Method for adjusting a wiring harness holder for a vehicle

Abstract

The application relates to the technical field of automobile wiring harnesses and discloses an adjusting method for an automobile wiring harness fixing frame. The method comprises the following steps: collecting position data, tension data and environmental factor data of the automobile wiring harness and the fixing frame to generate a real-time state data set; then, multi-dimensional interactive analysis is carried out on the real-time state data set, the correlation between the fixing frame and the wiring harness is understood in combination with context information, interactive modes and potential deviation modes are identified, and an adjusting mark data set with priorities is generated; subsequently, a dynamic reference model is established based on the real-time state data set and the adjusting mark data set, and model parameters are adjusted by using the interactive analysis result; then, the real-time state data is compared with the dynamic reference model, position or tension deviation behaviors are identified, and adjusting requirements are marked, the adjusting background information is fed back to a wiring harness layout log; finally, the wiring harness layout log is analyzed, deep adjusting modes and optimization signals are identified, the adjusting traceability is carried out, the root cause and the adjusting path are determined.

Description

Technical Field

[0001] This invention relates to the field of automotive wiring harness technology, specifically to an adjustment method for an automotive wiring harness mounting bracket. Background Technology

[0002] In the manufacturing and use of automobiles, automotive wiring harnesses, as key components for transmitting electrical signals and energy, directly affect the normal operation of various vehicle systems due to their installation stability. Automotive wiring harnesses typically require mounting brackets for positioning and fixation to prevent displacement, wear, or stretching caused by vibrations and bumps during vehicle operation, which could lead to short circuits, signal interruptions, and other malfunctions. As automotive technology advances towards intelligence and electrification, the number of wiring harnesses is constantly increasing, and their layout is becoming more complex, placing higher demands on the adjustment precision and adaptability of the mounting brackets. The adjustment of automotive wiring harness mounting brackets often relies on pre-set fixing parameters or manual judgment based on experience. In the state parameter acquisition stage, existing technologies often only focus on information from a single dimension of the wiring harness or mounting bracket, such as only collecting position data or tension data, ignoring the influence of environmental factors (such as temperature, humidity, and vibration intensity) on the fit between the two. This results in the generated state data lacking comprehensiveness and failing to accurately reflect the real-time fit between the wiring harness and the mounting bracket under actual working conditions. In the data analysis and adjustment decision-making process, existing methods struggle to achieve multi-dimensional interactive analysis and fail to deeply understand the relationship between the mounting bracket and the wiring harness. In most cases, determining whether adjustment is needed relies solely on simple threshold comparisons, making it difficult to identify the interaction patterns between the mounting bracket and the wiring harness across different dimensions, and also failing to promptly detect potential deviation patterns. This results in a lack of scientific rigor in the generation of adjustment signals, often leading to untimely or directional adjustments, which in turn affect the stability of the wiring harness. In existing technologies, the benchmark models used to determine adjustment needs are mostly static models, whose parameters are difficult to adjust according to real-time operating conditions after being set. When the vehicle's driving environment changes, the wiring harness ages, or the mounting bracket has slight wear, the static benchmark model cannot adapt to these dynamic changes. This can easily lead to misjudgments when comparing real-time status data with the benchmark model, either identifying normal status changes as deviations or overlooking actual deviation problems. After identifying deviation behavior, existing methods typically only make simple adjustments to address the current deviation, lacking in-depth analysis of the adjustment context. The failure to integrate deviation identification results with harness layout logs prevents the uncovering of deeper adjustment patterns and optimization signals, and makes it difficult to trace the root cause of adjustment needs. This results in a blind adjustment process that fails to address the problem at its source, potentially leading to repeated occurrences of the same deviation problem, increasing vehicle maintenance costs and the risk of malfunctions. Summary of the Invention

[0003] The purpose of this invention is to provide an adjustment method for an automotive wiring harness mounting bracket to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides an adjustment method for an automotive wiring harness mounting bracket, the method comprising: Collect status parameters of automotive wiring harnesses and mounting brackets, including position data, tension data, and environmental factor data, and generate real-time status datasets; Multi-dimensional interactive analysis is performed on the real-time status dataset. At the same time, the context information is used to understand the relationship between the fixture and the wire harness, the interaction mode and potential deviation mode of the fixture under different dimensions are identified, and the analysis results are marked as preliminary adjustment signals to generate an adjustment mark dataset with priority. A dynamic baseline model is established based on the real-time state dataset and the adjustment marker dataset. The accuracy of the baseline model is enhanced by the interactive analysis results, and the parameters of the baseline model are adjusted in real time. The real-time collected status data is compared with the dynamic benchmark model to identify deviations in position or tension, and the adjustment requirements for the deviations are marked. The deviation identification results are fed back to the harness layout log for analysis to obtain adjustment background information. Based on the deviation identification results of the dynamic benchmark model, the wiring harness layout log is analyzed to identify deep-seated adjustment patterns and optimization signals. The adjustment patterns and optimization signals are then traced back to their source to identify the root cause and adjustment path.

[0005] Preferably, the acquisition of the status parameters of the automotive wiring harness and mounting bracket, including position data, tension data, and environmental factor data, to generate a real-time status dataset specifically includes: Determine the monitoring area of ​​the automotive wiring harness mounting bracket and capture real-time status parameters; The captured data is classified, distinguishing between location, tension, and environmental dimensions, and the data is parsed to extract location data, tension data, and environmental factor data. The parsed data is integrated and formatted to generate a structured real-time status dataset.

[0006] Preferably, the step of performing multi-dimensional interactive analysis on the real-time status dataset, simultaneously utilizing contextual information to understand the relationship between the fixture and the wiring harness, identifying the interaction patterns and potential deviation patterns of the fixture in different dimensions, and marking the analysis results as preliminary adjustment signals to generate a priority-based adjustment label dataset, specifically includes: The real-time status dataset is organized according to dimensional hierarchy, and contextual information is extracted from the data; Correlation analysis is performed between different dimensions to identify normal and deviation interaction patterns. Deviation interaction patterns are labeled to generate preliminary regulation signals and record regulation type, frequency, and severity. Priorities are assigned to each regulation signal based on its characteristics, generating a regulation label dataset with priorities. All analysis and labeling results are then integrated to output a structured regulation label dataset.

[0007] Preferably, the step of establishing a dynamic benchmark model based on the real-time state dataset and the adjustment marker dataset, enhancing the accuracy of the benchmark model using interactive analysis results, and adjusting the benchmark model parameters in real time specifically includes: Feature extraction is performed on the real-time state dataset and the adjustment label dataset to establish a dynamic benchmark model, and the dynamic benchmark model is trained using the extracted features; Based on the results of real-time interactive analysis, the parameters and structure of the baseline model are dynamically adjusted.

[0008] Preferably, the step of comparing the real-time collected status data with the dynamic benchmark model to identify deviations in position or tension, marking the adjustment requirements for the deviations, and feeding back the deviation identification results to the harness layout log for analysis to obtain adjustment background information specifically includes: The real-time collected automotive wiring harness mounting bracket status data is input into the dynamic benchmark model. The dynamic benchmark model is used to compare the real-time input data, analyze the differences between the real-time data and the benchmark behavior pattern, and identify potential adjustment needs. Detected deviations are flagged, and the degree of deviation and related contextual information are recorded. Assess the severity of the deviation behavior and assign a regulation level to each regulation requirement based on the magnitude and frequency of the deviation; Extract the deviation identification results and feed them back to the harness layout log. Search the harness layout log and read the corresponding adjustment background information.

[0009] Preferably, the analysis of the harness layout log based on the deviation identification results of the dynamic benchmark model identifies deep-seated adjustment patterns and optimization signals, and traces the adjustment patterns and optimization signals to identify the root causes and adjustment paths, specifically including: Collect harness layout logs related to the deviation identification results and obtain complete contextual information; The wiring harness layout log is analyzed to identify deep-seated adjustment patterns and optimization signals, and potential adjustment clues are extracted from the log to identify the adjustment type. The identified optimization signals are analyzed to trace their origins and identify the path through which they occur, thus identifying the initial source of the regulation. A consolidated report is generated based on all the identified information.

[0010] Preferably, the method further includes: A multi-level adjustment framework is generated based on the adjustment mark dataset. The multi-level adjustment framework includes a structure layer, a position layer, and a tension layer. The structure layer is used to optimize the overall layout process of the wire harness fixing frame, the position layer is used to ensure position accuracy, and the tension layer is used to adjust the tension to meet safety specifications. Based on the multi-level adjustment framework, an adjustment thinking chain is generated, which includes a state analysis step and an adjustment optimization step. The state analysis step includes identifying the key parameters and core requirements of the automotive wiring harness fixing bracket, and the adjustment optimization step includes position adjustment, tension compensation, and layout optimization. Using the multi-level adjustment framework and the adjustment thought chain, the global structure of the automotive wiring harness fixing bracket is adjusted to generate a primary adjustment scheme.

[0011] Preferably, the method further includes: By performing geometric analysis and parameter annotation on the standard wiring harness layout, the standard wiring harness layout is decomposed into fixed templates and variable parameters to extract specific adjustment structures in the automotive field and build a reusable adjustment knowledge base including point level, segment level and overall level. The primary adjustment scheme is segmented to generate a sequence of segments with context markers.

[0012] Preferably, the method further includes: Using a vectorized similarity calculation technique, the matching degree between each segment in the segment sequence and the adjustment template in the reusable adjustment knowledge base is calculated, and the adjustment template with the highest similarity is matched for each segment; Based on the matched adjustment template and the segment sequence with context markers, the segments in the segment sequence are finely adjusted to generate the final adjustment scheme.

[0013] Preferably, the method further includes: Based on the final adjustment scheme, adjustment control information is generated; The automotive wiring harness mounting bracket is adjusted according to the aforementioned adjustment control information; The condition of the automotive wiring harness mounting bracket after the adjustment operation is evaluated and analyzed.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This adjustment method for automotive wiring harness brackets, during the state parameter acquisition phase, goes beyond single-dimensional data acquisition. It comprehensively collects positional data, tension data, and environmental factor data of both the wiring harness and the bracket. The resulting real-time state dataset fully reflects the interaction between the wiring harness and the bracket under actual working conditions. By integrating multiple types of data, it avoids biased judgments about the operating status of both components due to missing data from a single source. This allows subsequent analysis to be based on a more realistic understanding, providing comprehensive information support for accurately identifying adjustment needs. In the data analysis phase, this method employs a multi-dimensional interactive analysis approach, combining contextual information to understand the relationship between the mounting bracket and the wiring harness. This not only identifies the interaction patterns of the mounting bracket across different dimensions but also promptly detects potential deviation patterns. Compared to simple threshold comparisons in traditional methods, this multi-dimensional interactive analysis delves deeper into the hidden relationships behind the data, accurately determining which situations constitute normal operational fluctuations and which represent deviation trends requiring attention. This results in the generation of a priority-based adjustment marker dataset. Prioritization helps staff rationally arrange the processing order when faced with multiple adjustment requests, prioritizing issues that have a greater impact on wiring harness operation and improving the efficiency of the adjustment work. The establishment and real-time adjustment of a dynamic benchmark model is one of the key advantages of this method. This model is constructed based on real-time state datasets and adjustment marker datasets, rather than using fixed parameter settings. In practical applications, the accuracy of the benchmark model is continuously enhanced using interactive analysis results, and the model parameters are dynamically adjusted based on real-time acquired data. This dynamic adjustment mechanism ensures that the benchmark model remains synchronized with the current operating conditions. When environmental factors change, the wiring harness shows slight aging, or the mounting bracket experiences minor wear, the benchmark model can adapt to these changes promptly. This avoids the misjudgment problems caused by static benchmark models failing to keep up with changes in operating conditions, ensuring the reliability of the comparison results between real-time state data and the benchmark model, thereby accurately identifying deviations in position or tension. Feeding deviation identification results back to the harness layout log for analysis provides rich background information on adjustments. By combining this information with the harness layout log, staff can understand the harness's installation history, past adjustment records, and the layout of surrounding components, avoiding isolated judgments based solely on current deviation data. Further analysis of the harness layout log based on this background information can identify deeper adjustment patterns and optimization signals, such as deviation types prone to occur under specific environmental conditions or common adjustment needs after driving on specific road sections. Simultaneously, by tracing the adjustment source, the root cause of the deviation problem can be found—whether it's due to improper initial installation location, aging of the mounting bracket material, or vibration transmission from surrounding components. This allows for the determination of the most reasonable adjustment path, avoiding blind adjustments, addressing the problem at its source, and reducing the recurrence of similar deviation problems. This method, through the organic integration of the aforementioned series of steps, forms a closed-loop adjustment system. From data acquisition, analysis, model building, deviation identification to source tracing and optimization, each step supports and feeds back to the others, continuously optimizing the adjustment strategy. This closed-loop system can continuously improve the accuracy and adaptability of the mounting bracket adjustment, ensuring that the automotive wiring harness maintains a stable operating state throughout its entire service life, reducing faults caused by wiring harness fixing problems, lowering automotive maintenance costs, and providing a reliable guarantee for the stable operation of various automotive systems. Attached Figure Description

[0015] Figure 1 This is a schematic diagram illustrating the working principle of an adjustment method for an automotive wiring harness fixing bracket according to the present invention. Figure 2 Workflow diagram for collecting status parameters and generating real-time status datasets for automotive wiring harness mounting brackets; Figure 3 A workflow diagram for generating multi-dimensional interactive analysis and adjustment of the marker dataset for real-time status datasets of automotive wiring harness fixtures; Figure 4 A flowchart illustrating the workflow for comparing automotive wiring harness mounting bracket status data and providing feedback on deviation identification results. Detailed Implementation

[0016] 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.

[0017] Please see Figure 1 This invention provides a method for adjusting an automotive wiring harness mounting bracket, the method comprising: The system collects state parameters of automotive wiring harnesses and mounting brackets, including position data, tension data, and environmental factor data, to generate a real-time state dataset. Subsequently, multi-dimensional interactive analysis is performed on this dataset to understand the relationship between the mounting brackets and wiring harnesses by incorporating contextual information, identifying interaction patterns and potential deviation patterns, and generating a priority-based adjustment marker dataset. A dynamic benchmark model is built based on the real-time state dataset and the adjustment marker dataset. The model accuracy is enhanced using the results of the interactive analysis, and model parameters are adjusted in real-time. The real-time collected state data is compared with the dynamic benchmark model to identify deviations in position or tension, mark adjustment needs, and feed them back to the wiring harness layout log to obtain adjustment background information. Finally, the wiring harness layout log is analyzed based on the deviation identification results to identify deep-seated adjustment patterns and optimization signals, and adjustments are traced to determine the root cause and adjustment path.

[0018] Example 1: See Figure 2 In the adjustment method of automotive wiring harness mounting brackets, the process of collecting state parameters involves the coordinated work of multiple stages. First, the monitoring area of ​​the mounting bracket is determined, based on the layout characteristics of the wiring harness and the structural design of the bracket. For example, in the front compartment area of ​​a vehicle, the wiring harness is typically arranged along the firewall and longitudinal beams, and the mounting bracket is installed in sheet metal holes or welded nuts. The monitoring area covers the main mounting points of the wiring harness, bending transition sections, and areas susceptible to vibration. The deployment of the sensor network must consider space constraints and signal interference. Position sensors, using non-contact photoelectric encoders or Hall effect sensors, are installed near the contact point between the mounting bracket and the wiring harness to measure the displacement of the wiring harness relative to the mounting bracket in real time. Tension sensors, using miniature strain gauges or fiber Bragg grating sensors, are embedded within the outer sheath of the wiring harness to monitor tensile and compressive forces. Environmental sensors, including temperature sensors and accelerometers, are placed in the high-heat areas of the engine compartment and high-vibration areas near the suspension.

[0019] Raw data acquisition is accomplished through a distributed data acquisition module, with each sensor node independently sampling and transmitting the data to the central processing unit. The sampling frequency of the position sensor is dynamically adjusted according to the vehicle's operating status; for example, low-frequency sampling is used during idling, and high-frequency mode is switched during high-speed driving. Data acquisition from the tension sensor must consider the material characteristics of the wiring harness; for rubber-sheathed wiring harnesses, a dynamic compensation algorithm is used to eliminate measurement errors caused by temperature. Environmental sensor data synchronously records timestamps and operating condition identifiers for easy subsequent correlation analysis. The captured raw data undergoes preprocessing, employing a sliding window filter to eliminate transient interference, such as electromagnetic pulses caused by engine ignition. Outlier detection is based on historical data statistics, removing sampling points that exceed reasonable ranges.

[0020] Positional dimension data includes the three-dimensional coordinates, angular offsets, and motion trajectories of the harness fixing points. For example, the coordinate data of a fixing point includes the deviations of the X, Y, and Z axes relative to the design reference, while the angular data records the changes in the tangent angle at the bend of the harness. Tension dimension data is divided into static reference values ​​and dynamic fluctuation components. Static values ​​reflect the installation preload, while dynamic values ​​capture force changes during operation. Environmental dimension data integrates temperature gradients, vibration spectra, and humidity changes. Temperature data indicates the distance between the measurement point and the heat source, and vibration data distinguishes the acceleration amplitudes of different frequency bands.

[0021] The parsing of location data includes calculating the magnitude and direction of the spatial offset vector and identifying the motion trend formed by continuous sampling points. The parsing of tension data extracts peak force, valley force, and root mean square value, and performs frequency domain analysis on the dynamic components to obtain the dominant frequency. The parsing of environmental data establishes a temperature-vibration coupling matrix and marks the start and end times of abnormal operating conditions. The extracted feature parameters are formatted; location data is converted to both polar and rectangular coordinate formats, tension data retains both time-domain and frequency-domain views, and environmental data is generated in both time-series and spatial distribution formats. The structured real-time status dataset is stored hierarchically, with raw sampling data, feature parameters, and associated metadata stored separately and indexed to support multi-granularity queries and analysis.

[0022] Sensor nodes are equipped with self-diagnostic capabilities and periodically report their operational status. Data transmission employs a redundancy check mechanism, triggering retransmission requests for erroneous data packets. The central processing unit performs data integrity checks, compensating for missing data segments through interpolation. The database is version-controlled, retaining historical snapshots with each update. The generation cycle of real-time status datasets is linked to vehicle operating status; long-cycle batch processing is used for idling conditions, while real-time streaming processing is enabled for aggressive driving modes.

[0023] The dynamic adjustment mechanism of the monitoring area is adaptively optimized based on operational data. When the location data of a fixed point remains stable, its sampling frequency can be gradually reduced; conversely, the monitoring density is increased for areas with frequent fluctuations. The layout strategy of the tension sensors takes into account the segmented characteristics of the wire harness, sparsely arranging them in rigid connection sections and densely monitoring flexible transition sections. The location optimization of environmental sensors is based on the simulation results of thermal and vibration fields, prioritizing coverage of areas with significant gradient changes.

[0024] In the event of a primary sensor failure, a backup sensor automatically takes over the data acquisition task. During network interruptions, a local caching mechanism ensures no data loss. The processing unit employs a heterogeneous architecture, with a general-purpose processor handling routine calculations and a dedicated coprocessor handling tasks requiring high real-time performance. System resource allocation utilizes dynamic priority scheduling, ensuring critical data channels receive the highest bandwidth guarantee. Position data is combined with vehicle attitude information to distinguish between actual offset and parallax caused by vehicle motion. Tension data is analyzed synchronously with throttle opening and gear position signals to identify the impact of driving operations on the wiring harness stress. Environmental data is correlated with geographic location information to establish a mapping relationship between external climate and the cabin microenvironment.

[0025] Example 2: See Figure 3 In the adjustment method of automotive wiring harness brackets, a multi-dimensional interactive analysis process achieves accurate identification of adjustment signals through hierarchical data processing and dynamic modeling. The real-time status dataset is first structured according to dimensional hierarchy. Position data is indexed by fixed point number and acquisition sequence; tension data is categorized and stored according to wiring harness segment identification and sampling frequency; and environmental data is grouped according to sensor type and physical property. Contextual information extraction involves obtaining wiring harness material parameters, including sheath elastic modulus and coefficient of thermal expansion, from the vehicle configuration database; querying bracket installation torque and positioning tolerances from production process records; and retrieving vibration tolerance levels and applicable temperature ranges from vehicle platform specifications. These background data are correlated with the real-time status dataset to form an information network with complete semantics.

[0026] Cross-analysis of position and tension dimensions detects the coupling relationship between spatial offset and force changes. For example, an increase in Y-direction displacement at a fixed point accompanied by a decrease in tension readings may indicate that loose clips are causing the harness to sag. Joint analysis of position and environmental dimensions identifies deformation patterns caused by temperature gradients. For instance, the axial expansion of the harness in the high-temperature zone of the engine compartment exhibits a non-linear relationship with the temperature rise curve. Coordinated observation of tension and environmental dimensions reveals the resonance phenomenon between vibration frequency and tension fluctuations; mechanical vibrations in specific frequency bands amplify the dynamic forces on the harness. The normal interaction mode obtains a baseline range through training with historical data, such as position offset not exceeding 30% of the design tolerance at room temperature and tension fluctuation amplitude maintained within ±5% of the static value under idling conditions.

[0027] The identification of deviation interaction patterns employs a sliding window comparison algorithm. For location data, a marker is triggered when the cumulative offset of the same fixed point exceeds a threshold within three consecutive sampling periods, while simultaneously examining the tension change trend in the adjacent region of that point. Anomaly detection in tension data focuses on the abrupt change rate and duration. Instantaneous peaks accompanied by rapid decay are classified as transient interference, while stepwise increases are identified as structural deviations. Complex deviations caused by environmental factors are assessed through multi-condition logic. When the temperature exceeds the material's tolerance limit and the vibration intensity reaches a critical value, a regulation signal is generated even if a single dimension of the data does not exceed the limit. Each marked deviation pattern is accompanied by an attribute description, including spatial distribution characteristics, temporal evolution patterns, and associated dimension weights.

[0028] The scoring elements include deviation magnitude, duration, impact range, and urgency of repair. In position-related signals, offsets of critical fixed points affecting the insertion and removal accuracy of electrical connectors are given the highest priority. In tension-related signals, continuous loads exceeding 80% of the material's yield strength are marked as urgent. Environmentally related signals are graded according to equipment protection requirements, with high-temperature alarms that may cause insulation aging taking precedence over ordinary vibration alerts. A dynamic weight adjustment mechanism is introduced in the priority calculation, automatically increasing the decision weight of environmental factors under extreme operating conditions. All adjustment signals and their attributes constitute an adjustment tag dataset, stored in a tree structure for rapid retrieval and updates.

[0029] Spatial features such as location distribution entropy, temporal features such as tension autocorrelation function, and environmental features such as temperature-vibration coupling coefficient are extracted from the real-time state dataset. The annotation information provided by the labeled dataset is used for supervised learning, dynamically optimizing the classification boundary between normal and deviant patterns. Model training employs an incremental learning strategy, with newly acquired data batches triggering parameter fine-tuning, and the model structure automatically expanding or simplifying according to complexity requirements. The location prediction sub-model uses spatiotemporal sequence analysis methods, the tension estimation sub-model constructs a hybrid architecture combining physical mechanisms and data-driven approaches, and the environmental compensation sub-model implements multi-sensor data fusion.

[0030] The location benchmark is updated considering material creep effects, with tolerances gradually relaxed for long-term service fixed points. Dynamic correction of the tension threshold, combined with harness aging test results, increases the safety factor by 0.1 levels for every 10,000 km increase in service life. Environmental compensation parameters are optimized based on seasonal variations, with different temperature-deformation mapping relationships used for summer and winter modes. Model structure evolution is driven by feature importance assessment, reducing the dimensionality of input dimensions whose contribution consistently falls below the threshold, and automatically generating new detection channels for emerging deviation patterns. Real-time data streams are divided into training and testing sets, and prediction errors on new data trigger a self-diagnostic process. When the average error of location prediction exceeds 1.5 times the required positioning accuracy, sensor calibration is automatically initiated; when the root mean square error of tension estimation continues to increase, a comprehensive inspection of the signal acquisition link is triggered. The model health monitoring interface displays the confidence index of each submodule in real time, allowing maintenance personnel to quickly locate components with degraded performance using color coding.

[0031] The adjusted labeled dataset is uploaded to the workshop management system via a standard protocol, and 3D annotations of deviation locations are overlaid and displayed on AR glasses at the maintenance station. Parameter snapshots of the dynamic benchmark model are periodically backed up to the cloud, supporting configuration migration across vehicle platforms. Statistical analysis reports of abnormal patterns are automatically generated as PDF documents, including trend charts and root cause inferences, and distributed to relevant design departments through the enterprise knowledge system. Metadata records for the entire analysis process include algorithm version, computation time, and resource utilization, providing quantitative evidence for system performance optimization.

[0032] When the data stream for a certain dimension is interrupted, the system automatically switches to degrade mode, using historical averages and mechanistic models to fill in the missing information. When a computing node fails, the task scheduler migrates the load to a backup server, and critical analysis tasks are equipped with checkpoint recovery mechanisms. Data consistency is guaranteed through distributed transactions; any write operation of analysis results is confirmed by a majority of nodes. System resource allocation implements a service quality tiered system, with real-time analysis tasks receiving the highest priority, and batch processing jobs scheduled for execution during idle periods.

[0033] The actual operation records of maintenance personnel are compared and analyzed with the adjustment schemes suggested by the system to identify algorithm deviations and correct decision rules. Disassembly and inspection data from decommissioned vehicles are input back into the system to verify the accuracy of historical analysis conclusions. A continuous learning mechanism enables the system to adapt to changes in the characteristics of new wiring harness materials. When installation records of new materials such as carbon fiber sheaths are detected, the corresponding physical parameter library is automatically loaded and the analysis model is updated. This self-evolutionary capability allows the multi-dimensional interactive analysis system to maintain optimal performance in line with the development of automotive technology.

[0034] Example 3: See Figure 4In the adjustment method of automotive wiring harness fixing brackets, the identification of deviation behavior and log analysis constitute the core links of closed-loop adjustment. When real-time acquired state data is input into the dynamic benchmark model for comparison, the model uses a difference quantization engine to process multi-dimensional inputs. The comparison of position data calculates the Euclidean distance between the actual coordinates of the fixing point and the benchmark coordinates, and analyzes the vector direction of the angular deviation. The comparison of tension data focuses on three aspects: the absolute error of the static benchmark value, the spectral difference of the dynamic fluctuation component, and the degree of deviation of the load change rate. The comparison of environmental data focuses on the mismatch of the temperature-vibration coupling relationship, such as the percentage deviation between the actual measured thermal expansion coefficient and the model prediction value.

[0035] Positional deviations are categorized by their spatial impact range into localized deviations (single-point out-of-tolerance) and systematic deviations (multi-point associated out-of-tolerance). Tension deviations are classified according to their mechanical characteristics: overload type (continuously exceeding the safety threshold), oscillation type (periodic amplitude exceeding the limit), and attenuation type (abnormal decrease in preload). Environmentally related deviations are labeled as temperature-dominant, vibration-dominant, or combined types. Each deviation event is recorded with a precise timestamp, spatial coordinates, deviation amount, and environmental snapshot, forming a structured deviation record set.

[0036] Position deviation level By offset Duration and influence weight Joint decision: in: These are the dimension weight coefficients. The maximum allowable offset, For the longest tolerance duration, Assign values ​​based on the importance of fixed points (critical connection points) Auxiliary fixed points Tension deviation level Using a similar architecture, add frequency components. Considerations: in: This is the measured tension value. As the baseline value, This represents the ultimate strength of the material. The final adjustment level is taken as... and The weighted maximum value is then superimposed with the environmental degradation coefficient.

[0037] When deviation identification results are fed back to the harness layout log, a spatiotemporal indexing mechanism is used for retrieval. A time window of ±5 minutes is used for the timestamp of the deviation event, and a spatial range of 30cm radius centered on the fixed point is used to extract relevant log entries. Log data includes installation records (torque value, installation angle), maintenance history (list of replaced parts), design parameters (harness length, bending radius), and environmental records (historical temperature and humidity curves). The retrieval results generate a contextual report containing background information in five dimensions: installation configuration, service history, status of adjacent components, design constraints, and historical anomalies.

[0038] Time series analysis detects periodic adjustment events, such as monthly position reset requirements at a fixed point. Association rule mining uncovers hidden patterns, for example, when the transmission vibration intensity exceeds 4.5g, the tension fluctuation of adjacent wiring harnesses will inevitably exceed the limit. Cluster analysis identifies the spatial distribution characteristics of high-frequency adjustment areas, such as the frequent collective shift of fixed point groups behind the engine. The pattern recognition engine categorizes these findings into displacement compensation patterns, tension buffering patterns, or structural reinforcement patterns.

[0039] For positional deviation patterns, reverse trace the torque application curve in the installation record to check if process requirements were not met; review the positioning tolerance markings on the design drawings to verify if they exceeded manufacturing capabilities. Trace abnormal tension signals to material selection data and verify the fatigue strength parameters of the wiring harness sheath; check environmental protection measures implementation records to confirm whether the insulation sleeve provides complete coverage. Generate a probability score for each tracing path, and output the root cause in order of confidence level.

[0040] The primary path is based on direct cause-and-effect relationships, such as tightening bolts with torque to address loose bolts, or replacing components to address material aging. The secondary path considers indirect factors; when excessive vibration is the root cause, derivative solutions such as adding damping bushings or modifying the location of fixing points are derived. Each path is labeled with its implementation complexity, required resources, and expected performance indicators, forming an executable set of adjustment instructions.

[0041] The merged report integrates four types of information: deviation feature matrix (spatial distribution, temporal characteristics, mechanical performance), background correlation map (installation, design, environmental factors), pattern recognition conclusions (periodicity, correlation, clustering), and source analysis results (root cause ranking, adjustment path tree). The report is packaged in machine-readable JSON-LD format and generates a visual summary diagram, marking problem areas on the 3D wiring harness model and using color coding to distinguish adjustment priorities.

[0042] Deviation detection results are cross-validated with the online monitoring system in real time. If the vibration sensor does not record any abnormalities but the model identifies vibration-related deviations, the sensor calibration process is triggered. Log analysis conclusions are compared with vehicle CAN bus data. When the maintenance record shows that a component has been replaced but the tension reading has not improved, a data reliability warning is automatically issued. The source analysis results are pushed to the engineering knowledge base for similarity matching with solutions for similar vehicle models, supplementing and recommending adjustment schemes.

[0043] Real-time deviation detection utilizes the Apache Flink stream processing engine, ensuring data comparison and labeling are completed within 200ms. Log analysis tasks are implemented using Spark batch processing, and complex pattern recognition algorithms are executed in parallel on a distributed cluster. The source tracing analysis module integrates a graph computing engine to construct an installation-design-maintenance relationship graph for rapid causal reasoning. All intermediate results are stored in a time-series database, supporting full-process data traceability and replay.

[0044] After each deviation event is handled, the actual adjustment effect data is fed back to the model training set. If stability improves after position adjustment, the tolerance threshold for that region is tightened; if fluctuations intensify after tension compensation, the allowable range of dynamic changes is widened. Model version management records the decision-making basis for each parameter adjustment, forming a self-evolving benchmark system.

[0045] Adjustment commands are issued to the intelligent tool system via the workshop's Internet of Things (IoT). The electric torque wrench receives bolt tightening parameters, and the laser positioning device acquires coordinate correction values. Execution results are transmitted back in real time via a sensor network. The adjustment effect evaluation algorithm calculates indicators such as the improvement rate of position deviation and the degree of improvement in tension stability, generates an adjustment effectiveness report, and archives it in the knowledge base.

[0046] Example 4: In the adjustment method for automotive wiring harness mounting brackets, the construction of a multi-level adjustment framework and the generation of a primary adjustment scheme involve the establishment of a structured knowledge base and scheme optimization. The design of the adjustment framework is based on the physical characteristics and functional requirements of the wiring harness system, decomposing the adjustment task into three synergistic levels. The structural layer defines the overall layout rules, including wiring harness segmentation principles, fixing point density distribution, and bending radius constraints. The position layer formulates coordinate control strategies, covering installation tolerance zones, dynamic offset allowable ranges, and reset accuracy standards. The tension layer specifies mechanical management specifications, including static preload ranges, dynamic fluctuation limits, and fatigue accumulation algorithms.

[0047] Forward reasoning begins with the state analysis steps, identifying key parameters such as the wire harness creep in high-temperature regions and the stress concentration factor at vibration nodes. Reverse reasoning starts with the adjustment target, deriving the fixed-point stiffness distribution required to achieve positional stability and the compensation scheme that satisfies tension balance requirements. The logical nodes in the thought process include conditional judgments (e.g., "If the ambient temperature > 120℃, then activate the heat-resistant material compensation rule") and action sequences (e.g., "First release the tension lock, then adjust the position, and finally reset the preload").

[0048] The implementation of the global structural adjustment is divided into two stages: spatial division and parameter optimization. Spatial division decomposes the vehicle wiring harness into functional areas: the powertrain area adopts a high-density fixed-point layout, the chassis area features enhanced vibration damping design, and the passenger compartment area focuses on optimizing space utilization. Parameter optimization calculates the ideal configuration for each area. The fixed-point spacing in the powertrain is set to 150±10mm, the chassis section tension compensation coefficient is taken as 1.2-1.5 times the standard value, and the bending radius of the instrument panel wiring harness is no less than 5 times the diameter. The output of the primary adjustment scheme is a structured instruction set, including geometric adjustment parameters, mechanical control values, and material change suggestions.

[0049] The computer-aided design system extracts baseline geometric features, including fixed-point topological relationships, harness centerline trajectories, and section control points. The parameter annotation process identifies two types of elements: fixed templates include snap-fit ​​type codes (e.g., U-shaped snap-fits are marked C-203) and bracket mounting surface normals; variable parameters cover length adaptation range (±15mm), angle adjustment margin (±5°), and tension buffer gap (2-3mm). The extraction of domain-specific adjustment structures establishes a knowledge graph, encoding engineering experience into computable rules.

[0050] Point-level knowledge stores the adjustment rules for individual fixed points, such as the torque curve and temperature compensation table for a certain type of clip. Segment-level knowledge describes the characteristics of harness segments, including the fluctuation absorption algorithm for flexible segments and the vibration transmission coefficient for rigid segments. Overall-level knowledge defines system-level constraints, such as the principle of total weight distribution of the entire vehicle's harness and electromagnetic compatibility layout specifications. The entity relationship model of the knowledge base records the derivation logic between each level; for example, changing the type of a fixed point will trigger a chain update of the characteristics of adjacent segments.

[0051] Each harness segment is labeled with its physical environmental characteristics (temperature range, vibration spectrum), spatial constraints (minimum bending radius, obstacle clearance), and functional importance level (Class A: safety critical, Class B: functionally important, Class C: auxiliary wiring). The labeling process is semi-automatic, with a computer-aided identification system suggesting candidate labels, which are then confirmed and supplemented by engineers. The labeled segment sequence generates a topology diagram, recording the connection methods between segments and parameter transfer rules, as shown in Table 1.

[0052] Table 1: Variable parameter annotations for typical harness segments.

[0053] PWR-01 ±10 ±3 1.5-2.0 1.2 >40dB SNS-05 ±5 ±1 0.8-1.2 1.0 >30dB COM-12 ±15 ±5 2.0-3.0 1.5 >35dB The experience gained in adjusting new vehicle models is imported through a standardized process and integrated into the knowledge system after case analysis, feature extraction, and rule verification. The on-site feedback system collects data on actual adjustment effects; when the success rate of adjustments at a certain fixed point consistently falls below a threshold, the corresponding rule revision process is triggered. The version control system records the content, time, and responsible party for each knowledge update, supporting historical queries and change tracking.

[0054] Spatial objectives include minimizing the total length and balancing the load at fixed points; mechanical objectives cover reducing peak stress and homogenizing tension distribution; and technological objectives consider assembly accessibility and tool compatibility. The optimization algorithm employs constraint satisfaction techniques, transforming design requirements into boundary conditions and searching for Pareto optimal solutions within the solution space. Each candidate solution is accompanied by a feasibility analysis report, outlining its matching degree with knowledge base rules and explanations of exceptions.

[0055] The natural language processing engine parses the experiential descriptions in the maintenance manual and automatically generates candidate tags. The computer vision module analyzes the wiring harness assembly images, identifies spatial conflict areas, and suggests adjustment solutions. The knowledge graph inference engine checks tag consistency; when a segment is labeled as a "high-frequency vibration zone" but insufficient buffer gaps are set, the system issues a logical conflict warning.

[0056] A multiphysics model of the wire harness system is constructed in a virtual environment to simulate its position holding capability and tension variation under different operating conditions. The simulation results are compared with the expected values ​​in the knowledge base, and framework parameter adjustment is initiated when the deviation exceeds a threshold. For physical verification, a prototype is built in a typical section, and the actual performance of the adjustment scheme is tested in an environmental test chamber. The results are fed back to the knowledge base to improve the rule system.

[0057] In terms of system integration, the adjustment framework engine interfaces with upstream design systems and downstream manufacturing execution systems through standard interfaces. When receiving CAD models, it automatically extracts harness topology data, and when outputting adjustment schemes, it generates executable process instructions. The mobile application supports on-site querying of adjustment rules, and an augmented reality interface overlays the ideal position and actual deviation of fixed points.

[0058] Marking integrity checks ensure that all harness segments have clear environmental classifications and functional definitions. Knowledge matching audits trace the source rules for each adjustment recommendation, recording deviations from standards and approval processes. Conflict detection is performed before solution release to identify potential issues such as spatial interference, mechanical overload, and process infeasibility.

[0059] This study analyzes the implementation effects of adjustment schemes over the years, identifying frequently modified areas and recurring problem patterns. It compares the differences in adjustment strategies across different vehicle platforms, extracting best practices that can be widely adopted. Data from the dismantling of retired vehicles is used to verify the long-term effectiveness of the adjustment schemes, improving the aging model and lifespan prediction algorithm of the knowledge base. The introduction of new mounting materials triggers the recalibration of mechanical rules, and the widespread adoption of lightweight design requires adjustments to the length compensation algorithm. The special requirements of high-voltage wiring harnesses in electric vehicles have given rise to new adjustment dimensions, such as maintenance strategies for electromagnetic shielding integrity. The system continuously learns to maintain the advancement and applicability of the adjustment schemes.

[0060] Example 5: In the adjustment method for automotive wiring harness brackets, the matching process between the segment sequence and the adjustment knowledge base employs vector space mapping technology. Each wiring harness segment with context markers is transformed into a feature vector, with vector dimensions including geometric attributes, mechanical properties, and environmental adaptation parameters. The geometric attribute vector components describe the length adaptation range, bending angle tolerance, and spatial degrees of freedom; the mechanical property components characterize tension buffering requirements, vibration attenuation coefficients, and fatigue accumulation factors; and the environmental adaptation components encode temperature compensation requirements, corrosion resistance levels, and electromagnetic shielding performance. Templates in the reusable adjustment knowledge base are also vectorized to form a standard reference vector set.

[0061] Feature vectors undergo standardization to eliminate dimensional differences; geometric parameters are normalized to percentage values ​​of the design baseline; and mechanical parameters are converted into dimensionless coefficients. Similarity calculation employs an improved cosine similarity algorithm, introducing dimensional weighting factors to enhance the influence of key features. The matching process first calculates the similarity score between the segment vector and all template vectors in the knowledge base, then sorts candidate templates in descending order of score. Constraint filtering is applied during the screening phase to exclude templates that do not meet environmental conditions or spatial limitations. Finally, the effective template with the highest similarity is selected as the adjustment baseline. The matching results are accompanied by a deviation analysis report, detailing the fit and differences across each dimension.

[0062] For geometric adjustments, the baseline values ​​in the template are adjusted based on the context markers of the segment sequence. In high-temperature regions, the length compensation margin is increased for harness segments, and in areas of high vibration, the lower limit of the bending radius is expanded. For space-constrained segments, a compact layout rule is implemented to optimize routing while maintaining electrical safety clearances. Mechanical adjustments dynamically adapt to tension control, introduce damping attenuation algorithms in high-fluctuation environments, and optimize preload distribution in static load areas. The material compatibility module checks the compatibility of the harness sheath with the environment, automatically triggering a replacement recommendation when the temperature marker exceeds the material tolerance threshold. The adjustment operation generates a detailed instruction sequence, including spatial coordinate correction values, torque application curves, and buffer component installation positions.

[0063] Geometric control commands define the target 3D coordinates of fixed points, the sequence of control points for the wiring harness path, and the bending forming angle. Mechanical control commands specify the target reading range of the tension sensor, dynamic fluctuation suppression strategies, and overload protection thresholds. Execution parameters clarify tool operating specifications, including the speed-torque curve of the electric torque wrench, the alignment tolerance of the laser positioning instrument, and the shaping temperature-time combination of the hot air gun. Control information is encoded in a machine-readable format and transmitted to the execution terminal via an Industrial Internet of Things (IIoT) protocol.

[0064] The electric actuator receives coordinate commands to drive the linear module, achieving millimeter-level positioning and adjustment of the fixed frame. The servo tension controller analyzes the mechanical commands and adjusts the wiring harness preload in real time through closed-loop feedback. Environmental compensation devices are automatically deployed according to the scheme requirements, such as installing ceramic fiber insulation sleeves in high-temperature areas and adding silicone damping pads in vibration areas. The execution process is monitored by multiple sensors: a force sensor provides real-time torque feedback, a photoelectric encoder tracks displacement, and an infrared thermal imager monitors material deformation. Execution data is continuously compared with command requirements; a dynamic correction process is triggered when the deviation exceeds a threshold.

[0065] Geometric evaluation measures the positional stability of fixed points and calculates the coordinate variance over consecutive sampling periods. Mechanical evaluation analyzes the statistical distribution of tension readings, examining mean shift and standard deviation variations. Environmental adaptability evaluation assesses the material's performance retention under extreme conditions using accelerated aging test data. Functional verification involves electrical parameter testing, including contact resistance and insulation withstand voltage tests. Evaluation data is compared with the pre-adjustment baseline to generate an improvement rate index.

[0066] When geometric evaluation reveals insufficient location reproducibility, it reports the positioning accuracy defect to the knowledge base, triggering a revision of the template's tolerance parameters. If mechanical evaluation detects that the tension fluctuation suppression effect is not as expected, it adjusts the control parameters of the damping algorithm. Material performance evaluation identifies abnormal aging phenomena and initiates the review process for material selection rules. An association mapping is established between evaluation results and adjustment schemes, forming a knowledge graph of scheme effectiveness.

[0067] After each adjustment operation, the actual execution parameters and effect data are stored in a structured manner. Successful cases are used to extract new rules, such as the optimal damper installation angle under a specific vibration spectrum. Cases that do not meet expectations are analyzed to identify the root causes and supplement constraints into the matching and filtering logic. The knowledge base version management records each update, and new templates must be verified through virtual simulation before release.

[0068] The matching calculation engine interfaces with the product lifecycle management system to obtain engineering change information for wiring harness components. The control command generation module interconnects with the manufacturing execution system to synchronize production cycle time and process parameters. The status data of the execution terminal is transmitted back to the cloud platform in real time to update the status of the digital twin model. Evaluation results are automatically imported into the quality management system, triggering preventive maintenance plans for relevant products.

[0069] When the matching similarity falls below the confidence threshold, the system automatically switches to expert intervention mode, providing multi-template comparison analysis to assist human decision-making. In the event of equipment failure during execution, the control system activates a backup tool switching program. During data transmission interruptions, local caching ensures that critical instructions are not lost. Abnormal evaluation data triggers a review process to prevent erroneous feedback from polluting the knowledge base.

[0070] This study analyzes successful patterns from past adjustment cases to identify frequently used template combinations. It investigates adaptation patterns across different vehicle platforms and establishes a brand-wiring harness type matching rule library. Furthermore, it statistically analyzes the impact of seasonal variations on adjustment effectiveness to refine the dynamic adjustment algorithm for environmental adaptation parameters.

[0071] The application of novel smart materials has spurred the development of specialized templates, such as deformation recovery strategies for shape memory alloy wire harnesses. The advancement of flexible electronics technology has introduced stretchable wire harness templates, increasing the adaptive parameter for curvature radius. The electrification trend is driving the expansion of adjustment dimensions for high-voltage wire harnesses, adding arc detection and spacing control rules. The system maintains its technological leadership in adjustment schemes through continuous learning.

[0072] The 3D harness model rendering compares the state before and after adjustment, using color gradients to display changes in tension distribution. Time series charts show historical trends of key parameters and mark the time nodes of adjustment operations. Multi-dimensional radar charts comprehensively evaluate the improvement degree of various indicators, intuitively presenting the overall effectiveness of the adjustment plan. Technical documents include matching calculation process logs, adjustment instruction details, execution parameter records, and effect evaluation data. Management reports summarize resource consumption, work hour distribution, and cost-benefit analysis. All document versions are linked to the adjustment plan number, forming a traceable knowledge asset.

[0073] 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.

[0074] 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 method for adjusting an automotive wiring harness mounting bracket, characterized in that, The method includes: Collect status parameters of automotive wiring harnesses and mounting brackets, including position data, tension data, and environmental factor data, and generate real-time status datasets; Multi-dimensional interactive analysis is performed on the real-time status dataset. At the same time, the context information is used to understand the relationship between the fixture and the wire harness, the interaction mode and potential deviation mode of the fixture under different dimensions are identified, and the analysis results are marked as preliminary adjustment signals to generate an adjustment mark dataset with priority. A dynamic baseline model is established based on the real-time state dataset and the adjustment marker dataset. The accuracy of the baseline model is enhanced by the interactive analysis results, and the parameters of the baseline model are adjusted in real time. The real-time collected status data is compared with the dynamic benchmark model to identify deviations in position or tension, and the adjustment requirements for the deviations are marked. The deviation identification results are fed back to the harness layout log for analysis to obtain adjustment background information. Based on the deviation identification results of the dynamic benchmark model, the wiring harness layout log is analyzed to identify deep-seated adjustment patterns and optimization signals. The adjustment patterns and optimization signals are then traced back to their source to identify the root cause and adjustment path.

2. The adjustment method for the automotive wiring harness fixing bracket according to claim 1, characterized in that, The process involves collecting status parameters of the automotive wiring harness and mounting bracket, including position data, tension data, and environmental factor data, to generate a real-time status dataset, specifically including: Determine the monitoring area of ​​the automotive wiring harness mounting bracket and capture real-time status parameters; The captured data is classified, distinguishing between location, tension, and environmental dimensions, and the data is parsed to extract location data, tension data, and environmental factor data. The parsed data is integrated and formatted to generate a structured real-time status dataset.

3. The adjustment method for the automotive wiring harness fixing bracket according to claim 2, characterized in that, The process involves performing multi-dimensional interactive analysis on the real-time status dataset, utilizing contextual information to understand the relationship between the mounting bracket and the wiring harness, identifying the interaction patterns and potential deviation patterns of the mounting bracket in different dimensions, and marking the analysis results as preliminary adjustment signals to generate a priority-based adjustment label dataset. Specifically, this includes: The real-time status dataset is organized according to dimensional hierarchy, and contextual information is extracted from the data; Correlation analysis is performed between different dimensions to identify normal and deviation interaction patterns. Deviation interaction patterns are labeled to generate preliminary regulation signals and record regulation type, frequency, and severity. Priorities are assigned to each regulation signal based on its characteristics, generating a regulation label dataset with priorities. All analysis and labeling results are then integrated to output a structured regulation label dataset.

4. The adjustment method for the automotive wiring harness fixing bracket according to claim 3, characterized in that, The step of establishing a dynamic baseline model based on the real-time state dataset and the adjustment marker dataset, enhancing the accuracy of the baseline model using interactive analysis results, and adjusting the baseline model parameters in real time specifically includes: Feature extraction is performed on the real-time state dataset and the adjustment label dataset to establish a dynamic benchmark model, and the dynamic benchmark model is trained using the extracted features; Based on the results of real-time interactive analysis, the parameters and structure of the baseline model are dynamically adjusted.

5. The adjustment method for the automotive wiring harness fixing bracket according to claim 4, characterized in that, The process involves comparing the real-time collected status data with the dynamic benchmark model to identify deviations in position or tension, marking the adjustment requirements for these deviations, and feeding the deviation identification results back to the harness layout log for analysis to obtain adjustment background information. Specifically, this includes: The real-time collected automotive wiring harness mounting bracket status data is input into the dynamic benchmark model. The dynamic benchmark model is then used to compare the real-time input data, analyze the differences between the real-time data and the benchmark behavior pattern, and identify potential adjustment needs. Detected deviations are flagged, and the degree of deviation and related contextual information are recorded. Assess the severity of the deviation behavior and assign a regulation level to each regulation requirement based on the magnitude and frequency of the deviation; Extract the deviation identification results and feed them back to the harness layout log. Search the harness layout log and read the corresponding adjustment background information.

6. The adjustment method for the automotive wiring harness fixing bracket according to claim 5, characterized in that, The deviation identification results based on the dynamic benchmark model are used to analyze the harness layout log, identify deep-seated adjustment patterns and optimization signals, and trace the source of these adjustment patterns and optimization signals to identify the root causes and adjustment paths. Specifically, this includes: Collect harness layout logs related to the deviation identification results and obtain complete contextual information; The wiring harness layout log is analyzed to identify deep-seated adjustment patterns and optimization signals, and potential adjustment clues are extracted from the log to identify the adjustment type. The identified optimization signals are analyzed to trace their origins and identify the path through which they occur, thus identifying the initial source of the regulation. A consolidated report is generated based on all the identified information.

7. The adjustment method for the automotive wiring harness fixing bracket according to claim 1, characterized in that, The method further includes: A multi-level adjustment framework is generated based on the adjustment mark dataset. The multi-level adjustment framework includes a structure layer, a position layer, and a tension layer. The structure layer is used to optimize the overall layout process of the wire harness fixing frame, the position layer is used to ensure position accuracy, and the tension layer is used to adjust the tension to meet safety specifications. Based on the multi-level adjustment framework, an adjustment thinking chain is generated, which includes a state analysis step and an adjustment optimization step. The state analysis step includes identifying the key parameters and core requirements of the automotive wiring harness fixing bracket, and the adjustment optimization step includes position adjustment, tension compensation, and layout optimization. Using the multi-level adjustment framework and the adjustment thought chain, the global structure of the automotive wiring harness fixing bracket is adjusted to generate a primary adjustment scheme.

8. The adjustment method for the automotive wiring harness fixing bracket according to claim 7, characterized in that, The method further includes: By performing geometric analysis and parameter annotation on the standard wiring harness layout, the standard wiring harness layout is decomposed into fixed templates and variable parameters to extract specific adjustment structures in the automotive field and build a reusable adjustment knowledge base including point level, segment level and overall level. The primary adjustment scheme is segmented to generate a sequence of segments with context markers.

9. The adjustment method for the automotive wiring harness fixing bracket according to claim 8, characterized in that, The method further includes: Using a vectorized similarity calculation technique, the matching degree between each segment in the segment sequence and the adjustment template in the reusable adjustment knowledge base is calculated, and the adjustment template with the highest similarity is matched for each segment; Based on the matched adjustment template and the segment sequence with context markers, the segments in the segment sequence are finely adjusted to generate the final adjustment scheme.

10. The adjustment method for the automotive wiring harness fixing bracket according to claim 9, characterized in that, The method further includes: Based on the final adjustment scheme, adjustment control information is generated; The automotive wiring harness mounting bracket is adjusted according to the aforementioned adjustment control information; The condition of the automotive wiring harness mounting bracket after the adjustment operation is evaluated and analyzed.

Images