Concrete production temperature real-time monitoring system

Abstract

The invention provides a concrete production temperature real-time monitoring system, and relates to the technical field of data processing, and the system comprises the steps: collecting the temperature data of a plurality of key links in the whole process from raw material mixing, stirring, transportation to pouring of concrete, and obtaining multi-source discrete temperature data; aggregation processing is carried out on the multi-source discrete temperature data to obtain a unified multi-dimensional time sequence data stream, and the multi-dimensional time sequence data stream is processed and mapped to obtain a dynamic data field; a reference analysis domain is defined in a dynamic data field, temperature data points in the reference analysis domain are preliminarily grouped according to feature similarity, and a temperature data point set of each group is solved and calculated to obtain a minimum surrounding circle of a complete temperature data point set. The concrete construction quality and the structural durability are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a real-time monitoring system for concrete production temperature. Background Technology

[0002] In the field of concrete engineering, temperature control is a core element in ensuring the quality of concrete construction and the durability of structures. Especially when constructing large-volume concrete or in high-temperature or low-temperature environments, temperature fluctuations throughout the concrete production process can easily lead to problems such as cracks and insufficient strength. Therefore, temperature monitoring has become a critical aspect of construction.

[0003] Existing technologies mostly employ a monitoring mode that combines local fixed-point sensors with manual spot checks. For example, in the foundation engineering of a high-rise building, the construction party only installed temperature sensors at the outlet of the mixer at the batching plant and at the pouring site, collected data through wired transmission, and required manual recording of the concrete temperature inside the transport truck every 2 hours.

[0004] In this case, the aggregate temperature was not monitored during the raw material storage stage, and the concrete temperature rise caused by changes in ambient temperature during transportation could not be captured in real time. Ultimately, because the amount of cooling water added was not adjusted according to the initial temperature of the aggregate during mixing, and the concrete with excessive temperature during transportation was directly poured, multiple surface cracks appeared in the foundation structure. The technical defects are that the monitoring links are incomplete, the data transmission is lagging and lacks real-time performance, and it is impossible to form a closed-loop control of temperature throughout the entire process. As a result, the control decision lags behind the temperature change, making it difficult to accurately control the temperature throughout the entire concrete production process. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a real-time monitoring system for concrete production temperature, thereby improving the quality of concrete construction and structural durability.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: In a first aspect, a real-time monitoring system for concrete production temperature includes: The data acquisition module is used to collect temperature data from multiple key stages of the entire process of concrete from raw material mixing, stirring, transportation to pouring, and obtain multi-source discrete temperature data. The module is used to aggregate multi-source discrete temperature data to obtain a unified multi-dimensional time-series data stream, and then process the multi-dimensional time-series data stream to map it into a dynamic data field. The solver module is used to define a baseline analysis domain within the dynamic data field, preliminarily group the temperature data points within the baseline analysis domain according to feature similarity, and perform solver calculations for each group of temperature data point sets to obtain the minimum enclosing circle of the complete temperature data point set. The partitioning module is used to divide the sub-region of the benchmark analysis domain covered by the minimum enclosing circle into a dynamic partition. The temperature data points in the dynamic partition have highly similar statistical characteristics and trend features, so as to realize the adaptive partitioning of the benchmark analysis domain into the dynamic partition. The association module is used for adaptive partitioning based on dynamic partitions. It associates each data item in the time series data stream with the corresponding dynamic partition based on feature similarity, and analyzes the data items gathered in each partition to obtain independent control parameters that reflect the statistical characteristics, rate of change and trend of each partition. The fusion module is used to perform weighted fusion and optimization decisions on independent control parameters to obtain global stirring process adjustment instructions; The processing module is used to adjust the amount of cooling water added and the mixing time in real time according to the mixing process adjustment instructions, so as to achieve adaptive closed-loop control of the concrete production temperature.

[0007] Furthermore, temperature data was collected at multiple key stages throughout the entire concrete process, from raw material mixing, stirring, and transportation to pouring, to obtain multi-source discrete temperature data, including: Temperature sensor nodes are deployed in the raw material storage area, the feeding port of the mixing plant, the inside of the mixer, the transport tanker and the pouring site to obtain a distributed sensor network covering the entire production process. The temperature of materials or concrete in the key process is collected synchronously by each sensor node in the distributed sensor network according to a preset period. The collected temperature values ​​are then encapsulated with the collection time and spatial location information to obtain the original temperature data packet with a time and space stamp. Each edge gateway performs timestamp synchronization and alignment, data format conversion, and filters out abnormal and invalid data from the multiple raw temperature data packets received, thus obtaining discrete temperature data with unified specifications.

[0008] Furthermore, the multi-source discrete temperature data are aggregated to obtain a unified multi-dimensional time-series data stream, which is then processed and mapped to obtain a dynamic data field, including: It receives multi-source discrete temperature data, buffers and temporarily stores the multi-source discrete temperature data stream, performs spatiotemporal alignment and fusion processing on the buffered multi-source discrete temperature data, and obtains a continuous and unified aggregated temperature data sequence in time and space based on the timestamp and spatial location information in the data. By associating and encapsulating the aggregated temperature data sequence with the corresponding production process metadata and environmental parameters, a structured multi-dimensional time-series data stream is obtained. Based on multi-dimensional time-series data streams, a dynamic data field of temperature status throughout the entire process is constructed, with time as the horizontal axis, spatial location of the production process as the vertical axis, and temperature value as the field strength.

[0009] Furthermore, a baseline analysis domain is defined within the dynamic data field. Temperature data points within this domain are initially grouped based on feature similarity. For each group of temperature data points, a solution is calculated to obtain the minimum bounding circle of the complete temperature data point set, including: In a dynamic data field, a baseline analysis domain is defined based on a preset time span and spatial range; Extract all temperature data points within the benchmark analysis domain, and calculate their multidimensional feature vectors based on the statistical characteristics and trends of each data point. Based on the similarity between the multidimensional feature vectors, perform preliminary clustering and grouping of the temperature data points. For each preliminary cluster group, obtain all temperature data points contained in the preliminary cluster group, and calculate the minimum geometric circle that completely surrounds all data points of the preliminary cluster group to obtain the minimum enclosing circle.

[0010] Furthermore, the sub-region of the benchmark analysis domain covered by the minimum enclosing circle is treated as a dynamic partition. Temperature data points within this dynamic partition all exhibit highly similar statistical characteristics and trends, enabling adaptive partitioning of the benchmark analysis domain into the dynamic partition. This includes: Based on all minimum bounding circles, the geometric coverage of each minimum bounding circle is mapped to a continuous spatiotemporal subregion within the baseline analysis domain, and the continuous spatiotemporal subregion is defined as an independent dynamic partition. The baseline analysis domain is then seamlessly spliced ​​and covered by mapping all the dynamic partitions obtained from the defined independent dynamic partitions to all the minimum bounding circles, thus completing the process of adaptively dividing the baseline analysis domain into multiple dynamic partitions.

[0011] Furthermore, based on adaptive partitioning with dynamic partitioning, each data item in the time-series data stream is associated with its corresponding dynamic partition according to feature similarity. The data aggregated within each partition are analyzed to obtain independent control parameters reflecting the statistical characteristics, rate of change, and trend of each partition, including: Based on the completed adaptive partitioning results, each temperature data point added in real time in the multi-dimensional time series data stream is associated with and assigned to the corresponding dynamic partition according to the feature similarity between the multi-dimensional feature vector and the features of each dynamic partition. A comprehensive analysis is performed on all temperature data points belonging to the same dynamic partition to extract the statistical characteristics of the data within the dynamic partition, calculate the rate of temperature change, and analyze the trend of change in order to obtain the analysis results. Based on the analysis results, a set of independent control parameters is obtained for each dynamic partition.

[0012] Furthermore, weighted fusion and optimization decisions are made on the independent control parameters to obtain global stirring process adjustment instructions, including: The system receives all independent control parameters corresponding to each dynamic partition. For each independent control parameter, a preset dynamic weight coefficient is assigned according to the spatial importance of the corresponding dynamic partition, the confidence level of data quality, and the effectiveness of historical control. The system then performs weighted fusion calculation to obtain a comprehensive global temperature status assessment index. Using the global temperature state assessment index as the core input, and combining the preset concrete production temperature target and process constraints, the mixing process adjustment command that satisfies the global optimal control objective is calculated through an optimized decision-making algorithm.

[0013] Furthermore, based on the mixing process adjustment instructions, the amount of cooling water added and the mixing time are adjusted in real time during the mixing process to achieve adaptive closed-loop control of the concrete production temperature, including: The mixing process adjustment command is transmitted down, and the transmitted mixing process adjustment command is received and parsed to obtain the specific cooling water flow rate adjustment amount and mixing time adjustment value. Based on the cooling water flow rate adjustment and stirring time adjustment values ​​obtained from the analysis, a control signal is obtained for synchronously adjusting the cooling water addition rate and the mixer running time. Based on the control signal, the corresponding cooling water addition and stirring time adjustment actions are executed to complete the real-time control of the current production temperature and start the next round of data acquisition and control cycle to achieve adaptive closed-loop control.

[0014] In a second aspect, a computing device includes: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to execute the system.

[0015] Thirdly, a computer-readable storage medium storing a program that, when executed by a processor, performs the system.

[0016] The above-described solution of the present invention has at least the following beneficial effects: By employing a distributed sensor network covering the entire concrete production process to collect data, and edge gateways to process heterogeneous data, combined with dynamic data field construction, dynamic partitioning based on feature clustering and minimum enclosing circle, extraction of independent control parameters for each partition, and dynamic weighted fusion optimization and closed-loop control of cooling water addition and mixing time, this integrated technology overcomes the technical problems of incomplete monitoring links, lagging and heterogeneous data transmission, fixed and unadaptable partitioning, lack of targeted and global coordination in control, and inability to achieve closed-loop control in traditional methods. This achieves real-time and accurate monitoring, dynamic adaptive analysis, and global optimal control of temperature throughout the entire concrete production process, ensuring that concrete production temperature meets process requirements and improving concrete construction quality and structural durability. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a real-time monitoring system for concrete production temperature provided in an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the process of defining a benchmark analysis domain in a dynamic data field, initially grouping temperature data points within the benchmark analysis domain according to feature similarity, and performing calculations for each group of temperature data point sets to obtain the minimum enclosing circle of the complete temperature data point set. Detailed Implementation

[0019] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0020] like Figure 1 As shown, an embodiment of the present invention proposes a real-time monitoring system for concrete production temperature, comprising: The data acquisition module is used to collect temperature data from multiple key stages of the entire process of concrete from raw material mixing, stirring, transportation to pouring, and obtain multi-source discrete temperature data. The module is used to aggregate multi-source discrete temperature data to obtain a unified multi-dimensional time-series data stream, and then process the multi-dimensional time-series data stream to map it into a dynamic data field. The solver module is used to define a baseline analysis domain within the dynamic data field, preliminarily group the temperature data points within the baseline analysis domain according to feature similarity, and perform solver calculations for each group of temperature data point sets to obtain the minimum enclosing circle of the complete temperature data point set. The partitioning module is used to divide the sub-region of the benchmark analysis domain covered by the minimum enclosing circle into a dynamic partition. The temperature data points in the dynamic partition have highly similar statistical characteristics and trend features, so as to realize the adaptive partitioning of the benchmark analysis domain into the dynamic partition. The association module is used for adaptive partitioning based on dynamic partitions. It associates each data item in the time series data stream with the corresponding dynamic partition based on feature similarity, and analyzes the data items gathered in each partition to obtain independent control parameters that reflect the statistical characteristics, rate of change and trend of each partition. The fusion module is used to perform weighted fusion and optimization decisions on independent control parameters to obtain global stirring process adjustment instructions; The processing module is used to adjust the amount of cooling water added and the mixing time in real time according to the mixing process adjustment instructions, so as to achieve adaptive closed-loop control of the concrete production temperature.

[0021] In this embodiment of the invention, the system overcomes the technical problems of traditional concrete temperature monitoring, such as incomplete monitoring links, difficulty in unified data integration and analysis, fixed and unadaptive partitioning, lack of targeted and global coordination in regulation, and inability to achieve adaptive closed-loop control, by employing a comprehensive technical approach that integrates full-process multi-source temperature data acquisition, multi-source data aggregation and dynamic data field construction, feature similarity-based clustering and dynamic partitioning based on minimum enclosing circles, extraction of independent control parameters through partitioned data correlation analysis, weighted fusion optimization decision-making based on control parameters, and closed-loop control of cooling water addition and mixing time based on adjustment commands. This results in achieving real-time and accurate monitoring and dynamic adaptive analysis of the temperature throughout the entire concrete production process, realizing globally optimal mixing process control, ensuring that the concrete production temperature meets requirements, and ultimately improving the technical effect of concrete construction quality and structural stability.

[0022] In a preferred embodiment of the present invention, temperature data are collected from multiple key stages throughout the entire process of concrete production, from raw material mixing, stirring, and transportation to pouring, to obtain multi-source discrete temperature data, including: Temperature sensor nodes are deployed in the raw material storage area, the feeding port of the mixing plant, inside the mixer, in transport tankers, and at the pouring site to form a distributed sensor network covering the entire production process. Specifically, this includes: considering the temperature influence characteristics of the entire concrete production process, addressing the incomplete coverage of monitoring links in the background technology, identifying the key links where temperature sensor nodes need to be deployed; in the raw material storage area, multiple temperature sensor nodes are arranged at the surface, middle, and bottom layers of the aggregate pile according to its volume and height; the sensor nodes are installed using waterproof and dustproof mounting brackets to ensure the accurate collection of the true temperature of aggregate at different depths; at the feeding port of the mixing plant, temperature sensor nodes are installed on both sides of the inner wall of the feeding chute, with the node positions avoiding areas directly impacted by materials to prevent damage to the sensors due to material collisions, for monitoring purposes. During the feeding stage, the initial temperature of the raw materials is monitored inside the mixer. Sensor mounting slots are pre-drilled at different locations on the inner wall of the mixing drum. Temperature sensor nodes are embedded in these slots and encapsulated with high-strength, wear-resistant sealing material to prevent wear or corrosion from the concrete materials during mixing. This enables real-time temperature monitoring of the concrete mixing process inside the mixer. In the transport tanker, one temperature sensor node is placed at the top, middle, and bottom of the tank's inner wall. These nodes are installed using a magnetic fixing structure for easy disassembly and maintenance, monitoring temperature changes at different locations during transport. At the pouring site, temperature sensor nodes are placed around the area to be poured and at temporary pre-mixed concrete storage points. These nodes are fixed with portable brackets, allowing for flexible position adjustments as the pouring progresses. After all temperature sensor nodes are deployed, each node is uniquely identified and coded. A network configuration tool connects all nodes to the same wireless sensor network, setting the network communication parameters for each node. This results in a distributed sensor network covering the raw material storage and mixing plant, the mixing machine, the transport tankers, and the pouring site.

[0023] Through the sensor nodes in the distributed sensor network, the temperature of materials or concrete at their respective key stages is synchronously collected at a preset cycle. The collected temperature values ​​are then appended with the collection time and spatial location information, encapsulating them into a raw temperature data packet with a time-stamp. Specifically, after the distributed sensor network is built, the collection cycle of the sensor nodes is set according to the process rhythm and temperature change rate of concrete production. The collection cycle setting references the single operation time of concrete mixing, the transportation time of the transport truck, and the fluctuation of ambient temperature to ensure that the collection frequency can capture the dynamic changes in temperature at each stage in a timely manner, while avoiding data redundancy caused by excessively frequent collection. A synchronous collection calibration procedure for the sensor nodes is initiated, using the master clock node in the network as a reference to unify the internal clocks of all sensor nodes. Calibration ensures complete synchronization of data acquisition time across all nodes. After calibration, each sensor node begins acquiring temperature data according to a preset cycle. Upon acquiring the temperature values ​​of materials or concrete at its critical stage, the sensor node automatically extracts its own identification code and corresponding spatial location information. This spatial location information clearly points to the specific monitoring stage and installation point. Simultaneously, it extracts the current acquisition time information, accurate to the second. The sensor node then integrates the acquired temperature values, acquisition time information, and spatial location information, encapsulating them in an orderly manner according to a preset data packet structure. The temperature value serves as the core data packet, while the acquisition time and spatial location information are additional information segments. All information segments are arranged in a fixed order, ultimately forming a raw temperature data packet with a time and space stamp. After completing one data acquisition and encapsulation cycle, each sensor node immediately sends the raw temperature data packet to the nearest edge gateway.

[0024] Each edge gateway performs timestamp synchronization and alignment, data format conversion, and filters out abnormal and invalid data from multiple received raw temperature data packets, resulting in discrete temperature data with unified specifications. Specifically, after receiving multiple raw temperature data packets from each sensor node, the edge gateway first initiates a timestamp synchronization and alignment process. In the background technology, the lack of a unified time reference for data from different monitoring points hinders effective data correlation analysis. To address this deficiency, the edge gateway uses its built-in high-precision real-time clock as a reference, reading the acquisition time information from each raw temperature data packet one by one, comparing it with the gateway's current system time, and calculating the time deviation value for each data packet. For data packets with time deviations, the edge gateway corrects and adjusts their acquisition time information based on the deviation value, ensuring that the timestamps of all raw temperature data packets remain completely synchronized, enabling temperature data collected from different points in different stages to be correlated and analyzed in the time dimension. After timestamp synchronization is complete, the edge gateway initiates a data format unification conversion process. Since sensor nodes in different monitoring stages may use different models, their output temperature data formats differ. Some nodes output temperature data with unit identifiers, and some nodes retain different decimal places. To address this issue, the edge gateway uniformly converts the temperature values ​​in all raw temperature data packets according to the system's preset standard data format, unifying the unit to degrees Celsius and retaining two decimal places. It also removes redundant identifiers from the data format, achieving standardization of all temperature data formats. After format conversion, the edge gateway initiates an abnormal and invalid data filtering process. The edge gateway has built-in normal temperature range thresholds for each stage of concrete production. These thresholds are set with reference to concrete production process standards and historical monitoring data. The edge gateway compares the temperature value in each data packet with the corresponding threshold, eliminating abnormal temperature data exceeding the threshold range. Simultaneously, the edge gateway checks the integrity of the data packets, identifying and deleting invalid data such as blank or garbled data packets caused by sensor node failures or network transmission interruptions.

[0025] After a series of processes including timestamp synchronization, data format unification conversion, and filtering of invalid data, the edge gateway finally obtains discrete temperature data with unified specifications. This solves the problems of inconsistent data formats and interference from abnormal data in the background technology. Specifically, after receiving multiple raw temperature data packets sent by each sensor node, the edge gateway initiates a timestamp synchronization and alignment process. In the background technology, the lack of a unified time reference for data from different monitoring points led to the inability to effectively correlate and analyze the data. To address this deficiency, the edge gateway uses its built-in high-precision real-time clock as a reference, reads the acquisition time information from each raw temperature data packet one by one, compares it with the gateway's current system time, calculates the time deviation value of each data packet, and corrects and adjusts the acquisition time information of data packets with time deviations based on the deviation value. This ensures that the timestamps of all raw temperature data packets remain completely synchronized, enabling temperature data collected from different points in different stages to be correlated and analyzed in the time dimension. After the timestamp synchronization is completed, the edge gateway initiates a unified data format conversion process. Since sensor nodes in different monitoring links may use different models, their output temperature data formats differ. Some nodes output temperature data with unit identifiers, and some nodes output temperature data with different decimal places. To address this issue, the edge gateway performs a unified conversion on the temperature values ​​in all raw temperature data packets according to the system's preset standard data format, unifying the unit of the temperature data to degrees Celsius, unifying the number of decimal places to two, and deleting redundant identifier information in the data format.

[0026] After format conversion, the edge gateway initiates an anomaly and invalid data filtering process. The edge gateway has built-in normal temperature range thresholds for each stage of concrete production. These thresholds are set with reference to concrete production process standards and historical monitoring data. The edge gateway compares the temperature value in each data packet with the corresponding threshold, eliminating abnormal temperature data exceeding the threshold range. Simultaneously, the edge gateway checks the integrity of the data packets, identifying and deleting invalid data such as blank or garbled data packets caused by sensor node failures or network transmission interruptions. After a series of processes including timestamp synchronization, data format unification conversion, and anomaly / invalid data filtering, the edge gateway finally obtains discrete temperature data with unified specifications.

[0027] In this embodiment of the invention, the preferred embodiment overcomes the technical problems of traditional temperature acquisition, such as incomplete coverage of monitoring links, lack of spatiotemporal traceability information, difficulty in integrating heterogeneous data, large interference from abnormal data, and transmission lag, by deploying sensor nodes to construct a distributed sensor network at key stages of the entire concrete production process, synchronously collecting temperature data and adding spatiotemporal stamps, aligning data timestamps, unifying formats and filtering anomalies through edge gateways, and then transmitting and aggregating data in real time through a wireless communication network. Thus, it achieves the acquisition of multi-source discrete temperature data streams that cover the entire process, are standardized, have spatiotemporal stamps, and are real-time and coherent.

[0028] In a preferred embodiment of the present invention, multi-source discrete temperature data are aggregated to obtain a unified multi-dimensional time-series data stream, and the multi-dimensional time-series data stream is processed and mapped to obtain a dynamic data field, including: This system receives multi-source discrete temperature data and buffers the data stream. It then performs spatiotemporal alignment and fusion processing on the buffered data. Based on the timestamps and spatial location information in the data, it obtains a continuous and unified aggregated temperature data sequence in time and space. Specifically, addressing the shortcomings of the background technology where multi-source temperature data is discretely distributed, spatiotemporally fragmented, and unable to be effectively correlated, a dedicated data receiving module is first built to receive multi-source discrete temperature data streams transmitted from wireless communication networks. This module has multi-channel parallel receiving capabilities, enabling it to simultaneously process data from sensor nodes at different stages, such as the raw material storage area mixing plant's feed inlet, the internal transport tankers, and the pouring site, avoiding data congestion or loss caused by single-channel reception. After reception, a buffering mechanism is immediately activated to store the multi-source discrete temperature data stream in a high-speed cache module. The buffering duration is set according to the data acquisition cycle and subsequent processing speed to ensure sufficient time for data preprocessing while preventing cache overflow.

[0029] After buffering and temporary storage are completed, the spatiotemporal alignment process is initiated. For time alignment, using the system's unified high-precision clock as a benchmark, the timestamp information of each temperature data point is extracted. This is compared with the system time at the time of data reception, and data with time deviations due to transmission delays is corrected. For data uploaded by different sensor nodes within the same acquisition cycle, data is strictly sorted according to timestamps to ensure that all data remains synchronized in the time dimension. For spatial alignment, the spatial location information of each data point is extracted. Combined with a preset production process spatial coordinate system, the installation location of each sensor node is mapped to specific spatial coordinates. For example, the sensor nodes for the surface aggregate in the raw material storage area are mapped to specific XYZ values ​​in the coordinate system, ensuring that data from different points in different stages can be accurately located in the spatial dimension.

[0030] After spatiotemporal alignment is completed, data fusion processing is performed. Pre-defined data fusion rules are used to calculate the final temperature value of a spatiotemporal point by averaging multiple temperature data collected under the same spatiotemporal coordinates. This avoids data deviation caused by the failure of a single sensor. For spatiotemporal points with missing data, linear interpolation is used to fill in the missing data by combining the temperature change trends of adjacent spatiotemporal points to ensure data continuity. After buffering and temporary storage of spatiotemporal alignment and fusion processing, the originally discrete and spatiotemporally misaligned multi-source temperature data is integrated into a set of aggregated temperature data sequences that are continuous and ordered in time and accurately located in space.

[0031] By associating and encapsulating aggregated temperature data sequences with corresponding production process metadata and environmental parameters, a structured multi-dimensional time-series data stream is obtained. Specifically, addressing the shortcomings of background technologies where temperature data only contains single temperature values ​​and has low correlation with production processes and environmental factors, the production process metadata and environmental parameters related to concrete temperature changes are first sorted out. The production process metadata includes raw material proportioning information, the amount of different aggregates, cement grade, mixing speed, feeding sequence, single mixing time, and the speed and transportation time of the transport truck, etc. The environmental parameters include real-time ambient temperature, humidity, wind speed, solar radiation intensity, and local ambient temperature at different production stages, etc. The data comes from the environmental monitoring station of the concrete production control system and the on-board terminal of the transport vehicle, respectively. A data interaction interface is built to achieve real-time data acquisition.

[0032] The data association process is initiated, using the obtained polymerization temperature data sequence as the core. Association links are established based on timestamps and spatial location information. For example, polymerization temperature data inside the mixer at a certain moment is bound to production process metadata such as the mixer's stirring speed and feeding sequence at the same moment, as well as environmental parameters such as ambient temperature and humidity in the mixing station area at the same moment. Similarly, polymerization temperature data inside the transport tanker at a certain time period is bound to metadata such as the tanker's travel speed and transport time during that time period, as well as environmental parameters along the transport route. During the association process, the time synchronization of the data is strictly verified to ensure that each piece of polymerization temperature data can accurately match the corresponding production and environmental data. After the association is completed, the data is encapsulated. The design employs a structured data encapsulation format, dividing each data entry into multiple fixed fields, including a time field, a spatial location field, a temperature value field, a production process metadata field, and an environmental parameter field. Each field specifies the data storage format and unit; for example, the time field is accurate to the second, the production process metadata field uses revolutions per minute (RPM) for stirring speed, and the environmental parameter field uses degrees Celsius. All associated data is uniformly encapsulated according to this format, ultimately resulting in a structured, multi-dimensional time-series data stream. This data stream not only contains temperature data but also integrates data on key factors influencing temperature changes.

[0033] Based on multi-dimensional time-series data streams, a dynamic data field for the entire process temperature state is constructed, with time as the horizontal axis, spatial location of the production process as the vertical axis, and temperature value as the field strength. Specifically, this includes: to address the shortcomings of existing technologies that cannot intuitively present the evolution of temperature state throughout the entire concrete production process, a dynamic data field construction platform is built based on multi-dimensional time-series data streams. The three core dimensions of the dynamic data field are clearly defined: with time as the horizontal axis, the time axis is subdivided according to the data acquisition cycle, with each subdivision corresponding to a specific acquisition time point, covering the entire production process from raw material storage to pouring; with the spatial location of the production process as the vertical axis, each key link is divided into multiple specific spatial points according to the sequence of raw material storage area, mixing plant, mixing port, internal mixer, transport tanker, and pouring site, with each point corresponding to a unique spatial coordinate, ensuring that the vertical axis can completely cover the spatial range of the entire production process; and with temperature value as the field strength, changes in temperature values ​​are converted into changes in the field strength of the data field, with higher temperatures resulting in larger field strength values ​​and lower temperatures resulting in smaller field strength values.

[0034] The data from the multi-dimensional time-series data stream are brought into a three-dimensional system one by one. For each data point, its position on the horizontal axis is determined by its time field, its position on the vertical axis by its spatial location field, and its corresponding field strength value by its temperature value field. At the same time, the production process metadata and environmental parameters are associated with the corresponding data field nodes as additional attributes. Using data visualization technology, the field strength value of each data field node is transformed into an intuitive visual presentation. Different color gradients are used to represent the magnitude of the field strength, with red representing high temperature areas and blue representing low temperature areas. The darker the color, the higher the temperature.

[0035] To achieve the dynamism of the data field, a real-time update mechanism is established. Whenever new multi-dimensional time-series data is generated, the dynamic data field construction platform immediately receives the new data, maps it to the corresponding three-dimensional coordinate position, and updates the field strength value and visual presentation effect at that position. The platform supports backtracking and extrapolation in the time dimension. By adjusting the time scale of the horizontal axis, the temperature field distribution status at any time period can be viewed. It can also extrapolate the subsequent temperature field change pattern based on historical data trends. The final constructed dynamic data field of the entire process temperature status can intuitively and clearly present the temperature changes of concrete at each stage and at each time point from raw material storage to pouring, as well as the impact of production process and environmental factors on temperature.

[0036] In this embodiment of the invention, the preferred embodiment employs a technique of buffering, spatiotemporally aligning, and fusing real-time transmitted multi-source discrete temperature data streams to generate a temporally and spatially continuous aggregated temperature data sequence. This aggregated temperature data sequence is then associated and encapsulated with corresponding production process metadata and environmental parameters. Finally, based on a structured multi-dimensional time-series data stream, a dynamic data field of the entire process temperature state is constructed, with time as the horizontal axis, the spatial location of the production process as the vertical axis, and temperature values ​​as the field strength. Therefore, this overcomes the technical problems of traditional concrete temperature monitoring data, such as multi-source discreteness, spatiotemporal dimensional fragmentation, low data correlation, and the inability to intuitively present the evolution law of the entire process temperature state. This achieves the technical effect of generating a unified and standardized multi-dimensional time-series data stream and constructing a dynamic data field that accurately reflects the spatiotemporal evolution characteristics of temperature throughout the entire concrete production process, providing intuitive, comprehensive, and continuous data support for temperature zoning analysis and control decisions.

[0037] like Figure 2 As shown, in another preferred embodiment of the present invention, a reference analysis domain is defined within the dynamic data field. Temperature data points within the reference analysis domain are initially grouped according to feature similarity. For each group of temperature data points, a solution calculation is performed to obtain the minimum enclosing circle of the complete temperature data point set, including: In the dynamic data field, a baseline analysis domain is defined based on a preset time span and spatial range. Specifically, this includes addressing the shortcomings of the background technology in temperature analysis, such as the lack of a clear target range and difficulty in focusing on core monitoring areas. Combining the temperature control requirements of the entire concrete production process, the basis for defining the baseline analysis domain is determined. The preset time span should refer to the key cycles of concrete production, such as the total time from the input of raw materials for mixing to the transportation of a single batch of concrete to the pouring site, while reserving a certain buffer time to ensure that the complete key stages of temperature change of that batch of concrete are covered. The preset spatial range is based on the core links of the entire concrete production process, accurately corresponding to monitoring areas such as raw material storage areas, mixing plant feeding ports, the inside of mixers, transport tankers, and pouring sites. The spatial coordinate boundaries of each area are clearly defined to avoid omitting key monitoring points or including irrelevant areas.

[0038] In the dynamic data field platform, a dedicated region delineation function allows users to input preset time span parameters and select the corresponding time axis interval. This ensures that the interval fully covers all temperature data within the target analysis period. Based on preset spatial range parameters, a spatial area encompassing all core monitoring links is delineated along the vertical axis of the production process, clearly defining the upper and lower boundary coordinates of the area. During the delineation process, the system automatically verifies whether the selected area contains sufficient temperature data points. If the number of data points is insufficient, the system will adjust the time span or spatial range appropriately until the analysis requirements are met. Ultimately, a clearly defined baseline analysis domain covering the core analysis objects is obtained in the dynamic data field, providing a clear scope definition for accurate analysis.

[0039] All temperature data points within the benchmark analysis domain are extracted, and their multidimensional feature vectors are calculated based on the statistical characteristics and trends of each data point. Based on the similarity between the multidimensional feature vectors, the temperature data points are initially clustered. Specifically, to address the shortcomings of relying on manual experience, strong subjectivity, and lack of objective feature basis for temperature data grouping, the data point extraction program is first started. Through the filtering function of the dynamic data field platform, all temperature data points within the benchmark analysis domain are accurately extracted. Each extracted data point carries a complete timestamp, spatial location information, and corresponding temperature value, and is also associated with its corresponding production process metadata and environmental parameters, providing comprehensive data support for subsequent feature analysis.

[0040] After extraction, the statistical characteristics and trends of each data point are analyzed. The extraction of statistical characteristics covers multiple dimensions, including the average temperature, temperature variance, maximum temperature, minimum temperature, and median temperature of the monitoring point over a period of time. The characteristics can clearly reflect the overall temperature distribution at the point. The trend analysis is time-centric. By analyzing the temperature values ​​of adjacent collection periods before and after the data point, it is determined whether the temperature is rising, falling, or stable. At the same time, the rate of temperature change, i.e. the amount of temperature change per unit time, is calculated to accurately capture the dynamic change pattern of temperature.

[0041] Based on the extracted statistical features and trends, a multidimensional feature vector is calculated for each temperature data point. Each dimension of the multidimensional feature vector corresponds to a feature index, such as the first dimension being the temperature mean, the second dimension being the temperature variance, the third dimension being the temperature change rate, and the fourth dimension being the temperature change trend indicator, etc., to ensure that the vector can comprehensively represent the temperature characteristics of the data point. A feature similarity comparison algorithm is used to calculate the similarity between the multidimensional feature vectors of any two data points. A reasonable similarity threshold is set, and data points with similarity higher than the threshold are grouped together, while data points with similarity lower than the threshold are divided into different groups.

[0042] For each preliminary cluster group, all temperature data points contained in the preliminary cluster group are obtained, and the minimum geometric circle that completely surrounds all data points in the preliminary cluster group is calculated to obtain the minimum enclosing circle. Specifically, in order to solve the defects of the background technology where the boundaries of temperature data groups are fuzzy and it is difficult to accurately define the range of each group, the data of each preliminary cluster group is first sorted out, and the complete information of all temperature data points contained in each group is exported. The spatiotemporal coordinates of each data point in the dynamic data field are extracted, namely the time axis coordinates and the spatial location coordinates of the production process, and the temperature value is used as an auxiliary reference.

[0043] After the data is processed, the minimum enclosing circle calculation process is initiated to filter out the edge data points in each group of data points, which are the data points whose spatiotemporal coordinates are located at the boundary of the group. Edge points are the key to determining the range of the minimum enclosing circle. Using geometric analysis, an initial circle is first determined with the two farthest edge data points as the diameter. It is then checked whether the circle can completely enclose all data points within the group. If there are data points that exceed the range of the initial circle, the point that exceeds the range is combined with the two diameter endpoints of the initial circle to form a new triangle. The circumcircle of the triangle is used as the new initial circle, and the process is checked again to see if it encloses all data points.

[0044] Repeat the above process, continuously adjusting the center position and radius of the initial circle until a minimum geometric circle that completely encloses all data points in the group is found. During the adjustment process, the relationship between the spatiotemporal coordinates of each data point and the center and radius of the current circle needs to be verified in real time using system tools to ensure that no data point exceeds the circle's range, while ensuring that the radius of the circle is the smallest among all feasible enclosing circles. After the minimum enclosing circle for each initial clustering group is calculated, the center coordinates and radius of each circle are recorded and associated with the corresponding group for storage. The minimum enclosing circle clearly defines the spatiotemporal range of each group in the dynamic data field, making the boundaries of each group more explicit and laying a solid foundation for mapping them to dynamic partitions, thus solving the technical defects of traditional grouping boundaries that are vague and difficult to accurately divide.

[0045] In this embodiment of the invention, the preferred embodiment overcomes the technical problems of traditional concrete temperature analysis, such as lack of a clear target analysis range, reliance on subjective experience for data grouping without objective feature basis, and insufficient analysis accuracy due to ambiguous grouping boundaries. This is achieved by defining a benchmark analysis domain in a dynamic data field according to a preset time span and spatial range, extracting statistical features and changing trends of temperature data points within the domain to calculate multidimensional feature vectors, performing preliminary clustering based on vector similarity, and solving the minimum enclosing circle that completely surrounds all data points for each preliminary clustering group.

[0046] In a preferred embodiment of the present invention, the sub-region of the benchmark analysis domain covered by the minimum enclosing circle is regarded as a dynamic partition. The temperature data points within the dynamic partition all have highly similar statistical characteristics and trend features, so as to achieve adaptive partitioning of the benchmark analysis domain into the dynamic partition, including: Based on all minimum bounding circles, the geometric coverage of each minimum bounding circle is mapped to a continuous spatiotemporal sub-region within the baseline analysis domain. This continuous spatiotemporal sub-region is defined as an independent dynamic partition. Specifically, addressing the shortcomings of traditional temperature analysis partition boundaries in the background technology, which are fixed and rigid and unable to adapt to dynamic temperature changes, the core parameters of all minimum bounding circles are first compiled, including the center coordinates, radius, and corresponding preliminary clustering group identifiers for each circle. These parameters are the core basis for mapping the spatiotemporal sub-regions. The center coordinates correspond to specific spatiotemporal locations in the dynamic data field, the radius determines the coverage, and the group identifier is associated with the characteristic attributes of the data points within the circle. This establishes the minimum bounding circle... The mapping rules between the coverage area and the spatiotemporal coordinates of the benchmark analysis domain are as follows: In the time dimension, based on the timestamps of all temperature data points within the minimum enclosing circle, the time interval corresponding to the circle is determined. The starting point of the interval is the earliest acquisition time of the data points within the circle, and the ending point is the latest acquisition time, ensuring that the time range can completely cover the time distribution of all data points within the circle. In the spatial dimension, based on the spatial location information of the data points within the minimum enclosing circle and combined with the spatial coordinate system of the benchmark analysis domain, the geometric radius of the circle is transformed into the corresponding spatial region boundary, clarifying the specific coverage area of ​​this region in core links such as the raw material storage area and the feeding port of the mixing plant, ensuring that the spatial range accurately matches the spatial distribution of the data points within the circle.

[0047] During the mapping process, the coordinate calibration function of the dynamic data field platform is used to accurately calibrate the mapping results of each minimum bounding circle to avoid inaccurate spatiotemporal sub-region range due to coordinate deviation. After calibration, the continuous spatiotemporal sub-region obtained by mapping each minimum bounding circle is defined as an independent dynamic partition, and a unique partition identifier code is assigned to each dynamic partition. The code contains the corresponding minimum bounding circle group identifier and spatiotemporal range information. The core characteristics of temperature data points in each dynamic partition are recorded, such as the statistical characteristic mean and the type of change trend, clearly indicating that the data points in the dynamic partition have highly similar statistical characteristics and change trends.

[0048] The defined independent dynamic partition is mapped from all the minimum bounding circles to form all dynamic partitions. These dynamic partitions are then seamlessly stitched and covered across the baseline analysis domain to complete the adaptive division of the baseline analysis domain into multiple dynamic partitions. Specifically, to address the shortcomings of the background technique where temperature partition coverage is incomplete and fails to fully cover the analysis range, information on all defined dynamic partitions is compiled. This includes summarizing core information such as the spatiotemporal boundary coordinates, partition identifier, and the number of covered data points for each partition, resulting in a complete dynamic partition information table. This facilitates subsequent stitching and coverage verification, initiating the dynamic partition stitching and coverage process. Following the spatiotemporal range of the baseline analysis domain, and using the time axis as a guide, starting from the earliest time interval, the dynamic partitions within the corresponding time periods are spatially stitched together sequentially. During the stitching process, the boundary comparison function of the dynamic data field platform is used to verify in real time whether the spatial boundaries of adjacent dynamic partitions are smoothly connected. If partition boundaries overlap, the overlapping area is automatically identified. Combining the feature similarity of data points in the two partitions, the partition boundary with more uniform features is retained to ensure that the overlapping area is reasonably assigned. If there are gaps between partitions, it is checked whether there are uncovered temperature data points in the gap area. If so, the minimum bounding circle calculation result of the corresponding area is re-verified, adjusted, and remapped as a dynamic partition to fill the gap. If there are no valid data points in the gap area, it is marked as an invalid area and is not included in the scope of valid dynamic partitions.

[0049] After the stitching is completed, the coverage of the benchmark analysis domain is fully verified. The system tools are used to count the ratio of the coverage area of ​​all dynamic partitions to the total area of ​​the benchmark analysis domain to ensure that the coverage ratio reaches 100% and no effective areas are missed. The feature similarity of data points in each dynamic partition is checked again to ensure that the stitched partitions still maintain the core requirement of similar features in the same area and different features in different areas. After the verification is passed, the entire benchmark analysis domain is adaptively divided into multiple dynamic partitions with clear boundaries, complete coverage and unified features.

[0050] In this embodiment of the invention, the preferred embodiment overcomes the technical problems of traditional concrete temperature analysis, such as fixed and rigid partition boundaries, inability to adapt to dynamic temperature changes, omissions or overlaps in partition coverage, and low similarity of data features within partitions leading to a lack of targeted analysis and control. This is achieved by using a technique that maps the geometric coverage of each circle to a continuous spatiotemporal sub-region within the baseline analysis domain based on all minimum enclosing circles and defines them as independent dynamic partitions. Then, the baseline analysis domain is spliced ​​and covered without omissions by all dynamic partitions. This achieves the technical effect of adaptively dividing the baseline analysis domain into multiple dynamic partitions, ensuring that the temperature data points in each dynamic partition have highly similar statistical characteristics and trend characteristics, and that all dynamic partitions completely cover the baseline analysis domain without omissions. This provides a reliable partitioning foundation for partition data correlation analysis and precise control.

[0051] In a preferred embodiment of the present invention, based on adaptive partitioning of dynamic partitions, each data item in the time-series data stream is associated with the corresponding dynamic partition according to feature similarity, and the data items aggregated in each partition are analyzed to obtain independent control parameters reflecting the statistical characteristics, rate of change, and trend of each partition, including: Based on the completed adaptive partitioning results, each newly added temperature data point in the multi-dimensional time-series data stream is associated with and assigned to the corresponding dynamic partition according to the feature similarity between the multi-dimensional feature vector and the features of each dynamic partition. Specifically, to address the shortcomings of the background technology, such as the lag in the association between temperature data and analysis partitions and the inability to match in real time, the core feature information of all dynamically partitioned data points that have been adaptively partitioned is first sorted out to establish a dynamic partition feature benchmark library. Specifically, the multi-dimensional feature vectors of the existing temperature data points in each dynamic partition are extracted, and the mean or median of the vectors are calculated to obtain a unique feature template for each partition. The feature template contains key information such as the typical statistical characteristics of the data in the partition, the range of the normal rate of change, and the main trend type of change, which serve as the matching benchmark for the association and assignment of newly added data points.

[0052] When a new temperature data point is generated in real time in a multi-dimensional time-series data stream, the data point feature extraction process is initiated. According to the determined feature extraction rules, a complete multi-dimensional feature vector is calculated for the new data point, covering all key dimensions such as the temperature statistical characteristics, real-time rate of change, and current trend of change of the data point. After the extraction is completed, the feature similarity comparison program is initiated to calculate the similarity between the multi-dimensional feature vector of the new data point and the feature templates of all dynamic partitions in the feature benchmark library one by one.

[0053] During the comparison process, a preset similarity threshold is set based on the sensitivity of concrete temperature characteristics to ensure that only data points with highly consistent features are assigned to the same partition. If the similarity of a new data point with the feature template of a dynamic partition is higher than the threshold, the data point is directly associated with and assigned to that partition. If the similarity with all partitions is lower than the threshold, the system will automatically mark the data point as an abnormal feature point, temporarily assign it to a temporary pending partition, and trigger an early warning check. The system will update the feature benchmark library of each dynamic partition in real time, integrating the features of the new data point into the template of the corresponding partition to ensure that the partition features can dynamically adapt to data changes.

[0054] A comprehensive analysis is performed on all temperature data points belonging to the same dynamic partition. The statistical characteristics of the data within the dynamic partition are extracted, the rate of temperature change is calculated, and the trend of change is analyzed to obtain the analysis results. Specifically, in view of the shortcomings of traditional temperature analysis in the background technology, which focuses only on the temperature value itself and has a single dimension, all temperature data points belonging to the same dynamic partition are first comprehensively organized. The summarized data not only includes the temperature value, timestamp, and spatial location information of each data point, but also covers the associated production process metadata and environmental parameters, forming a complete partition dataset. During the organization process, the data is sorted in chronological order to ensure that the analysis can clearly present the temperature change pattern over time.

[0055] After data processing, a multi-dimensional comprehensive analysis process is initiated. In terms of statistical characteristic extraction, not only are the mean and variance of all temperature data within a zone calculated, but also detailed features such as the maximum, minimum, and median temperatures, as well as the dispersion of temperature distribution, are extracted to comprehensively understand the overall temperature distribution and fluctuation range of the zone. Regarding temperature change rate calculation, the temperature difference between two adjacent acquisition periods is calculated based on timestamps, and combined with the acquisition period duration, the temperature change rate per unit time is obtained. Simultaneously, the maximum, minimum, and average rates are statistically analyzed to accurately capture the speed of temperature change. In terms of trend analysis, by combining long-term temperature data and analyzing the patterns of temperature increases and decreases, it is determined whether the overall temperature within the zone is rising, falling, or stable. The persistence and frequency of trends are also analyzed, such as whether there are periodic fluctuations or sudden changes. Furthermore, temperature data is cross-analyzed with associated production process metadata and environmental parameters to clarify the influence of factors such as raw material ratios, stirring speed, and ambient temperature on the temperature changes in the zone, identifying the core causes of temperature fluctuations. Through multi-dimensional and comprehensive analysis, a complete analytical result covering statistical characteristics, change rates, trends, and influencing factors is finally obtained.

[0056] Based on the analysis results, a set of independent control parameters is obtained for each dynamic zone. Specifically, to address the shortcomings of the control parameters in the background technology, which lack specificity and cannot adapt to the temperature characteristics of different areas, the temperature control target for each dynamic zone is first defined. This target is set with reference to the temperature requirements of the corresponding stage in the concrete production process standard. Different dynamic zones have different control targets due to differences in their production stages and temperature characteristics. For example, the temperature control target for the dynamic zone inside the mixer is lower than that for the transport tanker zone to ensure that the concrete temperature does not become too high during mixing. A mapping rule between the analysis results and the control parameters is established. If the zone analysis results show that the average temperature is higher than the control target and the fluctuation range is large, it indicates that the current cooling water addition is insufficient, and the corresponding control parameters should be directed to increasing the basic cooling water addition amount. If the temperature change rate is too fast, especially a rapid increase in a short period, it indicates that the response speed of cooling water addition needs to be improved, and the corresponding control parameters should include the dynamic adjustment range of cooling water flow. If the analysis finds that the temperature change is strongly correlated with the ambient temperature, the corresponding control parameters need to reserve adaptive adjustment space according to changes in ambient temperature.

[0057] During the mapping process, the control parameters obtained from the initial mapping are calibrated in conjunction with the process constraints of concrete production. The amount of cooling water added must not exceed the maximum supply capacity of the equipment, and the mixing time must not be less than the minimum time required for uniform concrete mixing, ensuring that the control parameters are executable in actual production. After calibration, an independent set of control parameters is obtained for each dynamic zone. The parameter set includes specific indicators such as the basic amount of cooling water added, the dynamic adjustment range of cooling water flow, the baseline value of mixing time, and the adjustment range of mixing time. Each indicator is precisely matched with the temperature analysis results of that zone. Finally, each set of control parameters is bound and stored with the corresponding dynamic zone identifier.

[0058] In this embodiment of the invention, the preferred embodiment adopts a technique based on adaptive partitioning of dynamic partitions. This technique associates and assigns newly added temperature data points in the multi-dimensional time-series data stream with the feature similarity of each dynamic partition through multi-dimensional feature vectors. It comprehensively analyzes all data points within the same dynamic partition to extract statistical characteristics, calculate the rate of temperature change, and analyze the trend of change. Based on the analysis results, it generates a set of independent control parameters for each dynamic partition. Therefore, this technique overcomes the technical problems of traditional concrete temperature control, such as lagging data and analysis partition association, single dimension of partition temperature analysis, lack of specificity of control parameters, and inability to adapt to the temperature characteristics of different partitions. As a result, it achieves accurate association and assignment of newly added data with dynamic partitions, fully grasps the temperature operation characteristics of each dynamic partition, and generates independent control parameters for each partition that accurately reflect its statistical characteristics, rate of change, and trend.

[0059] In a preferred embodiment of the present invention, weighted fusion and optimization decisions are made on the independent control parameters to obtain a global stirring process adjustment command, including: The system receives all independent control parameters corresponding to each dynamic partition. For each independent control parameter, a preset dynamic weight coefficient is assigned based on the spatial importance of the corresponding dynamic partition, data quality confidence, and historical control effectiveness. Weighted fusion calculations are then performed to obtain a comprehensive global temperature state assessment index. Specifically, to address the shortcomings of previous techniques in control parameter fusion—namely, the lack of scientific weight differentiation and neglect of partition differences and data reliability—a parameter receiving and integration module is first built. This module has multi-channel parallel receiving capabilities, enabling real-time reception of independent control parameters from all dynamic partitions. During reception, the module automatically verifies the integrity of each parameter package, ensuring that it contains core information such as partition identifier, specific control parameter values, parameter generation time, and corresponding temperature analysis results. After successful verification, the module binds all independent control parameters to the basic information of the corresponding dynamic partition, obtaining a complete parameter list containing partition characteristics and control requirements, providing a clear data foundation for weight assignment and fusion calculations.

[0060] The process of assigning dynamic weight coefficients is initiated, which unfolds from three core dimensions: spatial importance, data quality confidence, and historical control effectiveness. In the spatial importance dimension, different importance levels are preset based on the degree of influence of each stage of the concrete production process on the final temperature control. For example, the dynamic zoning inside the mixer directly determines the temperature changes during concrete mixing and has the greatest impact on overall temperature control, thus being set as the highest importance level; the dynamic zoning of the transport tanker affects the temperature stability during concrete transportation and is set as the second highest level; the dynamic zoning of the raw material storage area only affects the initial temperature and is set as a general level. Each level corresponds to a different basic weight coefficient, with higher levels having larger basic weight coefficients. In the data quality confidence dimension, the data quality of each dynamic zone is quantitatively scored by verifying the completeness of temperature data collection, transmission stability, and the proportion of abnormal data. Partitions with a data collection integrity rate above 95%, no packet loss during transmission, and an abnormal data ratio below 1% are assigned the highest confidence weight. Partitions with minor data quality flaws have their confidence weight appropriately reduced. Partitions with poor data quality are assigned the lowest confidence weight to ensure that control parameters for partitions with high data quality occupy a more reasonable proportion during the fusion process. In terms of historical control effectiveness, the execution records of past control parameters for each dynamic partition are retrieved, and the percentage of times the partition temperature reached the preset control target after parameter execution is statistically analyzed, i.e., the historical control compliance rate. Partitions with a compliance rate above 90% indicate that their independent control parameters are highly effective and are assigned a higher historical effectiveness weight. Partitions with a compliance rate below 60% indicate that their parameter effectiveness is insufficient and are assigned a lower weight to ensure that parameters for partitions with good historical control effects receive more attention.

[0061] After assigning basic weights to the three dimensions, the weight coefficients for each dynamic partition are comprehensively calculated. The weights for spatial importance, data quality confidence, and historical control effectiveness are superimposed according to a preset ratio to obtain the final dynamic weight coefficients for the independent control parameters of each dynamic partition. During the calculation process, the system automatically verifies the rationality of the weight coefficients, ensuring that the sum of the weight coefficients for all partitions is one, avoiding imbalances in weight allocation. A weighted fusion calculation process is then initiated, multiplying the specific value of the independent control parameter for each dynamic partition by its corresponding final dynamic weight coefficient to obtain the weighted value of the control parameter for that partition. The weighted values ​​of the control parameters for all partitions are then aggregated and calculated, combined with the temperature analysis results of each partition, to obtain a global temperature status assessment index that comprehensively reflects the temperature control needs of all dynamic partitions, taking into account both partition importance and data reliability. This index not only includes the overall global temperature deviation but also covers the priority of temperature issues in each key partition, providing accurate and comprehensive evaluation basis for global control decisions and addressing the technical shortcomings of traditional control methods that lack weight differentiation in parameter fusion and ignore partition differences.

[0062] Using a global temperature status assessment index as the core input, combined with preset concrete production temperature targets and process constraints, an optimized decision-making algorithm calculates mixing process adjustment instructions that meet the global optimal control objective. Specifically, this includes: clearly defining the preset concrete production temperature targets and process constraints. The concrete production temperature targets are set with reference to national concrete construction quality acceptance standards and specific project design requirements, including core indicators such as the target range of concrete mixer outlet temperature, the maximum temperature fluctuation during transportation, and the upper limit of temperature control before pouring. Process constraints are determined based on the performance of concrete production equipment and construction process requirements, including hard constraints such as the maximum and minimum cooling water supply flow rates, the minimum and maximum mixing times of the mixer, ensuring that the subsequently generated control instructions are executable in actual production.

[0063] The optimization decision-making algorithm execution process is initiated. This process uses the global temperature state assessment index as the core input, combined with preset temperature targets and process constraints, to generate the globally optimal control command in three steps. First, a control objective function is constructed. This function focuses on optimizing the global temperature state assessment index, aiming to minimize the deviation between the global temperature and the preset temperature target, while also considering the temperature control needs of each key dynamic zone, ensuring optimal global temperature control. Second, feasible region screening is performed by substituting process constraints. Process constraints such as the upper and lower limits of cooling water flow rate and stirring time are transformed into explicit numerical ranges and substituted into the control objective function. All possible combinations of control parameters are screened, eliminating infeasible combinations that exceed the constraints, ensuring that the remaining parameter combinations meet the hard requirements of the production equipment and process. Third, optimal solution screening is performed. By traversing all feasible control parameter combinations, the objective function value corresponding to each combination is calculated, i.e., the degree of global temperature deviation. The parameter combination with the smallest objective function value is selected as the globally optimal control solution.

[0064] During the optimal solution selection process, if multiple optimal solutions with the same objective function value emerge, a secondary selection process is conducted by combining the historical control costs of each dynamic partition. The solution with the lowest control cost is selected to ensure that the control command not only achieves the temperature control target but also considers production economy. After the optimal solution is determined, it is converted into a specific global mixing process adjustment command. This command includes core control indicators such as the adjustment amount of the basic cooling water supply flow rate, the dynamic adjustment range of the cooling water flow rate, the adjustment value of the mixer's baseline mixing time, and the dynamic adjustment range of the mixing time. The command clearly specifies the execution priority and execution time of each control indicator. For example, the cooling water supply flow rate is adjusted first, and then the mixing time is adjusted according to temperature changes to ensure that the command execution process is orderly and efficient. The generated mixing process adjustment command is finally verified by simulating the execution of the command to check whether the global temperature can reach the preset target and whether it meets all process constraints. After the verification is passed, the command is sent to the processing module to provide reliable decision support for the adaptive closed-loop control of concrete production temperature.

[0065] In this embodiment of the invention, the preferred embodiment, by receiving independent control parameters for each dynamic partition, assigning dynamic weights based on the spatial importance of the partition, data quality confidence, and historical control effectiveness, and then weighting and fusing them to obtain a global temperature state evaluation index, and using this index as the core in conjunction with preset temperature targets and process constraints to calculate instructions through an optimized decision-making algorithm, overcomes the technical problems of traditional mixing process control, such as parameter fusion without weight differentiation, neglect of partition differences and data reliability, disconnect between global decision-making and local needs, and easy violation of process constraints. This achieves the technical effect of generating mixing process adjustment instructions that take into account the characteristics of each partition and global optimality, and meet process requirements, thereby improving the scientificity and accuracy of control decisions and providing reliable instruction support for adaptive closed-loop control of concrete production temperature.

[0066] In a preferred embodiment of the present invention, the amount of cooling water added and the mixing time during the mixing process are adjusted in real time according to the mixing process adjustment instruction, so as to achieve adaptive closed-loop control of the concrete production temperature, including: The system transmits mixing process adjustment commands downwards, receives and parses these commands to obtain specific adjustments to the cooling water flow rate and mixing time. Specifically, addressing the shortcomings of delayed command transmission and inaccurate parsing in the background technology, a dedicated command transmission link is first established. This link employs low-latency wireless communication technology, directly connecting the optimization decision module and the core controller of the concrete production control system, ensuring that mixing process adjustment commands are transmitted downwards in real-time without packet loss. During transmission, the system encrypts the commands to prevent interference or tampering, and also adds a transmission checksum for the receiving end to verify the integrity of the commands.

[0067] Upon receiving a stirring process adjustment command, the core controller immediately initiates the command parsing program. During parsing, the controller extracts key information from the command segment by segment according to a preset command format. First, it identifies the execution priority of the command to determine whether the adjustment command is for emergency control or routine optimization. Then, it focuses on parsing parameters related to cooling water flow adjustment, including the adjustment amount of the basic cooling water supply flow, the threshold of the dynamic adjustment range, and the response time of the flow adjustment. Next, it analyzes parameters related to stirring time adjustment, covering the adjustment value of the mixer's baseline stirring time, the dynamic adjustment range of the stirring time, and the adjusted stirring speed matching parameters.

[0068] After parsing, the controller performs a double check on the results. The first check is a parameter range check, comparing the obtained cooling water flow rate adjustment with the maximum and minimum supply capacity of the cooling water pump, and the mixing time adjustment with the minimum and maximum allowable mixing time of the mixer, ensuring that the results are within the safe operating range of the equipment. The second check is a logical consistency check, verifying the matching between the cooling water flow rate and mixing time adjustment parameters. For example, when the cooling water flow rate is significantly increased, does the mixing time need to be adjusted accordingly to ensure uniform concrete mixing and avoid parameter inconsistencies? After passing the checks, the controller stores the specific cooling water flow rate adjustment and mixing time adjustment values ​​obtained from the parsing in a dedicated data cache, and marks the parsing completion status.

[0069] Based on the cooling water flow rate adjustment and mixing time adjustment values ​​obtained from the analysis, control signals are obtained for synchronously adjusting the cooling water addition rate and the mixer running time. Specifically, in response to the defects of asynchronous control actions and poor adaptability of control signals in the background technology, the signal requirements of the equipment executing the concrete production site are sorted out. The cooling water pump adopts a variable frequency control mode and requires analog signals to adjust the speed to change the flow rate; the running time of the mixer is driven by a variable frequency motor and requires pulse signals to set the running duration and speed. Therefore, the control signals need to be matched with the signal interface standards of the two types of equipment respectively.

[0070] After retrieving the parsed cooling water flow rate adjustment and stirring time adjustment values ​​from the data buffer, the controller initiates the control signal generation program. In generating the cooling water flow control signal, the flow rate adjustment is converted into a corresponding variable frequency motor speed command. Then, a digital-to-analog converter (DAC) transforms the digital speed command into an analog control signal. The voltage range and current intensity of the signal strictly match the input requirements of the cooling water pump inverter. Simultaneously, signal filtering is incorporated to prevent voltage fluctuations from causing fluctuations in the flow rate adjustment. Regarding the generation of the stirring time control signal, the stirring time adjustment value is converted into a precise pulse signal period and number of pulses. The frequency of the pulse signal matches the receiving standard of the mixer motor controller, ensuring that the motor can accurately control the running time according to the pulse signal. At the same time, it is associated with the stirring speed signal, so that the stirring time and speed are adjusted in tandem. To achieve the synchronization of cooling water addition and stirring time adjustment, a unified timing synchronization identifier is added to the control signal. When the controller sends the control signal, it will ensure that the cooling water flow control signal and the stirring time control signal arrive at the corresponding execution device controller at the same time, avoiding the situation where one is adjusted and the other has not yet started. After generation, the controller performs signal strength detection and format verification on the control signal to ensure that the signal can be stably transmitted to the execution device and will not cause equipment malfunction due to signal distortion.

[0071] Based on the control signal, the system executes corresponding cooling water addition and mixing time adjustment actions to achieve real-time control of the current production temperature and initiate the next round of data acquisition and control cycle to achieve adaptive closed-loop control. Specifically, to address the core defects of the background technology, such as the inability to form a full-process closed-loop management and control lag, the system first initiates the control signal issuance and execution process. Upon receiving the flow control signal, the cooling water pump frequency converter immediately adjusts the output frequency of its internal frequency conversion module, driving the pump motor to change its speed. The change in speed directly alters the cooling water output flow rate. The flow sensor collects the current cooling water flow rate in real time and feeds it back to the controller for comparison with the target adjustment amount. If a deviation exists, the signal is fine-tuned until the flow rate reaches the target value. Upon receiving the pulse control signal, the mixer motor controller precisely controls the motor's start and stop times and adjusts the motor speed to ensure that the mixing time strictly matches the adjustment value. During mixing, the timer provides real-time feedback on the running time to avoid under-mixing or over-mixing. During the execution of the control actions, the system collects concrete temperature data at key points such as the mixer's interior and mixing outlet in real time through a distributed sensor network and transmits it to the data processing module for real-time evaluation of the control effect. If the temperature data shows that it has dropped to the preset control target range, the controller will maintain the current cooling water flow rate and mixing time parameters; if the temperature is still higher than the target value, a secondary fine-tuning prompt will be triggered, waiting for the next round of instruction optimization; if the temperature is lower than the target value, an early warning will be issued to avoid affecting the workability of concrete due to excessive cooling.

[0072] Once the current batch of concrete has been mixed and the temperature control has reached the target, the controller immediately sends a signal to the acquisition module to initiate the next round of data acquisition. The acquisition module then restarts collecting temperature data from key stages throughout the entire process, including the raw material storage area, the feeding port, and the inside of the mixer. Subsequently, it sequentially enters the data aggregation, dynamic zoning, correlation analysis, weighted fusion, and optimization decision-making stages, generating new mixing process adjustment instructions and restarting the control process. Through this cyclical mechanism, a complete adaptive closed-loop control system is formed, from data acquisition, analysis and decision-making, instruction execution, effect feedback, to the next round of acquisition. The adaptive closed loop can respond in real time to temperature changes throughout the entire concrete production process, continuously optimizing control parameters. This solves the defects of traditional technologies, such as disconnect between monitoring and control, lack of closed loop, and strong lag, ensuring that the concrete production temperature remains stable within the preset target range. This effectively avoids quality problems such as cracks and insufficient strength caused by temperature fluctuations, ensuring construction quality and structural durability.

[0073] In this embodiment of the invention, the preferred embodiment overcomes the technical problems of traditional concrete temperature control, such as the inability to form a full-process adaptive closed-loop control, the lag of control actions behind temperature changes, and the inaccuracy of key control parameters. This is achieved by transmitting and parsing the mixing process adjustment command to obtain the specific cooling water flow rate adjustment and mixing time adjustment value, generating control signals to synchronously regulate the cooling water addition rate and mixer running time based on the adjustment value, and starting the next round of data acquisition and control cycle after executing the control action.

[0074] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the system as described above. All implementations in the above system embodiments are applicable to this embodiment and can achieve the same technical effects.

[0075] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the system as described above. All implementations in the above system embodiments are applicable to this embodiment and can achieve the same technical effects.

[0076] The above description represents the preferred embodiments of the present invention. It should be noted that, for those skilled in the art, various improvements and modifications can be made without departing from the principles of the present invention, and such improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A real-time monitoring system for concrete production temperature, characterized in that, include: The data acquisition module is used to collect temperature data from multiple key stages of the entire process of concrete from raw material mixing, stirring, transportation to pouring, and obtain multi-source discrete temperature data. The module is used to aggregate multi-source discrete temperature data to obtain a unified multi-dimensional time-series data stream, and then process the multi-dimensional time-series data stream to map it into a dynamic data field. The solver module is used to define a baseline analysis domain within the dynamic data field, preliminarily group the temperature data points within the baseline analysis domain according to feature similarity, and perform solver calculations for each group of temperature data point sets to obtain the minimum enclosing circle of the complete temperature data point set. The partitioning module is used to divide the sub-region of the benchmark analysis domain covered by the minimum enclosing circle into a dynamic partition. The temperature data points within the dynamic partition have highly similar statistical characteristics and trend features, so as to achieve adaptive partitioning of the benchmark analysis domain into dynamic partitions. The association module is used for adaptive partitioning based on dynamic partitions. It associates each data item in the time series data stream with the corresponding dynamic partition based on feature similarity, and analyzes the data items gathered in each partition to obtain independent control parameters that reflect the statistical characteristics, rate of change and trend of each partition. The fusion module is used to perform weighted fusion and optimization decisions on independent control parameters to obtain global stirring process adjustment instructions; The processing module is used to adjust the amount of cooling water added and the mixing time in real time according to the mixing process adjustment instructions, so as to achieve adaptive closed-loop control of the concrete production temperature.

2. The real-time monitoring system for concrete production temperature according to claim 1, characterized in that, Temperature data was collected at multiple key stages throughout the entire concrete process, from raw material mixing, stirring, and transportation to pouring, resulting in multi-source discrete temperature data, including: Temperature sensor nodes are deployed in the raw material storage area, the feeding port of the mixing plant, the inside of the mixer, the transport tanker and the pouring site to obtain a distributed sensor network covering the entire production process. The temperature of materials or concrete in the key process is collected synchronously by each sensor node in the distributed sensor network according to a preset period. The collected temperature values ​​are then encapsulated with the collection time and spatial location information to obtain the original temperature data packet with a time and space stamp. Each edge gateway performs timestamp synchronization and alignment, data format conversion, and filters out abnormal and invalid data from the multiple raw temperature data packets received, thus obtaining discrete temperature data with unified specifications.

3. The real-time monitoring system for concrete production temperature according to claim 2, characterized in that, Multi-source discrete temperature data are aggregated to obtain a unified multi-dimensional time-series data stream. This multi-dimensional time-series data stream is then processed and mapped to obtain a dynamic data field, including: It receives multi-source discrete temperature data, buffers and temporarily stores the multi-source discrete temperature data stream, performs spatiotemporal alignment and fusion processing on the buffered multi-source discrete temperature data, and obtains a continuous and unified aggregated temperature data sequence in time and space based on the timestamp and spatial location information in the data. The aggregated temperature data sequence is associated with and encapsulated with the corresponding production process metadata and environmental parameters to obtain a structured multi-dimensional time-series data stream; Based on multi-dimensional time-series data streams, a dynamic data field of temperature status throughout the entire process is constructed, with time as the horizontal axis, spatial location of the production process as the vertical axis, and temperature value as the field strength.

4. The real-time monitoring system for concrete production temperature according to claim 3, characterized in that, A baseline analysis domain is defined within the dynamic data field. Temperature data points within the baseline analysis domain are initially grouped according to feature similarity. For each group of temperature data points, calculations are performed to obtain the minimum enclosing circle of the complete temperature data point set, including: In a dynamic data field, a baseline analysis domain is defined based on a preset time span and spatial range; Extract all temperature data points within the benchmark analysis domain, and calculate their multidimensional feature vectors based on the statistical characteristics and trends of each data point. Based on the similarity between the multidimensional feature vectors, perform preliminary clustering and grouping of the temperature data points. For each preliminary cluster group, obtain all temperature data points contained in the preliminary cluster group, and calculate the minimum geometric circle that completely surrounds all data points of the preliminary cluster group to obtain the minimum enclosing circle.

5. The real-time monitoring system for concrete production temperature according to claim 4, characterized in that, The sub-region of the baseline analysis domain covered by the minimum bounding circle is treated as a dynamic partition. Temperature data points within the dynamic partition all exhibit highly similar statistical characteristics and trends, thus achieving adaptive partitioning of the baseline analysis domain into the dynamic partition. This includes: Based on all minimum bounding circles, the geometric coverage of each minimum bounding circle is mapped to a continuous spatiotemporal subregion within the baseline analysis domain, and the continuous spatiotemporal subregion is defined as an independent dynamic partition. The baseline analysis domain is then seamlessly spliced ​​and covered by mapping all the dynamic partitions obtained from the defined independent dynamic partitions to all the minimum bounding circles, thus completing the process of adaptively dividing the baseline analysis domain into multiple dynamic partitions.

6. The real-time monitoring system for concrete production temperature according to claim 5, characterized in that, Based on adaptive partitioning with dynamic partitioning, each data item in the time-series data stream is associated with its corresponding dynamic partition according to feature similarity. The data aggregated within each partition are then analyzed to obtain independent control parameters reflecting the statistical characteristics, rate of change, and trend of each partition, including: Based on the completed adaptive partitioning results, each temperature data point added in real time in the multi-dimensional time series data stream is associated with and assigned to the corresponding dynamic partition according to the feature similarity between the multi-dimensional feature vector and the features of each dynamic partition. A comprehensive analysis is performed on all temperature data points belonging to the same dynamic partition to extract the statistical characteristics of the data within the dynamic partition, calculate the rate of temperature change, and analyze the trend of change in order to obtain the analysis results. Based on the analysis results, a set of independent control parameters is obtained for each dynamic partition.

7. The real-time monitoring system for concrete production temperature according to claim 6, characterized in that, Weighted fusion and optimization decisions are made on independent control parameters to obtain global stirring process adjustment instructions, including: The system receives all independent control parameters corresponding to each dynamic partition. For each independent control parameter, a preset dynamic weight coefficient is assigned according to the spatial importance of the corresponding dynamic partition, the confidence level of data quality, and the effectiveness of historical control. The system then performs weighted fusion calculation to obtain a comprehensive global temperature status assessment index. Using the global temperature state assessment index as the core input, and combining the preset concrete production temperature target and process constraints, the mixing process adjustment command that satisfies the global optimal control objective is calculated through an optimized decision-making algorithm.

8. The real-time temperature monitoring system for concrete production according to claim 7, characterized in that, Based on the mixing process adjustment instructions, the amount of cooling water added and the mixing time are adjusted in real time during the mixing process to achieve adaptive closed-loop control of the concrete production temperature, including: The mixing process adjustment command is transmitted down, and the transmitted mixing process adjustment command is received and parsed to obtain the specific cooling water flow rate adjustment amount and mixing time adjustment value. Based on the cooling water flow rate adjustment and stirring time adjustment values ​​obtained from the analysis, a control signal is obtained for synchronously adjusting the cooling water addition rate and the mixer running time. Based on the control signal, the corresponding cooling water addition and stirring time adjustment actions are executed to complete the real-time control of the current production temperature and start the next round of data acquisition and control cycle to achieve adaptive closed-loop control.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the system as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, performs the system as described in any one of claims 1 to 8.

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