Wireless operation terminal used in extreme environment

Abstract

The invention relates to the technical field of communication, and provides a wireless operation terminal used in an extreme environment, which comprises an environment sensing module, a dynamic stability control module, a communication link reconstruction module, an anti-interference processing module, an emergency recovery module, a man-machine interaction module, a heat dissipation reinforcement module and a power management module. All the modules are connected with one another and work cooperatively. The environment sensing module collects various environment parameters in real time and constructs a multi-dimensional coupling model; the dynamic stability control module generates an environmental adaptation coefficient based on the model; the communication link reconstruction module generates an optimal channel switching path according to the channel switching path; the anti-interference processing module performs nonlinear signal shaping; the emergency recovery module predicts a failure risk and triggers a multi-stage emergency strategy; the heat dissipation reinforcing module dynamically adjusts the heat conductivity of the phase change material; and the power management module constructs a fault prediction model and executes a dynamic power supply strategy. According to the invention, the stability and reliability of the equipment can be improved, and the adaptive capacity and the emergency processing capacity in a complex environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and more particularly, to a wireless operation terminal used in extreme environments. Background Art

[0002] With the widespread application of modern communication technologies, wireless operation terminals, as important tools for information exchange, have been widely used in various scenarios. However, with the continuous expansion of application scenarios, especially the increasing demand for applications in extreme environments, such as those in complex environments with high cold, high temperature, high humidity, strong vibration, low air pressure, and strong electromagnetic interference, existing wireless operation terminals face many challenges. Existing wireless operation terminals are mostly designed for conventional environments, and their stability, reliability, and communication performance in extreme environments often fail to meet actual needs. For example, the heat dissipation design of existing terminals cannot effectively reduce the device temperature in high-temperature environments, causing the device to overheat, affecting performance or even damage. In environments with strong electromagnetic interference, communication links are easily interfered with, resulting in data transmission errors or interruptions. And under extreme temperature fluctuations, the dynamic stability of the device is difficult to ensure, making it prone to failure. In addition, existing terminals lack effective emergency recovery mechanisms when faced with sudden environmental changes. Once the device fails, recovery is difficult.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the existing technology: the existing wireless operation terminals have insufficient adaptability in extreme environments and are unable to effectively cope with complex and changeable environmental factors, resulting in poor equipment stability and low communication reliability, and lack of an efficient emergency recovery mechanism, making it difficult to meet the needs of high-reliability applications. Summary of the Invention

[0004] The present invention provides a wireless operation terminal for use in extreme environments, comprising:

[0005] Environmental perception module, dynamic stability control module, communication link reconstruction module, anti-interference processing module, emergency recovery module, human-computer interaction module, heat dissipation reinforcement module and power management module;

[0006] The environmental perception module collects temperature, humidity, vibration, air pressure and electromagnetic interference intensity parameters in real time and constructs a multi-dimensional environmental coupling model;

[0007] The dynamic stability control module executes a dynamic weight allocation algorithm based on a multi-dimensional environmental coupling model to generate an environmental adaptation coefficient;

[0008] The communication link reconstruction module establishes a channel migration cost model according to the environmental adaptation coefficient and generates an optimal channel switching path;

[0009] The anti-interference processing module performs nonlinear signal shaping based on the electromagnetic interference intensity parameter to generate an anti-interference communication waveform;

[0010] The emergency recovery module predicts the risk of equipment failure by the mutation rate of environmental parameters and triggers a multi-level emergency strategy;

[0011] The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material according to the temperature gradient;

[0012] The power management module combines environmental parameters and device status to build a fault prediction model and implement a dynamic power supply strategy.

[0013] Furthermore, the method for constructing the multidimensional environmental coupling model includes:

[0014] Step D1: Calculate the environmental parameter interaction factor matrix Φ, whose elements Represents the coupling coefficient of the i-th parameter to the j-th parameter, satisfying Where σ is the environmental type correction factor, P i 、P j is the normalized parameter value, δ is the coupling attenuation constant;

[0015] Step D2: Establish a dynamic weight distribution function Where V(t) is the parameter change rate vector, U(t) is the device state vector, represents element-wise product;

[0016] Step D3: Update the model parameters by sliding the time window, with the window length T = τ·(1+log(E risk )), τ is the reference time constant, E risk It is the failure risk level of the previous cycle.

[0017] Furthermore, the dynamic weight allocation algorithm includes:

[0018] Step W1: Calculate the parameter deviation vector Δ=[ΔT, ΔH, ΔV, ΔP, ΔE] according to the current environmental parameters, where ΔT=|T curr -T ref | / (T max -T min ), ΔH, ΔV, ΔP, and ΔE were normalized using the same method;

[0019] Step W2: Generate comprehensive deviation index G = ∑(w i ·Δ i 2 )+ρ·max(Δ i ), where w i is the real-time weight output by the dynamic weight allocation function W(t), and ρ is the extreme value sensitivity coefficient;

[0020] Step W3: When G exceeds the dynamic threshold G th When the stability adjustment instruction is triggered, the dynamic threshold G th =G base ×(1+tanh(α·dG / dt)), α is the adjustment rate coefficient, and dG / dt is the rate of change of G.

[0021] Furthermore, the construction of the channel migration cost model includes:

[0022] Step C1: Define the channel switching cost function C ij =μ1·|Q i -Q j |μ2·t switch +μ3·(1-S ij ), where Q i , Q j is the channel quality indicator, t switch is the switching delay, S ij is the channel state similarity, μ1-μ3 are the cost weight coefficients;

[0023] Step C2: Construct a channel migration graph, where the nodes represent channels and the edge weight is C ij ;

[0024] Step C3: Use the constrained shortest path algorithm to find the optimal path. The constraints include: the number of path hops ≤ N max , cumulative delay ≤ T max , path stability index ≥ S min ;

[0025] Step C4: Dynamically update the channel quality indicator Q i =ε·Q inst +(1-ε)·Q hist , where Q hist is the instantaneous measurement value, Q hist is the historical weighted average, and ε is the forgetting factor.

[0026] Furthermore, the nonlinear signal shaping process includes:

[0027] Step S1: Perform a joint time-frequency domain analysis on the received signal to extract the interference characteristic parameter set Ω = f peak ,BW,A rms , where f peak is the interference peak frequency, BW is the interference bandwidth, A rms is the RMS value of the interference amplitude;

[0028] Step S2: Construct the adaptive filter transfer function H(z)=K·(1+β·z -D) / (1+α·z -D ), where D is the delay parameter, α and β are notch coefficients dynamically adjusted according to Ω, and K is the gain compensation factor;

[0029] Step S3: Perform nonlinear phase compensation, the compensation amount Where γ is the environmental adaptation coefficient, f0 is the signal center frequency, and n is the nonlinear exponent.

[0030] Furthermore, the triggering method of the multi-level emergency strategy includes:

[0031] Step E1: Calculate the environmental parameter mutation rate vector V = [dT / dt, dH / dt, dV / dt], and obtain the mutation comprehensive index M = ||V||2·exp(σ·max(V)), where σ is the sensitivity coefficient;

[0032] Step E2: When M>M th1 Initiate the first-level emergency, reduce the communication rate and activate the backup power supply;

[0033] When M>M th2 When the secondary emergency is activated, switch to the anti-destruction topology network and activate emergency cooling;

[0034] When M>M th3 When the third level emergency is activated, data fuse protection is executed and equipment self-test is started;

[0035] Step E3: Generate an emergency strategy execution sequence to ensure that the time interval between adjacent strategy switching Δt ≥ t min , t min is the minimum stable time threshold.

[0036] Furthermore, the method for adjusting the thermal conductivity of the phase change material includes:

[0037] Step H1: Establish temperature gradient field model Where d is the thickness of the equipment shell;

[0038] Step H2: Calculate ideal thermal conductivity κ0 is the base thermal conductivity, ξ is the nonlinear adjustment coefficient;

[0039] Step H3: Control the lattice structure of the phase change material by current to make the actual thermal conductivity κ real =κ desired ×(1-e -t / τ ), τ is the response time constant, and t is the adjustment duration.

[0040] Furthermore, the construction of the fault prediction model includes:

[0041] Step F1: Collect power characteristic parameter set Ψ=Vripple ,I avg ,R internal , where V ripple is the voltage ripple, I avg is the average current, R internal is the internal resistance;

[0042] Step F2: construct a three-dimensional health space with the coordinate axis being the normalized Ψ parameter;

[0043] Step F3: Define the abnormality index

[0044]

[0045] Among them, w i is the parameter weight, ψ i is the value of the ith parameter in the power characteristic parameter set, ψ i_healthy is the value in the health state corresponding to the i-th parameter value;

[0046] Step F4: Use Hidden Markov Model to predict the failure probability P fault =1-exp(-λ·A·t), λ is the aging rate coefficient.

[0047] Furthermore, the method for constructing the indestructible topology network includes:

[0048] Step N1: According to the node remaining energy E i and link quality L ij Calculate node weight W i =log(E i / E min )×∑(L ij / L max );

[0049] Step N2: Build a virtual backbone network, select W i The largest first k nodes are used as cluster heads, k = ceil(N × ρ), N is the total number of nodes, ρ is the network density coefficient;

[0050] Step N3: Establish multi-path routing table, path reliability R path =Π(1-p j ), p j is the probability of each link being interrupted.

[0051] Furthermore, the human-computer interaction module performs:

[0052] Step O1: Detect the operator's glove deformation through the pressure sensor array and calculate the operation force vector F = [f x ,f y ,f z ];

[0053] Step O2: When ||F||2>F safe When the fault protection is activated, the reverse tactile feedback force F is generated. fb =-k·(FF safe )·F / ||F||;

[0054] Step O3: Adaptively adjust the operation interface according to the environmental risk level, and display element contrast C = C0×(1+η·E risk ), η is the visual enhancement coefficient.

[0055] The above-described embodiments of the present invention have at least the following beneficial effects: The wireless operation terminal of the present invention, by integrating multiple functional modules, can effectively improve the device's operating performance in extreme environments. The environmental perception module can collect multiple environmental parameters in real time and construct a multidimensional coupling model, providing a basis for dynamic adjustment of the device, thereby enhancing the device's adaptability to complex environments. The dynamic stability control module executes a dynamic weight allocation algorithm based on the multidimensional environmental coupling model to generate a precise environmental adaptability coefficient, thereby optimizing the device's operating state and ensuring its stability under extreme conditions. The communication link reconstruction module establishes a channel migration cost model based on the environmental adaptability coefficient, which can generate an optimal channel switching path, thereby improving the reliability and anti-interference capability of the communication link. The anti-interference processing module uses nonlinear signal shaping to generate an anti-interference communication waveform, further improving communication quality and ensuring the accuracy and integrity of data transmission. The emergency recovery module can predict the risk of device failure based on the environmental parameter mutation rate and trigger a multi-level emergency strategy, effectively reducing the risk of device failure under sudden environmental changes and improving the device's emergency response and survivability. The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material based on the temperature gradient, effectively controlling the device temperature and ensuring stable operation in high-temperature environments or environments with large temperature differences. The power management module combines environmental parameters and device status to build a fault prediction model and implement a dynamic power supply strategy, which can optimize the device's energy consumption management, extend the device's service life, and improve the device's reliability in extreme environments.

[0056] In addition, the human-computer interaction module detects the deformation of the operator's gloves through a pressure sensor array, calculates the operation force vector, and activates misoperation protection when necessary, generating reverse tactile feedback force, thereby improving the accuracy and safety of the operation. At the same time, the module can also adaptively adjust the operation interface according to the environmental risk level and enhance the contrast of display elements, which can improve the operator's visual experience and increase operational efficiency. Overall, the wireless operation terminal of the present invention can enhance the comprehensive performance of the device in extreme environments through the collaborative work of various modules, meeting the application requirements of high reliability and high adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0058] Figure 1 This is a structural diagram of a wireless operation terminal for use in extreme environments provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0060] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0061] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0062] Reference below Figure 1 , Figure 1 This is a schematic diagram of the structure of a wireless operation terminal for use in extreme environments provided by an embodiment of the present invention. Figure 1 As shown, a wireless operation terminal 100 for use in extreme environments includes:

[0063] Environmental perception module 101, dynamic stability control module 102, communication link reconstruction module 103, anti-interference processing module 104, emergency recovery module 105, human-computer interaction module 106, heat dissipation reinforcement module 107 and power management module 108;

[0064] The environmental perception module is connected to the dynamic stability control module and the emergency recovery module, the dynamic stability control module is respectively connected to the communication link reconstruction module and the heat dissipation reinforcement module, the communication link reconstruction module is connected to the anti-interference processing module, the anti-interference processing module is connected to the wireless communication unit, and the emergency recovery module is connected to the human-computer interaction module and the power management module;

[0065] The environmental perception module collects temperature, humidity, vibration, air pressure and electromagnetic interference intensity parameters in real time and constructs a multi-dimensional environmental coupling model;

[0066] The dynamic stability control module executes a dynamic weight allocation algorithm based on a multi-dimensional environmental coupling model to generate an environmental adaptation coefficient;

[0067] The communication link reconstruction module establishes a channel migration cost model according to the environmental adaptation coefficient and generates an optimal channel switching path;

[0068] The anti-interference processing module performs nonlinear signal shaping based on the electromagnetic interference intensity parameter to generate an anti-interference communication waveform;

[0069] The emergency recovery module predicts the risk of equipment failure by the mutation rate of environmental parameters and triggers a multi-level emergency strategy;

[0070] The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material according to the temperature gradient;

[0071] The power management module combines environmental parameters and device status to build a fault prediction model and implement a dynamic power supply strategy.

[0072] It should be noted that the wireless operation terminal of the present invention is designed for use in extreme environments. Its core lies in ensuring the stability and reliability of the device through the collaborative operation of multiple modules. The environmental perception module is the foundation of the entire system. It can collect parameters such as temperature, humidity, vibration, air pressure, and electromagnetic interference intensity in real time, and construct a multidimensional environmental coupling model. The multidimensional environmental coupling model here refers to a mathematical model that comprehensively considers the mutual influence of multiple environmental factors and is used to describe the interaction between different environmental parameters. The dynamic stability control module executes a dynamic weight allocation algorithm based on this model to generate an environmental adaptability coefficient. This coefficient is used to dynamically adjust subsequent modules to adapt to changing environmental conditions. The communication link reconstruction module establishes a channel migration cost model based on the environmental adaptability coefficient and generates the optimal channel switching path to ensure the stability and reliability of the communication link. The anti-interference processing module performs nonlinear signal shaping based on the electromagnetic interference intensity parameter to generate an interference-resistant communication waveform, thereby improving communication quality. The emergency recovery module predicts the risk of device failure based on the mutation rate of environmental parameters and triggers a multi-level emergency response strategy to cope with sudden environmental changes. The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material based on the temperature gradient to ensure stable operation of the device in environments with high temperatures or large temperature differences. The power management module combines environmental parameters and device status to build a fault prediction model, and implements dynamic power supply strategies to optimize the energy consumption management of the device.

[0073] Specifically, the environmental perception module collects parameters such as temperature, humidity, vibration, air pressure, and electromagnetic interference intensity. These parameters are acquired in real time via a sensor network. For example, a temperature sensor can be a thermistor or semiconductor temperature sensor, used to measure temperature changes around the device; a humidity sensor can be a capacitive or resistive humidity sensor, used to detect the humidity level in the air; a vibration sensor can be an accelerometer, used to measure the intensity of vibration experienced by the device; a pressure sensor can be a piezoresistive sensor, used to measure ambient air pressure; and an electromagnetic interference sensor can be an electromagnetic induction sensor, used to detect the intensity of electromagnetic interference. A multidimensional environmental coupling model is constructed by calculating the interaction factor matrix between these parameters. The elements of this matrix represent the coupling coefficient of one parameter to another, reflecting the degree of mutual influence between different environmental factors. A dynamic weight allocation algorithm dynamically adjusts the weight of each parameter based on the parameter's rate of change and the device's status, thereby generating an environmental adaptability coefficient. For example, if a particular environmental parameter changes rapidly and has a significant impact on device operation, its weight will be increased accordingly. The communication link reconstruction module establishes a channel migration cost model based on the environmental adaptability coefficient by defining a channel switching cost function. This function takes into account factors such as channel quality, switching delay, and channel state similarity. A constrained shortest path algorithm is used to solve the optimal channel switching path, ensuring the stability and reliability of the communication link. The anti-interference processing module extracts a set of interference characteristic parameters through joint time-frequency domain analysis. It then constructs an adaptive filter transfer function and performs nonlinear phase compensation to generate an interference-resistant communication waveform. The emergency recovery module predicts device failure risk by calculating the mutation rate vector of environmental parameters and triggers multi-level emergency strategies based on the risk level, such as reducing communication rate, activating backup power, and switching to a destruction-resistant network topology. The heat dissipation reinforcement module establishes a temperature gradient field model to calculate the ideal thermal conductivity. It then dynamically adjusts the thermal conductivity of the phase change material by controlling the lattice structure of the phase change material through current to adapt to temperature changes. The power management module collects power supply characteristic parameters, constructs a three-dimensional health space, defines anomaly indicators, and uses a hidden Markov model to predict failure probability. This allows the module to implement dynamic power supply strategies and optimize device energy management.

[0074] Preferably, the sensor network in the environmental perception module can be deployed in a distributed manner to improve the accuracy and reliability of data collection. For example, temperature sensors can be distributed across multiple key locations on the device to obtain more comprehensive temperature information. In the dynamic weight allocation algorithm, the specific calculation methods for the parameter change rate vector and the device state vector can be optimized based on the actual application scenario. For example, the parameter change rate can be calculated by differentially calculating continuously acquired data, and the device state vector can include information such as the device's current operating mode and load status. The weight coefficient of the channel switching cost function in the communication link reconstruction module can be adjusted based on the actual communication environment to better reflect the impact of different factors on the communication link. For example, in environments with high electromagnetic interference, the weight of the channel quality indicator can be increased. The notch coefficient and gain compensation factor of the adaptive filter transfer function in the anti-interference processing module can be dynamically adjusted based on interference characteristic parameters to improve anti-interference effectiveness. The multi-level emergency response strategy in the emergency recovery module can be customized based on the specific application scenario of the device. For example, in military communications, a higher emergency response level can be added to cope with extreme situations. The phase change material in the heat dissipation reinforcement module can be made of high-performance composite materials to improve the adjustable range of thermal conductivity and response speed. The fault prediction model in the power management module can be combined with machine learning algorithms to further improve the accuracy of fault prediction.

[0075] In some embodiments, the method for constructing the multidimensional environmental coupling model includes:

[0076] Step D1: Calculate the environmental parameter interaction factor matrix Φ, whose elements Represents the coupling coefficient of the i-th parameter to the j-th parameter, satisfying Where σ is the environmental type correction factor, P i 、P j is the normalized parameter value, δ is the coupling attenuation constant;

[0077] Step D2: Establish a dynamic weight distribution function Where V(t) is the parameter change rate vector, U(t) is the device state vector, represents element-wise product;

[0078] Step D3: Update the model parameters by sliding the time window, with the window length T = τ·(1+log(E risk )), τ is the reference time constant, E risk It is the failure risk level of the previous cycle.

[0079] It should be noted that the method for constructing a multidimensional environmental coupling model in the present invention is achieved by calculating the environmental parameter interaction factor matrix. The elements of this matrix represent the coupling coefficient of one environmental parameter to another parameter, which is used to quantify the mutual influence between different environmental factors. The dynamic weight allocation function dynamically adjusts the weight of each parameter according to the parameter change rate and the device status to adapt to environmental changes. The sliding time window is used to update the model parameters, and its length is dynamically adjusted according to the failure risk level of the previous cycle to ensure the real-time and accuracy of the model. These steps together constitute the method for constructing a multidimensional environmental coupling model, which provides a basis for subsequent dynamic stability control.

[0080] Specifically, in the calculation of the environmental parameter interaction factor matrix, the coupling coefficient reflects the intensity of the interaction between environmental parameters. For example, the coupling coefficient between temperature and humidity can be determined through experimental data or historical data, indicating the degree of influence of temperature changes on humidity. The parameter change rate vector and the device state vector in the dynamic weight allocation function are key factors. The parameter change rate vector can be obtained by differential calculation of continuously collected environmental parameters, indicating the trend of changes in environmental parameters over time; the device state vector includes information such as the current working mode and load conditions of the device, which is used to reflect the operating status of the device in the current environment. The length of the sliding time window is dynamically adjusted according to the failure risk level of the previous cycle. The failure risk level can be evaluated through the device's fault history data or real-time monitoring data. The adjustment of the window length can ensure that the update frequency of the model parameters matches the severity of environmental changes, thereby improving the adaptability of the model.

[0081] Preferably, the calculation of the coupling coefficient can introduce an environmental type correction coefficient to adapt to different types of extreme environments. For example, in a high-cold environment, the coupling coefficient of temperature and humidity needs to be adjusted according to the extreme cold conditions to more accurately reflect the interaction between environmental factors. The calculation of the parameter change rate vector can use more complex algorithms, such as Kalman filtering, to improve the estimation accuracy of the change rate. The device state vector can be further refined to include information such as the health status of the device and the remaining power to more comprehensively reflect the operating status of the device. The adjustment strategy of the sliding time window can be combined with a machine learning algorithm to automatically optimize the window length based on historical data and real-time data to achieve more accurate model parameter updates.

[0082] In some embodiments, the dynamic weight allocation algorithm includes:

[0083] Step W1: Calculate the parameter deviation vector Δ=[ΔT, ΔH, ΔV, ΔP, ΔE] according to the current environmental parameters, where ΔT=|T curr -T ref | / (T max -T min), ΔH, ΔV, ΔP, and ΔE were normalized using the same method;

[0084] Step W2: Generate comprehensive deviation index G = ∑(w i ·Δ i 2 )+ρ·max(Δ i ), where w i is the real-time weight output by the dynamic weight allocation function W(t), and ρ is the extreme value sensitivity coefficient;

[0085] Step W3: When G exceeds the dynamic threshold G th When the stability adjustment instruction is triggered, the dynamic threshold G th =G base ×(1+tanh(α·dG / dt)), α is the adjustment rate coefficient, and dG / dt is the rate of change of G.

[0086] It should be noted that the dynamic weight allocation algorithm is a key technical means used in the present invention to optimize the operating status of equipment in extreme environments. The algorithm evaluates the deviation between the current environmental parameters and the normal operating parameters by calculating the parameter deviation vector, generates a comprehensive deviation index to quantify the overall degree of deviation, and triggers the stability adjustment instruction according to the dynamic threshold. Each element in the parameter deviation vector represents the degree of deviation of an environmental parameter, which is calculated by a normalization method to ensure the comparability between different parameters. The comprehensive deviation index combines the real-time weight and extreme value sensitivity coefficient of the dynamic weight allocation function. The dynamic weight allocation function dynamically adjusts the weight according to the parameter change rate and the equipment status, so that the equipment can flexibly adjust the operating strategy according to the current environment and its own status. The dynamic threshold is dynamically adjusted according to the adjustment rate coefficient and the change rate of the comprehensive deviation index to ensure that the equipment can respond in time when the environment changes and maintain stable operation.

[0087] Specifically, in the calculation of the parameter deviation vector, the deviation of each parameter is obtained by dividing the difference between the current measured value and the normal operating value by the parameter's range. The same normalization method is used to ensure that the deviations of different parameters are comparable. For example, the deviation of the temperature parameter can be calculated by dividing the difference between the current temperature and the normal operating temperature range by the temperature range. In the calculation of the comprehensive deviation index, the real-time weight of the dynamic weight allocation function is dynamically adjusted based on the parameter change rate and device status. The parameter change rate can be calculated by differentially calculating continuously collected data. The device state vector may include information such as the device's current operating mode and load conditions. The extreme value sensitivity coefficient is used to adjust the sensitivity of the comprehensive deviation index to extreme deviations and can be set according to the actual application scenario. In the calculation of the dynamic threshold, the adjustment rate coefficient is used to dynamically adjust the threshold based on the rate of change of the comprehensive deviation index, ensuring that the device can promptly trigger stability adjustment instructions when the environment changes.

[0088] Preferably, the calculation of the parameter deviation vector can introduce more environmental parameters, such as electromagnetic interference intensity, vibration frequency, etc., to more comprehensively reflect the environmental status of the device. In the calculation of the comprehensive deviation index, the real-time weight of the dynamic weight allocation function can be adjusted according to the different operating modes of the device. For example, in a high vibration environment, the weight of the vibration parameter can be appropriately increased. The extreme value sensitivity coefficient can be optimized according to the operational safety requirements of the equipment. For equipment with higher safety requirements, the extreme value sensitivity coefficient can be appropriately increased. In the calculation of the dynamic threshold, the adjustment rate coefficient can be adjusted according to the response capability of the equipment. For equipment with a faster response speed, the adjustment rate coefficient can be appropriately reduced to improve the adaptability of the equipment to environmental changes.

[0089] In some embodiments, the construction of the channel migration cost model includes:

[0090] Step C1: Define the channel switching cost function C ij =μ1·|Q i -Q j |μ2·t switch +μ3·(1-S ij ), where Q i , Q j is the channel quality indicator, t switch is the switching delay, S ij is the channel state similarity, μ1-μ3 are the cost weight coefficients;

[0091] Step C2: Construct a channel migration graph, where the nodes represent channels and the edge weight is C ij ;

[0092] Step C3: Use the constrained shortest path algorithm to find the optimal path. The constraints include: the number of path hops ≤ N max , cumulative delay ≤ T max , path stability index ≥ S min ;

[0093] Step C4: Dynamically update the channel quality indicator Q i =ε·Q inst +(1-ε)·Q hist , where Q hist is the instantaneous measurement value, Q hist is the historical weighted average, and ε is the forgetting factor.

[0094] It should be noted that the construction of the channel migration cost model is a key technology used in the present invention to optimize the stability and reliability of the communication link. The model quantifies the cost of channel switching by defining a channel switching cost function, including factors such as channel quality index, switching delay and channel state similarity. The channel quality index is used to evaluate the communication performance of the channel. The switching delay represents the time required to switch from one channel to another, and the channel state similarity reflects the degree of similarity between the two channels. By constructing a channel migration graph, the channel is used as a node, the edge weight is the channel switching cost, and the constrained shortest path algorithm is used to solve the optimal path, ensuring that the communication link can switch quickly and stably when the environment changes. Dynamically updating the channel quality index further improves the real-time and adaptability of the model. By combining instantaneous measurements and historical weighted averages, it can more accurately reflect the current state of the channel.

[0095] Specifically, in the channel switching cost function, channel quality indicators can be measured using parameters such as the signal-to-noise ratio (SNR) and bit error rate (BER), which reflect the communication performance of the channel. Switching latency can be determined by measuring the time delay during the channel switching process, typically in milliseconds. Channel state similarity can be calculated by comparing parameters such as the frequency, bandwidth, and modulation scheme of two channels. The higher the similarity, the lower the switching cost. Cost weight coefficients (w1, w2, and w3) are used to balance the impact of different factors on the switching cost and can be adjusted based on the actual communication environment. For example, in environments with high electromagnetic interference, the weight of the channel quality indicator can be appropriately increased. The constraints of the constrained shortest path algorithm include the number of path hops, the accumulated delay, and the path stability indicator. These constraints ensure the feasibility and stability of the switching path. The number of path hops limits the length of the switching path, the accumulated delay limits the total switching time, and the path stability indicator ensures the reliability of the switching path. When dynamically updating the channel quality indicator, the forgetting factor (α) is used to balance the impact of instantaneous measurements and the historical weighted average. The smaller the forgetting factor, the more sensitive the model is to instantaneous changes.

[0096] Preferably, the cost weight coefficient in the channel switching cost function can be optimized according to different application scenarios. For example, in a highly dynamic environment, the weight of the switching delay can be appropriately increased to reduce the communication interruption time. The constraints of the constrained shortest path algorithm can be adjusted according to the actual network topology. For example, in a multi-hop network, the limit on the number of path hops can be appropriately relaxed to improve the flexibility of path selection. When dynamically updating the channel quality index, more historical data can be introduced to predict the channel quality through machine learning algorithms to further improve the adaptability of the model. In addition, the calculation of channel state similarity can introduce more parameters, such as the type and intensity of channel interference, to more accurately reflect the similarity between channels.

[0097] In some embodiments, the nonlinear signal shaping process includes:

[0098] Step S1: Perform a joint time-frequency domain analysis on the received signal to extract the interference characteristic parameter set Ω = f peak ,BW,A rms , where f peak is the interference peak frequency, BW is the interference bandwidth, A rms is the RMS value of the interference amplitude;

[0099] Step S2: Construct the adaptive filter transfer function H(z)=K·(1+β·z -D ) / (1+α·z -D ), where D is the delay parameter, α and β are notch coefficients dynamically adjusted according to Ω, and K is the gain compensation factor;

[0100] Step S3: Perform nonlinear phase compensation, the compensation amount Where γ is the environmental adaptation coefficient, f0 is the signal center frequency, and n is the nonlinear exponent.

[0101] It should be noted that nonlinear signal shaping processing is a key technology used in the present invention to improve the anti-interference capability of communications. The processing process mainly includes performing a joint time-frequency domain analysis on the received signal, extracting the interference feature parameter set, constructing an adaptive filter transfer function, and performing nonlinear phase compensation. The joint time-frequency domain analysis can analyze the signal in both time and frequency dimensions at the same time, thereby more comprehensively identifying the interference characteristics. The interference feature parameter set includes interference peak frequency, interference bandwidth, and interference amplitude root mean square value, etc. These parameters are used to describe the characteristics of the interference signal. The adaptive filter transfer function dynamically adjusts the filter parameters according to the interference characteristics to effectively suppress the interference signal. Nonlinear phase compensation is used to correct the phase distortion caused by nonlinear factors during signal transmission, thereby improving signal integrity and communication quality.

[0102] Specifically, joint time-frequency domain analysis can be achieved through methods such as the short-time Fourier transform (STFT) or wavelet transform. The short-time Fourier transform can decompose the signal into two dimensions: time and frequency, facilitating the extraction of interference characteristics. The interference peak frequency refers to the frequency point where the interference signal has the highest energy in the frequency domain. The interference bandwidth represents the frequency range of the interference signal, and the root mean square value of the interference amplitude reflects the strength of the interference signal. The notch coefficient and gain compensation factor in the adaptive filter transfer function are dynamically adjusted based on the interference characteristics. For example, when the interference peak frequency is high, the notch coefficient can be increased accordingly to enhance the filtering effect. The environmental adaptation coefficient in the nonlinear phase compensation is used to adjust the compensation amount to adapt to different environmental conditions. The nonlinear index reflects the degree of signal phase distortion and can be set according to the actual communication environment.

[0103] Preferably, the time-frequency domain joint analysis can adopt more advanced methods, such as the Hilbert-Huang transform (HHT), to improve the accuracy of interference feature extraction. The notch coefficient and gain compensation factor in the adaptive filter transfer function can be optimized by machine learning algorithms to achieve more accurate interference suppression. The environmental adaptation coefficient in the nonlinear phase compensation can be dynamically adjusted according to the actual operating status of the equipment. For example, in a high electromagnetic interference environment, the environmental adaptation coefficient can be appropriately increased to enhance the compensation effect. In addition, the nonlinear index can be adaptively adjusted according to the modulation mode and transmission environment of the signal to improve the anti-interference ability of the signal.

[0104] In some embodiments, the triggering method of the multi-level emergency strategy includes:

[0105] Step E1: Calculate the environmental parameter mutation rate vector V = [dT / dt, dH / dt, dV / dt], and obtain the mutation comprehensive index M = ||V||2·exp(σ·max(V)), where σ is the sensitivity coefficient;

[0106] Step E2: When M>M th1 Initiate the first-level emergency, reduce the communication rate and activate the backup power supply;

[0107] When M>M th2 When the secondary emergency is activated, switch to the anti-destruction topology network and activate emergency cooling;

[0108] When M>M th3 When the third level emergency is activated, data fuse protection is executed and equipment self-test is started;

[0109] Step E3: Generate an emergency strategy execution sequence to ensure that the time interval between adjacent strategy switching Δt ≥ t min , t min is the minimum stable time threshold.

[0110] It should be noted that the triggering method of the multi-level emergency strategy is a key technology used in the present invention to deal with the risk of equipment failure in extreme environments. This method evaluates the degree of drastic changes in the environment in which the equipment is located by calculating the mutation rate vector of the environmental parameters, and triggers emergency strategies at different levels accordingly. The mutation rate vector reflects the speed of change of the environmental parameters over time, while the mutation comprehensive index is used to quantify the combined impact of these changes. Depending on the size of the mutation comprehensive index, the device can sequentially activate the first, second, and third level emergency strategies to gradually enhance the response measures to ensure the stable operation of the equipment and data security in extreme situations.

[0111] Specifically, the calculation of the environmental parameter mutation rate vector involves multiple environmental parameters, such as temperature, humidity, vibration, etc. The mutation rate of each parameter is obtained by calculating its time derivative. The calculation of the comprehensive mutation index combines the sensitivity coefficient, which is used to adjust the weight of the mutation rate of different parameters in the comprehensive index to reflect the degree of influence of different parameters on the operation of the equipment. The first-level emergency strategy includes reducing the communication rate and enabling the backup power supply to reduce equipment energy consumption and maintain basic functions; the second-level emergency strategy further switches to a destruction-resistant topology network and activates emergency heat dissipation to enhance the survivability and heat dissipation performance of the equipment; the third-level emergency strategy implements data fuse protection and initiates equipment self-test to prevent data loss and quickly locate faults. The emergency strategy execution sequence ensures that the time interval between adjacent strategy switches meets the minimum stability time threshold to avoid unstable equipment status due to excessively fast strategy switching.

[0112] Preferably, the calculation of the mutation rate vector can introduce more environmental parameters, such as electromagnetic interference intensity and air pressure change rate, to more comprehensively reflect the dynamic changes in the environment. The sensitivity coefficient can be adjusted according to the specific application scenario and importance of the equipment. For example, in scenarios with high requirements for communication quality, the sensitivity coefficient of communication-related parameters can be appropriately increased. The reduction in communication rate in the first-level emergency strategy can be dynamically adjusted according to the importance and urgency of the current communication task to balance communication performance and energy consumption. The anti-destruction topology network in the second-level emergency strategy can adopt a more advanced network architecture, such as a distributed self-healing network, to improve the network's anti-destruction capability and self-recovery capability. The data fuse protection mechanism in the third-level emergency strategy can be combined with data backup and recovery technology to ensure the integrity and recoverability of data in extreme situations.

[0113] In some embodiments, the method for adjusting the thermal conductivity of a phase change material includes:

[0114] Step H1: Establish temperature gradient field model Where d is the thickness of the equipment shell;

[0115] Step H2: Calculate ideal thermal conductivity κ0 is the base thermal conductivity, ξ is the nonlinear adjustment coefficient;

[0116] Step H3: Control the lattice structure of the phase change material by current to make the actual thermal conductivity κ real =κ desired ×(1-e -t / τ ), τ is the response time constant, and t is the adjustment duration.

[0117] It should be noted that the method for adjusting the thermal conductivity of the phase change material is a key technology used in the present invention to optimize the heat dissipation performance of the device. This method calculates the ideal thermal conductivity by establishing a temperature gradient field model, and dynamically adjusts the actual thermal conductivity of the phase change material by controlling the lattice structure of the phase change material through current. The temperature gradient field model is used to describe the temperature distribution inside the device and its changing law, providing a theoretical basis for thermal conductivity regulation. The ideal thermal conductivity is the optimal thermal conductivity value calculated based on the operating requirements and environmental conditions of the device. By controlling the lattice structure of the phase change material through current, dynamic adjustment of the thermal conductivity can be achieved to adapt to the heat dissipation requirements of the device under different operating conditions.

[0118] Specifically, the temperature gradient field model is established based on the thickness and temperature distribution of the device shell. The temperature gradient is calculated by measuring the temperature difference between the inside and outside of the device and combining it with the thermal conductivity characteristics of the shell. The calculation of ideal thermal conductivity takes into account factors such as the baseline thermal conductivity, the nonlinear adjustment coefficient, and the response time constant. The baseline thermal conductivity is the thermal conductivity of the phase change material under standard conditions. The nonlinear adjustment coefficient is used to adjust the amplitude of the thermal conductivity change. The response time constant reflects the rate of thermal conductivity adjustment. By controlling the lattice structure of the phase change material through electric current, the microstructure of the material can be changed, thereby achieving dynamic regulation of thermal conductivity. The actual thermal conductivity adjustment process is a dynamic equilibrium process. By controlling the magnitude and duration of the current, the actual thermal conductivity can be gradually brought close to the ideal thermal conductivity.

[0119] Preferably, the establishment of the temperature gradient field model can be combined with the finite element analysis method to more accurately simulate the temperature distribution inside the device. The nonlinear adjustment coefficient can be adjusted according to the actual operating conditions and heat dissipation requirements of the equipment. For example, under high-load operation, the nonlinear adjustment coefficient can be appropriately increased to increase the adjustment amplitude of the thermal conductivity. The response time constant can be optimized according to the physical properties of the phase change material to achieve a faster thermal conductivity adjustment speed. In addition, the method of current controlling the lattice structure of the phase change material can adopt pulse current technology to achieve more accurate thermal conductivity adjustment by controlling the frequency and amplitude of the pulse current. A feedback mechanism can also be introduced to monitor the actual thermal conductivity of the phase change material in real time, and dynamically adjust the current parameters according to the deviation from the ideal thermal conductivity to achieve a more stable thermal conductivity adjustment effect.

[0120] In some embodiments, the construction of the fault prediction model includes:

[0121] Step F1: Collect power characteristic parameter set Ψ=V ripple ,I avg ,R internal , where V ripple is the voltage ripple, I avg is the average current, R internal is the internal resistance;

[0122] Step F2: construct a three-dimensional health space with the coordinate axis being the normalized Ψ parameter;

[0123] Step F3: Define the abnormality index

[0124]

[0125] Among them, w i is the parameter weight, ψ i is the value of the ith parameter in the power characteristic parameter set, ψ i_healthy is the value in the health state corresponding to the i-th parameter value;

[0126] Step F4: Use Hidden Markov Model to predict the failure probability P fault =1-exp(-λ·A·t), λ is the aging rate coefficient.

[0127] It should be noted that the construction of a fault prediction model is a key technology used in the present invention to optimize power management. The model collects a set of power characteristic parameters, constructs a three-dimensional health space, defines anomaly indicators, and finally uses a hidden Markov model to predict the probability of failure. The power characteristic parameter set includes parameters such as voltage ripple, average current, and internal resistance, which can reflect the operating status of the power supply. The three-dimensional health space is a coordinate system based on normalized power characteristic parameters, which is used to evaluate the health status of the power supply. The anomaly indicator is used to quantify the deviation between the power characteristic parameters and the healthy state, and the hidden Markov model predicts the failure probability of the power supply based on these parameters, thereby optimizing the dynamic power supply strategy.

[0128] Specifically, the voltage ripple in the power supply characteristic parameter set reflects the degree of fluctuation in the power supply output voltage, the average current represents the average load current of the power supply, and the internal resistance reflects the internal resistance characteristics of the power supply. These parameters are collected in real time by sensors and normalized for evaluation in a three-dimensional health space. In the calculation of the abnormality index, the parameter weights can be set according to the degree of impact of different parameters on power supply health. For example, changes in internal resistance have a greater impact on power supply health and can therefore be assigned a higher weight. The hidden Markov model is a statistical model used to predict future failure probabilities based on historical data and current status. The aging rate coefficient is used to adjust the predicted failure probability to take into account the aging of the power supply.

[0129] Preferably, the power supply characteristic parameter set can be further expanded to include parameters such as temperature and startup time to more comprehensively reflect the operating status of the power supply. In the three-dimensional health space, more dimensions can be introduced or more complex evaluation models, such as machine learning algorithms, can be adopted to improve the accuracy of health assessment. In the calculation of the abnormality index, a dynamic weight adjustment mechanism can be introduced to dynamically adjust the parameter weights according to the actual operating status and historical data of the power supply. The aging rate coefficient can be adjusted according to the actual use environment and maintenance conditions of the power supply. For example, in a high temperature environment, the aging rate of the power supply is faster, so the aging rate coefficient can be appropriately increased. In addition, the parameters of the hidden Markov model can be optimized through a machine learning algorithm in combination with real-time monitoring data and historical fault data to improve the accuracy of fault prediction.

[0130] In some embodiments, the method for constructing the indestructible topology network includes:

[0131] Step N1: According to the node remaining energy E i and link quality L ij Calculate node weight W i =log(E i / E min )×∑(L ij / L max );

[0132] Step N2: Build a virtual backbone network, select W i The largest first k nodes are used as cluster heads, k = ceil(N × ρ), N is the total number of nodes, ρ is the network density coefficient;

[0133] Step N3: Establish multi-path routing table, path reliability R path =Π(1-p j ), p j is the probability of each link being interrupted.

[0134] It should be noted that the construction of a destruction-resistant topology network is a key technology used in the present invention to improve the reliability of communication networks in extreme environments. This method calculates node weights, selects cluster head nodes, constructs a virtual backbone network, and establishes a multipath routing table, thereby ensuring that the network can continue to operate normally even when some nodes fail or links are interrupted. Node weights comprehensively consider the node's remaining energy and link quality, reflecting the node's importance and reliability in the network. The cluster head node is the core node of the virtual backbone network, responsible for coordinating communications with surrounding nodes. The multipath routing table provides a variety of communication path options, enhancing the network's destruction resistance.

[0135] Specifically, node weights are calculated based on the node's residual energy and link quality, weighted by the energy coefficient and quality coefficient to obtain a comprehensive node weight. Residual energy reflects the node's endurance, while link quality indicates the reliability of a node's communication with other nodes. The virtual backbone network is constructed by selecting the top k nodes with the highest weights as cluster heads, where the value of k depends on the scale and density of the network. The multipath routing table is constructed by taking path reliability into account, assessing path stability by calculating the outage probability of each link. Higher path reliability indicates a greater ability to maintain communication in extreme environments.

[0136] Optimally, node weight calculations can incorporate additional factors, such as a node's geographic location and communication range, to more comprehensively assess node importance. Cluster head selection can utilize a distributed algorithm, allowing nodes to autonomously compete for the cluster head position, enhancing the network's self-organizing capabilities. Multipath routing tables can incorporate dynamic update mechanisms to adjust path selection based on real-time link status to accommodate rapidly changing network environments. Furthermore, redundant designs can be implemented to provide backup for critical nodes and links, further enhancing the network's resilience.

[0137] In some embodiments, the human-computer interaction module performs:

[0138] Step O1: Detect the operator's glove deformation through the pressure sensor array and calculate the operation force vector F = [f x ,f y ,f z ];

[0139] Step O2: When ||F||2>F safe When the fault protection is activated, the reverse tactile feedback force F is generated. fb =-k·(FF safe )·F / ||F||;

[0140] Step O3: Adaptively adjust the operation interface according to the environmental risk level, and display element contrast C = C0×(1+η·E risk ), η is the visual enhancement coefficient.

[0141] It should be noted that the design of the human-computer interaction module is a key technology used in the present invention to improve the convenience and accuracy of operation. The module detects the deformation of the operator's gloves through the pressure sensor array, calculates the operation force vector, and activates the misoperation protection mechanism when necessary to generate a reverse tactile feedback force to prevent misoperation. At the same time, the contrast of the display elements of the operation interface is adaptively adjusted according to the environmental risk level to enhance the visual effect. The pressure sensor array is a sensor network that can detect pressure distribution. It is used to sense the deformation of the operator's gloves and thus infer the operation force. The misoperation protection mechanism helps the operator correct the operation by detecting abnormal operation force and providing tactile feedback. The environmental risk level is the operation risk level assessed according to the current environmental conditions, and is used to adjust the display effect of the operation interface to adapt to the operation requirements in different environments.

[0142] Specifically, the pressure sensing array can be composed of multiple pressure sensors distributed in key parts of the operating gloves, such as the fingertips, palms, etc. The operation force vector is obtained by processing the pressure data collected by the pressure sensing array, which reflects the direction and magnitude of the force applied by the operator. The activation threshold of the misoperation protection mechanism can be set according to actual operation requirements. For example, when the operation force exceeds a certain threshold, it is considered that there is a risk of misoperation, and the protection mechanism is activated. The magnitude and direction of the reverse tactile feedback force can be adjusted according to the operation force vector to provide intuitive feedback. The environmental risk level can be evaluated based on environmental parameters (such as temperature, humidity, electromagnetic interference, etc.). The higher the level, the greater the adjustment of the contrast of the display elements of the operation interface. The visual enhancement coefficient is used to adjust the contrast of the display elements and can be dynamically set according to the environmental risk level to ensure that the operator can clearly see the operation interface in different environments.

[0143] Preferably, the layout of the pressure sensing array can be further optimized to improve the accuracy of operation force detection. For example, the number of sensors can be increased or higher-precision pressure sensors can be used. The activation threshold of the misoperation protection mechanism can be dynamically adjusted according to the complexity and importance of the operation task. For example, in high-precision operation tasks, a lower activation threshold can be set to improve the protection sensitivity. The calculation of the reverse tactile feedback force can introduce more complex algorithms, such as adaptive feedback algorithms based on machine learning, to provide feedback that is more in line with the operator's habits. The assessment of environmental risk levels can be combined with real-time environmental monitoring data and historical operation data to make more accurate predictions through machine learning models. The adjustment of the visual enhancement coefficient can be optimized based on the operator's visual feedback. For example, the optimal contrast adjustment range can be determined through user testing to improve the comfort and accuracy of the operation.

[0144] The aforementioned embodiments of the present invention have the following beneficial effects: The wireless operation terminal of the present invention, by integrating multiple functional modules, can effectively improve the device's operational performance in extreme environments. The environmental perception module can collect multiple environmental parameters in real time and construct a multidimensional coupling model, providing a basis for dynamic device adjustment, thereby enhancing the device's adaptability to complex environments. The dynamic stability control module executes a dynamic weight allocation algorithm based on the multidimensional environmental coupling model to generate a precise environmental adaptability coefficient, thereby optimizing the device's operating state and ensuring its stability under extreme conditions. The communication link reconstruction module establishes a channel migration cost model based on the environmental adaptability coefficient, generating an optimal channel switching path, thereby improving the reliability and anti-interference capability of the communication link. The anti-interference processing module uses nonlinear signal shaping to generate an anti-interference communication waveform, further improving communication quality and ensuring the accuracy and integrity of data transmission. The emergency recovery module predicts the risk of device failure based on the environmental parameter mutation rate and triggers a multi-level emergency response strategy, effectively reducing the risk of device failure under sudden environmental changes and improving the device's emergency response and survivability. The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material based on the temperature gradient, effectively controlling the device temperature and ensuring stable operation in high-temperature environments or environments with large temperature differences. The power management module combines environmental parameters and device status to build a fault prediction model and implement a dynamic power supply strategy, which can optimize the device's energy consumption management, extend the device's service life, and improve the device's reliability in extreme environments.

[0145] In addition, the human-computer interaction module detects the deformation of the operator's gloves through a pressure sensor array, calculates the operation force vector, and activates misoperation protection when necessary, generating reverse tactile feedback force, thereby improving the accuracy and safety of the operation. At the same time, the module can also adaptively adjust the operation interface according to the environmental risk level and enhance the contrast of display elements, which can improve the operator's visual experience and increase operational efficiency. Overall, the wireless operation terminal of the present invention can enhance the comprehensive performance of the device in extreme environments through the collaborative work of various modules, meeting the application requirements of high reliability and high adaptability.

[0146] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0147] The above descriptions are merely some preferred embodiments of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also encompass other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by mutually replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A wireless operation terminal for use in extreme environments, characterized in that: It includes environmental perception module, dynamic stability control module, communication link reconstruction module, anti-interference processing module, emergency recovery module, human-computer interaction module, heat dissipation reinforcement module and power management module; The environmental perception module collects temperature, humidity, vibration, air pressure and electromagnetic interference intensity parameters in real time and constructs a multi-dimensional environmental coupling model; The dynamic stability control module executes a dynamic weight allocation algorithm based on a multi-dimensional environmental coupling model to generate an environmental adaptation coefficient; The communication link reconstruction module establishes a channel migration cost model according to the environmental adaptation coefficient and generates an optimal channel switching path; The anti-interference processing module performs nonlinear signal shaping based on the electromagnetic interference intensity parameter to generate an anti-interference communication waveform; The emergency recovery module predicts the risk of equipment failure by the mutation rate of environmental parameters and triggers a multi-level emergency strategy; The heat dissipation reinforcement module dynamically adjusts the thermal conductivity of the phase change material according to the temperature gradient; The power management module combines environmental parameters and device status to build a fault prediction model and implement a dynamic power supply strategy.

2. The wireless operation terminal according to claim 1, wherein: The method for constructing the multidimensional environmental coupling model includes: Step D1: Calculate the environmental parameter interaction factor matrix Φ, whose elements Represents the coupling coefficient of the i-th parameter to the j-th parameter, satisfying Where σ is the environmental type correction factor, P i 、P j is the normalized parameter value, δ is the coupling attenuation constant; Step D2: Establish a dynamic weight distribution function, as shown in the following formula; Among them, V(t) is the parameter change rate vector, U(t) is the device state vector, represents the element-by-element product, W(t) is the dynamic weight; Step D3: Update the model parameters by sliding the time window, as shown in the following formula; T=τ·(1+log(E risk )), τ is the reference time constant, E risk is the failure risk level of the previous cycle, and T is the window length.

3. The wireless operation terminal according to claim 2, wherein: The dynamic weight allocation algorithm includes: Step W1: Calculate the parameter deviation vector based on the current environmental parameters, as shown in the following formula; Δ=[ΔT,ΔH,ΔV,ΔP,ΔE], where ΔT=|T curr -T ref | / (T max -T min ), ΔH, ΔV, ΔP, and ΔE use the same normalization method; T curr is the current temperature measurement value, T ref is the device temperature reference value, T max is the maximum value of the temperature sensor range, T min It is the minimum value of the temperature sensor range; Step W2: Generate a comprehensive deviation index, as shown in the following formula; G=∑(w i ·Δ i 2 )+ρ·max(Δ i ), where w i is the real-time weight output by the i-th dynamic weight allocation function W(t), ρ is the extreme value sensitivity coefficient; Δ i is the deviation of the i-th parameter, Step W3: When G exceeds the dynamic threshold G th When the stability adjustment instruction is triggered, the dynamic threshold G th =G base ×(1+tanh(α·dG / dt)), α is the adjustment rate coefficient, dG / dt is the rate of change of G, G base is the baseline deviation threshold.

4. The wireless operation terminal according to claim 1, wherein: The construction of the channel migration cost model includes: Step C1: Define the channel switching cost function as shown in the following formula; C ij =μ1·|Q i -Q j |μ2·t switch +μ3·(1-S ij ), where Q i , Q j is the channel quality indicator, t switch is the switching delay, S ij is the channel state similarity, μ1-μ3 are the cost weight coefficients; Step C2: Construct a channel migration graph, where the nodes represent channels and the edge weight is C ij ; Step C3: Use the constrained shortest path algorithm to find the optimal path. The constraints include: the number of path hops ≤ N max , cumulative delay ≤ T max , path stability index ≥ S min ; Step C4: Dynamically update the channel quality indicator, as shown in the following formula; Q i =ε·Q inst +(1-ε)·Q hist , where Q hist is the instantaneous measurement value, Q hist is the historical weighted average, and ε is the forgetting factor.

5. The wireless operation terminal according to claim 1, wherein: The nonlinear signal shaping process comprises: Step S1: Perform a joint time-frequency domain analysis on the received signal to extract the interference characteristic parameter set Ω = f peak ,BW,A rms , where f peak is the interference peak frequency, BW is the interference bandwidth, A rms is the RMS value of the interference amplitude; Step S2: Construct an adaptive filter transfer function, as shown in the following formula; H(z)=K·(1+β·z -D ) / (1+α·z -D ), where D is the delay parameter, α and β are notch coefficients dynamically adjusted according to Ω, and K is the gain compensation factor; Step S3: Perform nonlinear phase compensation, and the compensation amount is as shown in the following formula; Where γ is the environmental adaptation coefficient, f0 is the signal center frequency, and n is the nonlinear exponent.

6. The wireless operation terminal according to claim 1, wherein: The triggering method of the multi-level emergency strategy includes: Step E1: Calculate the environmental parameter mutation rate vector, as shown in the following formula; V = [dT / dt, dH / dt, dV / dt], and the comprehensive mutation index is calculated based on the environmental parameter mutation rate vector, as shown in the following formula; M = ||V||2·exp(σ·max(V)), σ is the sensitivity coefficient; ||V||2 is the Euclidean norm of vector V, and M is the comprehensive mutation index; Step E2: When M>M th1 Initiate the first-level emergency, reduce the communication rate and activate the backup power supply; When M>M th2 When the secondary emergency is activated, switch to the anti-destruction topology network and activate emergency cooling; When M>M th3 When the third level emergency is activated, data fuse protection is executed and equipment self-test is started; th1 is the preset first threshold, N th2 To preset the second threshold, M th3 A third threshold is preset; Step E3: Generate an emergency strategy execution sequence to ensure that the time interval between adjacent strategy switching Δt ≥ t min , t min is the minimum stable time threshold.

7. The wireless operation terminal according to claim 1, wherein: The method for adjusting the thermal conductivity of the phase change material comprises: Step H1: Establish a temperature gradient field model, as shown in the following formula; Where d is the thickness of the equipment shell, T surface is the measured value of the surface temperature of the equipment shell, T core The core temperature measurement of the device; Step H2: Calculate the ideal thermal conductivity as shown in the following formula; k0 is the base thermal conductivity, ξ is the nonlinear adjustment coefficient, k desired is the ideal thermal conductivity; Step H3: Controlling the lattice structure of the phase change material by current so that the actual thermal conductivity satisfies the following conditions; κ real =κ desired ×(1-e -t / τ ), τ is the response time constant, and t is the adjustment duration.

8. The wireless operation terminal according to claim 1, wherein: The construction of the fault prediction model includes: Step F1: Collect power characteristic parameter set Ψ=V ripple ,I avg ,R internal , where V ripple is the voltage ripple, I avg is the average current, R internal is the internal resistance; Step F2: construct a three-dimensional health space with the coordinate axis being the normalized Ψ parameter; Step F3: Define the abnormality index as shown in the following formula; Among them, w i is the parameter weight, ψ i is the value of the ith parameter in the power characteristic parameter set, ψ i_healthy is the value under healthy state corresponding to the i-th parameter value, and A is the abnormality index; Step F4: Use the Hidden Markov Model to predict the failure probability, as shown in the following formula; P fault =1-exp(-λ·A·t), λ is the aging rate coefficient, P fault To predict the probability of failure.

9. The wireless operation terminal according to claim 6, wherein: The method for constructing the indestructible topology network includes: Step N1: According to the node remaining energy E i and link quality L ij The node weight is calculated as shown in the following formula; W i =log(E i / E min )×∑(L ij / L max ); where E min is the minimum power threshold, L max Support maximum link quality for the system; Step N2: Build a virtual backbone network, select W i The largest first k nodes are used as cluster heads, where k = ceil(N × ρ), N is the total number of nodes, and ρ is the network density coefficient; Step N3: Establish a multi-path routing table, where the path reliability is shown in the following formula; R path =Π(1-p j ), p j is the probability of each link being interrupted, R path is the path reliability.

10. The wireless operation terminal according to claim 1, wherein: The human-computer interaction module performs: Step O1: Detect the operator's glove deformation through the pressure sensor array and calculate the operation force vector F = [f x ,f y ,f z ]; Step O2: When ||F||2>F safe When the fault protection is activated, the reverse tactile feedback force F is generated. fb =-k·(FF safe )·F / ||F||; Step O3: Adaptively adjust the operation interface according to the environmental risk level, and display element contrast as shown in the following formula; C=C0×(1+η·E risk ), η is the visual enhancement coefficient, E risk is the risk level value, C0 is the reference contrast, and C is the display element contrast.

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