A monitoring system and method for high-density connector connection state

Abstract

The application 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 library management module, a multi-frequency 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 connector type; the rule library management module loads the corresponding rule library, evaluates the feature coverage, generates new rules and updates when the feature coverage is insufficient; the monitoring array generates a scanning strategy; the main control processing unit drives the scanning and receives the optical signal response feature set; the contact state mapping module matches the feature set with the standard library to generate the connection state identification of each contact; and the abnormal state iteration module sends a reconfiguration instruction to the monitoring array when the connection integrity is not satisfied. The system can comprehensively and accurately monitor the connection state of the high-density connector, and timely identify and handle the connection abnormality.

Description

Technical Field

[0001] This invention relates to the field of connector monitoring technology, specifically to a monitoring system and method for the connection status of high-density connectors. Background Technology

[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 typically contain a large number of contacts with minute spacing, and must maintain stable connection performance in complex environments. However, during long-term use, factors such as vibration, temperature changes, and dust corrosion can easily cause abnormalities in the connection status of high-density connectors, such as loose contacts, oxidation, and poor contact.

[0003] If these connection anomalies are not detected and handled in a timely manner, they may lead to signal transmission interruptions, data loss, or even equipment failure, causing serious economic losses. Traditional connector condition monitoring methods mostly rely on manual inspection or simple electrical parameter measurements, which have many limitations. Manual inspection is not only inefficient but also difficult to accurately identify minute connection anomalies; electrical parameter measurements are easily affected by electromagnetic interference, have limited monitoring accuracy, and cannot comprehensively reflect the multi-dimensional condition characteristics of the connector.

[0004] As electronic devices evolve towards higher density, miniaturization, and higher reliability, higher demands are placed on the real-time performance, accuracy, and comprehensiveness of high-density connector connection status monitoring. Existing monitoring technologies are insufficient to meet these requirements, necessitating a monitoring system capable of comprehensively collecting multi-dimensional physical parameters, intelligently determining connection status, and dynamically iteratively handling abnormal states. This will improve the operational reliability of high-density connectors and ensure the stable operation of related equipment and systems. Summary of the Invention

[0005] The purpose of this invention is to provide a monitoring system and method for the connection status of high-density connectors, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a monitoring system for the connection status of high-density connectors, the system comprising:

[0007] The status feature analysis module is used to collect multi-dimensional physical parameters of high-density connectors and identify connector types.

[0008] The connection status determination rule base management module is used to load the corresponding status determination rule base according to the connector type, determine the feature coverage of the status determination rule base relative to the standard connector samples of the same type, and if the coverage is lower than a preset threshold, generate new determination rules based on the analysis model and update the status determination rule base.

[0009] A multi-band optical signal monitoring array is used to generate a multi-band optical signal scanning strategy based on the updated state determination rule base.

[0010] The main control processing unit is used 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 to receive the optical signal response feature set returned by the multi-band optical signal monitoring array;

[0011] The contact status mapping module is used to match the optical signal response feature set with the standard connection status feature library to generate the connection status identifier of each contact.

[0012] An abnormal state iteration module is used to determine whether the connection integrity condition is met based on the connection status identifier, and when it is not met, it sends a reconfiguration command to the multi-band optical signal monitoring array.

[0013] Preferably, the feature coverage determination module in the connection status determination rule base management module includes:

[0014] The physical parameters of the standard connector sample are input into the state determination rule base. The number of valid features identified by the state determination rule base is counted, and the ratio of the number of valid features to the total number of features of the standard connector sample is calculated as the feature coverage.

[0015] When the feature coverage is lower than a preset threshold, by comparing the feature differences between the standard connector sample and the connector under test, a new judgment rule is generated based on the analysis model, which includes feature dimensions, judgment thresholds and anomaly location parameters.

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

[0017] Obtain the impedance fluctuation trajectory and signal attenuation coefficient of each contact point, and generate the basic characteristic values ​​of the contact points;

[0018] The contact points are divided into clusters according to physical regions. The distribution density of the basic characteristic values ​​of the contacts within each contact cluster is calculated to generate regional characteristic values.

[0019] The crosstalk intensity between adjacent contacts is statistically analyzed to generate topological correlation feature values.

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

[0021] Based on the connector type and a preset frequency band allocation rule, determine the state characteristic range corresponding to each monitoring unit;

[0022] The region feature value is matched with the state feature interval. When the region feature value is within the state feature interval of a monitoring unit, the physical region is defined as the target scanning region of the monitoring unit.

[0023] The target scanning areas of all monitoring units are integrated to form a scanning path configuration table.

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

[0025] A carrier frequency distribution spectrum is generated based on the scan path configuration table, where 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 basic characteristic value of the contact point;

[0027] A weighted carrier signal is applied to the target scanning area, and the curve of the reflected signal intensity change is collected.

[0028] A timing delay is applied to the intensity variation curve of the reflected signal to cause time shifts in signals of different frequency bands;

[0029] The multi-band signals after time delay processing are superimposed to generate a comprehensive response waveform.

[0030] Preferably, the feature matching performed by the touch state mapping module includes:

[0031] Extract the peak fluctuation characteristics and energy decay characteristics of the comprehensive response waveform;

[0032] Calculate the similarity index between the peak fluctuation feature and the reference feature in the standard connection state feature library;

[0033] When the similarity index is lower than the matching threshold, the touch point area is marked as an abnormal state area, and a connection status identifier containing the abnormality level and location coordinates is generated.

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

[0035] Count the number of contacts that do not meet the connection integrity condition and their distribution density;

[0036] New frequency band allocation rules and timing delay parameters are generated based on the distribution density.

[0037] The new frequency band allocation rules and timing delay parameters are encapsulated into reconfiguration instructions.

[0038] Preferably, the system further includes:

[0039] The thermal distribution monitoring module is used to collect real-time temperature field data on the surface of high-density connectors;

[0040] When the contact state mapping module detects an abnormal state area, it extracts the temperature gradient change rate of the corresponding area.

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

[0042] Preferably, the secondary verification performed by the thermal distribution monitoring module includes:

[0043] The anomaly level is corrected based on the rate of change of the temperature gradient;

[0044] When the corrected anomaly level exceeds the critical threshold, the multi-band optical signal monitoring array is triggered to perform directional enhancement scanning.

[0045] Preferably, the present invention also includes a connection status monitoring method for high-density connectors, the method comprising all the modules and method flow of the connection status monitoring system for high-density connectors described above.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] Through the collaborative work of multiple modules, comprehensive and intelligent monitoring of connector status is achieved. The status feature analysis module can collect multi-dimensional physical parameters and identify connector types, providing comprehensive and targeted basic data for subsequent status determination. This avoids the limitations of single-parameter monitoring and makes the description of connector status richer and more accurate.

[0048] The connection status 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, it generates new rules based on the analysis model and updates the library, ensuring that the determination rules always match the actual status characteristics of the connector. This enhances the adaptability and accuracy of status determination and can cope with the status changes of different types of connectors and the same type of connector at different stages of use.

[0049] The multi-band optical signal monitoring array generates scanning strategies based on an updated rule base. Leveraging the advantages of optical signal monitoring, it is unaffected by electromagnetic interference and can accurately capture subtle changes in the connector's state. Compared to traditional electrical measurement methods, it improves the stability and sensitivity of monitoring and can identify earlier connection anomalies.

[0050] As the core of the system, the main control processing unit coordinates the work of each module, drives the monitoring array to perform scanning and receive optical signal response feature sets, ensures the orderly progress of the monitoring process and the efficient transmission and processing of data, and provides reliable data support for subsequent state mapping.

[0051] The contact status mapping module matches the optical signal response feature set with the standard feature library to generate the connection status identifier of each contact, realizing the accurate positioning of the status of each contact. This allows staff to quickly understand which specific contact is abnormal, facilitating targeted maintenance and handling, and reducing the time and cost of troubleshooting.

[0052] When the abnormal state iteration module determines that the connection integrity is not met, it sends a reconfiguration command to the monitoring array. By dynamically adjusting the scanning strategy, it further monitors and confirms the abnormal state, avoiding possible misjudgments that may occur during one-time monitoring, improving the reliability of abnormal state identification, and ensuring timely response and handling of connection anomalies. Attached Figure Description

[0053] Figure 1 This is a timing diagram of the high-density connector connection status monitoring system described in this invention;

[0054] Figure 2 This is a flowchart for feature coverage determination and rule updating;

[0055] Figure 3 A flowchart for generating a multi-band optical signal scanning strategy;

[0056] Figure 4 Flowchart for multi-band optical signal scanning execution;

[0057] Figure 5 This is a flowchart for the secondary verification of thermal distribution. Detailed Implementation

[0058] 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.

[0059] Please see Figure 1 This invention provides a monitoring system for the connection status of high-density connectors, the system comprising:

[0060] The system operation begins with the state feature analysis module. This module uses a sensor array deployed near the connector interface to collect basic physical parameters such as impedance fluctuation trajectories and signal attenuation coefficients at each contact point of the connector in real time. Simultaneously, the module incorporates a connector type identification algorithm, which determines the specific model of the connector under test by comparing contact layout patterns, electrical characteristic fingerprints, or preset identification codes. The connection state determination rule base management module loads the corresponding state determination rule base from the storage unit based on the identified connector type. This module calculates the effective coverage of the rule base with the physical parameter features of standard connector samples of the same type. Specifically, it inputs all physical parameters of the standard samples into the rule base, counts the number of physical parameter features that the rule base can successfully identify and match, and calculates the ratio of this effective feature count to the total number of features in the standard samples. If this ratio is lower than a preset threshold, it indicates that the existing rule base does not adequately cover the features of the current connector model. At this point, the module starts the analysis model, compares the differences in physical parameter features between the standard samples and the connector under test, generates new determination rules including descriptions of the new feature dimensions, determination threshold settings, and abnormal location location parameters, and automatically updates the rule base. The multi-band optical signal monitoring array receives updated rule base information and generates a targeted optical signal scanning strategy. Based on this scanning strategy, the main control processing unit drives the multi-band optical signal monitoring array to perform scanning operations on the high-density connector and receives the optical signal response feature dataset returned by the array. The contact state mapping module performs pattern matching between this dataset and a pre-stored standard connection state feature library, generating 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 preset integrity conditions. If the integrity conditions are not met, this module generates a reconfiguration command and sends it to the multi-band optical signal monitoring array, triggering a refined retest of the abnormal area. Through the collaborative work of these modules, the system forms a closed-loop monitoring process of "acquisition-analysis-judgment-scanning-mapping-feedback-optimization".

[0061] Example 1: See Figure 2 This demonstrates that in the implementation of the connection state determination rule base management module, feature coverage evaluation and dynamic rule base updates constitute the key to the system's adaptive capability. The module's operation begins with the confirmation of the type of the high-density connector under test, information provided by the state feature analysis module. Based on this type identifier, the module retrieves and loads the pre-associated state determination rule base from the system storage unit. This rule base contains a series of preset logical conditions and feature matching patterns used to determine the connection state based on physical parameters. Simultaneously, the module accesses a stored standard connector sample database, which stores comprehensive and validated multi-dimensional physical parameter datasets collected under standard connection states for each known connector type. These data represent the characteristic benchmark for that connector model under ideal connection conditions.

[0062] Feature coverage calculation is a core step in evaluating whether the currently loaded rule base is sufficient to accurately determine the status of the connector model. The module inputs all physical parameter features of the selected standard sample of the same model as the connector under test into the currently active status determination rule base. These physical parameter features typically contain multi-dimensional information, such as: detailed trajectory data of impedance changes over time or frequency for each contact under a specific test sequence; quantitative values ​​of signal attenuation coefficients for 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 input feature. The module continuously tracks the processing results of the rule base, accurately counting the number of features successfully identified, matched to predefined status categories, and outputting valid determination conclusions by the rule base; this number is the number of valid features. Simultaneously, the module obtains the total number of physical parameter feature entries contained in the standard sample, i.e., the total number of features in the standard connector sample. Feature coverage is obtained through a ratio calculation, which is the number of valid features divided by the total number of features in 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. This threshold is a configurable parameter, typically set within a certain range based on experience and the required monitoring reliability. If the calculated feature coverage is lower than this preset threshold, it indicates that the currently loaded state determination rule base has insufficient characterization capability for that specific connector model. This means that the rule base fails to fully cover or effectively process all or part of the key features exhibited by this connector model under standard conditions, potentially leading to omissions or errors in state determination. When insufficient coverage is detected, the module automatically activates its built-in analysis model. The model is designed to identify feature differences and generate new determination rules. The model first focuses on features in the standard samples that were not successfully identified and determined by the current rule base—that is, the feature subset not covered by the rule base. The model analyzes the specific performance of these uncovered features in the standard samples, including their numerical range, distribution patterns, and variation patterns. Next, the model acquires the actual measurement data of the connector under test on the same feature dimensions. By carefully comparing the specific numerical differences, distribution shifts, or pattern changes of the standard samples and the connector under test on these uncovered features, the model performs feature difference analysis.

[0064] Based on the results of the aforementioned difference analysis, the model executes the logic for generating new rules. This logic aims to define new feature dimensions, set applicable decision thresholds, and associate anomaly location information. For example, the model might identify an impedance trajectory pattern that is stable in standard samples but exhibits abnormal fluctuations on the connector under test, a pattern not defined in the current rule base. The model will describe the characteristics of this new pattern and, based on the statistical distribution of standard samples and the degree of deviation of the connector under test, set a numerical threshold for determining whether the pattern is abnormal. Simultaneously, the model will analyze the physical contacts or regions primarily associated with this feature pattern, generating anomaly location parameters. Finally, the model outputs one or more new decision rule entries containing complete information. Each entry explicitly specifies the new feature dimension, the decision threshold for that feature dimension, and the corresponding location parameters when the feature is abnormal.

[0065] The connection status determination rule base management module receives newly added determination rule entries generated by the analysis model. This module integrates these new entries into the currently loaded status determination rule base. The integration process must ensure that the new rules are logically consistent with the existing rules to avoid conflicts. After integration, the updated status determination rule base replaces the old version and becomes the basis for subsequent status determination of the currently tested connector (and subsequent connectors of the same model). Through this series of operations—coverage assessment, difference analysis, rule generation, and library updates—the system achieves self-improvement and dynamic adaptability of the status determination rule base, enabling it to more accurately respond to characteristic changes and potential new feature patterns of different connector models, maintaining effective monitoring of connector status. This process requires no manual intervention, automatically responding to insufficient rule base coverage and continuously optimizing the accuracy and reliability of monitoring.

[0066] Example 2: See Figure 3This demonstrates how the state characteristic analysis module performs multi-dimensional physical parameter acquisition and analysis on high-density connectors, forming the foundational data source for the system to identify connection status. The module achieves synchronous data acquisition through a high-precision sensor network deployed near the connector interface. Sensor types are selected based on parameter characteristics, including miniature probe arrays for electrical characteristic measurements, couplers for high-frequency signal analysis, and miniature position encoders for spatial positioning. One core dimension of the acquisition is the impedance fluctuation trajectory of each individual contact. This trajectory is obtained by applying a test excitation signal of specific frequency and amplitude to the contact and measuring its response voltage and current changes in real time. The measurement process covers a preset frequency range or time window, recording a complete curve of impedance value changing with frequency or time. These curves reflect the contact resistance, material properties, and possible oxidation or contamination status of the contact. Another key acquisition dimension is the signal attenuation coefficient. The module injects a test signal of known amplitude and frequency into selected contacts 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 and received signals, the attenuation coefficient, the proportion of signal loss during transmission, 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 from 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 within a specific frequency band, the peak-to-peak value of the impedance fluctuation, the zero-crossing rate, or the frequency of occurrence of a specific fluctuation pattern. For the signal attenuation coefficient, the algorithm extracts its attenuation value at key frequencies, the slope of attenuation as a function of frequency, or the integral value within a preset frequency band. After calculation, each contact generates a set of quantified values, called the contact's basic characteristic values. These characteristic values ​​are stored in structured data form, with each value corresponding to a specific attribute describing the electrical state of that contact.

[0068] To reveal the spatial relationships within the connector, the module divides the densely packed contacts into logical physical regions, forming contact clusters, based on the connector's physical structure design and layout. This division can be based on the connector PCB's wiring partitions, functional block divisions, or row / column matrix divisions. Each contact cluster contains a group of spatially adjacent or functionally related contacts. For each defined contact cluster, the module performs statistical analysis on the fundamental characteristic values ​​of all contacts within it. The analysis focuses on the distribution characteristics of these characteristic values ​​within the cluster. For example, for the fundamental characteristic value of "mid-frequency average impedance" for all contacts within a cluster, the module calculates its statistical distribution across the entire cluster. Specific calculations include: determining the average value of the characteristic value (reflecting the overall level of the cluster); calculating the variance or standard deviation (reflecting the dispersion of the characteristic value among contacts within the cluster); counting the number and percentage of contacts whose characteristic value falls within a preset range; or applying kernel density estimation to depict the probability density distribution profile of the characteristic value within the cluster. Based on these distribution characteristic calculations, the module generates one or more comprehensive indicators representing the overall electrical characteristics of the physical region, referred to as region characteristic values. For example, "cluster average impedance" represents the overall conductivity of the area, "cluster impedance dispersion" reflects the consistency of contact impedance within the area, and "high impedance contact percentage" indicates the proportion of potentially defective contacts within the area. These regional characteristic values ​​macroscopically characterize the electrical state of different blocks of the connector.

[0069] Furthermore, the module analyzes the interaction between contacts, i.e., crosstalk. A specific test signal is injected into one contact (the driving contact), while the amplitude of the induced signal is measured at one or more physically adjacent contacts (the victim contacts). Measurements are performed at multiple frequency points, and crosstalk intensity is typically quantified in decibels (dB), representing the amplitude ratio of the driving signal to the induced signal. The module iterates through all possible adjacent contact pairs (based on the adjacency relationships defined by the connector layout), measuring and recording the crosstalk intensity value between each pair. Based on all measured crosstalk intensity data, the module generates topology correlation feature values. These feature values ​​describe the isolation performance between contacts within the connector, for example: calculating the average crosstalk intensity of all adjacent contact pairs (reflecting average isolation); identifying the maximum crosstalk intensity and its corresponding contact pair location (identifying the most severe crosstalk path); and counting the number of contact pairs with crosstalk intensity exceeding a preset threshold (identifying high-risk crosstalk areas). Topology correlation feature values ​​reveal the strength and distribution of electrical coupling between contacts, which is crucial for identifying short-circuit risks or signal integrity degradation.

[0070] Multi-band optical signal monitoring arrays utilize regional feature values ​​output by a state feature analysis module to formulate their scanning strategies. The array consists of multiple independent optical signal monitoring units, each capable of independently controlling the wavelength (frequency band) of its emitted and received optical signals. Internally, the array maintains a preset configuration mapping table, indexed according to the connector type identified by the state feature analysis module. For each connector type, the mapping table defines one or more state feature intervals that each optical signal monitoring unit is responsible for monitoring. A state feature interval refers to the numerical range of regional feature values. For example, monitoring unit A might be assigned to monitor the physical region where the "cluster average impedance" value is within the interval [Z_low_A, Z_high_A]; monitoring unit B might be responsible for the region where the "cluster impedance dispersion" value is within the interval [D_low_B, D_high_B]; and monitoring unit C might be responsible for the region where the "high impedance contact ratio" exceeds the threshold P_C. Different units may focus on different dimensions of regional feature values ​​or different numerical intervals.

[0071] The array receives the regional feature values ​​of each physical region calculated by the state feature analysis module. For each physical region, the array compares its calculated regional feature value with the state feature intervals defined in the configuration mapping table, which are the responsibility of each monitoring unit. When a regional feature value of a physical region falls within a specific state feature interval of a monitoring unit, the system determines that the physical region meets the monitoring range of that monitoring unit and marks it as the "target scanning area" of that monitoring unit. A physical region may be assigned to multiple monitoring units for scanning (multi-dimensional scanning) because its multiple regional feature values ​​meet the conditions of different units. Similarly, a monitoring unit may be responsible for scanning multiple physical regions that meet its state feature interval conditions. The array summarizes the target scanning area allocation results of all monitoring units and generates a detailed scanning path configuration table. This table is the core output of the scanning strategy, and it 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 to the unit for scanning; the optical signal frequency band (wavelength) to be used for each target physical region; and the suggested scanning parameters (such as basic signal strength) that may be required based on the regional feature values. The scan path configuration table provides a clear basis for path planning and resource allocation for the multi-band optical signal monitoring array to perform accurate, efficient and targeted scanning operations, ensuring that scanning energy is focused on key areas that reflect 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 comprehensive response waveform S(t) output by the multi-band optical signal monitoring array. The module applies signal processing algorithms to extract features from S(t). The primary feature extracted is the peak fluctuation feature. The algorithm detects local maxima (peaks) in S(t), records the amplitude of each peak, calculates the time interval between adjacent peaks, and analyzes the trend of peak amplitude changes over time or sequence. Secondly, energy attenuation features are extracted. The algorithm calculates the energy integral of S(t) within a specific time window, or analyzes the descent slope of the signal envelope (obtained through methods such as Hilbert transform) to quantify the signal energy attenuation rate. The module accesses a pre-stored standard connection state feature library. This library contains a set of reference features obtained from the same high-density connectors of the same model under standard connection states through the same scanning process. The module calculates the similarity index Sim between the observed features and the reference features. Similarity calculation can employ various algorithms, such as calculating the Euclidean distance between feature vectors (a smaller distance indicates high similarity) or calculating cosine similarity (a value close to 1 indicates high similarity). A specific similarity calculation can be expressed as:

[0075]

[0076] Where: N represents the number of features involved in the calculation; F obs,i F represents the i-th feature value extracted from the observed waveform; ref,i φ(F) represents the i-th reference feature value in the standard feature library. obs,i ,F ref,i The expression represents the similarity function used to calculate the similarity of a single feature i. i This represents the weight coefficient of the i-th feature, used to reflect the importance of different features in state determination. The weights can be preset based on experience or feature sensitivity; Sim is a 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 preset matching threshold T. mtch A comparison is made. This threshold is a configurable parameter used to define the boundary between "normal" and "abnormal". If Sim ≥ T mtch If the response characteristics of the contact area are sufficiently similar to the standard state, it is considered "normal" and marked as such. <T mtch If the Sim value is lower than T, it indicates that the response characteristics of the contact area differ significantly from the standard state, and the module marks the contact area as an "abnormal state area". Simultaneously, the module determines the abnormal state based on the Sim value being lower than T. mtchThe degree of anomaly is assessed by measuring the difference between the anomaly level and the measured value. This difference is mapped to a preset anomaly level range. Combined with the location information recorded during the scanning process, the module generates a connection status identifier that includes the anomaly level and precise location coordinates. This identifier is the system's specific judgment output regarding the local state of the connector.

[0078] Example 4: The abnormal state iteration module performs a final determination of the overall connection integrity of the high-density connector based on the set of connection status identifiers output by the contact status mapping module. Connection integrity conditions are a set of preset logical rules used to determine whether the current connection status meets the functional and safety requirements of the device or system. Typical integrity condition definitions may include: the status identifiers of all contacts marked as "critical paths" must be "normal"; the number of non-critical contacts marked as "abnormal state areas" must not exceed the maximum allowable threshold; and these abnormal contacts cannot form high-density clusters in physical space, i.e., the number of abnormal contacts in any local area must not exceed another preset local density threshold. This condition aims to prevent complete functional loss or overheating risks caused by the failure of local contact groups. After completing the scanning and matching, the contact status mapping module outputs the following set of connection status identifiers, see Table 1.

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

[0080] Touch ID Position coordinates (row, column) Abnormal level Area code C23 (2,3) medium R_Z1 C24 (2,4) slight R_Z1 C25 (2,5) medium R_Z1 C32 (3,2) slight R_Z1 C56 (5,6) serious R_Z3 C78 (7,8) slight R_Z5

[0081] The abnormal state iteration module receives this set of state identifiers. The module first checks the status of all critical path contacts, assuming all critical contacts are normal in this example. Next, the module counts the total number of contacts marked as "abnormal state areas" on the entire connector, denoted as N. abnormal Assume that the statistics yield N abnormal =15, while the maximum allowed number of abnormal contacts in the system is Max. global =20. Because N abnormal <Max globalThe global quantity condition is met. Subsequently, the module analyzes the spatial distribution density of abnormal contacts. The module divides the connector contact array into analysis grid cells of a preset size. The module determines that the connection integrity condition is not met because there is a local area where the density of abnormal contacts exceeds the maximum allowable value. At this point, the module initiates 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 to which it exceeds 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 this high-density abnormal area. Specific measures may include: allocating more optical signal monitoring units to this area for collaborative scanning; or allocating a wider bandwidth or higher-density subcarriers to the monitoring units responsible for scanning this area to improve the scanning accuracy in this area; or specifying the use of characteristic frequency bands that are more sensitive to detecting the identified abnormal types (moderate, 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] Simultaneously, the module generates new timing delay parameters. In the initial scan, reflected signals from different frequency bands (regions) are subjected to a fixed timing delay for separation. However, in high-density anomaly areas, contact failures may interact, complicating the characteristics of reflected signals. The initial settings may be insufficient to clearly separate subtle signal differences or potential new patterns within this area. The module calculates new delay parameters based on the anomaly level distribution (including moderate and slight) and density of the area. These new parameters may include: setting differentiated delay amounts for different sub-regions or feature points within the high-density area to enhance signal discriminability on the time axis; or increasing the overall signal delay offset for the area, allowing it to occupy a wider time window in the composite waveform S(t) for detailed analysis. For non-high-density areas, the timing delay parameters may remain unchanged or only undergo adaptive adjustments.

[0083] The module encapsulates the newly calculated frequency band allocation rules and new timing delay parameters into a structured command message, namely the reconfiguration command. This command includes a clear command identifier, a target connector identifier, a list of monitoring units requiring reconfiguration, new frequency band parameters (center frequency, bandwidth) specified for each unit, a new timing delay parameter table (specifying new delay values ​​for different regions or frequency bands), and a list of high-priority regions requiring immediate enhanced scanning. After encapsulation, the module sends the reconfiguration command to the control interface of the multi-band optical signal monitoring array via the system's internal communication bus.

[0084] Upon receiving a reconfiguration command, the multi-band optical signal monitoring array parses its contents. Based on the new frequency band allocation rules in the command, the array controller reconfigures the operating parameters of the light source and receiver of the relevant monitoring units. Simultaneously, the controller loads a new timing delay parameter table and updates the delay settings of the signal acquisition and processing units. After the configuration update, the array, according to the command requirements, prioritizes and focuses on the high-priority area specified in the command, performing one or more enhanced scans according to the new frequency band and timing parameters. This scan aims to acquire more refined, lower-noise, or more characteristic optical signal response data for this high-priority area. After the scan, the array returns the new response data to the contact status mapping module for further analysis and status label updates. Through this iterative process, the system specifically improves its diagnostic capabilities for complex and abnormal areas.

[0085] Example 5: See Figure 5 This demonstrates the system-integrated thermal distribution monitoring module as a means of auxiliary verification and enhanced monitoring. This module operates independently of the optical signal monitoring array; its core component is a non-contact infrared thermal imager array deployed above or to the side of the high-density connector, or a network of miniature distributed temperature sensors embedded in the connector carrier plate. The module continuously collects infrared radiation or temperature readings across the entire connector surface at a fixed sampling period. The sensor data undergoes calibration and spatial registration to generate a high-resolution, real-time surface temperature field distribution map. This temperature field data not only includes the instantaneous temperature value at each pixel or sensor location 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 and identifying high-temperature points. The module is in standby mode, and its in-depth analysis function is triggered by the judgment results of the contact state mapping module.

[0086] When the contact status mapping module completes the analysis and matching of the optical signal response characteristics and outputs a set of connection status identifiers, this set is simultaneously sent to the abnormal status iteration module and the thermal distribution monitoring module. The thermal distribution monitoring module parses the received status identifier set and filters out all entries marked as "abnormal status areas." For each physical area marked as abnormal (uniquely identified by location coordinates or area number), the module initiates a targeted thermal data analysis process. The module extracts the local temperature data corresponding to the abnormal physical area from the real-time stored temperature field data. The local temperature data contains a sequence of temperature values ​​from all sampling points within the area over the most recent time window. Based on this temperature sequence, the module calculates the key thermal characteristic of the abnormal area: the rate of change of the temperature gradient. The calculation process involves time-series analysis of the temperature sequence. For example, the module might calculate the average rate of temperature rise (the amount of temperature change per unit time) of the area over a specific time interval. Alternatively, it might apply a linear fitting method to calculate the slope of temperature change over time, which represents the rate of change of the temperature gradient. More complex analyses might include detecting abrupt changes or nonlinear patterns in the temperature sequence. The rate of change of temperature gradient quantifies the dynamic characteristics of heat generation or heat dissipation in the anomalous region before, during, or after optical signal scanning.

[0087] The calculated temperature gradient change rate is input into the connection status determination rule base maintained by the connection status determination rule base management module. This rule base not only contains determination rules based on optical signal characteristics but also pre-sets logic for comprehensive status verification by integrating thermal characteristics. The rule base stores the correlation knowledge between different connection states (especially various abnormal states) and typical temperature behavior patterns. For example, the rule base may define: "increased contact resistance" anomalies are usually accompanied by a medium to high positive temperature gradient change rate (accelerated temperature rise); "cold solder joint" or "microcrack" anomalies may manifest as slight or unstable temperature changes; "complete open circuit" may have no significant temperature rise or even a temperature decrease; "partial short circuit" may lead to a sharp temperature rise. The rule base receives the preliminary determination result of the optical signal for a specific abnormal area (such as "moderate poor contact") and its corresponding temperature gradient change rate value. The rule base applies the built-in rule logic for secondary verification. One of the core tasks of secondary verification is to correct the anomaly level initially given based on optical signal characteristics. For example, if the optical signal characteristics of a certain area are initially determined to be "slightly abnormal," but its temperature gradient change rate shows a rapid temperature rise (high positive value), the rules in the rule base may indicate that this thermal behavior is more consistent with the characteristics of a "moderate" or "severe" contact resistance anomaly, thus increasing the anomaly level. Conversely, if the optical signal of a certain area is determined to be "moderately abnormal," but the temperature gradient change rate is stable or very low, the rule base may consider that the thermal characteristics do not support a severe anomaly, decreasing the anomaly level or maintaining the original judgment but marking it as requiring further observation. The correction process is performed based on the mapping relationships defined in the rule base, established based on historical data or physical models.

[0088] The rule base defines critical threshold parameters related to temperature characteristics. These thresholds are used to distinguish temperature behavior at different risk levels. For example, a "critical threshold for temperature rise rate" is set. When the corrected anomaly level (which integrates optical signal characteristics and temperature characteristics) exceeds the preset critical threshold, the system determines that the anomaly area is high-risk and may cause overheating failure or functional malfunction. At this time, the thermal distribution monitoring module triggers an enhanced scan command. This command is directional, explicitly specifying the target location for the enhanced scan, i.e., the location coordinates or area number of the high-risk anomaly area. The command also includes a scan mode identifier, indicating that the multi-band optical signal monitoring array needs to adopt the preset "directional enhanced scan" mode.

[0089] After receiving a trigger command from the thermal distribution monitoring module, the multi-band optical signal monitoring array parses the command content to obtain the target scanning position and scanning mode requirements. The array controller adjusts the operating status of the relevant monitoring units according to the preset "directional enhanced scanning" mode configuration parameters. The enhanced scanning mode may include one or more of the following strategies: significantly increasing the power of the optical signal transmitted to the target area to enhance the reflected signal strength and improve the signal-to-noise ratio; increasing the scanning bandwidth or using a denser set of subcarrier frequencies in the target area to obtain richer frequency domain response information; increasing the scanning sampling rate to obtain signal waveforms with higher time resolution; performing multiple repeated scans and signal averaging in the target area to suppress random noise; or using a combination of characteristic frequency bands specifically optimized for the identified anomaly type (inferred from the corrected level and thermal characteristics). Based on the new configuration parameters, the array focuses on the high-risk anomaly area specified by the command and performs one or more enhanced scanning operations. The scanning process is strictly limited to the target area or a small extended area centered on it to avoid unnecessary global scanning that consumes resources. The optical signal response data acquired by the enhanced scan (which may be a higher resolution or higher quality composite response waveform or raw data) is sent back to the contact status mapping module.

[0090] The contact status mapping module receives new data from the enhanced scan. This module applies the same feature extraction and similarity matching process, but this time it processes high-quality scan data targeting high-risk areas. The module generates updated connection status identifiers based on more precise scans, specifically for the target area. This result may confirm, correct, or further refine previous anomaly level and type judgments. The updated status identifiers are fed back to the anomaly status iteration module for reassessing overall connection integrity. The intervention of the thermal distribution monitoring module, by introducing independent temperature dimension features, cross-validates and reassesses the initial optical signal detection results, triggering more refined optical re-testing when high-risk anomalies are identified. This dual-modal data fusion and iterative verification mechanism of optical signals and thermal distribution improves the system's sensitivity and diagnostic reliability to potential heat-related failure modes, while providing a higher confidence level for identifying high-risk areas. Through closed-loop feedback, the system continuously optimizes its understanding and response to key anomaly areas.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of 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, include: The status feature analysis module is used to collect multi-dimensional physical parameters of high-density connectors and identify connector types. The connection status determination rule base management module is used to load the corresponding status determination rule base according to the connector type, determine the feature coverage of the status determination rule base relative to the standard connector samples of the same type, and if the coverage is lower than a preset threshold, generate new determination rules based on the analysis model and update the status determination rule base. The status determination rule base includes determination rules based on optical signal features, presets the logic for comprehensive status verification by fusing thermal features, and stores the correlation knowledge between different connection states and typical temperature behavior patterns. A multi-band optical signal monitoring array is used to generate a multi-band optical signal scanning strategy based on the updated state determination rule base. The main control processing unit is used 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 to receive the optical signal response feature set returned by the multi-band optical signal monitoring array; The contact state mapping module is used to match the optical signal response feature set with the standard connection state feature library to generate the connection state identifier of each contact. The standard connection state feature library contains a set of reference features obtained by the same scanning process for high-density connectors of the same model under the standard connection state. An abnormal state iteration module is used to determine whether the connection integrity condition is met based on the connection status identifier, and when the condition is not met, a reconfiguration command is sent to the multi-band optical signal monitoring array. The state feature analysis module collects multi-dimensional physical parameters, including: Obtain the impedance fluctuation trajectory and signal attenuation coefficient of each contact point, and generate the basic characteristic values ​​of the contact points; The contact points are divided into clusters according to physical regions. The distribution density of the basic characteristic values ​​of the contacts within each contact cluster is calculated to generate regional characteristic values. Statistical analysis of crosstalk intensity between adjacent contacts is performed to generate topological correlation feature values. The multi-band optical signal monitoring array generates a scanning strategy including: Based on the connector type and a preset frequency band allocation rule, determine the state characteristic range corresponding to each monitoring unit; The region feature value is matched with the state feature interval. When the region feature value is within the state feature interval of a monitoring unit, the physical region is defined as the target scanning region of the monitoring unit. The target scanning areas of all monitoring units are integrated to form a scanning path configuration table.

2. The monitoring system of high-density connector connection state according to claim 1, wherein, The feature coverage determination module for connection status determination includes: The physical parameters of the standard connector sample are input into the state determination rule base. The number of valid features identified by the state determination rule base is counted, and the ratio of the number of valid features to the total number of features of the standard connector sample is calculated as the feature coverage. When the feature coverage is lower than a preset threshold, by comparing the feature differences between the standard connector sample and the connector under test, a new judgment rule is generated based on the analysis model, which includes feature dimensions, judgment thresholds and anomaly location parameters.

3. The monitoring system of a high-density connector connection state according to claim 2, wherein The multi-band optical signal monitoring array performs scanning including: A carrier frequency distribution spectrum is generated based on the scan path configuration table, where different frequency bands correspond to different physical regions; A frequency weighting coefficient is applied to the carrier signal, and the frequency weighting coefficient is dynamically adjusted according to the basic characteristic value of the contact point; A weighted carrier signal is applied to the target scanning area, and the curve of the reflected signal intensity change is collected. A timing delay is applied to the intensity variation curve of the reflected signal to cause time shifts in signals of different frequency bands; The multi-band signals after time delay processing are superimposed to generate a comprehensive response waveform.

4. The monitoring system of a high-density connector connection state according to claim 3, wherein The feature matching performed by the touch point state mapping module includes: Extract the peak fluctuation characteristics and energy decay characteristics of the comprehensive response waveform; Calculate the similarity index between the peak fluctuation feature and the reference feature in the standard connection state feature library; When the similarity index is lower than the matching threshold, the touch point area is marked as an abnormal state area, and a connection status identifier containing the abnormality level and location coordinates is generated.

5. The monitoring system of a high-density connector connection state according to claim 4, wherein The abnormal state iteration module sends reconfiguration instructions including: Count the number of contacts that do not meet the connection integrity condition and their distribution density; New frequency band allocation rules and timing delay parameters are generated based on the distribution density. The new frequency band allocation rules and timing delay parameters are encapsulated into reconfiguration instructions.

6. The monitoring system of a high-density connector connection state according to claim 5, wherein Also includes: The thermal distribution monitoring module is used to collect real-time temperature field data on the surface of high-density connectors; When the contact state mapping module detects an abnormal state area, it extracts the temperature gradient change rate of the corresponding area. The temperature gradient change rate is input into the state determination rule base for secondary verification.

7. The monitoring system of a high-density connector connection state according to claim 6, wherein The secondary verification performed by the thermal distribution monitoring module includes: The anomaly level is corrected based on the rate of change of the temperature gradient; When the corrected anomaly level exceeds the critical threshold, the multi-band optical signal monitoring array is triggered to perform directional enhancement scanning.

8. A method of monitoring the connection state of a high-density connector, characterized by, The steps performed by all modules of the connection status monitoring system for the high-density connector as described in any one of claims 1 to 7.

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