System and method for monitoring connection state of high-density connector

Abstract

The invention relates to the technical field of connector monitoring, and discloses a monitoring system and method for the connection state of a high-density connector. The system comprises a state feature analysis module, a connection state judgment rule base management module, a multi-band optical signal monitoring array, a main control processing unit, a contact state mapping module and an abnormal state iteration module. The state feature analysis module collects multi-dimensional physical parameters and identifies the type of the connector; the rule base management module loads a corresponding rule base, evaluates the feature coverage degree, and generates and updates a newly added rule when the feature coverage degree is insufficient; the monitoring array generates a scanning strategy; the main control processing unit drives, scans and receives the optical signal response feature set; the contact state mapping module matches the feature set with a standard library to generate connection state identifiers of all contacts; and when the connection integrity is not met, the abnormal state iteration module sends a reconfiguration instruction to the monitoring array. The system can comprehensively and accurately monitor the connection state of the high-density connector, and timely identify and process connection abnormity.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of connector monitoring, in particular to a high-density connector connection state monitoring system and method. BACKGROUND

[0002] In modern electronic devices and systems, high-density connectors, as key components for signal and power transmission, are widely used in precision fields such as communication equipment, aerospace, and industrial control. These connectors usually contain a large number of contacts with small spacing between them, and need to maintain stable connection performance in complex environments. However, during long-term use, affected by factors such as vibration, temperature change, and dust erosion, the connection state of high-density connectors is prone to abnormalities, such as contact loosening, oxidation, and poor contact.

[0003] If these connection abnormalities are not discovered and handled in time, it may lead to signal transmission interruption, data loss, and even device failure, causing serious economic losses. Traditional connector state monitoring methods rely on manual inspection or simple electrical parameter measurement, which have many limitations. Manual inspection is not only inefficient, but also difficult to accurately identify small connection abnormalities; electrical parameter measurement is easily affected by electromagnetic interference, with limited monitoring accuracy and unable to fully reflect the multi-dimensional state characteristics of the connector.

[0004] With the development of electronic devices towards high density, miniaturization, and high reliability, higher requirements are put forward for the real-time, accuracy, and comprehensiveness of high-density connector connection state monitoring. Existing monitoring technologies cannot meet these needs, and a monitoring system that can comprehensively collect multi-dimensional physical parameters, intelligently determine the connection state, and dynamically iterate the processing of abnormal states is needed to improve the operation reliability of high-density connectors and ensure the stable operation of related devices and systems. SUMMARY

[0005] The purpose of the present application is to provide a high-density connector connection state monitoring system and method to solve the problems raised in the background.

[0006] To achieve the above purpose, the present application provides a high-density connector connection state monitoring system, which comprises:

[0007] A state feature analysis module for collecting multi-dimensional physical parameters of the high-density connector and identifying the connector type;

[0008] A connection state determination rule library management module for loading the corresponding state determination rule library according to the connector type, judging the feature coverage degree of the state determination rule library relative to the same type of standard connector sample, and if the coverage degree is lower than the preset threshold, generating new determination rules based on the analysis model and updating the state determination rule library;

[0009] a multi-band optical signal monitoring array, configured to generate a multi-band optical signal scanning strategy according to the updated state judgment rule library;

[0010] a master processing unit, configured to drive the multi-band optical signal monitoring array to perform scanning on the high-density connector according to the multi-band optical signal scanning strategy, and receive an optical signal response feature set returned by the multi-band optical signal monitoring array;

[0011] a contact state mapping module, configured to match the optical signal response feature set with a standard connection state feature library, and generate a connection state identifier of each contact;

[0012] an abnormal state iteration module, configured to determine whether a connection integrity condition is met according to the connection state identifier, and send a reconfiguration instruction to the multi-band optical signal monitoring array when the condition is not met.

[0013] Preferably, the judgment feature coverage in the connection state judgment rule library management module comprises:

[0014] inputting the physical parameters of the standard connector sample into the state judgment rule library, counting the number of effective features identified by the state judgment rule library, and calculating the ratio of the number of effective features to the total number of features of the standard connector sample as the feature coverage;

[0015] when the feature coverage is lower than a preset threshold, generating new judgment rules containing feature dimensions, judgment thresholds and abnormal positioning parameters based on an analysis model by comparing the feature differences between the standard connector sample and the connector to be tested.

[0016] Preferably, the state feature analysis module collects multi-dimensional physical parameters, which comprises:

[0017] obtaining impedance fluctuation trajectories and signal attenuation coefficients of each contact to generate contact basic feature values;

[0018] dividing the contact clusters according to physical regions, calculating the distribution density of the contact basic feature values in each contact cluster, and generating regional feature values;

[0019] counting the crosstalk intensity between adjacent contacts to generate topological correlation feature values.

[0020] Preferably, the multi-band optical signal monitoring array generates a scanning strategy, which comprises:

[0021] matching a preset frequency band allocation rule according to the connector type to determine the state feature interval corresponding to each monitoring unit;

[0022] Matching the region characteristic value with the state characteristic interval, when a region characteristic value is in the state characteristic interval of a monitoring unit, the physical region is defined as the target scanning region of the monitoring unit;

[0023] Integrating the target scanning regions of all monitoring units forms a scanning path configuration table.

[0024] Preferably, the multi-band optical signal monitoring array performs scanning, which includes:

[0025] According to the scanning path configuration table, a carrier frequency distribution spectrum is generated, wherein different frequency bands correspond to different physical regions;

[0026] A frequency weighting coefficient is applied to the carrier signal, and the frequency weighting coefficient is dynamically adjusted according to the contact basic characteristic value;

[0027] The weighted carrier signal is loaded to the target scanning region, and a reflected signal intensity change curve is collected;

[0028] A time delay processing is applied to the reflected signal intensity change curve, so that the time offset of different frequency band signals is generated;

[0029] The multi-band signal after the time delay processing is superimposed to generate a comprehensive response waveform.

[0030] Preferably, the contact state mapping module performs feature matching, which includes:

[0031] The peak fluctuation feature and the energy attenuation feature of the comprehensive response waveform are extracted;

[0032] The similarity index of the peak fluctuation feature and the reference feature in the standard connection state feature library is calculated;

[0033] When the similarity index is lower than the matching threshold, the contact region is marked as an abnormal state region, and a connection state identifier containing an abnormal level and a position coordinate is generated.

[0034] Preferably, the abnormal state iteration module sends a reconfiguration instruction, which includes:

[0035] The number and distribution density of contacts that do not meet the connection integrity condition are counted;

[0036] According to the distribution density, a new frequency band allocation rule and a time delay parameter are generated;

[0037] The new frequency band allocation rule and the time delay parameter are packaged as a reconfiguration instruction.

[0038] Preferably, the system further includes:

[0039] A thermal distribution monitoring module is configured to collect surface temperature field data of the high-density connector in real time;

[0040] extracting the temperature gradient change rate of the corresponding area when the contact state mapping module detects the abnormal state area;

[0041] inputting the temperature gradient change rate into the connection state determination rule base for secondary verification.

[0042] Preferably, the secondary verification of the thermal distribution monitoring module comprises:

[0043] correcting the abnormal level according to the temperature gradient change rate;

[0044] triggering the multi-band optical signal monitoring array to perform directional enhanced scanning when the corrected abnormal level exceeds the critical threshold.

[0045] Preferably, the present application further comprises a connection state monitoring method of a high-density connector, which comprises all the modules and method processes of the above-mentioned connection state monitoring system of a high-density connector.

[0046] Compared with the prior art, the present application has the following beneficial effects:

[0047] Through the cooperative work of multiple modules, comprehensive and intelligent monitoring of the connector state is realized. The state feature analysis module can collect multi-dimensional physical parameters and identify the connector type, providing comprehensive and targeted basic data for subsequent state determination, avoiding the limitations of single parameter monitoring, and making the description of the connector state more rich and accurate.

[0048] The connection state determination rule base management module loads the corresponding rule base according to the connector type, and can evaluate the feature coverage of the rule base. When the coverage is insufficient, new rules are generated based on the analysis model and the library is updated, ensuring that the determination rules always match the actual state features of the connector, enhancing the adaptability and accuracy of state determination, and being able to cope with different types of connectors and state changes of the same type of connector at different use stages.

[0049] The multi-band optical signal monitoring array generates a scanning strategy based on the updated rule base, takes advantage of optical signal monitoring, and can accurately capture subtle state changes of the connector without being affected by electromagnetic interference. Compared with traditional electrical measurement methods, the stability and sensitivity of monitoring are improved, and earlier connection abnormalities can be identified.

[0050] The main control processing unit, as the core of the system, coordinates the work of each module, drives the monitoring array to perform scanning and receives the optical signal response feature set, ensuring the orderly progress of the monitoring process and the efficient transmission and processing of data, and providing reliable data support for subsequent state mapping.

[0051] The contact state mapping module matches the optical signal response feature set with the standard feature library, generates the connection state identification of each contact, realizes the accurate positioning of each contact state, enables the worker to quickly understand which contact has an abnormality, facilitates targeted maintenance and processing, and reduces the time and cost of troubleshooting.

[0052] The abnormal state iteration module sends a reconfiguration instruction to the monitoring array when determining that the connection integrity does not meet the requirements, further monitors and confirms the abnormal state by dynamically adjusting the scanning strategy, avoids the possible misjudgment of one-time monitoring, improves the reliability of abnormal state identification, and ensures timely response and processing of the connection abnormality. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 The timing diagram of the high-density connector connection state monitoring system according to the present application;

[0054] Figure 2 The flowchart of feature coverage judgment and rule updating;

[0055] Figure 3 The flowchart of multi-band optical signal scanning strategy generation;

[0056] Figure 4 The flowchart of multi-band optical signal scanning execution;

[0057] Figure 5 The flowchart of thermal distribution secondary verification. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0059] Please refer to Figure 1 The present application provides a high-density connector connection state monitoring system, which comprises:

[0060] The system operation starts from the state feature analysis module. This module collects the impedance fluctuation trajectory, signal attenuation coefficient and other basic physical parameters of each contact of the connector in real time through the sensor array deployed near the connector interface. At the same time, the module has a built-in connector type identification algorithm, which determines the specific model of the current connector under test by comparing the contact layout pattern, electrical characteristic fingerprint or pre-set identification code. The connection state judgment rule library management module loads the corresponding state judgment rule library from the storage unit according to the identified connector type. This module calculates the effective coverage of the rule library on the physical parameter characteristics of the standard connector samples of the same type. The specific method is as follows: input all the physical parameters of the standard samples into the rule library, count the number of physical parameter characteristics that can be successfully identified and matched by the rule library, and calculate the ratio of the number of effective characteristics to the total number of standard sample characteristics. If the ratio is lower than the pre-set threshold, it indicates that the existing rule library has insufficient coverage of the characteristics of the current connector model. At this time, the module starts the analysis model, compares the differences in physical parameter characteristics between the standard samples and the current connector under test, generates new judgment rules containing new characteristic dimension descriptions, judgment threshold settings and abnormal position positioning parameters, and automatically updates the rule library. The multi-band optical signal monitoring array receives the updated rule library information and generates a targeted optical signal scanning strategy. The main control processing unit drives the multi-band optical signal monitoring array to perform scanning operations on the high-density connector according to the scanning strategy, and receives the optical signal response characteristic data set returned by the array. The contact state mapping module performs pattern matching on the data set and the pre-stored standard connection state characteristic library to generate a connection state identifier for each contact or contact area of the connector. The abnormal state iteration module analyzes the state identifiers of all contacts to determine whether the overall connection meets the pre-set integrity condition. If the integrity condition is not met, the module generates a reconfiguration instruction and sends it to the multi-band optical signal monitoring array to trigger fine re-measurement of the abnormal area. Through the cooperative work of the above modules, the system forms a closed-loop monitoring process of "collection-analysis-judgment-scanning-mapping-feedback-optimization".

[0061] Embodiment 1: refer to Figure 2 , which shows that in the implementation process of the connection state judgment rule library management module, the evaluation of the feature coverage and the dynamic update of the rule library constitute the key to the adaptive ability of the system. The operation of the module starts from the confirmation of the type of the high-density connector under test, which is provided by the state feature analysis module. The module retrieves and loads the state judgment rule library pre-associated with the type from the system storage unit according to the type identifier. This rule library contains a series of pre-set logical conditions and feature matching patterns for judging the connection state according to the physical parameters. At the same time, the module accesses the stored standard connector sample database, which stores a comprehensive and verified multi-dimensional physical parameter data set collected under the standard connection state for each known type of connector. These data represent the feature benchmark of the connector under ideal connection conditions.

[0062] The calculation of feature coverage is a core step to evaluate whether the current loaded rule base is sufficient to accurately determine the status of the connector under test. The module inputs all the physical parameter features of the selected standard sample of the same model as the connector under test into the currently activated status determination rule base one by one. These physical parameter features usually contain multiple dimensions of information, such as: detailed trajectory data of the impedance of each contact under a specific test sequence over time or frequency; quantitative values of signal attenuation coefficients of each contact at a preset operating frequency; and a set of crosstalk strength values obtained by measuring the signal coupling strength between adjacent contacts. The rule base executes its built-in matching and determination logic for each feature input. The module continuously tracks the processing results of the rule base, accurately counts the number of features that are successfully identified, matched to a predefined status category, and output valid determination conclusions by the rule base, which is the number of valid features. At the same time, the module obtains the total number of physical parameter features contained in the standard sample, which is the total number of features of the standard connector sample. The feature coverage is obtained by a ratio calculation, which is the ratio of the number of valid features to the total number of features of the standard sample. This ratio quantifies the proportion of standard sample features that the current rule base can cover and process.

[0063] The system compares the calculated feature coverage value with a preset threshold. The threshold is a configurable parameter, usually set within a certain interval based on experience and monitoring reliability requirements. If the calculated feature coverage is lower than the preset threshold, it indicates that the current loaded status determination rule base has insufficient representation ability for the connector of this particular model. This means that the rule base has not fully covered or effectively processed all or part of the key features exhibited by the connector of this model in the standard state, which may lead to missed or incorrect status determination. When detecting insufficient coverage, the module automatically activates its built-in analysis model. The design goal of the model is to identify feature differences and generate new determination rules. The model first focuses on the features in the standard sample that are not successfully identified and determined by the current rule base, i.e., the subset of features that the rule base has not covered. The model analyzes the specific performance of these uncovered features in the standard sample, including their numerical range, distribution law, change pattern, etc. Then, the model obtains the actual measurement data of the connector under test in the same feature dimension. By carefully comparing the specific numerical differences, distribution shifts or pattern changes of the standard sample and the connector under test in these uncovered features, the model performs feature difference analysis.

[0064] Based on the results of the above difference analysis, the model executes the generation logic of new rules. This logic aims to define new feature dimension descriptions, set applicable decision thresholds, and associate abnormal location information. For example, the model may identify a kind of impedance trajectory pattern that is stable in standard samples but appears abnormal fluctuations on the connector under test, which is not defined in the current rule library. The model will describe the characteristics of this new pattern, and set a numerical threshold for determining whether this pattern is abnormal according to the statistical distribution of standard samples and the deviation of the connector under test. At the same time, the model will analyze the main associated physical contacts or areas of this feature pattern, and generate abnormal positioning parameters. Finally, the model outputs one or more new decision rule entries containing complete information, each entry clearly specifies the new feature dimension, the decision threshold of the feature dimension, and the location positioning parameters corresponding to the feature abnormality.

[0065] The connection state decision rule library management module receives the new decision rule entries generated by the analysis model. The module integrates these new entries into the currently loaded state decision rule library. The integration process needs to ensure that the new rules are logically consistent with the original rules, avoiding conflicts. After integration, the updated state decision rule library replaces the old version and becomes the basis for subsequent state decisions on the current connector under test (and subsequent connectors of the same model). Through this series of operations: coverage evaluation, difference analysis, rule generation, and library update, the system realizes the self-improvement and dynamic adaptation ability of the state decision rule library, enabling it to more accurately cope with the characteristics changes and potential new feature patterns of different models of connectors, and maintain effective monitoring of the state of the connector. This process does not require human intervention and automatically responds to the lack of rule library coverage, continuously optimizing the accuracy and reliability of monitoring.

[0066] Example 2: see Figure 3, demonstrates the multi-dimensional physical parameter acquisition and analysis of the state feature analysis module on high-density connectors. This process constitutes the basic data source for the system to identify the connection state. The module achieves synchronous data capture through a high-precision sensor network deployed near the connector interface. The sensor types are selected according to parameter characteristics, including a micro-probe array for electrical characteristic measurement, a coupler for high-frequency signal analysis, and a micro-position encoder for spatial positioning. One of the core dimensions of acquisition is the impedance fluctuation trajectory of each individual contact. This trajectory is obtained by applying a test excitation signal of a specific frequency and amplitude to the contact and measuring the real-time response voltage and current changes. The measurement process covers a pre-set frequency range or time window, recording the complete curve of impedance value changes with frequency or time evolution. These curves reflect the contact resistance, material properties, and possible oxidation or contamination conditions of the contact. Another key acquisition dimension is the signal attenuation coefficient. The module injects a test signal of known amplitude and frequency into the selected contact and measures the received signal amplitude at the end of the signal transmission path or adjacent contacts. By comparing the amplitude difference between the injected signal and the received signal, the loss ratio of the signal in the transmission process, i.e., the attenuation coefficient, is calculated. This coefficient is measured at different operating frequencies to form a characteristic spectrum reflecting the high-frequency transmission performance of the contact.

[0067] Based on the raw impedance fluctuation trajectory and signal attenuation coefficient data collected for each contact, the module performs feature extraction operations. For the impedance fluctuation trajectory, the analysis algorithm identifies its key patterns, such as calculating the average impedance value in a specific frequency band, the peak-to-peak value of impedance fluctuation, the zero-crossing rate, or the occurrence frequency of a specific fluctuation pattern. For the signal attenuation coefficient, the algorithm extracts its attenuation value at key frequencies, the slope of attenuation change with frequency, or the integral value in a pre-set frequency band. After calculation, a set of quantified numerical values, called contact basic feature values, is generated for each contact. These feature values are stored in structured data form, with each value corresponding to a specific attribute describing the electrical state of the contact.

[0068] To reveal the spatial correlation characteristics inside the connector, the module designs the layout according to the physical structure of the connector, divides the densely arranged contacts into logical physical regions, and forms contact clusters. The division can be based on the wiring partition of the connector PCB, functional block division, or row / column matrix division. Each contact cluster contains a group of spatially adjacent or functionally related contacts. For each divided contact cluster, the module performs statistical analysis on the contact basic characteristic values of all contacts contained therein. The analysis focuses on the distribution characteristics of the characteristic values in the cluster. For example, for the basic characteristic value of "average impedance in the middle frequency band" of all contacts in the cluster, the module calculates the statistical distribution of the characteristic value in the entire cluster. The specific calculation includes: determining the average value of the characteristic value (reflecting the overall level of the cluster); calculating the variance or standard deviation (reflecting the dispersion degree of the characteristic value between the contacts in the cluster); counting the number of contacts whose characteristic value falls within a preset numerical interval and its proportion; or applying kernel density estimation method to depict the probability density distribution profile of the characteristic value in the cluster. Based on the calculation results of these distribution characteristics, the module generates one or more comprehensive indicators representing the overall electrical characteristics of the physical region, called regional characteristic values. For example, "cluster average impedance" represents the overall conductivity of the region, "cluster impedance dispersion" reflects the consistency of the contact impedance in the region, and "high impedance contact proportion" indicates the proportion of potential bad contacts in the region. These regional characteristic values depict the electrical state of different blocks of the connector from a macroscopic perspective.

[0069] In addition, the module analyzes the mutual influence between contacts, i.e., crosstalk phenomenon. By injecting a specific test signal into a contact (driven contact), the module measures the induced signal amplitude at one or more physically adjacent contacts (victim contacts). The measurement is performed at multiple frequency points, and the crosstalk strength is usually quantified in decibels (dB), representing the amplitude ratio of the driving signal to the induced signal. The module traverses all possible adjacent contact pairs (according to the adjacency relationship defined by the connector layout) and measures and records the crosstalk strength value between each pair of contacts. Based on all the measured crosstalk strength data, the module generates topological correlation characteristic values. These characteristic values describe the isolation performance between contacts inside the connector, such as: calculating the average value of the crosstalk strength of all adjacent contact pairs (reflecting the average isolation); finding the maximum crosstalk strength and its corresponding contact pair position (identifying the most serious crosstalk path); and counting the number of contact pairs with crosstalk strength exceeding a preset threshold (identifying high-risk crosstalk areas). The topological correlation characteristic values reveal the strength and distribution of electrical coupling between contacts, which is crucial for identifying short circuit risks or signal integrity degradation.

[0070] The multi-band optical signal monitoring array utilizes the regional characteristic values outputted by the state characteristic analysis module to formulate its scanning strategy. The array is composed of multiple independent optical signal monitoring units, each of which can independently control the wavelength (band) of the emitted and received optical signals. The array maintains a preset configuration mapping table inside, which is indexed according to the connector types identified by the state characteristic analysis module. For each connector type, the mapping table defines one or more state characteristic intervals that each optical signal monitoring unit is responsible for monitoring. A state characteristic interval refers to a numerical range of a regional characteristic value. For example, monitoring unit A may be assigned to monitor physical regions with a "cluster average impedance" value in the interval [Z_low_A, Z_high_A]; monitoring unit B is responsible for regions with a "cluster impedance dispersion" value in the interval [D_low_B, D_high_B]; and monitoring unit C may be responsible for regions with a "high impedance contact ratio" exceeding the threshold P_C. Different units may focus on different dimensions of regional characteristic values or different numerical intervals.

[0071] The array receives the regional characteristic value results of each physical region calculated by the state characteristic analysis module. For each physical region, the array compares its calculated regional characteristic values with the state characteristic intervals defined in the configuration mapping table for each monitoring unit. When a certain regional characteristic value of a certain physical region falls within a certain state characteristic interval responsible by a certain monitoring unit, the system determines that the physical region meets the monitoring range of the monitoring unit, and marks the physical region as the "target scanning region" of the monitoring unit. A physical region may be assigned to multiple monitoring units for scanning (multi-dimensional scanning) because its multiple regional characteristic values meet the conditions of different units. Similarly, a monitoring unit may be responsible for scanning multiple physical regions that meet its state characteristic interval conditions. The array aggregates the target scanning region allocation results of all monitoring units to generate a detailed scanning path configuration table. This table is the core output of the scanning strategy, which explicitly lists the following information: the identifier of each optical signal monitoring unit; the identifiers or location range descriptions of all target physical regions assigned for scanning by the unit; the optical signal band (wavelength) to be used for each target physical region; and the scanning parameter suggestions (such as the basic signal strength) that may be required according to the regional characteristic values. The scanning path configuration table provides clear path planning and resource configuration basis for the subsequent execution of accurate, efficient, and targeted scanning operations by the multi-band optical signal monitoring array, ensuring that the scanning energy is focused on key regions reflecting potential state characteristics.

[0072] Example 3: see Figure 4This demonstrates how a multi-band optical signal monitoring array performs a physical scan of a high-density connector based on a scan path configuration table. The array parses the configuration table to identify the target physical area to be scanned by each optical signal monitoring unit and its corresponding specified optical frequency band (wavelength). Based on this information, the array generates a carrier frequency distribution spectrum. This spectrum is a mapping table that explicitly defines the correspondence between different optical carrier frequencies (or bands) and the specific physical areas to be scanned. Before transmitting optical carrier signals to the target physical area, the array performs signal weighting processing. This processing is based on the contact basic characteristic values ​​corresponding to the contacts within the target area, provided by the state feature analysis module. The array's built-in algorithm is based on Z... avg Or other relevant characteristic values, dynamically calculate a frequency weighting coefficient W f This coefficient acts on the carrier signal, potentially adjusting its transmit power or modulation depth. The calculation logic aims to adapt the signal strength to the electrical characteristics of the target area; for example, for areas where eigenvalues ​​indicate the potential for high impedance, W... f This may be increased to enhance signal strength and ensure a measurable reflected signal. The weighted optical carrier signal (whose frequency is specified by the carrier frequency distribution spectrum) is precisely guided and projected onto the surface of the target physical area defined in the configuration table.

[0073] The photodetectors in the array are activated synchronously to capture the light signals reflected from the target physical region. The detectors record the trajectory of the reflected light signal intensity changing over time, forming a reflected signal intensity variation curve I(t). For each scanned physical region, an independent I(t) curve is generated. To clearly separate and identify reflected signals from different frequency bands in subsequent processing and avoid confusion caused by signal overlap in the time domain, the array applies a preset timing delay Δt to each acquired I(t) curve. This delay Δt is set according to the offset parameters pre-defined for different carrier frequencies (regions) in the carrier frequency distribution spectrum. For example, the offset parameter assigned to region R... A The reflected signal is delayed by Δt1 and assigned to region R. B The reflected signal is delayed by Δt2, where Δt1 ≠ Δt2. The delayed signal is represented as I(t-Δt). This operation causes a relative shift in the time axis of reflected signals from different frequency bands that might have arrived simultaneously or at similar times. Finally, the array superimposes and fuses all the time-delayed signal waveforms I(t-Δt) representing the reflection characteristics of different physical regions. This superposition process generates a single, comprehensive response waveform S(t) containing information from all scanned regions. S(t) is the core input data for the system's subsequent state determination.

[0074] The contact state mapping module receives the integrated response waveform S(t) outputted by the multi-band optical signal monitoring array. The module applies signal processing algorithms to extract features from S(t). The primary extracted feature is the peak fluctuation feature. The algorithm detects the local maximum points (peaks) in S(t), records the amplitude value of each peak, calculates the time interval between adjacent peaks, and analyzes the trend of peak amplitude change over time or sequence. The second extracted feature is the energy decay feature. The algorithm calculates the energy integral of S(t) within a certain time window, or analyzes the descending slope of the signal envelope (obtained by Hilbert transform or other methods) to quantify the decay rate of signal energy. The module accesses the pre-stored standard connection state feature library. This library contains a set of reference features obtained by the same scanning process for the same type of high-density connector in the standard connection state. The module calculates the similarity index Sim between the observed features and the reference features. The similarity calculation can use various algorithms, such as calculating the Euclidean distance between feature vectors (smaller distance indicates higher similarity), or calculating the cosine similarity (value close to 1 indicates high similarity). A specific similarity calculation can be expressed as:

[0075]

[0076] where: N represents the number of features participating in the calculation; F obs,i represents the i-th feature value extracted from the observed waveform; F ref,i represents the corresponding i-th reference feature value in the standard feature library; φ(F obs,i ,F ref,i ) represents the similarity function of a single feature i. w i represents the weight coefficient of the i-th feature, which reflects the importance of different features in state determination. The weight can be pre-set according to experience or feature sensitivity; Sim is the calculated comprehensive similarity index, whose value range is usually in the [0, 1] interval, and the larger the value, the higher the similarity.

[0077] The calculated similarity index Sim is compared with a pre-set matching threshold T mtch . This threshold is a configurable parameter used to define the boundary between "normal" and "abnormal". If Sim≥T mtch , it is considered that the response features of the contact area are similar enough to the standard state, and it is marked as "normal". If Sim<T mtch , it indicates that the response features of the contact area are significantly different from the standard state, and the module marks this contact area as an "abnormal state area". At the same time, the module marks the contact area as an "abnormal state area" according to the value of Sim being lower than T mtchThe difference is mapped to a pre-defined abnormality level interval. Combining the location information recorded during the scanning process, the module generates a connection status signature containing the abnormality level and the precise location coordinates. This signature is the system's specific decision output for the local status of the connector.

[0078] Example 4: The abnormality status iteration module makes the final decision on the overall connection integrity of the high-density connector based on the set of connection status signatures output by the contact status mapping module. The connection integrity condition is a set of pre-defined logical rules to determine whether the current connection status meets the functional and safety requirements of the device or system. A typical integrity condition definition can include: the status signature of all contacts labeled as "critical path" must be "normal"; the number of contacts labeled as "abnormality zone" in non-critical contacts must not exceed the allowed maximum threshold; and, these abnormal contacts cannot form a high-density cluster in physical space, i.e., the number of abnormal contacts in any local area must not exceed another pre-defined local density threshold. This condition aims to prevent complete loss of function or overheating risk due to local contact group failure. After the contact status mapping module completes scanning and matching, it outputs the following set of connection status signatures, refer to Table 1.

[0079] Table 1: Example abnormal contact status signatures.

[0080] Contact ID Position coordinates (row, column) Abnormality level Area number belonging to C23 (2,3) Medium R_Z1 C24 (2,4) Mild R_Z1 C25 (2,5) Medium R_Z1 C32 (3,2) Mild R_Z1 C56 (5,6) Severe R_Z3 C78 (7,8) Mild R_Z5

[0081] The abnormality status iteration module receives this set of status signatures. The module first checks the status of all critical path contacts, assuming in this example that all critical contacts are normal. Next, the module counts the total number of contacts labeled as "abnormality zone" throughout the connector, denoted as N abnormal . Assume that the count results in N abnormal = 15, while the system allows a maximum abnormal contact number threshold of Max global = 20. Since N abnormal < Max globalThe global quantity condition is satisfied. Subsequently, the module analyzes the spatial distribution density of abnormal contacts. The module divides the connector contact array into preset size analysis grid units. The module determines that the connection integrity condition is not satisfied because there is a local area of abnormal contact density exceeding the maximum allowed value. At this time, the module starts the reconfiguration instruction generation process. The module first focuses on the identified high-density abnormal area. Based on the number of abnormal contacts in this area and the degree of exceeding the threshold (4-2=2), the module generates a new frequency band allocation rule. The core of the new rule is to increase the scanning resolution and signal strength of the high-density abnormal area. Specific measures may include: allocating more optical signal monitoring units for cooperative scanning in this area; or allocating a wider frequency band or higher density of subcarriers to the monitoring unit responsible for scanning this area to improve the scanning accuracy in this area; or specifying the use of feature frequency bands that are more sensitive to detecting the identified abnormal types (medium, slight) in this area. For other low-density or isolated abnormal areas, the new rule may maintain the original scanning frequency band allocation or only make minor adjustments.

[0082] At the same time, the module generates new timing delay parameters. In the initial scan, different frequency bands (areas) of reflected signals are applied with fixed timing delays for separation. However, in the high-density abnormal area, contact failures may interact with each other, causing the characteristics of reflected signals to be complex, and the initial setting may not be sufficient to clearly separate the subtle signal differences or potential new patterns in this area. The module calculates new delay parameters based on the abnormal level distribution (including medium and slight) and density of the area. New parameters may include: setting different delay amounts for different sub-areas or feature points in the high-density area to enhance the distinguishability of signals in the time axis; or increasing the delay offset of the overall signal in this area, so that it occupies a wider time window in the comprehensive waveform S(t), facilitating detailed analysis. For non-high-density areas, the timing delay parameters may remain unchanged or only be adaptively adjusted.

[0083] The module encapsulates the new frequency band allocation rule and the new timing delay parameter generated by the above calculation into a structured instruction message, i.e., a reconfiguration instruction. The instruction includes a clear command identifier, a target connector identifier, a list of monitoring units that need to be reconfigured, new frequency band parameters (center frequency, bandwidth) specified for each unit, a new timing delay parameter table (specifying new delay values for different areas or frequency bands), and a list of high-priority areas that need to be immediately enhanced scanning. After encapsulation, the module sends the reconfiguration instruction to the control interface of the multi-frequency optical signal monitoring array through the system internal communication bus.

[0084] Upon receiving the reconfiguration instruction, the multi-band optical signal monitoring array parses its content. The array controller reconfigures the working parameters of the light source and receiver of the relevant monitoring unit according to the new frequency band allocation rule in the instruction. At the same time, the controller loads the new time delay parameter table and updates the delay settings of the signal acquisition and processing unit. After the configuration update is completed, the array focuses on the high-priority area specified in the instruction according to the new frequency band and time sequence parameters, and performs one or more enhanced scans. The purpose of this scan is to obtain more detailed, lower noise or more obvious feature optical signal response data in the high-priority area. After the scan is completed, the array returns the new response data to the contact state mapping module for further analysis and state identification update. Through this iterative process, the system can improve the diagnostic ability of complex abnormal areas.

[0085] Embodiment 5: Referring to Figure 5 , the system integrates a thermal distribution monitoring module as a means of auxiliary verification and enhanced monitoring. The module operates independently of the optical signal monitoring array, and its core component is a non-contact infrared thermal imager array deployed above or to the side of the high-density connector, or a micro-distributed temperature sensor network embedded in the connector carrier board. The module continuously collects infrared radiation or temperature readings from the entire surface of the connector at a fixed sampling period. After calibration and spatial registration processing, the sensor data generates a high-resolution, real-time surface temperature field distribution map. This temperature field data not only contains the instantaneous temperature value of each pixel point or sensor position, but also records the temperature sequence over time. The temperature field data is continuously transmitted to the module's processing unit for storage and preliminary analysis, such as calculating the average temperature, identifying high-temperature points, and other basic information. The module is in standby state, and its deep analysis function is triggered by the judgment result of the contact state mapping module.

[0086] When the contact state mapping module finishes the analysis and matching of the optical signal response characteristics, and outputs the connection state identification set, the set is sent to the abnormal state iteration module and the thermal distribution monitoring module at the same time. The thermal distribution monitoring module analyzes the received state identification set and filters out all entries marked as "abnormal state area". For each physical area marked as abnormal (identified by location coordinates or area number), the module starts the targeted thermal data analysis process. From the real-time stored temperature field data, the module extracts the local temperature data corresponding to the abnormal physical area. The local temperature data includes the temperature value sequence of all sampling points in the area within the recent time window. Based on the temperature sequence, the module calculates the key thermal feature of the abnormal area: temperature gradient change rate. The calculation process involves time series analysis of the temperature sequence. For example, the module may calculate the average temperature rise rate (temperature change per unit time) of the area within a certain time interval. Or, using linear fitting method, the slope of temperature change with time is calculated, and the slope value represents the temperature gradient change rate. More complex analysis may include detecting mutation points or nonlinear change patterns in the temperature sequence. The temperature gradient change rate quantifies the heating or cooling dynamic characteristics of the abnormal area before, during or after the optical signal scan.

[0087] The calculated temperature gradient change rate is input into the connection state judgment rule library maintained by the connection state judgment rule library management module. The rule library not only contains judgment rules based on optical signal characteristics, but also predefines logic for comprehensive state verification by integrating thermal features. The rule library stores the association knowledge of different connection states (especially various abnormal states) and typical temperature behavior patterns. For example, the rule library may define that "contact resistance increase" type of abnormality usually accompanies moderate to high positive temperature gradient change rate (temperature rise accelerates); "virtual welding" or "micro-crack" type of abnormality may show slight or unstable temperature change; "complete open circuit" may have no significant temperature rise or even temperature drop; "local short circuit" may cause rapid temperature rise. The rule library receives the input of the preliminary judgment result of the specific abnormal area (such as "moderate contact failure") and its corresponding temperature gradient change rate value. The rule library applies the built-in rule logic for secondary verification. One of the core tasks of secondary verification is to correct the abnormal level initially given based on the optical signal characteristics. For example, if the optical signal characteristics of a certain area are preliminarily judged as "slight abnormality", but its temperature gradient change rate shows rapid temperature rise (high positive value), the rules in the rule library may indicate that the thermal behavior is more consistent with the characteristics of "moderate" or "severe" contact resistance abnormality, so the abnormal level is upgraded. Conversely, if the optical signal of a certain area is judged as "moderate abnormality", but the temperature gradient change rate is stable or very low, the rule library may consider that the thermal feature does not support severe abnormality, and the abnormal level is downgraded or maintained but marked for further observation. The correction process is performed according to the mapping relationship defined in the rule library based on historical data or physical model.

[0088] The rule base defines critical threshold parameters related to temperature signatures. These thresholds are used to distinguish different risk levels of temperature behaviors. For example, a "temperature rise rate critical threshold" is set. When the revised anomaly level (which has integrated both optical signature and temperature signature) exceeds the pre-set critical threshold, the system determines that the anomaly region is at high risk, which can lead to overheat failure or functional failure. At this time, the thermal distribution monitoring module triggers an enhanced scanning instruction. The instruction is directional, explicitly specifying the target location that needs to perform enhanced scanning, i.e., the location coordinates or region number of the high-risk anomaly region. The instruction also contains a scanning mode identifier, indicating that the multi-band optical signal monitoring array needs to use the pre-set "directional enhanced scanning" mode.

[0089] After receiving the trigger instruction from the thermal distribution monitoring module, the multi-band optical signal monitoring array parses the instruction content and obtains the target scanning location and scanning mode requirements. The array controller adjusts the working state of the relevant monitoring units according to the pre-set "directional enhanced scanning" mode configuration parameters. The enhanced scanning mode can include one or more of the following strategies: significantly increasing the optical signal power emitted to the target region to enhance the reflected signal strength and improve the signal-to-noise ratio; increasing the scanning frequency band width or using a denser set of subcarrier frequencies in the target region to obtain more rich frequency domain response information; increasing the scanning sampling rate to obtain higher time resolution signal waveforms; performing multiple repeated scans in the target region and signal averaging to suppress random noise; or using a feature frequency band combination specially optimized for the identified anomaly type (inferred from the revised level and thermal signature). The array focuses on the high-risk anomaly region specified by the instruction and performs one or more enhanced scanning operations according to the new configuration parameters. The scanning process is strictly limited within the target region or a small extended region centered on it, avoiding unnecessary global scanning resource consumption. The optical signal response data (which can be higher resolution or higher quality integrated response waveforms or raw data) collected by the enhanced scanning is sent back to the contact state mapping module.

[0090] The contact status mapping module receives new data from the enhanced scan. The module applies the same feature extraction and similarity matching procedure, but this time processing the high-quality scan data for the high-risk area. The module generates updated connection status identification based on the more accurate scan, particularly the determination for the target area. The result can confirm, correct, or further refine the previous abnormality level and type judgment. The updated status identification is fed back to the abnormality status iteration module for re-evaluation of the overall connection integrity. The intervention of the thermal distribution monitoring module cross- validates and re-evaluates the preliminary optical detection result by introducing the independent temperature dimension feature, and triggers more detailed optical re-measurement when a high-risk abnormality is identified. This bimodal data fusion and iterative verification mechanism of optical signal and thermal distribution enhances the sensitivity and diagnostic reliability of the system to potential thermal-related failure modes, while providing a higher confidence basis for the determination of high-risk areas. The system continuously optimizes the cognition and response to critical abnormal areas through closed-loop feedback.

[0091] It should be noted that the relational terms herein, such as first and second, and the like, are used solely to distinguish one from another entity or action without necessarily requiring or implying any actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0092] While the embodiments of the application have been illustrated and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and alterations can be made therein without departing from the spirit and scope of the application, which is defined by the appended claims and their equivalents.

Claims

1. A system for monitoring the connection state of a high-density connector, characterized by, The application relates to a high-density connector state detection system, comprising: a state feature analysis module for collecting multi-dimensional physical parameters of a high-density connector and identifying the connector type; a connection state judgment rule library management module for loading a corresponding state judgment rule library according to the connector type, judging the feature coverage degree of the state judgment rule library relative to a standard connector sample of the same type, and generating new judgment rules based on an analysis model and updating the state judgment rule library if the coverage degree is lower than a preset threshold; a multi-frequency band optical signal monitoring array for generating a multi-frequency band optical signal scanning strategy according to the updated state judgment rule library; a main control processing unit for driving the multi-frequency band optical signal monitoring array to perform scanning on the high-density connector according to the multi-frequency band optical signal scanning strategy, and receiving an optical signal response feature set returned by the multi-frequency band optical signal monitoring array; a contact state mapping module for matching the optical signal response feature set with a standard connection state feature library to generate a connection state identifier of each contact; an abnormal state iteration module for judging whether a connection integrity condition is met according to the connection state identifier, and sending a reconfiguration instruction to the multi-frequency band optical signal monitoring array when the condition is not met.

2. The monitoring system of high-density connector connection state according to claim 1, wherein, The judgment of the feature coverage degree in the connection state judgment rule library management module comprises: inputting the physical parameters of the standard connector sample into the state judgment rule library, counting the number of effective features recognized by the state judgment rule library, and calculating the ratio of the number of effective features to the total number of features of the standard connector sample as the feature coverage degree; when the feature coverage degree is lower than the preset threshold, generating new judgment rules containing feature dimensions, judgment thresholds and abnormal positioning parameters based on an analysis model by comparing the feature differences between the standard connector sample and the connector to be detected.

3. The monitoring system of high-density connector connection state according to claim 1, wherein, The state feature analysis module collects multi-dimensional physical parameters, which comprises: obtaining the impedance fluctuation trajectory and signal attenuation coefficient of each contact to generate a contact basic feature value; dividing the contact clusters according to physical regions, calculating the distribution density of the contact basic feature values in each contact cluster, and generating regional feature values; statistically analyzing the crosstalk intensity between adjacent contacts to generate topological correlation feature values.

4. The monitoring system of a high-density connector connection state according to claim 3, wherein The multi-frequency band optical signal monitoring array generates a scanning strategy, which comprises: matching the connector type with a preset frequency band allocation rule to determine the state feature interval corresponding to each monitoring unit; matching the regional feature values with the state feature interval, and when a certain regional feature value is in the state feature interval of a certain monitoring unit, the physical region is divided into the target scanning region of the monitoring unit; integrating the target scanning regions of all monitoring units to form a scanning path configuration table.

5. The monitoring system of high-density connector connection state according to claim 1, wherein, The multi-frequency band optical signal monitoring array performs scanning, which comprises: generating a carrier frequency distribution spectrum according to the scanning path configuration table, wherein different frequency bands correspond to different physical regions; applying a frequency weighting coefficient to the carrier signal, and the frequency weighting coefficient is dynamically adjusted according to the contact basic feature value; loading the weighted carrier signal to the target scanning region to collect a reflected signal intensity change curve; applying time delay processing to the reflected signal intensity change curve to cause time offset of different frequency band signals; The superimposed multi-band signal after time delay processing generates a comprehensive response waveform.

6. The connection state monitoring system of a high-density connector according to claim 5, wherein The contact state mapping module performs feature matching, including: Extracting peak fluctuation features and energy attenuation features of the comprehensive response waveform; Calculating the similarity index of the peak fluctuation features and the reference features in the standard connection state feature library; When the similarity index is lower than the matching threshold, mark the contact area as an abnormal state area, and generate a connection state identifier containing an abnormal level and position coordinates.

7. The connection condition monitoring system of a high-density connector according to claim 1, wherein The abnormal state iteration module sends a reconfiguration instruction, including: Statistics of the number of contacts that do not meet the connection integrity condition and their distribution density; Generating new frequency band allocation rules and time delay parameters according to the distribution density; Encapsulating the new frequency band allocation rules and time delay parameters into a reconfiguration instruction.

8. The connection condition monitoring system of a high-density connector according to claim 1, wherein Further comprising: A thermal distribution monitoring module for real-time acquisition of high-density connector surface temperature field data; When the contact state mapping module detects an abnormal state area, extract the temperature gradient change rate of the corresponding area; Input the temperature gradient change rate into the connection state judgment rule library for secondary verification.

9. The connection state monitoring system of a high-density connector according to claim 8, wherein The secondary verification of the thermal distribution monitoring module includes: Correcting the abnormal level according to the temperature gradient change rate; When the corrected abnormal level exceeds the critical threshold, trigger the multi-band optical signal monitoring array to perform directional enhanced scanning.

10. A method of monitoring the connection state of a high-density connector, characterized by, All modules and method processes of the connection state monitoring system of the high-density connector according to any one of claims 1-9.

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