Complex environment-oriented wireless image transmission recorder signal enhancement and stable transmission method

Abstract

The invention discloses a wireless image transmission recorder signal enhancement and stable transmission method oriented to a complex environment, and relates to the technical field of digital information transmission. Comprising the steps of environment adaptability analysis and transmission demand evaluation, multi-path propagation modeling and interference analysis, dynamic adjustment of spectrum allocation and scheduling, channel coding and modulation optimization, cooperative transmission and network topology optimization, space-time coding and beam forming technology application and design of a low-delay protocol. According to the signal enhancement and stable transmission method of the wireless image transmission recorder for the complex environment, the signal enhancement and stable transmission performance of the wireless image transmission recorder in the complex environment is effectively improved by introducing the multi-path propagation modeling, the cooperative transmission model and the network topology optimization model. Accurate path loss prediction, reflection and refraction path energy optimization and multipath interference dynamic management are realized. Meanwhile, optimal allocation of resources and dynamic adjustment of a transmission path are realized through global optimization of cooperative transmission efficiency and a network topology structure.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information transmission, and in particular to a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment. Background Art

[0002] With the rapid development of wireless communication technology, wireless image recorders have been widely used in complex environments such as remote monitoring and drone data collection. However, signal transmission in complex environments is often interfered by various factors, such as path loss, reflection and refraction, multipath interference, etc., which puts higher requirements on transmission stability and signal quality. Existing technologies mostly rely on simple beamforming and spectrum allocation methods, and do not fully consider the multi-factor coupling effect in complex environments, resulting in the following deficiencies:

[0003] Signal attenuation problem: Existing methods fail to dynamically adjust the loss in path loss calculation, making it difficult to accurately reflect the multiple signal attenuation characteristics in complex environments.

[0004] Reflection and refraction interference: The lack of global optimization modeling of reflection and refraction paths makes it impossible to effectively reduce the impact of reflection and refraction energy loss and directional deviation on signal quality.

[0005] Insufficient multipath interference management: Traditional multipath interference processing methods fail to comprehensively consider complex factors such as time delay and interference intensity, resulting in reduced signal demodulation accuracy at the receiving end. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment, comprising the following steps:

[0009] S1. Conduct environmental adaptability analysis and transmission demand assessment in complex environments and formulate transmission requirements;

[0010] S2. Multipath propagation modeling based on transmission requirements, and path analysis and interference analysis of signal propagation in the environment;

[0011] S3. Dynamically adjust spectrum allocation and scheduling to optimize signal transmission effects based on the results of multipath propagation modeling and interference analysis;

[0012] S4. Optimize channel coding and modulation based on spectrum allocation and scheduling;

[0013] S5. Based on the channel coding and modulation optimization scheme, coordinated transmission and network topology optimization are performed;

[0014] S6. Use space-time coding and beamforming technology to further improve the spatial reliability of signal transmission;

[0015] S7. Design a low-latency protocol to achieve signal enhancement and stable transmission.

[0016] To further optimize the technical solution, in step S1:

[0017] Environmental adaptability analysis includes identification of environmental factors such as obstacle density, terrain changes, weather conditions, and signal interference sources;

[0018] Transmission requirements assessment includes signal attenuation, signal frequency requirements, bandwidth requirements, and optimal signal transmission path;

[0019] The transmission requirements formulated include the minimum threshold of signal quality, the required transmission rate, and the fault tolerance of the signal.

[0020] To further optimize the technical solution, in step S2, the multipath propagation modeling includes establishing a signal attenuation model, a reflection and refraction model, and a multipath interference model, which are used to predict the signal propagation behavior under different environmental conditions.

[0021] To further optimize the technical solution, in the multipath propagation modeling:

[0022] The signal attenuation model is as follows:

[0023] ;

[0024] in,

[0025] : received signal power;

[0026] : Transmit signal power;

[0027] : Environmental attenuation coefficient, which depends on the signal frequency and propagation medium;

[0028] : The distance between the transmitter and the receiver;

[0029] : Path loss index, ranging from 2 to 4;

[0030] : Dynamic interference factor, considering the impact of dynamic signal sources in the environment;

[0031] : Relative angle, describing the angle between the receiving antenna and the main interference source;

[0032] The reflection and refraction model is as follows:

[0033] ;

[0034] in,

[0035] : No. Signal strength after secondary reflection or refraction;

[0036] : incident signal strength;

[0037] : Reflection retention coefficient, which indicates the proportion of the signal retained after each reflection;

[0038] : Reflection attenuation factor, which describes the signal attenuation rate in multiple reflections;

[0039] : number of reflections, ;

[0040] : Refraction penetration coefficient, which describes the energy ratio of the signal penetrating the medium;

[0041] : The angle between the incident angle and the normal of the reflecting surface;

[0042] : The refraction path damping coefficient, which represents the attenuation effect of the medium on signal propagation;

[0043] : propagation distance of the refraction path;

[0044] The multipath interference model is as follows:

[0045] ;

[0046] in,

[0047] : The total power of the received signal;

[0048] : No. The strength of the signal along the reflection path;

[0049] : No. The signal frequency of each path;

[0050] :time;

[0051] : No. The signal phase of each path;

[0052] : No. The strength of the signal along the refracted path;

[0053] : No. The signal attenuation factor of the refraction path;

[0054] : No. The refraction propagation distance of each path;

[0055] : Total number of reflection paths;

[0056] : Total number of refraction paths.

[0057] To further optimize the technical solution, in step S3, in the dynamic adjustment of spectrum allocation and scheduling, dynamic spectrum management technology is used to adjust the frequency band usage of the signal according to the real-time signal quality and interference conditions.

[0058] To further optimize the technical solution, in step S4, the channel coding and modulation optimization includes: selecting different channel coding schemes according to real-time channel quality and transmission requirements, the channel coding schemes include convolutional coding, Turbo coding, and LDPC coding, and selecting different modulation schemes, the modulation schemes include QPSK, 16QAM, and 64QAM.

[0059] To further optimize the technical solution, in step S5, the collaborative transmission and network topology optimization are performed using a collaborative transmission model and a network topology optimization model;

[0060] The cooperative transmission model comprehensively considers the impact of node bandwidth allocation weight, bit error rate, link delay and interference coefficient on transmission efficiency;

[0061] The network topology optimization model optimizes the topology of the entire network by dynamically adjusting the link selection between nodes.

[0062] To further optimize this technical solution, the collaborative transmission model is as follows:

[0063] ;

[0064] in,

[0065] : Cooperative transmission efficiency;

[0066] : No. The bandwidth allocation weight of each node;

[0067] : No. The transmission rate of each node;

[0068] : No. The bit error rate of each node;

[0069] : The total number of nodes participating in cooperative transmission;

[0070] : No. The transmission delay of the link;

[0071] : No. The interference coefficient of the link indicates the interference degree of the link;

[0072] : The total number of transmission links;

[0073] The network topology optimization model is as follows:

[0074] ;

[0075] in,

[0076] : Unitized network transmission cost;

[0077] :node To Node The link length;

[0078] :node To Node The link transmission power of

[0079] :link The reliability loss rate, ;

[0080] : The total number of nodes in the network.

[0081] To further optimize the technical solution, in step S6:

[0082] Space-time coding, which uses the spatial dimension to improve the robustness of signals by encoding the signals sent from multiple antennas;

[0083] Beamforming adjusts the transmission direction and power of multiple antennas to form a directional signal beam, which is used to reduce interference during signal propagation.

[0084] To further optimize the technical solution, in step S7, the low-latency transmission protocol adopts a first-come-first-served FCFS queue strategy and is based on priority scheduling to ensure that important data is transmitted first.

[0085] In a second aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of a method for signal enhancement and stable transmission of a wireless image recorder for complex environments as described in the first aspect of the present invention are implemented.

[0086] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of a method for signal enhancement and stable transmission of a wireless image recorder for complex environments as described in the first aspect of the present invention are implemented.

[0087] Compared with the prior art, the present invention provides a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment, which has the following beneficial effects:

[0088] This method of signal enhancement and stable transmission of wireless image recorders in complex environments effectively improves the signal enhancement and stable transmission performance of wireless image recorders in complex environments by introducing multipath propagation modeling, collaborative transmission model and network topology optimization model, and using artificial intelligence machine learning and deep learning technology. Compared with the existing technology, this method can achieve accurate path loss prediction, reflection and refraction path energy optimization, and dynamic management of multipath interference, significantly reducing the transmission bit error rate. At the same time, through the global optimization of collaborative transmission efficiency and network topology structure, the optimal allocation of resources and dynamic adjustment of transmission paths are achieved, thereby improving the overall transmission efficiency and stability, and meeting the needs of high-quality signal transmission in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0090] Figure 1 A schematic diagram of a flow chart of a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment proposed by the present invention;

[0091] Figure 2This is a model composition diagram for multipath propagation modeling of a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment proposed by the present invention. DETAILED DESCRIPTION

[0092] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0093] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0094] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

[0095] Embodiment 1:

[0096] Reference Figure 1-2 , which is the first embodiment of the present invention, and provides a method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment, comprising the following steps:

[0097] S1. Conduct environmental adaptability analysis and transmission demand assessment in complex environments and formulate transmission requirements.

[0098] In this embodiment, in the signal enhancement and stable transmission method of the wireless image transmission recorder, the first step is to perform an environmental adaptability analysis. This analysis first involves the identification of complex environmental factors, such as obstacle density, terrain changes, weather conditions, signal interference sources, etc. By collecting these environmental data, the possible impact on the wireless signal is evaluated, and the specific requirements of the system for signal transmission are clarified. The evaluation content includes but is not limited to the degree of signal attenuation, signal frequency requirements, bandwidth requirements, and optimal signal transmission paths. Based on these evaluation results, reasonable transmission requirements are formulated, such as the minimum threshold of signal quality, the required transmission rate, the fault tolerance of the signal, etc., to lay the foundation for the subsequent design of enhanced and stable transmission solutions.

[0099] S2. Multipath propagation modeling is performed based on transmission requirements to conduct path analysis and interference analysis of signal propagation in the environment.

[0100] In this embodiment, in wireless signal transmission facing complex environments, the signal may be affected by the multipath propagation effect during the propagation process, especially in environments such as cities or mountainous areas, where the signal reaches the receiving end through different paths, which may cause attenuation, distortion, interference and other problems. Multipath propagation modeling is performed, and multipath propagation modeling includes establishing a signal attenuation model, a reflection and refraction model, and a multipath interference model to predict the signal propagation behavior under different environmental conditions. At the same time, the surrounding interference sources are analyzed, such as signals from other wireless devices, noise sources, electromagnetic wave interference, etc. These factors will affect the quality of the wireless signal. The comprehensive signal propagation model and interference source analysis provide a theoretical basis for subsequent signal enhancement methods and help design effective interference suppression and signal stable transmission strategies.

[0101] In the multipath propagation modeling:

[0102] The signal attenuation model is as follows:

[0103] ;

[0104] in,

[0105] : Received signal power (W);

[0106] : Transmitted signal power (W);

[0107] : Environmental attenuation coefficient, which depends on the signal frequency and propagation medium;

[0108] : The distance between the transmitter and the receiver (m);

[0109] : Path loss index, ranging from 2 to 4;

[0110] : Dynamic interference factor, considering the impact of dynamic signal sources in the environment;

[0111] : Relative angle, describing the angle between the receiving antenna and the main interference source.

[0112] By introducing dynamic interference factors and angular dependencies , used to describe the impact of interference signals on received power in complex environments. Dynamic interference factor During the calculation, the power proportion of the interference signal in the received signal is quantified to obtain the influence of the interference signal in the total received signal, that is, the interference proportion; the obtained interference proportion is smoothed, and the time-weighted average is calculated in combination with historical data to obtain the dynamic interference factor. In step S2, the environmental adaptability analysis is combined to calculate , and Parameters such as: by measuring different distances The signal strength changes under the environment are combined with the signal frequency and the characteristics of the propagation medium to estimate the environmental attenuation coefficient. You can also select multiple points to measure the signal power and use the attenuation law to Fit the data to find . Conduct field tests according to different environment types (such as cities, open areas, forests, etc.) to measure the relationship between path loss and distance. By fitting the actual measured signal attenuation curve, the path loss index is obtained. This model is used to estimate the power level of the signal at the receiving end, providing basic data for subsequent steps (such as channel coding).

[0113] The reflection and refraction model is as follows:

[0114] ;

[0115] in,

[0116] : No. Signal strength after the first reflection or refraction (W);

[0117] : incident signal intensity (W);

[0118] : Reflection retention coefficient, which indicates the proportion of the signal retained after each reflection;

[0119] : Reflection attenuation factor, which describes the signal attenuation rate in multiple reflections;

[0120] : number of reflections, ;

[0121] : Refraction penetration coefficient, which describes the energy ratio of the signal penetrating the medium;

[0122] : angle of incidence, the angle between the incident angle and the normal of the reflecting surface (radians);

[0123] : The refraction path damping coefficient, which represents the attenuation effect of the medium on signal propagation;

[0124] : Refraction path propagation distance (m).

[0125] This model is used to describe the signal reflection and refraction process. , Describe the reflection characteristics, , etc. to describe the refractive properties. By inputting the parameters, the , , , The value of is used to analyze the comprehensive impact of multiple reflections and refractions on signal strength in complex environments.

[0126] Reflection retention factor Determined by the material properties of the reflecting surface, surface roughness, and the frequency of the incident signal; refraction penetration coefficient Determined by the energy ratio of the signal after passing through the medium, it can be obtained through experimental measurement; reflection attenuation factor Through the measurement of multiple known reflection and refraction path signals in the environment, the optimal coefficient of the exponential decay model is fitted; the refraction path damping coefficient Combined with the path loss index, the propagation loss of the signal in different media is calculated to derive .

[0127] The multipath interference model is as follows:

[0128] ;

[0129] in,

[0130] : Total power of received signal (W);

[0131] : No. The strength of the reflected path signal (W);

[0132] : No. Signal frequency of each path (rad / s);

[0133] : time (s);

[0134] : No. The signal phase of each path (rad);

[0135] : No. The intensity of the signal along the refraction path (W);

[0136] : No. The signal attenuation factor of the refraction path;

[0137] : No. The refraction propagation distance of each path (m);

[0138] : Total number of reflection paths;

[0139] : Total number of refraction paths.

[0140] By modeling and estimating the parameters of the signal propagation path, the power contribution of each path is calculated and the total power of the received signal is obtained, providing data support for dynamic spectrum allocation (step S3) and cooperative transmission (step S5). For example, in dynamic spectrum allocation, the total power of the received signal calculated by the multipath interference model can provide the spectrum allocation strategy with data on the impact of different signal sources in the environment on spectrum usage, which helps to make accurate spectrum resource scheduling in a dynamic environment and ensure efficient and stable signal transmission. Cooperative transmission relies on collaboration and resource sharing between different nodes, and the efficiency of this collaboration is affected by signal quality and interference. Through the updated multipath interference model, the total power of the received signal obtained provides interference intensity data for each path (such as the signal strength of the reflection path and the refraction path). These data can help analyze the interference between nodes, thereby adjusting the cooperative transmission strategy and optimizing the bandwidth allocation and transmission path selection of each node. Ultimately, the efficiency of cooperative transmission can be improved and the negative impact of multipath interference can be reduced.

[0141] The three models jointly construct a mathematical description of signal propagation in complex environments, and are used to evaluate signal strength, reflection and refraction loss, and multipath interference. The output parameters of each model (such as received signal power) can be used as input for the next step of optimization (such as dynamic spectrum allocation and scheduling).

[0142] S3. Based on the results of multipath propagation modeling and interference analysis, dynamically adjust spectrum allocation and scheduling to optimize signal transmission effects.

[0143] In this embodiment, a dynamic signal spectrum allocation and priority scheduling scheme is designed based on the results of the analysis. In a complex environment, signal spectrum resources are limited and frequently interfered with, so it is necessary to reasonably allocate available spectrum to maximize signal quality and bandwidth efficiency. In this process, dynamic spectrum management technology is used to adjust the frequency band usage of the signal according to the real-time signal quality and interference conditions. For example, when the signal quality of a certain frequency band is poor, it can automatically switch to a frequency band with less interference. At the same time, a priority scheduling strategy is used to prioritize different data streams to ensure that important signals (such as high-quality video streams) are transmitted first to reduce delays and packet loss. This strategy monitors the signal status in real time and intelligently adjusts the transmission frequency and priority to ensure stable and efficient transmission of the signal.

[0144] S4. Channel coding and modulation optimization is performed based on spectrum allocation and scheduling.

[0145] In this embodiment, an adaptive channel coding and modulation scheme is designed based on the dynamic spectrum allocation and priority scheduling scheme. In a complex environment, the transmission quality of the signal may be affected by various factors, resulting in an increase in the bit error rate. In order to improve the reliability and efficiency of transmission, adaptive channel coding and modulation technology is essential. According to the real-time channel quality (such as signal-to-noise ratio) and transmission requirements, the system can select different channel coding schemes (such as convolutional coding, Turbo coding, LDPC coding, etc.) and modulation schemes (such as QPSK, 16QAM, 64QAM, etc.). This adaptive mechanism can dynamically adjust the coding and modulation methods according to the changes in the channel to ensure that the best signal quality and transmission rate can be provided under different environmental conditions.

[0146] The selection of channel coding and modulation schemes is dynamically adjusted based on the real-time channel quality (such as signal-to-noise ratio) and spectrum allocation results. Specifically, after spectrum resource allocation is completed, the system will evaluate the current channel conditions and select appropriate channel coding and modulation methods. For example:

[0147] Channel coding selection: When the channel quality is poor (such as low signal-to-noise ratio), the system will give priority to channel coding with higher strength (such as LDPC coding or Turbo coding) to enhance anti-interference ability; when the channel quality is good (such as high signal-to-noise ratio), you can choose coding with higher efficiency but poorer anti-interference (such as convolutional coding).

[0148] Modulation scheme selection: The system dynamically selects the modulation scheme based on the allocation of spectrum resources and the real-time quality of the channel. For example, when the signal-to-noise ratio is low, low-order modulation (such as QPSK) is used to increase the resistance to bit error rate; when the channel quality is good, high-order modulation (such as 16QAM, 64QAM) is selected to increase the data transmission rate.

[0149] S5. Based on the channel coding and modulation optimization scheme, collaborative transmission and network topology optimization are carried out.

[0150] To further improve the transmission stability of wireless image transmission signals, a collaborative transmission and network topology optimization solution is designed. In a complex environment, the transmission capacity of a single node may not be sufficient to cover a large area or penetrate many obstacles.

[0151] In this embodiment, the collaborative transmission and network topology optimization are performed using a collaborative transmission model and a network topology optimization model;

[0152] The cooperative transmission model comprehensively considers the impact of node bandwidth allocation weight, bit error rate, link delay and interference coefficient on transmission efficiency;

[0153] The network topology optimization model optimizes the topology of the entire network by dynamically adjusting the link selection between nodes.

[0154] The collaborative transmission model is as follows:

[0155] ;

[0156] in,

[0157] : Cooperative transmission efficiency;

[0158] : No. Bandwidth allocation weight of each node (Hz);

[0159] : No. The transmission rate of each node (bps);

[0160] : No. The bit error rate of each node;

[0161] : The total number of nodes participating in cooperative transmission;

[0162] : No. Transmission delay of the link (s);

[0163] : No. The interference coefficient of the link indicates the interference degree of the link;

[0164] : The total number of transmission links.

[0165] The collaborative transmission model comprehensively considers the impact of node bandwidth allocation weight, bit error rate, link delay and interference coefficient on transmission efficiency from the overall perspective of the system. and optimize the link interference coefficient , to achieve efficient collaborative transmission.

[0166] Bandwidth allocation weight Based on the channel quality of each node (such as signal-to-noise ratio or signal-to-noise ratio gain), nodes with better channel quality can be allocated higher bandwidth to increase data transmission rate. The bandwidth of nodes with low signal-to-noise ratio is reduced to avoid excessive consumption of system resources.

[0167] Optimize link interference coefficient It depends on the activities of other nodes in the current network environment, especially the interference of adjacent nodes. If the interference of a link is large, the interference coefficient will increase, which means the effective transmission capacity of the link decreases. In this case, the system will adjust the transmission path to avoid high interference areas or reduce the bandwidth allocation of the interfering link.

[0168] In step S5, calculate and After determining the parameters such as the transmission efficiency, the model is used to quantify the transmission efficiency and provide decision support for beamforming and dynamic spectrum allocation strategies.

[0169] Efficiency in the Co-Transfer Model Guide link selection and power optimization, and constrain the optimization process of network transmission costs.

[0170] The network topology optimization model is as follows:

[0171] ;

[0172] in,

[0173] : Unitized network transmission cost;

[0174] :node To Node Link length (m);

[0175] :node To Node Link transmission power (W);

[0176] :link The reliability loss rate, ;

[0177] : The total number of nodes in the network.

[0178] The model minimizes , optimize the network topology to achieve the best transmission performance. Link length , transmission power , reliability loss rate It is the key parameter that determines the transmission cost. The optimization process dynamically adjusts the transmission path and power allocation to avoid the selection of overly long links and high loss rate paths, thereby improving the overall transmission efficiency and stability. In step S5, the model combines the results of the collaborative transmission model , perform link optimization configuration and provide decision-making basis for signal transmission in complex environments.

[0179] Output of the network topology optimization model Provides a basis for dynamic spectrum allocation and signal routing planning.

[0180] S6. Use space-time coding and beamforming technology to further improve the spatial reliability of signal transmission.

[0181] In this embodiment, in a complex environment, multipath propagation and signal interference seriously affect the quality of wireless signals. In order to meet this challenge, space-time coding and beamforming technology are introduced to improve the spatial transmission performance of the signal. Space-time coding is a multi-antenna technology that can encode signals sent by multiple antennas and use the spatial dimension to improve the robustness of the signal. By rationally designing space-time coding, signal attenuation and interference can be reduced in complex environments to ensure accurate data transmission. Beamforming technology adjusts the transmission direction and power of multiple antennas to form a directional signal beam, thereby reducing the interference to the signal during propagation. The combination of the two can significantly improve the spatial reliability of the signal and effectively reduce signal loss and attenuation in a multipath environment.

[0182] S7. Design a low-latency protocol to achieve signal enhancement and stable transmission.

[0183] In this embodiment, as the real-time requirements of the wireless image transmission recorder system increase, a low-latency transmission protocol is designed. In order to achieve efficient and stable signal transmission in a complex environment, the system needs to have a low-latency response capability. This requires that during the data transmission process, operations such as signal reception, encoding, scheduling, and forwarding can be quickly processed to minimize signal transmission delays. The design of a low-latency transmission protocol includes an optimized queue management strategy, a fast data transmission path selection mechanism, and an efficient signal processing algorithm. For example, the use of a first-come, first-served (FCFS) queue strategy combined with priority scheduling can ensure that important data is transmitted first. At the same time, the protocol also needs to effectively manage network congestion to avoid data loss or quality degradation due to excessive latency.

[0184] Embodiment 2:

[0185] This embodiment also provides a computer device, which is suitable for a method for enhancing and stabilizing the transmission of wireless image transmission recorders in complex environments, and includes a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement a method for enhancing and stabilizing the transmission of wireless image transmission recorders in complex environments as proposed in the above embodiment.

[0186] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, a method for enhancing and stabilizing the signal transmission of a wireless image transmission recorder in a complex environment as proposed in the above embodiment is implemented.

[0187] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0188] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0189] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0190] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk case (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0191] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by 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 with a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit with a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0192] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for signal enhancement and stable transmission of a wireless image transmission recorder in a complex environment, characterized in that: The following steps are involved: S1. Conduct environmental adaptability analysis and transmission demand assessment in complex environments and formulate transmission requirements; S2. Multipath propagation modeling based on transmission requirements, and path analysis and interference analysis of signal propagation in the environment; S3. Dynamically adjust spectrum allocation and scheduling to optimize signal transmission effects based on the results of multipath propagation modeling and interference analysis; S4. Optimize channel coding and modulation based on spectrum allocation and scheduling; S5. Based on the channel coding and modulation optimization scheme, coordinated transmission and network topology optimization are performed; S6. Use space-time coding and beamforming technology to further improve the spatial reliability of signal transmission; S7. Design a low-latency protocol to achieve signal enhancement and stable transmission.

2. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment, characterized in that: In step S1: Environmental adaptability analysis includes identification of environmental factors such as obstacle density, terrain changes, weather conditions, and signal interference sources; Transmission requirements assessment includes signal attenuation, signal frequency requirements, bandwidth requirements, and optimal signal transmission path; The transmission requirements formulated include the minimum threshold of signal quality, the required transmission rate, and the fault tolerance of the signal.

3. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: In step S2, multipath propagation modeling includes establishing a signal attenuation model, a reflection and refraction model, and a multipath interference model, which are used to predict signal propagation behavior under different environmental conditions.

4. According to claim 3, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: In the multipath propagation modeling: The signal attenuation model is as follows: ； in, : received signal power; : Transmit signal power; : Environmental attenuation coefficient, which depends on the signal frequency and propagation medium; : The distance between the transmitter and the receiver; : Path loss index, ranging from 2 to 4; : Dynamic interference factor, considering the impact of dynamic signal sources in the environment; : Relative angle, describing the angle between the receiving antenna and the main interference source; The reflection and refraction model is as follows: ； in, : No. Signal strength after secondary reflection or refraction; : incident signal strength; : Reflection retention coefficient, which indicates the proportion of the signal retained after each reflection; : Reflection attenuation factor, which describes the signal attenuation rate in multiple reflections; : number of reflections, ; : Refraction penetration coefficient, which describes the energy ratio of the signal penetrating the medium; : The angle between the incident angle and the normal of the reflecting surface; : The refraction path damping coefficient, which represents the attenuation effect of the medium on signal propagation; : propagation distance of the refraction path; The multipath interference model is as follows: ； in, : The total power of the received signal; : No. The strength of the signal along the reflection path; : No. The signal frequency of each path; :time; : No. The signal phase of each path; : No. The strength of the signal along the refracted path; : No. The signal attenuation factor of the refraction path; : No. The refraction propagation distance of each path; : total number of reflection paths; : Total number of refraction paths.

5. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: In step S3, in the dynamic adjustment of spectrum allocation and scheduling, dynamic spectrum management technology is adopted to adjust the frequency band usage of the signal according to the real-time signal quality and interference conditions.

6. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: In step S4, channel coding and modulation optimization includes: selecting different channel coding schemes according to real-time channel quality and transmission requirements, the channel coding schemes include convolutional coding, Turbo coding, and LDPC coding, and selecting different modulation schemes, the modulation schemes include QPSK, 16QAM, and 64QAM.

7. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: In step S5, the collaborative transmission and network topology optimization are performed using a collaborative transmission model and a network topology optimization model; The cooperative transmission model comprehensively considers the impact of node bandwidth allocation weight, bit error rate, link delay and interference coefficient on transmission efficiency; The network topology optimization model optimizes the topology of the entire network by dynamically adjusting the link selection between nodes.

8. According to claim 7, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment is characterized in that: The collaborative transmission model is as follows: ； in, : Cooperative transmission efficiency; : No. The bandwidth allocation weight of each node; : No. The transmission rate of each node; : No. The bit error rate of each node; : The total number of nodes participating in cooperative transmission; : No. The transmission delay of the link; : No. The interference coefficient of the link indicates the interference degree of the link; : The total number of transmission links; The network topology optimization model is as follows: ； in, : Unitized network transmission cost; :node To Node The link length; :node To Node The link transmission power of :link The reliability loss rate, ; : The total number of nodes in the network.

9. According to claim 1, a method for enhancing and stabilizing the signal transmission of a wireless image recorder in a complex environment, characterized in that: In step S6: Space-time coding, which uses the spatial dimension to improve the robustness of signals by encoding the signals sent from multiple antennas; Beamforming adjusts the transmission direction and power of multiple antennas to form a directional signal beam, which is used to reduce interference during signal propagation.

10. The method for signal enhancement and stable transmission of a wireless image recorder in a complex environment according to claim 1, characterized in that: In step S7, the low-latency transmission protocol adopts a first-come-first-served FCFS queue strategy and is based on priority scheduling to ensure that important data is transmitted first.

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