Concrete precast block automatic curing production line and curing method

Abstract

The invention discloses a concrete precast block automatic curing production line and a curing method. The concrete precast block automatic curing production line comprises a conveying line matched with the output end of a distributing machine and used for conveying concrete precast blocks, a steam curing shed arranged on the conveying line, a steam curing device used for conveying steam into the steam curing shed and a plurality of temperature sensors evenly arranged in the steam curing shed. The conveying line is lengthened so as to be used for storing precast blocks, and a steam-curing shed is directly additionally arranged on the production line, so that the purpose of curing while producing is achieved, the efficiency of the precast blocks is improved, the labor cost is reduced, transferring of the concrete precast blocks after the production line is transformed is reduced, the curing time is shortened through steam curing, deviation of thickness and surface flatness is avoided, and the production efficiency is improved. And the qualified rate of products is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of concrete precast block production, in particular to an automatic curing production line and curing method for concrete precast blocks. BACKGROUND

[0002] With the rapid development of infrastructure projects in China, concrete precast components such as slope protection blocks, drainage ditches, kerbs, etc. are widely used in railway auxiliary projects, municipal rail projects, highway projects and fabricated building projects. The traditional production method has problems such as large site occupation, low efficiency of open-air operation, high dependence on manual labor, etc., resulting in unstable component quality and high production cost.

[0003] Moreover, many related production systems have significant limitations in practical applications. Many solutions lack dynamic coordination capabilities in environmental control and energy utilization, often failing to adapt to the complex and changing demands of the production process, especially in the balance of temperature regulation and energy input, which can easily lead to resource waste or environmental parameter fluctuations. This limitation makes it difficult to reconcile the contradiction between production efficiency and energy saving goals.

[0004] Focusing on specific challenges, the core problem in this field is how to achieve precise automation of temperature control and efficient stability of steam supply. First, the degree of automation of temperature control directly determines whether the production environment can meet the process requirements. If the temperature cannot be adjusted according to real-time feedback, it is easy to cause uneven product quality. The realization of temperature control is closely related to the stability of steam supply. If the process of steam transmission and injection through pipes lacks uniformity and controllability, it will further exacerbate the imbalance of temperature distribution, affecting the overall production effect. These two factors interact with each other and become a technical problem that needs to be solved urgently. SUMMARY

[0005] The purpose of the present application is to solve the above problems and provide an automatic curing production line and curing method for concrete precast blocks. The original production line is lengthened to store precast blocks, and a steam curing shed is directly installed on the production line to achieve the purpose of production and curing at the same time. This improves the efficiency of precast blocks, reduces labor costs, and reduces the transportation of concrete precast blocks after the production line is modified. Steam curing shortens the curing time, avoids deviations in thickness and surface flatness, and improves the product qualification rate.

[0006] The technical solution adopted by the present application to solve its technical problems is: An automatic curing production line for concrete precast blocks, comprising a conveying line for conveying concrete precast blocks in cooperation with the output end of a distributing machine, a steam curing shed provided on the conveying line, a steam curing device for conveying steam into the steam curing shed, and a plurality of temperature sensors uniformly provided in the steam curing shed. The steam curing device comprises a steam boiler, a heat preservation steam pipeline connected with the steam boiler, a plurality of jet pipelines with end portions penetrating into the steam curing shed and connected with the heat preservation steam pipeline, and electromagnetic valves arranged on the jet pipelines.

[0007] Further, the feeding mechanism comprises a chute, a discharging pipeline arranged at the bottom of the chute, and a biomass fuel throwing mechanism arranged at the bottom of the discharging pipeline and used for throwing the biomass fuel into the steam boiler.

[0008] Further, the throwing mechanism comprises a housing, a feeding passage arranged at the side of the housing and communicated with the steam boiler, a rotating shaft arranged in the housing and rotating in the housing, and a plurality of blades uniformly arranged on the outer cylindrical surface of the rotating shaft.

[0009] Further, the quantitative mechanism comprises a bottom plate arranged in the discharging pipeline, a partition plate arranged in the discharging pipeline and rotating in the discharging pipeline, and a moving frame arranged at the side of the discharging pipeline.

[0010] Further, the rotating shaft at the upper end of the partition plate penetrates through the side wall of the discharging pipeline and is rotationally connected with the side wall of the discharging pipeline, one end of the rotating shaft outside the partition plate is provided with a swing arm, and a tension spring is arranged between the lower end of the swing arm and the side wall of the discharging pipeline.

[0011] Further, the side of the discharging pipeline is provided with a cylinder used for driving the moving frame to move.

[0012] Further, an automatic curing method of a concrete precast block is applied to the production line and comprises the following steps. S101, collecting real-time temperature data of each region in a production environment through a distributed sensor network, and constructing a data matrix comprising temperature values, time stamps and position information; S102, analyzing temperature variation characteristics according to the data matrix, calculating temperature deviation of each region and generating an adjustment instruction; S103, acquiring steam state parameters, combining the adjustment instruction to calculate a steam distribution scheme; S104, executing the distribution scheme through a control device to release steam to a target region; and S105, collecting environmental feedback data, analyzing deviation degree and dynamically updating an adjustment strategy.

[0013] Further, step S102 comprises: extracting temperature variation gradient and time sequence characteristics from the data matrix; processing the characteristics by using a classification algorithm to determine temperature fluctuation modes of each region; calculating temperature deviation values according to the fluctuation modes, and generating corresponding deviation identifiers and adjustment instructions if the deviation values exceed a preset threshold range.

[0014] Further, step S103 comprises: The pressure, flow rate and density data of the main pipeline and branch pipeline are collected by the monitoring device to form a set of steam state parameters; the set of parameters is standardized, and if there is an abnormal parameter, the abnormal category is judged through a classification algorithm; according to the abnormal category and the pipeline position correlation, the distribution calculation weight is adjusted to generate optimized steam distribution parameters.

[0015] Further, step S104 comprises: The flow control value and timing data in the distribution scheme are analyzed; the on-off state and flow size of each pipeline branch are adjusted by the regulating valve group; if the flow size does not match the preset range, calibration processing is performed to update the flow parameter; the jetting device is driven according to the timing data to release steam to the target area in sequence.

[0016] The beneficial effects of the present application are: 1. The present application comprises a conveying line for conveying concrete precast blocks in cooperation with the output end of a cloth machine, a steam curing shed arranged on the conveying line, a steam curing device for conveying steam into the steam curing shed, and a plurality of temperature sensors uniformly arranged in the steam curing shed; the length of the conveying line is lengthened for storing precast blocks, and the steam curing shed is directly installed on the production line, achieving the purpose of production and curing simultaneously, improving the efficiency of precast blocks, reducing labor costs, and reducing the transportation of concrete precast blocks after the modification of the production line. The curing time is shortened, the deviation of thickness and surface flatness is avoided, and the product qualification rate is improved.

[0017] 2. The material throwing mechanism comprises a shell, a feeding channel arranged on the side surface of the shell and communicated with the steam boiler, and a rotating shaft arranged in the shell and rotating in the shell, wherein a plurality of blades are uniformly arranged on the outer cylindrical surface of the rotating shaft. The upper end of the shell is provided with a feeding port communicated with the material falling pipeline. The biomass fuel in the chute enters the shell through the feeding channel, the rotating shaft rotates, and the biomass fuel in the shell is thrown to the steam boiler under the action of the blades, achieving uniform spreading of the biomass fuel in the steam boiler and improving the combustion efficiency of the biomass fuel.

[0018] 3, The application acquires multi-dimensional temperature information matrix by arranging temperature sensor network at key positions of production line, calculates temperature deviation by using adaptive algorithm and generates adjustment instruction. According to steam demand parameter, real-time monitoring of pipeline steam state is carried out, and optimal steam distribution scheme is calculated by using optimization algorithm. Electric regulating valve group adjusts steam flow according to execution instruction, and injection device releases steam according to time sequence. The application also dynamically adjusts control strategy by analyzing environmental feedback data, forms closed-loop adjustment mechanism. Meanwhile, temperature and steam supply correlation model is established, and parameter prediction accuracy and response speed are continuously optimized. The method realizes accurate intelligent control of production environment temperature, improves energy utilization efficiency and guarantees product quality stability. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is a structural schematic diagram of the application; Figure 2 It is a cross-sectional view of the loading mechanism of the application; Figure 3 It is a flow chart of the application; Figure 4 It is a flow chart of instruction and parameter generation of the application; Figure 5 It is a parameter optimization flow chart of the application.

[0020] In the figure: conveying line 1, steaming shed 2, steam boiler 3, heat preservation steam pipeline 4, injection pipeline 5, electromagnetic valve 6, trough 7, dropping pipeline 8, shell 9, feeding channel 10, blade 11, bottom plate 12, partition plate 13, moving frame 14, swing arm 15, tension spring 16, air cylinder 17. DETAILED DESCRIPTION

[0021] As shown in Figure 1 A concrete precast block automatic curing production line, comprising a conveying line 1 cooperating with a distributor output end for conveying concrete precast blocks, a steaming shed 2 arranged on the conveying line 1, a steaming device for conveying steam into the steaming shed 2, and a plurality of temperature sensors uniformly arranged in the steaming shed 2. The length of the conveying line 1 is lengthened for storing precast blocks, and the steaming shed is directly installed on the production line, achieving the purpose of production and curing at the same time, improving the efficiency of precast blocks, reducing labor costs, and reducing the transportation of concrete precast blocks after the modification of the production line. The steaming and curing shorten the curing time, avoid deviations in thickness and surface flatness, and improve the product qualification rate.

[0022] As shown in Figure 1As shown in the figure, the steam curing device comprises a steam boiler 3, a heat preservation steam pipeline 4 connected with the steam boiler 3, a plurality of jet pipelines 5 connected with the heat preservation steam pipeline 4 and having end portions penetrating into the steam curing shed 2, an electromagnetic valve 6 arranged on each of the jet pipelines 5, and a feeding mechanism arranged at the bottom of the steam boiler 3 and used for adding the biomass fuel into the steam boiler 3. After the steam boiler 3 heats water, steam is delivered to the jet pipelines 5 through the heat preservation steam pipeline 4, and then is delivered into the steam curing shed 2 through the jet pipelines 5, so that the steam curing of the precast blocks in the steam curing shed 2 is realized. The electromagnetic valve is arranged on the jet pipeline 5, the uniform amount of steam sprayed by each jet pipeline 5 can be adjusted through the electromagnetic valve, and the amount of steam in different areas can be adjusted.

[0023] As shown in the figure, Figure 2 the feeding mechanism comprises a chute 7, a dropping pipeline 8 arranged at the bottom of the chute 7, a throwing mechanism arranged at the bottom of the dropping pipeline 8 and used for throwing the biomass fuel into the steam boiler 3, and a quantitative mechanism arranged on the dropping pipeline 8. The biomass fuel is stored in the chute 7, enters the throwing mechanism through the dropping pipeline 8, and is thrown into the steam boiler 3 by the throwing mechanism, so that the automatic feeding of the biomass fuel is realized.

[0024] As shown in the figure, Figure 2 the throwing mechanism comprises a housing 9, a feeding passage 10 arranged at the side of the housing 9 and communicated with the steam boiler 3, a rotating shaft 10 arranged in the housing 9 and rotating in the housing 9, a motor arranged at the side of the housing 9 and used for driving the rotating shaft 10 to rotate, a plurality of blades 11 uniformly arranged on the outer cylindrical surface of the rotating shaft 10, and a feeding port arranged at the upper end of the housing 9 and communicated with the dropping pipeline 8. The biomass fuel in the chute 7 enters the housing through the feeding passage 10, the rotating shaft 10 rotates, and the biomass fuel in the housing 9 is thrown into the steam boiler 3 under the action of the blades 11, so that the biomass fuel is uniformly spread in the steam boiler 3 and the combustion efficiency of the biomass fuel is improved.

[0025] As shown in the figure, Figure 2 the quantitative mechanism comprises a bottom plate 12 arranged in the dropping pipeline 8, a partition plate 13 arranged in the dropping pipeline 8 and rotating in the dropping pipeline 8, and a moving frame 14 arranged at the side of the dropping pipeline 8. The moving frame 14 comprises a vertical portion and a horizontal portion, an opening is arranged on the side wall of the dropping pipeline 8 and matched with the vertical portion of the moving frame 14, the bottom of the partition plate 13 is matched with one end of the inner side of the bottom plate 12, the biomass fuel in the chute 7 enters the dropping pipeline 8, the biomass fuel is in the space composed of the bottom plate 12 and the partition plate 13, the moving frame 14 moves into the dropping pipeline 8, pushes the biomass fuel to open the partition plate 13, the biomass fuel enters the housing 9 through the feeding port of the housing 9, and the horizontal portion of the moving frame 14 blocks the dropping pipeline 8 when the moving frame 14 moves, so that the quantitative feeding of the biomass fuel is realized.

[0026] As Figure 2 shown, the pivot shaft of the upper end of the partition plate 13 penetrates the sidewall of the material dropping pipe 8 and is rotatably connected with the sidewall of the material dropping pipe 8, the outer end of the pivot shaft of the partition plate 13 is provided with a swing arm 15, a tension spring 16 is arranged between the lower end of the swing arm 15 and the sidewall of the material dropping pipe 8, under the action of the tension spring 16, the bottom of the partition plate 13 is always in close contact with the inner end of the bottom plate 12, and the biomass fuel cannot enter the shell 9 when the biological fuel is not added.

[0027] As Figure 2 shown, the sidewall of the material dropping pipe 8 is provided with a cylinder 17 for driving the movement of the moving frame 14, the cylinder body end of the cylinder 17 is fixed to the part of the bottom plate 12 extending out of the material dropping pipe 8, and the piston rod end of the cylinder 17 is connected with the vertical part of the moving frame 14.

[0028] As Figure 2 shown, an automatic curing method of a concrete precast block is applied to the production line and includes the following steps: S101, collecting real-time temperature data of each area in the production environment through a distributed sensor network, and constructing a data matrix containing temperature values, time stamps and location information; S102, analyzing temperature variation characteristics according to the data matrix, calculating temperature deviation of each area and generating adjustment instructions; S103, obtaining steam state parameters, combining the adjustment instructions to calculate a steam distribution scheme; S104, executing the distribution scheme through a control device to release steam to the target area; S105, collecting environmental feedback data, analyzing the deviation degree and dynamically updating the adjustment strategy.

[0029] Step S102 includes: The sensors arranged at a preset interval obtain temperature data of each key position; the temperature data is integrated to generate a multi-dimensional information matrix containing temperature values, collection time stamps and position coordinates; if there is missing data in the multi-dimensional information matrix, the missing data is completed through a preset interpolation method to obtain a complete data matrix.

[0030] As Figure 4 shown, step S102 includes: extracting temperature variation gradient and time sequence characteristics from the data matrix; processing the characteristics by using a classification algorithm to determine temperature fluctuation patterns of each area; calculating temperature deviation values according to the fluctuation patterns, and if the deviation values exceed a preset threshold range, generating corresponding deviation identifiers and adjustment instructions.

[0031] Obtain a multi-dimensional temperature information matrix, which contains temperature data of each region; separate the change gradient and time sequence features from the multi-dimensional temperature information matrix by a pre-established feature extraction method to obtain preliminary temperature change trend data; classify the temperature change trend of each region according to the change gradient data and time sequence features, and determine the temperature fluctuation mode of each region by using a support vector machine algorithm; calculate the temperature deviation value of each region according to the temperature fluctuation mode, and generate a corresponding deviation anomaly identifier if the deviation value exceeds a preset threshold range; generate a targeted temperature adjustment instruction according to the deviation anomaly identifier in combination with region division information, and determine the adjustment direction and amplitude of the adjustment instruction; calculate the required steam distribution amount of each region according to the temperature adjustment instruction in combination with steam demand related parameters to obtain a steam demand parameter set.

[0032] When the support vector machine algorithm is used to classify the temperature change trend, the temperature fluctuation mode can be divided into stable type, periodic type and abnormal type. Assuming that the temperature of a certain region fluctuates once every 4 hours within a day, it can be classified as a periodic type, and the temperature of another region suddenly jumps more than 10 degrees, which is classified as an abnormal type.

[0033] When the deviation value is calculated according to the temperature fluctuation mode, the normal temperature range can be set to 22.0 to 26.0 degrees. If the temperature of a certain region reaches 28.5 degrees, the deviation value is 2.5 degrees, which exceeds the preset threshold of 1.0 degree, and a deviation anomaly identifier is generated.

[0034] When the temperature adjustment instruction is generated, the adjustment direction and amplitude can be determined according to the deviation anomaly identifier in combination with the region division information. Assuming that the temperature of a certain region is 3.0 degrees higher, the system generates an instruction to reduce the temperature, and the amplitude is 3.5 degrees to slightly over-adjust to ensure rapid recovery.

[0035] When the steam distribution amount is calculated, the steam demand related parameters can be combined to distribute the steam according to the temperature adjustment instruction and factors such as the area and equipment load of the region. Assuming that the temperature of a certain region needs to be reduced, the area is large and the equipment load is high, the steam distribution amount is 500 cubic meters per hour, while another small area region only needs 200 cubic meters. This distribution method ensures efficient use of resources and avoids waste.

[0036] As Figure 5As shown, step S103 includes: collecting data of the main pipeline and branch pipeline by the steam flow monitoring device, obtaining steam pressure, steam flow rate and steam density data, and forming an initial steam state data set. According to the initial steam state data set, a preset standardized processing method is used to normalize the steam pressure, steam flow rate and steam density data, and a standardized steam state parameter set is obtained. If a parameter in the standardized steam state parameter set exceeds a preset threshold range, the parameter is marked as abnormal, and the pipeline position and time point of the abnormality are recorded to determine an abnormal parameter subset. For the abnormal parameter subset, a support vector machine algorithm is used to classify and process the abnormal data to determine whether the abnormality is caused by equipment failure, and a classified abnormal category data set is obtained. Through the classified abnormal category data set, the correlation between the abnormal category and the pipeline position is analyzed in combination with the historical steam state parameter set to determine the distribution range of the potential fault pipeline. According to the distribution range of the potential fault pipeline, the weight coefficient of the distribution calculation is adjusted in combination with the real-time collected steam state parameter set to generate an optimized steam distribution parameter set.

[0037] In the actual application of steam flow monitoring, the steam flow monitoring device can collect data of the main pipeline and branch pipeline to obtain key data such as steam pressure, steam flow rate and steam density. After the pressure, flow rate and density data are normalized by a preset standardization method, the obtained values are between 0 and 1, which facilitates threshold comparison.

[0038] In analyzing the correlation between the abnormal category and the pipeline position, the historical steam state parameter set can be combined to find that a certain section of the main pipeline has appeared similar pressure abnormalities several times in the past month, and it is speculated that the pipeline may have a potential fault. For optimization of the steam distribution parameter set, more reference information can be extracted from historical data to analyze the abnormal frequency and influence range of different pipeline positions and dynamically adjust the distribution strategy. The operation risk of the fault area is effectively reduced, and the stability of the overall steam supply is ensured. Through the above analysis and adjustment, the operation efficiency of the steam system is improved, potential problems can be discovered and handled in time, and the continuity and reliability of steam supply in production are ensured.

[0039] Step S103 includes: obtaining a set of steam state parameters, performing preliminary processing on the parameter set, extracting data fields related to temperature regulation and regional demand, and determining temperature deviation values for each region. According to the temperature deviation values, combined with pipeline resistance and resistance coefficient data, a linear regression model is used to analyze the transmission loss of steam at each injection position, and a resistance influence factor for each injection position is obtained. Through the resistance influence factor, a preliminary scheme for flow distribution is calculated for each injection position in association with regional demand, and if the resistance influence factor of a certain injection position exceeds a preset threshold, the flow distribution ratio of that position is adjusted to determine a preliminary flow control value. According to the preliminary flow control value, combined with real-time data of temperature regulation, the trend of changes in regional demand is analyzed, and a flow adjustment parameter matching the trend is obtained to obtain an optimized flow control value. According to the optimized flow control value, a time scheduling scheme corresponding to each injection position is generated according to the logical rules of the opening timing, and if the fluctuation amplitude of regional demand in a certain time period exceeds a predetermined range, the opening timing of the related injection position is adjusted in priority to determine the final time scheduling result. Through the final time scheduling result, combined with the optimized flow control value, an execution instruction combination including flow distribution and opening timing is generated to obtain an instruction data set that can be called by the system.

[0040] After determining the temperature deviation value, when analyzing the transmission loss in combination with the pipeline resistance and resistance coefficient data, a linear regression model can be used to evaluate the resistance influence of steam at each injection position. Assuming that the pipeline length of a certain injection position is long and the roughness of the inner wall of the pipeline is high, the resistance coefficient is 0.03, and the system analysis shows that the resistance influence factor of this position is 0.75, which is higher than the preset threshold 0.6. This indicates that the steam transmission loss at this position is large, and the flow distribution ratio needs to be adjusted. By reducing the flow distribution ratio of this position, for example, from the initial 30% to 20%, the flow control value can be preliminarily determined.

[0041] When finally generating an execution instruction combination including flow distribution and opening timing, the system integrates the optimized flow control value and the time scheduling result into an instruction data set that can be called. For example, for a certain injection position, the instruction can include a flow setting of 40 cubic meters per hour, an opening time of 7:50 am, and a closing time of 5:00 pm. In this way, the system can achieve precise control of steam distribution, improve overall operating efficiency, and reduce the risk of insufficient supply due to demand fluctuations. This multi-dimensional analysis and adjustment method provides strong support for the stable operation of the steam system.

[0042] Step S104 includes: obtaining the execution instruction data of the external system from the pre-established instruction receiving module, performing instruction analysis on the electric regulating valve group to obtain specific control parameters of each valve. According to the control parameters, a flow calculation model is used to analyze the flow demand of each pipeline branch to determine the flow control value of each branch. Through the valve group control interface, the flow control value is transmitted to the electric regulating valve group to adjust the steam on-off state and flow size of each pipeline branch. If the adjusted flow size does not meet the preset threshold range, the information processing module is used to calibrate the flow data to obtain calibrated flow adjustment parameters. According to the calibrated flow adjustment parameters, the electric regulating valve group is controlled again to synchronously update the steam on-off state of each pipeline branch to obtain a stable flow output state. Through the control unit of the injection device, predetermined timing data is obtained, the time node of steam release is set for the target area, and the execution order of the release operation is determined. According to the execution order, the injection device is driven to release steam to the target area in time sequence.

[0043] When transmitting the flow control value through the valve group control interface and adjusting the steam on-off state, if the actual flow of area A is only 450 cubic meters per hour, which does not reach the preset threshold range of 470-500 cubic meters per hour, the data needs to be calibrated through the information processing module. After calibration, the valve opening may be adjusted from 60% to 65% to make the flow close to the target value. This calibration mechanism effectively avoids flow deviation and ensures stable system operation.

[0044] When the electric regulating valve group is controlled again and the steam on-off state is synchronously updated, the flow output can be monitored through a real-time feedback mechanism. Assuming that the flow of area A is stabilized at 485 cubic meters per hour and the flow of area B is 315 cubic meters per hour after adjustment, both are within a reasonable range. This stable output state helps to ensure that the temperature regulation requirements of the target area are met.

[0045] When obtaining the predetermined timing data and setting the steam release time node, for target areas A and B, area A can be set to release steam preferentially during the high demand period from 8 am to 10 am, with a release frequency of every 15 minutes and a duration of 5 minutes each time; while area B releases steam from 2 pm to 4 pm with a frequency of every 30 minutes. This time node design can match the fluctuation of regional demand and improve the efficiency of steam utilization.

[0046] Step S105 includes: collecting environmental feedback data in the steam injection process in real time through the sensor network, the environmental feedback data including humidity change, temperature response and distribution uniformity related indicators, obtaining a preliminary collection data set. According to the preliminary collection data set, a preset standardized processing method is used to normalize the humidity change, temperature response and distribution uniformity data, and a standardized feature data set is determined. For the standardized feature data set, a support vector machine algorithm is applied to classify the humidity change, temperature response and distribution uniformity indicators, and determine whether each indicator reaches a preset reference range. If the classification result shows that a certain indicator does not reach the reference range, a deviation quantization result is obtained.

[0047] The deployment of the sensor network adopts a distributed architecture, and temperature and humidity sensors are installed according to a grid layout in the steam injection area.

[0048] One comprehensive sensor node is provided in each square meter area, and the sampling frequency of the sensor is set to 10 times per second to ensure that transient changes during the steam release process can be captured. The measurement range of the humidity sensor covers 20% to 95% relative humidity, and the detection range of the temperature sensor is -10℃ to 150℃. The distribution uniformity is quantitatively evaluated by calculating the standard deviation of the values of each measurement point.

[0049] The standardized processing adopts a Z-score normalization method to convert different dimensional environmental parameters into a unified numerical range.

[0050] Specifically, the humidity change data is processed by subtracting the mean value and dividing by the standard deviation, and the temperature response data also adopts the same normalization strategy. The distribution uniformity indicator is standardized by calculating the coefficient of variation of the values of each measurement point. A coefficient of variation less than 0.15 indicates relatively uniform distribution, and a coefficient of variation greater than 0.3 indicates uneven distribution.

[0051] For example, the original humidity data ranges from 45% to 82%, and after standardized processing, it is converted to a standardized value of -1.2 to 2.1. The temperature data is standardized from 25℃ to 78℃ to a numerical interval of -0.8 to 1.9. This standardized processing eliminates the dimensional differences between different parameters, laying a foundation for accurate classification by the support vector machine algorithm.

[0052] The support vector machine algorithm uses a radial basis function as the kernel function, and classifies the environmental indicators by constructing an optimal separating hyperplane. The algorithm maps the standardized feature data to a high-dimensional space, and finds the decision boundary that can maximize the classification interval. The preset reference range is determined by historical data statistics, and the acceptable range of humidity change is set to be between -1.0 and 1.5, the acceptable range of temperature response is between -0.5 and 1.8, and the acceptable range of uniformity is between -1.2 and 1.0.

[0053] For example, when the humidity standardized value of a certain measurement point is 2.3, which exceeds the upper threshold of 1.5, the support vector machine algorithm classifies it as unqualified. The deviation quantization result shows that the deviation degree of this point is 0.8 standard deviation units, indicating that the steam injection parameters in this area need to be adjusted. This precise deviation quantization provides a quantitative basis for subsequent system optimization, enabling precise control and continuous improvement of the steam injection effect.

[0054] Step S105 includes: obtaining system operation data, calculating the deviation degree for the operation data, and comparing it with the preset tolerance range to determine whether to trigger the adjustment process. If the deviation degree exceeds the preset tolerance range, the preliminary strategy direction of temperature adjustment is determined according to the deviation direction and amplitude, using the pre-established mapping rule. For the preliminary strategy direction, the current state data of steam distribution is obtained, and the resource proportion is redistributed combined with the deviation amplitude to obtain the updated distribution parameters. If the distribution parameters differ from the current execution instructions, the difference between the two is compared through the information processing module to determine whether to update the control parameters and generate new instruction content. According to the new instruction content, the support vector machine algorithm is used to analyze the feedback data of the closed-loop adjustment to obtain the evaluation result of the adjustment effect. Through the comparison of the evaluation result with the preset threshold, if the effect is not as expected, the control parameters and steam distribution scheme are adjusted again to determine the optimized execution instruction. If the optimized execution instruction matches the system operation state, the instruction is sent to the execution unit through the data transmission module to complete the dynamic update of the closed-loop adjustment.

[0055] In the monitoring of the running data of the steam injection system, obtaining system operation data is the first step. For this topic, a multi-point sensor network can be deployed to collect key parameters such as temperature, humidity, and flow distribution in real time during the steam injection process. Suppose in a certain operation, the system collects temperature data of 85 degrees, while the preset standard is 90 degrees, and the deviation degree is 5 degrees. By comparing with the preset tolerance range of 3 degrees, it is obviously out of range, triggering the adjustment process. This way of data collection and deviation calculation can provide accurate basis for subsequent adjustment.

[0056] In the resource proportion reallocation phase, the system may adjust the steam distribution proportion from the original uniform distribution to an increase of 20% in the flow rate in the key area, combining the current state data and the deviation amplitude of the steam distribution. If the updated allocation parameter is 60% for the key area flow rate and the current execution instruction is 50%, the information processing module compares the difference between the two to determine whether the control parameter needs to be updated. This dynamic adjustment method helps to address the deviation problem and improve resource utilization efficiency.

[0057] After generating new instruction content, the support vector machine algorithm is used to analyze the feedback data of the closed-loop adjustment to evaluate the adjustment effect. Assuming that the feedback data shows that the temperature has been raised to 89 degrees, which is close to the preset threshold of 90 degrees but still not up to standard. By comparing with the threshold, the system will adjust the control parameters again, such as further increasing the steam flow by 5% and optimizing the distribution scheme. This algorithm-based analysis and iterative adjustment can continuously approach the expected target and ensure that the adjustment effect is gradually optimized.

[0058] From the overall process, the above-mentioned links form a complete closed loop from data collection to instruction execution. The core scheme is to quickly identify deviations and develop strategies through multi-parameter monitoring and algorithm analysis, while the extended scheme ensures that the adjustment effect reaches the expected target through multiple iterations and optimization. This logically progressive design not only improves the response speed of the system but also enhances the stability of the operation, providing strong support for the continuous and efficient operation of the steam injection system.

Claims

1. An automatic curing production line for precast concrete blocks, characterized in that: It comprises a conveyor line (1) for conveying precast concrete blocks in cooperation with an output end of a placing machine, a steaming shed (2) arranged on the conveyor line (1), and a steaming device for conveying steam into the steaming shed (2), wherein a plurality of temperature sensors are evenly arranged in the steaming shed (2); The steam curing device comprises a steam boiler (3), an insulated steam pipe (4) connected to the steam boiler (3), and a plurality of jet pipes (5) connected to the insulated steam pipe (4), the ends of which extend deep into the steam curing shed (2). The jet pipes (5) are each provided with a solenoid valve (6). A feeding mechanism for adding biomass fuel into the steam boiler (3) is provided at the bottom of the steam boiler (3).

2. The automatic curing production line for precast concrete blocks according to claim 1, characterized in that: The feeding mechanism comprises a trough (7), a feeding pipe (8) arranged at the bottom of the trough (7), and a throwing mechanism arranged at the bottom of the feeding pipe (8) for throwing the biomass fuel into the steam boiler (3); the feeding pipe (8) is provided with a quantitative mechanism.

3. The automatic curing production line for precast concrete blocks according to claim 2, characterized in that: The throwing mechanism comprises a shell (9), a feed channel (10) arranged on the side of the shell (9) and connected to the steam boiler (3), a rotating shaft (10) arranged in the shell (9) and rotating in the shell (9), and a plurality of blades (11) are evenly arranged on the outer cylindrical surface of the rotating shaft (10).

4. The automatic curing production line for precast concrete blocks according to claim 2, characterized in that: The quantitative mechanism comprises a bottom plate (12) arranged in the blanking pipe (8), a partition plate (13) arranged in the blanking pipe (8) and rotating in the blanking pipe (8), and a movable frame (14) arranged on the side of the blanking pipe (8), wherein the movable frame (14) comprises a vertical portion and a horizontal portion.

5. The automatic curing production line for precast concrete blocks according to claim 4, characterized in that: The rotating shaft at the upper end of the partition (13) passes through the side wall of the blanking pipe (8) and is rotatably connected to the side wall of the blanking pipe (8). A swing arm (15) is provided at one end outside the rotating shaft of the partition (13), and a tension spring (16) is provided between the lower end of the swing arm (15) and the side wall of the blanking pipe (8).

6. The automatic curing production line for precast concrete blocks according to claim 4, characterized in that: A cylinder (17) for driving the movable frame (14) to move is provided on the side of the blanking pipe (8).

7. An automatic curing method for precast concrete blocks, applied to the production line according to any one of claims 1 to 6, characterized in that: The following steps are involved: S101 collects real-time temperature data from various areas in the production environment through a distributed sensor network and constructs a data matrix containing temperature values, timestamps and location information; S102 analyzes temperature change characteristics based on the data matrix, calculates temperature deviations in each area and generates adjustment instructions; S103 obtains steam state parameters and calculates a steam distribution plan based on the adjustment instructions; S104 executes the distribution plan through a control device to release steam to the target area; S105 collects environmental feedback data, analyzes the degree of deviation and dynamically updates the adjustment strategy.

8. The automatic curing method for precast concrete blocks according to claim 7, characterized in that: Step S102 includes: The temperature change gradient and time series characteristics are extracted from the data matrix; the characteristics are processed using a classification algorithm to determine the temperature fluctuation pattern of each area; the temperature deviation value is calculated based on the fluctuation pattern, and if the deviation value exceeds a preset threshold range, a corresponding deviation mark and adjustment instruction are generated.

9. The automatic curing method for precast concrete blocks according to claim 7, characterized in that: Step S103 includes: The pressure, flow rate and density data of the main pipeline and branch pipelines are collected through monitoring devices to form a set of steam state parameters. The parameter set is standardized, and if any abnormal parameters exist, the abnormality category is determined through a classification algorithm. Based on the correlation between the abnormality category and the pipeline location, the distribution calculation weight is adjusted to generate optimized steam distribution parameters.

10. The automatic curing method for precast concrete blocks according to claim 7, characterized in that: Step S104 includes: Analyze the flow control value and timing data in the distribution plan; adjust the on-off status and flow size of each pipeline branch by regulating the valve group; if the flow size does not match the preset range, perform calibration and update the flow parameter; drive the injection device to release steam to the target area in sequence according to the timing data.

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