Concrete mixing plant equipment intelligent management platform system based on Internet of Things

Abstract

The invention relates to the technical field of concrete management, and discloses a concrete mixing plant equipment intelligent management platform system based on the Internet of Things, which comprises a data middle platform module in communication connection with a concrete mixing plant equipment management cloud platform; the equipment early warning diagnosis module is in communication connection with the concrete mixing plant equipment management cloud platform; an equipment maintenance module which is in communication connection with the equipment early warning diagnosis module; the offline resource integration module is in communication connection with the concrete mixing plant equipment management cloud platform; the intelligent terminal is in communication connection with the concrete mixing plant equipment management cloud platform; the concrete quality monitoring module is in communication connection with the concrete mixing plant equipment management cloud platform; the intelligent scheduling system is in communication connection with the concrete mixing plant equipment management cloud platform; according to the invention, the problems of low equipment management efficiency, insufficient productivity and untimely failure sudden response of the existing equipment management mode of the mixing plant are solved.

Description

Technical Field

[0001] The present invention relates to the field of mixing station equipment management, and in particular to an intelligent management platform system for concrete mixing station equipment based on the Internet of Things. Background Art

[0002] With the rapid development of the construction industry, concrete mixing plants are key suppliers of concrete for infrastructure construction in municipal and transportation sectors. The operational efficiency and management level of their equipment directly impact the construction accuracy and quality of infrastructure. Traditional methods for managing concrete mixing plant equipment rely primarily on manual operations and paper records, resulting in delayed information transmission, untimely equipment maintenance, and irrational resource allocation. Existing technologies, in particular, struggle to meet the requirements for efficient, accurate, and environmentally friendly concrete supply in modern construction, particularly in areas such as equipment failure warning, quality monitoring, and energy optimization. Existing management systems for concrete mixing plant equipment typically only offer basic equipment status monitoring capabilities, lacking in-depth analysis of equipment operating data and support for optimal utilization decisions. For example, equipment failures are often not discovered until after they occur, leading to production interruptions and increased repair costs. Key aspects such as the equipment's real-time status, operating environment, and fault prediction cannot be monitored in real time, indirectly leading to quality issues in the finished concrete product. Equipment scheduling and task allocation rely on manual experience, making it difficult to achieve optimal resource utilization.

[0003] In recent years, the rapid development of Internet of Things (IoT) technology has provided new solutions for the intelligent management of concrete mixing plant equipment. IoT technology enables the real-time collection, transmission, and analysis of equipment operating data, thereby providing data support for equipment early warning, maintenance, resource scheduling, and more. However, most existing IoT management systems are single-function and fail to fully integrate the various business needs of concrete mixing plants. In particular, their applications in production equipment operation monitoring, fault warning, and intelligent scheduling are still in their early stages. Therefore, how to provide an IoT-based intelligent management platform system for concrete mixing plant equipment that can achieve real-time monitoring of equipment operating status, fault warning, intelligent scheduling, quality analysis, and other functions, thereby improving equipment management efficiency and construction quality, has become a key issue that needs to be urgently addressed by those skilled in the art. Summary of the Invention

[0004] The present invention provides an intelligent management platform system for concrete mixing station equipment based on the Internet of Things to solve the problems of low equipment management efficiency, insufficient production capacity, and untimely response to sudden failures in existing mixing station equipment management methods.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: In the first aspect, an intelligent management platform system for concrete mixing station equipment based on the Internet of Things includes: Concrete mixing station equipment management cloud platform; A data center module connected to the concrete mixing station equipment management cloud platform; An equipment early warning diagnosis and evaluation module that is communicatively connected to the concrete mixing station equipment management cloud platform; an equipment repair and maintenance module communicatively connected to the equipment early warning and diagnosis module; An intelligent IoT module that is communicatively connected to the concrete mixing station equipment management cloud platform; An intelligent scheduling module that is communicatively connected to the concrete mixing station equipment management cloud platform.

[0006] Furthermore, the data middle platform module includes: a production capacity equipment model unit and a data large screen unit; The equipment model unit is used to display the 3D model structure of the mixing station equipment, including the equipment status library, parameter library, early warning library and other related information; The data large screen unit is used to obtain energy efficiency analysis, working hours, failure rates, maintenance logs, inspection records, etc. of all mixing station equipment, and display them in a digital and graphical manner.

[0007] Furthermore, the equipment early warning diagnosis and evaluation module includes: an early warning data unit, a data processing unit; The early warning data unit, based on the current real-time dynamic data of the equipment and the historical records of the dynamic library, combines big data analysis and intelligent algorithms to deeply mine and train dynamic data, accurately predict future trends, generate a multi-objective analysis model f(t0) for the equipment, and generate a potential fault feature model u(t0) through normalization calculation. Fit f(t0) with the health model g(t0) of the equipment for analysis. Under the condition of the same time as the fitting curve, when the early warning threshold curve model u(t0) of the early warning library is triggered, this module outputs alarm data such as time, status, and duration, and triggers the alarm mode of the equipment in the equipment model module; The data processing unit is used to call the data of the equipment in the early warning library in the equipment model unit when the equipment is working normally and compare it with the real-time data monitored by the sensor. When the early warning value of the early warning library is triggered, this module outputs alarm data such as time, status, and duration, and triggers the alarm mode of the equipment in the equipment model module, so that the problem can be discovered and solved in the early stage before the fault occurs.

[0008] Furthermore, the equipment repair and maintenance module includes: an equipment inspection unit, a repair unit, and a maintenance filing and recording unit; The equipment inspection unit is used to generate equipment inspection plans based on the daily maintenance requirements of the equipment after leaving the factory, and upload maintenance records by scanning codes and taking photos of the maintained parts; Maintenance unit, used to detect equipment damage, automatically triggering fault alarms and dispatching maintenance work orders; The maintenance record unit is used to store historical equipment maintenance data to assist in equipment failure analysis and maintenance work performance evaluation.

[0009] Furthermore, the intelligent IoT module includes: a device sensing unit and an edge computing unit; Equipment sensing unit, used to collect parameters such as temperature, humidity, dust, voltage, current, vibration, speed, resistance, load, and visual appearance through sensors to perform material archiving, status simulation, transmission detection, abnormality warning, and replacement prompt processing; The edge computing unit is used for edge computing gateways to preset predictive maintenance engines, multi-level early warning mechanisms, and floating-point filtering algorithms based on different parameter models of various devices, and transmit the processed time-sensitive data to the cloud server through the edge computing gateway.

[0010] Furthermore, the intelligent scheduling module further includes: a production line scheduling unit and a transport vehicle scheduling unit; The production line scheduling unit is used to analyze the current status, production capacity, usage cycle and other data of multiple production lines. The system automatically sends the production plan to the production line control system. The transport vehicle dispatching unit is used to analyze the daily status, transport volume, transport distance, maintenance warning and other data of the mixing station transport vehicles. The system automatically pushes each vehicle to the production line for loading and receiving materials according to the preset priority rules.

[0011] Beneficial effects: The present invention provides an intelligent management platform system for concrete mixing station equipment based on the Internet of Things. By integrating modules such as a data center, intelligent equipment management, intelligent IoT equipment, and intelligent scheduling, it realizes all-round visual supervision of equipment operation, significantly improves the life cycle of the equipment, and increases the equipment availability. At the same time, it reduces the management costs of equipment and maintenance personnel through information technology, meeting the requirements of modern production enterprises for high efficiency, precision, and digital transformation. Through the collaborative work of the concrete mixing station equipment management cloud platform and various modules, real-time monitoring of the equipment's operating status, fault warning, and intelligent scheduling are realized. The equipment early warning diagnosis module can detect potential faults in advance and reduce equipment downtime. The intelligent scheduling system scientifically allocates production tasks according to the production line capacity and equipment operating status, and optimizes resource utilization. Its equipment repair and maintenance module realizes preventive maintenance of equipment through equipment inspection, after-sales maintenance, and maintenance filing and recording functions, reducing the maintenance costs caused by sudden failures. The energy consumption optimization submodule analyzes equipment energy consumption data, generates energy-saving strategies, and further reduces energy consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a structural diagram of an intelligent management platform system for concrete mixing station equipment based on the Internet of Things in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following is a clear and complete description of the technical solutions of the present invention. It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0014] Unless otherwise defined, the technical or scientific terms used in the present invention shall have the usual meanings understood by persons of ordinary skill in the field to which the present invention belongs. The words "first", "second" and similar terms used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one" or "a" do not indicate a quantity limitation, but rather indicate the existence of at least one. Words such as "connected" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship also changes accordingly.

[0015] Example 1 See Figure 1 The embodiment of the present application provides an intelligent management platform system for concrete mixing station equipment based on the Internet of Things, including: Concrete mixing station equipment management cloud platform; A data center module connected to the concrete mixing station equipment management cloud platform; An equipment early warning diagnosis and evaluation module that is communicatively connected to the concrete mixing station equipment management cloud platform; an equipment repair and maintenance module communicatively connected to the equipment early warning and diagnosis module; An intelligent IoT module that is communicatively connected to the concrete mixing station equipment management cloud platform; An intelligent scheduling module that is communicatively connected to the concrete mixing station equipment management cloud platform.

[0016] The equipment model unit in the data center module is used to display the 3D model structure of the mixing station equipment, including relevant information such as the equipment's status database, parameter database, and early warning database. The status database and parameter database are based on the equipment model using Unity3D software and parametric modeling technology to construct 3D models of the mixing station production line equipment (including control valves, belt equipment, main engine, screw, etc.), mixing station concrete transportation equipment (including mixer trucks, loaders, pump trucks, etc.), and other equipment (such as scales, air compressors, cameras, etc.) installed in the mixing station. This module ensures the consistency of the 3D model with the entity based on the appearance size, location layout, shape structure, and operating parameters of key equipment. In combination with the motion mechanism, it generates the model's motion animation and physical behavior, providing a more realistic virtual dynamic environment. In addition, a standard status database and parameter database are generated based on the factory parameters and maintenance parameters of the equipment. Based on the equipment's original factory-defined warranty data and the equipment's native normal operation data, the equipment health model curve g(t0) is generated (equipment in sequence g(t1), g(t2)...). Through in-depth mining, comparison, analysis and training of the data of the physical equipment during actual operation, the curve g(t0) is optimized and adjusted to generate a characteristic model of potential faults.

[0017] The data center module's large data display unit is used to obtain energy efficiency analysis, operating hours, failure rates, maintenance logs, and inspection records for all mixing station equipment, presenting them in a digital and graphical manner. This system collects and records actual sensor data, uses Informatica and Kettle for data cleansing, deduplication, and conversion, Spark and Hive for batch processing, and Flink and Storm for real-time computing. CSS3 responsive design adapts to different screen sizes, and a microservices architecture is employed to fully consider real-time and scalability.

[0018] The equipment early warning diagnosis and assessment module includes: monitoring the equipment's temperature, voltage, current, vibration, resistance, and load changes through sensors, and uploading these to the cloud server's dynamic database via a gateway. Based on the current equipment's real-time dynamic data and dynamic library historical records, combined with big data analysis and intelligent algorithms, deep mining and training of dynamic data accurately predict future trends, generate a multi-objective analysis model f(t0) for the equipment, and generate a potential fault feature model u(t0) through normalized calculation. Fitting f(t0) with the equipment's health model g(t0) is analyzed. When the early warning threshold curve model u(t0) in the early warning library is triggered at the same time as the fitting curve, this module outputs alarm data such as time, status, and duration, and triggers the alarm mode for the equipment in the equipment model module.

[0019] In an embodiment of the present invention, the construction process of the pre-built system equipment multi-objective analysis model includes: using a machine learning method to build and train the system multi-objective analysis model based on the historical operation data of each device.

[0020] It should be noted that based on the historical operating data of the equipment, it is divided into a training library and a test library. Machine learning methods are used to establish a system multi-objective prediction model. The training library is input into the system multi-objective analysis model for training, and then tested with the test library to obtain a system multi-objective prediction model with high prediction accuracy. The system multi-objective analysis model predicts production capacity, temperature and humidity, voltage and current under different input conditions, providing data support and decision-making basis for subsequent system optimization analysis.

[0021] In this embodiment, the system's multi-objective analysis model includes multiple analysis models. These models can be used to predict faults and repairs at different locations or areas within the equipment, enabling a comprehensive analysis of the system's multi-dimensional operating parameters. This comprehensive analysis of multi-dimensional operating parameters, combined with the predictions from the prediction models, optimizes operations, reduces additional costs associated with equipment maintenance or damage caused by improper use, and improves economic efficiency.

[0022] The equipment maintenance module includes: equipment inspection unit, maintenance unit, and maintenance record unit. Among them, the equipment inspection unit is used to generate equipment inspection plans based on the daily maintenance requirements of the equipment after leaving the factory, take photos of the maintained parts, and upload maintenance records. The specific operation steps are as follows: In step 1, the system generates an inspection plan based on the factory maintenance data entered into the equipment parameter library, capturing conditions such as production volume, operating time, reset cycle or appearance changes.

[0023] In step 2, maintenance personnel arrive at the designated location in a timely manner according to the inspection plan. Using the program's integrated photo function (to prevent premature photography), they take photos of the components before and after maintenance and upload key photos. Maintenance personnel select the database equipment and component names, and then record the maintenance process and methods by maintaining the name and filtering the data.

[0024] The maintenance unit in the equipment repair and maintenance module automatically triggers fault alarms and dispatches repair work orders when equipment damage is detected. Based on the warning library models entered into the equipment parameter library, including thresholds for appearance, torque, load, and value, the system generates an alarm and automatically pushes the to-do items to the maintenance station. Maintenance workers can click to view abnormal equipment parameters and pre-set repair methods in the system.

[0025] The maintenance record unit stores historical equipment maintenance data, assisting with equipment failure analysis and maintenance performance evaluation. The system records inspections and maintenance processes, automatically generating graphical reports based on abnormal data, maintenance dates, maintenance personnel, and preset cycles. This facilitates plant management in evaluating equipment quality and maintenance personnel performance, and provides scientific maintenance improvement strategies.

[0026] The intelligent IoT module includes: a device sensing unit and an edge computing unit. The device sensing unit is used to perform material archiving, state simulation, transmission detection, abnormality warning, and replacement prompt processing on sensors collecting parameters such as temperature, humidity, dust, voltage, current, vibration, speed, resistance, load, and visual appearance. The edge computing unit is used by the edge computing gateway to pre-process data at the edge according to the different parameter model characteristics of various devices, and then transmit real-time data to the device multi-objective analysis model pre-built in the cloud server. The particle swarm algorithm is used to fit the model of the system to reduce the amount of data subsequently transmitted to the cloud, improve transmission efficiency, and ultimately obtain the best equipment maintenance and scheduling strategy.

[0027] It should be noted that the edge gateway collects sensor data through the 485 serial port connection, and the cloud server and the edge gateway communicate according to the MQTT protocol. The cloud server deploys the pre-built system equipment multi-objective analysis model f(t0) in advance. The edge gateway sends pre-processed real-time operation data to the cloud server through the MQTT protocol. After receiving the pre-processed real-time operation data, the cloud server analyzes and optimizes it through the model f(t0).

[0028] The intelligent scheduling module further includes: production line scheduling unit and transport vehicle scheduling unit. Among them, the production line scheduling unit is used to analyze the current status, production capacity, usage time, fault prediction, maintenance cycle and other data of multiple production lines. The system automatically sends the production plan to the production line control system. Specifically: based on data models such as equipment operating time, load changes, voltage and current, combined with multi-dimensional data such as mixing station order delivery time and mixing station production capacity, the optimal production plan is dynamically generated to avoid order conflicts and idle production lines. The steps are: Step 1: Preset the production line equipment archive in the scheduling module, including: batching equipment (valves, motors), transport belts, unloading equipment, stirring motors, lubrication equipment, etc.

[0029] Step 2: Configure the production line warning mechanism: For each equipment in the equipment file of each production line, associate the thresholds (ranges) of the potential fault feature model u(t0) with the first-level warning (damage warning, immediately stop and repair), the second-level warning (damage warning, stop operation and repair within a specified time or production range), and the third-level warning (fault hidden danger warning, the system alarm does not stop until the hidden danger is eliminated).

[0030] Step 3: Configure intelligent scheduling logic. For example, when the equipment model f(t0) of production line A triggers a level 1 warning, the platform accurately locates the fault point, outputs an alarm message, and automatically sends a shutdown signal to the industrial control system. The production plan for line A is then automatically allocated to lines B and C. This allocation process is adjusted based on parameters such as the current planned quantity, planned delivery time, production capacity, and equipment operating time of lines B and C. When the equipment model f(t0) of production line A triggers a level 2 warning, the platform accurately locates the fault point and outputs an alarm message. If the alarm is not reset after the specified time (output) set in the warning mechanism, the production plan for line A is automatically allocated to lines B and C. When the equipment model f(t0) of production line A triggers a level 3 warning, the platform accurately locates the fault point and outputs an alarm message until the fault warning is reset.

[0031] The transport vehicle dispatching unit is used to analyze the daily status, transport volume, transport distance, maintenance warning and other data of the mixing station transport vehicles. The system automatically pushes each vehicle to the production line for loading and receiving materials according to the preset priority rules.

[0032] The intelligent scheduling module further includes a production line scheduling unit and a transport vehicle scheduling unit. The latter analyzes the daily status, transport volume, transport distance, and maintenance warnings of the concrete mixing station's transport vehicles. The system automatically routes each vehicle to the production line for loading and receiving according to pre-set priority rules.

[0033] Step 1: Preset the transport vehicle archive in the scheduling module, including: engine vibration, deformation of load-bearing components, loading bin rotation speed, fuel quantity, maintenance time, etc.

[0034] Step 2: Configure the production line warning mechanism: For each equipment component in the transport vehicle file, associate the potential fault feature model u(t0) with the thresholds (ranges) for level 1 warning (damage warning, immediate stop and repair), level 2 warning (damage warning, stop operation and repair within a specified time or production range), and level 3 warning (fault hidden danger warning, system alarm continues until the hidden danger is eliminated).

[0035] Step 3: Configure intelligent scheduling logic. For example, when the model f(t0) for a component on vehicle A triggers a level 1 warning, the platform accurately locates the fault point, outputs an alarm message, and automatically assigns vehicle A's loading task to the next vehicle in the queue. Vehicle A cannot participate in the production loading queue until the fault is resolved. When the model f(t0) for a component on vehicle A triggers a level 2 warning, the platform accurately locates the fault point and outputs an alarm message. If the alarm is not reset after the specified time (production volume) set in the warning mechanism, vehicle A cannot participate in the production loading queue. When the model f(t0) for a component on vehicle A triggers a level 3 warning, the platform accurately locates the fault point and outputs an alarm message until the fault warning is resolved.

[0036] In the above embodiment, by integrating modules such as equipment management, equipment status monitoring, equipment fault analysis, automatic warning of equipment faults, intelligent scheduling, and 3D digital twin model display, all-round intelligent management of equipment operation is achieved, which significantly improves production efficiency and quality, reduces operating costs, and meets the requirements of modern, digital enterprise management for efficiency, precision, and intelligence. Through the collaborative work of the concrete mixing station equipment management cloud platform and various modules, real-time monitoring of equipment operating status, fault warning, and intelligent scheduling are achieved. The equipment warning and diagnosis module can detect potential faults in advance and reduce equipment downtime. The intelligent scheduling system automatically assigns equipment tasks according to the production schedule, optimizes resource utilization, and significantly improves equipment management efficiency and production schedule. Moreover, its equipment repair and maintenance module realizes preventive maintenance of equipment through equipment inspection, after-sales repair, and maintenance record keeping functions, reducing the maintenance costs caused by sudden failures.

[0037] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. An intelligent management platform system for concrete mixing station equipment based on the Internet of Things, characterized in that: include: Concrete mixing station equipment management cloud platform; A data center module connected to the concrete mixing station equipment management cloud platform; An equipment early warning diagnosis and evaluation module that is communicatively connected to the concrete mixing station equipment management cloud platform; an equipment repair and maintenance module communicatively connected to the equipment early warning and diagnosis module; An intelligent IoT module that is communicatively connected to the concrete mixing station equipment management cloud platform; An intelligent scheduling module that is communicatively connected to the concrete mixing station equipment management cloud platform.

2. The intelligent management platform system for concrete mixing station equipment based on the Internet of Things according to claim 1 is characterized in that: The data middle platform module includes: a production capacity equipment model unit and a data large screen unit; The equipment model unit is used to display the 3D model structure of the mixing station equipment, including the equipment status library, parameter library, early warning library and other related information; The data large screen unit is used to obtain energy efficiency analysis, working hours, failure rates, maintenance logs, inspection records, etc. of all mixing station equipment, and display them in a digital and graphical manner.

3. The intelligent management platform system for concrete mixing station equipment based on the Internet of Things according to claim 1 is characterized in that: The equipment early warning diagnosis and evaluation module includes an early warning data unit and a data processing unit; The early warning data unit, based on the current real-time dynamic data of the equipment and the historical records of the dynamic library, combines big data analysis and intelligent algorithms to deeply mine and train dynamic data, accurately predict future trends, generate a multi-objective analysis model f(t0) for the equipment, and generate a potential fault feature model u(t0) through normalization calculation. Fit f(t0) with the health model g(t0) of the equipment for analysis. Under the condition of the same time as the fitting curve, when the early warning threshold curve model u(t0) of the early warning library is triggered, this module outputs alarm data such as time, status, and duration, and triggers the alarm mode of the equipment in the equipment model module; The data processing unit is used to call the data of the equipment in the early warning library in the equipment model unit when the equipment is working normally and compare it with the real-time data monitored by the sensor. When the early warning value of the early warning library is triggered, this module outputs alarm data such as time, status, and duration, and triggers the alarm mode of the equipment in the equipment model module, so that the problem can be discovered and solved in the early stage before the fault occurs.

4. The intelligent management platform system for concrete mixing station equipment based on the Internet of Things according to claim 1 is characterized in that: The equipment repair and maintenance module includes: equipment inspection unit, maintenance unit, and maintenance filing and recording unit; The equipment inspection unit is used to generate equipment inspection plans based on the daily maintenance requirements of the equipment after leaving the factory, and upload maintenance records by scanning codes and taking photos of the maintained parts; Maintenance unit, used to detect equipment damage, automatically triggering fault alarms and dispatching maintenance work orders; The maintenance record unit is used to store historical equipment maintenance data to assist in equipment failure analysis and maintenance work performance evaluation.

5. The intelligent management platform system for concrete mixing station equipment based on the Internet of Things according to claim 1 is characterized in that: The intelligent IoT module includes: a device sensing unit and an edge computing unit; Equipment sensing unit, used to collect parameters such as temperature, humidity, dust, voltage, current, vibration, speed, resistance, load, and visual appearance through sensors to perform material archiving, status simulation, transmission detection, abnormality warning, and replacement prompt processing; The edge computing unit is used for edge computing gateways to preset predictive maintenance engines, multi-level early warning mechanisms, and floating-point filtering algorithms based on different parameter models of various devices, and transmit the processed time-sensitive data to the cloud server through the edge computing gateway.

6. The intelligent management platform system for concrete mixing station equipment based on the Internet of Things according to claim 1 is characterized in that: The intelligent scheduling module further includes: a production line scheduling unit and a transport vehicle scheduling unit; The production line scheduling unit is used to analyze the current status, production capacity, usage cycle and other data of multiple production lines. The system automatically sends the production plan to the production line control system. The transport vehicle dispatching unit is used to analyze the daily status, transport volume, transport distance, maintenance warning and other data of the mixing station transport vehicles. The system automatically pushes each vehicle to the production line for loading and receiving materials according to the preset priority rules.

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