A harness connection loosening special prevention and control and whole-process dynamic management method

Abstract

The application discloses a kind of harness connection loose special prevention and control and whole-process dynamic management and control method, the method is by collecting harness full life cycle loose related data, constructs special pre-judgment model and control benchmark library;Optimized design scheme forms design-loose-pre-judgment integrated technical document;Manufacturing stage is in real time by linkage model pre-judgment risk and triggers compensation;Assembly stage uses three-level loose detection mode to trace back and deal with;Running stage dynamically updates benchmark library and executes graded compensation;Integrate data to build iterative optimization system to continuously update model and strategy.The application realizes harness full life cycle closed-loop management and control, solves the problem of fragmentation, no linkage of prior art, improves loose risk pre-judgment accuracy and compensation pertinence, guarantees harness connection reliability and equipment operation safety.

Description

Technical Field

[0001] This invention relates to the field of wire harness connection control technology, specifically a method for the prevention and control of loose wire harness connections and dynamic control throughout the entire process. Background Technology

[0002] As a core connection component in electrical systems that enables power transmission and signal interaction, wire harnesses are widely used in numerous fields such as automotive, industrial automation, rail transportation, and aerospace. The reliability of their connections directly determines the operational stability, safety, and service life of the entire equipment. In practical applications, loose connections are the most prominent high-frequency fault throughout the entire life cycle of a wire harness. This fault can directly lead to increased terminal contact resistance, signal transmission attenuation, and current fluctuations. In severe cases, it can even cause short circuits, sudden equipment shutdowns, and even fires, negatively impacting industrial production efficiency and daily safety.

[0003] Current technologies for preventing loose wiring harness connections are generally fragmented and isolated, failing to form a closed-loop management system covering the entire process. During the design phase, existing solutions largely rely on engineers' experience for terminal selection, fixing point layout, and bundling node design. They lack a quantitative correlation between key design elements and loosening evolution, and lack targeted anti-loosening optimization designs. This makes it difficult to avoid potential loosening risks from the source, such as unreasonable terminal contact structures and improper fixing layouts. During manufacturing, the process parameters for core processes like terminal crimping and wire bundling are mostly controlled by fixed thresholds, unable to be dynamically adjusted based on real-time collected process data and loosening characteristic information. This makes it difficult to effectively address core manufacturing hazards that induce loosening, such as crimping parameter deviations and unreasonable bundling tension. During assembly, inspection methods are mostly based on overall sampling, without designing layered inspection mechanisms for easily loosened key nodes such as component connections and mating parts. This not only makes it difficult to accurately identify early loosening hazards but also makes it difficult to quickly trace the root cause after a fault occurs, resulting in a lack of targeted compensation measures. During operation, maintenance is mainly reactive, lacking the ability to predict loosening evolution trends. Repairs and replacements are often only carried out after the fault becomes apparent, leading to high maintenance costs and prolonged equipment downtime.

[0004] Furthermore, existing technologies fail to achieve data interoperability and coordinated optimization across design, manufacturing, assembly, and operation stages. Anti-loosening measures at each stage are fragmented, making it impossible to predict subsequent loosening risks using preceding data or to use subsequent fault data to optimize preceding design and manufacturing parameters. While some technologies attempt to introduce detection methods, these are mostly limited to localized detection at a single stage, lacking dynamic adaptability to all stages of loosening evolution. Moreover, compensation strategies are not designed with tiered risk levels based on loosening severity, resulting in limited anti-loosening effects and failing to fundamentally solve the connection loosening problem. Summary of the Invention

[0005] The purpose of this invention is to provide a method for the prevention and control of loose wire harness connections and dynamic management throughout the entire process, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for targeted prevention and dynamic management of loose wire harness connections, comprising: Data on the inducing mechanism, evolution stage and failure mode of connection loosening throughout the entire life cycle of the receiving harness are collected. Loosening characteristic parameters and source tracing data at each evolution stage are collected to construct a special prediction model for connection loosening and a special control benchmark library. Based on the aforementioned prediction model and benchmark library, the harness design scheme is optimized, and a loosening prediction process, anti-loosening compensation plan and special testing nodes are preset to form an integrated technical document of design-anti-loosening-prediction. According to the integrated technical document, configure the anti-loosening process parameters and loosening prediction threshold of the manufacturing process, collect process data and loosening characteristic monitoring data in real time, predict risks and trigger compensation strategies through the anti-loosening-prediction linkage model, and verify the compensation effect simultaneously. A three-level anti-loosening detection mode is adopted to carry out the inspection during the assembly stage. The root cause of the loosening risk is traced and targeted compensation measures are taken. The relevant data is recorded and fed back to the manufacturing control link. During the operation of the wire harness, the baseline evolution characteristics are collected and the baseline library is dynamically updated. The real-time evolution characteristics of loosening are monitored regularly. Trends are identified through prediction algorithms and a graded dynamic anti-loosening compensation strategy and early warning signals are triggered. Integrate data from the entire process of management and control, build an iterative optimization system, optimize the prediction model, linkage model and compensation strategy, and update the special management and control benchmark library.

[0007] Preferably, the typical inducing mechanisms of connection loosening include terminal crimping parameter deviation, unreasonable wire bundling tension, improper fixing layout of connection parts, vibration fatigue corrosion under working conditions, material aging caused by temperature stress, and non-standard assembly operations. The evolution stages include the initial loosening potential stage, the slight loosening stage, the moderate loosening stage, and the failure loosening stage. The loosening characteristic parameters include electrical characteristic parameters, mechanical characteristic parameters, and working condition stress-related characteristic parameters. Among them, the electrical characteristic parameters include the contact resistance change rate, signal transmission attenuation, and current fluctuation amplitude; the mechanical characteristic parameters include the tightening torque change, vibration response amplitude deviation, and insertion / removal contact gap change; and the working condition stress-related characteristic parameters include the vibration frequency and loosening rate correlation parameters and the temperature change amplitude and contact resistance deviation correlation parameters.

[0008] Preferably, the construction process of the special control benchmark library includes: integrating the output parameters of the prediction model, the loosening trend thresholds of each evolution stage, and the dynamic anti-loosening compensation strategies corresponding to different risk levels; establishing the correlation mapping relationship between risk levels and compensation strategies; the dynamic anti-loosening compensation strategies include design parameter fine-tuning, process parameter correction, assembly operation optimization, and operation and maintenance reinforcement anti-loosening strategies; the loosening prediction indicators include characteristic parameter thresholds, time-series trend change rate thresholds, and risk level judgment thresholds; and clarifying the handling procedures and compensation priorities for different prediction results.

[0009] Preferably, the key risk factors that are prone to loosening in the design process include the rationality of the terminal contact structure, the compatibility between the wire and the terminal, the scientific layout of the fixing points of the connection, the rationality of the distribution of the binding nodes, the vibration aging resistance of the protective material, and the compatibility of the wire harness routing with the vibration of the working conditions. The correlation between the risk factors and the loosening evolution is established through multiple sets of working condition simulation and comparison experiments. The targeted design optimization for loosening prevention includes adjusting the values ​​of risk factors, optimizing the fixing layout of the connection, the terminal contact structure, and the distribution of binding nodes. The preset loosening prevention compensation plan clarifies the compensation process, parameter range, and effect verification standards for each risk factor corresponding to the loosening type.

[0010] Preferably, the integrated design-loosening-prediction technical document integrates and optimizes the design parameters, loosening risk factors, prediction indicators, special testing nodes, compensation plans and verification standards, clarifies the linkage between design adjustments, indicator optimization and compensation strategy adaptation, adopts industry-standard format compilation, and reserves data update interfaces to support the adjustment of design parameters and prediction standards based on iterative data.

[0011] Preferably, the core manufacturing process includes wire cutting and stripping, terminal crimping, wire bundling, protective layer coating, and semi-finished product integration. The terminal crimping and wire bundling processes are the key control points. The anti-loosening process parameters include terminal crimping pressure and duration matching parameters, crimping depth accuracy parameters, wire bundling tension and spacing matching parameters, and bundling node reinforcement process parameters. The process operation data and loosening characteristic monitoring data are collected in real time through a time-series data acquisition unit to ensure the continuity, accuracy, and real-time nature of the data.

[0012] Preferably, the anti-loosening-prediction linkage model is an adaptive model based on time-series feature analysis. By comparing the deviation between the data and the prediction threshold, it accurately identifies core causes such as substandard terminal crimping and abnormal bundling tension. For minor loosening risks, it triggers fine-tuning of process parameters for compensation. For moderate and above risks, it triggers correction of process parameters and optimization of the operation process for compensation. After compensation, a special prediction model is used for re-evaluation. Only after verification that it is qualified can it proceed to the next process. If it is unqualified, the prediction-compensation-verification process is repeated.

[0013] Preferably, the three-level anti-loosening detection mode includes: component connection anti-loosening detection focuses on detecting the tightness of terminal connection and the reliability of core wire contact; docking part anti-loosening collaborative detection focuses on detecting the rationality of docking gap and connection tightness; overall connection anti-loosening comprehensive detection focuses on predicting the stability of collaborative work of each part and the related loosening risks of working conditions. The root cause tracing adopts the feature tracing-risk matching-cause confirmation process. After compensation and treatment, the corresponding level of detection is re-executed. Only after passing the test can the subsequent assembly process be carried out.

[0014] Preferably, the root cause data and compensation effect data include information such as risk location, type, core cause, prediction deviation, compensation process, parameters and effects, which are fed back to the manufacturing and design stages through standardized interfaces to make targeted adjustments to manufacturing process parameters, prediction thresholds and design parameters, and compensation plans, thereby suppressing the risk of loosening from the source.

[0015] Preferably, the benchmark evolution characteristics are non-loosening state characteristic data collected during the initial stage of harness operation. After removing anomalies, the data is entered into the benchmark library. The special control benchmark library periodically adjusts the benchmark characteristics, prediction thresholds, and compensation strategy parameters based on stress changes under operating conditions. The loosening special prediction algorithm combines time-series characteristic trend analysis and risk probability calculation to achieve accurate prediction of risk level, failure probability, and evolution cycle. The graded dynamic anti-loosening compensation strategy corresponds to mild, moderate, and severe risk levels, and simultaneously triggers graded early warning signals.

[0016] Compared with existing technologies, the beneficial effects of this invention are: by constructing a specialized predictive model and benchmark library, optimizing the anti-loosening strategies in all aspects of design, manufacturing, assembly and operation, and dynamically adjusting by collecting data in real time, a closed-loop management and control system is formed. This solves the problems of fragmentation and isolation in existing technologies, and has the advantages of being able to achieve closed-loop management and control of wire harness connection loosening throughout its entire life cycle, dynamically optimizing from design, manufacturing, assembly to operation, effectively preventing and compensating for loosening risks, and improving system reliability and safety. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the dynamic control method according to an embodiment of the present invention. Detailed Implementation

[0018] 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.

[0019] Please see Figure 1This paper presents a method for the prevention and control of loose connections in wire harnesses, along with dynamic management throughout the entire process. The method receives data on the inducing mechanisms, evolution stages, and failure modes of loose connections throughout the wire harness's lifecycle. It collects loosening characteristic parameters and source tracing data at each evolution stage to construct a special prediction model and a special management benchmark library for loose connections. Based on this prediction model and benchmark library, the method optimizes the wire harness design, pre-sets a loosening prediction process, a loosening prevention compensation plan, and special testing nodes, forming an integrated technical document encompassing design, loosening prevention, and prediction. Following this integrated technical document, the method configures manufacturing process loosening prevention parameters and loosening prediction thresholds, collects process data and loosening characteristic monitoring data in real time, predicts risks through the loosening prevention-prediction linkage model, triggers compensation strategies, and simultaneously verifies the compensation effect. A three-level loosening prevention detection mode is used to perform assembly stage inspections. For any loosening risks discovered, root cause tracing and targeted compensation measures are implemented, and relevant data is recorded and fed back to the manufacturing management control stage. During the wire harness operation phase, baseline evolution characteristics are collected and the baseline library is dynamically updated. Real-time loosening evolution characteristics are monitored periodically, and trends are identified through predictive algorithms to trigger tiered dynamic anti-loosening compensation strategies and early warning signals. Finally, the entire process control data is integrated to build an iterative optimization system, optimize the predictive model, linkage model, and compensation strategy, and update the specialized control baseline library, thereby achieving full-process dynamic closed-loop control of wire harness connection loosening.

[0020] For ease of understanding, the following explains some key terms in this embodiment: The connection loosening prediction model is used to predict and assess the risk of loosening at wire harness connections. This model analyzes historical and real-time monitoring data to identify the likelihood of loosening, its evolution trend, and potential impacts.

[0021] The specialized control benchmark library is used to store various data and standards related to the prevention and control of loose wiring harness connections. This benchmark library includes information such as loosening induction mechanisms, evolutionary stage characteristics, failure modes, prediction thresholds, and compensation strategies, providing data support and decision-making basis for the entire prevention and control process.

[0022] This integrated design-anti-loosening-prediction technical document is used to integrate all technical specifications and processes related to wire harness design, anti-loosening measures, and loosening prediction. This document ensures consistency and coordination of anti-loosening strategies and prediction standards from the design stage to subsequent manufacturing, assembly, and operation.

[0023] The anti-loosening-prediction linkage model is used to dynamically adjust anti-loosening process parameters and predict loosening risks in real time during the manufacturing process. This model automatically triggers corresponding compensation strategies by analyzing process data and loosening characteristic monitoring data to maintain the anti-loosening effect during manufacturing.

[0024] The three-level anti-loosening detection mode is used to conduct layered and multi-dimensional detection of the risk of loosening in wire harness connections during the assembly stage. This mode aims to improve the accuracy of identifying potential loosening hazards and provide a basis for subsequent root cause tracing and remedial measures.

[0025] The iterative optimization system is used to integrate end-to-end control data and continuously improve the predictive model, linkage model, and compensation strategy. Through data feedback and learning mechanisms, this system continuously enhances the overall effectiveness of preventing loose wiring harness connections.

[0026] This embodiment provides a method for the targeted prevention and dynamic management of loose connections in wire harnesses. First, it receives data on the inducing mechanism, evolution stages, and failure modes of loose connections throughout the entire lifecycle of the wire harness, and collects loosening characteristic parameters and source tracing data for each evolution stage. This data is received through manual entry of historical fault reports, review of industry standards, or expert experience summaries. Loosening characteristic parameters are collected by conducting simulated tests on the wire harness in a laboratory environment, recording its electrical or mechanical response under different stress conditions, measuring contact resistance with a multimeter, or recording tightening torque with a torque wrench. Source tracing data is obtained by manually recording environmental conditions and operator information at the time of the fault. Based on the received and collected data, a targeted prediction model for loose connections and a targeted management benchmark library are constructed. The targeted prediction model is a simple regression model based on statistical analysis, used to predict the probability of loosening. The targeted management benchmark library is an electronic spreadsheet containing information such as loosening type, frequency of occurrence, and typical characteristic values, used for preliminary guidance in the identification and management of loosening risks.

[0027] Furthermore, based on the aforementioned specialized predictive model and benchmark library, the wiring harness design scheme is optimized. This optimization process involves designers manually reviewing historical loosening cases in the benchmark library and adjusting the existing design based on the preliminary risk assessment provided by the predictive model, including adjusting connector selection or wiring harness routing. Simultaneously, a loosening prediction process, anti-loosening compensation plan, and specialized inspection points are pre-defined. The loosening prediction process is a simple checklist listing signs of loosening requiring attention. The anti-loosening compensation plan consists of general, predefined maintenance steps, such as retightening connections or replacing worn parts. Specialized inspection points are marked on the design drawings to highlight connection points requiring focused inspection. This results in an integrated design-anti-loosening-prediction technical document, which compiles independent documents such as design specifications, inspection requirements, and maintenance manuals to ensure information consistency.

[0028] During the manufacturing phase, in accordance with the aforementioned integrated technical documents, anti-loosening process parameters and loosening prediction thresholds are configured for each manufacturing step. The configuration of anti-loosening process parameters involves operators manually adjusting the parameters of the production equipment, such as the pressure of the crimping machine or the tension of the strapping machine, according to the document requirements. The loosening prediction threshold is a simple pass / fail standard set on the production line, used to visually inspect and determine the firmness of the connection. Real-time collection of process data and loosening characteristic monitoring data is performed. Process data includes manually recorded information such as production batch, operator, and equipment running time. Loosening characteristic monitoring data is obtained through sampling inspections by operators during production, recording the tightness or appearance defects of the connectors. A risk prediction and compensation strategy is triggered using an anti-loosening-prediction linkage model. This linkage model is a simple decision tree that compares the collected data with preset thresholds; when a deviation is detected, it prompts the operator to intervene. The compensation strategy is a simple corrective measure manually executed by the operator, such as re-crimping the terminals or adjusting the strapping force. After compensation, the compensation effect is verified simultaneously by conducting another visual inspection or simple functional test to confirm whether the problem has been resolved.

[0029] During the assembly phase, a three-level anti-loosening inspection mode is employed. This mode includes: Level 1, a simple functional test on individual connection components; Level 2, an overall performance test on modules composed of multiple connection components; and Level 3, a final power-on test on the entire wiring harness system. Loosening risks discovered during inspection are addressed through root cause analysis and targeted remedial measures. Root cause analysis involves manual inspection, checking assembly records, operating procedures, or component batch information to determine the possible causes of loosening. Targeted remedial measures involve assembly personnel using their experience to perform local repairs or component replacements for identified loosening issues. Relevant data, such as the location, type, and handling method of loosening, is recorded and fed back to the manufacturing control system via manual reports or emails, enabling the manufacturing department to understand the problems encountered during assembly.

[0030] During the wire harness operation phase, baseline evolution characteristics are collected and the baseline library is dynamically updated. Baseline evolution characteristics are electrical or mechanical parameters recorded manually or by simple sensors during the initial operation of the wire harness, serving as a reference for a secure state. The baseline library is dynamically updated by regularly reviewing operational data and manually adjusting some parameters or thresholds based on new operational experience or environmental changes. Real-time monitoring of loosening evolution characteristics is conducted periodically by inspection personnel who periodically check the appearance of wire harness connections or perform simple electrical tests using handheld devices. A predictive algorithm identifies trends and triggers a tiered dynamic anti-loosening compensation strategy and early warning signals. The predictive algorithm is a simple rule engine; when a monitored characteristic parameter exceeds a preset single threshold, a loosening risk is identified. The tiered dynamic anti-loosening compensation strategy is a preset maintenance plan manually executed by maintenance personnel based on the predictive results, tightening minor loosening and replacing severely loose parts. Early warning signals are issued via simple indicator lights or buzzers.

[0031] Finally, a comprehensive management and control data system was established to build an iterative optimization system. The comprehensive management and control data was collected manually from paper reports or electronic records at each stage of design, manufacturing, assembly, and operation. The iterative optimization system is a mechanism where an expert team holds regular meetings to review data from each stage and discuss improvement plans. Through this system, the predictive model, the linkage model, and the compensation strategy were optimized. Based on loosening issues discovered during actual operation, the expert team manually adjusted the parameters of the predictive model, modified the decision rules in the linkage model, and improved the specific steps of the compensation strategy. Simultaneously, the specialized management and control benchmark database was updated by manually entering new loosening cases, effective compensation measures, or updated threshold information to achieve continuous improvement of the control system.

[0032] The method described in this embodiment addresses the fragmented and isolated issues of traditional wire harness connection loosening prevention and control. By establishing a specialized predictive model and benchmark library, it achieves dynamic closed-loop management throughout the entire lifecycle, from design to operation and maintenance. This method ensures coordinated optimization and continuous improvement across all stages through integrated technical documentation, dynamic parameter adjustment during the manufacturing process, multi-level testing and root cause analysis during assembly, and proactive prediction and tiered compensation during operation, thereby enhancing the reliability and safety of wire harness connections.

[0033] In some of the embodiments described above in this application, data related to the inducing mechanism, evolution stage, and failure mode of connection loosening throughout the entire life cycle of the receiving harness are proposed to construct a predictive model and a benchmark library. In the process of its implementation, due to the unclear or non-specific content of the inducing mechanism, evolution stage, and characteristic parameters, the model construction may be inaccurate, the benchmark library may be incomplete, and the anti-loosening effect may be affected.

[0034] In this regard, this application further clarifies the typical inducing mechanisms, evolution stages, and loosening characteristic parameters of connection loosening. Specifically, the typical inducing mechanisms of connection loosening include deviations in terminal crimping parameters, unreasonable wire bundling tension, improper fixing layout of connection parts, vibration fatigue corrosion under operating conditions, material aging caused by temperature stress, and non-standard assembly operations. The evolution stages include the initial loosening potential stage, the slight loosening stage, the moderate loosening stage, and the failure loosening stage. The loosening characteristic parameters include electrical characteristic parameters, mechanical characteristic parameters, and operating stress-related characteristic parameters. Among them, electrical characteristic parameters include the rate of change of contact resistance, signal transmission attenuation, and current fluctuation amplitude; mechanical characteristic parameters include the change of tightening torque, vibration response amplitude deviation, and insertion / removal contact gap change; and operating stress-related characteristic parameters include parameters relating vibration frequency to loosening rate and parameters relating temperature amplitude to contact resistance deviation.

[0035] Among them, the typical triggering mechanism of loose connection is the root cause of the decline in the reliability of wire harness connection.

[0036] Terminal crimping parameter deviation refers to the failure of key parameters such as crimping force, crimping height, and crimping width to strictly meet design standards during the connection process between the terminal and the wire, resulting in unstable contact resistance or insufficient mechanical strength in the crimped area. This can be addressed by using high-precision crimping equipment combined with a real-time online monitoring system to ensure that the crimping parameters are within the preset tolerance range, or by introducing a visual inspection system to detect geometric dimensions and appearance defects in the crimped terminals.

[0037] Inappropriate wire bundling tension refers to the bundling of wires with excessive or insufficient tension. Excessive tension may damage the wire insulation, while insufficient tension will not effectively secure the wire bundle, causing relative displacement under vibration or impact. This can be addressed by using automated bundling equipment with tension control functions, or by using intelligent robotic systems to adaptively adjust the bundling tension based on the wire bundle diameter and material properties.

[0038] Improper connection layout refers to insufficient number of fixing points, unreasonable location selection, or insecure fixing methods for the wire harness within the overall equipment. This makes the wire harness susceptible to shaking or displacement due to external stress during operation. By simulating and optimizing the stress distribution of the wire harness under different operating conditions using finite element analysis (FEA), the optimal location and number of fixing points can be determined, or a modular and integrated fixing structure design can be adopted to enhance overall stability.

[0039] Vibration fatigue erosion refers to the fatigue damage that occurs when wiring harness connection points are exposed to a vibrating environment for a long time, resulting in fatigue damage to the material due to repeated stress and a gradual deterioration of the connection performance. Vibration energy can be absorbed and dispersed by selecting materials with excellent vibration fatigue resistance, such as high-strength alloys or damping composite materials, or by adding vibration damping pads or buffer structures at the connection points.

[0040] Temperature-induced stress aging refers to the thermal expansion and contraction of materials at joints caused by periodic changes in ambient temperature, leading to material performance degradation and increased joint gaps over long periods. Temperature-induced stress can be mitigated by selecting materials with low coefficients of thermal expansion and good resistance to temperature cycling, or by using elastic connection structures with thermal compensation capabilities.

[0041] Non-standard assembly operations refer to situations where operators fail to strictly follow standard operating procedures during manual or semi-automated assembly processes. Examples include improper insertion, insufficient tightening torque, and improper wiring harness routing, which can introduce potential loosening risks. Ensuring standardized operations can be achieved by implementing rigorous employee training and certification systems, equipping workers with power tools featuring torque control, and introducing automated assembly support systems.

[0042] The division into evolutionary stages helps to classify and manage the risk of loosening and to intervene early.

[0043] The initial loosening risk stage refers to a period where potential loosening risks already exist, but have not yet manifested obvious physical or electrical characteristics. At this stage, highly sensitive monitoring methods are required. This can be achieved through predictive analysis of high-risk points in the design and manufacturing process, or by identifying initial damage within the material through microstructural detection techniques.

[0044] The slight loosening stage refers to the stage where there are detectable minor changes in the physical or electrical characteristics of the connection points, but these changes have not yet affected the normal function of the wiring harness. This stage is identified through high-precision contact resistance measurement, micro-vibration analysis, or localized stress monitoring.

[0045] The moderate loosening stage refers to a further increase in the degree of loosening, which may lead to intermittent functional abnormalities, performance degradation, or sporadic failures. At this stage, changes in electrical and mechanical parameters will be more pronounced, requiring more proactive intervention measures.

[0046] The failure and loosening stage refers to a stage where the loosening has reached a severe level, causing the wiring harness to completely fail and even leading to a safety accident. This stage usually requires emergency handling and thorough repair or replacement.

[0047] Loosening characteristic parameters are key indicators for quantifying and monitoring the loosening state.

[0048] Electrical characteristic parameters directly reflect the electrical performance of the connection parts.

[0049] The rate of change of contact resistance refers to the rate at which the contact resistance at a connection point changes with time or operating conditions; it is a sensitive indicator for judging the degree of looseness. Real-time or periodic data is collected using a four-wire high-precision resistance meter, and its changing trend is calculated.

[0050] Signal transmission attenuation refers to the energy loss of a signal due to looseness when passing through a connection point, and it is particularly important for signal harnesses. It is measured using a network analyzer or time domain reflectometer (TDR) technology.

[0051] Current fluctuation amplitude refers to the abnormal fluctuation range of current flowing through a connection point under stable power supply conditions. It is monitored and analyzed in real time using high-precision current sensors and data acquisition systems.

[0052] Mechanical characteristic parameters reflect the physical state of the connection parts.

[0053] The change in tightening torque refers to the change in the tightening torque of a connector due to factors such as vibration, thermal expansion and contraction, or material creep. This is monitored in real time using intelligent torque sensors or bolts equipped with torque sensors.

[0054] Vibration response amplitude deviation refers to the deviation of the vibration response of a connection part from its normal state under specific vibration excitation. This is achieved by deploying accelerometers to collect vibration data and comparing it with reference data.

[0055] The change in insertion / removal contact gap refers to the minute change in the physical gap of the terminal insertion / removal connection due to loosening. It is monitored using a high-precision displacement sensor or optical measurement system.

[0056] The stress-related characteristic parameters under working conditions link environmental factors with the loosening evolution.

[0057] The correlation parameter between vibration frequency and loosening rate refers to the regularity of the evolution rate of connection loosening at different vibration frequencies. It is established by conducting fatigue tests at different frequencies on a vibration test bench and combining this with statistical analysis of field operation data.

[0058] The correlation parameter between temperature change amplitude and contact resistance deviation refers to the relationship between the amplitude of ambient temperature change and the contact resistance deviation of the connection part. This is analyzed by conducting experiments in a high and low temperature cycling test chamber and establishing a thermo-mechanical coupling model.

[0059] By clearly defining the typical inducing mechanisms of connection loosening, including terminal crimping parameter deviations, unreasonable wire bundling tension, improper fixing layout of connection parts, vibration fatigue corrosion under operating conditions, material aging caused by temperature stress, and non-standard assembly operations, this application can fundamentally identify and specifically prevent the potential root causes of loosening. Simultaneously, by refining the evolution stages of loosening into the initial loosening potential stage, the slight loosening stage, the moderate loosening stage, and the failure loosening stage, the monitoring of the loosening process becomes more precise, enabling the possibility of taking differentiated intervention measures at different stages. Furthermore, this application specifically defines loosening characteristic parameters, covering electrical characteristic parameters, including contact resistance change rate, signal transmission attenuation, and current fluctuation amplitude; mechanical characteristic parameters, including tightening torque change, vibration response amplitude deviation, and insertion / removal contact gap change; and operating stress-related characteristic parameters, including parameters relating vibration frequency to loosening rate and parameters relating temperature change amplitude to contact resistance deviation. These quantifiable, multi-dimensional monitoring indicators provide comprehensive and accurate data support for the construction of predictive models and benchmark libraries. In view of this, this application can construct a more accurate prediction model for loose connections and establish a more comprehensive special control benchmark library, thereby improving the accuracy of prediction and the effectiveness of control over loose connections in wire harnesses, effectively avoiding the problems of inaccurate models and incomplete benchmark libraries caused by insufficient data, and thus improving the reliability and safety of wire harnesses throughout their entire life cycle.

[0060] In some of the solutions mentioned above in this application, a special control benchmark library is proposed to store predictive data and control benchmarks to support loosening prevention compensation. In the process of its implementation, the construction of the benchmark library may lack a clear correlation mapping between risk level and compensation strategy, resulting in insufficient accuracy and efficiency of loosening prevention compensation. It is impossible to dynamically adjust the strategy according to the loosening risk, thereby affecting the overall loosening prevention effect.

[0061] In response, this application further proposes a process for constructing a special control benchmark library, including: integrating the output parameters of the prediction model, the loosening trend thresholds at each evolution stage, and the dynamic anti-loosening compensation strategies corresponding to different risk levels; establishing a correlation mapping relationship between risk levels and compensation strategies; the dynamic anti-loosening compensation strategies include design parameter fine-tuning, process parameter correction, assembly operation optimization, and operation and maintenance reinforcement anti-loosening strategies; the loosening prediction indicators include characteristic parameter thresholds, time-series trend change rate thresholds, and risk level judgment thresholds; and clarifying the handling procedures and compensation priorities for different prediction results.

[0062] Specifically, integrating the output parameters of the predictive model refers to incorporating the prediction results, risk probabilities, confidence levels, and other data generated by the specific predictive model into the specific control benchmark library. These output parameters include the predicted occurrence time of loose wiring harness connections, the quantified value of the degree of loosening, or the probability of a specific failure mode. The predictive model might output a probability of Y% for a slight loosening at a connection point within the next X hours, or that its contact resistance change rate will reach Z. By integrating these model outputs, the benchmark library can obtain forward-looking risk information based on data analysis and pattern recognition, thereby providing a scientific basis for subsequent risk assessment and strategy formulation. Another approach is for the predictive model to directly output the risk level of the current connection status, such as low risk, medium risk, or high risk, and store and manage these levels as key parameters in the benchmark library.

[0063] The loosening trend thresholds for each evolution stage refer to the quantitative boundary values ​​set to distinguish these stages based on the typical evolution stages of loosening in wiring harness connections. These thresholds are determined based on historical fault data, experimental test data, or industry standards. For example, when the rate of change in contact resistance exceeds a certain percentage, the system transitions from the initial loosening hazard stage to the slight loosening stage; when the change in tightening torque reaches a certain value, the system transitions from the slight loosening stage to the moderate loosening stage. These thresholds enable the system to accurately identify the current stage of loosening, thereby allowing for phased and targeted intervention measures. Another approach is to statistically analyze loosening characteristic parameters, including electrical, mechanical, and stress-related parameters, to determine the typical distribution range of these parameters at different evolution stages, and then use the boundary values ​​of these ranges as trend thresholds.

[0064] Dynamic anti-loosening compensation strategies corresponding to different risk levels refer to a series of predefined, executable, and adaptive compensation measures for different risk levels that may occur due to loosening of wire harness connections. These strategies are dynamic, meaning they are adjusted and optimized according to actual conditions. For minor risks, they may trigger fine-tuning of design parameters or correction of process parameters; for severe risks, they may require optimization of assembly operations or reinforcement of anti-loosening strategies during operation and maintenance. The definition of these strategies ensures that the system can provide appropriate and effective solutions under different risk scenarios. Another approach is to establish a rule-based expert system that intelligently recommends the most suitable combination of anti-loosening compensation strategies based on multiple factors such as the current risk level, wire harness type, and operating environment.

[0065] Establishing a mapping relationship between risk levels and compensation strategies refers to explicitly binding the identified loosening risk level with the corresponding dynamic anti-loosening compensation strategy in a specialized control benchmark database. This mapping relationship can be a direct one-to-one relationship, or a complex many-to-one or one-to-many relationship. Through a lookup table or decision tree structure, when the system determines the current risk level to be a moderate loosening risk, it automatically associates it with a combined strategy of process parameter correction and assembly operation optimization. This mapping relationship ensures seamless integration between risk identification and compensation execution, improving the automation and accuracy of decision-making. Another approach is to utilize ontology technology to construct a semantic association between risk levels and compensation strategies, enabling the system to more flexibly understand and apply these relationships.

[0066] The dynamic anti-loosening compensation strategy includes design parameter fine-tuning, process parameter correction, assembly operation optimization, and operation and maintenance reinforcement anti-loosening strategies. Design parameter fine-tuning refers to making small-scale adjustments to parameters such as the structure, materials, and dimensions of the wire harness during the product design phase to improve its anti-loosening ability, such as adjusting the terminal contact structure and optimizing the layout of fixing points. Process parameter correction refers to adjusting process parameters such as crimping pressure, bundling tension, and heating temperature during the manufacturing process to ensure connection quality, such as adjusting the matching parameters between terminal crimping pressure and time. Assembly operation optimization refers to improving operating procedures, tool usage, or personnel training during the assembly stage to reduce loosening caused by human factors, such as optimizing the tightening torque control process. Operation and maintenance reinforcement anti-loosening strategies refer to extending the service life of the wire harness and preventing loosening during the product operation phase through regular inspection, reinforcement, and replacement, such as regularly inspecting connection fasteners and applying anti-loosening adhesive. These strategies cover all stages of the wire harness's entire life cycle, forming a multi-level, comprehensive anti-loosening system.

[0067] The loosening prediction indicators include characteristic parameter thresholds, time-series trend change rate thresholds, and risk level determination thresholds. Characteristic parameter thresholds refer to absolute warning values ​​set for various characteristic parameters indicating loosening of the wiring harness connection. When the contact resistance exceeds a certain preset value, a loosening risk is considered to exist. The time-series trend change rate threshold refers to the rate at which the characteristic parameters change over time. When the rate of change exceeds a certain preset value, it indicates that the loosening is accelerating and requires attention. Contact resistance rises rapidly within a short period. The risk level determination threshold is a comprehensive threshold that combines multiple characteristic parameters and trend change rates to classify different risk levels. Through weighted averaging or fuzzy logic judgment, when the comprehensive risk score reaches a certain range, it is determined to be a moderate risk. These multi-dimensional prediction indicators together constitute a comprehensive and refined assessment system for loosening risk.

[0068] Clearly defining the handling procedures and compensation priorities for different predicted outcomes refers to pre-determining detailed response steps and the execution sequence of compensation measures for various results given by the predictive model. The handling procedures specify the concrete operational path from risk discovery to problem resolution. For minor risks, only recording and continuous monitoring may be necessary; for severe risks, immediate shutdown for inspection and emergency repairs may be required. Compensation priorities determine which strategy should be prioritized when multiple compensation options are available to achieve the best anti-loosening effect and resource utilization efficiency. Where possible, process parameter correction should be prioritized, followed by assembly operation optimization, and finally, operation, maintenance, and reinforcement anti-loosening strategies. This ensures that the system can respond quickly, orderly, and efficiently to different predicted outcomes.

[0069] Through the above technical solutions, the construction of the special control benchmark database is no longer a simple data set, but an intelligent decision support system. Integrating the output parameters of the prediction model enables the benchmark database to reflect the latest risk predictions in real time, improving the accuracy and foresight of risk identification. The introduction of loosening trend thresholds for each evolution stage allows the system to accurately locate the stage of loosening, thus avoiding a one-size-fits-all compensation strategy and achieving phased and refined management. The establishment of dynamic anti-loosening compensation strategies corresponding to different risk levels and the correlation mapping between risk levels and compensation strategies directly solves the problem of insufficient correlation between risk levels and compensation strategies in the benchmark database, ensuring that the most appropriate and effective compensation measures can be quickly matched in any risk scenario, greatly improving the accuracy and efficiency of anti-loosening compensation.

[0070] Furthermore, the dynamic anti-loosening compensation strategy covers the entire lifecycle, including design, manufacturing, assembly, operation, and maintenance. This ensures comprehensive coverage of anti-loosening measures from source to end, forming a closed-loop management system that effectively suppresses the occurrence and evolution of loosening risks. The multi-dimensional design of loosening prediction indicators, combined with characteristic parameter thresholds, time-series trend change rate thresholds, and risk level judgment thresholds, makes the assessment of loosening risks more comprehensive and objective, enabling earlier and more accurate identification of potential risks. Clearly defined handling procedures and compensation priorities for different prediction results ensure rapid and orderly response and processing when risks occur, reducing losses caused by delayed or chaotic decision-making.

[0071] The above-described embodiments of this application make the entire method for preventing and controlling loose connections in wire harnesses more intelligent and adaptable. Under the constructed full lifecycle management framework, the specialized management benchmark library built in this embodiment can provide more accurate and dynamic decision-making basis for design optimization, manufacturing process loosening prevention, assembly stage inspection, and dynamic loosening compensation during operation. Based on typical inducing mechanisms, evolution stages, and loosening characteristic parameters, this embodiment can more meticulously set loosening trend thresholds and loosening prediction indicators for each evolution stage. This allows the prediction model and linkage model to more accurately identify risks and trigger graded dynamic loosening compensation strategies, improving the effectiveness and robustness of the entire prevention and control system.

[0072] In some of the embodiments described above in this application, a design phase is proposed to optimize the wire harness design and pre-set anti-loosening measures. However, in the process of implementation, due to the failure to accurately identify the key risk factors that are prone to loosening in the design phase, and the lack of quantitative correlation between these risk factors and the evolution of loosening, the anti-loosening optimization design lacks pertinence and is difficult to effectively avoid potential loosening risks such as unreasonable terminal contact structure and improper fixing layout from the source, thereby affecting the overall anti-loosening effect.

[0073] In response, this application further proposes key risk factors that can easily lead to loosening of connections during the design phase, including the rationality of terminal contact structure, compatibility between wires and terminals, scientific layout of connection fixing points, rational distribution of binding nodes, vibration aging resistance of protective materials, and the adaptability of wiring harness routing to operating vibration conditions. The correlation between these risk factors and the evolution of loosening is established through multiple sets of comparative experiments simulating operating conditions. The targeted design optimization for preventing loosening includes adjusting the values ​​of risk factors, optimizing the fixing layout of connection points, terminal contact structure, and distribution of binding nodes. The pre-set anti-loosening compensation plan clarifies the compensation process, parameter range, and effectiveness verification standards for each risk factor corresponding to the loosening type.

[0074] Among these, key risk factors that can easily lead to loose connections during the design phase refer to internal or external factors that may directly or indirectly cause loosening of wire harness connections during the product design stage. These factors form the basis for systematically identifying and quantifying loosening risks. Besides the rationality of terminal contact structure, compatibility between wires and terminals, scientific layout of connection fixing points, rational distribution of bundling nodes, the vibration and aging resistance of protective materials, and the compatibility of wire harness routing with operating vibration conditions, these factors may also include the rigidity of connector housing materials, the design of wire harness bending radius, and the selection and installation method of wire harness fixing clamps.

[0075] The correlation between risk factors and loosening evolution is established through comparative experiments with multiple sets of operating conditions. This involves simulating various operating conditions that wiring harnesses might encounter in actual operation under controlled environments, such as vibration, temperature cycling, and humidity changes. The evolution process and degree of loosening of the wiring harness connection are compared under different risk factor values, thereby establishing a quantitative relationship model between risk factors and loosening evolution. This correlation can be established not only through comparative experiments with multiple sets of operating conditions but also through historical fault data analysis, finite element simulation analysis, and accelerated aging tests, aiming to provide a scientific basis for design optimization.

[0076] Anti-loosening targeted design optimization refers to purposeful and evidence-based improvements to the design scheme based on the established correlation between risk factors and the evolution of loosening. Specifically, this includes adjusting the values ​​of risk factors, such as selecting more vibration-resistant protective materials, optimizing the fixing layout of connection points to reduce stress concentration, improving terminal contact structures to enhance contact stability, and adjusting the distribution of bundling nodes to even out wire harness stress. Furthermore, it includes using new high-elasticity or high-wear-resistant materials, improving connector locking mechanism design, and adding additional stress-relieving structures to fundamentally improve the anti-loosening performance of the wire harness.

[0077] A pre-planned anti-loosening compensation plan refers to strategies for addressing different types of loosening risks that are planned in advance during the design phase. This plan clarifies the compensation process to be taken when a specific risk factor is detected that may cause loosening; whether this involves fine-tuning design parameters, correcting process parameters, or optimizing assembly operations; the range of parameters required for compensation; the range of torque adjustments; the specifications of materials to be replaced; and the verification standards for the compensation effect, specifying the testing methods and performance indicators required for acceptance. This plan exists in the form of a Standard Operating Procedure (SOP) to ensure a rapid and effective response to and handling of potential loosening issues during subsequent manufacturing, assembly, and operation phases.

[0078] Through the aforementioned technical solution, this application can accurately identify key risk factors in the design process that are prone to loosening of connections, and establish a quantitative correlation between these risk factors and the evolution of loosening based on multiple sets of operational condition simulation comparative experiments. This makes wire harness design optimization no longer dependent on engineers' experience and judgment, but based on objective data and scientific evidence, thereby effectively avoiding and suppressing potential loosening risks such as unreasonable terminal contact structures and improper fixing layouts from the source. By specifically optimizing the values ​​of risk factors, the fixing layout of connection parts, the terminal contact structure, and the distribution of binding nodes, the anti-loosening capability of the wire harness design is improved. At the same time, the pre-set anti-loosening compensation plan clarifies the compensation process, parameter range, and effect verification standards for each risk factor corresponding to the loosening type, ensuring that there is a standardized response strategy for potential loosening risks in the design stage, improving the efficiency of risk response and the accuracy of compensation. Overall, these measures, combined with the special prevention and control of connection loosening throughout the entire life cycle of the wire harness and the dynamic management and control method of the whole process, make the entire prevention and control system forward-looking, systematic, and accurate from the design source, thereby improving the reliability of wire harness connections and the operational stability of the entire equipment.

[0079] In some of the solutions mentioned above in this application, an integrated design-anti-loosening-prediction technical document is proposed to integrate design, anti-loosening and prediction elements to guide the whole process control. In this process, the document may lack a standardized format, clear inter-element linkage relationship and dynamic update mechanism, which makes it impossible to effectively support the real-time adjustment of design parameters and prediction standards, affecting the data communication and optimization effect of the whole process.

[0080] To address this, this application further proposes optimizing the integrated design-loosening-prediction technical document, integrating optimized design parameters, loosening risk factors, prediction indicators, specific inspection points, compensation plans, and verification standards. This technical document serves as the core guiding document for the entire process of controlling loosening in wire harness connections, and its integrated content aims to systematically integrate the key aspects of design, loosening prevention, and prediction. This can be achieved through a structured database or document management system, containing all optimized design parameters, identified loosening risk factors, prediction indicators for assessing loosening status, specific inspection points set during production and assembly, compensation plans for different loosening risks, and verification standards for evaluating compensation effectiveness. Alternatively, electronic documents based on data exchange formats such as XML or JSON can be used to store and manage this information in a parsable and machine-readable manner, ensuring data correlation and traceability between various elements.

[0081] To address the lack of clearly defined inter-element linkages in existing documents, this application clarifies the linkage between design adjustments, indicator optimization, and compensation strategy adaptation. This linkage refers to a mutual influence and automatic adjustment mechanism established among the three core stages of design, prediction, and compensation. When one stage changes, the others can adjust and adapt accordingly to maintain the coordination and effectiveness of the entire prevention and control system. Specifically, this is achieved by defining a series of business rules and logical judgments. When design parameters are slightly adjusted, the system automatically assesses their impact on prediction indicators and recommends or automatically updates corresponding compensation strategies. Alternatively, this can be achieved by establishing parameter mapping tables or decision tree models to link changes in design parameters with adjustments to prediction indicators and the selection of compensation strategies, ensuring that prediction and compensation can automatically adapt when design changes occur.

[0082] To address the issue of insufficient document standardization, this application adopts an industry-standardized format for compiling the integrated technical document. An industry-standardized format refers to a document or data format widely accepted and used within a specific industry. Its purpose is to improve the readability, exchangeability, and compatibility of information, facilitating data sharing and integration between different systems and departments. It adopts document structures and data field definitions recommended in standards such as ISO / TS 16949 or AS9100, or uses common engineering data exchange formats such as standard data models in STEP and PLM systems.

[0083] To address the shortcomings of the dynamic document update mechanism, this application provides a data update interface to support adjustments to design parameters and prediction criteria based on iterative data. This data update interface is a mechanism for dynamically updating the content of technical documents. It allows external systems or manual operations to input new data into the technical documents, thereby iteratively optimizing design parameters and prediction criteria. Specifically, it can be an API (Application Programming Interface) that allows other software systems to programmatically access and modify specific data fields in the technical documents. Alternatively, it can be a user-friendly data import / export module that supports updating data via file upload or graphical interface operations, automatically triggering adjustment logic for design parameters and prediction criteria.

[0084] Through the aforementioned technical solutions, the integrated design-loosening-prediction technical document is no longer a static guideline but possesses dynamic adaptability and optimization capabilities. Specifically, by integrating optimized design parameters, loosening risk factors, prediction indicators, specific testing nodes, compensation plans, and verification standards, this document comprehensively covers all key aspects of harness connection loosening prevention and control, ensuring data interoperability and collaboration across design, manufacturing, assembly, and operation stages. It clarifies the linkage between design adjustments, indicator optimization, and compensation strategy adaptation, enabling prediction indicators and compensation strategies to adjust automatically or semi-automatically when design schemes are fine-tuned or operational data feedback requires optimization. This avoids the lag and inaccuracy of manual intervention, enhancing the responsiveness and consistency of the entire prevention and control system. Adopting an industry-standard format significantly improves the readability, exchangeability, and integration of the technical document across different systems and departments, promoting seamless data flow and sharing throughout the entire process. In addition, a data update interface is reserved to support the adjustment of design parameters and prediction criteria based on iterative data. This allows the technical document to continuously learn and optimize based on the loosening evolution data and compensation effect data accumulated in actual operation, thereby continuously improving the accuracy of the prediction model and the effectiveness of the compensation strategy, suppressing the risk of loosening from the source, and ensuring the connection reliability throughout the entire life cycle of the harness.

[0085] In some of the solutions mentioned above in this application, it is proposed to configure manufacturing process anti-loosening process parameters and loosening prediction thresholds to collect process data and loosening characteristic monitoring data in real time. In this process, due to the lack of specific definition of core manufacturing processes, identification of key control processes, and detailed description of anti-loosening process parameters, data collection may be discontinuous, inaccurate, or not real-time, thereby affecting the effect of the anti-loosening-prediction linkage model and failing to effectively solve core causes such as substandard terminal crimping and abnormal binding tension.

[0086] In response, this application further proposes that in the manufacturing process, the core manufacturing processes include wire cutting and stripping, terminal crimping, wire bundling, protective layer coating, and semi-finished product integration. The terminal crimping and wire bundling processes are the key control points. The anti-loosening process parameters include terminal crimping pressure and duration matching parameters, crimping depth accuracy parameters, wire bundling tension and spacing matching parameters, and bundling node reinforcement process parameters. The process operation data and loosening characteristic monitoring data are collected in real time through a time-series data acquisition unit to ensure the continuity, accuracy, and real-time nature of the data.

[0087] Specifically, the core manufacturing processes encompass the key stages of wire harness production, including wire cutting and stripping, terminal crimping, wire bundling, protective layer coating, and semi-finished product integration. These processes form the basis for the structural integrity and connection reliability of the wire harness during manufacturing. The wire cutting and stripping process utilizes automated equipment to precisely control the wire length and stripping length, ensuring the quality of subsequent terminal crimping. The terminal crimping process uses a high-precision crimping machine to ensure reliable electrical and mechanical connections between the terminals and wires. The wire bundling process uses specialized bundling equipment to neatly bundle the wires according to design requirements, preventing them from becoming tangled or subjected to undue stress. The protective layer coating process provides insulation and mechanical protection to the wire harness, improving its environmental adaptability. The semi-finished product integration process assembles the various wire harness components to form a complete semi-finished wire harness. Furthermore, laser wire stripping technology can be used to improve stripping accuracy, or a visual inspection system can be introduced to monitor crimping quality in real time. Robot-assisted bundling can also be used to achieve more complex bundling paths and more uniform tension distribution.

[0088] Given that terminal crimping and wire bundling are the primary causes of loose wire harness connections, this application focuses on their control. This means investing more testing resources, stricter process parameter control, and more frequent quality spot checks in these two processes. 100% pull-out force and resistance tests are performed on terminal crimping, and online tension monitoring is implemented for wire bundling. Furthermore, this includes providing specialized training to operators to ensure they are proficient in operating procedures, introducing advanced automated equipment to reduce human error, and establishing detailed work instructions and quality control points.

[0089] To achieve precise control, the anti-loosening process parameters are clearly defined, including terminal crimping pressure and duration matching parameters, crimping depth accuracy parameters, wire bundling tension and spacing matching parameters, and bundling node reinforcement process parameters. These parameters are key process variables that directly affect the reliability of the wire harness connection. The terminal crimping pressure and duration matching parameters ensure that the crimping force and duration are moderate, ensuring a tight connection while avoiding damage to the wires. The crimping depth accuracy parameters control the depth of contact between the terminal and the wire, directly affecting contact resistance and mechanical strength. The wire bundling tension and spacing matching parameters ensure that the bundling force is uniform and the spacing is reasonable, avoiding uneven force or excessive compression on the wires. The bundling node reinforcement process parameters enhance the stability of the bundling points through additional fixing measures, such as cable ties, tape, or clips. These parameters are optimized through prior experiments and simulations to form an optimal process window, and the optimal combination of crimping pressure and duration is determined through pull-out force tests.

[0090] To ensure data validity, the process operation data and loosening characteristic monitoring data are collected in real time through a time-series data acquisition unit, ensuring data continuity, accuracy, and real-time performance. The time-series data acquisition unit is integrated into the production equipment, directly acquiring data through sensors and transmitting the data to the central control system via industrial Ethernet or a wireless communication module. Alternatively, independent monitoring equipment can be used, such as force and displacement sensors installed near the crimping machine to record the crimping curve in real time, and tension sensors installed on the strapping machine to monitor the strapping force in real time. Simultaneously, online electrical testing equipment monitors loosening characteristic parameters such as contact resistance and signal attenuation. Data continuity is ensured through high-frequency data sampling and a stable data transmission link; accuracy is achieved through high-precision sensors, periodic calibration equipment, and data verification algorithms; and real-time performance is ensured by optimizing the data processing flow, employing edge computing technology, and low-latency communication protocols. Data quality can be further guaranteed through redundant data acquisition systems, data anomaly detection and repair mechanisms, and timestamp synchronization technology.

[0091] Through the aforementioned technical solution, this application clarifies the core manufacturing processes and focuses on two key aspects: terminal crimping and wire bundling. This allows limited resources to be concentrated on the sources most prone to loosening. Detailed definitions of anti-loosening process parameters enable more precise and quantifiable control during manufacturing, avoiding potential loosening risks caused by ambiguous parameters. Real-time, continuous, and accurate data acquisition provides high-quality input for the anti-loosening-prediction linkage model, enabling the model to more accurately identify core causes such as substandard terminal crimping and abnormal bundling tension, thereby triggering timely and effective compensation strategies. This enhances the manufacturing process's ability to prevent loosening of wire harness connections, fundamentally solving the loosening problem caused by manufacturing process defects and ensuring the reliability of the wire harness before it leaves the factory.

[0092] In some of the solutions mentioned above in this application, an anti-loosening-prediction linkage model is proposed to predict risks and trigger compensation strategies. However, in its implementation, the model may not be able to accurately identify core causes such as substandard terminal crimping or abnormal bundling tension, and the compensation strategy may not be adaptive enough and cannot be dynamically adjusted according to the risk level, resulting in poor compensation effect.

[0093] In this regard, this application further proposes that the anti-loosening-prediction linkage model is an adaptive model based on time-series feature analysis. By comparing the deviation between the data and the prediction threshold, it accurately identifies core causes such as substandard terminal crimping and abnormal bundling tension. For mild loosening risks, it triggers fine-tuning of process parameters for compensation. For moderate and above risks, it triggers correction of process parameters and optimization of the operation process for compensation. After compensation, a special prediction model is used for re-evaluation. Only after verification that it is qualified can it proceed to the next process. If it is unqualified, the prediction-compensation-verification process is repeated.

[0094] Specifically, the adaptive model based on time-series feature analysis refers to a model capable of in-depth analysis of manufacturing process data and loosening feature monitoring data that change over time, capturing time-series features such as trends, periodicity, and outliers. This model employs deep learning models such as recurrent neural networks and long short-term memory networks, or statistical methods based on Kalman filtering and autoregressive moving average models, to learn and model historical data. Furthermore, its adaptive nature is reflected in its ability to continuously adjust its internal parameters and prediction logic based on new real-time data streams. Through mechanisms such as online learning and reinforcement learning, its predictive ability and recognition accuracy are continuously optimized over time, rather than using fixed rules or parameters.

[0095] The deviation between the comparison data and the predicted threshold refers to comparing the real-time collected process data and loosening characteristic monitoring data with the pre-set predicted threshold. This predicted threshold is a safety range derived from historical normal operation data statistics, or it can be an upper or lower limit set according to design requirements and experience. By calculating the difference or deviation between the real-time data and these thresholds, and using statistical methods such as standard deviation, mean square error, or setting a percentage deviation range, the degree of abnormality in the current process status is quantified.

[0096] Accurate identification of core causes such as substandard terminal crimping and abnormal bundling tension involves analyzing the deviation between data and predicted thresholds, combined with the model's internal diagnostic logic, to accurately determine the specific manufacturing defects leading to loosening risks. The model utilizes classification algorithms such as decision trees and support vector machines, or rule-based reasoning systems, to map specific data deviation patterns to specific causes. A deviation consistently exceeding the crimping pressure limit may indicate a malfunction in the crimping equipment, while excessive fluctuations in bundling tension may indicate improper bundling process parameter settings or incorrect operator techniques.

[0097] The minor loosening risk triggering process parameter fine-tuning compensation refers to the system automatically or prompting operators to make small, precise adjustments to relevant manufacturing process parameters when the model identifies a loosening risk in its early or minor stages. For the crimping process, the crimping pressure or duration is fine-tuned by 0.5% to 2%; for the strapping process, the strapping tension or spacing is adjusted within ±5%. This fine-tuning aims to correct potential problems promptly without interrupting production and prevent further escalation of risks.

[0098] The combined compensation strategy of process parameter correction and operational process optimization for moderate to severe risks refers to the system triggering more significant adjustments to process parameters and optimization of operational processes when the risk of loosening reaches a moderate or higher level. Process parameter correction may involve increasing the adjustment range of crimping pressure or binding tension by more than 5%, or even recalibrating equipment parameters. Operational process optimization may include retraining operators, adjusting the work sequence, adding inspection steps, or introducing auxiliary tools. This combined compensation strategy aims to address severe loosening problems from multiple dimensions, ensuring a complete resolution of the issue.

[0099] Post-compensation reassessment using a specialized predictive model refers to the system re-evaluating the current process status and product quality using the specialized predictive model after implementing any form of compensation. This includes re-collecting data, performing time-series feature analysis again, and comparing with a benchmark database to verify whether the compensation measures effectively eliminated the risk of loosening and to ensure that no new problems were introduced.

[0100] The "verification before proceeding to the next process" principle means that only when the reassessment results indicate that the risk of loosening has been completely eliminated and the product quality meets the preset standards, is the batch of products allowed to enter the subsequent manufacturing stages. This is a strict quality gate control mechanism. Conversely, the "repeated prediction-compensation-verification" process for non-conforming products means that if the assessment results still indicate a risk, the system will restart the entire closed-loop process from risk prediction and compensation strategy formulation to effect verification until the problem is completely resolved. This ensures that only high-quality products can proceed to the next stage, thus forming a continuous improvement quality control cycle.

[0101] Through the above technical solution, the anti-loosening-prediction linkage model proposed in this application is no longer a static judgment based on a fixed threshold, but has evolved into an adaptive system based on time-series feature analysis. This model can capture in real time the subtle trends and abnormal patterns of changes in manufacturing process data and loosening characteristic monitoring data over time, thereby achieving early and accurate identification of potential loosening risks. By comparing the deviation between real-time data and dynamically adjusted prediction thresholds, the model can deeply analyze and accurately locate the core causes of loosening, such as substandard terminal crimping or abnormal binding tension. This improves the accuracy of problem diagnosis and avoids ineffective compensation caused by the inability to identify the root cause in traditional methods.

[0102] Furthermore, this application intelligently triggers a tiered compensation strategy based on the identified risk level. For mild loosening risks, the system only performs minor adjustments to process parameters, avoiding excessive intervention and resource waste, and ensuring production continuity. For moderate and higher risks, a more comprehensive combination of process parameter corrections and operational process optimizations is triggered, fundamentally resolving more severe loosening problems and effectively preventing the accumulation and deterioration of defects. This tiered, adaptive compensation mechanism makes resource allocation more rational and the compensation effect more accurate and efficient.

[0103] Furthermore, the mechanism of re-evaluating using a specialized predictive model after compensation forms a crucial feedback loop. This ensures that the effectiveness of each compensation measure is verified, and the next process can only proceed after the risk is confirmed to be eliminated and the product is qualified. If the evaluation fails, the predictive-compensation-verification process is immediately repeated until the problem is completely resolved. This iterative optimization mechanism not only effectively prevents defective products from flowing into subsequent stages, improving product quality from the source, but also, through continuous feedback and learning, enables the anti-loosening-predictive linkage model and its compensation strategy to continuously improve and optimize themselves. This enhances the anti-loosening capability and overall quality control level in the wire harness manufacturing process, effectively solving the problems of inaccurate model recognition and inflexible compensation strategies in existing technologies.

[0104] In some of the solutions mentioned above in this application, a three-level anti-loosening detection mode is proposed to perform assembly stage inspection and to trace the root cause and compensate for loosening risks. In this process, the detection mechanism may lack layered special inspection for key nodes that are prone to loosening, such as component connections and docking parts, which leads to inaccurate identification of early loosening risks. At the same time, the root cause tracing process is not systematic enough, making it difficult to quickly trace the cause of the failure, resulting in a lack of targeted compensation and treatment, and failing to effectively prevent the loosening risk from being transmitted to subsequent processes.

[0105] In this regard, this application further proposes that the three-level anti-loosening detection mode includes: component connection anti-loosening detection focuses on detecting the tightness of terminal connection and the reliability of core wire contact; docking part anti-loosening collaborative detection focuses on detecting the rationality of docking gap and connection tightness; overall connection anti-loosening comprehensive detection focuses on predicting the stability of collaborative work of each part and the related loosening risks of working conditions. The root cause tracing adopts the feature tracing-risk matching-cause confirmation process. After compensation and treatment, the corresponding level of detection is re-executed. Only after passing the test can the subsequent assembly process be carried out.

[0106] Specifically, the three-level anti-loosening detection mode is a layered and progressive detection strategy designed to implement differentiated and refined detection for different levels of connection structures and potential loosening risks during wire harness assembly. Automated detection equipment, combined with technologies such as visual recognition and torque sensors, is used to perform rapid, batch detection of connection points at different levels. Alternatively, manual operation combined with auxiliary tools, using specialized detection fixtures, torque wrenches, etc., along with the operator's experience and judgment, is used to inspect key connection points one by one.

[0107] Among these measures, component connection loosening detection aims to ensure the connection quality of the smallest connection unit within the wire harness, which is fundamental to preventing loosening. This is achieved through electrical parameter testing, such as measuring contact resistance with a micro-ohmmeter and detecting voltage drop using a high-precision current injection method, to assess the contact reliability between the terminal and the core wire. This can be combined with pull-out force testing or visual inspection of the crimp morphology to determine the mechanical tightness of the connection. Another approach is to use X-ray fluoroscopy or industrial CT scanning technology to non-destructively inspect the internal structure of the terminal crimp area, determining whether the core wire is fully crimped, whether there are any loose connections or broken strands. Simultaneously, vibration or impact testing simulates working stress, observing changes in electrical parameters and assessing its tightness under dynamic conditions.

[0108] The joint inspection for loosening at the connection points focuses on the quality of the connection points between the wiring harness and external components or between the wiring harness itself, ensuring stable mechanical and electrical connections at the interface. Specifically, high-precision calipers, feeler gauges, or laser displacement sensors are used to measure the fit clearance at the connection points to ensure it is within the design tolerance range. Simultaneously, a torque tester or dedicated tightening force sensor is used to detect the tightening torque of the connecting bolts or clips to verify the reliability of the connection. Furthermore, acoustic testing technology can be used to analyze the acoustic response of the connection points through tapping or vibration to determine if there are abnormal gaps or loosening. Alternatively, infrared thermal imaging technology can be used to monitor the temperature rise of the connection points under power-on conditions; abnormal temperature rises may indicate poor contact or insufficient tightening.

[0109] The comprehensive anti-loosening test for overall connections aims to assess the overall anti-loosening capability of the entire wiring harness system under simulated or actual operating conditions, and to identify potential risks at the system level. The wiring harness is installed on a test bench simulating the operating conditions of the entire machine, and subjected to environmental reliability tests such as vibration, shock, and temperature cycling. Simultaneously, key electrical and mechanical parameters are monitored in real time to assess its stability under complex stress. Another approach is to establish a finite element model of the wiring harness and perform structural dynamics simulation analysis to predict the stress distribution and deformation trends of each connection point under specific operating conditions, thereby predicting potential loosening risk points. Alternatively, miniature sensors can be installed on actual operating equipment to monitor micro-motions, vibrations, or temperature changes at the wiring harness connections over a long period, and risk prediction can be made in conjunction with big data analysis.

[0110] The root cause tracing employs a feature tracing-risk matching-cause confirmation process, a systematic fault diagnosis method. It aims to analyze loosening characteristic data, match it with preset risk types, and ultimately pinpoint the root cause of the loosening. A knowledge base is established, containing historical fault data, loosening characteristics, and association rules with triggering causes. When a loosening characteristic is detected, the system automatically performs feature tracing and risk matching in the knowledge base, and provides a list of possible causes based on the matching results, which are then further confirmed by engineers. Alternatively, a machine learning algorithm can be used to train a classification model, inputting loosening characteristic parameters and outputting the most probable risk type, then further refining the cause confirmation based on the risk type.

[0111] Following compensation measures, a new test at the corresponding level is performed to ensure that the compensation measures have truly resolved the loosening issue and prevent recurrence or potential problems. Immediately after compensating for the loosening risk, the affected connection points should be tested at the level of the previously identified problem to verify the compensation effect. Alternatively, an automated or semi-automated verification procedure can be designed to automatically trigger the corresponding testing process after the compensation operation is completed and compare the results with preset acceptance standards to ensure that the test results meet the requirements.

[0112] Only after passing inspection can the wire harnesses proceed to subsequent assembly processes. This establishes a quality threshold to prevent potentially loose wire harnesses from entering later production stages, thereby avoiding the propagation and amplification of defects. Process flow control points are set up in the production management system. Only when the inspection result is marked as qualified by the system can the production permission for the next process be unlocked; otherwise, the process will be locked, awaiting problem resolution. Alternatively, physical isolation or labeling management can be used to separate failed wire harnesses from qualified ones, requiring operators to confirm the qualified label before sending them to subsequent processes.

[0113] Through the above technical solution, this application introduces a three-level anti-loosening detection mode, achieving refined and hierarchical detection of loosening risks during the wire harness assembly stage. Component connection anti-loosening detection accurately identifies early potential problems related to terminal connection tightness and core wire contact reliability, effectively preventing initial loosening caused by poor crimping or unstable contact. The joint anti-loosening detection at the mating points focuses on the reasonableness of the gaps and connection tightness at the interfaces, preventing loosening risks caused by improper fit or insufficient tightening. The overall connection anti-loosening comprehensive detection further evaluates the collaborative working stability of the wire harness system under simulated operating conditions, thus comprehensively predicting and covering various potential loosening risks. This hierarchical detection mechanism improves the accuracy and comprehensiveness of identifying early loosening risks, overcoming the limitations of traditional overall sampling inspection. Furthermore, the root cause tracing process of feature tracing, risk matching, and cause confirmation makes fault location more systematic and efficient. When a loosening risk is detected, its characteristics can be quickly traced, the specific risk type matched, and the root cause of the loosening ultimately confirmed, providing clear guidance for subsequent compensation and avoiding blind repairs. After compensation, the effectiveness of the compensation measures was ensured by re-performing the corresponding level of testing. The requirement that only qualified products could proceed to the subsequent assembly process was used as a quality threshold, which effectively prevented the unresolved loosening risks from being passed on to downstream processes. This created a closed-loop control system in the assembly stage, from testing, traceability, compensation to verification, thereby improving the reliability of the wire harness connection and the assembly quality.

[0114] In some of the solutions mentioned above in this application, root cause data and compensation effect data are proposed to provide feedback for adjusting manufacturing and design parameters. However, in this process, the data feedback lacks standardization and systematicity, the information content is incomplete, and the communication mechanism is missing. This results in insufficient precision and pertinence when adjusting manufacturing process parameters, prediction thresholds, and design parameters, making it impossible to effectively trace the root cause and optimize the compensation plan, thus making it difficult to suppress the risk of loosening from the source.

[0115] In response, this application further proposes that the root cause data and compensation effect data include information such as risk location, type, core cause, prediction deviation, compensation process, parameters and effects, which are fed back to the manufacturing and design stages through standardized interfaces to make targeted adjustments to manufacturing process parameters, prediction thresholds and design parameters, and compensation plans, thereby suppressing the risk of loosening from the source.

[0116] Specifically, the root cause data and compensation effect data contain information designed to provide a comprehensive and in-depth understanding of loosening issues. Risk location precisely indicates the specific physical area where loosening occurs, such as a specific connection point of the harness, component number, or coordinates in three-dimensional space, facilitating rapid identification of the problem's source. Type describes the specific manifestation or nature of the loosening, whether caused by poor electrical contact, mechanical fastening failure, or thermal stress, providing a basis for subsequent classification analysis and treatment. Core causes aim to reveal the root causes of loosening, such as terminal crimping parameter deviations, unreasonable wire bundling tension, improper connection fixation layout, material aging, or non-standard assembly operations, providing direction for subsequent process or design improvements. Prediction deviation refers to the difference between the prediction results of the prediction model and the actual loosening situation; its role is to evaluate the accuracy and reliability of the prediction model and provide a quantitative basis for continuous model optimization. The compensation process details the specific compensation measures and operational steps taken to address loosening risks, which helps standardize operations and provides a reference for subsequent process optimization. The parameters record the key technical parameters involved in the compensation process, such as the adjusted crimping pressure value, tightening torque value, or material specifications. These parameters are important bases for quantitatively evaluating the compensation effect and guiding subsequent adjustments. The effect is an assessment of the actual effectiveness after the compensation measures are implemented, including whether the loosening risk has been eliminated, whether the electrical performance has returned to normal, or whether it has passed re-inspection. This directly reflects the effectiveness of the compensation strategy.

[0117] To ensure the efficient and accurate flow of this critical data across different stages, this application proposes feedback through standardized interfaces. These standardized interfaces refer to predefined data exchange specifications or protocols that ensure consistency in the format, content, and semantics of data transmission between different systems. Data exchange formats based on XML or JSON are adopted, and data is transmitted via API application programming interfaces or shared through a unified database structure. The core objective is to eliminate data silos and ensure seamless and accurate information flow between different platforms such as manufacturing execution systems, product lifecycle management systems, and design support tools, avoiding information loss or ambiguity during transmission.

[0118] Based on the comprehensive and standardized feedback data mentioned above, this application enables targeted adjustments to manufacturing process parameters, prediction thresholds, design parameters, and compensation plans. Targeted adjustments mean making precise and purposeful modifications to relevant parameters and plans based on detailed root cause data and compensation effect data from the feedback, rather than blind or empirical adjustments. According to the feedback on the causes of poor crimping, the pressure, speed, and dwell time of the terminal crimping machine are precisely adjusted. Based on the prediction deviation data, the alarm threshold for the contact resistance change rate and the warning threshold for signal attenuation are adjusted to better reflect the loosening evolution under actual working conditions. Based on feedback on improper fixing layout of connection parts or rationality of terminal contact structure, design parameters such as the fixing point position of the wire harness, increasing the number of fixing parts, or improving the terminal contact surface design and material selection are optimized. Simultaneously, based on the compensation process and effect data, the compensation steps for specific loosening types are optimized, the spare material list is adjusted, or the operating instructions are updated to make it more efficient and effective. Ultimately, by implementing data-driven optimization during the design and manufacturing phases, the risk of loosening was mitigated at its source. This proactive and preventative risk management strategy aims to avoid or reduce the occurrence of loosening failures in subsequent assembly and operation phases.

[0119] Through the aforementioned technical solution, this application effectively addresses the issues of lack of standardization, systematic approach, and information sharing mechanisms in data feedback by clearly defining the comprehensive content of root cause data and compensation effect data and employing standardized interfaces for feedback. This enables the manufacturing and design stages to obtain comprehensive, accurate, and easily understandable information on loosening risks, allowing for precise and targeted adjustments to manufacturing process parameters, prediction thresholds, design parameters, and compensation plans. This data-driven closed-loop optimization mechanism enhances the ability to trace the root causes of loosening risks and improves the efficiency of compensation plan optimization, effectively suppressing loosening risks from the design and manufacturing stages. This significantly reduces the probability of loosening failures in the wiring harness throughout its lifecycle, improving the overall reliability, stability, and safety of the wiring harness connection.

[0120] In some of the embodiments described above in this application, a benchmark evolution feature is collected during the operation phase and the benchmark library is dynamically updated to predict loosening risks and trigger compensation strategies. In this process, the benchmark features may be subject to abnormal data interference, resulting in inaccurate benchmark predictions. The benchmark library update does not fully consider changes in working stress, causing the prediction threshold and compensation strategy to be out of touch with the actual operating environment. The prediction algorithm lacks multi-dimensional comprehensive analysis capabilities and cannot accurately assess risk levels, failure probabilities, and evolution cycles. The compensation strategy is not designed in a hierarchical manner, resulting in untimely early warnings and unreasonable allocation of maintenance resources, which reduces the anti-loosening effect and maintenance efficiency.

[0121] In this regard, this application further proposes that the benchmark evolution characteristics are the non-loosening state characteristic data collected in the initial stage of the harness operation, and after removing anomalies, they are entered into the benchmark library. The special control benchmark library periodically adjusts the benchmark characteristics, prediction thresholds and compensation strategy parameters based on the stress changes of the operating conditions. The loosening special prediction algorithm combines time-series characteristic trend analysis and risk probability calculation to achieve accurate prediction of risk level, failure probability and evolution cycle. The graded dynamic anti-loosening compensation strategy corresponds to mild, moderate and severe risk levels and triggers graded early warning signals simultaneously.

[0122] The baseline evolution characteristics refer to the regularity of changes in the electrical, mechanical, and stress-related characteristic parameters of the wiring harness under normal, secure conditions over time or operating conditions. This data serves as a reference standard for subsequent loosening prediction, ensuring the accuracy of the prediction. In practice, rigorous secure-state tests are conducted on brand-new wiring harnesses in a laboratory environment, collecting characteristic data under different simulated operating conditions. Statistical analysis is performed, outliers are removed, and the data is then entered into the baseline database. Alternatively, in the early stages of practical application, continuous monitoring is conducted on wiring harnesses that have been installed and rigorously tested to confirm they are secure. Characteristic data during stable operation is collected, and data smoothing and filtering techniques are used to process abnormal fluctuations to obtain clean baseline data.

[0123] The specialized control benchmark library is a dynamically updated database that stores benchmark data, judgment criteria, and response measures for loosening prediction and control. The purpose of this benchmark library is to ensure that prediction and compensation strategies can adapt to the actual state of the wiring harness under different operating conditions, improving the system's adaptability and effectiveness. Specifically, the system has built-in models of various typical operating conditions, such as high temperature, low temperature, high vibration, and humidity. When the actual operating condition is detected to match a preset model, the corresponding benchmark features, prediction thresholds, and compensation strategy parameters are automatically loaded or adjusted. Furthermore, machine learning algorithms can be used to continuously analyze the actual operating data and loosening evolution trends of the wiring harness under different operating conditions, and periodically perform adaptive learning and optimization adjustments to the parameters in the benchmark library.

[0124] The loosening-specific prediction algorithm is an intelligent algorithm used to assess the risk of loosening in wiring harness connections. It comprehensively considers the changing trends of characteristic parameters over time and the probability of loosening. This algorithm aims to provide multi-dimensional, quantitative assessment results of loosening risk, thereby guiding subsequent anti-loosening compensation and maintenance. One approach involves using deep learning models such as Long Short-Term Memory (LSTM) networks or gated recurrent units (GRUs) to predict trends in time-series feature data, and then combining this with Bayesian networks or support vector machines for risk probability classification. Another implementation method uses Kalman filtering or moving averages to smooth and extract trends from time-series data, and then employs ensemble learning algorithms such as decision trees or random forests to calculate the risk probability based on the trend change rate and historical failure data.

[0125] The tiered dynamic anti-loosening compensation strategy refers to taking different levels and types of anti-loosening compensation measures based on the severity of the loosening risk, and issuing corresponding level warning information. This strategy aims to optimize resource allocation, avoid over-maintenance or under-maintenance, thereby improving anti-loosening efficiency and safety. When a minor loosening risk is identified, the system automatically triggers remote parameter fine-tuning, such as adjusting the power supply voltage or signal frequency, and sends a low-level notification to maintenance personnel. When the risk escalates to a moderate level, on-site inspection and local tightening are triggered, and a medium-level warning is sent. When a severe risk occurs, the system is immediately shut down for repair or component replacement, and a high-level emergency warning is sent. Another implementation method is to suggest adjusting the preventive maintenance plan for minor risks, trigger automatic or semi-automatic local compensation operations for moderate risks by tightening via intelligent actuators, requiring manual confirmation, and for severe risks, forcibly shut down the system and guide it to the nearest repair point, while simultaneously sending the highest-level warning to the relevant personnel.

[0126] Through the above technical solutions, this application effectively solves the problems of impure benchmark data, delayed updates, inaccurate predictions, and lack of graded compensation, achieving accurate risk identification and efficient dynamic maintenance. Specifically, the benchmark evolution characteristics ensure the purity and representativeness of the benchmark data by collecting non-loosening state characteristic data in the initial stage of harness operation and removing anomalies, providing a reliable reference for subsequent comparisons and avoiding prediction deviations caused by abnormal interference. The special control benchmark library periodically adjusts benchmark characteristics, prediction thresholds, and compensation strategy parameters based on changes in operating stress, enabling the system to dynamically adapt to changes in the actual environment, maintain the timeliness of predictions and strategies, and solve the problem of delayed updates caused by static benchmarks. The loosening-specific prediction algorithm comprehensively evaluates the loosening evolution trend by combining time-series characteristic trend analysis and risk probability calculation, achieving multi-dimensional accurate prediction of risk level, failure probability, and evolution cycle, improving the comprehensiveness and accuracy of risk identification. The graded dynamic anti-loosening compensation strategy triggers graded early warning signals simultaneously according to mild, moderate, and severe risk levels, adopting differentiated responses for different risks, optimizing resource allocation and early warning timeliness, and ensuring the efficiency and pertinence of maintenance measures. These improvements, combined with the full-process control method, have made the harness connection loosening prevention and control system more adaptable, accurate and responsive during the operation phase, thereby improving the reliability of harness connections and the operational stability of equipment.

[0127] The following example will provide a more detailed explanation of the above technical solution: Firstly, in the initial implementation phase, for high-voltage wiring harnesses in electric vehicles, the system receives data related to the inducing mechanisms, evolution stages, and failure modes of connection loosening throughout their entire lifecycle. Inducing mechanisms include deviations in terminal crimping parameters, unreasonable wire bundling tension, improper fixing layout of connection points, vibration fatigue corrosion under operating conditions, material aging due to temperature stress, and non-standard assembly operations. Evolution stages cover the initial loosening potential stage, the slight loosening stage, the moderate loosening stage, and the failure loosening stage. Simultaneously, the system collects loosening characteristic parameters and traceability data for each evolution stage, including electrical characteristic parameters, mechanical characteristic parameters, and operating condition stress-related characteristic parameters. Based on this data, the system constructs a dedicated connection loosening prediction model and a dedicated control benchmark library. This benchmark library integrates the prediction model's output parameters, loosening trend thresholds for each evolution stage, and dynamic anti-loosening compensation strategies corresponding to different risk levels. It also establishes a correlation mapping relationship between risk levels and compensation strategies, clarifying loosening prediction indicators and the handling procedures and compensation priorities for different prediction results. This solves the problem of the lack of quantitative understanding and unified data management of loosening induction mechanisms in existing technologies.

[0128] Secondly, based on the established predictive model and benchmark library, the design department optimized the design scheme for high-voltage wiring harnesses in electric vehicles. For key risk factors that easily lead to loosening during the design phase, multiple sets of operational condition simulation comparative experiments were conducted to establish the correlation between risk factors and loosening evolution. Targeted anti-loosening design optimizations were then implemented, including adjusting risk factor values, optimizing the fixing layout of connection parts, terminal contact structures, and the distribution of binding nodes. Simultaneously, a loosening prediction process, anti-loosening compensation plan, and specific testing points were pre-set. The anti-loosening compensation plan clearly defines the compensation process, parameter range, and effectiveness verification standards for each risk factor corresponding to its loosening type. Finally, an integrated technical document encompassing design, anti-loosening, and prediction was created. This document integrates optimized design parameters, loosening risk factors, prediction indicators, specific testing points, compensation plans, and verification standards, clarifying the linkage between design adjustments, indicator optimization, and compensation strategy adaptation, and reserving data update interfaces. This overcomes the limitations of existing design schemes that rely on experience, lack quantitative correlation, and fail to provide targeted anti-loosening optimization.

[0129] Next, during the manufacturing phase, in accordance with the integrated technical documentation, anti-loosening process parameters and loosening prediction thresholds are configured for core manufacturing processes. These anti-loosening process parameters include terminal crimping pressure and duration matching parameters, crimping depth accuracy parameters, wire bundling tension and spacing adaptation parameters, and bundling node reinforcement process parameters. A time-series data acquisition unit collects process data and loosening characteristic monitoring data in real time, ensuring data continuity, accuracy, and real-time performance. Subsequently, the anti-loosening-prediction linkage model compares the data with the prediction threshold deviation, accurately identifying core causes such as substandard terminal crimping and abnormal bundling tension, predicting risks, and triggering compensation strategies. Mild loosening risks trigger fine-tuning of process parameters, while moderate and higher risks trigger a combination of process parameter correction and operational process optimization compensation. After compensation, a specialized prediction model is used for reassessment. Only after verification can the next process proceed; otherwise, the prediction-compensation-verification process is repeated. This solves the problem of fixed threshold control of process parameters in existing manufacturing processes, which cannot be dynamically adjusted.

[0130] During the assembly phase, a three-tiered anti-loosening detection mode is employed. This mode includes: component connection anti-loosening detection, joint anti-loosening detection of mating parts, and comprehensive overall connection anti-loosening detection. For any loosening risks identified, a feature tracing-risk matching-cause confirmation process is used for root cause analysis and targeted remedial measures. If a connector is found to be loose, the system can trace it back to its manufacturing batch, crimping parameters, assembly personnel, etc., to determine whether the loosening is due to insufficient crimping force or improper assembly torque. Relevant data is recorded and fed back to the manufacturing control stage. After remedial measures, the corresponding level of detection is re-executed, and only those that pass the remedial test can proceed to the next assembly process. This overcomes the shortcomings of existing assembly inspection methods, which are mostly based on overall sampling, making it difficult to accurately identify early-stage problems and quickly trace their root causes.

[0131] During the wiring harness operation phase, the system collects baseline evolution characteristics. Data on the non-loosening state characteristics collected during the initial stage of wiring harness operation is filtered for anomalies and entered into the baseline database. The system also periodically and dynamically updates the baseline characteristics, prediction thresholds, and compensation strategy parameters in the database based on stress changes under operating conditions. Simultaneously, it periodically monitors the real-time evolution characteristics of loosening. Through a specialized loosening prediction algorithm, combined with time-series characteristic trend analysis and risk probability calculation, it achieves accurate prediction of risk level, failure probability, and evolution cycle, triggering a graded dynamic anti-loosening compensation strategy corresponding to mild, moderate, and severe risk levels and graded warning signals. Mild risk may trigger a system prompt for inspection at the next maintenance checkpoint; moderate risk may trigger an alarm from the vehicle diagnostic system and recommend prompt repair; and severe risk may trigger an emergency stop or power limitation. This changes the current situation where operation phases are primarily based on passive maintenance and lack predictive capabilities.

[0132] Finally, the system integrates all-process control data to build an iterative optimization system. This system continuously analyzes data, optimizes predictive models, linkage models, and compensation strategies, and updates the specialized control benchmark library. By analyzing loosening fault data during operation, it reverse-optimizes design parameters, such as adjusting the fixing layout of connection parts and manufacturing process parameters, such as correcting terminal crimping pressure and predictive thresholds, thereby suppressing the risk of loosening at the source. This end-to-end data interoperability and linkage optimization forms a closed-loop control system, improving the ability to prevent loosening of wire harness connections and solving the fundamental problem of data fragmentation and inability to achieve linkage optimization in existing technologies.

[0133] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for targeted prevention and dynamic management of loose wire harness connections, characterized in that, include: Data on the inducing mechanism, evolution stage and failure mode of connection loosening throughout the entire life cycle of the receiving harness are collected. Loosening characteristic parameters and source tracing data at each evolution stage are collected to construct a special prediction model for connection loosening and a special control benchmark library. Based on the aforementioned prediction model and benchmark library, the harness design scheme is optimized, and a loosening prediction process, anti-loosening compensation plan and special testing nodes are preset to form an integrated technical document of design-anti-loosening-prediction. According to the integrated technical document, configure the anti-loosening process parameters and loosening prediction threshold of the manufacturing process, collect process data and loosening characteristic monitoring data in real time, predict risks and trigger compensation strategies through the anti-loosening-prediction linkage model, and verify the compensation effect simultaneously. A three-level anti-loosening detection mode is adopted to carry out the inspection during the assembly stage. The root cause of the loosening risk is traced and targeted compensation measures are taken. The relevant data is recorded and fed back to the manufacturing control link. During the operation of the wire harness, the baseline evolution characteristics are collected and the baseline library is dynamically updated. The real-time evolution characteristics of loosening are monitored regularly. Trends are identified through prediction algorithms and a graded dynamic anti-loosening compensation strategy and early warning signals are triggered. Integrate data from the entire process of management and control, build an iterative optimization system, optimize the prediction model, linkage model and compensation strategy, and update the special management and control benchmark library.

2. The method according to claim 1, characterized in that, The typical inducing mechanisms of connection loosening include terminal crimping parameter deviation, unreasonable wire bundling tension, improper fixing layout of connection parts, vibration fatigue corrosion under working conditions, material aging caused by temperature stress, and non-standard assembly operations. The evolution stages include the initial loosening potential stage, the slight loosening stage, the moderate loosening stage, and the failure loosening stage. The loosening characteristic parameters include electrical characteristic parameters, mechanical characteristic parameters, and working condition stress-related characteristic parameters. Among them, electrical characteristic parameters include contact resistance change rate, signal transmission attenuation, and current fluctuation amplitude; mechanical characteristic parameters include fastening torque change, vibration response amplitude deviation, and insertion / removal contact gap change; and working condition stress-related characteristic parameters include vibration frequency and loosening rate correlation parameters and temperature change amplitude and contact resistance deviation correlation parameters.

3. The method according to claim 1 or 2, characterized in that, The construction process of the special control benchmark library includes: integrating the output parameters of the prediction model, the loosening trend thresholds of each evolution stage, and the dynamic anti-loosening compensation strategies corresponding to different risk levels; establishing the correlation mapping relationship between risk levels and compensation strategies; the dynamic anti-loosening compensation strategies include design parameter fine-tuning, process parameter correction, assembly operation optimization, and operation and maintenance reinforcement anti-loosening strategies; the loosening prediction indicators include characteristic parameter thresholds, time-series trend change rate thresholds, and risk level judgment thresholds; and clarifying the handling procedures and compensation priorities for different prediction results.

4. The method according to claim 1, characterized in that, Key risk factors that may lead to loose connections in the design process include the rationality of terminal contact structure, compatibility between wires and terminals, scientific layout of connection fixing points, rational distribution of binding nodes, vibration aging resistance of protective materials, and compatibility of wire harness routing with operating vibration conditions. The correlation between risk factors and loosening evolution is established through multiple sets of operating condition simulation and comparison experiments. The targeted anti-loosening design optimization includes adjusting the values ​​of risk factors, optimizing the fixing layout of connection parts, terminal contact structure, and binding node distribution. The pre-set anti-loosening compensation plan clarifies the compensation process, parameter range, and effect verification standards for each risk factor corresponding to the loosening type.

5. The method according to claim 1 or 4, characterized in that, The integrated design-loosening-prediction technical document integrates and optimizes design parameters, loosening risk factors, prediction indicators, special testing nodes, compensation plans and verification standards. It clarifies the linkage between design adjustments, indicator optimization and compensation strategy adaptation, adopts industry-standard format, and reserves data update interfaces to support adjustments to design parameters and prediction standards based on iterative data.

6. The method according to claim 1, characterized in that, The core manufacturing processes include wire cutting and stripping, terminal crimping, wire bundling, protective layer coating, and semi-finished product integration. The terminal crimping and wire bundling processes are the key control points. The anti-loosening process parameters include terminal crimping pressure and duration matching parameters, crimping depth accuracy parameters, wire bundling tension and spacing matching parameters, and bundling node reinforcement process parameters. The process operation data and loosening characteristic monitoring data are collected in real time through a time-series data acquisition unit to ensure the continuity, accuracy, and real-time nature of the data.

7. The method according to claim 1 or 6, characterized in that, The anti-loosening-prediction linkage model is an adaptive model based on time-series feature analysis. By comparing the deviation between the data and the prediction threshold, it accurately identifies core causes such as substandard terminal crimping and abnormal bundling tension. For minor loosening risks, it triggers fine-tuning of process parameters for compensation. For moderate and above risks, it triggers correction of process parameters and optimization of the operation process for compensation. After compensation, it is re-evaluated using a special prediction model. Only after verification that it is qualified can it proceed to the next process. If it is unqualified, the prediction-compensation-verification process is repeated.

8. The method according to claim 1, characterized in that, The three-level anti-loosening detection mode includes: component connection anti-loosening detection focuses on the tightness of terminal connection and the reliability of core wire contact; docking part anti-loosening collaborative detection focuses on the rationality of docking gap and connection tightness; overall connection anti-loosening comprehensive detection focuses on predicting the stability of collaborative work of each part and the related loosening risks of working conditions. The root cause tracing adopts the feature tracing-risk matching-cause confirmation process. After compensation and treatment, the corresponding level of detection is re-executed. Only after passing the test can the subsequent assembly process be carried out.

9. The method according to claim 1 or 8, characterized in that, The root cause data and compensation effect data include information such as risk location, type, core cause, prediction deviation, compensation process, parameters and effects. These are fed back to the manufacturing and design stages through standardized interfaces, allowing for targeted adjustments to manufacturing process parameters, prediction thresholds and design parameters, and compensation plans to suppress loosening risks from the source.

10. The method according to claim 1, characterized in that, The baseline evolution characteristics are the non-loosening state characteristic data collected in the initial stage of the harness operation. After removing anomalies, the data is entered into the baseline library. The special control baseline library periodically adjusts the baseline characteristics, prediction thresholds, and compensation strategy parameters based on the stress changes under operating conditions. The loosening special prediction algorithm combines time-series characteristic trend analysis and risk probability calculation to achieve accurate prediction of risk level, failure probability, and evolution cycle. The graded dynamic anti-loosening compensation strategy corresponds to mild, moderate, and severe risk levels and simultaneously triggers graded early warning signals.

Images