Precast beam manufacturing process automatic identification method and system based on multi-source data

Abstract

The application discloses a prefabricated beam manufacturing process automatic identification method and system based on multi-source data, which comprises the following steps: acquiring the position of steel bars and the binding condition, the position change of a template, the temperature change in a steam curing kiln, the key parameters in the prefabricated beam production process, the gantry crane and the position of the lifting hooks of the gantry crane, and the lifting weight information of the lifting hooks of the gantry crane; the data storage and processing module automatically identifies each process in sequence through an AI algorithm; when the steel bar binding process starts, a process warning unit of a process warning and prefabricated beam process binding module automatically creates a virtual beam number associated with the process, the process warning unit automatically generates the content of a short message notification according to the current production line data and automatically sends the short message notification to the relevant person in charge; the person in charge accesses the virtual beam number list provided by the process warning unit, selects the corresponding virtual beam number to bind the process data, dynamically adjusts the virtual beam number to the real beam structure number, and realizes the automatic identification of the prefabricated beam manufacturing process.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of bridge engineering, and more specifically relates to a prefabricated beam manufacturing process automatic identification method and system based on multi-source data. BACKGROUND

[0002] In the field of bridge construction, prefabricated beams play a key role. They are usually manufactured in factories according to standard procedures and then transported to the construction site for installation. When making these prefabricated components, there are many important steps to be completed, such as steel bar binding, concrete pouring, curing, and tension grouting, etc. The smooth progress of these steps is crucial to ensuring the quality and structural stability of the prefabricated beams. Traditional process identification mainly relies on manual recording or simple sensor monitoring, which is inefficient, inaccurate, and lacks real-time performance. SUMMARY

[0003] To address the above deficiencies or improvements in the prior art, the present application provides a prefabricated beam manufacturing process automatic identification method and system based on multi-source data, which integrates machine vision, sensor technology, Internet of Things, and artificial intelligence algorithms. By installing high-definition cameras and precision sensors, real-time capture of steel bar binding images and key physical parameters such as template displacement, steam curing kiln temperature, key parameters during prefabricated beam production, gantry crane and its hook position, and gantry crane hook load is achieved. After encoding and protocol processing, the data is transmitted to the cloud. The data storage and processing module analyzes the data, automatically records the timestamp of the process activity, and generates a virtual beam number. The data storage and processing module automatically captures key processes such as steel bar binding, concrete pouring, curing, tension grouting, and beam storage, and real-time monitoring is achieved through intelligent algorithms to ensure that each process is bound to the correct virtual beam number. In addition, relevant responsible persons will receive SMS warnings to facilitate timely process data binding. The present application improves the automation level of prefabricated beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for quality control of prefabricated beam manufacturing.

[0004] To achieve the above-mentioned purpose, one aspect of the present application provides a prefabricated beam manufacturing process automatic identification method based on multi-source data, comprising:

[0005] S1: Obtain the position and binding of steel bars, template position changes, temperature changes in the steam curing kiln, key parameters during prefabricated beam production, gantry crane and its hook position, and gantry crane hook load information through the data acquisition and transmission module, and transmit them to the object storage server and time series database of the data storage and processing module;

[0006] S2: The data storage and processing module's process automatic recognition unit automatically recognizes the steel bar binding process, concrete pouring process, precast beam curing process, tensioning and grouting process, and beam storage process in sequence through AI algorithm according to the data stored by the object storage server and time series database;

[0007] S3: When the camera at the steel bar binding station recognizes that the binding process starts, the process warning unit of the process binding module automatically creates a virtual beam number associated with the process; the process warning unit automatically generates an SMS notification content including the virtual beam number and a prompt for the start of the process according to the data of the current production line; and sends the SMS to the relevant person in charge to provide instant process start information; after receiving the SMS reminder, the person in charge accesses the virtual beam number list provided by the process warning unit, selects the corresponding virtual beam number for process data binding, and dynamically adjusts the virtual beam number to the real beam structure number.

[0008] Further, the data acquisition and transmission module in step S1 includes a camera for acquiring the position and binding of steel bars, a displacement sensor for real-time monitoring of template position changes, a temperature sensor for real-time monitoring and recording of temperature changes in the steam curing kiln, intelligent tensioning device sensors and intelligent grouting device sensors for real-time monitoring and control of key parameters in the precast beam production process, position sensors for real-time monitoring of the gantry crane and its hook position, and load sensors for real-time monitoring of the gantry crane hook load.

[0009] Further, the camera is installed in the steel bar binding area; a plurality of displacement sensors are installed on the template; a plurality of temperature sensors are installed in the steam curing kiln; intelligent tensioning device sensors and intelligent grouting device sensors are installed on the intelligent tensioning device and the intelligent grouting device respectively; the position sensors are installed on the gantry crane and its hook respectively; and the load sensor is installed on the gantry crane hook.

[0010] Further, the data storage and processing module in step S2 includes a streaming media server and an object storage server for storing steel bar binding area video and image data, an intelligent gateway, a cloud, and a time series database for transmitting and storing sensor data, and a process automatic recognition unit for automatically recognizing each process in the precast beam production process;

[0011] The process automatic recognition unit automatically recognizes the steel bar binding process, concrete pouring process, precast beam curing process, tensioning and grouting process, and beam storage process in sequence through AI algorithm;

[0012] The process automatic identification unit comprises a steel bar binding process automatic identification module, a concrete pouring process automatic identification module, a precast beam curing process automatic identification module, a tensioning and grouting process automatic identification module and a beam storage process automatic identification module.

[0013] Further, the process pre-warning and precast beam process binding module in step S3 comprises a process pre-warning unit and a precast beam process binding unit; when the camera of the steel bar binding station recognizes the start of the binding process, the process pre-warning unit automatically creates a virtual beam number associated with the process; the process pre-warning unit automatically generates the content of the short message notification according to the data of the current production line, including the virtual beam number and the prompt of the start of the process; and automatically sends the short message to the relevant person in charge to provide instant process start information; after receiving the short message reminder, the person in charge accesses the virtual beam number list provided by the process pre-warning unit, selects the corresponding virtual beam number to bind the process data, and dynamically adjusts the virtual beam number to the real beam structure number.

[0014] Further, the image data captured by the camera in the steel bar binding area in step S1 is transmitted to the streaming media server, which can perform real-time processing and distribution of the video stream, and at the same time store the video data in the object storage server; step S2 comprises the steel bar binding process automatic identification module periodically acquiring real-time image data stream from the object storage server, using a trained neural network model to analyze the collected image data to identify the characteristics of the steel bar binding activity; when the neural network model identifies the steel bar binding activity, the steel bar binding process automatic identification module automatically records the timestamp at that time as the start time of the steel bar binding process; the steel bar binding process automatic identification module continues to monitor the steel bar binding activity until the activity is completed, sets a time threshold, and if no steel bar binding related behavior is detected within the time threshold, the steel bar binding process automatic identification module determines that the steel bar binding process has ended and records the end timestamp, completing the time recording of the steel bar binding process.

[0015] Further, step S2 further comprises the concrete pouring process automatic identification module starting a timing task, which cycles every 10 minutes to extract data collected by the displacement sensor from the time sequence database, including time, oil cylinder displacement value, sensor ID, vibration switch state, vibration time, set vibration frequency and pouring switch state; in the screened data set, the time when the pouring switch state changes from 0 to 1 is identified, which represents the start of pouring; in order to improve data accuracy, it is also determined whether the oil cylinder displacement value is less than the set threshold at this time, and when it is less than the threshold, it is proved that the formwork is in a closed state, thereby determining the start time of the pouring process;

[0016] The relevant field information collected by the temperature sensor in step S1 includes: time, temperature and humidity type, monitoring value and sensor ID;

[0017] The precast beam curing process automatic identification module in step S2 uses a sliding window to calculate the sliding average value and the rate of change of the temperature of the steam curing kiln, and further determines the heating, constant temperature or cooling stage of the steam curing kiln; the precast beam curing process automatic identification module starts a timing task, which is cycled once every 10 minutes, and maintains a state variable to track the current stage; when the state changes from non-heating to heating and exceeds the ambient temperature, the start time is recorded; when the state changes from cooling to constant temperature, and the constant temperature is close to the ambient temperature, the end time is recorded; the start and end times at this time correspond to the start and end times of the curing process, respectively;

[0018] The relevant field information collected by the intelligent tensioning device sensor and the intelligent pressure grouting device sensor in step S1 includes: beam number, actual occurrence time of recording tensioning operation and actual occurrence time of recording pressure grouting operation; the tensioning and pressure grouting process automatic identification module in step S1 starts a timing task, which is cycled once every 10 minutes, records the start time of tensioning as the start time of the tensioning and pressure grouting process, and records the end time of pressure grouting as the end time of the tensioning and pressure grouting process.

[0019] Further, the hoisting sensor is used to monitor the hook load information of the gantry crane in real time, and the data is transmitted to the time series database of the data storage and processing module in real time; the beam storage process automatic identification module in step S2 retrieves the hook load information of the gantry crane from the time series database in real time, and sets a predetermined weight threshold; when the hook load information of the gantry crane exceeds the weight threshold, the beam storage process automatic identification module determines that the gantry crane is performing hoisting operation; at the same time, the beam storage process automatic identification module in step S2 analyzes the spatial position data of the gantry crane and its hook provided by the position sensor to determine whether the gantry crane is located in the specified lifting area of the production line; if the gantry crane is in the lifting area and the hook load exceeds the weight threshold, the beam storage process automatic identification module records the time stamp at this time as the starting time point of the beam storage process.

[0020] Further, after the concrete pouring process automatic identification module in step S3 identifies the start pouring time, the precast beam process binding unit will automatically perform data filtering operation in the database; the filtering condition is: on the same production line, query the recorded steel bar binding process within 24 hours in advance; the precast beam process binding unit identifies the virtual beam number of the corresponding steel bar binding process by matching the process records in the time window; once the matching is confirmed, the record of the concrete pouring process will be bound to the virtual beam number of the steel bar binding process;

[0021] Once the start time of the curing process is identified, the prefabricated beam process binding unit will trigger the data screening operation; the screening logic is: querying the concrete pouring process record within 24 hours on the same production line; through time matching and production line consistency, the prefabricated beam process binding unit identifies the virtual beam number corresponding to the concrete pouring process; after successful identification, the prefabricated beam process binding unit binds the record of the curing process to the virtual beam number;

[0022] After the start time of the beam storage process is recorded, the prefabricated beam process binding unit performs data screening in the background; the screening condition is: querying the tensioning and grouting process within 24 hours on the same production line; the prefabricated beam process binding unit identifies the virtual beam number of the corresponding tensioning and grouting process through time and production line matching; once the corresponding beam number is identified, the beam storage process will be bound to the beam number, ensuring the continuity and integrity of the production record.

[0023] The second aspect of the application provides a prefabricated beam manufacturing process automatic identification system based on multi-source data, which is used to realize the prefabricated beam manufacturing process automatic identification method based on multi-source data, comprising a data acquisition and transmission module, a data storage and processing module, and a process warning and prefabricated beam process binding module; wherein,

[0024] The data acquisition and transmission module comprises a camera for acquiring the position and binding condition of the steel bar, a displacement sensor for real-time monitoring of template position change, a temperature sensor for real-time monitoring and recording of temperature change in the steam curing kiln, intelligent tensioning device sensors and intelligent grouting device sensors for real-time monitoring and control of key parameters in the prefabricated beam production process, position sensors for real-time monitoring of the gantry crane and its hook position, and load sensors for real-time monitoring of the load of the gantry crane hook;

[0025] The data storage and processing module comprises a streaming media server and an object storage server for storing steel bar binding area video and image data, an intelligent gateway, a cloud and a time series database for transmitting and storing various sensor data, and a process automatic identification unit for automatically identifying various processes in the prefabricated beam production process;

[0026] The process early warning and precast beam process binding module comprises a process early warning unit and a precast beam process binding unit; when the camera of the steel bar binding station identifies that the binding process starts, the process early warning unit automatically creates a virtual beam number associated with the process; the process early warning unit automatically generates short message notification content including the virtual beam number and a prompt of the start of the process according to the data of the current production line; and the short message is automatically sent to the relevant person in charge to provide instant process start information; after the person in charge receives the short message prompt, the person in charge accesses the virtual beam number list provided by the process early warning unit, selects the corresponding virtual beam number to bind the process data, and dynamically adjusts the virtual beam number to the real beam structure number.

[0027] The third aspect of the present application provides an electronic device comprising a processor and a memory, the processor and the memory are connected to each other;

[0028] The memory is used for storing a computer program;

[0029] The processor is configured to execute the automatic identification method of the precast beam manufacturing process based on multi-source data when the computer program is called.

[0030] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by a processor to realize the automatic identification method of the precast beam manufacturing process based on multi-source data.

[0031] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0032] (1) The automatic identification method and system of the precast beam manufacturing process based on multi-source data, the system comprises a data acquisition and transmission module, a data storage and processing module, and a process early warning and precast beam process binding module, each module cooperates and works together to form a complete automatic identification system of the precast beam manufacturing process, which can realize full-process automatic management from data acquisition to process identification and data binding, and improve the intelligent level of precast beam production.

[0033] (2) The automatic identification method and system of the precast beam manufacturing process based on multi-source data, the data acquisition and transmission module covers multiple key links in the precast beam production process, such as the position and binding condition of the steel bar, the position change of the formwork, the temperature change in the steam curing kiln, the key parameters in the precast beam production process, the position and lifting weight information of the gantry crane and its hooks, etc., which can comprehensively obtain various data in the production process and provide sufficient data support for subsequent process identification.

[0034] (3) The precast beam manufacturing process automatic identification method and system based on multi-source data of the present application, the process automatic identification unit of the data storage and processing module sequentially identifies the steel bar binding process, the concrete pouring process, the precast beam curing process, the tensioning and grouting process, and the beam storage process through AI algorithm, and can accurately identify the characteristics of each process by using trained neural network model and other technologies, such as the steel bar binding process automatic identification module which identifies the characteristics of the steel bar binding activity by analyzing image data, and records the start and end time of the process, thereby improving the accuracy and efficiency of process identification.

[0035] (4) The precast beam manufacturing process automatic identification method and system based on multi-source data of the present application, when the camera at the steel bar binding station identifies the start of the binding process, the process warning unit can automatically create a virtual beam number associated with the process, and automatically generate an SMS notification content to send to the relevant person in charge, providing instant process start information, so that the person in charge can timely understand the production progress and arrange subsequent work in time; after receiving the SMS reminder, the person in charge can access the virtual beam number list, select the corresponding virtual beam number for process data binding, and dynamically adjust the virtual beam number to the real beam structure number, realizing flexible conversion between virtual number and actual number, and ensuring the accuracy and continuity of production records. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 A flowchart of a precast beam manufacturing process automatic identification method based on multi-source data according to an embodiment of the present application;

[0037] Figure 2 A structural schematic diagram of a precast beam manufacturing process automatic identification system based on multi-source data according to an embodiment of the present application;

[0038] Figure 3 A structural schematic diagram of an electronic device according to an embodiment of the present application;

[0039] Figure 4 A schematic diagram of concrete formwork displacement monitoring data of a precast beam manufacturing process automatic identification method based on multi-source data according to an embodiment of the present application;

[0040] Figure 5 A schematic diagram of steam curing kiln temperature monitoring of a precast beam manufacturing process automatic identification method based on multi-source data according to an embodiment of the present application;

[0041] Figure 6 A schematic diagram of steel bar binding process automatic identification of a precast beam manufacturing process automatic identification method based on multi-source data according to an embodiment of the present application.

[0042] In all the drawings, the same reference signs represent the same technical features, specifically:

[0043] MQTT (Message Queuing Telemetry Transport) is a lightweight messaging protocol designed for low-bandwidth, high-latency, or unreliable network environments. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0045] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, when an element is referred to as "fixed to", "provided with" or "provided in" another element, it can be directly on another element or indirectly on another element. When an element is referred to as "connected to" another element, it can be directly connected to another element or indirectly connected to another element; the terms "mounting", "connection", "connection", "provision" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0046] In bridge construction, high-quality production of precast beams is crucial, but traditional process monitoring methods are inefficient, inaccurate and lack real-time performance. With the development of industrial automation and information technology, it is feasible to automatically identify and monitor the precast beam manufacturing process. Automatic identification technology can improve production efficiency, reduce human error, and provide real-time data for quality control.

[0047] Based on the above reasons, as shown in Figure 1 An aspect of the present application provides a precast beam manufacturing process automatic identification method based on multi-source data, comprising the following steps:

[0048] S1: Obtain the position and binding of the steel bar, the position change of the formwork, the temperature change in the steam curing kiln, the key parameters in the precast beam production process, the gantry crane and its hook position, and the gantry crane hook load information through the data acquisition and transmission module, and transmit to the data storage and processing module and the object storage server and time series database of the data storage and processing module;

[0049] S2: The object storage server of the data storage and processing module stores the steel binding area video and image data, and the time series database stores the sensor data of the data collection and transmission module; the process automatic identification unit automatically identifies the steel binding process, concrete pouring process, precast beam curing process, tension grouting process and beam storage process in sequence according to the data stored in the object storage server and the time series database through AI algorithm;

[0050] S3: When the camera of the steel binding station recognizes that the binding process starts, the process warning unit of the process warning and precast beam process binding module automatically creates a virtual beam number associated with the process; the process warning unit automatically generates the content of the short message notification according to the data of the current production line, including the virtual beam number and the prompt of the start of the process; and automatically sends the short message to the relevant person in charge to provide instant process start information; after receiving the short message reminder, the person in charge accesses the virtual beam number list provided by the process warning unit, selects the corresponding virtual beam number for process data binding, and dynamically adjusts the virtual beam number to the real beam structure number.

[0051] Further, the data acquisition and transmission module in step S1 includes a camera for acquiring the position and binding condition of the steel bar, a displacement sensor for real-time monitoring of template position change, a temperature sensor for real-time monitoring and recording of temperature change in the steam curing kiln, intelligent tensioning equipment sensor and intelligent pressure grouting equipment sensor for real-time monitoring and control of key parameters in the production process of the precast beam, position sensor for real-time monitoring of the gantry crane and its hook position, and load sensor for real-time monitoring of the load of the gantry crane hook; the data storage and processing module in step S2 includes a streaming media server and an object storage server for storing steel bar binding area video and image data, an intelligent gateway, a cloud and a time series database for transmitting and storing sensor data, and a process automatic identification unit for automatically identifying each process in the production process of the precast beam; the process early warning and precast beam process binding module in step S3 includes a process early warning unit and a precast beam process binding unit; the position and binding condition of the steel bar are acquired by the data acquisition and transmission module, the template position change is monitored in real time, the temperature change in the steam curing kiln is monitored and recorded, the key parameters in the production process of the precast beam are monitored and controlled in real time, and are transmitted to the data storage and processing module; the data storage and processing module automatically identifies the steel bar binding process, the concrete pouring process, the precast beam curing process, the tensioning and pressure grouting process, and the beam storage process in sequence through AI algorithm; when the camera at the steel bar binding station identifies the start of the binding process, the process early warning unit automatically creates a virtual beam number associated with the process; the process early warning unit automatically generates an SMS notification content including the virtual beam number and a prompt for the start of the process according to the data of the current production line; and the SMS is automatically sent to the relevant person in charge to provide instant process start information; after receiving the SMS reminder, the person in charge accesses the virtual beam number list provided by the process early warning unit, selects the corresponding virtual beam number for process data binding, and dynamically adjusts the virtual beam number to the real beam structure number.

[0052] Further, the data acquisition and transmission module is connected with the data storage and processing module, and the data storage and processing module is connected with the process warning and precast beam process binding module; the camera in step S1 is installed in the steel bar binding area to collect the steel bar binding area video, to ensure that the collected video stream has sufficient resolution and image quality, so as to accurately identify the position and binding condition of the steel bar; the installation position of the camera needs to ensure that the picture completely covers the steel bar binding area of the production line without dead angles; the image data collected by the camera can automatically identify the steel bar binding process; the data transmission between the camera and the data storage and processing module includes four steps of video signal conversion, video data compression, video stream packaging and transmission and network transmission; specifically, the camera captures the video stream of the steel bar binding area in real time, and converts the analog signal of the video stream into a digital format and compresses it, that is, the analog signal is converted into a discrete digital signal by an analog-to-digital converter, realizing the digitization of video data; the digitized video data usually contains a large amount of data, and direct transmission will occupy a large amount of bandwidth resources. Therefore, the digitized video data needs to be compressed. In order to realize efficient data transmission, advanced video coding technology such as H.264 or H.265 is adopted to compress the digitized video data to reduce the size of the video data. The compressed video data is packaged into network transmission data packets, and common packaging formats such as RTSP (Real-Time Streaming Protocol) or RTP (Real-Time Transport Protocol) can support real-time transmission of video data. The video stream captured by the camera needs to be pushed to the streaming media server in real time, and the role of the streaming media server is to receive, process and distribute the video stream so as to be received by the back-end data storage and processing module. Through the streaming media server, the video data can be pushed to the monitoring system or other systems that need real-time video data in real time, realizing real-time monitoring and analysis of the steel bar binding process. Through the above steps, the video stream captured by the high-definition camera in the steel bar binding area can be transmitted to the data storage and processing module in real time and efficiently, providing basic data support for subsequent process identification and monitoring.

[0053] RTSP (Real-Time Streaming Protocol): RTSP is a network control protocol used to control streaming media servers. It allows interaction between clients and servers, such as pausing, playing, and stopping video streams. RTSP is usually used with RTP to transmit real-time video streams. RTP (Real-Time Transport Protocol): RTP is a network protocol specifically designed for transmitting real-time data such as audio and video. It can provide timestamp and sequence number information to ensure the real-time and sequential nature of data. RTP is usually used to package compressed video data for transmission over the network.

[0054] After the high-definition camera is installed in the steel binding area, the video stream captured by the camera will undergo a series of processing and transmission steps to ensure that the data can be transmitted to the data storage and processing module in real time and accurately.

[0055] Further, in step S1, during the production of precast beams, the installation of the formwork is a critical step that directly affects the quality of concrete pouring and molding. To ensure the accuracy and efficiency of formwork installation, several displacement sensors are installed on the formwork, which are usually composed of high-precision measuring devices and can monitor small displacements, thereby ensuring that the formwork is accurately opened and closed according to design requirements. By monitoring the displacement of the formwork, the usage status of the formwork can be automatically recorded, and the concrete pouring process can be automatically identified through the displacement monitoring data of the formwork. For example, Figure 4 Schematic diagram of concrete formwork displacement monitoring data.

[0056] Further, steam curing is a key step in the production of precast beams, which aims to accelerate the hardening process of concrete by providing suitable temperature and humidity conditions, thereby improving its strength and durability. Temperature control in the steam curing kiln is of great significance to ensure the quality of precast beams. To ensure that the steam curing process achieves the desired effect, several temperature sensors need to be installed in the steam curing kiln to monitor and record the temperature changes in the kiln in real time. These temperature sensors should be able to withstand the high temperature and high humidity environment in the steam curing kiln and provide accurate and continuous temperature data. The curing process can be automatically identified through the temperature monitoring data of the steam curing kiln. For example, Figure 5 Schematic diagram of steam curing kiln temperature monitoring.

[0057] Further, in the production of precast beams, tensioning and grouting are two crucial processes that directly affect the structural performance and durability of precast beams. To improve the accuracy and automation level of these two processes, the intelligent tensioning equipment sensor and the intelligent grouting equipment sensor are usually equipped with various sensors for real-time monitoring and control of key parameters in the production process; the intelligent tensioning equipment sensor is mainly used to monitor the tensioning stroke and tensioning force; the intelligent grouting equipment sensor is used to monitor the grouting amount and grouting pressure.

[0058] Further, the data transmission between the displacement sensor, temperature sensor, intelligent tensioning equipment sensor, and intelligent grouting equipment sensor and the data storage and processing module includes data acquisition and conversion, data packaging and transmission, data reception and distribution; wherein,

[0059] Data acquisition and conversion includes sensor monitoring parameters and analog signal to digital signal; sensor monitoring parameters specifically include: during the production of precast beams, the displacement of the formwork, the temperature of the steam curing kiln, the tension of the precast beam and the pressure of the intelligent pressure grouting device are monitored in real time through displacement sensors, temperature sensors, intelligent tensioning device sensors and intelligent pressure grouting device sensors, and the corresponding physical quantities are converted into analog electrical signals, providing basic data for subsequent data transmission and processing; analog signal to digital signal includes: through an analog-to-digital converter (ADC), the analog signal is converted into a digital signal. The analog-to-digital converter quantizes the continuous analog signal into discrete digital values, so that the data can be recognized and processed in the digital system;

[0060] Data packaging and transmission includes: through industrial computer or intelligent gateway, the converted digital signal is packaged to form a message format conforming to a specific protocol; the packaged message is uploaded to the cloud through industrial Ethernet, Wi-Fi or cellular network connection methods;

[0061] Specifically, the MQTT protocol is used to package the data into MQTT messages; the packaged MQTT messages are uploaded to the cloud through industrial Ethernet, Wi-Fi or cellular network connection methods; wherein the MQTT message contains two parts: topic (Topic) and message content. Topic: used to distinguish different data types or data sources, such as "formwork displacement", "steam curing kiln temperature", "tension" and the like. The setting of the topic makes the data receiver can subscribe and receive the interested data according to the topic. Message content: contains specific monitoring data such as displacement value, temperature value, force value, pressure value, etc. The message content is usually represented in text or binary format, and the specific format is determined according to the actual needs;

[0062] Data receiving and distribution includes the MQTT broker (Broker) in the cloud receiving messages from the device end, and distributing the messages to the data processing module subscribing to the topic according to the topic.

[0063] Further, in the production process of precast beams, a variety of data is collected, including video image data and numerical data collected by various sensors. Different types of data usually require different storage solutions for efficient storage, retrieval and analysis. Video and image data are usually large in size and require flexible access, so they are suitable for storage in object storage servers. Object storage is a storage architecture for storing large-scale unstructured data, which can provide high scalability and high availability. Sensor data such as displacement, temperature and tension are usually recorded in time series, which are suitable for storage in time series databases. Time series databases are optimized for the characteristics of time series data and can efficiently process and query large amounts of time series data. By automatically identifying each process, production efficiency can be effectively improved, human errors can be reduced, and real-time data support can be provided for quality control; Based on the above reasons, InfluxDB is a commonly used time series database that supports high-throughput data writing and fast data querying; The process automatic identification unit includes a steel bar binding process automatic identification module, a concrete pouring process automatic identification module, a precast beam curing process automatic identification module, a tensioning and grouting process automatic identification module, and a beam storage process automatic identification module.

[0064] Further, the image data captured by the camera in the steel bar binding area in step S1 is transmitted to the streaming server, which can perform real-time processing and distribution of video streams, and store video data in the object storage server; In step S2, the steel bar binding process automatic identification module periodically obtains real-time image data stream from the object storage server, uses the trained neural network model to analyze the collected image data to identify the characteristics of the steel bar binding activity; When the neural network model identifies the steel bar binding activity, the steel bar binding process automatic identification module automatically records the time stamp at that time as the start time of the steel bar binding process; The steel bar binding process automatic identification module continues to monitor the steel bar binding activity until the activity is completed, sets a time threshold, and if no steel bar binding related behavior is detected within the time threshold, the steel bar binding process automatic identification module determines that the steel bar binding process has ended and records the end time stamp, completing the time recording of the steel bar binding process.

[0065] Further, as Figure 6The schematic diagram of automatic identification of steel bar binding process, the automatic identification module of concrete pouring process opens a timing task, which is cycled once every 10 minutes, extracts the data collected by the displacement sensor from the time series database (InfluxDB time series database), including time, cylinder displacement value (cylinderStroke1 to cylinderStroke12), sensor ID, vibrator switch state (vibratorRun), vibrator time (vibratorTime), set vibration frequency (setFrequency) and pouring switch state (pouringStart) and the like. In the screened data set, the time when the pouring switch state (pouringStart) changes from 0 to 1 is identified, which represents the start of pouring. In order to improve the data accuracy, it is also judged whether the cylinder displacement value is less than a certain threshold value at this time, and when it is less than the threshold value, it is proved that the template is in a closed state, and accordingly the start time of the pouring process can be determined.

[0066] Further, the relevant field information collected by the temperature sensor includes: time (time), temperature humidity type (metric_type), monitoring value (value) and sensor ID (sensor_id). The whole curing process of the steam curing kiln is divided into three stages: the temperature rising stage, the constant temperature stage and the temperature falling stage. However, in each stage, the temperature will fluctuate, so the temperature data collected by the temperature sensor cannot directly determine the three stages. The sliding window algorithm is usually used to calculate the moving average or other statistical indicators of time series data to smooth the data or identify trends. In determining the temperature rising, constant temperature and temperature falling stages of the steam curing kiln, a sliding window is used to calculate the sliding average and rate of change of the temperature of the steam curing kiln. The automatic identification module of the precast beam curing process calculates the rate of change of the temperature of the steam curing kiln according to the sliding window size of the temperature of the steam curing kiln and the temperature values of the adjacent two time points collected by the temperature sensor; determines whether the steam curing kiln is in the temperature rising stage, the constant temperature stage or the temperature falling stage according to the rate of change of the temperature of the steam curing kiln and the pre-set temperature threshold value; when the rate of change of the temperature of the steam curing kiln is greater than the pre-set temperature change rate threshold value of the temperature rising stage, it is in the temperature rising stage; when the rate of change of the temperature of the steam curing kiln is within the pre-set temperature change rate threshold value range of the constant temperature stage, it is in the constant temperature stage; when the rate of change of the temperature of the steam curing kiln is less than the pre-set temperature change rate threshold value of the temperature falling stage, it is in the temperature falling stage.

[0067] The sliding average of the temperature of the steam curing kiln is represented by formula (1):

[0068]

[0069] wherein MA t is the sliding average of the temperature of the steam curing kiln at time point t, n is the sliding window size of the temperature of the steam curing kiln, T iis the temperature value of the steam-curing kiln at time point i;

[0070] Between the moving average values of the steam-curing kiln temperatures of two continuous time points, we can calculate the change rate to represent the change trend of the temperature over time: the change rate of the steam-curing kiln temperature is represented by formula (2):

[0071]

[0072] wherein, CR t is the change rate of the steam-curing kiln temperature at time point t, MA t and MA t-1 are the moving average values of the steam-curing kiln temperature at time points t and t-1 respectively, and Δt is the time difference between the two adjacent time points;

[0073] According to the change rate CR t of the steam-curing kiln temperature, compared with the pre-set threshold value, the current stage is determined:

[0074] If CR t > θ rise , it is in the heating-up stage;

[0075] If θ stable > CR t > -θ stable , it is in the constant-temperature stage;

[0076] If CR t < θ fall , it is in the cooling-down stage;

[0077] wherein, θ rise is the temperature change rate threshold value of the heating-up stage, which is a positive value; θ stable and -θ stable are the positive and negative values of the temperature change rate threshold value of the constant-temperature stage, respectively, and θ fall is the temperature change rate threshold value of the cooling-down stage, which is a negative value.

[0078] The precast beam curing process automatic identification module starts a timing task, which is recycled every 10 minutes, and maintains a state variable to track the current stage; when the state changes from non-heating to heating and exceeds the ambient temperature, the start time is recorded; when the state changes from cooling to constant temperature, and the constant temperature is close to the ambient temperature, the end time is recorded; at this time, the start and end times correspond to the start and end times of the curing process, respectively.

[0079] Further, the relevant field information collected by the intelligent tensioning device sensor and the intelligent mud jacking device sensor includes: beam number (line_no), actual occurrence time of recording tensioning operation (record_time), and actual occurrence time of recording mud jacking operation (mudjacking_date); the tensioning and mud jacking process automatic identification module starts a timing task, which is cycled once every 10 minutes, records the start time of tensioning as the start time of the tensioning and mud jacking process, and records the end time of mud jacking as the end time of the tensioning and mud jacking process.

[0080] Further, the position sensors are installed on the gantry crane and the hook thereof respectively, for collecting the position coordinates (x, y, z) of the gantry crane and the hook thereof in the three-dimensional space and the related time stamp (time); the load sensor is installed on the hook of the gantry crane, for monitoring the load information in real time and transmitting the data to the time sequence database of the data storage and processing module in real time; the beam storage process automatic identification module retrieves the load information of the hook of the gantry crane from the time sequence database in real time, and sets a predetermined weight threshold; when the load information of the hook of the gantry crane exceeds the weight threshold, the beam storage process automatic identification module determines that the gantry crane is performing hoisting operation; at the same time, the beam storage process automatic identification module analyzes the spatial position data of the gantry crane and the hook thereof provided by the position sensors, to determine whether the gantry crane is located in the specified hoisting area of the production line; if the gantry crane is in the hoisting area and the load exceeds the weight threshold, the beam storage process automatic identification module records the time stamp at this time as the starting time point of the beam storage process.

[0081] Further, the process warning and precast beam process binding module includes a process warning unit and a precast beam process binding unit; when the camera of the steel bar binding station recognizes the start of the binding process, the process warning unit automatically creates a virtual beam number associated with the process; the virtual number includes the current date, the production area, and the production line; the format of the virtual number is: “YYYY.MM.DD-Region name-Production line number”. For example, a process starts on December 16, 2024, on the No. 1 production line in the north area of the workshop, and its virtual beam number is “2024.12.16-North area 1# workshop-1# production line”; the process warning unit automatically generates the content of the short message notification according to the data of the current production line, including the virtual beam number and the prompt of the start of the process; the short message is automatically sent to the relevant person in charge, providing instant process start information; after receiving the short message reminder, the person in charge can access the virtual beam number list provided by the process warning unit, select the corresponding virtual beam number for process data binding, and dynamically adjust the virtual beam number to the real beam structure number.

[0082] Further, the precast beam process binding unit in step S3 is used to bind the virtual beam number of the concrete pouring process and the steel binding process, bind the virtual beam number of the precast beam curing process and the concrete pouring process, bind the virtual beam number of the tensioning and grouting process and the precast beam curing process, and bind the virtual beam number of the beam storage process and the tensioning and grouting process.

[0083] Step S3 includes: when the camera of the steel binding station recognizes the start of the binding process, the process warning unit of the process warning and precast beam process binding module automatically creates a virtual beam number associated with the process; through the precast beam process binding unit of the process warning and precast beam process binding module, the virtual beam number of the concrete pouring process and the steel binding process is bound, the virtual beam number of the precast beam curing process and the concrete pouring process is bound, the virtual beam number of the tensioning and grouting process and the precast beam curing process is bound, and the virtual beam number of the beam storage process and the tensioning and grouting process is bound, ensuring that each process is bound with the correct virtual beam number, dynamically adjusting the virtual beam number to the real beam structure number, and further ensuring the continuity and integrity of the production record;

[0084] After the concrete pouring process automatic recognition module recognizes the start pouring time, the precast beam process binding unit will automatically perform a data screening operation in the database; the screening condition is: on the same production line, querying the recorded steel binding process within 24 hours; the precast beam process binding unit identifies the virtual beam number of the corresponding steel binding process by matching the process record within the time window; once the match is confirmed, the record of the concrete pouring process will be bound to the virtual beam number, ensuring data consistency;

[0085] Binding of the curing process and the virtual beam number of the concrete pouring process: as soon as the start time of the curing process is recognized, the precast beam process binding unit will trigger a data screening operation; the screening logic is: querying the concrete pouring process record within 24 hours on the same production line; through time matching and production line consistency, the precast beam process binding unit identifies the virtual beam number corresponding to the concrete pouring process; after successful identification, the precast beam process binding unit binds the record of the curing process to the virtual beam number, forming a complete process chain;

[0086] Binding of the beam storage process and the tensioning and grouting process: after the start time of the beam storage process is recorded, the precast beam process binding unit performs data screening in the background; the screening condition is: on the same production line, querying the tensioning and grouting process within 24 hours; the precast beam process binding unit identifies the virtual beam number of the corresponding tensioning and grouting process through time and production line matching; once the corresponding beam number is identified, the beam storage process will be bound to the beam number, ensuring the continuity and integrity of the production record.

[0087] As Figure 2 The second aspect of the present application provides a prefabricated beam manufacturing process automatic identification system based on multi-source data, which comprises a data acquisition and transmission module, a data storage and processing module, and a process early warning and prefabricated beam process binding module; the data acquisition and transmission module is connected with the data storage and processing module, and the data storage and processing module is connected with the process early warning and prefabricated beam process binding module; the data acquisition and transmission module comprises a camera for acquiring the position and binding condition of the steel bar, a displacement sensor for real-time monitoring of the position change of the template, a temperature sensor for real-time monitoring and recording of the temperature change in the steam curing kiln, intelligent tensioning device sensors and intelligent jacking device sensors for real-time monitoring and control of key parameters in the prefabricated beam production process, a position sensor for real-time monitoring of the position of the gantry crane and its hook, and a load sensor for real-time monitoring of the load of the gantry crane hook; the data storage and processing module comprises a streaming media server and an object storage server for storing video and image data of the steel bar binding area, an intelligent gateway, a cloud and a time series database for transmitting and storing the data of each sensor, and a process automatic identification unit for automatically identifying each process in the prefabricated beam production process; the process early warning and prefabricated beam process binding module comprises a process early warning unit and a prefabricated beam process binding unit; the data acquisition and transmission module acquires the position and binding condition of the steel bar, real-time monitors the position change of the template, monitors and records the temperature change in the steam curing kiln, and real-time monitors and controls the key parameters in the prefabricated beam production process, and transmits them to the data storage and processing module; the data storage and processing module automatically identifies the steel bar binding process, the concrete pouring process, the prefabricated beam curing process, the tensioning and jacking process, and the beam storage process in sequence through AI algorithm; when the camera at the steel bar binding station identifies the start of the binding process, the process early warning unit automatically creates a virtual beam number associated with the process; the process early warning unit automatically generates a short message notification content including the virtual beam number and a prompt of the start of the process according to the data of the current production line; and the short message is automatically sent to the relevant person in charge to provide instant process start information; after receiving the short message prompt, the person in charge accesses the virtual beam number list provided by the process early warning unit, selects the corresponding virtual beam number for process data binding, and dynamically adjusts the virtual beam number to the real beam structure number.

[0088] It should be noted that the prefabricated beam manufacturing process automatic identification system based on multi-source data provided by the embodiments of the present application can be a computer program (including program code) running in a computer device, for example, the prefabricated beam manufacturing process automatic identification system based on multi-source data is an application software; the prefabricated beam manufacturing process automatic identification system based on multi-source data can be used to execute the corresponding steps in the above-mentioned method provided by the embodiments of the present application.

[0089] In some possible implementation manners, the precast beam manufacturing process automatic identification system based on multi-source data provided in the embodiment can be implemented in a combination of hardware and software. For example, the precast beam manufacturing process automatic identification system based on multi-source data provided in the embodiment can be a hardware decoding processor programmed to execute the precast beam manufacturing process automatic identification method based on multi-source data provided in the embodiment. For example, the hardware decoding processor can be one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic elements.

[0090] In some possible implementation manners, the precast beam manufacturing process automatic identification system based on multi-source data provided in the embodiment can be implemented in software. The software can be in the form of programs and plug-ins, and include a series of modules to implement the precast beam manufacturing process automatic identification method based on multi-source data provided in the embodiment.

[0091] The precast beam manufacturing process automatic identification system and method based on multi-source data provided in the embodiment integrate machine vision, sensor technology, the Internet of Things, and artificial intelligence algorithms. The system captures real-time images of steel binding and key physical parameters such as template displacement, steam curing kiln temperature, key parameters in the precast beam production process, gantry crane and its hook position, and gantry crane hook load through the installation of high-definition cameras and precision sensors. The data is transmitted to the cloud after encoding and protocol processing, and the data storage and processing module analyzes the data, automatically records the timestamp of the process activity, and generates a virtual beam number. The data storage and processing module automatically captures key processes such as steel binding, concrete pouring, curing, tension grouting, and beam storage, and monitors them in real time through intelligent algorithms to ensure that each process is bound to the correct virtual beam number. In addition, relevant responsible persons will receive a short message warning to bind the process data in time. The application improves the automation level of precast beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for quality control of precast beam manufacturing.

[0092] The third aspect of the application also provides an electronic device, Figure 3 FIG. 1 is a structural schematic diagram of the electronic device of the embodiment, like Figure 3As shown, the electronic device 1000 in the embodiment can include a processor 1001, a network interface 1004 and a memory 1005, in addition, the above-mentioned electronic device 1000 can also include a user interface 1003, and at least one communication bus 1002. Wherein, the communication bus 1002 is used to realize the connection communication between the components. Wherein, the user interface 1003 can include a display (Display), a keyboard (Keyboard), and the optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1005 can be a high-speed RAM memory, or a non-volatile memory, for example, at least one disk storage. The memory 1005 can also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer readable storage medium can include an operating system, a network communication module, a user interface module and a device control application.

[0093] As shown, the electronic device 1000, the network interface 1004 can provide network communication function; and the user interface 1003 is mainly used for providing the interface for the user to input; and the processor 1001 can be used to call the device control application stored in the memory 1005, to realize: Figure 3

[0094] Through the data acquisition and transmission module, the position and binding condition of the steel bar, the position change of the formwork, the temperature change in the steam curing kiln, the key parameters in the production process of the precast beam, the gantry crane and its hook position and the information of the gantry crane hook load are acquired, and are transmitted to the data storage and processing module;

[0095] The object storage server of the data storage and processing module stores the steel bar binding area video and image data, and the time sequence database stores the sensor data of the data acquisition and transmission module; the process automatic recognition unit automatically recognizes the steel bar binding process, the concrete pouring process, the precast beam curing process, the tensioning and grouting process and the beam storage process in turn through the AI algorithm according to the data stored in the object storage server and the time sequence database;

[0096] ​When the camera of the steel bar binding station recognizes that the binding process starts, the process warning unit of the process warning and precast beam process binding module automatically creates a virtual beam number associated with the process; through the precast beam process binding unit of the process warning and precast beam process binding module, the concrete pouring process and the virtual beam number of the steel bar binding process are bound, the precast beam curing process and the virtual beam number of the concrete pouring process are bound, the tensioning and grouting process and the virtual beam number of the precast beam curing process are bound, and the beam storage process and the virtual beam number of the tensioning and grouting process are bound, so as to ensure that each process is bound with the correct virtual beam number, and then ensure the continuity and integrity of the production record.

[0097] It should be understood that in some possible implementations, the above-mentioned processor 1001 can be a central processing unit (CPU), and the processor can also be other general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The memory can include read-only memory and random access memory, and provide instructions and data for the processor. Part of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0098] In specific implementations, the above-mentioned electronic device 1000 can execute the implementation mode provided by each step of the above-mentioned Figure 1 through its built-in various functional modules. For details, please refer to the implementation mode provided by each step above, which will not be described here.

[0099] The electronic device provided by the embodiment integrates machine vision, sensor technology, Internet of Things and artificial intelligence algorithm; through the installation of high-definition cameras and precision sensors, the steel bar binding image and key physical parameters such as template displacement, steam curing kiln temperature, key parameters in the precast beam production process, gantry crane and its hook position, and gantry crane hook load are captured in real time; after the data is encoded and protocol processed, it is transmitted to the cloud, the data storage and processing module analyzes the data, automatically records the timestamp of the process activity, and generates a virtual beam number, the data storage and processing module automatically captures the key processes such as steel bar binding, concrete pouring, curing, tensioning and grouting, and beam storage, and through intelligent algorithm real-time monitoring, ensures that each process is bound with the correct virtual beam number. In addition, the relevant person in charge will receive a short message warning, so as to timely carry out process data binding. The application improves the automation level of precast beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for quality control of precast beam manufacturing.

[0100] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the method provided by each step Figure 1 The method provided by each step can refer to the implementation manner provided by each step, which will not be repeated here.

[0101] The computer readable storage medium provided by the embodiment integrates machine vision, sensor technology, Internet of Things and artificial intelligence algorithm; through the high-definition camera and the precision sensor, the steel bar binding image and the key physical parameters such as the template displacement, the steam curing kiln temperature, the key parameters in the precast beam production process, the gantry crane and the hook position, and the hook load of the gantry crane are captured in real time; the data is transmitted to the cloud after being encoded and protocol processed, the data storage and processing module analyzes the data, automatically records the time stamp of the process activity, and generates a virtual beam number, the data storage and processing module automatically captures the key processes such as the steel bar binding, the concrete pouring, the curing, the tensioning and grouting and the beam storage, and through the intelligent algorithm, the real-time monitoring is realized, so that each process is bound with the correct virtual beam number. In addition, the relevant person in charge will receive a short message warning, so as to timely bind the process data. The application improves the automation level of the precast beam manufacturing, reduces the human error, enhances the production efficiency, and provides real-time data support for the quality control of the precast beam manufacturing.

[0102] Any reference to memory, storage, database, or other medium used in the various embodiments provided by the present application can include non-volatile and / or volatile memory. The non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. The volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0103] Those skilled in the art can easily understand that the above description is only the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for automatic identification of precast beam manufacturing processes based on multi-source data, characterized in that, include: S1: The data acquisition and transmission module acquires information on the position and binding status of reinforcing bars, changes in formwork position, temperature changes in the steam curing kiln, key parameters during the precast beam production process, the position of the gantry crane and its hook, and the lifting weight information of the gantry crane hook, and transmits this information to the object storage server and time-series database of the data storage and processing module. S2: The automatic process identification unit of the data storage and processing module automatically identifies the steel bar binding process, concrete pouring process, precast beam curing process, tensioning and grouting process and beam storage process in sequence according to the data stored in the object storage server and time-series database using AI algorithms. S3: When the camera at the rebar tying station detects the start of the tying process, the process warning unit of the process warning and precast beam process binding module automatically creates a virtual beam number associated with that process. The process warning unit automatically generates an SMS notification based on the current production line data, including the virtual beam number and a prompt indicating the start of the process. The SMS is then automatically sent to the relevant personnel, providing immediate process start information. After receiving the SMS notification, the personnel access the list of virtual beam numbers provided by the process warning unit and select the corresponding virtual beam number for process data binding. Through the precast beam process binding unit of the process warning and precast beam process binding module, the virtual beam numbers for the concrete pouring process and rebar tying process are bound together; the virtual beam numbers for the precast beam curing process and concrete pouring process are bound together; the virtual beam numbers for the tensioning and grouting process and precast beam curing process are bound together; and the virtual beam numbers for the beam storage process and tensioning and grouting process are bound together, ensuring that each process is bound to the correct virtual beam number and dynamically adjusting the virtual beam number to the actual beam structure number.

2. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 1, characterized in that: The data acquisition and transmission module in step S1 includes a camera for acquiring the position and binding status of the reinforcing bars, a displacement sensor for real-time monitoring of changes in the formwork position, a temperature sensor for real-time monitoring and recording of temperature changes in the steam curing kiln, intelligent tensioning equipment sensors and intelligent grouting equipment sensors for real-time monitoring and control of key parameters during the precast beam production process, a position sensor for real-time monitoring of the gantry crane and its hook position, and a load sensor for real-time monitoring of the load on the gantry crane hook.

3. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 2, characterized in that: The camera is installed in the rebar binding area; several displacement sensors are installed on the template; several temperature sensors are installed inside the steam curing kiln; intelligent tensioning equipment sensors and intelligent grouting equipment sensors are respectively installed on the intelligent tensioning equipment and the intelligent grouting equipment; the position sensors are respectively installed on the gantry crane and its hook; the lifting weight sensor is installed on the gantry crane hook.

4. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 3, characterized in that: The data storage and processing module in step S2 includes a streaming media server and an object storage server for storing video and image data of the rebar binding area, an intelligent gateway, cloud and time-series database for transmitting and storing data from various sensors, and an automatic process identification unit for automatically identifying various processes in the precast beam production process. The automatic process identification unit uses AI algorithms to automatically identify the rebar tying process, concrete pouring process, precast beam curing process, tensioning and grouting process, and beam storage process in sequence. The automatic process identification unit includes an automatic identification module for rebar tying, an automatic identification module for concrete pouring, an automatic identification module for precast beam curing, an automatic identification module for tensioning and grouting, and an automatic identification module for beam storage.

5. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to any one of claims 2-4, characterized in that: The process warning and precast beam process binding module mentioned in step S3 includes a process warning unit and a precast beam process binding unit. When the camera at the rebar binding station detects the start of the binding process, the process warning unit automatically creates a virtual beam number associated with that process. The process warning unit automatically generates an SMS notification based on the current production line data, including the virtual beam number and a prompt that the process has started. The SMS is then automatically sent to the relevant person in charge, providing immediate process start information. After receiving the SMS notification, the person in charge accesses the list of virtual beam numbers provided by the process warning unit, selects the corresponding virtual beam number, and binds the process data to dynamically adjust the virtual beam number to the actual beam structure number.

6. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 5, characterized in that: In step S1, the image data captured by the camera in the rebar binding area is transmitted to the streaming media server. The streaming media server can process and distribute the video stream in real time, and at the same time store the video data in the object storage server. Step S2 includes an automatic rebar tying process identification module periodically acquiring real-time image data streams from an object storage server. A trained neural network model is used to analyze the acquired image data to identify the characteristics of the rebar tying activity. When the neural network model identifies a rebar tying activity, the automatic rebar tying process identification module automatically records the timestamp as the start time of the rebar tying process. The module continues to monitor the rebar tying activity until it ends. A time threshold is set; if no rebar tying-related behavior is detected within this time threshold, the module determines that the rebar tying process has ended and records the end timestamp, thus completing the time recording for the rebar tying process.

7. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 6, characterized in that: Step S2 includes the automatic identification module for concrete pouring process starting a timed task, which cycles every 10 minutes, and extracts data collected by displacement sensors from the time-series database, including time, cylinder displacement value, sensor ID, vibration switch status, vibration time, set vibration frequency, and pouring switch status; in the filtered data set, the moment when the pouring switch status changes from 0 to 1 is identified, which indicates the start of pouring; to improve data accuracy, it is also determined whether the cylinder displacement value at this time is less than a set threshold. When it is less than the threshold, it proves that the formwork is in a closed state, and the start time of the pouring process is determined accordingly. The relevant field information collected by the temperature sensor in step S1 includes: time, temperature and humidity type, monitored value, and sensor ID; In step S2, the automatic identification module for the curing process of precast beams uses a sliding window to calculate the sliding average value and rate of change of the temperature in the steam curing kiln, thereby determining the heating, constant temperature or cooling stage of the steam curing kiln. The precast beam curing process automatic identification module starts a timed task, which cycles every 10 minutes to maintain a state variable to track the current stage. When the state changes from non-heating to heating and exceeds the ambient temperature, the start time is recorded. When the state changes from cooling to constant temperature and the constant temperature is close to the ambient temperature, the end time is recorded. The start and end times at this time correspond to the start and end times of the curing process, respectively. In step S1, the relevant field information collected by the sensors of the intelligent tensioning equipment and the intelligent grouting equipment includes: beam number, actual occurrence time of the tensioning operation, and actual occurrence time of the grouting operation. In step S2, the automatic identification module for the tensioning and grouting process starts a timed task, which cycles every 10 minutes, recording the start time of tensioning as the start time of the tensioning and grouting process, and recording the end time of grouting as the end time of the tensioning and grouting process.

8. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 6, characterized in that: The lifting weight information of the gantry crane hook is monitored in real time by the lifting weight sensor, and the data is transmitted to the time-series database of the data storage and processing module in real time. In step S2, the automatic identification module for the beam storage process retrieves the lifting weight information of the gantry crane hook from the time-series database in real time and sets a predetermined weight threshold. When the lifting weight information of the gantry crane hook exceeds the weight threshold, the automatic identification module for the beam storage process determines that the gantry crane is performing a lifting operation. At the same time, the automatic identification module for the beam storage process analyzes the spatial position data of the gantry crane and its hook provided by the position sensor to determine whether the gantry crane is located in the designated lifting area of ​​the production line. If the gantry crane is in the lifting area and the lifting weight exceeds the weight threshold, the automatic identification module for the beam storage process records the timestamp at this time as the start time of the beam storage process.

9. The method for automatic identification of precast beam manufacturing processes based on multi-source data according to claim 6, characterized in that: In step S3, after the concrete pouring process automatic identification module identifies the start time of pouring, the precast beam process binding unit will automatically perform a data filtering operation in the database. The filtering condition is: on the same production line, query the steel reinforcement binding process recorded within the past 24 hours. The precast beam process binding unit identifies the virtual beam number of the corresponding steel reinforcement binding process by matching the process records within the time window. Once the match is confirmed, the record of the concrete pouring process will be bound to the virtual beam number of the steel reinforcement binding process. Once the start time of the curing process is identified, the precast beam process binding unit will trigger a data filtering operation. The filtering logic is as follows: query the concrete pouring process records within 24 hours on the same production line; through time matching and production line consistency, the precast beam process binding unit identifies the virtual beam number corresponding to the concrete pouring process; after successful identification, the precast beam process binding unit binds the curing process record to the virtual beam number. After the start time of the beam storage process is recorded, the precast beam process binding unit performs data filtering in the background. The filtering condition is: on the same production line, query the tensioning and grouting processes within the past 24 hours. The precast beam process binding unit identifies the virtual beam number of the corresponding tensioning and grouting process by matching the time and production line. Once the corresponding beam number is identified, the beam storage process will be bound to that beam number to ensure the continuity and integrity of the production records.

10. An automatic identification system for precast beam manufacturing processes based on multi-source data, characterized in that, The method for automatically identifying precast beam manufacturing processes based on multi-source data, as described in any one of claims 1-9, includes a data acquisition and transmission module, a data storage and processing module, and a process early warning and precast beam process binding module; wherein... The data acquisition and transmission module includes a camera for acquiring the position and binding status of the reinforcing bars, a displacement sensor for real-time monitoring of template position changes, a temperature sensor for real-time monitoring and recording of temperature changes in the steam curing kiln, an intelligent tensioning equipment sensor and an intelligent grouting equipment sensor for real-time monitoring and control of key parameters in the precast beam production process, a position sensor for real-time monitoring of the gantry crane and its hook position, and a load sensor for real-time monitoring of the load on the gantry crane hook. The data storage and processing module includes a streaming media server and an object storage server for storing video and image data of the rebar binding area, an intelligent gateway, cloud and time-series database for transmitting and storing data from various sensors, and an automatic process identification unit for automatically identifying various processes in the precast beam production process. The process early warning and precast beam process binding module includes a process early warning unit and a precast beam process binding unit. When the camera at the rebar tying station detects the start of the tying process, the process early warning unit automatically creates a virtual beam number associated with that process. Based on the current production line data, the process early warning unit automatically generates an SMS notification, including the virtual beam number and a prompt that the process has started. The SMS is then automatically sent to the relevant person in charge, providing immediate process start information. After receiving the SMS notification, the person in charge accesses the list of virtual beam numbers provided by the process early warning unit, selects the corresponding virtual beam number, and binds the process data to dynamically adjust the virtual beam number to the actual beam structure number.

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