating system, the plug-in task computer runs a Ubuntu operating system, the communication module is in an Ad hoc network through a MESH, and the semi-physical simulation management system manages and schedules the real-time simulator cluster through target information of the digital simulation system.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the invention, a hybrid heterogeneous semi-physical simulation system is built in the initial development stage, and the energy-saving ad hoc network routing algorithm under the complex environments of communication delay, node failure, link congestion and the like is verified and optimized, so that the reliability and development quality of algorithm design are improved, and the reality and credibility of simulation results are improved;
(2) In the development research stage, the development time of the algorithm is saved and the development quality of the algorithm is improved by developing the research of the hardware of the key communication part in the loop verification technology;
(3) The invention designs a multi-objective measurement criterion considering the node position and the residual energy factors by comprehensively considering the factors such as transmission efficiency, transmission quality, transmission success rate and the like, so as to achieve the aim of saving energy. Verification is carried out through a semi-physical simulation system, so that the fidelity of a designed algorithm is ensured;
(4) The invention combines the semi-physical simulation verification technology with the energy-saving self-organizing network routing algorithm, innovatively introduces the hardware-in-loop technology aiming at the key communication part, so that the developed communication algorithm is more similar to the real situation and can be truly analyzed;
(5) The invention adopts the semi-physical simulation technology at the key part, so that the fidelity of the developed communication algorithm can be improved, the development quality of the related communication algorithm can be effectively improved, and the credibility and the authenticity of the key part of the algorithm can be further supported; meanwhile, the algorithm development time is saved, and the algorithm development efficiency is improved without reducing the algorithm development accuracy.
Drawings
FIG. 1 is a schematic diagram of an overall framework for algorithm implementation;
FIG. 2 is a diagram of a semi-physical simulation system;
fig. 3 is a schematic diagram of an energy curve of the relay unmanned aerial vehicle.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Example 1
As shown in fig. 1 to 3, in order to solve the problems of low algorithm state simulation degree, low development efficiency and the like caused by the fact that a pure digital virtual system is separated from the simulation of real hardware equipment, the hybrid heterogeneous semi-physical simulation system is built in the initial stage of development, and the energy-saving ad hoc network routing algorithm under the complex environments of communication delay, node failure, link congestion and the like is verified and optimized, so that the reliability and development quality of algorithm design are improved, and the authenticity and credibility of simulation results are improved.
Compared with the traditional pure digital simulation system, the communication algorithm designed by the invention has higher credibility.
Aiming at the traditional method, in the development research stage, the invention improves the development quality of the algorithm while saving the development time of the algorithm by developing the research of the hardware of the key communication part in the loop verification technology.
The invention designs a multi-objective measurement criterion considering the node position and the residual energy factors by comprehensively considering the factors such as transmission efficiency, transmission quality, transmission success rate and the like, so as to achieve the aim of saving energy. And the fidelity of the designed algorithm is ensured through verification of a semi-physical simulation system.
1. Algorithm design
The design distance factor is:
It is assumed that all unmanned aerial vehicles can acquire position information of all unmanned aerial vehicles participating in a task through a GPS.
And defining track measurement from two angles of distance and adjacent node density according to the position information of the current unmanned aerial vehicle. From the perspective of distance, in order to reduce the number of route hops, a node closer to the target unmanned aerial vehicle is required to be selected as the next hop; from the point of view of node density, in order to reduce the probability of occurrence of routing holes in the data transmission process, a node with a large adjacent node density needs to be selected as the next hop. The unmanned aerial vehicle track measurement value is defined as follows:
（1）
In the method, in the process of the invention, As a distance factor,/>Is a node density factor,/>For Euclidean distance between the neighboring node and the destination node,/>For Euclidean distance between source node and destination node,/>For the number of neighboring nodes owned by the current node,Is the number of drones in the network. Track metrics/>The method can be used as a basis for selecting the relay node, and the larger the value is, the larger the probability that the node is selected as the next hop relay node is.
The design energy consumption factors are as follows:
The unmanned aerial vehicle is limited in configuration energy due to the restriction of the battery volume, and the energy of the unmanned aerial vehicle in the process of executing tasks is mainly applied to propulsion energy consumption and communication energy consumption, wherein the propulsion energy consumption accounts for a main part and is mainly related to factors such as the weight, the flying speed and the wing area of the unmanned aerial vehicle. However, in the research of communication routing protocols, only the communication energy consumption among unmanned aerial vehicles is often considered, and the propulsion energy consumption is ignored. If the energy of the node is exhausted in the task execution of the unmanned aerial vehicle, the node cannot support the forwarding and receiving of the data packet, the data carried by the data packet is lost and exits the network, and therefore the energy balance among the unmanned aerial vehicles in the visible network is a key for prolonging the life cycle of the network.
Assume that the initial energy of the unmanned aerial vehicle isBoth its forwarding and receiving data follow a first order radio model when unmanned aerial vehicle/>The directional distance is/>Unmanned aerial vehicle/>Transmission/>When the bit information is bit, the energy consumption calculation formula is as follows:
(2)
In the method, in the process of the invention, The energy consumption coefficient is used for the operation of the electronic system; /(I)Is a free space propagation loss coefficient; /(I)The multipath fading propagation loss coefficient; /(I)The information transmission distance threshold value is as follows: /(I)When (when)When the method is used, a free space model is adopted; when/>In this case, a multipath fading model is used. When unmanned plane/>Accept/>The energy consumption of the bit information is as follows:
(3)
According to the energy consumption formula of the unmanned aerial vehicle sending data and the receiving data, the residual energy of the unmanned aerial vehicle node can be calculated:
(4)
In the method, in the process of the invention, For transmitting and receiving indicating variables, if the unmanned plane transmits and receives dataVice versa/>。
The utility function is designed as follows:
The unmanned aerial vehicle nodes in the network are all provided with information tables, and the contents in the tables mainly comprise own ID, predicted track metric values and residual energy. The larger the predicted track metric value is, the more probability of routing holes and the boundary packet loss rate can be reduced. The remaining energy is a key element of information transmission, the larger the remaining energy is, the more data packets are processed, the efficiency of the transmitted task is improved, if the remaining energy is insufficient, the unmanned aerial vehicle node loses the capability of executing the task and exits the network, so that a utility function is defined and determined by two factors of track measurement and energy, and the performance of the network is balanced. The utility function is defined as follows:
(5)
In the method, in the process of the invention, ，/>The weight factors of the trajectory metric and the residual energy, respectively. Each unmanned aerial vehicle node can calculate a utility function through the information table, the performance of the unmanned aerial vehicle node of the next hop is estimated by using the utility function, and the node with the largest utility function value is selected as the node of the next hop. It can be seen from the utility function that when one of the trajectory metric and the residual energy is zero, the utility function value of the node is zero, and the right of competing relays is lost.
2. Algorithm implementation
Each unmanned aerial vehicle can acquire the position information of the unmanned aerial vehicle and other unmanned aerial vehicles, and each unmanned aerial vehicle has a unique identifier. Suppose unmanned aerial vehicle/>Carrying control information to be forwarded, the unmanned aerial vehicle needs to judge whether the target unmanned aerial vehicle is a neighboring unmanned aerial vehicle or not, and if the target unmanned aerial vehicle is the neighboring unmanned aerial vehicle, data forwarding is directly carried out. Otherwise, selecting the relay unmanned aerial vehicle according to the formula (5) until the control information is successfully transmitted to the target unmanned aerial vehicle, and completing data transmission.
The designed algorithm implementation whole framework is shown in fig. 1 (the nodes in fig. 1 are unmanned aerial vehicles):
A. Input: n;
B. initializing: eres =10000, nextStep =0, id_ sou =id sou、ID_des=IDdes;
C. judging whether the current node is a source node or not: if yes, the control information is put into a buffer; if not, enter step D:
if yes, judging whether the current node is the next node of other nodes: if the current node is the next node of other nodes, copying buffer information, updating the residual energy Eres according to the formula (2), and ending; if the current node is not the next node of other nodes, directly ending;
if not, judging whether the current node is the next node of other nodes: copying buffer information if the current node is the next node of other nodes, updating the residual energy Eres according to the formula (2), and then entering the step D; if the current node is not the next node of other nodes, directly ending;
D. Judging whether the destination node is a neighboring node: if yes, then NextStep =id des is executed, and then the remaining energy Eres is updated according to formula (2) and then ends; if not, searching for the node ID j with the maximum calculated metric value according to the formula (5), executing NextStep =id j, updating the remaining energy Eres according to the formula (2), and ending;
Wherein, the parameter meaning is as follows: j represents the number of the current node, N represents the total number of nodes, eres represents the remaining energy, nextStep represents the next point of information transmission, id_ sou represents the ID of the source node, ID sou represents the ID initial value of the source node, id_des represents the ID of the destination node, ID des represents the ID initial value of the destination node, buffer represents the buffer area, and ID j represents the ID of the current node.
3. Algorithm simulation
Compared with pure digital simulation, the semi-physical simulation system is closer to the situations of delay, interruption, interference and the like existing in a simulation real environment, and has higher requirements on the robustness and feasibility of the scheme. Therefore, in order to improve the authenticity and the credibility, the semi-physical simulation is adopted for algorithm verification.
The cluster semi-physical simulation system in simulation is composed of hardware such as a real-time simulator, a flight control computer, a plug-in task computer, a wireless communication module, a semi-physical simulation management system and the like, as shown in fig. 2.
In fig. 2 DDS (Data Distribution Service) is a standard and protocol for data communication in a real-time system. It provides a distributed, high-performance data exchange mechanism that allows different parts of the software system to share real-time data over the network. The real-time simulator runs an RTX real-time operating system, models the unmanned aerial vehicle, and can the kinematic model of the unmanned aerial vehicle to simulate the movement of the unmanned aerial vehicle. The flight control computer completes the control law resolving, flight control, control guidance and other programs based on the VxWorks real-time operating system. The plug-in task computer is provided with a Ubuntu operating system, and the canned unmanned aerial vehicle clusters are cooperated to form a network and relay selection algorithm. The wireless communication module is used for simulating remote communication among unmanned aerial vehicle clusters through MESH ad hoc network. The semi-physical simulation management system performs management scheduling on the real-time simulator cluster through the target information of the cluster verification digital simulation system, and performs virtual-real combined joint simulation verification.
The simulation result of the algorithm in the semi-physical simulation system is shown in fig. 3.
In order to more intuitively display the energy consumed by the unmanned aerial vehicle for receiving and transmitting the control information in the operation process, the energy consumption of the unmanned aerial vehicle serving as a relay is subjected to energy curve drawing. In order to keep the unmanned aerial vehicle becoming a relay at an initial energy value all the time, the first unmanned aerial vehicle and the eighth unmanned aerial vehicle keep the sending energy consumption and the receiving energy consumption of data.
Fig. 3 shows the energy consumption process of unmanned aerial vehicles No. two, no. four and No. six in the flight process. As can be seen from the figure, the whole data transmission process is divided into three phases: in the first stage, the measurement values of the second unmanned aerial vehicle, the fourth unmanned aerial vehicle and the sixth unmanned aerial vehicle have larger differences, so that the sixth unmanned aerial vehicle with the largest measurement value is selected as a relay unmanned aerial vehicle to transmit control information for the eighth unmanned aerial vehicle; when the energy consumption of the unmanned aerial vehicle No. six reaches a certain condition, the measurement values of the unmanned aerial vehicle No. four and the unmanned aerial vehicle No. six are almost different, and the second stage is entered. In the second stage, the six unmanned aerial vehicle and the four unmanned aerial vehicle are alternately used as relay unmanned aerial vehicles to transmit control information for the eight unmanned aerial vehicle, so that the energy consumption process shows a slowing trend; and when the energy consumption of the fourth unmanned aerial vehicle and the sixth unmanned aerial vehicle reaches a certain condition, the measurement values of the second unmanned aerial vehicle, the fourth unmanned aerial vehicle and the sixth unmanned aerial vehicle are almost different, and the third stage is started. And in the third stage, the second unmanned aerial vehicle, the fourth unmanned aerial vehicle and the sixth unmanned aerial vehicle are alternately used as relay unmanned aerial vehicles to transmit control information for the eighth unmanned aerial vehicle, and the energy consumption process is slowed down again until the energy consumption of all three unmanned aerial vehicles is completed.
The invention combines the semi-physical simulation verification technology with the energy-saving self-organizing network routing algorithm for verification.
The invention combines the semi-physical simulation verification technology with the energy-saving self-organizing network routing algorithm, innovatively introduces the hardware-in-loop technology aiming at the key communication part, so that the developed communication algorithm is closer to the real situation and can be truly analyzed.
The invention adopts the semi-physical simulation technology at the key part, so that the fidelity of the developed communication algorithm can be improved, the development quality of the related communication algorithm can be effectively improved, and the credibility and the fidelity of the key part of the algorithm can be further supported. Meanwhile, the algorithm development time is saved, and the algorithm development efficiency is improved without reducing the algorithm development accuracy.
The energy-saving self-organizing network routing algorithm can be replaced by other communication algorithms, and reliability of algorithm development can be improved by semi-physical simulation of important scenes.
As described above, the present invention can be preferably implemented.
All of the features disclosed in all of the embodiments of this specification, or all of the steps in any method or process disclosed implicitly, except for the mutually exclusive features and/or steps, may be combined and/or expanded and substituted in any way.
The foregoing description of the preferred embodiment of the invention is not intended to limit the invention in any way, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. The semi-physical simulation method is characterized in that a plurality of factor metrics are adopted, and verified by a semi-physical simulation system, wherein the adopted factors comprise track metric values, and the calculation formula of the track metric values is as follows:
In the method, in the process of the invention, Is the track metric value,/>As a distance factor,/>Is a node density factor,/>For Euclidean distance between the neighboring node and the destination node,/>For Euclidean distance between source node and destination node,/>For the number of neighboring nodes owned by the current node,/>Is the number of nodes in the network.
2. The semi-physical simulation method of claim 1 wherein said factors include energy consumption assuming initial energy of said node isBoth node forwarding and receiving data follow a first order radio model when node/>The directional distance is/>Node/>Transmission/>Node/>, when bit informationThe energy consumption calculation formula is as follows:
In the method, in the process of the invention, For node/>Energy consumption of/>For node/>To node/>Bit number of transmitted bit information,/>For node/>To node/>Distance of/>For the running energy consumption coefficient of the electronic system,/>As a coefficient of free-space propagation loss,For multipath fading propagation loss coefficient,/>Is an information transmission distance threshold.
3. The semi-physical simulation method of claim 2, wherein,When (when)When the communication between the nodes adopts a free space model; when/>When the communication between the nodes adopts a multipath fading model.
4. A semi-physical simulation method of claim 3, wherein said method comprises the steps of,
When the nodeAccept/>Node/>, when bit informationThe energy consumption is as follows:
In the method, in the process of the invention, For node/>Is not limited by the energy consumption of (a).
5. The semi-physical simulation method of claim 4, wherein said factors include residual energy as calculated by the following formula:
In the method, in the process of the invention, For the remaining energy,/>For the transmitted indicator variable,/>As an accepted indicator variable, if the unmanned aerial vehicle sends data, then/>If unmanned aerial vehicle accepts data then/>If the unmanned aerial vehicle sends and receives dataIf the unmanned aerial vehicle does not send data/>If the unmanned aerial vehicle does not accept the data/>。
6. The semi-physical simulation method of claim 5 wherein said factors include utility functions calculated as follows:
In the method, in the process of the invention, As utility function,/>Is the weight factor of the track metric value,/>Is a weight factor for the remaining energy.
7. The semi-physical simulation method of claim 6, wherein the node judges whether the destination node is a neighboring node or not, if so, directly forwarding data, assuming that the node carries control information to be forwarded; otherwise, selecting the relay node according to the calculation formula of the utility function until the control information is transmitted to the destination node, and completing the data transmission.
8. A semi-physical simulation system is characterized by comprising a communication module, a plug-in task computer, a flight control computer, a real-time simulator, a reflective memory board card, semi-physical integrated software and a semi-physical simulation management system which are sequentially in communication connection.
9. The semi-physical simulation system according to claim 8, wherein the real-time simulator runs an RTX real-time operating system, the flight control computer runs a VxWorks operating system, the plug-in task computer runs a Ubuntu operating system, the communication module is self-organized through a MESH, and the semi-physical simulation management system manages and schedules the real-time simulator cluster through target information of the digital simulation system.
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