New energy automobile pressure sensor production visual management system and method

Abstract

The invention relates to the field of pressure sensor production management, and discloses a new energy automobile pressure sensor production visual management system and method, and the system and method achieve the real-time collection of working parameters, equipment states, product identifiers, circulation data and test result data through the deployment of an IIOT gateway in a production line. And key information in the production process is comprehensively digitalized. And meanwhile, by constructing pressure sensor production graph data, the production data is mapped into a graph database structure, so that a flexible and efficient data relationship management and analysis mechanism is formed. Using a graph data structure, quality alarms can be quickly responded, potential fault units are located through path queries and candidate sets are formed. Based on further aggregation and generality analysis, main suspected objects can be accurately identified, fault reasons can be revealed, and timely early warning can be carried out. Therefore, a traditional production management mode is converted into active problem positioning and process optimization, the production efficiency is remarkably improved through an intelligent method, and the fault risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of pressure sensor production management, and more specifically, to a visualization management system and method for the production of pressure sensors for new energy vehicles. Background Art

[0002] The increasing popularity of new energy vehicles is driving the continuous upgrading of automotive parts production technology. Pressure sensors, as key components, play an indispensable role in the development of new energy vehicles. Pressure sensors are widely used in core areas of new energy vehicles, including powertrains, braking systems, and suspension systems. Their quality and performance directly impact vehicle safety and stability. However, due to the higher performance requirements of new energy vehicles, the manufacturing process of their internal pressure sensors has become increasingly complex. Traditional production management methods make it difficult to monitor key parameters in real time during the production process and cannot quickly locate potential sources of failure when quality issues arise. This results in an inability to effectively improve product yield and also poses bottlenecks in production efficiency.

[0003] With the development of industrial internet technologies, the digitization, visualization, and intelligent management of production processes through IoT has become a key trend in manufacturing upgrades. Pressure sensor production lines involve a wide range of equipment, parameters, product identification, and flow data. The question of how to fully utilize this data to improve production process transparency and rapidly identify quality issues has become a pressing issue for the industry. Summary of the Invention

[0004] This application is proposed to address the aforementioned technical issues. The embodiments of this application propose a visualization management system and method for the production of pressure sensors for new energy vehicles. Through data collection, correlation, analysis, and visualization, this system transforms the traditional production management model into proactive problem location and process optimization. This intelligent approach significantly improves production efficiency, reduces failure risks, and promotes the digitalization and high-quality development of new energy vehicle parts production.

[0005] According to one aspect of the present application, a method for visual management of the production of pressure sensors for new energy vehicles is provided, including: real-time collection of working parameters, equipment status data, product identification and flow data, and test result data of key workstations through an IIoT gateway deployed on a pressure sensor production line; constructing pressure sensor production map data based on the working parameters, equipment status data, product identification and flow data, and test result data; obtaining input quality alarms; querying the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarms; aggregating and performing commonality analysis on the candidate set of potential fault unit path data to obtain main suspects; displaying the main suspects on a display screen and triggering an early warning prompt signal.

[0006] In one possible implementation, pressure sensor production graph data is constructed based on working parameters, equipment status data, product identification and flow data, and test result data, including: transmitting the working parameters, equipment status data, product identification and flow data, and test result data to a local data center and storing them in a time series database, wherein the time series database is indexed by product ID / batch ID, equipment ID, and parameter name; defining the node type and relationship type of the graph database, and updating the nodes and relationships in the graph database according to the working parameters, equipment status data, product identification and flow data, and test result data to obtain the pressure sensor production graph data.

[0007] In one possible implementation, the node types include product unit, batch, process, equipment, process parameters, test items, and defects; and the relationship types include belonging to a batch, passing a process, using equipment, generating parameters, executing tests, and associating defects.

[0008] In one possible implementation, querying the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm includes: querying the pressure sensor production map data for a target node that matches the quality alarm; and traversing the pressure sensor production map data in reverse from the target node to obtain the candidate set of potential fault unit path data.

[0009] In one possible implementation, querying the pressure sensor production graph data for a target node that matches the quality alert includes: extracting product units, batches, test items, and defects from the quality alert, and using the product units in the quality alert as query nodes; and querying the pressure sensor production graph data for a target node that matches the query node.

[0010] In one possible implementation, traversing the pressure sensor production map data in reverse from the target node to obtain a candidate set of the potential fault unit path data includes: traversing the pressure sensor production map data in reverse from the target node along the relationship between nodes to gradually identify nodes that match the batches, test items, and defects in the quality alert to obtain a candidate set of the potential fault unit path data.

[0011] In one possible implementation, the candidate set of the potential fault unit path data is aggregated and analyzed for commonalities to obtain a main suspect, including: counting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain statistical analysis results of the common nodes and statistical analysis results of the common relationships; taking the common node with the largest value in the statistical analysis results of the common nodes as the main suspect; and / or, taking the common relationship with the largest value in the statistical analysis results of the common relationships as the main suspect.

[0012] In one possible implementation, the number of common nodes and common relationships in the candidate set of the potential fault unit path data is counted to obtain the statistical analysis results of the common nodes and the statistical analysis results of the common relationships, and the method also includes: correcting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain the corrected number of common nodes and common relationships, and using the corrected number of common nodes and common relationships as the statistical analysis results of the common nodes and the statistical analysis results of the common relationships.

[0013] In one possible implementation, the number of common nodes and common relationships in the candidate set of the potential fault unit path data is corrected to obtain a corrected number of common nodes and common relationships, including: dividing the statistical number of common nodes and common relationships by the number of all nodes and all relationships to obtain a node probability value and a relationship probability value; based on the node probability value and the relationship probability value, obtaining a binary discrete state function representing the node activity and the relationship activity; based on the binary discrete state function representing the node activity and the relationship activity, calculating a topology constraint value and a common node / relationship density value; based on the topology constraint value and the common node / relationship density value, calculating a number correction coefficient based on active coverage to obtain a first number correction coefficient and a second number correction coefficient; and correcting the sum of the number of common nodes and common relationships by the first number correction coefficient and the second number correction coefficient to obtain a corrected number of common nodes and common relationships.

[0014] According to another aspect of the present application, a new energy vehicle pressure sensor production visualization management system is provided, which is used to execute the above-mentioned new energy vehicle pressure sensor production visualization management method, including: a data acquisition module, which is used to collect working parameters, equipment status data, product identification and flow data, and test result data of key workstations in real time through an IIoT gateway deployed on the pressure sensor production line; a production map data construction module, which is used to construct pressure sensor production map data based on working parameters, equipment status data, product identification and flow data, and test result data; a quality alarm acquisition module, which is used to obtain input quality alarms; a potential fault unit query module, which is used to query the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm; a main suspect object determination module, which is used to aggregate and perform commonality analysis on the candidate set of potential fault unit path data to obtain the main suspect object; and an early warning prompt module, which is used to display the main suspect object on a display screen and trigger an early warning prompt signal.

[0015] Compared with the existing technology, the new energy vehicle pressure sensor production visualization management system and method provided by this application realizes real-time collection of working parameters, equipment status, product identification, flow data and test result data by deploying IIoT gateways in the production line, and fully digitizes the key information in the production process. At the same time, by constructing pressure sensor production graph data, the production data is mapped to a graph database structure, thereby forming a flexible and efficient data relationship management and analysis mechanism. Using the graph data structure, it is possible to quickly respond to quality alerts, locate potential fault units through path queries and form candidate sets. Based on further aggregation and commonality analysis, the main suspects can be accurately identified, the cause of the fault can be revealed and timely warnings can be issued. In this way, the traditional production management model is transformed into active problem location and process optimization, and production efficiency is significantly improved and the risk of failure is reduced through intelligent methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other purposes, features, and advantages of the present application will become more apparent through a more detailed description of the embodiments of the present application in conjunction with the accompanying drawings. The accompanying drawings are intended to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 The figure illustrates a schematic flow chart of a method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application.

[0018] Figure 2 The figure illustrates a schematic flow chart of step S2 in the method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application.

[0019] Figure 3 The figure illustrates a schematic flow chart of step S4 in the method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application.

[0020] Figure 4 The figure illustrates a schematic flow chart of step S41 in the method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application.

[0021] Figure 5 The figure illustrates a schematic flow chart of step S5 in the method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application.

[0022] Figure 6 The figure shows a schematic block diagram of a new energy vehicle pressure sensor production visualization management system according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described herein.

[0024] Figure 1 The figure shows a schematic flow chart of a method for visual management of production of pressure sensors for new energy vehicles according to an embodiment of the present application. Figure 1 As shown, the present application provides a method for visual management of the production of pressure sensors for new energy vehicles, including: S1: real-time collection of working parameters, equipment status data, product identification and flow data, and test result data of key workstations through an IIoT gateway deployed on a pressure sensor production line; S2: constructing pressure sensor production map data based on the working parameters, equipment status data, product identification and flow data, and test result data; S3: obtaining input quality alarms; S4: querying the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm; S5: aggregating and performing commonality analysis on the candidate set of potential fault unit path data to obtain the main suspects; S6: displaying the main suspects on a display screen and triggering an early warning prompt signal.

[0025] Specifically, in step S1, the working parameters, equipment status data, product identification and flow data, and test result data of key workstations are collected in real time through the IIoT gateway deployed in the pressure sensor production line. It should be understood that in the production of new energy vehicle pressure sensors, the products involve many manufacturing and testing links, the process is complex and highly sensitive to uncontrollable factors in the production process. To achieve refined quality management and efficient link traceability, it is necessary to rely on data transparency throughout the production process. The fundamental premise for achieving data transparency and visualization is to be able to implement seamless, real-time, and automated collection of dynamic data from all key workstations in the production process, and the Industrial Internet of Things (IIoT) gateway deployed in the production line is the core facility supporting this work. Such data not only includes the operating status and operating parameters of the production equipment itself, but also includes the unique identification related to each specific product and its flow process, as well as information such as detection and test results at each key link.

[0026] In one embodiment, an IIoT gateway deployed on a pressure sensor production line collects real-time operating parameters, equipment status data, product identification and flow data, and test results from key workstations. This includes: First, a thorough review of the key workstations on the production line is conducted. Specifically, these key workstations include chip placement, soldering and wiring, potting and curing, calibration, final functional testing, and airtightness testing. These steps determine the functional stability and factory quality of the product. Each workstation is equipped with automated industrial control equipment and detection sensors, such as placement machines, automatic welders, curing ovens, and test stations. Each device is equipped with or has externally connected modules for measuring and acquiring various process parameters, such as temperature sensors, pressure sensors, flow transmitters, current and voltage signal acquisition instruments, etc. These modules output real-time device status data and operating process parameters. The device status data includes power on / off status, fault alarms, and self-diagnostic codes. The operating process parameters include heating temperature, pressure value, holding time, and vibration amplitude. These data are transmitted externally through standard industrial interfaces, including RS485, Ethernet, CAN, Modbus RTU / TCP, OPC-UA and other protocols.

[0027] In addition, the IIoT gateway, as a node aggregation device for physical devices and upper-level data platforms, supports the conversion and compatibility capabilities of multiple industrial Internet protocols. Each IIoT gateway can actively poll the real-time parameter data of various production equipment through configuration settings, and can also actively push data change events on the device side to the gateway based on the industrial subscription / publishing mechanism. The types of data collected are not limited to data from physical sensors, but also include process and action records from equipment controllers (such as PLCs, embedded MCUs, etc.), such as startup and shutdown times, operator IDs, alarm events, equipment switching records, etc., which will also be collected. The frequency of data collection is dynamically set according to the characteristics of the equipment and process: for parameters with high dynamic fluctuations (such as pressure and temperature), the sampling accuracy can reach multiple times per second, and for stable state data, flow records, and operation logs, they are collected according to the production rhythm and event-driven methods.

[0028] In order to collect information on product identification and flow data, industrial-grade barcode scanners, industrial cameras or RFID readers are deployed at each workstation to ensure that each pressure sensor unit or its subassembly has a unique identity tracking when passing through all workstations. In a specific embodiment, a unique serial number (SN) or batch ID (Batch ID) is assigned when the material is put online or at the initial workstation, and the product barcode, QR code or RFID electronic tag is automatically collected and written. After each process, the flow record is automatically uploaded to the IIoT gateway, including the timestamp of entering the workstation, the time of leaving the workstation, the flow sequence number, the process parameter template ID, etc. This move not only establishes a full life cycle traceability chain for the product flow path, but also realizes the hierarchical collection and isolation of multi-station and multi-batch parallel production data.

[0029] To automatically collect test result data, each test station or automated quality control console is connected to the gateway. By interoperating with the test equipment through communication protocols, not only is test result judgment data (such as pass / fail and NG reason codes) collected, but also the full amount of raw test data directly related to the test, including electrical performance curves, pressure change curves, and seal leakage values. Each test data packet is tagged with metadata such as the product's unique ID, relevant process, collection time, and test equipment ID, enabling high-level correlation and integration of data with other production information on the backend.

[0030] After the collection is completed at the network layer and edge side, the IIoT gateway can perform preliminary local processing on the raw data, such as multi-source format standardization, redundant value deduplication, sampling noise reduction, data packet loss compensation, automatic error retransmission, etc., to improve data quality and production safety. At the same time, to ensure the reliability of the data reporting link, the IIoT gateway integrates a cache queue and breakpoint resumption mechanism. When the network is weak or interrupted, the data can be temporarily stored locally and uploaded in batches when the communication is restored, eliminating the risk of data loss caused by physical and systemic interruptions. After the above local preprocessing, the IIoT gateway pushes the collected data to the enterprise's local data center or cloud platform in real time at the millisecond level through communication methods such as industrial Ethernet, 5G network or dedicated metropolitan area Internet of Things. The data is encapsulated and encrypted for transmission using standard industrial Internet of Things protocols (such as MQTT, AMQP, CoAP, HTTPS, etc.) to ensure the real-time and security of the enterprise's production data.

[0031] At the data center, this production data, which has undergone collection, preprocessing, and multi-source integration, enters a dedicated data reception and storage platform. The system distributes different data types into a time series database and an object database according to structured rules. The time series database is used to carry all streaming data closely related to timestamps, such as equipment parameters, state change curves, and original test waveforms. The data index uses product ID, equipment ID, workstation ID, parameter type, and time interval as a composite primary key to enable full-dimensional on-demand queries. The object database carries structured information such as flow records, process paths, and test judgments, spanning all process nodes within the production lifecycle.

[0032] While collecting data, the IIoT gateway also offers local intelligent analysis capabilities. For example, it applies edge-side rules to fluctuations in operating parameters and device status, instantly detecting anomalies. It can then flag abnormal events locally or proactively trigger alerts in upstream systems, quickly reporting important events online. The collection software uses configurable data adapters to map raw data fields from various collection devices to standardized interfaces, ensuring uniformity and compatibility during data storage and subsequent calls.

[0033] In a specific embodiment, taking the pressure sensor welding station as an example, the equipment is connected to a temperature sensor and a precise current acquisition module. The IIoT gateway periodically (e.g., every 100ms) accesses the PLC to obtain real-time heating temperature and current curves, while detecting the station barcode scanning gun information to identify the serial number and batch number of the sensor currently being processed. Whenever the welding process is completed, the scanned data is written into the database together with the process parameters and immediately uploaded to the local central system. The system groups the data of multiple parallel devices in the same process by index to ensure that the production history information of any single pressure sensor is stored completely and accurately and can be quickly retrieved later. Similarly, the final test station automatically detects airtightness and synchronously uploads all original information such as test judgment, airtightness data curve, and abnormal points through data bus communication with the detection equipment. Each data is accurately associated with a unique product ID and bound to metadata such as acquisition time, test equipment, operator, and process template to provide accurate data support for subsequent problem location and root cause analysis.

[0034] Specifically, in step S2, pressure sensor production map data is constructed based on working parameters, equipment status data, product identification and flow data, and test result data. It should be understood that pressure sensors are core components in new energy vehicles that have extremely high requirements for safety and reliability, and each step of their production process may affect the performance and quality of the final product. In actual industrial scenarios, the production line of pressure sensors often covers highly automated process flows, complex equipment systems, and strict testing standards. Relying solely on the linear acquisition and static storage of single data cannot reveal the complex dynamic associations between various links in the production process, such as equipment parameters, process links, product batches, and test processes. Based on this, in this application, a production map based on traceable data is constructed to achieve full visualization of the production flow, efficient and convenient problem location, and intelligent analysis of abnormal associations.

[0035] In one embodiment, Figure 2 As shown, based on the working parameters, equipment status data, product identification and flow data, and test result data, the pressure sensor production graph data is constructed, including: S21: the working parameters, equipment status data, product identification and flow data, and test result data are transmitted to the local data center and stored in a time series database, wherein the time series database is indexed by product ID / batch ID, equipment ID, and parameter name; S22: the node type and relationship type of the graph database are defined, and the nodes and relationships in the graph database are updated according to the working parameters, equipment status data, product identification and flow data, and test result data to obtain the pressure sensor production graph data.

[0036] First, operating parameters, equipment status data, product identification and flow data, and test result data are transmitted to the local data center and stored in a time series database, forming a full-cycle dynamic record of different times, different equipment, and different product units or batches. This basic storage allows for traceability of multi-time point and multi-angle parameters for any product unit or production process node, providing a solid data foundation for subsequent data modeling. Even more significant is the definition of pressure sensor production graph data on this basis. That is, with the support of a graph database, all the important entities involved and the relationships between entities are modeled as a node and edge structure. This operation makes the data no longer just a sequential log, but a highly structured and interconnected network.

[0037] Next, define the node types and relationship types of the graph database, and update the nodes and relationships in the graph database based on the working parameters, equipment status data, product identification and flow data, and test result data to obtain the pressure sensor production graph data. In one embodiment, the node types include product units, batches, processes, equipment, process parameters, test items, and defects. It should be understood that the full production process and full-dimensional coverage can be achieved by setting the node types. Specifically, each pressure sensor is an independent product unit before leaving the factory. All products produced in the same shift, batch, and raw material background belong to a batch; each production line / production process link is defined as a process; each device is a device in actual production; at the same time, parameters that affect the process level and product performance (such as temperature, pressure, duration, flow, etc.) are defined as process parameters; the coverage detection task of product quality is a test item; all unqualified judgments and defect classification results found are set as defects.

[0038] After having the above entity nodes, it is necessary to further define the relationship types between them. The relationship types include belonging to batches, going through processes, using equipment, generating parameters, executing tests, and associated defects. Specifically, for example, product SN0001 on a production line belongs to batch B20240601; the various production and testing processes it undergoes, such as patching, welding, potting, airtightness testing, and finished product marking, are all recorded in the form of nodes during its life cycle, and a process relationship is established between the product unit and the process; each process is equipped with production equipment, such as welding machines, placement machines, curing ovens, etc., which are recorded in the equipment relationship between the process and the equipment; the key process parameters (such as temperature, current, pressure, etc.) collected during the execution of the process are bound to the equipment or process by generating parameter relationships; a certain test process (such as airtightness testing) is linked to the corresponding test item through the execution of the test relationship; if the final product fails to meet the requirements in a certain test, such as leakage, an associated defect relationship is created between the product unit and the defect node, and the failure judgment and abnormality dimension are stored.

[0039] In a specific embodiment, taking a pressure sensor product unit SN10086 as an example, the data storage and graph modeling process for its entire production process is summarized as follows: When the product goes online, it is bound to batch B20240601. After scanning the code, edges are established between the product unit, the batch, and the batch. When the product enters the patch process, the current device ID is detected as SPM-01, and the process and device nodes are recorded to form a process-device relationship. During the patch process, the sensor collects and transmits data including temperature, feed speed, dwell time, and other parameters in real time. All of these parameters are aggregated and stored via the IIoT gateway. Subsequently, whether it is the completion determination of each process, equipment anomalies, process parameter anomalies, or the final test data and results, the product unit is used as the central node. Based on the data content, the various edge relationships with other process, parameter, equipment, test item, defect, and other nodes are automatically mapped, supplemented, and improved.

[0040] Under this production graph structure, each piece of production data collected is not just an isolated numerical value; it forms a full-process network association with the product's entire life cycle, batch parent-child relationships, detailed process paths, equipment operating status, key process parameters, and even final quality performance. For example, if a leak is ultimately discovered in product SN10086 during factory airtightness testing, the product's status at each process and the changes in the equipment's operating parameters can be traced back along the product unit - process - equipment used - parameter generation - test execution - associated defect network structure, quickly converging to the possible links that led to the problem's root cause, enabling multi-dimensional diagnosis and attribution.

[0041] Specifically, in step S3, input quality alerts are obtained. It's understandable that, given highly automated production lines, manual spot checks and periodic analysis alone often make it difficult to promptly detect product defects and potential risks caused by complex processes and rapid mass production. By collecting and analyzing real-time data from each workstation, production process anomalies, such as parameter outages, equipment failures, or unqualified test results, can be immediately detected. Simultaneously, these quality alerts are automatically integrated into the visual management system, making subsequent data mining, correlation analysis, root cause identification, and decision support efficient and accurate.

[0042] In one embodiment, receiving input quality alerts involves: In a pressure sensor production line, each key process and test stage carries a large amount of data reflecting product quality and equipment health. At test stations, such as final inspection, airtightness testing stations, or electrical performance testing stations, automated testing instruments output pass / fail determinations, detailed values, anomaly codes, and specific defect categories for each product unit. An IIoT gateway or host system automatically collects all test data and its metadata, including the product's unique identification code, test time, test items, and device ID, and pushes it to the data platform in real time. Based on these time-sensitive data streams, the data center's backend system sets multi-level thresholds and rules, or applies dynamic anomaly detection mechanisms based on statistical algorithms and machine learning models to automatically review and assess the collected data. A structured quality alert can be automatically generated when, for example, a pressure sensor's airtightness test result fails, leakage values ​​exceed the allowable range, or a parameter fluctuation is detected during production, equipment self-protection or abnormal shutdown occurs, or multiple products in the same batch and at the same station experience anomalies. The quality alarm includes information such as alarm ID, product unit, batch information, production link, abnormal parameters, test time, test items, defect type and severity level, and is pushed to the visual management system through the interface.

[0043] In other embodiments of the present application, quality alerts can also be entered manually or by external quality monitoring agencies. If an operator notices frequent anomalies in a batch of products on the monitoring interface, they can manually fill out an electronic form to submit a quality alert. This manual or external data is then integrated into the production map data system in a compatible and consistent manner with the production line's big data, achieving standardized management of quality alerts across all channels.

[0044] Specifically, in step S4, the candidate set of potential fault unit path data that matches the quality alarm is queried in the pressure sensor production graph data. It should be understood that each quality alarm generated in the production link of the new energy vehicle pressure sensor actually corresponds to a specific product failure, failure or potential failure event. However, these abnormal events are not isolated from a single data or process node, but are often rooted in specific processes, links or batches in the entire product manufacturing chain. These abnormalities may be caused by multiple factors such as raw material defects, parameter drift during the process, hidden failure of key equipment, or insufficient verification after batch process adjustment. Therefore, only by organizing different types of real-time production data in a graph structure with correlation relationships can a multi-dimensional, chain-based, causal systematic traceability of fault events be achieved.

[0045] In one embodiment, Figure 3As shown, querying the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm includes: S41: querying the pressure sensor production map data for a target node that matches the quality alarm; S42: traversing the pressure sensor production map data in reverse from the target node to obtain the candidate set of potential fault unit path data.

[0046] In one embodiment, Figure 4 As shown, in the pressure sensor production graph data query for the target node that matches the quality alarm, the steps include: S411: extracting product units, batches, test items and defects from the quality alarm, and using the product units in the quality alarm as query nodes; S412: querying the pressure sensor production graph data for the target node that matches the query node.

[0047] Specifically, when a quality alert is received, the product unit is first extracted from the alert. Furthermore, the same alert event also includes batch information, connecting the product node to the batch node via a batch edge. Furthermore, because the alert involves detection, rejection, or failure indicators, it also includes test item and defect type information. This information can be used as location attributes for production graph nodes, enhancing the relevance of node selection and path aggregation during subsequent reverse traversal.

[0048] Next, the analysis process is started with the product unit in the pressure sensor production graph as the target node. According to the node relationship of the product life cycle, multiple links such as process, equipment use, parameter generation, and test execution are queried and traversed in reverse step by step. Specifically, in one embodiment, the pressure sensor production graph data is traversed in reverse from the target node to obtain a candidate set of the potential fault unit path data, including: from the target node, the pressure sensor production graph data is traversed in reverse along the relationship between nodes to gradually identify nodes that match the batches, test items, and defects in the quality alarm to obtain a candidate set of the potential fault unit path data. By using this method of multi-path, multi-angle reverse traversal with the target node as the starting point and the node out-degree and in-degree relationships, it is possible to comprehensively capture the process links, equipment parameters, and process distribution related to abnormal products, realize multi-dimensional, link-based, and precise traceability of faults, and greatly improve the accuracy and efficiency of fault root cause investigation.

[0049] In a specific example, taking a pressure sensor failing its airtightness test as an example, SN001122 is identified as a product unit in the pressure sensor production graph. It is found that its batch edge points to Batch20240615, and its process edges sequentially point to welding, assembly, potting, and the final airtightness test. Along this production path, the equipment edge at each stage can be used to locate nodes such as welding machine AQ-05, potting equipment GF-03, and testing equipment TM-02. Parameter edges generated by each process step can be traced to parameter nodes and values ​​at the corresponding moment, such as welding temperature, welding current, equipment load, and motor speed. Furthermore, the test execution edge can be linked to all test type nodes performed, regardless of whether the result was pass or fail. Through reverse tracing and path traversal, all relevant process steps, equipment configuration, real-time process parameter status, execution operator codes, and related information about other products in the same batch are linked.

[0050] Furthermore, considering that the generation of candidate sets of potential fault unit path data cannot rely solely on the data of a single instance, it is necessary to jointly consider similar anomalies under the same batch, the same process, the same equipment or the same process parameters. In the graph data, combined with the test items and defect type attributes of the alarm node, by retrieving all other product unit nodes that are connected to the batch node with the target product unit, and again along the corresponding process, equipment used, and parameter generation edges, check whether there are abnormal reports or parameter drifts in adjacent time periods, when the same equipment or key parameters exceed the limit. Common candidate paths are defined as a set of links with possible common causes of failures. For example, it is found that among the same batch of products, all products that have undergone the potting process GF-03 have multiple unqualified items in the gas leakage of the airtightness test item. Then the potting process GF-03 and production environment parameters (such as silicone temperature, curing time) under this path are included as high-priority paths in the candidate set. Similarly, if the test equipment TM-02 frequently produces substandard products over a period of time, the device node, its usage process, and the parameters at that time are aggregated into the candidate set. This data-driven intelligent troubleshooting can avoid focusing only on a single product incident and quickly focus on potential systemic problems.

[0051] Specifically, in step S5, the candidate set of the potential fault unit path data is aggregated and analyzed for commonalities to obtain the main suspects. It should be understood that in analyzing quality problems in the production process of pressure sensors, traditional methods mostly rely on single-point anomaly analysis or manual tracing, which is difficult to cope with the cross-influence of data from multiple batches, multiple workstations, and multiple devices in large-scale, complex processes and high-concurrency production environments. Since anomalies in actual factory scenarios often have complex causes and scattered manifestations, it is necessary to conduct more scientific and intelligent traceability analysis on large and closely related data sets. Based on this, the present application aggregates and analyzes the candidate set of the potential fault unit path data for commonalities, so as to utilize large-scale, structured production graph data to implement statistical induction and cross-analysis on the retrieved candidate set of potential fault unit paths, and screen out the true root cause of the fault by discovering high-frequency co-occurring nodes and relationships.

[0052] In one embodiment, Figure 5 As shown, the candidate set of the potential fault unit path data is aggregated and analyzed for commonality to obtain the main suspect object, including: S51: counting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain the statistical analysis results of the common nodes and the statistical analysis results of the common relationships; S52: taking the common node with the largest value in the statistical analysis results of the common nodes as the main suspect object; and / or, taking the common relationship with the largest value in the statistical analysis results of the common relationships as the main suspect object.

[0053] Specifically, the candidate path set is based on a quality alert. By traversing the production graph data backward and filtering relevant conditions, we obtain all node-link combinations corresponding to the alert. Each path represents a sequence of nodes and relationships, starting from the abnormal product unit node and continuing upstream through the entire production cycle, passing through all associated processes, key equipment, process parameters, test items, and other physical nodes and process relationships. Together, these paths provide detailed data support and a structured description of all links that may lead to product defects.

[0054] However, it's difficult to identify the systemic common cause of a problem through a single path for a single product. Most anomalies in actual manufacturing sites are localized or sporadic, and isolated paths alone can't reveal global risks. Only when multiple anomaly paths from different products, batches, and time periods overlap and intersect at key nodes or relationships can it indicate a process bottleneck, systemic failure, or a specific process, equipment, or parameter as the primary cause of batch defects. Therefore, aggregation and commonality analysis primarily involves comprehensive statistics on the candidate path set. This involves performing union statistics, quantitative analysis, and intersection screening on the node and relationship sets of all paths. The goal is to identify the most common entity nodes or relationship types within a large sample set. These high-number nodes and relationships, known as shared nodes and relationships, are common. Their large-scale co-occurrence across the entire anomaly traceability chain typically represents a systemic root cause or high-risk area driving the spread of anomalies.

[0055] In a specific embodiment, all candidate paths extracted by reverse traversal are first saved in a structured manner. Each path consists of a series of node-relationship-node-relationship-node link structures. For example, starting from product SN0001, its traceability path can be specifically as follows: product unit node → belongs to batch → batch node → passes through process → potting process node → uses equipment → GF-01 equipment node → generates parameters → temperature parameter node (abnormal) → performs test → airtightness test item node → associated defects → leakage defect node. With the increase of abnormal products, multiple similar but different paths continue to converge on the system backend. The graph calculation engine accumulates the number of nodes in all paths by type (such as equipment, process, parameters, test items, etc.) and relationship type (passes through process, uses equipment, generates parameters, etc.). For each node / relationship, the total number of nodes included or passed through in all candidate paths is recorded, and these repeated nodes and relationships are defined as common nodes and common relationships. The number of each common node or common relationship, that is, the total number of times it is hit in the candidate path, is called the statistical analysis result of the node or the relationship.

[0056] When performing reverse graph traversal from the target node based on the relationship between nodes, the selection of the critical path can be achieved by mining the global correlation of the nodes / relationships in the pressure sensor production graph data. However, when performing quantitative statistics of the nodes-relationships on the critical path, it is also expected to optimize the coverage density of the common nodes / common relationships in the overall graph structure, so as to improve the accuracy of aggregation and commonality analysis.

[0057] Based on this, in another embodiment, the number of common nodes and common relationships in the candidate set of the potential fault unit path data is counted to obtain the statistical analysis results of the common nodes and the statistical analysis results of the common relationships, and also includes: correcting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain the corrected number of common nodes and common relationships, and using the corrected number of common nodes and common relationships as the statistical analysis results of the common nodes and the statistical analysis results of the common relationships.

[0058] Specifically, the number of common nodes and common relationships in the candidate set of the potential fault unit path data is corrected to obtain the corrected number of common nodes and common relationships, including: first, dividing the statistical number of common nodes and common relationships by the number of all nodes and all relationships to obtain a node probability value p1 and a relationship probability value p2, and based on the node probability value and the relationship probability value, obtaining a binary discrete state function representing the node activity and the relationship activity, which is expressed as:

[0059] Φ=(p1,1-p1)

[0060] Ψ=(p2,1-p2)

[0061] Among them, Φ represents the binary discrete state function representing the node activity, and Ψ represents the binary discrete state function representing the relationship activity.

[0062] Next, based on the binary discrete state function representing the node activity and relationship activity, the topology constraint value and the shared node / relationship density value are calculated, which can be expressed as:

[0063] α=||Φ-Ψ||2

[0064]

[0065] Among them, ||·||2 represents the two-norm, α represents the topological constraint value, β1 represents the shared nodes, and β2 represents the relationship density value. The topological constraint value α is used to implement the set probabilistic path selection constraint by calculating the Wasserstein-like distance that reflects the topological activity, while the shared node β1 / relationship density value β2 is used to mark high-density traversal paths through high-density shared node / shared relationship graph coverage.

[0066] Then, based on the topology constraint value and the common node / relationship density value, a number correction coefficient based on active coverage is calculated to obtain a first number correction coefficient and a second number correction coefficient, which are expressed as:

[0067]

[0068] Wherein, ω1 represents the first number correction coefficient, and ω2 represents the second number correction coefficient.

[0069] Finally, the sum of the numbers of shared nodes and shared relationships is corrected using the first number correction coefficient and the second number correction coefficient to obtain a corrected number of shared nodes and shared relationships, which is expressed as:

[0070] n′1=ω1n1

[0071] n′2=ω2n2

[0072] Wherein, n1 represents the number of common nodes, n2 represents the number of common relationships, n'1 represents the number of common nodes after correction, and n'2 represents the number of common relationships after correction.

[0073] That is, the node / relationship activity is used as a dynamic constraint meta-entity in the traversal meta-path to ensure density coverage in the overall transfer distribution of the graph structure, that is, the density aggregation degree of the overall probability space is quantified through probability measurement to achieve dynamic adjustment of statistical characteristics based on commonality analysis, and improve the accuracy of the statistical analysis results of the common nodes and the statistical analysis results of the common relationships.

[0074] Furthermore, in this application, the nodes with the largest number are identified as the main suspects. In a specific embodiment, among the dozens of abnormal product sample paths obtained through analysis, the GF-01 equipment node, the potting process node, and the curing temperature abnormal process parameter node appear repeatedly in most candidate paths, and the actual statistical number is much higher than that of other nodes. These nodes are then considered to be the main suspects that are most likely to be directly related to the current quality abnormality. Similarly, at the relationship analysis level, if the number of hits for relationships such as potting workers and the use of equipment GF-01 is extremely high in most paths, or if a combination of upstream and downstream relationships of several process flows appears repeatedly at a high density, then the corresponding relationship links should also be highly vigilant, and the nodes or chains they point to can be regarded as potential process out-of-control conduction veins.

[0075] In other examples of the present application, not only a simple quantitative statistics is performed on the candidate paths, but also multivariate data such as time period, spatial distribution (such as different workstations, different production lines), and parameter change trends can be integrated to improve the spatiotemporal accuracy of the aggregated commonality analysis. For example, during the production batch B20240620 of a certain pressure sensor, the traceability paths of 10 consecutive abnormal finished products were hit on the potting process node and the GF-01 device node, and the number of abnormal curing temperature parameters collected also increased significantly. Based on such statistics, the number of hits for the potting process, GF-01 equipment, and corresponding parameter nodes will rise sharply in a very short period of time. In this embodiment, it is also possible to combine the actual workshop management strategy and preset judgment rules. For example, if the number of hits for a common node or common relationship accounts for more than 80% of the number of all candidate paths, it will be automatically identified as the main suspect. For more complex scenarios, the number of common relationships appearing in multiple link combinations can also be recursively counted. For example, the link combination of potting process → GF-01 equipment → curing temperature appears frequently in all candidate paths and covers multiple products and batches. It is identified as the main suspect.

[0076] Specifically, in step S6, the main suspect is displayed on the display screen and an early warning prompt signal is triggered. It should be understood that in the complex process of producing pressure sensors for new energy vehicles, the amount of production data is huge and changes extremely rapidly. Condition drift, equipment failure or process out of control in any link may cause large quantities of defective products. Therefore, timely, intuitive and informative feedback of fault analysis results to on-site operators, managers and engineering and technical teams is the key to ensuring overall quality coordination efficiency. Based on this, in this application, the main suspect is displayed on the display screen in a visual manner and an early warning prompt signal is triggered.

[0077] In one embodiment, after the main suspect is identified, it is structured and packaged into a push object together with the relevant node attributes (such as equipment number, process name, batch number, parameter index, defect type, number of anomalies, scope of influence, etc.). Subsequently, this information is integrated into the preset monitoring and analysis module through the production and manufacturing execution system, the factory management information platform or a customized data bus interface. On the display side, a variety of display forms are used, such as large-size real-time monitoring screens, regional management terminals, personal operation and maintenance agent PCs, and even mobile apps or on-site HMI (human-machine interface) terminals. Among them, large screens and distributed terminals will be arranged in production monitoring centers, process research and development offices, and main entrances and exits of factory workshops. The front end uses a visual development framework or professional industrial visualization tools to build a data display interface with multiple viewing dimensions, including entity topology maps, production flow paths, and abnormal statistical bar / heat maps. The information of the main suspects will be directly presented through exclusive sections, pop-ups, alarm lists or highlighted color blocks. For example, the borders of the suspected equipment will be flashing in the equipment hierarchy diagram, the key paths will be bolded in the process flow diagram, and warning signs of abnormal nodes ranked at the top in the statistical table will be displayed, ensuring that the information is prominent and the positioning is intuitive.

[0078] In summary, the method for visual management of the production of new energy vehicle pressure sensors provided by this application realizes real-time collection of working parameters, equipment status, product identification, flow data and test result data by deploying an IIoT gateway in the production line, thereby fully digitizing key information in the production process. At the same time, by constructing pressure sensor production graph data, the production data is mapped to a graph database structure, thereby forming a flexible and efficient data relationship management and analysis mechanism. By utilizing the graph data structure, it is possible to quickly respond to quality alerts, locate potential fault units through path queries and form candidate sets. Based on further aggregation and commonality analysis, the main suspects can be accurately identified, the cause of the fault can be revealed and timely warnings can be issued. In this way, the traditional production management model is transformed into active problem location and process optimization, and production efficiency is significantly improved and the risk of failure is reduced through intelligent methods.

[0079] This application also provides a new energy vehicle pressure sensor production visualization management system, which is used to implement the above-mentioned new energy vehicle pressure sensor production visualization management method, such as Figure 6As shown, the new energy vehicle pressure sensor production visualization management system 600 includes: a data acquisition module 601, which is used to collect working parameters, equipment status data, product identification and flow data, and test result data of key workstations in real time through the IIoT gateway deployed on the pressure sensor production line; a production map data construction module 602, which is used to construct pressure sensor production map data based on working parameters, equipment status data, product identification and flow data, and test result data; a quality alarm acquisition module 603, which is used to obtain input quality alarms; a potential fault unit query module 604, which is used to query the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm; a main suspect object determination module 605, which is used to aggregate and perform commonality analysis on the candidate set of potential fault unit path data to obtain the main suspect object; and an early warning prompt module 606, which is used to display the main suspect object on the display screen and trigger an early warning prompt signal.

[0080] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0081] The flowcharts of the methods involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the flowcharts. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems may be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and may be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and may be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and may be used interchangeably therewith.

[0082] It should also be noted that in the method of the present application, each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0083] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0084] The above description has been provided for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A visual management method for the production of pressure sensors for new energy vehicles, characterized in that: include: The IIoT gateway deployed on the pressure sensor production line collects real-time working parameters of key workstations, equipment status data, product identification and flow data, and test result data; Construct pressure sensor production map data based on operating parameters, equipment status data, product identification and flow data, and test result data; Get quality alerts for input; Querying the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alert; Aggregating and analyzing commonalities of the candidate set of the potential fault unit path data to obtain a main suspect; The main suspect is displayed on the display screen and an early warning signal is triggered.

2. The method for visual management of production of pressure sensors for new energy vehicles according to claim 1, characterized in that: Based on operating parameters, equipment status data, product identification and flow data, and test result data, pressure sensor production map data is constructed, including: Transmitting operating parameters, equipment status data, product identification and flow data, and test result data to a local data center and storing them in a time series database indexed by product ID / batch ID, equipment ID, and parameter name; The node types and relationship types of the graph database are defined, and the nodes and relationships in the graph database are updated according to the working parameters, equipment status data, product identification and flow data, and test result data to obtain the pressure sensor production graph data.

3. The method for visual management of production of pressure sensors for new energy vehicles according to claim 2, characterized in that: The node types include product unit, batch, process, equipment, process parameter, test item and defect; the relationship types include belonging to a batch, passing through a process, using equipment, generating parameters, executing tests and associated defects.

4. The method for visual management of production of pressure sensors for new energy vehicles according to claim 1, characterized in that: Querying the pressure sensor production map data for a candidate set of potential fault unit path data matching the quality alert includes: Querying the pressure sensor production graph data for a target node that matches the quality alert; The pressure sensor production graph data is traversed backward from the target node to obtain a candidate set of the potential fault unit path data.

5. The method for visual management of production of pressure sensors for new energy vehicles according to claim 4, characterized in that: Querying the pressure sensor production graph data for a target node that matches the quality alert includes: Extracting product units, batches, test items, and defects from the quality alert, and using the product units in the quality alert as query nodes; The pressure sensor production graph data is searched for a target node that matches the query node.

6. The method for visual management of production of pressure sensors for new energy vehicles according to claim 5, characterized in that: Traversing the pressure sensor production graph data backward from the target node to obtain a candidate set of the potential fault unit path data includes: The pressure sensor production graph data is traversed backward from the target node along the relationships between nodes to gradually identify nodes matching the batch, test item, and defect in the quality alert to obtain a candidate set of the potential fault unit path data.

7. The method for visual management of production of pressure sensors for new energy vehicles according to claim 1, characterized in that: Aggregate and analyze the commonality of the candidate set of the potential fault unit path data to obtain the main suspects, including: Counting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain statistical analysis results of the common nodes and statistical analysis results of the common relationships; The shared node with the largest value in the statistical analysis results of the shared nodes is selected as the main suspect; and / or the shared relationship with the largest value in the statistical analysis results of the shared relationships is selected as the main suspect.

8. The method for visual management of production of pressure sensors for new energy vehicles according to claim 7, characterized in that: Counting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain statistical analysis results of the common nodes and statistical analysis results of the common relationships, further comprising: The number of common nodes and common relationships in the candidate set of the potential fault unit path data is corrected to obtain the corrected number of common nodes and common relationships, and the corrected number of common nodes and common relationships is used as the statistical analysis result of the common nodes and the statistical analysis result of the common relationships.

9. The method for visual management of production of pressure sensors for new energy vehicles according to claim 8, characterized in that: Correcting the number of common nodes and common relationships in the candidate set of the potential fault unit path data to obtain a corrected number of common nodes and common relationships includes: Divide the statistical number of common nodes and common relationships by the number of all nodes and all relationships to obtain the node probability value and relationship probability value; Based on the node probability value and the relationship probability value, a binary discrete state function representing the node activity and the relationship activity is obtained; Based on the binary discrete state function representing node activity and relationship activity, the topology constraint value and the shared node / relationship density value are calculated; Calculating an active coverage-based number correction coefficient based on the topology constraint value and the common node / the relationship density value to obtain a first number correction coefficient and a second number correction coefficient; The sum of the numbers of common nodes and common relationships is corrected using the first number correction coefficient and the second number correction coefficient to obtain a corrected number of common nodes and common relationships.

10. A new energy vehicle pressure sensor production visualization management system, used to implement the new energy vehicle pressure sensor production visualization management method according to claim 1, characterized in that: include: The data acquisition module is used to collect real-time operating parameters, equipment status data, product identification and flow data, and test results of key workstations through the IIoT gateway deployed on the pressure sensor production line; A production map data construction module is used to construct pressure sensor production map data based on operating parameters, equipment status data, product identification and flow data, and test result data; A quality alarm acquisition module, used to acquire input quality alarms; A potential fault unit query module, configured to query the pressure sensor production map data for a candidate set of potential fault unit path data that matches the quality alarm; A main suspect object determination module is used to aggregate and perform commonality analysis on the candidate set of the potential fault unit path data to obtain the main suspect object; The early warning prompt module is used to display the main suspect on the display screen and trigger an early warning prompt signal.

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