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

Abstract

The invention discloses an automatic identification method and system for a precast beam manufacturing process based on multi-source data. The method comprises the steps that the position and binding condition of reinforcing steel bars, the position change of a formwork, the temperature change in a steam-curing kiln, key parameters in the precast beam production process, the positions of a gantry crane and a lifting hook of the gantry crane and lifting weight information of the lifting hook of the gantry crane are obtained; the data storage and processing module automatically identifies each process in sequence through an AI algorithm; when a reinforcing steel bar binding process starts, a process early warning unit of a process early warning and precast beam process binding module automatically creates a virtual beam number associated with the process, and the process early warning unit automatically generates short message notification content according to current production line data and automatically sends the short message notification content to a related principal; and the person in charge accesses a virtual beam number list provided by the process early warning unit, selects a corresponding virtual beam number to carry out process data binding, and dynamically adjusts the virtual beam number into a real beam structure number, so that automatic identification of the prefabricated beam manufacturing process is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge engineering, and more specifically, relates to an automatic identification method and system for precast beam manufacturing processes based on multi-source data. Background Art

[0002] In the field of bridge construction, precast beams play a crucial role. They are usually manufactured in factories according to standard processes and then transported to the construction site for installation. When fabricating these precast components, many important steps need to be completed, such as steel bar binding, concrete pouring, curing, and tensioning and grouting. The smooth progress of these steps is extremely critical for ensuring their quality and structural stability. Traditional process identification mainly relies on manual records or simple sensor monitoring, which are inefficient, inaccurate, and lack real-time performance. Summary of the Invention

[0003] Aiming at the above defects or improvement requirements of the prior art, the present invention provides an automatic identification method and system for precast beam manufacturing processes based on multi-source data, integrating machine vision, sensor technology, the Internet of Things, and artificial intelligence algorithms; by installing high-definition cameras and precision sensors, it captures images of steel bar binding and key physical parameters in real time, such as formwork displacement, steam curing kiln temperature, key parameters during the production process of precast beams, the position of the gantry crane and its hook, and the lifting weight of the gantry crane hook; after the data is encoded and processed by protocols, it is transmitted to the cloud. The data storage and processing module analyzes the data, automatically records the timestamps of process activities, and generates virtual beam numbers. The data storage and processing module automatically captures key processes such as steel bar binding, concrete pouring, curing, tensioning and grouting, and beam storage, and through intelligent algorithms, it monitors in real time to ensure that each process is bound to the correct virtual beam number. In addition, relevant responsible persons will receive SMS warnings for timely binding of process data. The present invention 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.

[0004] To achieve the above object, one aspect of the present invention provides an automatic identification method for precast beam manufacturing processes based on multi-source data, including:

[0005] S1: Obtain information on the position and binding of steel bars, changes in formwork position, changes in temperature inside the steam curing kiln, key parameters during the production process of precast beams, the position of the gantry crane and its hook, and the lifting weight of the gantry crane hook 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 process automatic recognition unit of the data storage and processing module 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 an AI algorithm based on the data stored in the object storage server and the time series database;

[0007] S3: When the camera at the steel bar 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 this process; the process warning unit automatically generates a text message notification content according to the data of the current production line, including the virtual beam number and a prompt for the start of the process; and automatically sends the text message to the relevant person in charge to provide instant process start information; after receiving the text message reminder, the person in charge accesses the list of virtual beam numbers 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 actual beam structure number.

[0008] Further, the data acquisition and transmission module in step S1 includes a camera for obtaining the position and binding situation of steel bars, a displacement sensor for real-time monitoring of the change in the position of the formwork, a temperature sensor for real-time monitoring and recording of the temperature change 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 production process of precast beams, 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.

[0009] Further, the camera is installed in the steel bar binding area; several displacement sensors are installed on the formwork; several temperature sensors are installed in the steam curing kiln; the intelligent tensioning equipment sensor and the intelligent grouting equipment sensor are respectively installed on the intelligent tensioning equipment and the intelligent grouting equipment; the position sensor is respectively installed on the gantry crane and its hook; 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 video and image data in the steel bar binding area, an intelligent gateway, a cloud, and a time series database for transmitting and storing data of each sensor, and a process automatic recognition unit for automatically recognizing each process in the production process of precast beams;

[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 an AI algorithm;

[0012] The process automatic recognition unit includes an automatic recognition module for steel bar binding process, an automatic recognition module for concrete pouring process, an automatic recognition module for precast beam curing process, an automatic recognition module for tensioning and grouting process, and an automatic recognition module for beam storage process.

[0013] Further, the process warning and precast beam process binding module in step S3 includes a process warning unit and a precast beam process binding unit; when the camera at the steel bar binding station recognizes the start of the binding process, the process warning unit automatically creates a virtual beam number associated with this process; the process warning unit automatically generates a text message notification content according to the data of the current production line, including the virtual beam number and a prompt for the start of the process; and automatically sends the text message to the relevant person in charge to provide immediate information on the start of the process; after receiving the text message reminder, the person in charge accesses the list of virtual beam numbers 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 actual beam structure number.

[0014] Further, in step S1, the image data captured by the camera in the steel bar binding area 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 includes that the automatic recognition module for steel bar binding process regularly obtains the real-time image data stream from the object storage server, and 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 recognizes the steel bar binding activity, the automatic recognition module for steel bar binding process automatically records the timestamp at this time as the start time of the steel bar binding process; the automatic recognition module for steel bar binding process continues to monitor the steel bar binding activity until the activity ends, sets a time threshold, and if no steel bar binding-related behavior is detected within this time threshold, the automatic recognition module for steel bar binding process determines that the steel bar binding process has ended and records the end timestamp to complete the time record of the steel bar binding process.

[0015] Further, step S2 also includes that the automatic recognition module for concrete pouring process starts a timing task, which loops every 10 minutes, extracts the data collected by the displacement sensor from the time series database, including time, oil cylinder displacement value, sensor ID, vibration switch status, vibration time, set vibration frequency, and pouring switch status; in the filtered data set, by identifying the moment when the pouring switch status changes from 0 to 1, this moment represents the start of pouring; in order to improve the data accuracy, at the same time, it is judged whether the oil cylinder displacement value is less than the set threshold at this time. When it is less than this threshold, it proves that the formwork is in the closed state, and accordingly, the start time of the pouring process is determined;

[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] In step S2, the precast beam curing process automatic recognition module calculates the sliding average value and change rate of the steam curing kiln temperature using a sliding window, and then determines the heating, constant temperature, or cooling stage of the steam curing kiln; the precast beam curing process automatic recognition module starts a timing task, which loops every 10 minutes, and maintains a status variable to track the current stage; when the status changes from non-heating to heating and exceeds the ambient temperature, the start time is recorded; when the status 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 equipment sensor and the intelligent grouting equipment sensor in step S1 includes: beam number, the actual occurrence time of recording the tensioning operation, and the actual occurrence time of recording the grouting operation; the tensioning and grouting process automatic recognition module in step S1 starts a timing task, which loops every 10 minutes, records the start time of tensioning as the start time of the tensioning and grouting process, and records the end time of grouting as the end time of the tensioning and grouting process.

[0019] Furthermore, the lifting weight information of the gantry crane hook is monitored in real time through the load cell, 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 recognition module in step S2 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 beam storage process automatic recognition module determines that the gantry crane is performing a lifting operation; at the same time, the beam storage process automatic recognition 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 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 beam storage process automatic recognition module records the time stamp at this time as the start time point of the beam storage process.

[0020] Furthermore, after the concrete pouring process automatic recognition module in step S3 identifies the start pouring time, the precast beam process binding unit will automatically perform a data screening operation in the database; the screening condition is: query forward for the recorded steel bar binding process within 24 hours on the same production line; the precast beam process binding unit identifies the virtual beam number of the corresponding steel bar 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 bar binding process;

[0021] Once the start time of the curing process is identified, the precast beam process binding unit will trigger a data screening operation. The screening logic is as follows: Query forward 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 records of the curing process to this virtual beam number.

[0022] After the start time of the beam storage process is recorded, the precast beam process binding unit conducts data screening in the background. The screening condition is: Query forward the tensioning and grouting processes within 24 hours on the same production line. 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 this beam number to ensure the continuity and integrity of production records.

[0023] The second aspect of the present invention provides an automatic identification system for precast beam manufacturing processes based on multi-source data, which is used to implement the automatic identification method for precast beam manufacturing processes based on multi-source data, including a data collection and transmission module, a data storage and processing module, and a process warning and precast beam process binding module. Among them,

[0024] The data collection and transmission module includes a camera for obtaining the position and binding situation of steel bars, a displacement sensor for real-time monitoring of the change in the position of the formwork, a temperature sensor for real-time monitoring and recording of the temperature change 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 during the production of precast beams, 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.

[0025] The data storage and processing module includes 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 data from each sensor, and a process automatic identification unit for automatically identifying each process during the production of precast beams.

[0026] The process warning and precast beam process binding module includes a process warning unit and a precast beam process binding unit; when the camera at 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 process warning unit automatically generates a text message notification content based on the data of the current production line, including the virtual beam number and a prompt for the start of the process; and automatically sends the text message to the relevant person in charge to provide immediate information on the start of the process; after receiving the text message reminder, the person in charge accesses the list of virtual beam numbers 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 actual beam structure number.

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

[0028] The memory is used to store computer programs;

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

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

[0031] Generally speaking, compared with the prior art through the above technical solutions conceived by the present invention, the following beneficial effects can be achieved:

[0032] (1) The automatic identification method and system of precast beam manufacturing processes based on multi-source data of the present invention, the system includes a data collection and transmission module, a data storage and processing module, and a process warning and precast beam process binding module. The modules cooperate with each other and work together to form a complete automatic identification system for precast beam manufacturing processes, which can realize the full-process automatic management from data collection to process identification and then to data binding, and improve the intelligent level of precast beam production.

[0033] (2) The automatic identification method and system of precast beam manufacturing processes based on multi-source data of the present invention, the data collection and transmission module covers multiple key links in the precast beam production process, such as the position and binding situation of steel bars, the change of template position, 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 hook, etc., and can obtain various types of data in the production process in an all-round way, providing sufficient data support for subsequent process identification.

[0034] (3) The automatic identification method and system for precast beam manufacturing processes based on multi-source data of the present invention. The process automatic 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 through AI algorithms. By using technologies such as the trained neural network model, it can accurately identify the characteristics of each process. For example, the steel bar binding process automatic identification module identifies the characteristics of the steel bar binding activity by analyzing image data and records the start and end times of the process, improving the accuracy and efficiency of process identification.

[0035] (4) The automatic identification method and system for precast beam manufacturing processes based on multi-source data of the present invention. When the camera at the steel bar binding station recognizes the start of the binding process, the process warning unit can automatically create a virtual beam number associated with this process and automatically generate SMS notification content to send to the relevant person in charge, providing instant process start information, enabling the person in charge to timely understand the production progress and facilitating the timely arrangement of subsequent work. 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 actual beam structure number, realizing the flexible conversion between the virtual number and the actual number and ensuring the accuracy and continuity of production records. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic flow chart of an automatic identification method for precast beam manufacturing processes based on multi-source data according to an embodiment of the present invention;

[0037] Figure 2 It is a schematic structural diagram of an automatic identification system for precast beam manufacturing processes based on multi-source data according to an embodiment of the present invention;

[0038] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention;

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

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

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

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

[0043] MQTT (Message Queuing Telemetry Transport) is a lightweight message transport protocol designed specifically for low - bandwidth, high - latency, or unreliable network environments. Specific implementation manners

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention 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 invention, it should be noted that unless otherwise clearly specified and limited, when an element is referred to as "fixed to", "arranged on" or "disposed on" another element, it can be directly on the other element or indirectly on the other element. When an element is referred to as "connected to" another element, it can be directly connected to the other element or indirectly connected to the other element; the terms "installed", "connected", "connected", "provided with" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

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

[0047] For the above reasons, as Figure 1 shown, one aspect of the present invention provides an automatic identification method for precast beam manufacturing processes based on multi - source data, including the following steps:

[0048] S1: Obtain the position and binding condition of steel bars, the change of formwork position, the change of temperature in the steam curing kiln, the key parameters during the production process of precast beams, the position of the gantry crane and its hook, and the lifting weight information of the gantry crane hook 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.

[0049] S2: The object storage server of the data storage and processing module stores the video and image data of the steel bar binding area, and the time series database stores the data of each sensor of the data acquisition and transmission module; 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 algorithms based on the data stored in the object storage server and the time series database;

[0050] S3: When the camera at the steel bar 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 this process; the process warning unit automatically generates the SMS notification content according to the data of the current production line, including the virtual beam number and the prompt for the start of the process; and automatically sends the SMS to the relevant person in charge to provide the 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.

[0051] Further, the data acquisition and transmission module in step S1 includes a camera for obtaining the position and binding condition of steel bars, a displacement sensor for real-time monitoring of the change in the position of the formwork, a temperature sensor for real-time monitoring and recording of the temperature change in the steam curing kiln, intelligent tensioning equipment sensors and intelligent grouting equipment sensors for real-time monitoring and control of key parameters in the production process of precast beams, 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 in step S2 includes 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 data of each sensor, and a process automatic recognition unit for automatically recognizing each process in the production process of precast beams; the process warning and precast beam process binding module in step S3 includes a process warning unit and a precast beam process binding unit; the position and binding condition of steel bars, the real-time monitoring of the change in the position of the formwork, the monitoring and recording of the temperature change in the steam curing kiln, and the real-time monitoring and control of key parameters in the production process of precast beams are obtained through the data acquisition and transmission module and transmitted to the data storage and processing module; the data storage and processing module 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 sequence through AI algorithms; when the camera at 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 process warning unit automatically generates a short message notification content based on the data of the current production line, including the virtual beam number and a prompt for the start of the process; and automatically sends the short message to the relevant person in charge to provide instant information on the start of the process; after receiving the short message reminder, the person in charge accesses the list of virtual beam numbers 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.

[0052] Furthermore, the data acquisition and transmission module is connected to the data storage and processing module, and the data storage and processing module is connected to the process warning and precast beam process binding module; in step S1, the camera is installed in the steel bar binding area to collect the video of the steel bar binding area, ensuring that the collected video stream has sufficient resolution and image quality, so as to accurately identify the position and binding situation of the steel bars; 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: video signal conversion, video data compression, video stream encapsulation and transmission, and network transmission; specifically, the video stream of the steel bar binding area is captured in real time by the camera, and the analog signal of the video stream is converted into a digital format and compressed, that is, the continuous waveform of the analog signal is converted into discrete digital signals through an analog-to-digital converter to realize the digitization of video data; the digitized video data usually contains a large amount of data volume, and direct transmission will occupy a large amount of bandwidth resources. Therefore, it is necessary to compress the digitized video data. To achieve efficient data transmission, advanced video coding technologies such as H.264 or H.265 are used to compress the digitized video data to reduce the size of the video data. The compressed video data is encapsulated into network transmission data packets, and common encapsulation formats such as RTSP (Real-Time Streaming Protocol) or RTP (Real-Time Transport Protocol) can support the 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. The role of the streaming media server is to receive, process and distribute the video stream so that the backend data storage and processing module can receive it. Through the streaming media server, the video data can be pushed to the monitoring system or other systems that require real-time video data in real time to realize the 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 interactions between clients and servers, such as pausing, playing, and stopping video streams. RTSP is usually used together with RTP for transmitting real-time video streams. RTP (Real-Time Transport Protocol): RTP is a network protocol specifically used for transmitting real-time data, such as audio and video. It can provide information such as timestamps and sequence numbers to ensure the real-time nature and order of data. RTP is usually used to encapsulate compressed video data for transmission over the network.

[0054] After installing high-definition cameras in the steel bar binding area, the video streams captured by the cameras will go through 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] Furthermore, in step S1, during the production of precast beams, the installation of the formwork is a key step, directly affecting the quality of concrete pouring and shaping. To ensure the accuracy and efficiency of formwork installation, several of the displacement sensors are installed on the formwork. These displacement sensors are usually composed of high-precision measuring devices and can monitor minute displacements, thereby ensuring that the formwork opens and closes precisely according to the 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. As Figure 4 It is a schematic diagram of the displacement monitoring data of the concrete formwork.

[0056] Furthermore, steam curing is a key link in the production of precast beams. Its purpose is to accelerate the hardening process of concrete by providing appropriate temperature and humidity conditions and improve its strength and durability. The temperature control in the steam curing kiln is of great significance for ensuring the quality of precast beams. To ensure that the steam curing process achieves the expected effect, several of the temperature sensors need to be installed in the steam curing kiln to monitor and record the temperature changes in the steam curing 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. As Figure 5 It is a schematic diagram of the temperature monitoring of the steam curing kiln.

[0057] Furthermore, during the production of precast beams, tensioning and grouting are two crucial processes, directly affecting the structural performance and durability of precast beams. To improve the accuracy and automation level of these two processes, the intelligent tensioning equipment sensors and intelligent grouting equipment sensors are usually equipped with a variety of sensors for real-time monitoring and control of key parameters during the production process; the intelligent tensioning equipment sensors are mainly used to monitor the tensioning stroke and tensioning force; the intelligent grouting equipment sensors are used to monitor the incoming and returning slurry volume and the incoming and returning slurry pressure.

[0058] Furthermore, the data transmission between the displacement sensors, temperature sensors, intelligent tensioning equipment sensors, and intelligent grouting equipment sensors and the data storage and processing module includes: data acquisition and conversion, data encapsulation and transmission, data reception and distribution; among which,

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

[0060] Data encapsulation and transmission include: encapsulating the converted digital signals through an industrial control computer or an intelligent gateway to form a message format that conforms to a specific protocol; the encapsulated message is uploaded to the cloud through connection methods such as industrial Ethernet, Wi-Fi, or cellular networks;

[0061] Specifically, the MQTT protocol is used to encapsulate data into MQTT messages; the encapsulated MQTT messages are uploaded to the cloud through connection methods such as industrial Ethernet, Wi-Fi, or cellular networks; among them, the MQTT message consists of two parts: a topic and a message content; Topic: used to distinguish different data types or data sources, such as "formwork displacement", "steam curing kiln temperature", "tensile force", etc. The setting of the topic enables the data receiver to subscribe to and receive the data of interest according to the topic. Message content: contains specific monitoring data, such as displacement values, temperature values, force values, pressure values, etc. The message content is usually represented in text or binary format, and the specific format depends on the actual requirements;

[0062] Data reception and distribution include the MQTT broker in the cloud receiving messages from the device side and distributing the messages to the data processing modules that subscribe to the topic according to the topic.

[0063] Furthermore, during the production process of precast beams, a wide 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 volume and require flexible access methods, so they are suitable for storage in an object storage server. Object storage is a storage architecture for storing large-scale unstructured data, capable of providing high scalability and high availability. Sensor data such as displacement, temperature, and tensile force are usually recorded in time series and are suitable for storage in a time series database. The time series database is optimized specifically 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; for 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] Furthermore, the image data captured by the camera in the steel bar binding area in step S1 is transmitted to the streaming media server. The streaming media server can perform real-time processing and distribution of the video stream and store the video data in the object storage server at the same time; in step S2, the steel bar binding process automatic identification module regularly obtains the 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 timestamp at this 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 ends. A time threshold is set. If no behavior related to steel bar binding is detected within this time threshold, the steel bar binding process automatic identification module determines that the steel bar binding process has ended and records the end timestamp to complete the time record of the steel bar binding process.

[0065] Furthermore, as Figure 6It is a schematic diagram for automatic identification of the steel bar binding process. The automatic identification module for the concrete pouring process starts a timing task, which cycles every 10 minutes. It extracts the data collected by the displacement sensor from the time series database (InfluxDB time series database), including time, cylinder displacement values (cylinderStroke1 to cylinderStroke12), sensor ID, vibrator switch status (vibratorRun), vibration time (vibratorTime), set vibration frequency (setFrequency), and pouring switch status (pouringStart), etc. In the filtered dataset, by identifying the moment when the pouring switch status (pouringStart) changes from 0 to 1, this moment indicates the start of pouring. To improve the data accuracy, it also judges whether the cylinder displacement value is less than a certain threshold at this time. When it is less than the threshold, it proves that the formwork is in the closed state, and based on this, the start time of the pouring process can be determined.

[0066] Furthermore, the relevant field information collected by the temperature sensor includes: time (time), temperature and humidity type (metric_type), monitored value (value), and sensor ID (sensor_id). The entire curing process of the steam curing kiln is divided into three stages: heating stage, constant temperature stage, and cooling stage. However, in each stage, the temperature fluctuates, so it is impossible to directly judge these three stages based on the temperature data collected by the temperature sensor. 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. When determining the heating, constant temperature, and cooling stages of the steam curing kiln, a sliding window is used to calculate the sliding average and change rate of the steam curing kiln temperature. The automatic identification module for the precast beam curing process calculates the change rate of the steam curing kiln temperature according to the sliding window size of the steam curing kiln temperature and the temperature values at two adjacent time points collected by the temperature sensor; determines whether the steam curing kiln is in the heating stage, constant temperature stage, or cooling stage according to the change rate of the steam curing kiln temperature and the pre-set temperature threshold; when the change rate of the steam curing kiln temperature is greater than the pre-set temperature change rate threshold for the heating stage, it is in the heating stage; when the change rate of the steam curing kiln temperature is within the pre-set temperature change rate threshold range for the constant temperature stage, it is in the constant temperature stage; when the change rate of the steam curing kiln temperature is less than the pre-set temperature change rate threshold for the cooling stage, it is in the cooling stage.

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

[0068]

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

[0070] Between the moving averages of the temperatures of two consecutive steam curing kilns, we can calculate the rate of change to represent the changing trend of temperature over time: The rate of change of the steam curing kiln temperature is expressed by Equation (2):

[0071]

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

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

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

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

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

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

[0078] The automatic identification module for the curing process of precast beams starts a timed task, which loops every 10 minutes, and maintains a status variable to track the current stage; when the status changes from non - heating to heating and exceeds the ambient temperature, the start time is recorded; when the status 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.

[0079] Furthermore, the relevant field information collected by the sensors of the intelligent tensioning equipment and the intelligent grouting equipment includes: beam number (line_no), the actual occurrence time of recording the tensioning operation (record_time), and the actual occurrence time of recording the grouting operation (mudjacking_date); the tensioning and grouting process automatic identification module starts a timing task, which loops every 10 minutes. The start time of tensioning is recorded as the start time of the tensioning and grouting process, and the end time of grouting is recorded as the end time of the tensioning and grouting process.

[0080] Furthermore, the position sensors are respectively installed on the gantry crane and its hook, and are used to collect the position coordinates (x, y, z) of the gantry crane and its hook in the three-dimensional space and the relevant time stamps (time). The load cell is installed on the hook of the gantry crane to monitor the load information in real time and transmit the data to the time series 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 series 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 a hoisting operation; at the same time, the beam storage process automatic identification module analyzes the spatial position data of the gantry crane and its hook provided by the position sensors to determine whether the gantry crane is located in the designated 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 start time point of the beam storage process.

[0081] Furthermore, the process warning and precast beam process binding module includes a process warning unit and a precast beam process binding unit; when the camera at the steel bar binding station recognizes the start of the binding process, the process warning unit automatically creates a virtual beam number associated with this process; the virtual number includes the current date, production area, and production line; the format of the virtual number is: "YYYY.MM.DD - area name - production line number". For example, if a process starts on December 16, 2024, on production line 1 in workshop 1 of the north area, its virtual beam number is "2024.12.16 - North Area #1 Workshop - #1 Production Line"; the process warning unit automatically generates the SMS notification content according to the data of the current production line, including the virtual beam number and the prompt of the start of the process; the SMS is automatically sent to the relevant person in charge to provide the instant start information of the process; after receiving the SMS reminder, the person in charge can access the list of virtual beam numbers 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 numbers of the concrete pouring process and the steel bar binding process, bind the virtual beam numbers of the precast beam curing process and the concrete pouring process, bind the virtual beam numbers of the tensioning and grouting process and the precast beam curing process, and bind the virtual beam numbers of the beam storage process and the tensioning and grouting process.

[0083] Step S3 includes: when the camera at the steel bar 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 this process; through the precast beam process binding unit of the process warning and precast beam process binding module, bind the virtual beam numbers of the concrete pouring process and the steel bar binding process, bind the virtual beam numbers of the precast beam curing process and the concrete pouring process, bind the virtual beam numbers of the tensioning and grouting process and the precast beam curing process, and bind the virtual beam numbers of the beam storage process and the tensioning and grouting process, 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, thereby 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, query forward for the recorded steel bar binding process within 24 hours; the precast beam process binding unit identifies the corresponding virtual beam number of the steel bar 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 this virtual beam number to ensure data consistency;

[0085] Binding of the curing process and the virtual beam number of the concrete pouring process: Once 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: query forward for 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 record of the curing process to this virtual beam number to form a complete process chain;

[0086] Binding of the beam storage process and the beam number of 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, query forward for the tensioning and grouting process within 24 hours; the precast beam process binding unit identifies the corresponding virtual beam number of the 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 this beam number to ensure the continuity and integrity of the production record.

[0087] As shown Figure 2 In the second aspect of the present invention, an automatic recognition system for precast beam manufacturing processes based on multi-source data is provided, including a data acquisition and transmission module, a data storage and processing module, and a process warning and precast beam process binding module; the data acquisition and transmission module is connected to the data storage and processing module, and the data storage and processing module is connected to the process warning and precast beam process binding module; the data acquisition and transmission module includes a camera for obtaining the position and binding condition of steel bars, a displacement sensor for real-time monitoring of the change in the position of the formwork, a temperature sensor for real-time monitoring and recording of the temperature change 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 during the production process of precast beams, 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 includes 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 data of each sensor, and a process automatic recognition unit for automatically recognizing each process during the production process of precast beams; the process warning and precast beam process binding module includes a process warning unit and a precast beam process binding unit; the position and binding condition of steel bars, the real-time monitoring of the change in the position of the formwork, the monitoring and recording of the temperature change in the steam curing kiln, and the real-time monitoring and control of key parameters during the production process of precast beams are obtained through the data acquisition and transmission module and transmitted to the data storage and processing module; the data storage and processing module 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 sequence through an AI algorithm; when the camera at the steel bar binding station recognizes the start of the binding process, the process warning unit automatically creates a virtual beam number associated with this process; the process warning unit automatically generates a text message notification content according to the data of the current production line, including the virtual beam number and a prompt for the start of the process; and automatically sends the text message to the relevant person in charge to provide instant information on the start of the process; after receiving the text message reminder, the person in charge accesses the list of virtual beam numbers 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.

[0088] It should be noted that the automatic recognition system for precast beam manufacturing processes based on multi-source data provided in the embodiment of the present invention may be a computer program (including program code) running in a computer device. For example, the automatic recognition system for precast beam manufacturing processes based on multi-source data is an application software; the automatic recognition system for precast beam manufacturing processes based on multi-source data may be used to execute the corresponding steps in the above method provided in the embodiment of the present application.

[0089] In some feasible embodiments, the automatic identification system for precast beam manufacturing processes based on multi-source data provided in this embodiment can be implemented in a combination of software and hardware. As an example, the automatic identification system for precast beam manufacturing processes based on multi-source data provided in the embodiments of the present application can be a processor in the form of a hardware decoding processor, which is programmed to execute the automatic identification method for precast beam manufacturing processes based on multi-source data provided in the embodiments of the present application. For example, a processor in the form of a hardware decoding processor can employ 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 components.

[0090] In some feasible embodiments, the automatic identification system for precast beam manufacturing processes based on multi-source data provided in this embodiment can be implemented in software, which can be software in the form of programs and plugins, and includes a series of modules to implement the automatic identification method for precast beam manufacturing processes based on multi-source data provided in the embodiments of the present invention.

[0091] The automatic identification system and method for precast beam manufacturing processes based on multi-source data provided in this embodiment integrate machine vision, sensor technology, the Internet of Things, and artificial intelligence algorithms; by installing high-definition cameras and precision sensors, it captures in real time images of steel bar binding and key physical parameters, such as formwork displacement, curing kiln temperature, key parameters during the production process of precast beams, the position of the gantry crane and its hook, and the lifting weight of the gantry crane hook; after the data is encoded and processed by the protocol, it is transmitted to the cloud. The data storage and processing module analyzes the data, automatically records the timestamps of process activities, and generates virtual beam numbers. The data storage and processing module automatically captures key processes such as steel bar binding, concrete pouring, curing, tensioning and grouting, and beam storage, and through intelligent algorithms, it monitors in real time 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 binding of process data. The present invention improves the automation level of precast beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for the quality control of precast beam manufacturing.

[0092] The third aspect of the present invention also provides an electronic device, Figure 3 which is a schematic structural diagram of the electronic device in this embodiment, as Figure 3As shown in the figure, the electronic device 1000 in this embodiment may include: a processor 1001, a network interface 1004, and a memory 1005. In addition, the above-mentioned electronic device 1000 may further include: a user interface 1003, and at least one communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. Among them, the user interface 1003 may include a display screen (Display) and a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. Optionally, the memory 1005 may also be at least one storage device located far from the aforementioned processor 1001. As Figure 3 shown, the memory 1005, as a computer-readable storage medium, may include an operating system, a network communication module, a user interface module, and a device control application program.

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

[0094] Obtain the position and binding condition of steel bars, the change of formwork position, the change of temperature in the steam curing kiln, the key parameters during the production process of precast beams, the position of the gantry crane and its hook, and the lifting weight information of the gantry crane hook through the data acquisition and transmission module, and transmit them to the data storage and processing module;

[0095] The object storage server of the data storage and processing module stores the video and image data of the steel bar binding area, and the time series database stores the data of each sensor 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 sequence through the AI algorithm according to the data stored in the object storage server and the time series database;

[0096] When the camera at the steel bar 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 this process; through the precast beam process binding unit of the process warning and precast beam process binding module, the concrete pouring process is bound to the virtual beam number of the steel bar binding process, the precast beam curing process is bound to the virtual beam number of the concrete pouring process, the tensioning and grouting process is bound to the virtual beam number of the precast beam curing process, and the beam storage process is bound to the virtual beam number of the tensioning and grouting process, ensuring that each process is bound to the correct virtual beam number, thereby ensuring the continuity and integrity of production records.

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

[0098] In specific implementation, the above-mentioned electronic device 1000 can execute the implementation methods provided by each step as described above Figure 1 through its built-in various functional modules. Specifically, reference can be made to the implementation methods provided by each step as described above, which will not be elaborated here.

[0099] The electronic device provided in this embodiment integrates machine vision, sensor technology, the Internet of Things, and artificial intelligence algorithms; by installing high-definition cameras and precision sensors, it captures steel bar binding images and key physical parameters in real time, such as formwork displacement, steam curing kiln temperature, key parameters during the precast beam production process, gantry crane and its hook position, and gantry crane hook lifting weight; after the data is encoded and processed by the protocol, it is transmitted to the cloud. The data storage and processing module analyzes the data, automatically records the time stamps of process activities, and generates virtual beam numbers. The data storage and processing module automatically captures key processes such as steel bar binding, concrete pouring, curing, tensioning and grouting, and beam storage, and through intelligent algorithms, monitors in real time 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 binding of process data. The present invention improves the automation level of precast beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for the quality control of precast beam manufacturing.

[0100] The embodiments of the present application also provide a computer-readable storage medium storing a computer program, which is executed by a processor to implement Figure 1 the methods provided in each step in

[0101] The computer-readable storage medium provided in this embodiment integrates machine vision, sensor technology, Internet of Things, and artificial intelligence algorithms; by installing high-definition cameras and precision sensors, it captures steel bar binding images and key physical parameters in real time, such as formwork displacement, steam curing kiln temperature, key parameters during precast beam production, gantry crane and its hook position, and gantry crane hook load; after the data is encoded and processed by the protocol, it is transmitted to the cloud, and the data storage and processing module analyzes the data, automatically records the time stamps of the process activities, and generates virtual beam numbers. The data storage and processing module automatically captures key processes such as steel bar binding, concrete pouring, curing, tensioning and 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 SMS warnings to facilitate timely binding of process data. The present invention improves the automation level of precast beam manufacturing, reduces human errors, enhances production efficiency, and provides real-time data support for the quality control of precast beam manufacturing.

[0102] Any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many 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), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0103] Those skilled in the art can easily understand that the above description is only a preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically identifying the manufacturing process of precast beams based on multi-source data, characterized in that: include: S1: The data acquisition and transmission module obtains the position and binding of the steel bars, the position change of the template, the temperature change in the steaming kiln, the key parameters in the precast beam production process, the position of the gantry crane and its hook, and the gantry crane hook weight information, and transmits them to the object storage server and time series database of the data storage and processing module; S2: The process automatic 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 the time series database through an AI algorithm; S3: When the camera at the rebar binding station recognizes the start of the binding process, the process warning unit of the process warning and prefabricated beam process binding module automatically creates a virtual beam number associated with the process; the process warning unit automatically generates SMS notification content based on the data of the current production line, including the virtual beam number and the reminder of the start of the process; and automatically sends the SMS to the relevant person in charge to provide immediate 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.

2. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 1, characterized in that: The data acquisition and transmission module described in step S1 includes a camera for obtaining the position and binding status of the steel bars, a displacement sensor for real-time monitoring the position changes of the template, a temperature sensor for real-time monitoring and recording the temperature changes in the steaming kiln, an intelligent tensioning equipment sensor and an intelligent grouting equipment sensor for real-time monitoring and controlling key parameters in the precast beam production process, a position sensor for real-time monitoring the position of the gantry crane and its hook, and a weight sensor for real-time monitoring the weight of the gantry crane hook.

3. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 2 is characterized in that: The camera is installed in the steel bar binding area; several of the displacement sensors are installed on the formwork; several of the temperature sensors are installed in the steaming kiln; the intelligent tensioning equipment sensor and the intelligent grouting equipment sensor are installed on the intelligent tensioning equipment and the intelligent grouting equipment respectively; the position sensor is installed on the gantry crane and its hook respectively; the lifting weight sensor is installed on the gantry crane hook.

4. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 3 is 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 steel bar binding area, an intelligent gateway, a cloud and a time series database for transmitting and storing various sensor data, and an automatic process identification unit for automatically identifying each process in the prefabricated beam production process; The process automatic identification unit automatically identifies the steel bar binding process, concrete pouring process, precast beam curing process, tensioning and grouting process, and beam storage process in sequence through an AI algorithm; 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.

5. A method for automatically identifying manufacturing processes of precast beams based on multi-source data according to any one of claims 2 to 4, characterized in that: The process warning and prefabricated beam process binding module in step S3 includes a process warning unit and a prefabricated beam process binding unit; when the camera of the steel bar binding station recognizes that the binding process has started, the process warning unit automatically creates a virtual beam number associated with the process; the process warning unit automatically generates SMS notification content based on the data of the current production line, including the virtual beam number and the reminder of the start of the process; and automatically sends the SMS to the relevant person in charge to provide immediate 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.

6. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 5, characterized in that: In step S1, the image data captured by the camera in the steel bar binding area is transmitted to the streaming media server, and the streaming media server can process and distribute the video stream in real time and store the video data in the object storage server; Step S2 includes the steel bar binding process automatic identification module periodically obtaining a 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 this 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 ends, and sets a time threshold. 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 to complete the time recording of the steel bar binding process.

7. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 6 is characterized by: Step S2 includes starting a timing task of the automatic identification module of the concrete pouring process, which is cycled once every 10 minutes, and extracting data collected by the displacement sensor from the time series database, including time, cylinder displacement value, sensor ID, vibration switch state, vibration time, set vibration frequency and pouring switch state; in the filtered data set, by identifying the moment when the pouring switch state changes from 0 to 1, this moment indicates the start of pouring; in order to improve the data accuracy, it is also determined whether the cylinder displacement value is less than the set threshold at this time. When it is less than the threshold, it proves that the template 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, monitoring value and sensor ID; The automatic identification module for curing process of precast beams in step S2 calculates the sliding average and change rate of the temperature of the steam curing kiln using a sliding window, and then determines the heating, constant temperature or cooling stage of the steam curing kiln; the automatic identification module for curing process of precast beams starts a timing task, cycles 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; The relevant field information collected by the intelligent tensioning equipment sensor and the intelligent grouting equipment sensor in step S1 includes: beam number, actual occurrence time of tensioning operation and actual occurrence time of grouting operation; the automatic identification module of tensioning and grouting process in step S2 starts the timing task, which cycles every 10 minutes, records the start time of tensioning as the start time of tensioning and grouting process, and records the end time of grouting as the end time of tensioning and grouting process.

8. The method for automatically identifying the manufacturing process of precast beams based on multi-source data according to claim 6, characterized in that: The weight sensor is used to monitor the weight information of the gantry crane hook in real time, and transmit the data to the time series database of the data storage and processing module in real time; in step S2, the automatic identification module of the beam storage process retrieves the weight information of the gantry crane hook from the time series database in real time, and sets a predetermined weight threshold; when the weight information of the gantry crane hook exceeds the weight threshold, the automatic identification module of the beam storage process determines that the gantry crane is performing a lifting operation; at the same time, the automatic identification module of 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 weight exceeds the weight threshold, the automatic identification module of the beam storage process records the timestamp at this time as the start time point of the beam storage process.

9. The method for automatically identifying the manufacturing process of precast beams 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 pouring start time, the precast beam process binding unit will automatically perform data screening operations in the database; the screening conditions are: on the same production line, forward query the steel bar binding process recorded within 24 hours; the precast beam process binding unit identifies the virtual beam number of the corresponding steel bar binding process by matching the process records within the time window; once the match is confirmed, the concrete pouring process record will be bound to the virtual beam number of the steel bar binding process; Once the start time of the curing process is identified, the precast beam process binding unit will trigger a data screening operation; the screening logic is: forward 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 prefabricated beam process binding unit performs data screening in the background; the screening conditions are: on the same production line, query the tensioning and grouting process within 24 hours; the prefabricated 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 the beam number to ensure the continuity and integrity of the production records.

10. An automatic identification system for prefabricated beam manufacturing process based on multi-source data, characterized in that: The method for automatically identifying the manufacturing process of prefabricated beams based on multi-source data according to any one of claims 1 to 9 comprises a data acquisition and transmission module, a data storage and processing module, and a process warning and prefabricated beam process binding module; wherein: The data acquisition and transmission module includes a camera for obtaining the position and binding of the steel bars, a displacement sensor for real-time monitoring of changes in the position of the template, a temperature sensor for real-time monitoring and recording changes in the temperature in the steaming kiln, an intelligent tensioning equipment sensor and an intelligent grouting equipment sensor for real-time monitoring and controlling key parameters in the precast beam production process, a position sensor for real-time monitoring of the position of the gantry crane and its hook, and a weight sensor for real-time monitoring of the weight of 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 steel bar binding area, an intelligent gateway, a cloud and a time series database for transmitting and storing various sensor data, and an automatic process identification unit for automatically identifying various processes in the prefabricated beam production process; The process warning and prefabricated beam process binding module includes a process warning unit and a prefabricated 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 process warning unit automatically generates SMS notification content based on the data of the current production line, including the virtual beam number and the reminder of the start of the process; and automatically sends the SMS to the relevant person in charge to provide immediate 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.

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