MES system management and control method and system based on wire harness production and manufacturing

Abstract

The invention relates to the technical field of wire harness production management, in particular to an MES system management and control method and system based on wire harness production and manufacturing. The method comprises the following steps: acquiring and analyzing wire harness structure data and process configuration data, splitting a plurality of wire harness process units, associating corresponding process parameter sets, station identification data and resource identification data for the wire harness process units, and constructing wire harness process unit description data; and based on the wire harness process unit description data, mapping is established between each operation state and the wire harness process unit in the production process, the operation state is marked as a qualified process parameter, and a parameter dynamic baseline is constructed. According to the method, a refined MES management and control mechanism which takes a wire harness process unit as a core and is based on a dynamic parameter baseline and risk grading is constructed, so that intelligent optimization of quality, risk and efficiency in the production process is realized.

Description

Technical Field

[0001] This invention relates to the field of wire harness production management technology, and in particular to a method and system for controlling MES systems based on wire harness production and manufacturing. Background Technology

[0002] As a crucial electrical connection carrier in automobiles, rail transportation, and high-end equipment, wire harnesses are characterized by complex structures, numerous process steps, diverse parameters, and strong transmissibility of quality risks during their manufacturing process. Existing wire harness manufacturing MES systems typically manage processes or workstations as basic objects, recording and tracing the production process by collecting process parameters such as wire cutting length, crimping height, and tensile strength.

[0003] However, with the increasing complexity of wire harness structures, the impact of different wire numbers, terminal types, and assembly positions on the overall reliability of the harness varies significantly, and existing technologies are gradually revealing their shortcomings: On the one hand, traditional MES systems mostly use processes or workstations as management units, making it difficult to accurately characterize the relationship between the smallest controllable process behavior and the wire harness function, resulting in coarse quality analysis granularity and low positioning efficiency; on the other hand, process parameter control mostly relies on fixed thresholds or manually set standards, lacking the ability to dynamically evolve based on historical qualified data, making it difficult to adapt to actual production conditions such as changes in equipment status, material batch differences, and process drift.

[0004] Furthermore, existing technologies often equate parameter anomalies with risk assessment in a simplistic way, failing to comprehensively consider the type of process behavior, parameter deviation characteristics, and their weighting of impact on harness continuity, signal integrity, and power supply stability. This results in homogenized control strategies, making it difficult to achieve differentiated and refined interventions. Summary of the Invention

[0005] Therefore, it is necessary for the present invention to provide a method and system for MES system management based on wire harness production and manufacturing, in order to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, a method for managing a MES system based on wire harness manufacturing includes the following steps: Step S1: Obtain and parse the wire harness structure data and process configuration data, split into multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data to construct wire harness process unit description data; Step S2: Based on the wire harness process unit description data, establish a mapping between each operation status in the production process and the wire harness process unit, mark the operation status as qualified process parameters, and construct a dynamic baseline for parameters; Step S3: Determine the process risk level of the wire harness process unit based on the degree of impact of the process behavior of the wire harness process unit on the functional reliability of the wire harness. Step S4: Obtain the current process parameters of the wire harness process unit, correlate them with the corresponding parameter dynamic baseline, and determine the wire harness process unit control decision based on the corresponding process risk level.

[0007] Preferably, the present invention also provides a MES system management and control system based on wire harness manufacturing, used to execute the above-described MES system management and control method based on wire harness manufacturing, wherein the MES system management and control system based on wire harness manufacturing includes: The process unit modeling module is used to acquire and parse wire harness structure data and process configuration data, split multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data, and construct wire harness process unit description data. The parameter baseline construction module is used to establish a mapping between each operation status in the production process and the wire harness process unit based on the wire harness process unit description data, mark the operation status as qualified process parameters, and construct a dynamic parameter baseline. The process risk assessment module is used to determine the process risk level of a wire harness process unit based on the degree of impact of the process behavior of the wire harness process unit on the functional reliability of the wire harness. The control and management decision generation module is used to obtain the current process parameters of the wire harness process unit, correlate them with the corresponding dynamic baseline parameters, and determine the control and management decision of the wire harness process unit in combination with the corresponding process risk level.

[0008] This invention refines wire harness production from traditional process or workstation levels to wire harness process units with clearly defined process boundaries and functional orientations. This establishes a one-to-one mapping between the smallest controllable process behaviors in production and wire harness conductivity, signal integrity, and power supply stability, significantly improving the precision of quality analysis and problem localization. Simultaneously, by introducing a dynamic baseline of parameters based on the continuous evolution of historical pass / fail status, it avoids the failure of fixed thresholds under equipment aging, material batch changes, and process drift scenarios, enabling process parameter determination to adaptively adjust with changes in production status. Furthermore, it combines process behavior types, parameter offset amplitude and frequency characteristics, and... Its influence weights on different wire harness functional items are used to classify and quantify the quality risks of wire harness process units, breaking through the crude judgment method of simply equating parameter abnormalities with risks in existing technologies. On this basis, based on the differences in process risks, structural positions, and functional criticality, differentiated control decisions are made to achieve key constraints on critical process units and flexible management of general process units, reducing the production efficiency losses caused by excessive intervention. Overall, this invention constructs a refined MES control mechanism with process units as the core, dynamic baselines as support, and risk classification as the guide, effectively improving the quality stability, risk controllability, and intelligent management level of the manufacturing system in the wire harness production process. Attached Figure Description

[0009] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the steps of a MES system control method based on wire harness manufacturing according to the present invention; Figure 2 This is a schematic diagram of the modules of the MES system for wire harness manufacturing. Figure 3 This is a schematic diagram illustrating the logic of splitting and aggregating wire harness process units. Detailed Implementation

[0010] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for managing a MES system based on wire harness manufacturing, the method comprising the following steps: Step S1: Obtain and parse the wire harness structure data and process configuration data, split into multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data to construct wire harness process unit description data; Step S2: Based on the wire harness process unit description data, establish a mapping between each operation status in the production process and the wire harness process unit, mark the operation status as qualified process parameters, and construct a dynamic baseline for parameters; Step S3: Determine the process risk level of the wire harness process unit based on the degree of impact of the process behavior of the wire harness process unit on the functional reliability of the wire harness. Step S4: Obtain the current process parameters of the wire harness process unit, correlate them with the corresponding parameter dynamic baseline, and determine the wire harness process unit control decision based on the corresponding process risk level.

[0014] Preferably, step S1 includes: The MES system is used to read the wire harness structure data and process configuration data of the wire harness products. By combining wire harness structure data and process configuration data, multiple wire harness process units are broken down to form a process unit table; Each wire harness process unit corresponds to at least one specific process behavior and is associated with the wire core segment, terminal crimping position, solder joint area and plug-in interface. The process behavior includes cutting, crimping and plugging of a single wire. Based on the process unit table, the set of process parameters required for the corresponding wire harness process unit, as well as the corresponding workstation identification data and resource identification data, are used to form the wire harness process unit description data.

[0015] In this embodiment of the invention, the task is accomplished by a MES system deployed on the production site. The MES system pre-establishes data interfaces with the product data management system and the process planning system to synchronize the wire harness structure data and process configuration data of the wire harness product.

[0016] The MES system retrieves the wire harness structure data of the wire harness product. The wire harness structure data is stored in a hierarchical format, including at least the wire harness product number, wire number, core specification, conductor cross-sectional area, insulation type, terminal type, terminal mounting cavity number, and wire harness branch topology. Each wire number corresponds to a unique core segment identifier, and each terminal type corresponds to a specific crimping position and insertion interface number.

[0017] Simultaneously, the process configuration data corresponding to the wire harness product is retrieved through the MES system. The process configuration data includes at least the process route number, process sequence number, workstation number, equipment number, and resource number, where the resource number is used to identify at least one of the crimping die, wire cutting tool, and insertion fixture.

[0018] After acquiring the two types of data mentioned above, the MES system performs process unit decomposition on the wire harness product based on the wire harness structure data and process configuration data. During the decomposition process, the smallest operating node that meets the independent process control conditions is used as the decomposition benchmark.

[0019] The minimum operating node is limited to one of the following three categories: wire cutting operation node for a single wire; crimping operation node for a single wire end corresponding to a specified terminal model; and insertion operation node for a terminal corresponding to a specified cavity position number.

[0020] The MES system, based on the process sequence number in the process configuration data, connects the aforementioned minimum operation nodes in series according to the actual production sequence. Furthermore, based on the uniqueness constraint of resource number and workstation number, it aggregates the minimum operation nodes sharing the same workstation number and arranged consecutively into a wire harness process unit. Each wire harness process unit thus formed has a clearly defined process start and end point.

[0021] After forming the process unit, the MES system assigns a unique process unit identifier to each wire harness process unit and establishes a one-to-one correspondence between the process unit identifier and the corresponding wire core segment number, terminal crimping position number or plug-in interface number, thereby forming a process unit table.

[0022] Subsequently, the MES system associates a set of process parameters for each wire harness process unit based on the process unit table. The set of process parameters is determined according to the type of process behavior: when the process behavior is a wire cutting operation, the set of process parameters includes at least the wire cutting length value and the length deviation value; when the process behavior is a crimping operation, the set of process parameters includes at least the crimping height value, the peak crimping force value, and the crimping displacement value; when the process behavior is a plugging operation, the set of process parameters includes at least the plugging depth value and the peak insertion force value.

[0023] All process parameters are stored in numerical data form and are associated with specific units of measurement. The unit for crimping height is limited to millimeters, the unit for peak crimping force is limited to Newtons, and the unit for peak insertion force is limited to Newtons.

[0024] Simultaneously, the MES system associates the workstation number and resource number in the process configuration data with the corresponding wire harness process unit. The workstation number identifies the specific production location executing the process unit, while the resource number identifies the specific tool or equipment component participating in that process unit.

[0025] After the association is completed, the MES system will encapsulate the process unit identifier, process behavior type, associated core segment or terminal location information, process parameter set, workstation identifier data and resource identifier data in a structured manner to generate wire harness process unit description data and store it in the process unit data table of the MES system.

[0026] It is worth noting that when associating resource identification data, the MES system also records the cumulative number of times each resource number is used. The cumulative number of uses is obtained by counting and incrementing the resource number after each process unit is completed.

[0027] The MES system incorporates the cumulative usage count as part of the resource identification data into the wire harness process unit description data, so that the wire harness process unit description data contains not only resource identity information but also resource usage status information.

[0028] Preferably, the process of splitting multiple wire harness process units by combining wire harness structure data and process configuration data is as follows: By combining wire harness structure data and process configuration data, identify all the smallest process element nodes in the wire harness product that meet the independent process control conditions; Based on preset process continuity rules and resource coupling degree, the smallest process element nodes are dynamically aggregated to form a wire harness process unit with clear process boundaries and logical operation sequence. During the decomposition process, the process dependencies between each wire harness process unit are constructed and recorded. These dependencies include physical connection dependencies and operation sequence dependencies. Construct a process unit table with process unit identifier, process behavior type, and associated physical location as its core.

[0029] In this embodiment of the invention, the splitting of the wire harness process unit is completed by the MES system after reading the wire harness structure data and process configuration data. The wire harness structure data includes at least the wire number, wire core segment number, terminal model, terminal mounting cavity number, and wire harness branch connection relationship; the process configuration data includes at least the process sequence number, workstation identification data, and resource identification data.

[0030] The MES system analyzes the wire number and terminal connection relationship line by line based on the wire harness structure data, and combines this with the process sequence number in the process configuration data to identify the smallest process element node in the wire harness product that meets the independent process control conditions. The smallest process element node is limited to an operation node that is completed at a single physical location and corresponds to a unique process behavior. Specifically, it includes wire cutting nodes, crimping nodes, and insertion nodes. Among them, the wire cutting node corresponds one-to-one with the wire core segment number, the crimping node corresponds one-to-one with the terminal model and crimping position, and the insertion node corresponds one-to-one with the terminal model and cavity position number.

[0031] The MES system aggregates the smallest process element nodes based on preset process continuity rules and resource coupling. The process continuity rule requires that adjacent nodes have consecutive and uninterrupted process sequence numbers in the process configuration data. Resource coupling is determined by comparing the workstation identifier data associated with the smallest process element node with the resource identifier data. When consecutive smallest process element nodes correspond to the same workstation identifier data and the same resource identifier data, the MES system aggregates the nodes into the same wiring harness process unit and records the sequence of process actions.

[0032] During the decomposition process, the MES system simultaneously constructs the process dependencies between wire harness process units. Physical connection dependencies are determined by the connection relationships between wire core segments and terminals in the wire harness structure data; operation sequence dependencies are determined by the process sequence numbers in the process configuration data, and the relationships between preceding and subsequent wire harness process units are recorded in a directed association manner.

[0033] The MES system assigns a unique process unit identifier to each wire harness process unit, and records the process unit identifier, process behavior type, and corresponding wire core segment number, terminal crimping position number or plug-in interface number in a structured manner to form a process unit table and store it.

[0034] Preferably, step S2 includes: Based on the wire harness process unit description data, the production operation status is associated with the specific wire number, terminal and cavity to which the wire harness process unit belongs, forming a status mapping set; Based on the state mapping set, the operation state attributes of the process parameters are identified to form a parameter state set; Select qualified parameters that are consistent with the wire harness functional test or process judgment in the parameter status set to form a qualified parameter set; Extract the parameter feature set from the qualified parameter set according to the preset wire size, terminal type and process category; The parameter reference range for each wire harness process unit is determined based on the parameter feature set and used as the dynamic baseline for the parameters.

[0035] In this embodiment of the invention, wire harness process unit description data is invoked, wherein each wire harness process unit includes a unique process unit identifier, a corresponding wire number, a terminal type, a cavity number, a station identifier, and a list of process parameter identifiers. During production, the MES system receives operation status data from the crimping and insertion stations. This operation status data includes at least: a station status code, the currently processed wire number, the terminal number, the cavity number, the production batch number, and the corresponding real-time value of the process parameters. Using a ternary matching rule of wire number, terminal number, and cavity number, each piece of operation status data is uniquely mapped to its corresponding wire harness process unit, forming a status mapping set.

[0036] Based on the state mapping set, process parameters within the same wire harness process unit are assigned explicit operational state attributes. These attributes are limited to "qualified state" or "unqualified state." A qualified state requires simultaneously meeting two constraints: first, the corresponding batch is judged to be normally conductive and the contact resistance is no greater than 50 milliohms in the wire harness functional testing process; second, no stoppage or rework flag is triggered in the corresponding process's judgment. The MES system records process parameters that meet these constraints as qualified state parameters, forming a parameter state set.

[0037] Within the parameter status set, the results are filtered according to the consistency rules between the harness functional test results and the process judgment results. Only parameter records that are marked as qualified in both types of judgment results are retained, forming the qualified parameter set. Each record in the qualified parameter set must include at least the parameter value, wire specification, terminal type, process category, and data acquisition timestamp.

[0038] Parameter feature sets are extracted according to preset parameter grouping rules. These rules stipulate that parameters with the same wire size, terminal type, and process category are grouped into the same parameter feature group. For example, in the crimping process, the wire size is limited to 0.5. 0.75 Or 1.0 The terminal type is limited to open or closed terminals, and the process parameters are limited to crimping height and crimping tension. For the same parameter feature group, the maximum, minimum, mean, and standard deviation of its qualified parameters are statistically analyzed to form a parameter feature set.

[0039] Based on this, parameter reference intervals are determined for each wire harness process unit. The upper and lower boundaries of the parameter reference intervals are calculated using the mean ± 3 times the standard deviation, and the width of the reference interval is limited to no less than 5 times the rated resolution of the equipment. When the mean change of the same parameter feature group exceeds 10% of the mean of the previous reference interval in three consecutive production batches, the reference interval boundaries are recalculated and synchronously updated to the corresponding wire harness process unit, forming a dynamic parameter baseline.

[0040] Preferably, determining the parameter reference range for each wire harness process unit based on the parameter feature set specifically involves: Multi-dimensional cluster analysis was performed on qualified process parameter data in the parameter feature set to identify the core clusters of parameter distribution under different wire specifications, terminal types and process categories; Calculate the statistical boundary in the multidimensional space based on the core cluster of each parameter distribution, and perform boundary fitting to form the initial static reference interval; Real-time monitoring of newly added qualified parameters flowing into the core clusters of each parameter distribution, and calculation of the movement trajectory and convergence rate relative to the centroid of the static reference interval; When the movement trajectory indicates that the process has experienced controlled drift or the convergence rate exceeds a preset threshold, a progressive adjustment of the reference interval is triggered, generating and updating it as a dynamic baseline for parameters.

[0041] In this embodiment of the invention, the parameter feature set is derived from qualified process parameter records under the same wire size, terminal type, and process category. The parameter dimensions are limited to crimping height and crimping tensile force, and the wire size is limited to 0.75. The terminal type is limited to closed terminals. The MES system performs multi-dimensional grouping processing on qualified parameters according to the similarity of parameter values ​​and the continuity of time, so that the parameters within the group are clustered within a range where the difference in crimping height does not exceed 0.05mm and the difference in crimping tension does not exceed 10N, forming a core cluster of parameter distribution.

[0042] For each parameter distribution core cluster, the mean vector and covariance matrix are calculated in the two-dimensional space formed by the pressing height and pressing tension. The statistical boundary is determined based on the mean ± 3 times the standard deviation of each dimension. The statistical boundary is fitted with a linear boundary to form the initial static reference interval.

[0043] During production, the MES system continuously receives new qualified parameters, maps them to the corresponding parameter distribution core clusters, and calculates the displacement vector of the core cluster mean vector relative to the centroid of the static reference interval in real time. Simultaneously, it records the displacement increment within each production batch as the movement trajectory and convergence rate. When the centroid displacement direction is consistent across five consecutive production batches and the cumulative displacement exceeds 15% of the width of the static reference interval, or when the convergence rate exceeds the 2% threshold per batch, a gradual adjustment of the reference interval is triggered. The gradual adjustment uses a 7:3 weighted average of the old reference interval boundary and the new statistical boundary to form a dynamic parameter baseline, which is then synchronously bound to the corresponding wire harness process unit.

[0044] It is worth noting that, for welding process categories, the parameter dimensions are limited to welding temperature and heating time, and the core cluster of parameter distribution is based on a temperature difference not exceeding 8. The rule that the duration difference does not exceed 0.2s is formed, and the static reference interval and dynamic update method are consistent with the implementation example.

[0045] Preferably, step S3 includes: Identify the process behavior type corresponding to each wire harness process unit in the wire harness process unit description data, and construct a behavior type set; Based on the behavior type set, combined with the electrical purpose of the wire number, the terminal connection position, and the wire harness branch structure, the relationship between process behavior and wire harness continuity, signal integrity, and power supply stability is marked, and a functional association set is generated. Based on the functional association set, the offset state of the current process parameters relative to the parameter dynamic baseline is mapped to the corresponding wire harness process unit, and the influence weight of each wire harness process unit is assigned by combining the sensitivity of process behavior to wire harness functional failure, forming an influence weight set. Based on the influence weight set and preset classification rules, the process risk level of each wire harness process unit is identified, forming a risk level set.

[0046] In this embodiment of the invention, the process behavior type bound to each wire harness process unit is read from the wire harness process unit description data. The process behavior types are limited to three categories: wire cutting, crimping, and plugging. Each wire harness process unit corresponds to only one process behavior type, forming a behavior type set, which is stored in the MES system as an enumeration field.

[0047] Based on the behavior type set, a functional association set is constructed by combining the electrical purpose of the wire number, the terminal connection location, and the harness branch structure. The electrical purpose of the wire number is limited to power line, signal line, or ground line; the terminal connection location is limited to main connection end, branch connection end, or terminal connection end; the harness branch structure is represented by the branch level number defined in the harness structure data. For crimping process units, if the electrical purpose of the wire number is a power line and the terminal connection location is a main connection end, then the process behavior is marked as having a strong correlation with power supply stability and harness continuity; if the electrical purpose of the wire number is a signal line and it is located at a branch connection end, then the marking is marked as having a strong correlation with signal integrity. The above marking rules are recorded in the form of association weight values, with strong association limited to a weight of 1.0, medium association limited to a weight of 0.6, and weak association limited to a weight of 0.3, thus forming a structured functional association set.

[0048] Based on this, the offset state of the current process parameters relative to the dynamic baseline is mapped to the corresponding wire harness process unit. The offset state is obtained by the pre-determination in step S4. The offset magnitude is expressed as the ratio of the difference between the actual parameter value and the center value of the dynamic baseline to the standard deviation of the dynamic baseline. The offset frequency is expressed as the proportion of the number of batches with offsets in continuous production to the total number of batches. The MES system binds the above offset states to the corresponding wire harness process units according to the functional association set.

[0049] Each wire harness process unit is assigned an influence weight based on the sensitivity of its process behavior to wire harness functional failure. Different process behaviors have preset inherent risk coefficients: 0.4 for wire cutting, 0.8 for crimping, and 0.6 for insertion. The influence weight is calculated as follows: Influence Weight = Functional Association Weight Inherent risk coefficient of behavior Offset Amplitude Level Coefficient Offset frequency level coefficients. Specifically, the offset amplitude level coefficients are limited to 0.5 for low, 1.0 for medium, and 1.5 for high, and the offset frequency level coefficients are limited to 0.6 for low, 1.0 for medium, and 1.4 for high. This forms the influence weight set.

[0050] The process risk level is identified based on the influence weight set and preset classification rules. The classification rules are as follows: an influence weight less than 0.5 is identified as low risk, an influence weight greater than or equal to 0.5 and less than 1.0 is identified as medium risk, and an influence weight greater than or equal to 1.0 is identified as high risk, forming a risk level set, which is uniquely associated with the corresponding wire harness process unit.

[0051] Preferably, mapping the offset state of the current process parameters relative to the dynamic baseline of the parameters to the corresponding wire harness process unit includes: Extract the specific functional items related to wire harness conduction, signal integrity, and power supply stability associated with each wire harness process unit based on the functional association set; An initial basic influence coefficient is preset for each specific functional item; Obtain historical offset data of the process parameters relative to the dynamic baseline of the parameters for each wire harness process unit; Calculate the average offset magnitude and offset frequency of each process parameter using historical offset data; Based on the magnitude of the average offset, the offset amplitude is divided into three levels: low, medium, and high. The low level is the average offset amplitude. The standard deviation of the dynamic baseline of the parameter The moderate amplitude level is 1 / 3 of the standard deviation of the dynamic baseline of the parameter. times Average offset The standard deviation of the dynamic baseline of the parameter The higher the amplitude level, the greater the average offset amplitude. Parametric dynamic baseline times, of which , ; Based on the offset frequency, the offset frequency is divided into three frequency levels: low, medium, and high. The low frequency level is the offset occurrence frequency. Total production batches The medium frequency level is the total production batch. Offset frequency Total production batches The high-frequency level is the frequency at which the offset occurs. Total production batches , ; Construct a two-dimensional weight adjustment matrix based on amplitude and frequency levels; The historical offset of each process parameter is mapped onto a two-dimensional weight adjustment matrix to obtain the first weight adjustment factor.

[0052] In this embodiment of the invention, based on the functional association set constructed in step S3, the specific functional items associated with the wire harness process unit are extracted. The functional items are limited to wire harness conduction and power supply stability, where wire harness conduction corresponds to terminal contact reliability, and power supply stability corresponds to terminal current carrying capacity. Each functional item is bound to a unique identifier in the functional association set.

[0053] A basic influence coefficient is preset for each specific functional item. The basic influence coefficient is stored in the MES system in a fixed value manner. The basic influence coefficient corresponding to wire harness conduction is limited to 0.6, the basic influence coefficient corresponding to power supply stability is limited to 0.4, and the sum of the basic influence coefficients is limited to 1.0. This is used to characterize the initial importance of different functional items within the wire harness process unit.

[0054] Obtain historical offset data of the process parameters for this wire harness process unit in the most recent 300 consecutive production batches. The historical offset data is calculated by subtracting the center value of the parameter's dynamic baseline from the actual parameter value and dividing by the standard deviation of the parameter's dynamic baseline, forming a dimensionless offset value sequence. For the same process parameter, the average absolute value of its offset is calculated as the average offset amplitude; the proportion of batches with an absolute offset value greater than 1 out of the 300 batches is calculated as the offset frequency.

[0055] For example, the standard deviation of the dynamic baseline of the parameter is denoted as Amplitude grading parameters Limited to 0.6, Limited to 1.2. Average offset amplitude not exceeding 0.6. The parameters are categorized into low amplitude levels, greater than 0.6. And not greater than 1.2 The parameters are classified as medium amplitude levels, greater than 1.2. The parameters are divided into high-amplitude levels. In the offset frequency classification, Limited to 5, The parameters with an offset frequency of no more than 5% of the total batch are classified as low frequency, those with an offset frequency of more than 5% but no more than 15% are classified as medium frequency, and those with an offset frequency of more than 15% are classified as high frequency.

[0056] Construct a two-dimensional weight adjustment matrix. The row index of the two-dimensional weight adjustment matrix represents the amplitude level, the column index represents the frequency level, and the matrix cell value is limited to the weight adjustment coefficient: 0.5 for low amplitude and low frequency, 1.0 for medium amplitude and medium frequency, and 1.6 for high amplitude and high frequency. The remaining combinations are arranged in ascending order of amplitude level and frequency level.

[0057] Finally, the average offset magnitude level and offset frequency level of each process parameter are mapped to a two-dimensional weight adjustment matrix, the corresponding unit value is read as the first weight adjustment factor, and a one-to-one binding relationship is established with the corresponding functional item and wire harness process unit.

[0058] In another embodiment, for the plug-in process unit, the function is limited to signal integrity, the basic influence coefficient is limited to 1.0, and the other historical offset calculation methods, amplitude and frequency classification rules, and two-dimensional weight adjustment matrix construction methods are consistent with the above embodiments.

[0059] Preferably, the weighting of the impact of process behavior on the functional failure of the wire harness on each wire harness process unit includes: Identify the process behavior type corresponding to each wire harness process unit and classify it into a predefined behavior category; Pre-determine the inherent risk coefficient for each type of process behavior; Based on the station identification data of each wire harness process unit, query the historical operation data of the corresponding station, calculate the average comprehensive equipment efficiency of the corresponding station, and calculate the equipment status impact factor based on the average comprehensive equipment efficiency. Based on the resource identification data corresponding to each wire harness process unit, query the corresponding historical usage count, and calculate the resource wear impact factor based on the historical usage count; The basic influence coefficient, first weight adjustment factor, behavioral inherent risk coefficient, equipment status influence factor and resource wear influence factor of the specific functional items associated with each wire harness process unit are weighted and integrated to generate the preliminary comprehensive influence value of each wire harness process unit. The preliminary comprehensive influence values ​​of each wire harness process unit are mapped to a preset, unified weight scale and calibrated to form an influence weight set.

[0060] In this embodiment of the invention, the process behavior type corresponding to each wire harness process unit is read from the wire harness process unit description data. The process behavior type is limited to four categories: wire cutting, crimping, plugging, and welding, and is classified into behavior categories according to predefined rules. Among them, wire cutting is classified into the low failure sensitivity category, crimping and plugging are classified into the medium and high failure sensitivity categories, and welding is classified into the high failure sensitivity category, forming a set of behavior categories.

[0061] Preset inherent risk coefficients for each behavior category. These inherent risk coefficients are stored in the MES system as fixed values: 0.3 for wire cutting, 0.6 for splicing, 0.8 for crimping, and 0.9 for welding. These coefficients reflect the inherent sensitivity of different process behaviors to wire harness functional failure.

[0062] Based on the workstation identification data in the wire harness process unit description data, query the historical equipment operation data for the corresponding workstation over the past 180 days. The historical equipment operation data must include at least equipment uptime, planned downtime, downtime due to malfunctions, and actual output cycle time. Average overall equipment efficiency is calculated as follows: Average Overall Equipment Efficiency = Equipment Uptime (Equipment uptime + downtime due to malfunction) Actual output rhythm Design cycle time. The equipment condition impact factor is determined by subtracting the average overall equipment efficiency from 1, and its value is limited to the range of 0 to 0.4.

[0063] The historical usage count of the corresponding resource is queried based on the resource identification data. The resource is limited to crimping molds, insertion jigs, or welding fixtures. The resource wear impact factor is calculated as the ratio of historical usage count to the rated life of the resource, and is limited to 0.1 when the ratio is less than 0.3, 0.3 when it is greater than or equal to 0.3 and less than 0.7, and 0.5 when it is greater than or equal to 0.7.

[0064] After completing the above factor calculations, a weighted fusion calculation is performed on the specific functional items associated with each wire harness process unit. The weighted fusion is expanded using a product form, and the calculation method is limited to: Preliminary comprehensive impact value = basic impact coefficient First weighting adjustment factor Inherent risk coefficient of behavior (1 + Equipment Status Influence Factor) (1 + resource wear and tear impact factor).

[0065] Among them, the basic influence coefficient and the first weight adjustment factor are derived from the preceding steps and correspond one-to-one with the specific functional items.

[0066] The preliminary comprehensive impact values ​​of each functional item under the same wire harness process unit are summed and divided by the number of functional items to form the preliminary comprehensive impact value of that wire harness process unit.

[0067] The initial comprehensive impact value is mapped to a unified weight scale. The unified weight scale is limited to the range of 0 to 1 and is calibrated using a linear normalization method. The calibration method is: Impact Weight = (Initial Comprehensive Impact Value - Minimum Impact Value) (Maximum influence value - minimum influence value). The calibrated influence weights are uniquely bound to the corresponding wire harness process unit, forming an influence weight set.

[0068] In another embodiment, for the welding process unit, the inherent risk coefficient of behavior is fixed at 0.9, the resource wear impact factor is calculated based on the ratio of the number of times the welding head is used to its rated life, and the calculation method of the other equipment status impact factors is the same as in the above embodiment, and the weighted fusion and unified weight scale calibration methods are consistent.

[0069] Preferably, step S4 includes: Real-time process parameters are obtained based on the current wire harness process unit to form the current parameter set; Based on the current parameter set, each process parameter is compared with the reference interval of the corresponding parameter dynamic baseline, the offset state of the parameter is marked, and an offset state set is formed. The parameter offset features are associated with the corresponding risk level set based on the offset state set to form a risk state set; The control intensity level corresponding to the wire harness process unit is determined based on the risk status set combined with the branch location of the wire harness, the criticality of the terminals, and their functional uses; Based on the control intensity level, design corresponding control decisions for the current wire harness process unit.

[0070] In this embodiment of the invention, real-time process parameters are acquired based on the current wire harness process unit to form a current parameter set. The real-time process parameters are directly output by the crimping station acquisition device, and the parameter types are limited to crimping height and crimping tension. The acquisition frequency is limited to acquiring one set of data after each crimping operation. The MES system uniquely binds the acquired real-time parameters to the corresponding wire harness process unit based on the station identification data and the wire harness process unit identifier, thus forming the current parameter set.

[0071] Each process parameter in the current parameter set is compared with the parameter dynamic baseline reference range established in step S2. The parameter dynamic baseline is stored in the form of upper and lower boundaries of the reference range. If the current parameter value is between the upper and lower boundaries of the reference range, it is marked as normal; if it is outside the upper and lower boundaries of the reference range and the deviation does not exceed 20% of the width of the reference range, it is marked as slightly off; if the deviation exceeds 20% of the width of the reference range, it is marked as severely off. The MES system summarizes the above marking results to form an offset state set.

[0072] After the offset state set is formed, the parameter offset features are associated with the risk level set generated in step S3. The association method is limited to using the wire harness process unit identifier as an index, merging and recording the offset state of all process parameters under that process unit with their corresponding process risk levels to form a risk state set. The risk state set must at least contain the offset state type, risk level identifier, and corresponding parameter name.

[0073] The control intensity level of the wire harness process unit is determined based on the risk status set. The determination of the control intensity level incorporates three structural factors: wire harness branch location, terminal criticality, and functional purpose. Wire harness branch location is represented by the hierarchical number in the wire harness structure data: main trunk location is assigned a value of 1.0, first-level branch 0.7, and second-level branch 0.4. Terminal criticality is categorized by terminal type: power terminals are assigned a value of 1.0, signal terminals 0.8, and grounding terminals 0.6. Functional purpose is determined based on the functional association set: power supply stability corresponds to 1.0, signal integrity to 0.9, and wire harness continuity to 0.7. The MES system quantifies the risk level from the risk status set, multiplies it by the above three structural factors, and sums the results to calculate the control intensity score.

[0074] A wire harness process unit with a control intensity score of less than 0.6 is identified as a Level 1 control intensity unit, a score greater than or equal to 0.6 and less than 1.0 is identified as a Level 2 control intensity unit, and a score greater than or equal to 1.0 is identified as a Level 3 control intensity unit.

[0075] The control decisions for the wire harness process unit are designed according to the control intensity level. Level 1 control intensity corresponds to continuous monitoring and recording; Level 2 control intensity corresponds to increasing the sampling frequency and triggering process parameter review prompts; Level 3 control intensity corresponds to locking the current wire harness process unit and generating a process re-inspection task. These control decisions are issued to the corresponding workstations through the MES system and recorded in the production history.

[0076] Most importantly, the specific control intensity level corresponding to the wire harness process unit is determined as follows: Identify the parameter offset status and associated process risk level of each wire harness process unit in the risk status set, and extract the risk feature vector; Based on risk feature vectors and harness structure data, the propagation influence factor of the branch position of each harness process unit on the overall harness network reliability is determined. The terminals are divided into power terminals, signal terminals, ground terminals, and redundant terminals, and the terminal key weights are set according to the criticality of the terminal connection circuit. The propagation impact factor, terminal key weight and risk feature vector are integrated and calculated in multiple dimensions and dynamically corrected to output the control intensity level corresponding to the wire harness process unit.

[0077] In this embodiment of the invention, the parameter offset state and process risk level of the wire harness process unit are read from the risk state set formed in step S4. The parameter offset state is limited to three categories: normal, slight offset, and severe offset, and the process risk level is limited to low risk, medium risk, and high risk. The offset state and risk level are encoded in numerical form, with 0 corresponding to normal, 1 to slight offset, and 2 to severe offset; 1 to low risk, 2 to medium risk, and 3 to high risk. These are combined to form a risk feature vector, which is limited to the form of [offset state value, risk level value].

[0078] The propagation impact factor of branch location on the overall reliability of the wiring harness network is calculated based on the wiring harness structure data. The wiring harness structure data describes the hierarchical relationship of each wiring harness process unit in a tree structure, with the hierarchical number of the trunk position limited to 0, first-level branches to 1, and second-level branches to 2. The propagation impact factor is calculated as follows: Propagation impact factor = 1 (1 + hierarchical number). Therefore, the propagation influence factor at the main branch is 1, 0.5 for first-level branches, and 0.33 for second-level branches. This propagation influence factor is used to characterize the degree of impact on the overall wire harness network when an anomaly occurs in this wire harness process unit.

[0079] The terminals associated with this wire harness process unit are identified by type. Terminal types are limited to power terminals, signal terminals, ground terminals, and redundant terminals. Terminal critical weights are assigned based on the criticality of the terminal connection circuit, with power terminals corresponding to 1.0, signal terminals to 0.8, ground terminals to 0.6, and redundant terminals to 0.3. These terminal critical weights are maintained in a fixed parameter table within the MES system.

[0080] After completing the factor extraction described above, a multi-dimensional fusion calculation is performed. The fusion calculation uses a weighted summation method, and the calculation formula is limited to: Control intensity score = (offset state value) 0.4+ risk level value 0.6) Dissemination Influence Factors Terminal key weights.

[0081] Among them, the offset state value and risk level value are derived from the risk feature vector, while the propagation impact factor and terminal key weight are derived from the harness structure data and terminal type identifier, respectively.

[0082] For example, if the offset status is severe offset, the risk level is high risk, and it is located at the main power supply terminal, then the control intensity score is (2). 0.4 + 3 0.6) 1 1.0 = 2.6.

[0083] Dynamic adjustments are made to the control intensity score. The average control intensity score of the most recent 50 production batches is used as the adjustment benchmark. When the current score is more than 30% higher than the historical average, the adjustment coefficient is 1.1; when it does not exceed this threshold, the adjustment coefficient is 1.0. The adjusted score is used to determine the control intensity level: a score less than 1.0 is designated as Level 1 control intensity, a score greater than or equal to 1.0 and less than 2.0 is designated as Level 2 control intensity, and a score greater than or equal to 2.0 is designated as Level 3 control intensity.

[0084] In another embodiment, for the signal plugging process unit located in the first-level branch, the propagation influence factor is 0.5, the terminal key weight is 0.8, and the other risk feature vector extraction methods, fusion calculation methods and dynamic correction rules are consistent with the above embodiment, thereby forming a differentiated wire harness process unit control intensity level.

[0085] Of particular importance, step S4 is followed by: By integrating the description data, dynamic baseline parameters, and process risk levels of all wire harness process units under the same wire harness product, a product-level process quality traceability map is constructed. In the process quality traceability map, the offset status, risk level and control decision execution of each wire harness process unit are marked, and the coupled process defect patterns across wire harness process units are identified through the status association path in the process quality traceability map. Based on the coupled process defect pattern, the process of generating the dynamic baseline of the corresponding process parameters is traced back to the process of generating the dynamic baseline of the parameters, and the selection logic of the historical qualified parameter set on which the dynamic baseline of the parameters is based is self-optimized and adjusted. The self-optimized and adjusted parameter dynamic baseline, updated process risk level, and defect mode characteristics will be synchronized to the process planning of all products in production and awaiting production for the same wire harness product.

[0086] In this embodiment of the invention, for the same wire harness product, the wire harness process unit description data, parameter dynamic baselines, and process risk levels of all its wire harness process units are summarized. The wire harness process unit description data includes at least the process unit identifier, process behavior type, wire number, terminal number, cavity number, workstation identifier, and resource identifier; the parameter dynamic baseline is stored in the form of the upper and lower boundaries of the reference interval and the statistical center value of each process parameter; the process risk level is stored in the form of enumerated values ​​for low risk, medium risk, and high risk. The MES system uses the wire harness product model as an index to organize the above data into nodes according to the process unit sequence, and establishes directed connections between nodes based on the process dependencies recorded in step S1, thereby constructing a product-level process quality traceability map.

[0087] In the process quality traceability map, status marking is performed on each wire harness process unit. The status marking content is limited to the current offset status, process risk level, and corresponding control decision execution status. The offset status is taken from the offset status set formed in step S4, and the control decision execution status is limited to executed or not executed. The MES system writes the above status information as node attributes into the process quality traceability map and marks the physical connection dependency or operation sequence dependency type on the node connection edge.

[0088] After status marking is completed, coupled process defect patterns across wire harness process units are identified through the status association paths in the process quality traceability map. The identification rules for coupled process defect patterns are defined as follows: in the same wire harness product, if two or more dependent wire harness process units exhibit medium or higher risk levels in three consecutive production batches, and their offset states show a synchronous or sequential diffusion relationship on the time axis, then it is determined to be a coupled process defect pattern. The MES system records the set of process units involved in this defect pattern, the corresponding parameter names, and the time interval.

[0089] After identifying the coupled process defect pattern, a reverse tracing operation is performed. The reverse tracing starts from the process unit involved in the defect pattern and traces back to the generation process of the corresponding parameter dynamic baseline to locate the historical qualified parameter set used. The filtering logic of the historical qualified parameter set is adjusted, with the adjustment method limited to: removing parameter records within the N production batches prior to the defect occurrence that have a synchronous offset relationship with the defect parameter, where N is limited to 50; and recalculating the mean and standard deviation of the parameter feature set to generate an updated parameter dynamic baseline.

[0090] After completing the dynamic baseline update of parameters, the process risk level of the corresponding wire harness process unit is updated synchronously, and the defect mode characteristics are written into the process quality traceability map as historical markers. Finally, the updated dynamic baseline of parameters, process risk level, and defect mode characteristics are synchronously distributed to the process planning data of all in-production and out-of-production products of the same wire harness product to ensure that subsequent production batches directly reference the updated control benchmark.

[0091] Please see Figure 2 The present invention also provides a MES system management and control system based on wire harness manufacturing, used to execute the above-described MES system management and control method based on wire harness manufacturing, wherein the MES system management and control system 100 based on wire harness manufacturing includes: The process unit modeling module 101 is used to acquire and parse wire harness structure data and process configuration data, split multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data, and construct wire harness process unit description data. The parameter baseline construction module 102 is used to establish a mapping between each operation status in the production process and the wire harness process unit based on the wire harness process unit description data, mark the operation status as qualified process parameters, and construct a dynamic parameter baseline. The process risk assessment module 103 is used to determine the process risk level of the wire harness process unit based on the degree of influence of the process behavior of the wire harness process unit on the functional reliability of the wire harness. The control and management decision generation module 104 is used to obtain the current process parameters of the wire harness process unit, correlate and determine them with the corresponding parameter dynamic baseline, and determine the control and management decision of the wire harness process unit in combination with the corresponding process risk level.

[0092] Please see Figure 3This demonstrates the logical process of a wire harness manufacturing unit from its smallest process element node to its aggregated unit, with the core corresponding to the decomposition and aggregation rules of the process unit modeling. First, the smallest process element nodes such as wire cutting, crimping, and insertion are decomposed. Then, nodes that can be aggregated are selected through two core logics: the process continuity rule connects the processes in the order of wire cutting, crimping, and insertion, and the resource coupling logic associates the operation nodes of the same workstation or equipment. Finally, nodes that meet the rules are aggregated into a complete wire harness manufacturing unit.

[0093] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is not limited by the foregoing description. Thus, all changes falling within the meaning and scope of the equivalents of the application are intended to be included within the scope of the invention.

[0094] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for managing a MES system based on wire harness manufacturing, characterized in that, Includes the following steps: Step S1: Obtain and parse the wire harness structure data and process configuration data, split into multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data to construct wire harness process unit description data; Step S2: Based on the wire harness process unit description data, establish a mapping between each operation status in the production process and the wire harness process unit, mark the operation status as qualified process parameters, and construct a dynamic baseline for parameters; Step S3: Determine the process risk level of the wire harness process unit based on the degree of impact of the process behavior of the wire harness process unit on the functional reliability of the wire harness. Step S4: Obtain the current process parameters of the wire harness process unit, correlate them with the corresponding parameter dynamic baseline, and determine the wire harness process unit control decision based on the corresponding process risk level.

2. The MES system control method based on wire harness manufacturing according to claim 1, characterized in that, Step S1 includes: The MES system is used to read the wire harness structure data and process configuration data of the wire harness products. By combining wire harness structure data and process configuration data, multiple wire harness process units are broken down to form a process unit table; Each wire harness process unit corresponds to at least one specific process behavior and is associated with the wire core segment, terminal crimping position, solder joint area and plug-in interface. The process behavior includes cutting, crimping and plugging of a single wire. Based on the process unit table, the set of process parameters required for the corresponding wire harness process unit, as well as the corresponding workstation identification data and resource identification data, are used to form the wire harness process unit description data.

3. The MES system control method based on wire harness manufacturing according to claim 2, characterized in that, The wire harness process units are broken down into multiple units by combining wire harness structure data and process configuration data, specifically: By combining wire harness structure data and process configuration data, identify all the smallest process element nodes in the wire harness product that meet the independent process control conditions; Based on preset process continuity rules and resource coupling degree, the smallest process element nodes are dynamically aggregated to form a wire harness process unit with clear process boundaries and logical operation sequence. During the decomposition process, the process dependencies between each wire harness process unit are constructed and recorded. These dependencies include physical connection dependencies and operation sequence dependencies. Construct a process unit table with process unit identifier, process behavior type, and associated physical location as its core.

4. The MES system control method based on wire harness manufacturing according to claim 1, characterized in that, Step S2 includes: Based on the wire harness process unit description data, the production operation status is associated with the specific wire number, terminal and cavity to which the wire harness process unit belongs, forming a status mapping set; Based on the state mapping set, the operation state attributes of the process parameters are identified to form a parameter state set; Select qualified parameters that are consistent with the wire harness functional test or process judgment in the parameter status set to form a qualified parameter set; Extract the parameter feature set from the qualified parameter set according to the preset wire size, terminal type and process category; The parameter reference range for each wire harness process unit is determined based on the parameter feature set and used as the dynamic baseline for the parameters.

5. The MES system control method based on wire harness manufacturing according to claim 4, characterized in that, The parameter reference ranges for each wire harness process unit determined based on the parameter feature set include: Multi-dimensional cluster analysis was performed on qualified process parameter data in the parameter feature set to identify the core clusters of parameter distribution under different wire specifications, terminal types and process categories; Calculate the statistical boundary in the multidimensional space based on the core cluster of each parameter distribution, and perform boundary fitting to form the initial static reference interval; Real-time monitoring of newly added qualified parameters flowing into the core clusters of each parameter distribution, and calculation of the movement trajectory and convergence rate relative to the centroid of the static reference interval; When the movement trajectory indicates that the process has experienced controlled drift or the convergence rate exceeds a preset threshold, a progressive adjustment of the reference interval is triggered, generating and updating it as a dynamic baseline for parameters.

6. The MES system control method based on wire harness manufacturing according to claim 1, characterized in that, Step S3 includes: Identify the process behavior type corresponding to each wire harness process unit in the wire harness process unit description data, and construct a behavior type set; Based on the behavior type set, combined with the electrical purpose of the wire number, the terminal connection position, and the wire harness branch structure, the relationship between process behavior and wire harness continuity, signal integrity, and power supply stability is marked, and a functional association set is generated. Based on the functional association set, the offset state of the current process parameters relative to the parameter dynamic baseline is mapped to the corresponding wire harness process unit, and the influence weight of each wire harness process unit is assigned by combining the sensitivity of process behavior to wire harness functional failure, forming an influence weight set. Based on the influence weight set and preset classification rules, the process risk level of each wire harness process unit is identified, forming a risk level set.

7. The MES system control method based on wire harness manufacturing according to claim 6, characterized in that, Mapping the offset state of the current process parameters relative to the dynamic baseline of the parameters to the corresponding wire harness process unit includes: Extract the specific functional items related to wire harness conduction, signal integrity, and power supply stability associated with each wire harness process unit based on the functional association set; An initial basic influence coefficient is preset for each specific functional item; Obtain historical offset data of the process parameters relative to the dynamic baseline of the parameters for each wire harness process unit; Calculate the average offset magnitude and offset frequency of each process parameter using historical offset data; Based on the magnitude of the average offset, the offset amplitude is divided into three levels: low, medium, and high. The low level is the average offset amplitude. The standard deviation of the dynamic baseline of the parameter The moderate amplitude level is 1 / 3 of the standard deviation of the dynamic baseline of the parameter. times Average offset The standard deviation of the dynamic baseline of the parameter The higher the amplitude level, the greater the average offset amplitude. Parametric dynamic baseline times, of which , ; Based on the offset frequency, the offset frequency is divided into three frequency levels: low, medium, and high. The low frequency level is the offset occurrence frequency. Total production batches The medium frequency level is the total production batch. Offset frequency Total production batches The high-frequency level is the frequency at which the offset occurs. Total production batches , ; Construct a two-dimensional weight adjustment matrix based on amplitude and frequency levels; The historical offset of each process parameter is mapped onto a two-dimensional weight adjustment matrix to obtain the first weight adjustment factor.

8. The MES system control method based on wire harness manufacturing according to claim 6, characterized in that, The weighting of the impact of process behavior on the functional failure of wire harnesses on each wire harness process unit includes: Identify the process behavior type corresponding to each wire harness process unit and classify it into a predefined behavior category; Pre-determine the inherent risk coefficient for each type of process behavior; Based on the station identification data of each wire harness process unit, query the historical operation data of the corresponding station, calculate the average comprehensive equipment efficiency of the corresponding station, and calculate the equipment status impact factor based on the average comprehensive equipment efficiency. Based on the resource identification data corresponding to each wire harness process unit, query the corresponding historical usage count, and calculate the resource wear impact factor based on the historical usage count; The basic influence coefficient, first weight adjustment factor, behavioral inherent risk coefficient, equipment status influence factor and resource wear influence factor of the specific functional items associated with each wire harness process unit are weighted and integrated to generate the preliminary comprehensive influence value of each wire harness process unit. The preliminary comprehensive influence values ​​of each wire harness process unit are mapped to a preset, unified weight scale and calibrated to form an influence weight set.

9. The MES system control method based on wire harness manufacturing according to claim 1, characterized in that, Step S4 includes: Real-time process parameters are obtained based on the current wire harness process unit to form the current parameter set; Based on the current parameter set, each process parameter is compared with the reference interval of the corresponding parameter dynamic baseline, the offset state of the parameter is marked, and an offset state set is formed. The parameter offset features are associated with the corresponding risk level set based on the offset state set to form a risk state set; The control intensity level corresponding to the wire harness process unit is determined based on the risk status set combined with the branch location of the wire harness, the criticality of the terminals, and their functional uses; Based on the control intensity level, design corresponding control decisions for the current wire harness process unit.

10. A MES system control system based on wire harness manufacturing, characterized in that, The MES system management and control system for implementing the wire harness manufacturing-based MES system management and control method as described in claim 1 includes: The process unit modeling module is used to acquire and parse wire harness structure data and process configuration data, split multiple wire harness process units, and associate each wire harness process unit with the corresponding set of process parameters, station identification data and resource identification data, and construct wire harness process unit description data. The parameter baseline construction module is used to establish a mapping between each operation status in the production process and the wire harness process unit based on the wire harness process unit description data, mark the operation status as qualified process parameters, and construct a dynamic parameter baseline. The process risk assessment module is used to determine the process risk level of a wire harness process unit based on the degree of impact of the process behavior of the wire harness process unit on the functional reliability of the wire harness. The control and management decision generation module is used to obtain the current process parameters of the wire harness process unit, correlate them with the corresponding dynamic baseline parameters, and determine the control and management decision of the wire harness process unit in combination with the corresponding process risk level.

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