Intelligent production system for tomato products

Abstract

The embodiment of the specification provides a tomato product intelligent production system, wherein the tomato product intelligent production system breaks the data barrier among various business systems by constructing an intelligent production system integrated with cross-system data acquisition, production task execution control, energy consumption correlation analysis and unified data interaction, realizes data penetration and business cooperation from enterprise planning to workshop execution to energy consumption monitoring. The system can dynamically correlate the real-time collected production process parameters with production orders and quality indexes, and provide a unified platform for transparent monitoring and accurate scheduling of the production process. At the same time, by directly linking the energy consumption data with the production output for calculation, the system realizes fine measurement and deep analysis of energy consumption, provides a reliable data basis for continuously optimizing the process and reducing the production unit consumption, and thus comprehensively improves the automation, informatization and intelligent management level of the tomato product production.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of intelligent technology, and in particular to an intelligent production system for tomato products. Background Technology

[0002] In current tomato product production management, multiple independent information systems are commonly used to handle planning, quality, equipment control, and energy consumption monitoring. This results in fragmented and disjointed storage of production data, quality data, equipment operating parameters, and energy consumption information, hindering effective communication and integration. This data silo phenomenon makes it difficult for enterprises to grasp the overall production situation in real time. Production scheduling relies on manual experience and offline reports, lacking precise control capabilities based on real-time data. Furthermore, process data at the equipment control layer is disconnected from planning and quality information at the management level. Energy consumption cannot be correlated with specific production batches or process stages, leading to a lack of data support for process optimization, inefficient production cost control, and hindering improvements in production efficiency and refined operational levels.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide an intelligent production system for tomato products to solve the technical defects existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, a smart production system for tomato products is provided, comprising: The data acquisition module is used to connect to the enterprise resource planning system, the quality management information system, and the programmable logic controller production control system. It obtains weighing data, impurity deduction data, and product grading results of tomato raw materials from the quality management information system, and obtains process parameters during the production process from the programmable logic controller production control system. The production control module is used to generate production work orders based on production tasks received from the enterprise resource planning system, allocate production work orders to the corresponding production workshops, and generate production visualization dashboards and data reports based on data obtained from the data acquisition module. The energy consumption management module is used to collect water consumption data and electricity consumption data in real time from the programmable logic controller production control system, and combine them with the output data in the production control module to calculate the water consumption and electricity consumption per ton of sauce and generate an energy consumption analysis report. The data interaction module is used for data format conversion and data synchronization between enterprise resource planning systems, quality management information systems, and programmable logic controller production control systems.

[0006] In one possible implementation, the data acquisition module is also used to connect to an unattended weighing and metering system and acquire the metering data of vehicles transporting tomato raw materials.

[0007] In one possible implementation, the data acquisition module is specifically used to acquire at least one of the following process parameters from the programmable logic controller production control system: pulping concentration, concentration temperature, and sterilization time.

[0008] In one possible implementation, the production control module is specifically used to decompose the overall production task into workshop-specific production work orders based on the capacity data of each production workshop and the preset production process requirements.

[0009] In one possible implementation, the production control module generates a production visualization dashboard that includes the work order execution progress and real-time production data for each workshop.

[0010] In one possible implementation, the production control module is also used to automatically generate daily operation reports containing historical trend charts of core indicators, and to store production data for a long time for querying and tracing.

[0011] In one possible implementation, the energy consumption analysis reports generated by the energy management module support filtering by workshop and date, and display material consumption ratio data.

[0012] In one possible implementation, the data interaction module enables data interaction between the quality management information system and the programmable logic controller production control system by developing a dedicated data interface and configuring data mapping rules.

[0013] One possible implementation also includes a network security module for connecting devices, personnel, and the intelligent tomato production system based on the existing network, and for enforcing network security policies.

[0014] In one possible implementation, the system connects with the procurement management system and the warehousing and logistics system through a data interaction module to achieve the integration and sharing of production, procurement, and warehousing data.

[0015] This invention constructs an intelligent production system that integrates cross-system data acquisition, production task execution control, energy consumption correlation analysis, and unified data interaction. This effectively breaks down data barriers between various business systems, achieving data connectivity and business collaboration from enterprise planning to workshop execution and energy consumption monitoring. The system dynamically correlates real-time collected production process parameters with production orders and quality indicators, providing a unified platform for transparent monitoring and precise scheduling of the production process. Simultaneously, by directly linking energy consumption data to production output, it achieves refined energy consumption measurement and in-depth analysis, providing reliable data for continuous process optimization and reducing unit energy consumption, thereby comprehensively improving the automation, informatization, and intelligent management level of tomato product production. Attached Figure Description

[0016] Figure 1 This is a system schematic diagram of an intelligent production system for tomato products provided in one embodiment of this specification. Detailed Implementation

[0017] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0018] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0019] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0020] This specification provides an intelligent production system for tomato products, which will be described in detail in the following embodiments.

[0021] See Figure 1 , Figure 1This diagram illustrates a system schematic of an intelligent tomato product production system according to an embodiment of this specification. Specifically, it includes a data acquisition module for interfacing with an enterprise resource planning (ERP) system, a quality management information system (QMI) system, and a programmable logic controller (PLC) production control system. This module acquires weighing data, impurity deduction data, and product grading results of tomato raw materials from the QMI system, and process parameters from the PLC production control system. A production control module generates production work orders based on production tasks received from the ERP system, allocates these work orders to corresponding production workshops, and generates production visualization dashboards and data reports based on the data acquired by the data acquisition module. An energy consumption management module collects real-time water and electricity consumption data from the PLC production control system and, combined with production data from the production control module, calculates water and electricity consumption per ton of tomato sauce and generates an energy consumption analysis report. A data interaction module performs data format conversion and synchronization between the ERP system, QMI system, and PLC production control system.

[0022] Among these, Enterprise Resource Planning (ERP) systems refer to information systems used to integrate and manage core business processes, such as finance, procurement, sales, and planning, such as the NC65 system, whose annual and monthly production plans serve as the source of all production activities. Quality Management Information Systems (QMS) refer to systems specifically designed to record and manage product quality-related data, such as the Yuanshan system, which records quality information throughout the entire process from raw tomato testing to finished tomato sauce labeling. Programmable Logic Controller (PLC) production control systems refer to systems used to automate the operation of equipment on production lines, such as a PLC system controlling nine production lines including pulpers, concentration tanks, and sterilization equipment, capable of outputting equipment operating parameters in real time. Weighing data refers to the gross weight, tare weight, and net weight values ​​obtained by weighing tomato raw material transport vehicles upon arrival at the factory. Impurity deduction data refers to the net weight of raw materials after deducting impurities such as mud and sand, used to calculate the actual payment to farmers based on raw material testing results. Product grading results refer to the raw material grades classified according to indicators such as sugar content and color difference. Process parameters refer to key parameter values ​​monitored and set by the PLC control system during production, such as pulping concentration, temperature and pressure of the concentration tank, and sterilization temperature and time. Production work orders refer to instructional documents that break down macro-level production tasks issued by the enterprise resource planning system into specific production lines, specifying production varieties, quantities, and time requirements. Production visualization dashboards refer to interfaces that centrally display key performance indicators such as production progress, output, and quality in graphical and chart formats. Data reports refer to formatted data files automatically generated by the system, such as daily operation reports. Water and electricity consumption per ton of tomato sauce refer to the amount of water and electricity consumed per ton of finished tomato sauce, which are core indicators for measuring production energy efficiency. Energy consumption analysis reports refer to reports that centrally display water and electricity consumption and energy consumption data per ton of tomato sauce for each workshop and time period. Data format conversion and data synchronization refer to the process of unifying data from different systems with different encoding rules and structures into a standard format through mapping rules, and ensuring that data between systems remains updated.

[0023] The system will be further described below through a detailed embodiment: During the production season of a tomato product company, the aforementioned intelligent tomato product production system began operation. The company's management issued a production plan of 100,000 tons of large barrels of tomato paste for the month through the Enterprise Resource Planning (NC65) system.

[0024] First, the data acquisition module is activated. This module connects to the Yuanshan system (quality management information system) and the 26 PLC control systems deployed in various workshops via a pre-developed data interface. When a truck loaded with tomatoes enters the factory, the unattended weighing system writes the weighing data into the Yuanshan system in real time. Simultaneously, quality inspectors enter the impurity deduction ratio and grading result (e.g., Grade 1) of the batch of raw materials into the Yuanshan system. The data acquisition module automatically retrieves this weighing data, impurity deduction data, and product grading result from the Yuanshan system. At the same time, the module continuously reads real-time process parameters from each PLC control system, such as the current temperature of the concentration tank on production line 3 being 92℃, and the operating time of the sterilization equipment on production line 5 being 45 seconds.

[0025] Next, the production control module begins operation. It receives the macro-level task of "producing 100,000 tons of tomato paste this month" from the Enterprise Resource Planning (ERP) system. Combining this with pre-stored production capacity data for each workshop in the database (e.g., 500 tons per day for Workshop 1, 550 tons per day for Workshop 2), and the available raw material grades and quantities obtained from the data acquisition module, the module automatically performs production scheduling calculations, generating and issuing detailed production work orders. For example, it issues a work order to Workshop 1: "Produce 500 tons of Grade 1 tomato paste on August 25th." During the execution of work orders on the production line, the production control module continuously integrates real-time production data and work order status from the data acquisition module, driving dynamic updates to the production visualization dashboard. The dashboard displays the planned and completed output for each workshop using bar charts, and shows the completion percentage of each production line's work orders using progress bars. After each day's production, the system automatically summarizes the data, generating a daily operation report including indicators such as raw material consumption, finished product output, and equipment operating rate, and plots a trend chart of the ratio of raw material consumption per ton of paste over the past week.

[0026] Meanwhile, the energy management module operates independently. It reads real-time energy consumption data directly from the smart meters and water meters attached to the PLC control system via the data interaction module. For example, it records that workshop No. 2 consumes 1500 kWh of electricity and 200 tons of water during the production batch from 2 PM to 3 PM. When the batch production ends, the production control module provides the output data for 150 tons of finished sauce. The energy management module automatically performs the calculations: electricity consumption per ton of sauce = 1500 kWh / 150 tons = 10 kWh / ton; water consumption per ton of sauce = 200 tons / 150 tons = 1.33 tons / ton. These calculation results, along with the original energy consumption data, are processed to form energy consumption analysis reports that can be filtered by workshop or day.

[0027] Throughout the process, the data interaction module acts as both a "translator" and a "scheduler." Because the Yuanshan system, PLC system, and Enterprise Resource Planning (ERP) system are developed by different vendors, their data formats vary (e.g., date formats, equipment coding rules). The data interaction module's built-in data mapping rule library converts this heterogeneous data into a unified format within the system. Simultaneously, it ensures that once a piece of data (such as the entry information of a batch of raw materials) is recorded in the Yuanshan system, it can be promptly synchronized to the relevant accounts in both the Yuanshan system and the ERP system, breaking down data silos.

[0028] The beneficial effect of this embodiment is that by constructing an integrated system that combines data acquisition, production control, energy consumption management, and data interaction, it achieves digital management and monitoring of the entire tomato product production process. The system effectively integrates multi-source data from the planning, execution, and quality layers, enabling production managers to schedule work orders and make decisions based on real-time and accurate data. At the same time, it achieves refined measurement and analysis of energy consumption, providing reliable data support for improving production efficiency and optimizing energy costs.

[0029] In one possible implementation, based on the aforementioned intelligent tomato product production system, the data acquisition module is also used to connect to the unattended metering and weighing system and obtain the metering data of the tomato raw material transport vehicles.

[0030] Among them, the unattended weighing system can refer to a system that automatically identifies vehicles, reads tare weight and gross weight, and calculates net weight to realize the unmanned weighing process of raw materials, such as a system that interfaces with the Xiaotieniu platform. The metering data of tomato raw material transport vehicles can refer to the records generated in this system that contain complete information such as vehicle information, weighing time, gross weight, tare weight, and net weight.

[0031] Building upon the aforementioned embodiments, the data acquisition module's functionality has been further expanded. In addition to connecting to the Yuanshan system and the PLC system, this module also establishes a connection with the unattended weighing system at the factory gate via a dedicated network protocol. When a pre-booked tomato transport vehicle enters the weighing platform, the vehicle and driver information registered on the "Little Iron Bull" platform is automatically retrieved. After the vehicle completes weighing, the unattended weighing system not only uploads the gross weight, tare weight, and net weight data to the Yuanshan system, but also pushes a complete data package containing license plate number, driver's name, weighing time, raw material origin (from the contract), gross weight, tare weight, and net weight to the data acquisition module of the aforementioned intelligent tomato product production system in real time via a data interface. This allows the system to obtain the most original weight data as soon as the raw materials enter the factory, providing source data assurance for subsequent raw material batch tracking, supplier settlement, and production input calculations, enhancing the integrity and timeliness of the data chain.

[0032] The beneficial effect of this embodiment is that by directly incorporating the unattended metering and weighing system into the data collection scope, the source of raw material entry data is automatically collected and acquired in real time, avoiding the errors and delays that may be caused by manual copying or secondary entry across systems, and ensuring the accuracy and real-time nature of the starting point of the production management data chain.

[0033] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the data acquisition module is specifically used to acquire at least one of the following process parameters from the programmable logic controller production control system: pulping concentration, concentration temperature, and sterilization time.

[0034] Pulping concentration refers to the percentage of solids in the tomato pulp after processing in a pulping machine; it is a key parameter affecting subsequent concentration efficiency. Concentration temperature refers to the material temperature maintained during the evaporation of moisture in the tomato paste in the concentration tank, directly affecting the product's color and flavor. Sterilization time refers to the processing time of the tomato paste in sterilization equipment at a specific temperature to achieve commercial aseptic standards.

[0035] Specifically, regarding the key control points of the production process, the process parameters acquired by the data acquisition module from the PLC control system in the aforementioned embodiments are further clarified and refined. This system pays particular attention to parameters that have a decisive impact on the final product quality and energy consumption. For example, in the pulping section of Workshop 1, the data acquisition module reads the pulping concentration value (e.g., 4.2%) detected by the online concentration meter and fed back to the PLC in real time by parsing specific data blocks of the Siemens S7-1200 PLC. In the concentration section of Workshop 3, the module reads the opening degree of the steam regulating valve in the concentration tank jacket and the material temperature inside the tank (e.g., 92℃) from the Omron NJ controller. In the sterilization section, it reads the temperature setpoint of the sterilizer's insulation section and the time it takes for the material to pass through that section (e.g., 125℃, 45 seconds). These key process parameters, acquired in real time, are not only used for display on the production visualization dashboard but also provide a core data foundation for subsequent process optimization analysis, quality traceability, and energy consumption correlation analysis.

[0036] The beneficial effect of this embodiment is that it clarifies the key points of the system for collecting critical quality and energy consumption control process parameters, enabling transparent and refined control of the production process to be implemented down to specific process steps, and providing the possibility for data-based process optimization and quality consistency control.

[0037] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the production control module is specifically used to decompose the overall production task into workshop-specific production work orders based on the capacity data of each production workshop and the preset production process requirements.

[0038] The production capacity data for each production workshop can refer to the maximum or average production capacity of each workshop per unit time, calculated from historical statistics or equipment theory. The preset production process requirements can refer to the process routes and quality standards pre-set in the system according to different product grades (such as premium, first-grade sauce) or customer requirements.

[0039] The process of the production control module generating work orders in the aforementioned embodiments is described in more detail here. When the Enterprise Resource Planning (ERP) system issues the task of "producing 10,000 tons of premium tomato sauce," the production control module does not simply allocate the production evenly. It first queries the historical production capacity data of each workshop in the database, for example: Line A (old line) has an average daily production capacity of 450 tons, and Line B (new line) has an average daily production capacity of 600 tons. At the same time, it calls the preset process requirement library to confirm that "premium sauce" must use specific varieties of tomatoes as raw materials, and the concentration temperature must be controlled between 90-92℃. Combining the quantity of premium raw materials that have entered the factory and their storage locations in different workshops obtained by the data acquisition module on the same day, the production control module runs the production scheduling algorithm. The algorithm considers production capacity constraints, process constraints, and raw material logistics costs, and finally generates refined workshop-specific production work orders such as "allocate a production work order for 4,000 tons of premium sauce to Workshop A, using raw materials from storage tanks 1-3; allocate a production work order for 6,000 tons of premium sauce to Workshop B, using raw materials from storage tanks 4-5." This makes production instructions more scientific and executable, and optimizes the allocation of overall production resources.

[0040] The beneficial effect of this embodiment is that by introducing capacity data and process requirements as the decision basis for work order decomposition, it realizes the intelligent and refined decomposition of production tasks from macro planning to workshop-level execution, thereby improving the rationality of production resource utilization and the feasibility of the plan.

[0041] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the production control module generates a visualization dashboard that includes the work order execution progress and real-time output data for each workshop.

[0042] The work order execution progress can refer to the percentage of the current completed output relative to the total planned output of the work order. Real-time output data can refer to the number of qualified finished products produced on the production line within the most recent statistical period (such as the current hour or current shift).

[0043] The core content of the production visualization dashboard mentioned in the previous embodiments is specifically presented here. On the large screen in the company's headquarters production command center, this dashboard is divided into multiple areas. One core area, titled "Workshop Work Order Execution Dashboard," lists all currently executing production work orders in tabular form. Each record includes the workshop number, product grade, planned output, completed output, completion rate (progress), and current shift's cumulative output (real-time output). For example, it might display "Workshop 3, Grade 1 sauce, planned 500 tons, completed 320 tons, progress 64%, current shift output 85 tons." This data is calculated in real-time by the production control module from the output counter signals fed back from the PLC systems of each workshop. Another area of ​​the dashboard may highlight the company's "Today's Real-Time Cumulative Output" in a scrolling numerical format. This display method allows management to clearly grasp the overall production dynamics, quickly locate workshops with lagging progress or abnormal output, and facilitate timely intervention.

[0044] The beneficial effect of this embodiment is that by using work order execution progress and real-time output as the core elements of the visual dashboard, it provides production managers with the most direct and critical dynamic monitoring information, greatly improving the transparency of the production site status and the timeliness of management response.

[0045] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the production control module is also used to automatically generate daily operation reports containing historical trend charts of core indicators, and to store production data for a long period of time for querying and traceability.

[0046] Among them, the historical trend chart of core indicators can refer to a chart showing the changes of key performance indicators such as the material consumption ratio per ton of sauce, the overall efficiency of equipment, and the first-pass yield over time, in the form of line charts, bar charts, etc. Long-term storage can refer to storing data in a dedicated historical database or data warehouse and creating indexes to support fast retrieval across months and years.

[0047] In addition to real-time monitoring, the aforementioned production control module also possesses powerful data archiving and analysis capabilities. At midnight each day, the system automatically triggers a report generation task. It extracts all relevant production data from the database for the past 24 hours, including the net weight of raw materials consumed in each workshop, the weight of finished sauce produced, equipment uptime, downtime, and quality inspection results. Using preset formulas, the system calculates a series of core indicators, such as the plant's average daily ton-to-ton sauce consumption ratio and the overall efficiency of equipment in each workshop, and inputs these into a formatted report template to generate a daily operation report in PDF format. Simultaneously, the system appends the daily "ton-to-ton sauce consumption ratio" value to the historical sequence, automatically drawing a line graph showing the trend of this indicator over the past 30 days, which is then attached to the report. All raw process data and calculation results are compressed and stored in a dedicated time-series database, with a composite index established based on time, workshop, and product batch. Three years later, when detailed production records for a specific batch of products are needed, the administrator only needs to enter the batch number to query all process parameters, operation records, raw material sources, and shift energy consumption data for that batch's production.

[0048] The beneficial effects of this embodiment are that real-time data is transformed into management knowledge through automated daily report generation and historical trend analysis; and a complete digital archive of product production is established through long-term secure data storage, achieving full-process traceability and meeting the long-term data needs of quality management and process improvement.

[0049] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the energy consumption management module generates energy consumption analysis reports that can be filtered by workshop and date, and displays material consumption ratio data.

[0050] Among these features, filtering by workshop and date allows users to limit the range of data displayed in the report by selecting the workshop name from the drop-down menu and the date range from the calendar control. The material consumption ratio refers to the ratio of the net weight of tomato raw materials consumed in producing one ton of finished tomato sauce; it is a core indicator for measuring raw material utilization efficiency.

[0051] The reports generated by the aforementioned energy management module have flexible interactive analysis capabilities. The generated energy consumption analysis report is an online interactive page, not a static file. The top of the report features a dropdown menu for "Select Workshop" (containing options such as All, Workshop 1, Workshop 2, etc.) and a calendar control for "Select Date Range." When a production manager wants to analyze the energy consumption performance of Workshop 2 in August, they simply select the appropriate option and click "Query." The main content of the report will immediately refresh, displaying the daily electricity and water consumption, finished product output, and calculated daily electricity and water consumption per ton of sauce from August 1st to 31st. Simultaneously, a new column, "Material Consumption Ratio," is added to the report. This data is calculated by the system from the raw material consumption and finished product output data retrieved from the production control module (for example, if 610 tons of raw materials are used to produce 100 tons of sauce, the material consumption ratio is 6.1). Below the report, a comparison chart of the three trend curves for electricity consumption per ton of sauce, water consumption per ton of sauce, and material consumption ratio over the past month will be automatically generated. This integrated display method allows managers to intuitively analyze the relationship between energy consumption and production efficiency. For example, they may find that dates with increased material consumption ratios are often accompanied by abnormal consumption of water per ton of soy sauce, thus guiding them to investigate whether there are any abnormalities in the raw material cleaning process.

[0052] The beneficial effect of this embodiment is that by providing multi-dimensional data filtering and integrated display of related indicators (such as material consumption ratio), energy consumption analysis is elevated from simple data listing to the level of correlation analysis, helping managers to deeply explore the production and operation reasons behind abnormal energy consumption and achieve the dual goals of energy saving and efficiency improvement.

[0053] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the data interaction module enables data interaction between the quality management information system and the programmable logic controller production control system by developing a dedicated data interface and configuring data mapping rules.

[0054] Among them, a dedicated data interface can refer to a software interface program written for the communication protocol and data access method of a specific system (such as the Yuanshan system or Siemens PLC). A data mapping rule can refer to a configuration file or rule table that defines how to convert between the source data field names, types, meanings, and corresponding fields in the target system.

[0055] The specific implementation of the aforementioned data interaction module is detailed here. Since the Yuanshan system (quality management information system) typically runs on a general-purpose server and uses a database for data storage, while the PLC control system is an industrial real-time control device, their communication protocols and data structures differ significantly. To achieve data interoperability between them, the data interaction module adopted the following specific measures: For the Yuanshan system's Oracle database, a standard database query interface based on ODBC / JDBC was developed for periodically or triggered reading of the latest quality inspection results. For the Siemens PLC, a read / write interface based on industrial Ethernet protocols (such as Profinet or S7 protocols) was developed. The most crucial step is configuring the data mapping rules. For example, the rule file defines: when it is necessary to synchronize the "sterilization temperature" in the PLC to the Yuanshan system, first read the floating-point number (e.g., 125.5) at address DB10.DBD100 through the PLC interface, then convert it to 12.55 according to the rule "value / 10", and then map it to the "temperature setpoint" field in the "sterilization process record table" of the Yuanshan system, completing the unit "°C". This combination of interfaces and rules enables accurate and automatic data flow between two heterogeneous systems.

[0056] The beneficial effect of this embodiment is that it clarifies that developing dedicated interfaces and configuring mapping rules are key technical means to achieve heterogeneous system integration, ensuring that data from different sources and in different formats can be accurately understood and converted, thus laying a solid data pathway foundation for building an integrated system.

[0057] In one possible implementation, the aforementioned intelligent tomato product production system also includes a network security module, which is used to connect equipment and personnel to the intelligent tomato product production system based on the existing network and to execute network security policies.

[0058] Existing networks can refer to an enterprise's existing factory LAN, office network, and potential wireless network infrastructure. Network security strategies can refer to a series of rules established to protect the system from attacks and unauthorized access, including firewall configuration, access control lists, user authentication and permission management, and network traffic monitoring.

[0059] During the deployment of the aforementioned system, a network security module was integrated simultaneously. This module does not alter the factory's existing network topology but instead deploys integrated hardware and software security gateways at key nodes. For example, an industrial firewall is deployed between the industrial switch connecting the PLC control network and the core switch connecting the server area, strictly limiting access to the PLC network to specific IP addresses and ports of the aforementioned data acquisition module, blocking any unauthorized access. For personnel accessing the production visualization dashboard via computer or mobile terminal, the network security module enforces unified authentication. Each user must log in with a personal account and password, and the system assigns different data viewing and operation permissions based on their role (e.g., operator, team leader, production manager). Furthermore, the module enables network log auditing to record all important data access and operation activities. These measures collectively constitute the system for implementing network security strategies, ensuring system interconnectivity while effectively protecting against security threats from internal and external networks, ensuring the security of production control data and the stable operation of the system.

[0060] The beneficial effect of this embodiment is that by integrating a network security module and executing the system's security policies, a security protection system is simultaneously built during the process of promoting the digital transformation of widespread interconnection of equipment, systems, and personnel. This reduces the risk of production interruption or data leakage due to network attacks or misoperations, and ensures the reliable operation of the intelligent production system.

[0061] In one possible implementation, based on the aforementioned intelligent production system for tomato products, the system connects with the procurement management system and the warehousing and logistics system through a data interaction module to achieve the integration and sharing of production, procurement, and warehousing data.

[0062] The procurement management system can refer to an information system that manages an enterprise's material procurement applications, approvals, orders, and delivery tracking. The warehousing and logistics system can refer to an information system that manages the warehousing, storage, and outbound processes of raw materials, spare parts, and finished products, as well as their inventory status. Data integration and sharing refers to eliminating barriers between systems, enabling information such as production needs, procurement plans, and inventory status to flow and synchronize across system boundaries in real time and accurately.

[0063] The integration scope of the aforementioned intelligent tomato product production system has been further expanded, extending upstream and downstream. Through the development of new interfaces via the data interaction module, the system has been integrated with the company's procurement management system and warehousing and logistics system. The specific process is as follows: When the production control module generates a production work order based on the production schedule, the system automatically analyzes the consumption quota of packaging auxiliary materials (such as bucket lids and labels) required for that work order. Subsequently, through the data interaction module, it queries the warehousing and logistics system for the real-time inventory of these auxiliary materials. If the inventory is below the safety threshold, the system automatically triggers a process to send a purchase requisition to the procurement management system. The requisition includes the required material codes, quantities, and required delivery times. After approval by the procurement personnel, the order information is generated. When the supplier delivers goods to the warehouse, warehouse personnel use a barcode scanner to process the warehousing and logistics system entry, and the inventory quantity is updated in real time. This warehousing status information is then synchronized back to the main system in real time through the data interaction module. When the production line needs to requisition this batch of auxiliary materials, a material requisition form is created in the system and directly linked to the production work order; the material requisition and issuance information is also synchronized bidirectionally. Thus, a complete closed-loop data flow has been formed, from production planning driving procurement demand, to procurement arrival driving inventory updates, and then to inventory materials serving production consumption, enabling cross-departmental business collaboration.

[0064] The beneficial effect of this embodiment is that by deeply integrating the production system with the procurement and warehousing system, data flow and business linkage of key links in the supply chain are realized, which effectively reduces material shortages or inventory backlogs caused by poor information flow and optimizes the supply chain response speed and overall operating costs.

[0065] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0066] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0067] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent production system for tomato products, characterized in that, include: The data acquisition module is used to connect to the enterprise resource planning system, the quality management information system, and the programmable logic controller production control system. It obtains the weighing data, impurity deduction data, and product grading results of tomato raw materials from the quality management information system, and obtains the process parameters during the production process from the programmable logic controller production control system. The production control module is used to generate production work orders based on production tasks received from the enterprise resource planning system, allocate the production work orders to the corresponding production workshops, and generate production visualization dashboards and data reports based on the data acquired by the data acquisition module. The energy consumption management module is used to collect water consumption data and electricity consumption data in real time from the programmable logic controller production control system, and combine them with the output data in the production control module to calculate the water consumption per ton of sauce and the electricity consumption per ton of sauce, and generate an energy consumption analysis report. The data interaction module is used to perform data format conversion and data synchronization between the enterprise resource planning system, the quality management information system, and the programmable logic controller production control system.

2. The intelligent production system for tomato products according to claim 1, characterized in that, The data acquisition module is also used to connect to the unattended metering and weighing system and acquire the metering data of the tomato raw material transport vehicles.

3. The intelligent production system for tomato products according to claim 1, characterized in that, The data acquisition module is specifically used to acquire at least one process parameter from the programmable logic controller production control system, including pulping concentration, concentration temperature, and sterilization time.

4. The intelligent production system for tomato products according to claim 1, characterized in that, The production control module is specifically used to decompose the overall production task into workshop-specific production work orders based on the capacity data of each production workshop and the preset production process requirements.

5. The intelligent production system for tomato products according to claim 1, characterized in that, The production visualization dashboard generated by the production control module includes the work order execution progress and real-time output data for each workshop.

6. The intelligent production system for tomato products according to claim 1, characterized in that, The production control module is also used to automatically generate daily operation reports containing historical trend charts of core indicators, and to store production data for a long time for querying and tracing.

7. The intelligent production system for tomato products according to claim 1, characterized in that, The energy consumption analysis report generated by the energy consumption management module supports filtering by workshop and date, and displays material consumption ratio data.

8. The intelligent production system for tomato products according to claim 1, characterized in that, The data interaction module enables data interaction between the quality management information system and the programmable logic controller production control system by developing a dedicated data interface and configuring data mapping rules.

9. The intelligent production system for tomato products according to claim 1, characterized in that, It also includes a network security module, which is used to connect devices and personnel to the intelligent tomato product production system based on the existing network and to execute network security policies.

10. The intelligent production system for tomato products according to claim 1, characterized in that, The system connects with the procurement management system and the warehousing and logistics system through the data interaction module to achieve the integration and sharing of production, procurement and warehousing data.

Images