Maritime cooperative key system failure mode mechanism analysis method, system and medium

Abstract

The application discloses a kind of offshore cooperative key system failure mode mechanism analysis method, system and medium, comprising: establishing task-driven complex electromagnetic environment-key system coupling model;Wherein, the establishment of coupling model includes: S1. establishing parameterized complex electromagnetic environment model;S2. establish task-oriented key system failure mechanism chain model, the model is for at least one key task system in offshore cooperative combat, builds from "electromagnetic environment parameter input" to "system function state output" causal mapping relationship;S3. based on the parameterized complex electromagnetic environment model and the task-oriented key system failure mechanism chain model, coupling simulation is carried out, to evaluate the performance state of specified task system under specific electromagnetic environment, and predict its failure boundary.The present application lays a foundation for typical frequency equipment reliability modeling, reliability simulation, reliability test verification, semi-physical verification platform development and application verification, etc.

Description

Technical Field

[0001] This invention belongs to the field of critical system failure mode and mechanism analysis technology, and relates to a method, system and medium for failure mode and mechanism analysis of a marine collaborative critical system. Background Technology

[0002] With the rapid development of electronic information technology and its deep integration into the military field, modern naval warfare has evolved into a system-wide confrontation unfolding across multiple dimensions, including land, sea, air, space, electromagnetic spectrum, and cyberspace. Among these, victory in the electromagnetic spectrum has become a prerequisite for seizing the initiative on the battlefield. Maritime joint operational formations rely on various radar, communication, navigation, and other electronic information systems to achieve situational awareness, command and control, and coordinated strikes. The stable operation of these critical systems is highly dependent on the complex electromagnetic environment.

[0003] Currently, the battlefield electromagnetic environment is becoming increasingly complex, characterized by a wide variety of radiation sources, complex and variable signal patterns, highly congested spectrum, and the coexistence of intentional and unintentional interference. This Complex Electromagnetic Environment (CEME) can interfere with, block, deceive, and even damage electronic information systems through various coupling pathways, potentially leading to serious consequences such as false alarms / missed alarms, communication interruptions, and navigation and positioning drift, directly weakening or even dismantling the effectiveness of the entire maritime collaborative combat system. Therefore, accurately analyzing, assessing, and predicting the impact of the complex electromagnetic environment on key maritime collaborative systems has become an urgent issue that must be addressed in equipment demonstration, tactical formulation, and battlefield survivability.

[0004] Currently, the research and practice on the effects of complex electromagnetic environments mainly have the following ideas and methods, but there are still significant shortcomings: (1) Qualitative analysis based on "environmental description" and "phenomenon induction". This type of method is a common starting point for current research. Usually, by classifying and describing the constituent elements of complex electromagnetic environments (such as natural interference and human interference), and combining historical experience or theoretical analysis, various failure phenomena that may occur in electronic systems (such as receiver saturation, signal misjudgment, etc.) are summarized. However, this method relies heavily on expert experience and lacks quantitative evaluation methods. It can only answer "what problems may occur", but cannot answer "under what specific environmental conditions will the problem inevitably occur" and "how to quantify the severity of the problem". For example, it points out that "communication may be blocked by interference", but cannot specify "what is the probability of communication link interruption under a specific power and specific pattern of interference signal", which makes it difficult to directly use its conclusions for precise combat planning and quantitative verification of equipment performance indicators. (2) Isolated testing and evaluation method based on "single device - single interference". This method typically applies one or more typical interference signals to a single electronic device (such as a radar or a radio) under laboratory or field conditions and observes its performance changes. This method can obtain quantitative data in specific scenarios, but its limitations are very obvious: First, it severs the connection between "environment" and "mission". The test focuses on the changes in the technical indicators of the device itself (such as sensitivity and dynamic range) under interference, rather than the overall effectiveness of the device in completing specific combat missions (such as continuously tracking targets and maintaining command links). Second, it ignores the complexity of system coordination. Maritime collaborative operations are an organic whole formed by multiple systems linked through a network. The interference effect on a single device will spread and amplify in the network through information flow, triggering "cascading failure". The isolated test method cannot assess this system-level vulnerability and chain reaction. (3) Rough evaluation method based on "static parameters" and "statistical models". Some studies attempt to establish mathematical models to correlate environmental interference parameters (such as interference power and interference-to-signal ratio) with system performance parameters (such as detection probability and bit error rate). This approach represents a step forward from qualitative analysis, but the models are often built upon numerous simplifications and statistical averages. Its core flaws lie in its "static" and "black-box" nature. The models typically deal with steady-state, average environmental parameters, failing to simulate dynamic evolution and sudden, complex electromagnetic events. Furthermore, it treats the complex signal processing flows within the system (such as radar signal processing chains and communication encoding / decoding and synchronization processes) as a "black box," establishing only macroscopic statistical relationships between inputs and outputs. This method cannot reveal the underlying mechanisms by which interference signals penetrate system defenses, affect critical processing nodes, and ultimately lead to functional failure. Therefore, its reliability and accuracy are insufficient when predicting new types of interference and assessing system behavior in unknown environments. Summary of the Invention

[0005] The primary objective of this invention is to provide a method, system, and medium for analyzing the failure modes of critical marine collaborative systems, in order to overcome the aforementioned shortcomings of the prior art.

[0006] To achieve this objective, in the basic implementation scheme, the present invention provides a method for analyzing the failure mode mechanism of a key marine collaborative system, characterized by including: establishing a task-driven complex electromagnetic environment-key system coupling model; The establishment of the coupling model includes: S1. Establish a parameterized complex electromagnetic environment model, wherein the electromagnetic environment of the combat zone is decomposed into mutually independent or related natural electromagnetic environment sub-models and artificial electromagnetic environment sub-models, and the core interference parameters of each sub-model are quantitatively characterized. S2. Establish a mission-oriented critical system failure mechanism chain model. The model is for at least one critical mission system in maritime collaborative operations. It constructs a causal mapping relationship from "electromagnetic environment parameter input" to "system functional state output". The functional state includes at least normal, degraded and failed. S3. Based on the parameterized complex electromagnetic environment model and the task-oriented critical system failure mechanism chain model, perform coupled simulation to evaluate the performance status of a specified task system under a specific electromagnetic environment and predict its failure boundary.

[0007] Furthermore, in S1, the core interference parameters include at least one of interference type, signal density, energy intensity, and time / frequency / spatial distribution characteristics; the artificial electromagnetic environment sub-model integrates at least a simulation signal sample library of radar interference, communication interference, and electromagnetic pulse.

[0008] Furthermore, in S2, the task-oriented critical system failure mechanism chain model is established for at least one collaborative task among target detection, communication assurance, and navigation and positioning; the causal mapping relationship is specifically defined and quantified through a four-dimensional analysis space of "receiver processing depth - interference mode - interference power - interference distance".

[0009] Furthermore, when the task is "target detection", the failure mechanism chain model includes: A signal processing and track management logic sub-model is used to simulate the data processing flow from radar echo to stable track; A set of electromagnetic environment effect injection points are used to map the interference parameters in the S1 model to specific processing steps in the logic sub-model; A set of failure criteria based on track accuracy, continuity, and confidence level is used to determine the functional status of the system based on the output of the logical sub-model.

[0010] Furthermore, when the task is "communication assurance" or "navigation and positioning," the failure mechanism chain model defines at least three reversible failure paths: Channel degradation path: This refers to the direct impact of electromagnetic environment parameters on the physical transmission channel; the path effect disappears when the interference is removed. Front-end suppression path: The corresponding electromagnetic environment parameters cause the receiver front-end to saturate or lose lock. The function can be restored when the interference is removed. Information deception path: Deceptive interference signals corresponding to a specific structure are processed by the receiver processing chain, resulting in erroneous output.

[0011] Furthermore, the method also includes a failure tracing step: in the coupled simulation of S3, when the system functional state is determined to be degraded or failed, the specific failure path leading to the state and the threshold of the triggering electromagnetic environment parameters are traced back according to the failure mechanism chain model.

[0012] Furthermore, the coupled simulation in S3 is a dynamic simulation, the input of which includes a sequence of electromagnetic environment parameters that evolve with time or combat phase, and the output is a dynamic curve of the system performance status and key failure time points within the corresponding time period.

[0013] Furthermore, the method also includes: S4. Sensitivity Analysis Steps: Based on the simulation results of S3, analyze the impact weight of each core interference parameter on system performance in order to identify the key vulnerability parameters that lead to system failure.

[0014] Another objective of this invention is to provide a failure mode mechanism analysis system for a key maritime collaborative system, characterized in that it includes: a processor and a memory; the memory stores a computer program, which, when executed by the processor, implements the method described therein.

[0015] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method described thereon.

[0016] The beneficial effects of this invention are as follows: First, it represents a leap from "qualitative cognition" to "quantitative assessment and prediction," significantly improving the objectivity and accuracy of assessments. Existing technologies for analyzing the effects of complex electromagnetic environments largely remain at the level of phenomenon description, classification discussion, or experience-based qualitative judgment, failing to answer key questions such as "at what level of interference will the system fail" and "when will it fail?" This invention, by constructing a parameterized complex electromagnetic environment model and a task-oriented failure mechanism chain model, transforms the vague concepts of "environmental complexity" and "system vulnerability" into quantifiable, injectable parameters for simulation models. Through coupled simulation of the models, it can dynamically and quantitatively output curves showing the changes in system performance indicators over time or environmental parameters, thereby achieving accurate prediction of system failure thresholds and objective assessment of mission success probabilities, providing a reliable quantitative basis for command and decision-making.

[0017] Second, it reveals the inherent causal logic of the "environment-system" interaction, supporting in-depth failure diagnosis and tracing. Traditional analysis methods often only indicate that the system "failed," but struggle to pinpoint the root cause and specific link in the failure. The failure mechanism chain model proposed in this invention, along with its three typical failure paths (channel degradation, front-end suppression, and information deception), constructs a clear causal relationship diagram from electromagnetic interference input to abnormal system output. In simulation or actual testing, once system performance degrades, the specific failure path leading to the problem can be quickly identified by tracing the mechanism chain backward, and further pinpointing which environmental parameter(s) exceeded the design tolerance. This "traceable and locatable" capability greatly facilitates fault diagnosis, anti-interference strategy optimization, and improvement of weak links in system design.

[0018] Third, it provides dynamic and forward-looking operational effectiveness simulation capabilities, supporting "effect-based" electromagnetic spectrum operational planning. Unlike static, single-point effect analysis, the coupled model of this invention supports input of time-varying environmental parameter sequences, enabling simulation of the dynamic evolution of the effectiveness of our key systems under complex scenarios such as enemy dynamic interference, our frequency switching, and changes in the battlefield environment. This allows commanders and spectrum managers to proactively assess the impact of the electromagnetic situation on our coordination capabilities under different operational phases and tactical actions, and to identify risk windows in advance. For example, it can predict how long a communication link will be interrupted or navigation accuracy will become unacceptable after a certain jamming pattern is continuously applied, thus providing scientific support for proactive electromagnetic spectrum maneuvering, jamming countermeasures, and operational tempo control, realizing a shift from "passively adapting to the environment" to "actively shaping and utilizing the environment."

[0019] Fourth, by identifying key vulnerabilities through sensitivity analysis, this invention provides direct and efficient input for equipment development and system protection. This invention not only assesses failures but also, through the system's sensitivity analysis function, automatically identifies the key vulnerability parameters with the highest weight influencing the effectiveness of a specific mission system. This technical effect has extremely high engineering application value. For equipment developers, it clarifies the design indicators that newly developed systems must prioritize hardening and protection in complex electromagnetic environments; for system warfare designers, it identifies which nodes and links within the system are key targets for electromagnetic protection, thereby optimizing the allocation of protection resources and achieving "maximum system electromagnetic interference resistance at the lowest cost."

[0020] Fifth, a standardized and scalable evaluation framework has been constructed, possessing good versatility and promotional value. The "mission-environment-system" coupled modeling framework proposed in this invention has highly structured and modular characteristics. The parameterized environment model and failure mechanism chain model can serve as standard modules. For new missions (such as electronic reconnaissance and coordinated strikes), new equipment (such as new radars and data links), or new electromagnetic threats (such as new jamming aircraft), new evaluation capabilities can be quickly constructed simply by updating or expanding the corresponding model library and parameter set. This makes the method not only applicable to maritime collaborative scenarios, but also, after adaptation, can be extended to the evaluation of electromagnetic environment effects in multi-domain coordinated operations such as air and land, demonstrating good military benefits and broad applicability. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of a failure mode mechanism analysis method for a key marine collaborative system according to the present invention; Figure 2 The diagram shown illustrates the complex electromagnetic environment of the research object. Figure 3 The image shows a schematic diagram of a complex electromagnetic signal environment model library. Figure 4 The diagram shown is a block diagram of a radar signal model. Figure 5 The diagram shows the mechanism of action of a typical interference mode. Figure 6 The diagram shows a typical radar system failure mode diagram. Figure 7 The diagram shows a schematic representation of the electromagnetic environment effect characterization scheme of the target detection system. Figure 8 The diagram shows a typical failure mode diagram of a communication system. Figure 9 The diagram shows a schematic representation of the electromagnetic environment effect of a communication support system. Figure 10 The diagram shows a typical failure mode of a navigation and positioning system. Figure 11 The diagram shows a schematic representation of the electromagnetic environment effect of a navigation and positioning system. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Please refer to Figures 1 to 11 The purpose of this invention is to provide a method for analyzing the failure mode mechanism of a key marine collaborative system, characterized by including: establishing a task-driven complex electromagnetic environment-key system coupling model; The establishment of the coupling model includes: S1. Establish a parameterized complex electromagnetic environment model, wherein the electromagnetic environment of the combat zone is decomposed into mutually independent or related natural electromagnetic environment sub-models and artificial electromagnetic environment sub-models, and the core interference parameters of each sub-model are quantitatively characterized. S2. Establish a mission-oriented critical system failure mechanism chain model. The model is for at least one critical mission system in maritime collaborative operations. It constructs a causal mapping relationship from "electromagnetic environment parameter input" to "system functional state output". The functional state includes at least normal, degraded and failed. S3. Based on the parameterized complex electromagnetic environment model and the task-oriented critical system failure mechanism chain model, perform coupled simulation to evaluate the performance status of a specified task system under a specific electromagnetic environment and predict its failure boundary.

[0025] The technical solution of the present invention will now be described through specific embodiments, including: Step 1: Analyze the complex electromagnetic environment at sea. Based on the definition, characteristics, and basic components of complex electromagnetic environments, model and analyze them into two categories: natural electromagnetic environments and man-made electromagnetic environments. The complex electromagnetic environment is further classified and modeled according to its constituent elements and equipment types. Specifically: Step 1.1: Define the complex electromagnetic environment, such asFigure 1 Since this invention focuses on the complex electromagnetic environment effects of key marine collaborative systems, it particularly focuses on modeling and analyzing the failure modes and failure mechanisms of key systems under complex electromagnetic environments. Therefore, this invention models and analyzes complex electromagnetic environments into three categories: natural electromagnetic environments and man-made electromagnetic environments.

[0026] Step 1.2: Modeling of Complex Electromagnetic Environments Step 1.2.1: Modeling the natural electromagnetic environment. The natural electromagnetic environment mainly includes various clutter, which refers to the echoes generated by electromagnetic waves affected by weather, ground, ocean, flocks of birds, etc. Sometimes the target echo can be submerged by clutter echoes with very high amplitude, which severely limits the capabilities of electromagnetic equipment.

[0027] Step 1.2.1.1: Construct the surface clutter model: (1) Rayleigh distribution Within the radar illumination area, clutter consists of numerous independent scatterers randomly distributed across the clutter surface. When a linear detector is used in the receiver, the probability density of the amplitude envelope of this distribution can be expressed as: in, It is the amplitude envelope The mean square value. The mean of this distribution is proportional to its standard deviation. Standard deviation = Mean = 0.523 mean For median The probability density function that is not normalized to the square mean is... in This is the normalized amplitude envelope.

[0028] If the receiver uses square-law detection, its standard deviation is equal to its average value, and its probability density function is: in, It is the average power. This refers to instantaneous power.

[0029] (2) Log-normal distribution When the radar resolution cell is very small, a log-normal distribution is more suitable. When using square-law detection, the probability density function of the clutter echo is: in, yes The standard deviation, yes The median average.

[0030] When using a linear receiver, the normalized amplitude of the output signal is ,So, yes The probability density function at the median is: Unlike the Rayleigh distribution, which requires only one parameter value—its mean square value—this distribution is determined by two parameters: the standard deviation and the median.

[0031] (3) Weibull distribution The first two distributions can be said to solve clutter in opposite situations: one is when the resolving unit is too large, and the other is when the resolving unit is too small. The Weibull distribution is a statistical model for measurement data with characteristics between these two types. In fact, with the selection of reasonable parameters, the Weibull distribution can be infinitely close to the first two distributions.

[0032] The probability density function of this distribution is in, Related to the slope, It is the median of the distribution.

[0033] When the receiver uses square-law detection, In this case, the Weibull probability density function is expressed as follows: in, , For normalized power, and yes The median. This statistical model can be used for land clutter, sea clutter, and meteorological clutter.

[0034] (4) K-distribution The K-distribution is a two-parameter clutter statistical model. If we use... It's the amplitude. It is a scale factor. Gamma function, These are shape parameters. If it is a modified Bessel function of the second kind, then the probability density function of this distribution is expressed as follows: Step 1.2.1.2: Construct a sea clutter model, as shown in Table 1, using sea state as an indicator to describe wave height in the ocean, and then determine the sea clutter index.

[0035] Table 1 Step 1.2.1.3: Construct a meteorological clutter model: Taking raindrops as an example, if each raindrop meteorological particle is considered as a particle with a diameter of... If we consider a sphere as a whole, then in a distribution of raindrops of any size, a few large raindrops will become the main cause of radar echoes. In the simulation, the backscattering coefficient... As shown in the following formula: in, and It's experience points. It is the precipitation rate, and its unit is... .

[0036] Step 1.2.2: Electromagnetic environment modeling. This involves analyzing complex electromagnetic environments and identifying typical electromagnetic signals as research objects, namely radar radiation signals, communication radiation signals, radar jamming signals, communication jamming signals, and electromagnetic pulse signals. A typical electromagnetic signal model library is established, such as... Figure 2 .

[0037] Taking radar signal modeling as an example, this section explains typical electromagnetic signals in an electromagnetic environment. The following mainly introduces common non-coherent pulse signal models, linear frequency modulated signal models, phase-coded signal models, and coherent pulse train signal models. Figure 3 (1) Continuous wave radar signal model The signal expression is: Where: A is the amplitude; The carrier angular frequency; This is the initial phase.

[0038] (2) Monopulse radar signal model A single-frequency pulse signal can be represented as In the formula: Let be the carrier angular frequency; u(t) be the modulation function. The modulation function is a rectangular pulse. In the formula: T is the pulse width; in fact, the above formula is the signal transmitted by a common pulse radar, which is obtained by modulating the carrier signal with a rectangular pulse. rect is a function used to cut a continuous carrier signal into a pulse of finite width, and it is the most basic modulation function in radar single-pulse signal modeling.

[0039] rect(t) is a rectangle function, defined as: Of course, a single-frequency signal waveform can also be represented in complex form as follows: In the formula: j is the imaginary unit, The cutoff frequency and the angular frequency in real form satisfy .

[0040] (3) Coherent pulse train signal model Coherent pulse trains (CPLs) are large time-bandwidth product signals that increase signal duration without reducing bandwidth. They retain the high range resolution of pulse signals while also possessing the velocity resolution of continuous wave signals, making them a widely used signal type. The simplest PCL is the uniform PCL, composed of N equally spaced pulse trains of the same frequency and a non-uniform PCL. More complex PCLs include weighted PCLs, frequency-coded PCLs, and non-uniform PCLs. In weighted PCLs, time-domain weighting suppresses Doppler sidelobes and improves the Doppler response. In frequency-coded PCLs, the frequencies of sub-pulses change according to a certain rule, reducing autocorrelation function sidelobes and improving radar signal effectiveness.

[0041] Considering the choice of pulse repetition frequency (PRF), coherent pulse trains are generally classified into three categories: low repetition rate, medium repetition rate, and high repetition rate. Low repetition rate pulse trains exhibit velocity ambiguity, high repetition rate pulse trains exhibit distance ambiguity, while medium repetition rate pulse trains exhibit both distance and velocity ambiguity.

[0042] Coherent pulse train can be represented as In the formula The complex envelope of the sub-pulse. Let N be the pulse repetition period and N be the number of sub-pulses. This considers the case where the complex envelope of the pulse is a rectangular pulse, i.e. And T is the sub-pulse width.

[0043] (4) Linear frequency modulated pulse signal model The complex envelope of a linear frequency modulated pulse signal is In the formula Let T be the complex envelope of the signal. Here, T is the pulse width, K = B / T is the frequency modulation slope, and B is the frequency range. (5) Phase-coded pulse signal model Phase-coded waveforms differ from frequency-modulated waveforms in that they divide a relatively wide pulse train into many short pulses. These sub-pulses have equal widths but are transmitted with a specific phase. The phase of each sub-pulse is selected according to phase coding. Currently, the most commonly used phase-coded waveform is the binary phase-coded waveform, and there are many types of phase-coded sequences, such as Barker code sequences, complementary sequences, m-sequences, and L-sequences.

[0044] 1) Buck sequence binary code encoding The waveform of the Buck sequence binary phase code encoded signal has the following expression: In the formula: P is the code length; T is the sub-pulse width; A is the signal amplitude; f0 is the carrier frequency; ak is the value of the k-th word in the binary code sequence, which is 1 or -1. The complex envelope function of the signal. The delta function is a mathematical tool that connects discrete sequences with continuous-time signals. 2) Binary phase coding based on m-sequences The m-sequence, also known as the maximum-length sequence, is a binary pseudo-random sequence. It possesses a relatively ideal periodic autocorrelation function, and its fuzzy function exhibits an isotropic, thumbtack-shaped pattern. However, in aperiodic operation, the autocorrelation function will have relatively high sidelobes; when K >> 1, the main-sidelobe ratio approaches... .

[0045] The phase-coded waveform based on the m-sequence is similar in principle to Barker codes, and has the expression... In the formula: P is the code length; T is the sub-pulse width; A is the signal amplitude; f0 is the carrier frequency; ak is the value of the k-th word in the binary code sequence, which is 1 or -1. The complex envelope function of the signal. in Step 2: Analyze the failure modes and mechanisms of key maritime collaborative systems, classifying them according to collaborative equipment type or operational task, and conducting in-depth analysis based on component structure, failure modes, and failure mechanisms. For the failure mode and mechanism analysis of typical electronic information systems, this invention focuses on analyzing the electromagnetic effects of intentional human interference on radar, communication, and navigation systems. To facilitate analysis, the electromagnetic environment effect analysis of the three typical electronic information systems is unified. This invention defines and models these effects from four dimensions: receiver processing depth, interference method, interference power, and interference distance, as detailed below. Figure 4 As shown. Step 2.1: Perform failure mode and mechanism analysis on the target detection system, which mainly includes navigation radar, search radar, radar receiver, and signal processor.

[0046] Step 2.1.1: Filter and predict the signal processing output of the key components of the target detection system to obtain the target's position at this time and the next time. Combine the current detection results with the historical information of the target's trajectory. Based on the trajectory association criteria, maintain the target's trajectory, terminate the trajectory, or start a new trajectory.

[0047] Step 2.1.2: Perform failure mode analysis on the target detection system. Based on the working principle of the radar system and the mechanism of active radar interference, the electromagnetic effect analysis principle for the co-located transmit and receive radar system is as follows: Figure 5 As shown.

[0048] The first type is irreversible hard damage, where the system cannot recover even without countermeasures; this is considered irreversible damage. The latter three types involve affected modules failing to function under interference, but the system automatically recovers when no countermeasures are taken; these are considered soft failures. The costs and effects of these two types differ significantly; not only are the costs or consequences of the attack different, but the effects and impacts on the invention also differ considerably.

[0049] Step 2.1.3: Analyze the failure mechanism of the target detection system, determine the characterization of the complex electromagnetic environment effect, and determine the criteria for successful mission implementation through the typical interference modes of the target detection system.

[0050] Step 2.1.3.1: Determine the characterization of complex electromagnetic environment effects. Based on the different effects produced by different nodes in the receiving front-end, establish a parameterized characterization sample set for the effects of different nodes. This will determine the causal relationships between the effects of different nodes and provide measurement methods for different characteristic parameters. Similarly, establish parameterized characterization sample sets for the effects produced by different nodes in the signal processing unit and data processing unit, along with corresponding measurement methods, and determine the qualitative or quantitative relationships between the samples, such as...Figure 6 .

[0051] Step 2.1.3.2: Determine the criteria for mission success by analyzing typical jamming methods of the target detection system. Apply active suppression jamming and active deception jamming to the radar for error analysis. In practical applications, the radar's detection range, detection probability, and tracking range significantly increase with the signal-to-interference ratio (SIR), while the obstruction area and measurement error decrease accordingly. Based on the above indicators, the success of target detection can be determined using the following criteria: (1) The detection probability (the percentage of successful target detection) meets the mission requirements; (2) The false alarm rate (the proportion of false alarm targets (the situation where the target is mistakenly believed to exist) to the total reported targets) is less than the task requirement; (3) The false negative rate (the proportion of actual targets that were not detected out of all actual targets) is lower than the mission requirements; (4) If the positioning accuracy (the deviation between the detected target position and the actual position) is higher than the mission requirements, the measurement error formula, such as root mean square error (RMSE) or other statistical methods can be used to evaluate the mission.

[0052] Step 2.2: Perform failure mode and mechanism analysis on the communication assurance system.

[0053] Step 2.2.1: Perform failure mode analysis on the communication support system. Based on the working principle of the communication system and the mechanism of active interference, the failure mode analysis of the communication system is as follows: Figure 7 As shown.

[0054] The first type of failure mode is electromagnetic interference (EMI) targeting the transmission channel. This involves altering the electromagnetic characteristics of the communication transmission channel, rendering it unable to meet normal communication requirements. Without countermeasures, the transmission channel will function normally. The latter two failure modes target EMI against the receiver of the communication system. Under interference, relevant functional modules of the affected communication system malfunction, but the system will automatically return to normal without countermeasures. Clearly, all three failure modes are soft failures. However, the costs and effects of these three failure modes differ significantly; not only are the costs or consequences of countermeasures different, but the interference effects and impacts on the invention also vary considerably.

[0055] Step 2.2.2: Perform failure mechanism analysis on the communication support system, determine the characterization of complex electromagnetic environment effects, and then determine the criteria for successful mission implementation.

[0056] Step 2.2.2.1: Determine the characterization of complex electromagnetic environment effects. Based on the different effects produced by different nodes in the communication receiving front-end, establish a parameterized characterization sample set of the effects of different nodes. This will determine the causal relationship between the effects of different nodes and provide measurement methods for different characteristic parameters, such as... Figure 8 As shown.

[0057] Step 2.2.2.2: Determine the criteria for task execution. While signal power decreases with increasing communication distance, interference power may also change. In some cases, interference may be relatively stable, thus reducing the signal-to-interference ratio (SIR). Correspondingly, optimizing the SIR increases the success rate of communication equipment while simultaneously reducing bit error rate, packet loss rate, and communication latency. Based on these indicators, the success of the communication task can be determined using the following methods: (1) Whether the communication timeout occurs, i.e. whether the delay exceeds the set time threshold; (2) Whether the packet loss rate is too high, that is, whether the historical packet loss rate is greater than the set threshold; (3) Whether the bit error rate is less than the range required by the task; (4) Whether the smooth flow rate is greater than the task requirement range.

[0058] Step 2.3: Perform failure mode and mechanism analysis on the navigation and positioning system. The navigation and positioning system is mainly implemented using a Beidou receiver as an example.

[0059] Step 2.3.1: Failure Mode Analysis of Navigation and Positioning System. Based on the working principle of navigation and positioning system and the mechanism of active interference in navigation, the failure mode principle of navigation and positioning system is as follows: Figure 9 .

[0060] The first failure mode is electromagnetic interference targeting the navigation and positioning transmission channel. This involves altering the electromagnetic characteristics of the channel, preventing it from meeting normal signal transmission requirements. Without countermeasures, the transmission channel will function normally. The latter two failure modes target the receiver of the navigation and positioning system. Under interference, relevant functional modules of the affected system malfunction; however, without countermeasures, the system will automatically return to normal. Clearly, all three failure modes are soft failures. However, their costs and effects differ significantly; not only are the costs or consequences of countermeasures different, but the interference effects and impacts also vary considerably.

[0061] Step 2.3.2: Analyze the failure mechanism of the navigation and positioning system, determine the characterization of the effects of the complex electromagnetic environment, and determine the criteria for successful mission implementation.

[0062] Step 2.3.2.1: Based on the different effects produced by different nodes of the navigation and positioning system receiver, establish a parameterized characterization sample set of the effects of different nodes to determine the causal relationship between the effects of different nodes, and provide measurement methods for different characteristic parameters, such as... Figure 10 As shown.

[0063] Based on the signals or data of each node in the navigation and positioning system, corresponding parameters, including time and frequency domains, transient and statistical quantities, are extracted as characterization parameters of the complex electromagnetic environment effects of the navigation and positioning system. This allows us to examine how the navigation and positioning system changes with electromagnetic environment elements.

[0064] For testing receivers of navigation and positioning systems, there are already some indicators used to describe the characteristics of the navigation and positioning system itself, such as signal-to-noise ratio and the number of received signals. However, these indicators will change with the external environment, so they can be used as effect parameters.

[0065] Based on the type of environmental factors, corresponding indicators are added to form a new indicator system that comprehensively reflects the effect characteristics of navigation and positioning systems in complex electromagnetic environments.

[0066] Step 2.3.2.2: Determine the criteria for task success. By analyzing the interference, the mechanism by which interference affects the navigation receiver is clarified. Referring to the GPS navigation system, its C / A code spreading gain is... In the above formula, the spread spectrum signal bandwidth is BWCA, and the baseband signal bandwidth is denoted by BBW. From this formula, it can be concluded that the baseband signal can achieve a certain gain at the output through spread spectrum. Assuming the loss during the entire signal transmission process is Lr, the final output signal-to-noise ratio of the baseband signal is... Therefore, although the navigation system uses spread spectrum, which provides a certain tolerance to interference, when the interference power is sufficiently high, after despreading the signal, the interference is broadened in frequency, effectively increasing the noise power and still affecting the signal output. For navigation signals, the carrier-to-noise ratio (carrier power / noise power) is generally used. ( ) is used to measure the quality.

[0067] In the above formula, Pr is the signal power received by the antenna, GA is the gain of the receiving antenna, and L is the energy loss of the receiver throughout the process. K is the Boltzmann constant, and the noise temperatures of the receiving antenna and amplifier are Tant and Tamp, respectively. The thermal noise power density can be calculated from these values. The noise figure of the receiver is denoted as NF.

[0068] According to GPS system requirements, the minimum C / A code level power in the L1 band is [missing information]. Therefore, assuming a receiver noise figure NF = 4dB, loss L = 2dB, and antenna gain for the satellite signal GA = 1.5dB, the above formula can be used to calculate... When interference signals are present, the equivalent C / N0 carrier-to-noise ratio is: In the above formula, Q is the quality factor for interference immunity; it is typically 1 for narrowband interference, 2.22 for wideband interference, and 1.5 for wideband spread spectrum. Rc is the code rate of the spreading code. , This represents the ratio of interference signal power to signal power.

[0069] As can be seen from the above formula, the mechanism of interference is to reduce the signal carrier-to-noise ratio, thereby degrading the signal quality. As a result, the success rate of signal acquisition and tracking operations will decrease or even fail completely, ultimately causing the receiver to be unable to complete the positioning work.

[0070] When the signal-to-interference ratio (SIR) increases, the positioning accuracy and offset will improve accordingly. However, due to system offset (due to hardware or software defects, the navigation device itself may have systematic errors), system errors, and dynamic offset (during movement, the device's measurements may be affected by dynamic factors such as changes in speed and direction), the actual navigation system offset cannot be completely eliminated, resulting in a positioning accuracy of 0.

[0071] Based on the above indicators, the success of a navigation task can be determined by checking whether these indicators meet the following criteria: (1) The positioning accuracy is less than the target set for the task; (2) The navigation system offset is less than the set target; If the above conditions are met, the navigation and positioning task is considered successful; if any one of the conditions is not met, the navigation and positioning task is considered to have failed.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Those skilled in the art should understand that modifications can be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the failure mode mechanism of a key maritime collaborative system, characterized in that, include: Establish a task-driven coupled model of complex electromagnetic environment and key system; The establishment of the coupling model includes: S1. Establish a parameterized complex electromagnetic environment model, wherein the electromagnetic environment of the combat zone is decomposed into mutually independent or related natural electromagnetic environment sub-models and artificial electromagnetic environment sub-models, and the core interference parameters of each sub-model are quantitatively characterized. S2. Establish a mission-oriented critical system failure mechanism chain model. The model is for at least one critical mission system in maritime collaborative operations. It constructs a causal mapping relationship from "electromagnetic environment parameter input" to "system functional state output". The functional state includes at least normal, degraded and failed. S3. Based on the parameterized complex electromagnetic environment model and the task-oriented critical system failure mechanism chain model, perform coupled simulation to evaluate the performance status of a specified task system under a specific electromagnetic environment and predict its failure boundary.

2. The method according to claim 1, characterized in that, In S1, the core interference parameters include at least one of interference type, signal density, energy intensity, and time / frequency / spatial distribution characteristics; the artificial electromagnetic environment sub-model integrates at least a simulation signal sample library of radar interference, communication interference, and electromagnetic pulse.

3. The method according to claim 1, characterized in that, In S2, the task-oriented critical system failure mechanism chain model is established for at least one collaborative task among target detection, communication assurance, and navigation and positioning; the causal mapping relationship is specifically defined and quantified through a four-dimensional analysis space of "receiver processing depth - interference mode - interference power - interference distance".

4. The method according to claim 3, characterized in that, When the task is "target detection", the failure mechanism chain model includes: A signal processing and track management logic sub-model is used to simulate the data processing flow from radar echo to stable track; A set of electromagnetic environment effect injection points are used to map the interference parameters in the S1 model to specific processing steps in the logic sub-model; A set of failure criteria based on track accuracy, continuity, and confidence level is used to determine the functional status of the system based on the output of the logical sub-model.

5. The method according to claim 3, characterized in that, When the task is "communication assurance" or "navigation and positioning", the failure mechanism chain model defines at least three reversible failure paths: Channel degradation path: This refers to the direct impact of electromagnetic environment parameters on the physical transmission channel; the path effect disappears when the interference is removed. Front-end suppression path: The corresponding electromagnetic environment parameters cause the receiver front-end to saturate or lose lock. The function can be restored when the interference is removed. Information deception path: Deceptive interference signals corresponding to a specific structure are processed by the receiver processing chain, resulting in erroneous output.

6. The method according to claim 5, characterized in that, The method also includes a failure tracing step: in the coupled simulation of S3, when the system functional state is determined to be degraded or failed, the specific failure path leading to the state and the threshold of the triggering electromagnetic environment parameters are traced back according to the failure mechanism chain model.

7. The method according to claim 1, characterized in that, The coupled simulation in S3 is a dynamic simulation. Its input includes a sequence of electromagnetic environment parameters that evolve over time or during combat phases, and its output is a dynamic curve of the system's performance status and key failure time points within the corresponding time period.

8. The method according to claim 1, characterized in that, The method further includes: S4. Sensitivity Analysis Steps: Based on the simulation results of S3, analyze the impact weight of each core interference parameter on system performance in order to identify the key vulnerability parameters that lead to system failure.

9. A failure mode mechanism analysis system for a key maritime collaborative system, characterized in that, include: Processor and memory; The memory stores a computer program that, when executed by the processor, implements the method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

Images