Intelligent temperature control system of concrete mixing plant

Abstract

The invention relates to the technical field of concrete production, in particular to a concrete mixing plant intelligent temperature control system which comprises a temperature distribution and control module, a temperature inspection module, a trend processing module and an instruction generation module. A temperature field monitoring network is formed by performing equal-interval temperature measurement on a working area of the stirrer; performing temperature curve modeling on each grid unit in the temperature field monitoring network, and checking the change trend of a temperature change curve in a plurality of temperature stages according to a boundary condition corresponding to the temperature change curve; performing difference analysis on the detected temperature change curves, extracting the trend coincidence degree of each temperature change curve, and setting a trend coincidence sequence according to the position of each grid unit under the trend coincidence degree; according to the temperature values in the trend conforming sequence, control adjusting instructions are generated, and temperature control processing is conducted on the stirring machine according to the temperature obtained after the adjusting instructions are executed; and the stability and efficiency of concrete production are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete production, and in particular to an intelligent temperature control system for a concrete mixing station. Background Art

[0002] During concrete production, temperature control is a key factor affecting concrete performance. Temperature control is essential throughout the concrete processing phase to prevent significant temperature variations that can cause cracks and other problems. Traditional temperature control systems often rely on single-point data collection, making it impossible to identify localized overheating or cooling, or temperature fluctuations.

[0003] For example, Chinese patent publication number CN106738350A discloses a multi-mode temperature control method, device, and system for a concrete mixing plant. The method includes: receiving the real-time material temperature collected by a material temperature measuring device; determining the real-time concrete temperature based on the real-time material temperature; receiving a target concrete temperature input by a user; comparing the real-time concrete temperature with the target concrete temperature; and, based on the comparison result, employing appropriate temperature control measures to adjust the real-time concrete temperature.

[0004] For example, Chinese patent publication number CN115871109A discloses an intelligent temperature regulation system and method for concrete mixing stations. The system includes a raw material weighing module, a raw material temperature monitoring module, a concrete outlet temperature monitoring module, and a data processing module. The raw material weighing module is used to weigh the feed amount of each raw material for concrete; the raw material temperature monitoring module is used to monitor the feed temperature of each raw material for concrete; the concrete outlet temperature monitoring module is used to monitor the outlet temperature of concrete; the data processing module is used to obtain the temperature regulation relationship between the feed amount, feed temperature, and concrete outlet temperature of each raw material, and control and adjust the feed temperature of each raw material to adjust the concrete outlet temperature.

[0005] The prior art describes how to adjust the temperature of concrete by comparing the real-time temperature with the target temperature, and describes the relative conditions of the concrete feed temperature and the outlet temperature to illustrate the current concrete adjustment process. However, the prior art ignores the temperature changes at different temperature stages during the concrete processing process, and the temperature recognition method used tends to be single-point recognition, resulting in ignoring the temperature fluctuations of the concrete at local locations. As a result, the final generated concrete cannot be processed according to its multi-point temperature changes, reducing the stability of the concrete during processing. Summary of the Invention

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an intelligent temperature control system for a concrete mixing station, including: a temperature distribution control module, which is used to measure the temperature of the working area of the mixer at equal intervals, generate grid cells based on the position identifiers on the working area, form a temperature field monitoring network, and set the boundary conditions of each grid cell in the temperature field monitoring network.

[0007] The temperature inspection module is used to model the temperature curve of each grid unit in the temperature field monitoring network, set the temperature change curve corresponding to each grid unit, and inspect the change trend of the temperature change curve under multiple temperature stages based on the boundary conditions corresponding to the temperature change curve.

[0008] The trend processing module is used to perform difference analysis on the temperature change curve after inspection, extract the trend compliance degree of each temperature change curve, and set the trend compliance sequence according to the position of each grid unit under the trend compliance degree.

[0009] The instruction generation module is used to generate control adjustment instructions according to the temperature values in the trend sequence, and perform temperature control on the mixer according to the temperature after each adjustment instruction is executed.

[0010] The beneficial effects of the present invention are: 1. The present invention establishes a temperature field monitoring network, measures the temperature of the mixer working area at equal intervals, generates grid units, and sets initial boundary conditions, logical boundary conditions and delayed boundary conditions for the grid units in sequence, extracts the temperature changes at multiple currently identified positions, and describes the changes at each position in different temperature stages with its heating and cooling peak values and rates, thereby completing the precise identification of the temperature field at multiple positions and the setting of dynamic boundary conditions.

[0011] Second, the present invention examines the temperature values included in the temperature change curve to check the differences between the current temperature change curve and the expected temperature change curve to identify the relative deviation of the current temperature change curve. The relative form of the current temperature change curve is described based on the degree of trend conformity of each temperature stage to illustrate whether there is spatial and positional consistency in the temperature change in the area represented by each grid unit. A trend conformity sequence is then generated to illustrate the degree of matching between the temperature change trend and the historical pattern, facilitating subsequent adjustment of the control strategy and timely temperature control of the current concrete.

[0012] 3. The present invention identifies adjustment nodes based on trend compliance sequences, retrieves alternative nodes and their control instructions, and clusters the alternative nodes according to temperature stages, spatial positions, and trend compliance levels using cluster analysis. After checking the priority of each alternative node, the present invention combines control instructions and eliminates conflicts to perform temperature control on the current concrete. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below with reference to the accompanying drawings and examples.

[0014] Figure 1 It is a system diagram of an intelligent temperature control system for a concrete mixing station.

[0015] Figure 2 The present invention is a flow chart of the temperature control module of the intelligent temperature control system of a concrete mixing station.

[0016] Figure 3 The present invention is a flow chart of the temperature inspection module of the intelligent temperature control system of a concrete mixing station.

[0017] Figure 4 The present invention is a flow chart of a trend processing module of an intelligent temperature control system of a concrete mixing plant.

[0018] Figure 5 The present invention is a flow chart of an instruction generation module of an intelligent temperature control system of a concrete mixing plant. DETAILED DESCRIPTION

[0019] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0020] See Figure 1 , an intelligent temperature control system for a concrete mixing station, including: a temperature control module, a temperature inspection module, a trend processing module and an instruction generation module; wherein the output end of the temperature control module is connected to the temperature inspection module, the output end of the temperature inspection module is connected to the trend processing module, and the output end of the trend processing module is connected to the instruction generation module.

[0021] The temperature control module is used to measure the temperature of the mixer's working area at equal intervals, generate grid cells based on the position identifiers on the working area, form a temperature field monitoring network, and set the boundary conditions of each grid cell in the temperature field monitoring network.

[0022] The temperature inspection module is used to model the temperature curve of each grid unit in the temperature field monitoring network, set the temperature change curve corresponding to each grid unit, and inspect the change trend of the temperature change curve under multiple temperature stages based on the boundary conditions corresponding to the temperature change curve.

[0023] The trend processing module is used to perform difference analysis on the temperature change curve after inspection, extract the trend compliance degree of each temperature change curve, and set the trend compliance sequence according to the position of each grid unit under the trend compliance degree.

[0024] The instruction generation module is used to generate control adjustment instructions according to the temperature values in the trend sequence, and perform temperature control on the mixer according to the temperature after each adjustment instruction is executed.

[0025] The aforementioned working areas refer to the feed area, mixing-related areas, and the final placement area of the concrete after pouring in the mixing station. An array of high-precision temperature sensors is evenly spaced on the mixer's inner wall and mixing arm. Corresponding temperature sensors are then installed in the feed area and areas requiring pouring, forming a three-dimensional temperature field monitoring network. The temperature sensors installed in these areas are used to obtain the temperature of the aggregate during feeding, mixing, and pouring to prevent cracks in the concrete caused by large temperature differences. The temperature of the aggregate during feeding is primarily affected by the environment. During the mixing process, various cooling measures, such as air conditioning, cold water, and ice cubes, are added based on the feed temperature. This results in the temperature values being measured in three stages: heating, cooling, and stabilization. It is then necessary to determine whether the temperature curve meets expectations and whether the expected temperature output after mixing meets the standard.

[0026] The above position identifier is used to illustrate the relative position of the current grid unit in the mixer, and to illustrate the temperature value measured at the corresponding position.

[0027] At the same time, the temperature rise peak and cooling peak in multiple stages such as early, middle and late stages are measured, the delay time measured at these peaks is marked, and all these measured temperature values are formed into a three-dimensional temperature field monitoring network according to their relative positions during stirring.

[0028] like Figure 2 As shown, the implementation method of the temperature control module includes: checking the initial temperature of the grid unit in the feeding area according to the position identification of each grid unit, and setting the initial boundary condition based on the initial temperature checked by each grid unit.

[0029] The temperature of each grid cell is extracted, and the peak temperature rise and drop of each grid cell are marked. The grid cells are logically grouped according to the growth rate when each peak occurs, and multiple logical boundary conditions are set under the logical grouping.

[0030] The delay time is set by using the time difference between the temperatures of each grid unit reaching the target value, and the delay time is marked as a delayed boundary condition.

[0031] The initial boundary conditions, logical boundary conditions and delayed boundary conditions are output as boundary conditions on each grid cell.

[0032] At this point, the boundary conditions corresponding to the temperature in the temperature field monitoring network are set. After that, the measured temperature needs to be preliminarily identified and formed into a temperature cloud map or a temperature level change form to form its temperature field monitoring network.

[0033] The initial boundary conditions above describe the temperature of the aggregate input to the mixer under the current circumstances, and whether the aggregate temperature after mixing reaches the normal paving temperature. The input temperature determines the amount and method of adding ice, cold water, or air conditioning during mixing. The output temperature then indicates whether the current internal area is being mixed normally during mixing, and whether the temperature control after mixing can reduce problems such as cracking of the concrete.

[0034] The logical boundary conditions describe the peak values that appear during heating and cooling within each grid unit. The heating peak reflects the degree of heat accumulation within the concrete, determining whether a large temperature difference between the inside and outside of the concrete could lead to cracks. Excessive heating peaks require immediate cooling with ice, cold water, and air conditioning. Cooling peaks indicate whether the concrete has cooled completely and whether the temperature is too low after cooling. The data for each grid unit is then grouped based on the average growth rate of the heating and cooling peaks at the time of their occurrence to promote initial concrete stability.

[0035] As for the delayed boundary condition, it describes the time difference between the time taken to reach the expected temperature value after mixing, hydration reaction and cooling treatment, and the expected time, to illustrate whether each grid unit can achieve stable growth during the concrete mixing process and its reaction rate. For example, the earlier the temperature rise peak appears, the more intense the hydration reaction is. The speed at which the temperature drop peak appears can reflect the effectiveness of the cooling method. By combining the time with the average rate at which the peak appears, it can be known whether the temperature control at each position is normal.

[0036] After that, it is necessary to look at the relative stages of temperature change on each grid cell and then identify the time spent in each stage to determine the overall situation of the current concrete mixing.

[0037] Therefore, the implementation method of the temperature field monitoring network also includes: checking the temperature stages on the grid units, and identifying the time proportions of the heating stage, cooling stage and stable stage in turn.

[0038] Each grid cell is semantically labeled with the endpoint temperature corresponding to the time proportion of the temperature stage, and the value of each grid cell in the corresponding temperature stage after semantic labeling is added to the boundary conditions of each grid cell.

[0039] The end point temperature described at this time is the typical temperature value in each temperature stage, such as the end point of heating, the end point of cooling, and the average value of the stable period. After being extracted as the end point temperature, these values are synchronized to the recorded boundary conditions.

[0040] Each temperature stage is set based on the temperature value. For example, the temperature rise from the initial value to the peak is considered the heating stage, the temperature drop from the peak is considered the cooling stage, and the stable part is considered the stabilization stage. For example, if the heating stage percentage is greater than 40% and the heating end temperature is greater than 35°C, these recorded data are placed at the boundary conditions to more specifically describe the current boundary conditions.

[0041] In one embodiment of the present invention, the temperature inspection module mainly identifies the change trend of the temperature curve in each grid unit, analyzes the expression of the obtained temperature value in each temperature stage, and determines whether the change trend reflected under its boundary conditions is in line with expectations.

[0042] like Figure 3 As shown, the implementation method of the temperature inspection module includes: forming a temperature change curve corresponding to each grid unit based on the temperature measurement frequency of each grid unit; the temperature change curve is actually a time-temperature series data set, which is used to represent the change trend of the working area where the mixer is located under the corresponding time series.

[0043] Based on the boundary conditions corresponding to each temperature change curve, check the fluctuation range of the temperature change curve under the corresponding boundary conditions. The boundary conditions checked at this time will include initial temperature, heating rate, cooling rate, peak temperature, stable temperature range, etc., that is, it is required to check whether the range of identified temperature values at the boundary conditions extracted at the beginning meets its standards, and combine the data on multiple grid units to identify whether there are fluctuations in cooling rate, heating rate and peak temperature. Based on the existing fluctuations, it is determined whether the multi-unit data currently obtained during mixing is valid and whether the execution of the concrete mixing task is stable.

[0044] The above-mentioned initial temperature is identified based on the temperature of the feeding area contained in the boundary conditions. The heating rate and cooling rate describe that the rate of change relative to time cannot exceed the normal safety threshold. The peak temperature is the part that constrains its heating peak and cooling peak. The stable temperature range constraint describes the range of its temperature in the stable stage to illustrate its relative value.

[0045] Data verification is performed based on the fluctuation range of the temperature change curve under the corresponding boundary conditions, forming a sample label corresponding to the temperature change curve, and the verified temperature change curve is output. During data verification, in addition to verifying the content identified by the boundary conditions under different temperature conditions, this part of the content is used as the subject of the inspection at this time, such as the peak value and rate during the heating and cooling phases, and the range and specific value during the stable phase to determine whether the changes conform to the normal trend.

[0046] Preferably, the temperature measurement frequency can be controlled to 2h / time in the heating stage, 4h / time in the cooling stage, and 8h / time in the stabilization stage to view the temperature data that needs to be analyzed currently and avoid cracks caused by control deviation.

[0047] Therefore, the implementation method of data verification includes: extracting key parameters corresponding to the temperature change curve based on the corresponding boundary conditions of the temperature change curve, performing verification with the value range of the key parameters, and setting sample labels for the temperature change curve according to the verification content.

[0048] At this time, after determining whether the value range meets expectations, it is also necessary to identify whether there are abnormal fluctuations in the temperature change curve on each grid unit. At this time, the identified grid units are mainly located in the mixing area of the mixer. At this time, the temperature curve near the inlet and outlet is not considered. The spatial consistency of the temperature change is tested to statistically determine whether there are any grid unit anomalies.

[0049] Preferably, the data verification is processed according to the contents corresponding to the initial temperature, heating rate, cooling rate, peak temperature, and stable temperature range.

[0050] The key parameter to be checked is the temperature value of the feeding area, which is used to verify whether the temperature of the feeding area meets the process requirements. For example, the aggregate feeding temperature must be within a specific range. The measured feeding temperature is compared with the preset initial temperature threshold to calculate the deviation. The deviation obtained at this time is mainly used for subsequent cooling decisions. The initial temperature constraint is extracted to more comprehensively display the current working conditions of the mixer feeding. The preset initial temperature threshold set at this time will be set according to the average temperature during normal feeding and discharging. The temperature value checked here is required to be within the normal environmental range to avoid extreme temperature aggregate feeding.

[0051] The key parameters in the heating and cooling stages are mainly the heating rate and the cooling rate, which are used to describe the rate of change of the temperature value under heating and cooling, and to judge whether it meets the preset change rate threshold. At this time, two thresholds will be set, a heating rate threshold and a cooling rate threshold. The thresholds used can be set based on the thresholds that can maintain stable concrete production under historical data, to prevent local overheating from causing rapid evaporation of water, and to control thermal stress to prevent cracks.

[0052] Peak temperature identifies the peak value that can be reached in each stage under the current initial temperature conditions. The key parameters viewed here are the maximum and minimum peak values under cooling and heating to check whether the peak value exceeds the relative safety threshold. The deviation obtained at the initial temperature can be used to query data from the database and set the corresponding peak threshold to prevent material phase change or freezing.

[0053] The stable temperature range is the range of temperature values recorded in the stable stage. For example, the temperature range and fluctuation amplitude of the stable node are selected as key parameters, and then the confidence interval of the temperature value in the stable stage in the historical data is used as the threshold used here to identify whether the value range of the key parameter exceeds the normal range to ensure uniform curing of the concrete.

[0054] During consistency testing, the temperature curves of adjacent grid cells are compared to determine whether their standard deviations are within the normal range. If the calculated standard deviation meets the safety threshold, no alarm is triggered. Otherwise, the corresponding abnormal point is marked on the temperature curve. The safety threshold is the average standard deviation of historical data used in comparing different grid cells. The spatial consistency of each grid cell is tested using three standard deviations.

[0055] At this time, the boundary conditions extracted by the temperature value will be used to extract the relevant key parameters, and they will be divided according to their temperature stages and other contents to determine the data and corresponding content that need to be tested.

[0056] At this time, checking the changing trend of the temperature change curve in multiple temperature stages also includes: checking the time proportion of each grid unit in each temperature stage, extracting the difference points of each grid unit based on the standard deviation of the temperature value under the time proportion, and outputting the sample label of the temperature change curve based on the location of each difference point.

[0057] At this time, the points where abnormal values appear are checked through their temperature standard deviation, such as the temperatures corresponding to the heating, cooling, and stabilization stages, and the peak temperature when taking the value, to determine the parts that obviously exceed three times the standard deviation. After finding the corresponding data points, these parts are marked as difference points, and sample labels are set for them based on the position of the grid unit to illustrate the abnormalities in different grid units.

[0058] Preferably, the inspection time proportion is used to identify the time of the current grid unit in multiple temperature stages, count the duration proportions of each grid unit in heating, cooling and stabilization, and then calculate the standard deviation of the temperature value of each grid unit in each temperature stage, mark the part with a temperature value greater than three times the temperature standard deviation, and use it as an abnormal point, and mark at what time proportion and temperature stage it appears.

[0059] Then, the temperature data of each grid cell is synchronized according to the absolute timestamp, and the collective standard deviation of the temperature values of all grid cells at each time point is identified. This collective standard deviation represents the standard deviation calculated from the temperature values within all grid cells at each time point. At this time, the part with a temperature value greater than three times the collective standard deviation is identified, and then the location of the difference point is identified. The time and location of its occurrence are marked, and the difference points identified in these two cases are comprehensively judged to extract the comprehensive difference point, and the part tested at this time is output as its sample label.

[0060] In one embodiment of the present invention, Figure 4 As shown, the implementation method of the trend processing module includes: based on the changing trend of the temperature change curve in each temperature stage, the trend compliance degree of the heating stage, the cooling stage and the stabilization stage is calculated in turn, and the weighted value of the trend compliance degree of the heating stage, the cooling stage and the stabilization stage is regarded as the trend compliance degree corresponding to the current temperature change curve.

[0061] The temperature rising points obtained at this time are mainly used to describe the process of how the current concrete reacts in the temperature rising stage of the temperature change curve. At the same time, the extracted temperature rising points will also be included in the temperature recovery part after reaching the cooling peak to illustrate how the current temperature reaches a relatively stable temperature state. The data in these parts are used as the temperature rising points identified at this time to determine whether there is a problem with the current processing. As for the cooling part and the stable part in the temperature change curve, the trend conformity is calculated by the rate of change of the curve slope corresponding to its cooling rate. For the stable part, the trend conformity is obtained by its standard deviation variance.

[0062] The trend conformity is adjusted based on the difference in trend conformity between adjacent grid cells, and the adjusted trend conformity is sorted according to its spatial position and output as a trend conformity sequence.

[0063] The implementation method of the trend compliance degree of the heating stage includes: based on the changing trend of the temperature change curve, checking the historical heating points under each heating stage, judging whether the current time point is a historical heating point, if it is a historical heating point, calculating the trend compliance degree by comparing the slope of the temperature change curve with the historical temperature change curve; if it is not a historical heating point, calculating the trend compliance degree by comparing the rate of change of the slope of the temperature change curve with the historical temperature change curve.

[0064] As for the implementation method of the trend compliance degree in the cooling stage, it includes: based on the changing trend of the temperature change curve, checking the historical cooling points under each cooling stage, judging whether the current time point is a historical cooling point, if it is a historical cooling point, calculating the trend compliance degree by using the slope change rate of the temperature change curve and the historical temperature change curve; if it is not a historical cooling point, calculating the trend compliance degree by using the cooling rate of the temperature change curve and the historical temperature change curve.

[0065] As for the implementation method of the trend conformity in the stable stage, the method includes: calculating the trend conformity with the historical temperature change curve based on the temperature standard deviation in the current stable stage.

[0066] When calculating the degree of trend conformity between the current temperature change curve and the historical temperature change curve, the currently identified temperature values are calculated using the Pearson correlation coefficient to determine the degree of similarity between the curves. The weighted summation is then performed, using the time proportion of each stage as a weight, to determine the degree of trend conformity for each temperature stage.

[0067] As for judging whether the current temperature rise point and temperature drop point are historical temperature rise points and historical temperature drop points, the judgment is made by comparing the Euclidean distance between the temperature value of the current point and the corresponding data in the historical data. First, the time series is aligned to align the time points of the current temperature change curve with the historical temperature change curve. Then, the distance value between the two temperature values is calculated. If the distance value is less than the distance threshold, the current point is judged to be a historical temperature rise point or a historical temperature drop point. At this time, the distance threshold is set using the average value of the historical data judged as a historical temperature rise point or a temperature drop point. At this time, the distance needs to be close after the corresponding time alignment. It should be noted that the current time series alignment is to align the temperature data under the same or similar working conditions with the time when they started working. It is not a complete alignment. At this time, the data in the temperature change curve also needs to be normalized to eliminate its dimension.

[0068] The aforementioned rate of change of the slope of the curve is expressed as the value obtained by taking a derivative of the slope of the curve. When using the derived value to calculate multiple temperature values with a large distance between them, a multi-dimensional description is preferred to express the degree of conformity of the currently calculated trend. The two trend conformities calculated during the current warming phase, those that conform to the historical warming point and those that do not conform to the historical warming point, are then combined in the form of a weighted sum to obtain the degree of conformity of the trend during the warming phase. The sum of the weights set at this time is 1. The two weights can be adjusted according to actual needs, such as selecting 0.6 and 0.4, or setting the weights based on the support of the data in the historical data for the data that conforms to the historical warming point and the data that does not conform to the historical warming point. The same applies to the cooling and warming phases and will not be explained here.

[0069] Preferably, when adjusting the trend conformity degree based on the difference in trend conformity degrees between adjacent grid cells, for example, the adjusted trend conformity degree = trend conformity degree × (1-attenuation coefficient × difference in trend conformity degrees between adjacent grid cells); at this time, the attenuation coefficient can be selected as 0.2, or a smaller value, so that the trend conformity degree obtained by each grid cell can tend to be consistent, thereby reducing the situation where the temperature trend is inconsistent in the working area where the mixer is located.

[0070] At this time, the main thing to check is whether there are similar and different trends in the temperature changes of each grid unit. For example, during the current concrete mixing process, the temperature change rate and peak value of the two adjacent grid units should be similar. If they are not similar, it means that there are differences, which may indicate that there are abnormalities in the overall cooling and heating stages, and adjustments need to be made to prevent the stability of concrete production from being affected. At the same time, when the grid unit is close to the discharge port and describes the temperature curve of the actual discharge process, its temperature curve should be consistent with the temperature curve under normal discharge conditions to prevent abnormal output of colloidal concrete. At the same time, checking the temperature near the inlet can determine the temperature corresponding to the current incoming aggregate, which facilitates subsequent adjustments to the degree of cooling and the temperature of the concrete.

[0071] Preferably, the adjusted trend conformity is sorted according to its spatial position and output as a trend conformity sequence. At this time, the trend conformity calculated by the temperature change curve of each grid unit is sorted according to the position of the grid unit and output in sequence to obtain curve data on whether the temperature at different positions conforms to the expected trend.

[0072] In one embodiment of the present invention, the instruction generation module determines whether the current stage is heating up or cooling down based on the read concrete temperature data. If it is heating up, it is necessary to issue relevant instructions for controlling the cooling circulating water according to the user setting value; if it is cooling down, it first calculates the cooling rate and issues relevant instructions according to the user setting value.

[0073] The system reads the current temperature and selects inputs and speed control commands such as cold water, ice, and air conditioning to determine if there is a temperature error after the corresponding command is executed. Finally, it can issue cooling water flow rate control commands and flow control commands to achieve precise control of the inlet and outlet temperature difference.

[0074] like Figure 5 As shown, the implementation method of the instruction generation module includes: identifying the adjustment nodes under each temperature stage based on the trend compliance degree of the trend compliance sequence, retrieving the operating status of each adjustment node based on the temperature value and trend compliance degree of each adjustment node, and obtaining an alternative node corresponding to at least one adjustment node.

[0075] Monitor the candidate nodes corresponding to each adjustment node, select the control commands of the candidate nodes based on the temperature values of the candidate nodes, and combine the control instructions of the candidate nodes into control adjustment instructions.

[0076] Preferably, the adjustment node represents a node that requires active intervention in the temperature stage of the temperature change curve. The characteristics of this node mainly include significant temperature deviation, low trend compliance, and dynamic sensitive points. Significant temperature deviation means that the temperature value of the current temperature change curve has a significant deviation from the temperature value of the historical temperature change curve, the trend compliance value is low, and the temperature value of the peak point represented by the dynamic sensitive point is significantly different from that of the historical temperature curve.

[0077] An alternative node refers to a point in the vicinity of the regulating node that is most similar to the regulating node in terms of temperature value and trend compliance. It is used to provide a control basis for the regulating node. The vicinity will indicate that there are data points adjacent to the position that currently needs to be adjusted in the spatial and temporal dimensions, indicating that it is an alternative node. Subsequently, the control command of the alternative node will select its relative control command according to its position in the cooling stage or the heating stage.

[0078] If it is in the heating stage and the current candidate node has the problem of heating up too quickly, select the control commands corresponding to adding cold water spray, starting the cooling water pump, etc.

[0079] If the system is at a cooling node and the current candidate node is cooling too slowly, control commands such as increasing the pump speed or extending the cooling time will be executed. At this point, it is necessary to generate the corresponding operation mode for the candidate node based on the candidate node's stage and actual performance. These control commands are then combined and the identical control commands are removed to obtain the control adjustment command for the adjustment node.

[0080] The above-mentioned adjustment nodes filter out points with trend compliance lower than the threshold as their adjustment nodes based on the trend compliance threshold, and use the temperature deviation that occurs in each temperature stage to sort the adjustment nodes according to each temperature stage for processing in sequence; the trend compliance threshold used here will be set based on historical data, and the average value of the trend compliance threshold when filtering the adjustment nodes with historical data will be used as the threshold used here.

[0081] Then, the alternative nodes are screened, and the temperature value and trend compliance value of the current adjustment node are used to search from the currently collected temperature change curve to view the alternative nodes similar to the current adjustment node. Then, the alternative nodes are used to select the control adjustment instructions used by the current adjustment node from the historical control instruction set.

[0082] When screening candidate nodes for the adjustment node, the Euclidean distance can be used to calculate the normalized temperature value and trend conformity between the adjustment node and the candidate stage in terms of temperature value and trend conformity. Then, for each candidate node B, its matching degree S with the adjustment node A is calculated, as follows: ;in, represents the Euclidean distance between candidate node B and adjustment node A, Represents the maximum allowable distance, which is used to normalize the calculated Euclidean distance to obtain a matching degree relative to the range of 0 to 1. The matching degree obtained here is more inclined to indicate that the nodes in the historical data have similar temperature values and trend conformity values as the current adjustment node. In this case, the candidate nodes obtained are in the same temperature stage as the current adjustment node. The control instructions selected by the candidate nodes are then combined to form the control adjustment instructions for the current adjustment node.

[0083] Combining the control instructions of the candidate nodes into control adjustment instructions also includes obtaining a matching degree between the candidate nodes and the adjustment node, screening the candidate nodes using a matching degree threshold, and searching a historical control instruction set using the screened candidate nodes. The matching degree threshold is set to 0.7, which selects multiple points that are very similar to the current situation.

[0084] The control instructions of the historical control instruction set under the corresponding temperature stage are used to extract the control instructions corresponding to each alternative node. When extracting the control instructions of the alternative node, the similarity between the alternative node and the historical control instruction set is calculated, and the control instruction with the largest similarity value is used as the control instruction of the corresponding alternative node. The method of calculating the similarity can be based on the Pearson correlation coefficient or cosine similarity, and is calculated using the temperature value of the alternative node.

[0085] When the control instructions of the alternative nodes are combined into the output control adjustment instructions, it is necessary to consider the conflict relationship between the control instructions of the alternative nodes, and use cluster analysis to resolve the conflicts between the instructions. The alternative nodes are clustered in turn according to their temperature stage, spatial position and trend compliance. The comprehensive weight of each alternative node is set according to the weight of each alternative node in each clustering. Then, the priority of the alternative node is set according to the comprehensive weight of the alternative node to execute its control instructions in turn, and the repeated parts of the control instructions are eliminated. The control instructions with larger priority values are executed. After eliminating conflicts and repeated parts of these instructions, they are combined into the final output control adjustment instructions.

[0086] The candidate nodes are clustered according to the temperature stage, spatial position and trend compliance, and the priority of each candidate node is set after clustering. The control instructions are combined from high to low in priority to obtain the control adjustment instructions.

[0087] Preferably, when clustering the temperature stages, clustering is performed based on the temperature values, and clustering is performed with three labels: heating up, stable, and cooling down; when clustering the spatial positions, arbitrarily select multiple candidate nodes as the initial center, calculate the Euclidean distance of each candidate node to the initial center, and assign it to the nearest cluster, and then recalculate the center of each cluster, and repeat this process until the center temperatures of all clusters are reached, thereby completing the spatial position clustering; as for the trend conformity clustering, clustering is performed based on the difference between the trend conformity, which is the same as the spatial clustering method, until the difference between the trend conformity of each point and the center point of the multiple clusters divided after iteration reaches a stable level, and the clustering is completed.

[0088] For example, let the priority of candidate node i be Expressed as: ;in, It is expressed as the intra-cluster weight of the time stage, which is set by dividing the temperature value of the current candidate node by the maximum value within its cluster; The cluster weight is expressed as the trend conformity degree. The value is set based on the ratio of the trend conformity degree of the current candidate node to the maximum trend conformity degree in the cluster. It is expressed as the intra-cluster weight of the spatial position, and its value is set as the ratio of the distance from the current candidate node to the adjustment node to the maximum distance to the same adjustment node in the cluster; Indicates the adjustment coefficient of the time stage, which is set according to the temperature stage of the current alternative stage. For example, if the current stage is heating up, the value can be 0.5; if it is in the cooling stage, the value can be 0.4; and if it is in the stable stage, the value can be 0.1; The adjustment coefficient of the trend compliance is set based on the relative importance of identifying the trend compliance in the current calculation stage. Its setting value can be expressed as 0.3. They are respectively expressed as adjustment coefficients of spatial positions, and their values can be set to 0.2 to balance the relative situation of the currently set priorities. At this time, a single candidate node is described in three clustering ways to describe the importance of each node relative to the node that currently needs to be adjusted in a similar manner. Starting from the control command executed by the candidate node with the largest priority value, multiple control commands corresponding to the current adjustment node are deduplicated, and after deleting their conflicting commands, the command corresponding to the current adjustment node is executed, and the temperature value after execution is read. The read temperature value is used to identify whether there is an error between it and the expected temperature change curve, and the read data is forwarded to the temperature control module to complete the closed-loop control of the current system. Until the temperature error is identified to be less than the expected value, such as 0.5 degrees Celsius, or a smaller error, the current command control cycle is terminated to complete the cyclic control of the concrete mixing in the current mixer.

[0089] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered by the scope of protection of the present invention.

Claims

1. An intelligent temperature control system for a concrete mixing station, characterized in that: include: The temperature control module is used to measure the temperature of the mixer's working area at equal intervals, generate grid cells based on the location identifiers on the working area, form a temperature field monitoring network, and set the boundary conditions of each grid cell in the temperature field monitoring network; The temperature inspection module is used to model the temperature curve of each grid unit in the temperature field monitoring network, set the temperature change curve corresponding to each grid unit, and verify the change trend of the temperature change curve under multiple temperature stages based on the boundary conditions corresponding to the temperature change curve; The trend processing module is used to perform difference analysis on the temperature change curve after inspection, extract the trend compliance degree of each temperature change curve, and set the trend compliance sequence according to the position of each grid unit under the trend compliance degree; The instruction generation module is used to generate control adjustment instructions according to the temperature values in the trend sequence, and perform temperature control on the mixer according to the temperature after each adjustment instruction is executed.

2. The intelligent temperature control system for a concrete mixing station according to claim 1, characterized in that: The implementation methods of the temperature control module include: According to the position mark of each grid cell, check the initial temperature of the grid cell in the feeding area, and set the initial boundary conditions based on the initial temperature of each grid cell; Extract the temperature of each grid unit, mark the peak temperature rise and cooling peak of each grid unit, logically group each grid unit according to the growth rate when each peak occurs, and set multiple logical boundary conditions under the logical grouping; The delay time is set by using the time difference between the temperatures of each grid unit reaching the target value, and the delay time is marked as a delay boundary condition; The initial boundary conditions, logical boundary conditions and delayed boundary conditions are output as boundary conditions on each grid cell.

3. The intelligent temperature control system for a concrete mixing station according to claim 2, characterized in that: The implementation of the temperature field monitoring network also includes: View the temperature stages on the grid cells and identify the time proportions of the heating stage, cooling stage, and stable stage in turn; Each grid cell is semantically labeled with the endpoint temperature corresponding to the time proportion of the temperature stage, and the value of each grid cell in the corresponding temperature stage after semantic labeling is added to the boundary conditions of each grid cell.

4. The intelligent temperature control system for a concrete mixing station according to claim 1, characterized in that: The implementation of the temperature detection module includes: Based on the temperature measurement frequency of each grid unit, a temperature change curve corresponding to each grid unit is formed; Based on the boundary conditions corresponding to each temperature change curve, check the fluctuation range of the temperature change curve under the corresponding boundary conditions; The data is checked based on the fluctuation range of the temperature change curve under the corresponding boundary conditions to form a sample label corresponding to the temperature change curve, and the checked temperature change curve is output.

5. The intelligent temperature control system for a concrete mixing station according to claim 4, characterized in that: The implementation methods of data verification include: Based on the temperature change curve at the corresponding boundary conditions, the key parameters corresponding to the temperature change curve are extracted, and the key parameters are tested with their value ranges, and sample labels are set for the temperature change curve according to the test content.

6. The intelligent temperature control system for a concrete mixing station according to claim 4, characterized in that: The changing trend of the inspection temperature change curve at multiple temperature stages also includes: Check the time proportion of each grid unit in each temperature stage, extract the difference points of each grid unit based on the standard deviation of the temperature value under the time proportion, and output the sample label of the temperature change curve based on the location of each difference point.

7. The intelligent temperature control system for a concrete mixing station according to claim 1, characterized in that: The implementation of the trend processing module includes: Based on the changing trend of the temperature change curve in each temperature stage, the trend conformity of the heating stage, the cooling stage and the stable stage is calculated in sequence, and the weighted values of the trend conformity of the heating stage, the cooling stage and the stable stage are regarded as the trend conformity corresponding to the current temperature change curve; The trend conformity is adjusted based on the difference in trend conformity between adjacent grid cells, and the adjusted trend conformity is sorted according to its spatial position and output as a trend conformity sequence.

8. The intelligent temperature control system for a concrete mixing station according to claim 7, characterized in that: The ways to achieve the trend compliance degree in the warming stage include: Based on the changing trend of the temperature change curve, check the historical temperature rising points in each temperature rising stage to determine whether the current time point is a historical temperature rising point. If it is a historical temperature rising point, calculate the trend conformity by comparing the slope of the temperature change curve with the historical temperature change curve. If it is not a historical temperature rising point, calculate the trend conformity by comparing the slope change rate of the temperature change curve with the historical temperature change curve. The implementation method of the trend conformity degree of the cooling stage includes: based on the changing trend of the temperature change curve, checking the historical cooling points under each cooling stage, judging whether the current time point is a historical cooling point, and if it is a historical cooling point, calculating the trend conformity degree by comparing the slope change rate of the temperature change curve with the historical temperature change curve; if it is not a historical cooling point, calculating the trend conformity degree by comparing the cooling rate of the temperature change curve with the historical temperature change curve; The implementation method of the trend conformity degree in the stable stage includes: calculating the trend conformity degree with the historical temperature change curve based on the temperature standard deviation in the current stable stage.

9. The intelligent temperature control system for a concrete mixing plant according to claim 1, characterized in that: The implementation of the instruction generation module includes: Based on the trend compliance degree of the trend compliance sequence, the regulating nodes in each temperature stage are identified, the operating status of each regulating node is retrieved based on the temperature value and trend compliance degree of each regulating node, and a candidate node corresponding to at least one regulating node is obtained; Monitor the candidate nodes corresponding to each adjustment node, select the control commands of the candidate nodes based on the temperature values of the candidate nodes, and combine the control instructions of the candidate nodes into control adjustment instructions.

10. The intelligent temperature control system for a concrete mixing station according to claim 9, characterized in that: The implementation method of combining the control instructions of the candidate nodes into control adjustment instructions also includes: Obtaining the matching degree between the candidate node and the adjustment node, screening the candidate nodes based on the matching degree threshold, and retrieving the historical control instruction set using the screened candidate nodes; Extract the control instructions corresponding to each candidate node based on the control instructions of the historical control instruction set at the corresponding temperature stage; The candidate nodes are clustered according to the temperature stage, spatial position and trend compliance, and the priority of each candidate node is set after clustering. The control instructions are combined from high to low in priority to obtain the control adjustment instructions.

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