Whole life cycle management method for intelligent mixing station

Abstract

The invention provides a full-life-cycle management method for an intelligent mixing station, belongs to the technical field of intelligent management of constructional engineering, and realizes full-process closed-loop management from production task issuing, production scheduling execution, progress monitoring to production information tracing through Internet of Things sensing, data collaboration and intelligent decision technologies. By breaking through a data barrier between a digital management platform and a mixing station control system, a'task-production-monitoring-feedback 'full-life-cycle management chain is constructed, dynamic optimal configuration of production resources is realized, and the engineering project execution efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent management of construction projects, and particularly to a full life cycle management method for a smart batching plant, which is applicable to the closed-loop management and resource optimization allocation in the links of concrete production, transportation, and construction, etc. Background Art

[0002] With the deep application of Internet of Things and big data technologies in the construction industry, as the core facility for concrete production, the efficient operation of a smart batching plant is crucial for project progress, cost control, and quality control. However, the production management of traditional batching plants relies on manual scheduling, resulting in the problem of data islands. There is a lack of real-time data interaction between the digital management platform and the batching plant production control system, leading to low production scheduling efficiency and serious resource waste. Therefore, there is an urgent need to develop a full life cycle management method for a smart batching plant. Summary of the Invention

[0003] In order to solve the core problems such as serious data islands, fragmentation of the whole process coordination, and lag in resource allocation existing in the management of traditional smart batching plants, the present invention provides a full life cycle management method for a smart batching plant. By breaking through the data barrier between the digital management platform and the batching plant control system, a full life cycle management chain of "task - production - monitoring - feedback" is constructed to realize the dynamic optimization allocation of production resources and improve the execution efficiency of engineering projects and resource utilization rate.

[0004] The technical solution of the present invention is as follows:

[0005] A full life cycle management method for a smart batching plant includes the following steps:

[0006] 1. Task coordination stage: The data synchronization system obtains the production tasks of the digital management platform in real time and automatically synchronizes them to the batching plant production control system;

[0007] 2. Production scheduling and production execution: The batching plant production control system performs intelligent production scheduling based on task priorities, synchronously collects data such as production progress, material consumption, and equipment status during the production process, and writes them into a multi-source heterogeneous database;

[0008] 3. Progress dynamic monitoring: By real-time parsing of the database, log files, etc. of the batching plant production control system, accurately track the production task progress and identify abnormal working conditions;

[0009] 4. Resource closed-loop feedback: When the production task reaches a preset node (such as batch completion, transportation ready), automatically synchronize data such as shipping information and material consumption to the digital management platform to support subsequent construction scheduling and resource planning.

[0010] Furthermore,

[0011] The digital management platform generates structured production task sheets according to the requirements of engineering projects. The data synchronization system calls the RESTful API interface of the digital platform through a polling mechanism to obtain task data, and writes it into the database of the batching plant production control system after format conversion.

[0012] The production task sheet includes information such as concrete mix ratio, strength grade, demand quantity, construction location, and delivery time.

[0013] The data synchronization system converts the task data of the platform into the format required by the batching plant production control system and writes it into the corresponding table of the database file of the batching plant production control system.

[0014] Furthermore,

[0015] Scheduling and production execution: The batching plant production control system generates a production plan based on a dynamic scheduling algorithm, and real-time collects three-state data of production status, delivery information model, and material consumption model through Internet of Things devices, and persists the data after cleaning and verification.

[0016] The batching plant production control system dynamically schedules production in combination with task requirements and equipment production capacity, monitors the operating status of equipment in real time through the Internet of Things, establishes a full-life-cycle production status perception system, collects three-state data of production - pause - end, synchronously integrates multi-dimensional delivery information models and material consumption models, and realizes the tracking of data such as production status, delivery information, and material consumption;

[0017] Clean, verify, and perform correlation analysis on the collected data, and achieve data persistence through storage media, providing data support for production scheduling optimization, resource dynamic configuration, and decision-making analysis.

[0018] The delivery information model contains the following associated mappings of fields:

[0019] Task order number, supplier, production date, project name, construction location;

[0020] Mix ratio number, pouring method, supply quantity, cumulative quantity, driver, vehicle number;

[0021] Quality monitoring index fields, including measured slump value, out-of-tank temperature, and strength grade.

[0022] Furthermore,

[0023] Progress dynamic monitoring: Build a real-time data pipeline based on the Change Data Capture (CDC) technology, perform digital tracking through a three-dimensional progress index system, and use a time series pattern matching algorithm to identify abnormal working conditions and trigger warning work orders.

[0024] The progress dynamic monitoring system adopts a high-throughput data synchronization engine based on the Change Data Capture (CDC) technology to parse the database transaction logs of the batching plant production control system and construct a data acquisition pipeline with millisecond-level response.

[0025] Through the three-dimensional progress index system, the entire production process is digitally tracked. The time series pattern matching algorithm is used to identify abnormal conditions such as delayed delivery and ratio deviation, and warning work orders are automatically triggered, forming an intelligent monitoring closed-loop of collection - analysis - warning - response to ensure data consistency between the production execution and the planning system.

[0026] Among them, the three-dimensional progress index system includes:

[0027] Task completion rate: percentage of planned volume completed, compliance rate of mixing batches;

[0028] Delivery timeliness rate: on-time delivery rate, count of in-transit abnormal events;

[0029] Material matching degree: deviation standard deviation between actual consumption and theoretical ratio.

[0030] Furthermore,

[0031] Resource closed-loop feedback: When a preset business node is triggered, a full-dimensional production data packet is generated by the rule engine and sent back to the digital management platform. After integrity verification, the project status is updated and management reports are generated, realizing digital management of the entire chain.

[0032] The resource closed-loop feedback mechanism adopts a node listening architecture based on the rule engine. When key nodes such as batch completion and transportation readiness of production tasks are triggered, a full-dimensional production data packet is automatically pushed to the digital platform in real time. The digital management platform performs integrity verification and business logic matching on the received data through the closed-loop feedback processing engine, updates the project status, generates reports, and conducts cost accounting, realizing digital management of the entire chain.

[0033] The beneficial effects of the present invention are

[0034] Improve production efficiency and collaboration ability

[0035] The data synchronization system breaks through the data islands between the digital management platform and the batching plant production control system, realizes automatic issuance of production tasks, avoids manual intervention, reduces task transfer delay and error rate, and improves production efficiency and accuracy.

[0036] Enhance production transparency and precise control

[0037] Real-time collection of "production - pause - end" three-state data, combined with multi-dimensional data models such as delivery information and material consumption, realizes visual monitoring of the entire production process.

[0038] Optimize resource management and cost control

[0039] The digital management platform monitors the material consumption of the batching plant in real time, avoids resource waste, and improves resource utilization rate; at key nodes (such as "batch completion" and "transportation ready"), production data packets are automatically pushed to support the digital management platform for cost accounting, report generation, and project status update, improving management efficiency.

[0040] In summary, the present invention effectively solves the core problems existing in the management of traditional intelligent batching plants, such as serious data islands, fragmentation of the entire process collaboration, and lag in resource allocation, and realizes the management of the full life cycle of "task - production - monitoring - feedback". BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a schematic diagram of the working process of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0043] As Figure 1 shown, the core modules and data flow relationships of the present invention include a digital management platform, a data synchronization system, and a batching plant production control system.

[0044] Digital management platform: It has a task generation module, a production monitoring interface, a report center, and an open API interface; the digital management platform formulates production plans, issues production tasks, receives production data, and conducts statistical analysis, coordinating the data management and decision-making support for the entire production process;

[0045] Data synchronization system: It includes a task synchronization unit, a progress monitoring unit, a feedback execution unit, and a monitoring and alarm center. The data synchronization system is a support system for the digital management platform and the batching plant production control system, receives the production tasks of the digital management platform, and synchronizes them to the batching plant production control system in real time;

[0046] Batching plant production control system: It includes an intelligent production scheduling module, a multi-source data acquisition module, and a local database; monitors the production data of the batching plant production control system and transmits the data back to the digital management platform to ensure the efficient and accurate intertransmission of tasks and data between systems.

[0047] The mixing plant production control system receives and executes production tasks, collects production data (such as raw material consumption, equipment status, etc.) in real time, processes and solidifies the data for storage, dynamically adjusts production arrangements to optimize efficiency, and realizes the implementation of production tasks and the collection of data sources.

[0048] The three work together to achieve a full-process closed-loop management from task collaboration to production execution, progress monitoring, and data feedback.

[0049] The specific working steps are as follows:

[0050] (1) Task collaboration stage: The digital management platform generates specific production task orders according to the requirements of the engineering project, including information such as concrete mix ratio, strength grade, demand quantity, construction location, delivery time, etc. The data synchronization system polls and requests the Restful-style API interface provided by the digital platform to obtain the production tasks of the station, converts the task data of the platform into the format required by the mixing plant production control system, and writes it into the corresponding table of the database file of the mixing plant production control system to achieve the task synchronization function between the digital management platform and the mixing plant production control system.

[0051] (2) Production scheduling and execution: The mixing plant production control system dynamically schedules production in combination with task requirements and equipment production capacity, real-time monitors the operating status of equipment through the Internet of Things, establishes a production status perception system for the entire life cycle, accurately collects three-state data of "in production - paused - ended", synchronously integrates multi-dimensional delivery information models (including task order numbers, supply units, project names, construction locations, delivered volumes, etc.) and material consumption models (including raw materials such as cement, sand, gravel, and admixtures), and realizes the tracking of data such as production status, delivery information, and material consumption. The system cleans, validates, and performs correlation analysis on the collected data, and realizes the persistence of data through storage media such as databases or files, providing data support for production scheduling optimization, dynamic resource allocation, and decision-making analysis.

[0052] (3) Dynamic progress monitoring:

[0053] The dynamic progress monitoring system adopts a high-throughput data synchronization engine based on change data capture (CDC) technology to realize the parsing of the database transaction log of the mixing plant production control system and construct a data acquisition pipeline with millisecond-level response. The system digitally tracks the entire production process through a three-dimensional progress index system (task completion rate, delivery timeliness rate, material matching degree), uses a time series pattern matching algorithm to identify abnormal working conditions such as delivery delays and ratio deviations, and automatically triggers warning work orders. It forms an intelligent monitoring closed-loop of "collection - analysis - warning - response" to ensure the data consistency between production execution and the planning system.

[0054] (4) Resource Closed-loop Feedback: The resource closed-loop feedback mechanism adopts a node listening architecture based on a rule engine. When key nodes such as "batch completion" and "transportation ready" are triggered in the production task, it automatically triggers the real-time push of a full-dimensional production data packet (including shipping information, material consumption list, error analysis, etc.) to the digital platform. The digital management platform performs integrity verification and business logic matching on the received data through a closed-loop feedback processing engine, updates the project status, generates reports, conducts cost accounting, etc., to achieve digital management of the entire chain.

[0055] The above are only the preferred embodiments of the present invention, which are only used to illustrate the technical solutions of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. A full - life - cycle management method for intelligent batching plants, characterized in that: It includes the following steps: 1) Task collaboration stage: The data synchronization system obtains the production tasks of the digital management platform in real - time and automatically synchronizes them to the batching plant production control system; 2) Production scheduling and production execution: The batching plant production control system performs intelligent production scheduling based on task priorities, synchronously collects data on production progress, material consumption, and equipment status during the production process, and writes them into a multi - source heterogeneous database; 3) Progress dynamic monitoring: By real - time parsing the database and log files of the batching plant production control system, track the production task progress and identify abnormal working conditions; 4) Resource closed - loop feedback: When the production task reaches a preset node, automatically synchronize data on shipping information and material consumption to the digital management platform to support subsequent construction scheduling and resource planning.

2. The method according to claim 1, characterized in that: The digital management platform generates a structured production task order according to the project requirements. The data synchronization system obtains task data by calling the RESTful API interface of the digital platform through a polling mechanism, and writes it into the database of the batching plant production control system after format conversion.

3. The method according to claim 2, characterized in that: The production task order includes information such as concrete mix ratio, strength grade, demand quantity, construction location, and delivery time; The data synchronization system converts the task data of the platform into the format required by the batching plant production control system and writes it into the corresponding table of the database file of the batching plant production control system.

4. The method according to claim 1, characterized in that: Production scheduling and production execution: The batching plant production control system generates a production plan based on a dynamic production scheduling algorithm, real - time collects three - state data of production status, shipping information model, and material consumption model through Internet of Things devices, and performs data cleaning and verification before persistent storage.

5. The method according to claim 4, characterized in that: The batching plant production control system dynamically schedules production in combination with task requirements and equipment production capacity, real - time monitors the operating status of equipment through the Internet of Things, establishes a full - life - cycle production status perception system, collects three - state data of production - pause - end, synchronously integrates multi - dimensional shipping information models and material consumption models, and realizes the tracking of data such as production status, shipping information, and material consumption; Perform data cleaning, verification, and correlation analysis on the collected data, and achieve data persistence through storage media, providing data support for production scheduling optimization, resource dynamic configuration, and decision - making analysis.

6. The method according to claim 1, characterized in that: Progress dynamic monitoring: Build a real - time data pipeline based on the Change Data Capture (CDC) technology, perform digital tracking through a three - dimensional progress index system, and use a time - series pattern matching algorithm to identify abnormal working conditions and trigger warning work orders.

7. The method according to claim 6, characterized in that: The progress dynamic monitoring system adopts a high - throughput data synchronization engine based on the Change Data Capture (CDC) technology to realize the parsing of the database transaction log of the batching plant production control system and construct a data acquisition pipeline with millisecond - level response; Digitally track the entire production process through a three-dimensional progress indicator system, use a time series pattern matching algorithm to identify abnormal conditions such as delivery delays and ratio deviations, and automatically trigger warning work orders; form an intelligent monitoring closed loop of collection - analysis - warning - response to ensure data consistency between the production execution and planning systems.

8. The method according to claim 1, wherein Resource closed-loop feedback: When a preset business node is triggered, generate a full-dimensional production data packet through a rule engine and send it back to the digital management platform. After integrity verification, update the project status and generate management reports to achieve digital management of the entire chain.

9. The method according to claim 8, wherein The resource closed-loop feedback mechanism adopts a node listening architecture based on a rule engine. When key nodes such as batch completion and transportation readiness of production tasks are triggered, automatically trigger the real-time push of the full-dimensional production data packet to the digital platform; the digital management platform performs integrity verification and business logic matching on the received data through a closed-loop feedback processing engine, updates the project status, generates reports, and conducts cost accounting to achieve digital management of the entire chain.

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