Method and apparatus for tracking quality results throughout an enterprise

Abstract

The application relates to a method and device for tracking enterprise whole-process quality results, which comprises the following steps: generating a process flow transfer diagram and a link quality result panel dual-area cockpit interface; collecting quality data of each process link, obtaining quality result data of each process link through linkage calculation, and storing the quality result data to a specified path; comparing the quality result data corresponding to each process link with a preset quality threshold value, and performing early warning display on the cockpit interface when the quality result data exceeds the preset quality threshold value; in response to the early warning click operation, automatically jumping to a traceability interface to display an abnormal whole-process transmission path and a matching optimization suggestion. Through automatic data collection of a whole-process special terminal, a linkage calculation engine, a dynamic visualization cabin and an abnormal traceability mechanism, the method and device for tracking enterprise whole-process quality results according to the embodiment of the application can effectively solve problems such as cockpit data fragmentation and no linkage, realize automatic control and traceability, and reduce cost and improve efficiency.

Description

Technical Field

[0001] This application relates to the intersection of enterprise management and data visualization technology, and in particular to a method and apparatus for tracking the quality results of an enterprise throughout its entire process. Background Technology

[0002] In current enterprise management, operational dashboards have become a common tool for assisting decision-making. However, their technological applications are mostly limited to the static visualization of single-dimensional indicators such as financial data and production output, failing to achieve deep integration with quality outcome data generated throughout the entire business process, including marketing, product development, procurement, manufacturing, and after-sales service. Existing dashboards have data sources scattered across various business units and lack a unified data interaction and integration mechanism, resulting in the ability to present only partial operational information. They cannot provide operational decision support from a full-process collaborative management perspective, and thus cannot meet the actual needs of enterprises for dynamic control of quality and cost across the entire chain.

[0003] Meanwhile, existing technologies have significant shortcomings in data processing and control capabilities: on the one hand, the quality result data of the entire business process relies on manual export and input across devices, which not only consumes a lot of manpower maintenance costs, but also poses risks of data delays, omissions or input errors, making it difficult to guarantee the real-time performance and accuracy of the data; on the other hand, the quality result data of each process link is stored independently in different devices, lacking cross-link linkage calculation logic and dynamic early warning mechanism. It is impossible to quantify the transmission impact of quality anomalies in a certain link on the overall operating cost of the entire process, nor can it automatically identify anomalies and push early warnings. It requires manual periodic checks, making it difficult for enterprises to discover and resolve quality cost risks in the entire process in a timely manner, which seriously restricts the efficiency and accuracy of enterprise operation and control. Summary of the Invention

[0004] In view of this, this application proposes a method for tracking the quality results throughout the entire enterprise process, including the following steps: Generate a dual-area dashboard interface that includes a process flow diagram and a panel for process quality results. Collect quality data from each process stage and store it in a specified path; Compare the quality result data corresponding to each process step with the preset quality threshold. When the quality result data exceeds the preset quality threshold, an early warning will be displayed on the cockpit interface. Clicking the alert will automatically redirect you to the source tracing interface, which displays the entire transmission path of the anomaly and corresponding optimization suggestions.

[0005] In one possible implementation, collecting quality data from each process stage and storing it to a specified path includes the following steps: Start the process node data monitoring process, scan the dedicated ports 9601-9610, pop up the data acquisition status interface and display the connection status and synchronization progress of each terminal in real time. Each port's corresponding data acquisition terminal automatically collects quality data from each process stage; By loading preset benchmark weights and cross-process correlation weights, the quality data of each process step are linked and calculated to obtain the quality result data of each process step. After receiving the quality result data of each process step, perform data matching and verification. If the verification fails, push a correction prompt. If the verification passes, store the quality result data of each process step in the specified path.

[0006] In one possible implementation, generating a dual-area dashboard interface consisting of a process flow diagram and a stage quality results panel includes the following steps: The stored quality result data of each process step is retrieved to determine the dual-area layout parameters of the cockpit interface; Generate corresponding size process rectangles based on the quality result data size of each step to form a process flow diagram; and display warnings for abnormal nodes whose quality result data exceeds the preset quality threshold by using preset color markings. Independent panels are generated from the quality results data of each process stage to display detailed data and year-on-year / month-on-month change rates. The panels can be configured to expand relationship diagrams and process mode diagrams to represent the process quality results panel.

[0007] In one possible implementation, comparing the quality result data corresponding to each process step with a preset quality threshold, and displaying a warning on the cockpit interface when the quality result data exceeds the preset quality threshold, includes the following steps: The system automatically retrieves the latest stored quality result data every 5 minutes and synchronously updates the node dimensions of the process flow diagram and the process quality result panel data on the cockpit interface. The quality result data corresponding to each process step is compared with the preset quality threshold one by one, and abnormal nodes whose quality result data exceeds the quality threshold are marked. Warning pop-ups appear at abnormal nodes in each process step on the cockpit interface, indicating the abnormality type, abnormal value, and the upstream and downstream links affected. At the same time, the warning information is pushed to the terminal of the management personnel.

[0008] In one possible implementation, responding to a warning click automatically redirects to the source tracing interface, displaying the entire abnormality propagation path and corresponding optimization suggestions, including the following steps: Upon detecting a click on the warning pop-up, the system automatically triggers a command to retrieve data from the source tracing interface, loading the full-process data associated with the abnormal node. The system generates a full-process transmission path diagram of the anomaly on the source tracing interface, showing the transmission links and the associated impact values ​​of each link. Based on the anomaly type and transmission path, it matches and pushes corresponding optimization suggestions from the preset suggestion library, and also sets a confirmation option for the processed status on the interface.

[0009] In one possible implementation, the data acquisition terminal corresponding to each port automatically collects quality data for each process stage, including the following steps: By sending data acquisition commands to dedicated ports 9601-9610 respectively, with the commands carrying the target data fields of the corresponding process steps, the command response signals of each port are obtained. Based on the command response signal, the data acquisition terminal corresponding to each port automatically reads the real-time business data of the relevant process link, filters out the information that matches the target data field, and generates an initial quality data file; The data acquisition terminal encapsulates the initial quality data file into data packets using the TCP / IP protocol. The cockpit main computer receives the data packets and parses them to obtain the quality data for each process stage.

[0010] This application also provides a device for tracking the quality results of an enterprise's entire process, comprising: The process data acquisition module consists of dedicated data acquisition terminals deployed in 10 business process stages of the enterprise. Each terminal is equipped with a dedicated communication port corresponding to 9601-9610, which is used to automatically collect quality result data of each stage. The cockpit main computing module is equipped with a high-speed computing module, a visualization rendering module, and a built-in full-process quality result linkage calculation engine, which is used to perform data verification, storage, linkage calculation and interface rendering. The visualization module is a multi-screen terminal deployed in the operation and control center, used to receive data from the main computing module of the cockpit and display the dual-area cockpit interface, anomaly warning and traceability interface; The early warning push module communicates with the main computing module in the cockpit and is used to push abnormal early warning information to the terminal of the operation and management personnel, and supports feedback on the processed status.

[0011] In one possible implementation, each dedicated data acquisition terminal in the process data acquisition module has a built-in port binding unit and a data filtering unit. The port binding unit is used to bind the terminal to a unique dedicated port among 9601-9610, and only receive collection instructions for the corresponding process step; The data filtering unit is used to perform preliminary screening of the collected quality result data, automatically remove invalid data with values ​​<0, and push valid data to the cockpit main computing module.

[0012] In one possible implementation, the cockpit main computing module includes a data storage unit and a verification unit: The data storage unit has a pre-defined storage path for storing verified quality result data according to the business cycle. The verification unit is used to perform data and process matching verification, comparing whether the data fields are consistent with the preset fields of the process to which they belong. When the verification fails, it generates and pushes a data correction prompt to the corresponding acquisition terminal.

[0013] In one possible implementation, the visualization module includes an interface control unit and an interactive response unit: The interface control unit is used to trigger data refresh commands every 5 minutes to synchronously update the node size and panel data of the process flow diagram, and also supports receiving manual real-time refresh commands. The interactive response unit is used to detect click operations on the warning pop-up, trigger the retrieval command in the tracing interface, and also supports receiving confirmation operations of the processed status in the tracing interface and feeding back to the cockpit main computing unit.

[0014] The beneficial effects of this invention are: By deploying dedicated data acquisition terminals and port monitoring mechanisms at every node of the enterprise's business processes, automatic collection, matching, verification, and categorized storage of quality data are achieved. This completely solves the data delay and omission problems caused by traditional manual cross-device data entry, significantly reducing manual maintenance costs. Simultaneously, it ensures accurate correspondence between data and business processes, providing a high-quality data foundation for subsequent quality analysis. Utilizing a dual-area visualization interface design of process flow diagrams and process quality result panels, combined with dynamic refresh functionality, scattered quality result data is transformed into an intuitive and real-time operational view. This not only breaks through the limitations of traditional static dashboard displays but also allows managers to quickly grasp the quality status of each process and cross-process relationships, providing clear data support for business decisions. Through automatic anomaly detection using preset thresholds, pop-up warnings, and linkage with the anomaly tracing interface displaying the transmission path and optimization suggestions, a closed-loop management mechanism from anomaly identification to problem resolution is constructed. This effectively compensates for the shortcomings of traditional models, such as lack of risk warnings and difficulty in anomaly tracing, helping enterprises to promptly intercept quality risks, reduce overall operational cost losses, and improve overall operational control efficiency and accuracy. Based on the technological innovations of this application, the fully automated data collection mechanism, the process-oriented dynamic visualization interface design, and the anomaly warning and traceability closed-loop system can effectively solve multiple problems in data integration, function display, and risk control of existing enterprise management dashboards. This enables enterprises to shift from monitoring single-dimensional indicators to dynamic control of full-process quality results, significantly improving the scientific nature and timeliness of enterprise business decisions, reducing the impact of quality anomalies on operating costs, and enhancing the comprehensive competitiveness of enterprises in a dynamic market environment.

[0015] Other features and aspects of this application will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] The accompanying drawings, which are included in and form part of this specification, illustrate exemplary embodiments, features, and aspects of this application together with the specification and serve to explain the principles of this application.

[0017] Figure 1 A flowchart illustrating a method for tracking enterprise-wide quality results according to an embodiment of this application; Figure 2 A block diagram illustrating an apparatus for tracking enterprise-wide quality results according to an embodiment of this application; Detailed Implementation Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0018] It should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application or to simplify the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0020] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0021] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0022] The method and apparatus for tracking enterprise full-process quality results in this application is a system solution based on dedicated port data acquisition, cross-link linkage calculation and dynamic visualization technology. It realizes automated acquisition of quality results of the entire business process of an enterprise, anomaly early warning and closed-loop management of source tracing. It is applied in the field of enterprise operation and management and data visualization technology intersection. It plays a role in solving the pain points of traditional enterprise operation dashboard data fragmentation, single visualization function and lack of risk early warning. It provides enterprises with full-chain operation and management support from accurate acquisition of full-process data to dynamic visualization of quality status, and then to rapid source tracing and optimization of anomalies. It helps enterprises reduce manual data maintenance costs, improve quality risk response efficiency and enhance the scientific nature of business decision-making.

[0023] The specific implementation of this application is referred to Figures 1-2 ,like Figure 1 The image shows a specific embodiment of the enterprise whole-process quality result tracking method of this application. The enterprise whole-process quality result tracking method includes: 100. Generate a dual-area dashboard interface that includes a process flow diagram and a process quality result panel.

[0024] Specifically, step 100 involves the computer activating the visualization rendering module to construct a dashboard interface based on data calculated by the full-process quality result linkage calculation engine. The interface employs a dual-area layout: a process flow diagram and a stage quality result panel. The process flow diagram area uses blue arrows to represent the complete process chain of marketing, product development, process development, production planning, procurement, raw material warehousing and logistics, quality inspection, manufacturing, logistics warehousing and delivery, and after-sales service. Each process stage node is designed as a rectangle of different sizes based on its corresponding quality result value. When the quality result value of a stage is greater than or equal to a preset threshold, that stage node is highlighted in red. The stage quality result panel area provides an independent panel for each process stage. Each panel displays detailed quality result data for the corresponding stage, as well as year-on-year and month-on-month change rates. Clicking on a panel expands a relationship diagram and process mode diagram to show the impact of that stage on the quality results of upstream and downstream stages. Simultaneously, the computer is configured to automatically refresh the interface data every 5 minutes and also supports manual triggering of real-time refresh by staff. With its dual-zone layout, staff can intuitively grasp the overall flow of the company's entire business process and the details of the quality results at each stage. Different sized process nodes and highlighted abnormal nodes quickly attract attention, helping staff identify key and abnormal steps in the process immediately. The combination of automatic and manual refresh ensures both the timeliness of the interface data and meets the staff's need to quickly access the latest data in special circumstances, providing clear, real-time visualized data support for enterprise operation and management, and improving decision-making efficiency.

[0025] 200. Collect quality data from each process stage and store it in the specified path.

[0026] Specifically, step 200 involves the computer initiating a process node data monitoring process. Simultaneously, it scans dedicated communication ports 9601-9610, corresponding to 10 process stages: marketing, product development, process development, production planning, procurement, raw material warehousing and logistics, quality inspection, manufacturing, logistics warehousing and delivery, and after-sales service. The system displays the connection status (online / offline) and data synchronization progress of the data acquisition terminals for each stage in real time. Each process stage's data acquisition terminal automatically collects relevant data on the quality results of that stage, such as rework hours and scrap costs in the product development stage, and customer complaint compensation costs and after-sales repair hours in the after-sales service stage. After receiving the data, the computer transmits it to the built-in end-to-end quality result linkage calculation engine. The engine combines preset benchmark weights and cross-process correlation weights to perform linkage calculations, deriving quality result data for each process stage. Then, it automatically performs a "data-stage" matching check to verify whether data fields correspond to their respective process stages. If the check fails, a data field correction prompt is pushed to the corresponding terminal. If the check passes, the quality result data for each process stage is categorized and stored in the path / data / cockpit / [operating cycle] / stage data / . This step achieves automatic collection of quality data for each process stage, eliminating the need for manual cross-system data export and entry, effectively avoiding data delays and omissions, and reducing manual data maintenance costs. The "data-stage" matching check mechanism ensures the accuracy and validity of the collected data. The linkage calculation engine, combined with reasonable weight settings, can scientifically quantify the quality results of each stage. Categorized storage in designated paths facilitates data management, querying, and subsequent retrieval, providing a reliable data foundation for enterprise end-to-end quality result tracking and business decision-making.

[0027] 300. Compare the quality result data corresponding to each process step with the preset quality threshold. When the quality result data exceeds the preset quality threshold, a warning will be displayed on the cockpit interface.

[0028] Specifically, step 300 involves the computer retrieving quality result data for each process stage from / data / cockpit / [operating cycle] / stage data / path and automatically comparing it with preset quality thresholds. When a stage's quality result data exceeds the preset threshold, an anomaly warning pop-up immediately appears at the corresponding stage node in the process flow diagram area of ​​the dashboard interface. The pop-up indicates the anomaly type, the anomaly value, and the upstream and downstream stages affected by the anomaly. Simultaneously, the warning information is pushed to the terminals of operational management personnel. In the stage quality result panel area of ​​the dashboard, the corresponding stage's data is also highlighted in red to further remind staff to pay attention to the anomaly. This automatic comparison and warning mechanism replaces regular manual checks, enabling rapid and accurate identification of cost anomalies in each process stage and timely notification of staff, preventing the anomaly from escalating and causing greater losses to the company's operations. The dual reminder method of pop-up windows and red highlighting ensures that staff do not ignore anomaly information, helping them to respond quickly and take measures to resolve the problem.

[0029] 400. Upon responding to the alert and clicking the button, the system will automatically redirect to the source tracing interface, displaying the entire transmission path of the anomaly and corresponding optimization suggestions.

[0030] Specifically, step 400 involves the computer automatically responding to an anomaly warning pop-up on the dashboard interface by automatically redirecting the user to the anomaly tracing interface when a staff member clicks on the pop-up. In this interface, the computer uses data correlation analysis to display the entire transmission path of the anomaly and marks the associated impact values ​​of each stage. Simultaneously, based on the anomaly type and the company's historical handling experience, targeted optimization suggestions are pushed to the tracing interface. After handling the anomaly, the staff member can confirm the "handled" status on the interface. Upon confirmation, the anomaly warning pop-up turns gray on the dashboard interface to distinguish between handled and unhandled anomalies. This one-click redirection to the tracing interface eliminates the tedious process of manually querying and tracing the anomaly transmission path, significantly improving anomaly tracing efficiency. The clearly displayed transmission path and associated impact values ​​help staff fully understand the scope and extent of the anomaly's impact on the company's entire operational process, facilitating the development of scientific and effective solutions. The pushed optimization suggestions provide practical references for staff, helping them to quickly and properly handle anomalies, while the gray-marked handled anomalies facilitate tracking and management of the anomaly handling status, enhancing the company's ability to control operational anomalies.

[0031] Furthermore, such as Figure 1As shown, the enterprise's full-process quality result tracking method is a sequential, closed-loop process: First, step 100 is executed, generating a dual-area dashboard interface consisting of a process flow diagram and a stage quality result panel, providing a visual platform for full-process quality monitoring. Next, step 200 is executed, collecting quality data from each process stage, calculating the quality results data for each stage through linkage, and storing it in a designated path to provide reliable data support for subsequent analysis. Then, step 300 is executed, comparing the quality result data corresponding to each process stage with preset quality thresholds. When the quality result data exceeds the preset quality threshold, an alert is displayed on the dashboard interface, enabling rapid identification and notification of anomalies. Finally, step 400 is executed, responding to the alert click operation, automatically redirecting to the source tracing interface, displaying the entire process transmission path of the anomaly and corresponding optimization suggestions, helping to quickly locate the root cause of the problem and formulate solutions, forming a closed-loop management process of visualization, data collection and calculation, anomaly alerts, and source tracing optimization.

[0032] In one possible implementation, the process of collecting quality data from each process stage, performing linked calculations to obtain quality result data for each process stage, and storing it in a designated path includes the following steps: starting the process node data monitoring process, scanning dedicated ports 9601-9610, popping up the data collection status interface and displaying the connection status and synchronization progress of each terminal in real time, the data collection terminal corresponding to each port automatically collecting quality data from each process stage, performing linked calculations on the quality data of each process stage by loading preset stage benchmark weights and cross-stage association weights to obtain quality result data for each process stage, performing data and stage matching verification after receiving the quality result data of each process stage, pushing a correction prompt if the verification fails, and storing the quality result data of each process stage in a categorized manner in a designated path if the verification passes.

[0033] Specifically, the automatic data collection phase for quality results throughout the entire process: The computer starts the "process node data monitoring process" and scans ports 9601-9610. The process data acquisition status interface pops up, displaying the connection status (online / offline) and data synchronization progress of the data acquisition terminals at each stage in real time.

[0034] Each process data acquisition terminal automatically collects relevant data on the quality results of the corresponding process.

[0035] Marketing process (port 9601): Collect contract and order deviation data.

[0036] Product development phase (port 9602): Collect rework time and scrap cost.

[0037] Manufacturing process (port 9608): Collect production defect rework costs and material scrap value.

[0038] After-sales service (port 9610): Collect customer complaint compensation costs and after-sales repair man-hours.

[0039] After receiving the data, the computer automatically performs a "data-process" matching check (such as whether the data field corresponds to the process process). If the check fails, a "data field correction prompt" is pushed to the corresponding terminal. If the check passes, the data is categorized and stored in the path / data / cockpit / [operating cycle] / process data / .

[0040] In one possible implementation, generating a dual-area dashboard interface consisting of a process flow diagram and a process quality result panel includes the following steps: retrieving stored quality result data for each process stage; determining the dual-area layout parameters of the dashboard interface; generating corresponding size process rectangles based on the size of each process quality result data to form a process flow diagram; and marking abnormal nodes with preset colors for early warning display when the quality result data exceeds a preset quality threshold; generating independent panels from the quality result data of each process stage to display detailed data and year-on-year / month-on-month change rates; and configuring the panel to expand into a relationship diagram and process mode diagram when clicked to represent the process quality result panel.

[0041] In one possible implementation, comparing the quality result data corresponding to each process step with a preset quality threshold, and displaying an early warning on the cockpit interface when the quality result data exceeds the preset quality threshold, includes the following steps: automatically retrieving the latest stored quality result data every 5 minutes, synchronously updating the node size of the process flow diagram and the process quality result panel data on the cockpit interface, comparing the quality result data corresponding to each process step with the preset quality threshold one by one, marking abnormal nodes where the quality result data exceeds the quality threshold, popping up an early warning pop-up window on the corresponding abnormal node of each process step on the cockpit interface, marking the abnormality type, abnormal value and the affected upstream and downstream links, and simultaneously pushing the early warning information to the terminal of the management personnel.

[0042] Specifically, the dynamic visualization phase of the cockpit interface: The computer starts the "visual rendering module" and generates a full-process operation dashboard interface based on the calculation results. The interface is laid out in two areas: "process flow diagram + process quality result panel".

[0043] a. Flowchart area: The blue arrows show the process link of "Marketing → Product Development → ... → After-sales Service". Each node is displayed as a rectangle of different sizes according to the quality result value (the larger the value, the larger the size). Abnormal nodes (quality result value ≥ threshold) are displayed in red.

[0044] b. Process Quality Results Panel Area: Independent panels for each process stage. Each panel displays detailed data on the quality results of that stage, as well as year-on-year / month-on-month change rates. Clicking on a panel will expand to include a "Relationship Diagram" and a "Process Pattern Diagram" (showing the impact of that stage on the quality results of upstream and downstream stages).

[0045] The computer automatically refreshes the interface data every 5 minutes, and supports manual triggering of "real-time refresh" to ensure that the content displayed in the cockpit is synchronized with the latest data.

[0046] In one possible implementation, responding to a click on an alert automatically redirects to the source tracing interface, displaying the entire transmission path of the anomaly and corresponding optimization suggestions. This includes the following steps: Upon detecting a click on the alert pop-up, the source tracing interface is automatically triggered to retrieve the entire process data associated with the anomaly node, generating a full-process transmission path diagram of the anomaly on the source tracing interface, showing the transmission links and the associated impact values ​​of each stage; based on the anomaly type and transmission path, corresponding optimization suggestions are matched and pushed from a preset suggestion library, while a confirmation option for the processed status is set on the interface.

[0047] Specifically, the anomaly tracing and early warning stage: The computer compares the quality results of each stage with preset thresholds (e.g., the quality result of a single stage is ≥ 120% of the historical average of that stage). If an anomaly is detected, an anomaly warning pop-up window immediately appears on the corresponding stage node of the cockpit interface, indicating the anomaly type (e.g., "abnormal rework cost in manufacturing stage"), the anomaly value, and the upstream and downstream stages affected.

[0048] Clicking the warning pop-up will automatically redirect the computer to the anomaly tracing interface, displaying the entire transmission path of the anomaly (such as "manufacturing defect → logistics resend → after-sales complaint"), the associated impact value of each link, and pushing "optimization suggestions".

[0049] The warning information is simultaneously pushed to the terminal of the operation and management personnel, and the "processed" status can be confirmed through the interface. After confirmation, the pop-up window turns into a gray mark.

[0050] The specific key data items, data sources, and computer execution rules are shown in Table 1: Table 1 Execution Rules In one possible implementation, the data acquisition terminal corresponding to each port automatically collects quality data for each process stage, including the following steps: sending data acquisition commands to dedicated ports 9601-9610, with the commands carrying the target data fields for the corresponding process stage, obtaining command response signals from each port, and the data acquisition terminal corresponding to each port automatically reading the real-time business data of its respective process stage based on the command response signals, filtering out information that matches the target data fields, generating an initial quality data file, and the data acquisition terminal encapsulating the initial quality data file into data packets using the TCP / IP protocol. After receiving the data packets, the main computer in the cockpit parses them to obtain the quality data for each process stage.

[0051] This application also provides a device 100 for tracking the quality results of an enterprise's entire process, comprising: The process data acquisition module 110 consists of dedicated data acquisition terminals deployed in 10 business process stages of the enterprise. Each terminal is equipped with a dedicated communication port corresponding to 9601-9610 for automatically collecting quality result data of each stage.

[0052] Specifically, the process data acquisition module 110 is the foundation for enterprise quality data acquisition, consisting of dedicated data acquisition terminals deployed in 10 key business process stages. These terminals act like sensitive tentacles distributed along the enterprise's operational network. Each terminal is precisely configured with a dedicated communication port from 9601 to 9610, thus achieving a close correspondence with a specific process stage. For example, in the product development stage, the corresponding terminal automatically collects quality result data such as development cycle, number of design changes, and percentage of new technology applications through port 9602. In the manufacturing stage, the terminal using port 9608 collects data such as product pass rate, number of defective products, and operating parameters of production equipment. Each terminal only receives acquisition commands from the corresponding port-related stage, effectively avoiding data collection chaos and ensuring that the collected data is accurate and targeted, providing a reliable data source for subsequent in-depth analysis of the quality status of each process stage.

[0053] The cockpit main computing module 120 is equipped with a high-speed computing module, a visualization rendering module, and a built-in full-process quality result linkage calculation engine, which is used to perform data verification, storage, linkage calculation, and interface rendering.

[0054] Specifically, the main computing module 120 of the cockpit serves as the core computing and processing hub of the entire device. It integrates a high-speed computing module, a visualization rendering module, and a built-in full-process quality result linkage calculation engine. When the process data acquisition module 110 transmits quality data from each stage, the high-speed computing module quickly verifies the data, comparing the data fields with the preset fields of the respective process stages. If inconsistencies are found, a data correction prompt is generated and pushed to the corresponding acquisition terminal. Verified data is stored in the preset designated path of the data storage unit 121, categorized according to the business cycle for easy retrieval and querying later. Simultaneously, the full-process quality result linkage calculation engine performs complex calculations on the quality data of each stage based on the stage benchmark weight and cross-stage correlation weight, deriving the linkage result. Finally, the visualization rendering module combines the raw data and the linkage calculation result, generating a dual-area cockpit interface for display according to predetermined rules. This realizes a series of key processing flows from raw data acquisition to visual presentation, providing intuitive and comprehensive data visualization support for enterprise business decisions.

[0055] The visualization module 130 is a multi-screen terminal deployed in the operation and control center. It is used to receive data from the cockpit main computing module 120 and display the dual-area cockpit interface, anomaly warning and traceability interface.

[0056] Specifically, the visualization module 130 is deployed in the enterprise's operation and control center, presented in the form of a multi-screen terminal. Its main task is to receive data processed by the main computing module 120 in the cockpit and transform it into intuitive visual content for managers to view and analyze. During daily operation, the interface control unit 131 triggers a data refresh command at a fixed interval of 5 minutes, synchronously updating the node dimensions and process panel data in the flow chart to ensure that the displayed information is consistent with the enterprise's real-time quality status. Simultaneously, the visualization module 130 also supports managers manually issuing real-time refresh commands, flexibly meeting the need for the latest data in different scenarios. When the main computing module 120 detects a quality anomaly and pushes an early warning, the visualization module 130 clearly displays the anomaly warning content on the interface. If a manager clicks on the warning pop-up, the interaction response unit 132 quickly detects this operation, triggering a retrieval command for the traceability interface, displaying the entire anomaly transmission path and corresponding optimization suggestions. It also supports receiving confirmation from managers on the traceability interface regarding the anomaly's handling status and promptly transmitting this feedback to the main computing unit in the cockpit, forming a complete information interaction loop and helping the enterprise efficiently resolve quality issues.

[0057] The early warning push module 140 is communicatively connected to the cockpit main computing module 120 and is used to push abnormal early warning information to the terminal of the operation and management personnel, and supports feedback on the processed status.

[0058] Specifically, the early warning push module 140 plays a crucial information transmission role in the entire quality result tracking device, maintaining a close communication connection with the main computing module 120 in the cockpit. When the main computing module 120 detects abnormal data during the comparison of quality results with preset thresholds, the early warning push module 140 immediately activates. It accurately pushes abnormal warning information, including the type of abnormality, the abnormal value, and key information such as potentially affected upstream and downstream links, to the terminal devices of management personnel, such as mobile phones, tablets, or office computers, ensuring that relevant personnel can obtain quality abnormality information as soon as possible and take timely countermeasures. Furthermore, the early warning push module 140 also supports receiving feedback from management personnel on the status of abnormality handling. After the manager handles the abnormal situation, they can provide feedback on the handling status through the terminal operation. The early warning push module 140 then sends this information back to the main computing module 120 in the cockpit to update the warning status, realizing a complete closed-loop management from abnormality detection, early warning push, problem handling to status feedback, effectively improving the enterprise's ability to control quality risks.

[0059] Furthermore, based on the same inventive concept, this application also provides a device 100 for tracking the quality results of an enterprise's entire process. For example... Figure 2 As shown, the enterprise-wide quality result tracking device consists of a process data acquisition module 110, a main cockpit computing module 120, a visualization module 130, and an early warning push module 140. The process data acquisition module 110 incorporates a port binding unit 111 and a data filtering unit 112. By configuring dedicated acquisition terminals for 10 business process stages with corresponding ports 9601-9610, it achieves accurate acquisition of quality data for each stage, port binding, and filtering of invalid data. The main cockpit computing module 120 includes a data storage unit 121 and a verification unit 122, undertaking the core processing tasks of data verification, classified storage, linked calculation, and interface rendering. The visualization module 130, relying on the interface control unit 131 and the interactive response unit 132, displays a dual-area cockpit interface, anomaly warning, and source tracing interface through a multi-screen terminal in the business control center, and supports data refresh and warning click interaction. The early warning push module 140 communicates with the cockpit main computing module 120, and is responsible for pushing abnormal information to the terminal of the management personnel and receiving feedback on the processing status. All modules and units work together to realize the collection, processing, display and risk control of the quality results of the entire enterprise process.

[0060] Specifically, the process data acquisition nodes are as follows: Data acquisition terminals are deployed at each stage of the enterprise's business processes (marketing, product development, process development, production planning, procurement, raw material warehousing and logistics, quality inspection, manufacturing, logistics warehousing and delivery, and after-sales service). Each terminal is equipped with a dedicated communication port (port numbers: 9601-9610, corresponding to 10 stages) to establish real-time communication with the main computer in the cockpit via the TCP / IP protocol. The main computer in the cockpit is equipped with a high-speed computing unit and a visualization rendering module, and has a built-in engine for linking and calculating the quality results of the entire process. The data storage path is uniformly set to / data / cockpit / [operating cycle] / (the operating cycle is named in the format of "year-month"). The visualization terminal in the cockpit is deployed in the enterprise's business control center, supports multi-screen linkage display, and receives visualization interface data pushed by the main computer.

[0061] In one possible implementation, each dedicated data acquisition terminal in the process data acquisition module 110 has a built-in port binding unit 111 and a data filtering unit 112.

[0062] The port binding unit 111 is used to bind the terminal to the only dedicated port among 9601-9610, and only receive the collection instructions of the corresponding process link.

[0063] The data filtering unit 112 is used to perform preliminary screening of the collected quality result data, automatically remove invalid data with values ​​<0, and push valid data to the cockpit main computing module 120.

[0064] In one possible implementation, the cockpit main computing module 120 includes a data storage unit 121 and a verification unit 122.

[0065] The data storage unit 121 has a preset specified storage path for storing the verified quality result data according to the business cycle.

[0066] The verification unit 122 is used to perform data and process matching verification, comparing whether the data fields are consistent with the preset fields of the process to which they belong. When the verification fails, it generates and pushes a data correction prompt to the corresponding acquisition terminal.

[0067] In one possible implementation, the visualization module 130 includes an interface control unit 131 and an interactive response unit 132.

[0068] The interface control unit 131 is used to trigger a data refresh command every 5 minutes to synchronously update the node size and panel data of the process flow diagram, and also supports receiving manual real-time refresh commands.

[0069] The interactive response unit 132 is used to detect the click operation of the warning pop-up, trigger the retrieval command of the tracing interface, and at the same time support receiving the processing status confirmation operation in the tracing interface and feeding it back to the cockpit main computing unit.

[0070] Specifically, this application provides a method and apparatus for tracking the quality results of an enterprise throughout the entire process. By deploying data acquisition units at all business process nodes using a computer, constructing a quality result linkage calculation engine, and developing a process-oriented visual dashboard interface, the full-process automation of "automatic data acquisition throughout the entire process - linkage calculation of quality results across links - dynamic visualization of the dashboard - anomaly tracing and early warning" is achieved.

[0071] The method and apparatus for tracking the quality results of the entire enterprise process in this application can automatically collect quality data of each process by deploying and configuring collection terminals with dedicated communication ports corresponding to 9601-9610 in 10 business process links such as enterprise marketing, product development, manufacturing, and after-sales service, starting the data monitoring process to scan the ports, and storing the data in a specified path after matching and verifying the data with the process. The main computing module in the cockpit then calls the stored data and the calculation results from the built-in linkage calculation engine to activate the visualization rendering module, generating a dual-area interface of "process flow diagram and process quality result panel," which is presented through a multi-screen terminal of the visualization display module. At the same time, the interface data is automatically refreshed at 5-minute intervals, comparing the quality results with preset thresholds. Once an anomaly is detected, the anomaly information is marked in a pop-up window on the interface and pushed to the terminal of the management personnel through the early warning push module. When an early warning click operation is detected, the anomaly-related data is automatically retrieved to generate a traceability interface, displaying the entire process transmission path and matching optimization suggestions. It also supports the confirmation feedback of the anomaly handling status, realizing the automated collection and accurate storage of enterprise full-process quality data, dynamic visualization of quality status, real-time early warning of anomalies and rapid traceability closed-loop management. It effectively solves the problems of high maintenance costs, data fragmentation, single visualization function and lack of risk warning in traditional manual data, providing full-process data support for enterprise business decision-making, reducing the impact of quality anomalies on operating costs, and improving the overall efficiency and accuracy of business management.

[0072] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for tracking the quality results throughout the entire process of an enterprise, characterized in that, Includes the following steps: Generate a dual-area dashboard interface that includes a process flow diagram and a panel for process quality results. Collect quality data from each process stage and store it in a specified path; Compare the quality result data corresponding to each process step with the preset quality threshold. When the quality result data exceeds the preset quality threshold, an early warning will be displayed on the cockpit interface. Clicking the alert will automatically redirect you to the source tracing interface, which displays the entire transmission path of the anomaly and corresponding optimization suggestions.

2. The method for tracking enterprise-wide quality results according to claim 1, characterized in that, The process of collecting quality data from each stage of the process and storing it to a designated path includes the following steps: Start the process node data monitoring process, scan the dedicated ports 9601-9610, pop up the data acquisition status interface and display the connection status and synchronization progress of each terminal in real time. Each port's corresponding data acquisition terminal automatically collects quality data from each process stage; By loading preset benchmark weights and cross-process correlation weights, the quality data of each process step are linked and calculated to obtain the quality result data of each process step. After receiving the quality result data of each process step, perform data matching and verification. If the verification fails, push a correction prompt. If the verification passes, store the quality result data of each process step in the specified path.

3. The method for tracking enterprise-wide quality results according to claim 2, characterized in that, The dual-area cockpit interface for generating the process flow diagram and the stage quality result panel includes the following steps: The stored quality result data of each process step is retrieved to determine the dual-area layout parameters of the cockpit interface; Generate corresponding size process rectangles based on the quality result data size of each step to form a process flow diagram; and display warnings for abnormal nodes whose quality result data exceeds the preset quality threshold by using preset color markings. Independent panels are generated from the quality results data of each process stage to display detailed data and year-on-year / month-on-month change rates. The panels can be configured to expand relationship diagrams and process mode diagrams to represent the process quality results panel.

4. The method for tracking enterprise-wide quality results according to claim 3, characterized in that, The process of comparing the quality result data corresponding to each process step with the preset quality threshold, and displaying a warning on the cockpit interface when the quality result data exceeds the preset quality threshold, includes the following steps: The system automatically retrieves the latest stored quality result data every 5 minutes and synchronously updates the node dimensions of the process flow diagram and the process quality result panel data on the cockpit interface. The quality result data corresponding to each process step is compared with the preset quality threshold one by one, and abnormal nodes whose quality result data exceeds the quality threshold are marked. Warning pop-ups appear at abnormal nodes in each process step on the cockpit interface, indicating the abnormality type, abnormal value, and the upstream and downstream links affected. At the same time, the warning information is pushed to the terminal of the management personnel.

5. The method for tracking enterprise-wide quality results according to claim 4, characterized in that, The aforementioned click action to respond to the warning will automatically redirect to the source tracing interface, which will display the entire abnormal transmission path and corresponding optimization suggestions, including the following steps: Upon detecting a click on the warning pop-up, the system automatically triggers a command to retrieve data from the source tracing interface, loading the full-process data associated with the abnormal node. The system generates a full-process anomaly propagation path diagram on the source tracing interface, showing the propagation links and the associated impact values ​​of each stage. Based on the anomaly type and propagation path, it matches and pushes corresponding optimization suggestions from a pre-set suggestion library. At the same time, a confirmation option for the processed status is set in the interface.

6. The method for tracking enterprise-wide quality results according to claim 2, characterized in that, The data acquisition terminal corresponding to each port automatically collects quality data for each process step, including the following steps: By sending data acquisition commands to dedicated ports 9601-9610 respectively, with the commands carrying the target data fields of the corresponding process steps, the command response signals of each port are obtained. Based on the command response signal, the data acquisition terminal corresponding to each port automatically reads the real-time business data of the relevant process link, filters out the information that matches the target data field, and generates an initial quality data file; The data acquisition terminal encapsulates the initial quality data file into data packets using the TCP / IP protocol. The cockpit main computer receives the data packets and parses them to obtain the quality data for each process stage.

7. A device for tracking the quality results of an enterprise throughout its entire process, characterized in that, include: The process data acquisition module consists of dedicated data acquisition terminals deployed in 10 business process stages of the enterprise. Each terminal is equipped with a dedicated communication port corresponding to 9601-9610, which is used to automatically collect quality result data of each stage. The cockpit main computing module is equipped with a high-speed computing module, a visualization rendering module, and a built-in full-process quality result linkage calculation engine, which is used to perform data verification, storage, linkage calculation and interface rendering. The visualization module is a multi-screen terminal deployed in the operation and control center, used to receive data from the main computing module of the cockpit and display the dual-area cockpit interface, anomaly warning and traceability interface; The early warning push module communicates with the main computing module in the cockpit and is used to push abnormal early warning information to the terminal of the operation and management personnel, and supports feedback on the processed status.

8. The device for tracking enterprise-wide quality results according to claim 7, characterized in that, Each dedicated data acquisition terminal in the process data acquisition module has a built-in port binding unit and a data filtering unit; The port binding unit is used to bind the terminal to a unique dedicated port among 9601-9610, and only receive collection instructions for the corresponding process step; The data filtering unit is used to perform preliminary screening of the collected quality result data, automatically remove invalid data with values ​​<0, and push valid data to the cockpit main computing module.

9. The device for tracking enterprise-wide quality results according to claim 7, characterized in that, The cockpit main computing module includes a data storage unit and a verification unit: The data storage unit has a pre-defined storage path for storing verified quality result data according to the business cycle. The verification unit is used to perform data and process matching verification, comparing whether the data fields are consistent with the preset fields of the process to which they belong. When the verification fails, it generates and pushes a data correction prompt to the corresponding acquisition terminal.

10. The device for tracking enterprise-wide quality results according to claim 7, characterized in that, The visualization module includes an interface control unit and an interactive response unit: The interface control unit is used to trigger data refresh commands every 5 minutes to synchronously update the node size and panel data of the process flow diagram, and also supports receiving manual real-time refresh commands. The interactive response unit is used to detect click operations on the warning pop-up, trigger the retrieval command in the tracing interface, and also supports receiving confirmation operations of the processed status in the tracing interface and feeding back to the cockpit main computing unit.

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