Concrete pouring site material demand intelligent prediction method and system

Abstract

The invention relates to the technical field of material prediction, and discloses a concrete pouring site material demand intelligent prediction method and system, and the method comprises the steps: analyzing engineering structure data and design capacity from a pouring part, generating a basic demand list, building a progress sequence according to the accumulated pouring capacity, forming a pouring progress set through combining with the unloading time mark, and carrying out the calculation of the pouring progress set. Comparing the real-time pouring volume with the basic demand list step by step, locking the current structural member, dynamically distributing the theoretical demand quantity of subsequent members by taking the pouring strength correction coefficient of the current member as a constraint to obtain a dynamic consumption rate index, and calculating a safety stock threshold value based on the index and the entering time length of a secondary vehicle. Generating a secondary vehicle scheduling instruction in combination with on-site real-time inventory, updating the material vehicle number and the predicted arrival time of the remaining tasks according to the basic demand list, the pouring progress set and the secondary vehicle scheduling instruction, and generating a dynamic demand list; the method can improve the intelligent prediction efficiency of the material demand on the concrete pouring site.

Description

Intelligent Prediction Method and System for Material Demand at Concrete Pouring Sites Technical Field

[0001] This invention relates to the field of material forecasting technology, and in particular to an intelligent method and system for forecasting material demand at concrete pouring sites. Background Technology

[0002] Existing technologies for predicting material demand at concrete pouring sites generally rely on static construction schedules and fixed material consumption coefficients. This approach fails to accurately track actual construction progress based on real-time output of pumping equipment, unloading rhythm of transport vehicles, and dynamic accumulation of poured volume. Consequently, the structural components currently being poured cannot be accurately identified, and the predicted material demand for subsequent structural components is severely out of sync with the actual consumption process on site. This results in a significant discrepancy between the timing of material dispatch instructions and the actual timing of on-site demand.

[0003] Existing technologies typically rely solely on the design volume of structural components to calculate the total material demand for subsequent structural components. They fail to incorporate the actual pouring intensity, rate fluctuations, and supply rhythm trends exhibited by the structural components during the actual pouring process to dynamically adjust the predicted consumption rate. Furthermore, the setting of safety stock thresholds lacks a linkage mechanism with the actual consumption rate and transportation response time. The generation of vehicle dispatch instructions lags behind the on-site inventory consumption rate, resulting in either forced interruptions to pouring due to insufficient inventory or long waiting times for vehicles due to inventory backlog. Consequently, a dynamic demand list synchronized with the pouring progress cannot be formed. Summary of the Invention

[0004] This invention provides a method and system for intelligent prediction of material demand at concrete pouring sites, the main purpose of which is to solve the problem of low efficiency in intelligent prediction of material demand at concrete pouring sites.

[0005] To achieve the above objectives, this invention provides an intelligent prediction method for material requirements at concrete pouring sites, comprising: acquiring engineering structural data and corresponding design volumes of pouring locations during a target process to obtain a basic requirement list for the target process; constructing a progress sequence based on the cumulative pouring volume during the target process, and marking the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain a pouring progress set for the target process; comparing the real-time pouring volume of the pouring progress set with the theoretical requirements of structural components in the basic requirement list step by step to identify the current structural component of the target process; and using the pouring volume corresponding to the current structural component as the basis for prediction. Using the strength correction coefficient as a constraint, the theoretical demand for subsequent structural components in the basic demand list is dynamically allocated to obtain the dynamic consumption rate index of the target process. Based on the dynamic consumption rate index and the arrival time of each vehicle in the target process, a safety stock threshold is calculated. Then, based on a comparison between the safety stock threshold and the real-time on-site inventory in the target process, a vehicle dispatch instruction for the target process is determined. Finally, based on the basic demand list, the pouring schedule set, and the vehicle dispatch instruction, the number of material vehicles and their estimated arrival time for the remaining pouring tasks in the target process are updated to obtain the dynamic demand list for the target process.

[0006] In a preferred embodiment, obtaining the engineering structural data and corresponding design volumes of the pouring sections in the target process to obtain the basic requirements list of the target process includes: parsing the engineering structural data and corresponding design volumes constituting the pouring sections from the construction design documents of the pouring sections in the target process; associating and pairing the engineering structural data with the design volumes to obtain the basic dataset of the target process; and sorting the structural components of the basic dataset according to the construction flow sequence of the pouring sections to obtain the basic requirements list of the target process.

[0007] In a preferred embodiment, the step of constructing a progress sequence based on the cumulative pouring volume in the target process, and marking the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain the pouring progress set of the target process, includes: acquiring the cumulative pumping stroke count and the corresponding single-stroke displacement coefficient of the pumping equipment in the target process at fixed time intervals; using the product of the cumulative pumping stroke count and the single-stroke displacement coefficient as the cumulative pouring volume of the target process; recording the sampling timestamp of the cumulative pouring volume in chronological order, and binding the sampling timestamp with the corresponding cumulative pouring volume to obtain the progress sequence of the target process; and aligning the unloading start time and unloading end time of the vehicle in the target process as the unloading time with the time point position of the progress sequence to obtain the pouring progress set of the target process.

[0008] In a preferred embodiment, the step of comparing the real-time pouring volume of the pouring schedule set with the theoretical demand of structural components in the basic demand list step by step to lock the current structural component in the target process includes: accumulating the corresponding theoretical demand according to the arrangement order of structural components in the basic demand list to obtain the upper limit of the cumulative volume of structural components in the target process; comparing the real-time pouring volume of the pouring schedule set with the upper limit of the cumulative volume: if the real-time pouring volume is less than or equal to the upper limit of the cumulative volume of the first structural component in the target process, then the first structural component is determined as the target. The current structural component of the process; if the real-time pouring volume is greater than the upper limit of the cumulative volume of the first structural component, then the real-time pouring volume is compared sequentially with the upper limit of the cumulative volume of subsequent structural components in the target process: when the real-time pouring volume is greater than the upper limit of the cumulative volume of the preceding structural component in the target process and less than or equal to the upper limit of the cumulative volume of the structural component to be inspected in the target process, the structural component to be inspected is determined as the current structural component of the target process; if the real-time pouring volume is greater than the upper limit of the cumulative volume of all structural components in the target process, then the pouring task of the target process is determined to be completed.

[0009] In a preferred embodiment, the step of dynamically allocating the theoretical demand of subsequent structural components in the basic demand list, constrained by the pouring strength correction coefficient corresponding to the current structural component, to obtain the dynamic consumption rate index of the target process, includes: interpolating and encrypting the time point and real-time pouring volume corresponding to the current structural component to obtain the time sequence of the current structural component; calculating the average pouring rate of the current structural component over the time interval based on the volume difference and time difference between adjacent points in the time sequence, and associating the average pouring rate with the midpoint of the time interval to obtain the speed-corresponding point of the current structural component; and connecting the speed pairs in chronological order. The actual casting rate curve of the current structural component is obtained. The ratio of the peak rate to the average rate of the actual casting rate curve is determined as the peak rate ratio, and the ratio of the standard deviation to the average value of the instantaneous rate values ​​on the actual casting rate curve is used as the rate fluctuation index of the current structural component. The peak rate ratio and the rate fluctuation index are combined to obtain the casting strength correction coefficient of the current structural component. Based on the correspondence between the physical property parameters and process property parameters of subsequent structural components in the target process, the baseline consumption rate of the subsequent structural components is calculated. The product of the baseline consumption rate and the casting strength correction coefficient is used as the dynamic consumption index of the target process.

[0010] In a preferred embodiment, the formula for calculating the baseline consumption rate includes: in, The baseline consumption rate, The volume of the subsequent structural component. As a unit time base value, The surface area influence coefficient. The external surface area of ​​the subsequent structural component. It is a natural constant. The coefficient representing the influence of reinforcement ratio. The reinforcement ratio of the subsequent structural members. This refers to the concrete slump. The slump reference value, The slump influence index, As the reference value for layer thickness, For the thickness of the layered pouring, The effect index is determined by the layer thickness. This represents the vibration influence coefficient.

[0011] In a preferred embodiment, the step of calculating the safety stock threshold of the target process based on the dynamic consumption rate index and the vehicle arrival time of the target process, and determining the vehicle dispatch instruction for the target process based on the comparison result between the safety stock threshold and the real-time inventory in the target process, includes: using the product of the dynamic consumption rate index and the vehicle arrival time of the target process as the safety stock threshold of the target process; when the real-time inventory in the target process is lower than the safety stock threshold, assembling the status information of subsequent vehicles in the target process with the current location information to obtain the vehicle dispatch instruction for the target process.

[0012] In a preferred embodiment, the step of updating the number of material trips and estimated arrival times required for the remaining pouring tasks in the target process based on the basic demand list, the pouring schedule set, and the trip dispatch instructions to obtain the dynamic demand list for the target process includes: taking the difference between the theoretical demand for the remaining structural components in the basic demand list and the accumulated pouring volume in the pouring schedule set as the remaining total quantity for the target process; determining the current supply rhythm level of the target process according to the dynamic consumption rate index of the pouring schedule set, and matching the remaining total quantity with the current supply rhythm level to obtain the total number of trips for the target process; sorting the vehicle identifiers of the trip dispatch instructions according to the departure time to obtain the vehicle entry sequence for the target process; allocating estimated arrival times to the vehicles in the target process according to the location information of the vehicle entry sequence and the current supply rhythm level; and summarizing the total number of trips and the estimated arrival times to obtain the dynamic demand list for the target process.

[0013] In a preferred embodiment, determining the current supply rhythm level of the target process based on the dynamic consumption rate index of the pouring progress set, and matching the remaining total quantity with the current supply rhythm level to obtain the total number of trips for the target process, includes: performing a trend comparison between the dynamic consumption rate index and the dynamic consumption rate index of the historical period of the pouring progress set; dividing the current supply rhythm into acceleration rhythm, constant speed rhythm, and deceleration rhythm based on the trend comparison result; and dividing the remaining total quantity into portions using the standard loading volume per vehicle of the trip dispatch instruction in the target process as the modulus to obtain the total departure frequency of the target process.

[0014] The total frequency of departures is allocated to corresponding departure intervals according to the requirements of the acceleration rhythm, the constant speed rhythm and the deceleration rhythm, to obtain the subsequent departure time sequence of the target process, and the number of departures in the subsequent departure time sequence is determined as the total number of trains in the target process.

[0015] To address the aforementioned problems, this invention also provides an intelligent prediction system for material demand at concrete pouring sites. The system includes: a basic demand list module, which acquires the engineering structural data and corresponding design volumes of the pouring locations during the target process to obtain a basic demand list for the target process; a pouring progress set module, which constructs a progress sequence based on the cumulative pouring volume during the target process and marks the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain a pouring progress set for the target process; a current structural component module, which compares the real-time pouring volume of the pouring progress set with the theoretical demand of structural components in the basic demand list level by level to lock the current structural component of the target process; and a dynamic consumption rate index module, which... The pouring strength correction coefficient corresponding to the current structural component is used as a constraint to dynamically allocate the theoretical demand of subsequent structural components in the basic demand list, thereby obtaining the dynamic consumption rate index of the target process. The next-vehicle dispatching instruction module calculates the safety stock threshold of the target process based on the dynamic consumption rate index and the next-vehicle arrival time of the target process, and determines the next-vehicle dispatching instruction of the target process based on the comparison result between the safety stock threshold and the real-time inventory on site in the target process. The dynamic demand list module updates the number of material vehicles and the estimated arrival time required for the remaining pouring tasks in the target process based on the basic demand list, the pouring progress set, and the next-vehicle dispatching instruction, thereby obtaining the dynamic demand list of the target process.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] 1. This technology acquires the cumulative pumping strokes and unloading time of the pumping equipment in real time, constructs a pouring progress set that is completely synchronized with the actual pouring progress on site, and compares the real-time pouring volume with the upper limit of the cumulative volume of the structural components step by step to accurately locate the structural components currently being poured, so that the starting point of the material demand prediction is precisely aligned with the actual construction position, eliminating the positional deviation between the prediction and the site.

[0018] 2. This technology uses the pouring strength correction coefficient exhibited by the current structural components during actual pouring as a constraint to dynamically allocate the theoretical demand for subsequent structural components. This allows the dynamic consumption rate index to be adjusted in real time according to changes in the on-site pouring rhythm. Simultaneously, a safety stock threshold is calculated based on the product of the dynamic consumption rate index and the arrival time of each material truck. When the real-time on-site inventory falls below this threshold, a dispatch instruction for each truck is automatically generated. Finally, a dynamic demand list is formed by updating the number of material trucks and the estimated arrival time required for the remaining pouring tasks. This creates a closed-loop linkage between the rhythm and quantity of material supply and the progress and intensity of on-site pouring, ensuring synchronous matching between material replenishment and on-site consumption. Attached Figure Description

[0019] Figure 1 is a flowchart illustrating the intelligent prediction method for material demand at a concrete pouring site according to an embodiment of the present invention.

[0020] Figure 2 is a functional block diagram of an intelligent prediction system for material demand at a concrete pouring site provided in an embodiment of the present invention.

[0021] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0023] This application provides an intelligent prediction method for material demand at concrete pouring sites. The executing entity of this intelligent prediction method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the intelligent prediction method for material demand at concrete pouring sites can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0024] Referring to Figure 1, a flowchart illustrating an intelligent prediction method for material demand at a concrete pouring site according to an embodiment of the present invention is provided. In this embodiment, the intelligent prediction method for material demand at a concrete pouring site includes: Specifically, when obtaining the engineering structural data and corresponding design volumes of the pouring location in the target process to obtain the basic demand list of the target process, the method involves: parsing the engineering structural data and corresponding design volumes constituting the pouring location from the construction design documents of the pouring location in the target process; associating and pairing the engineering structural data with the design volumes to obtain the basic dataset of the target process; and sorting the structural components of the basic dataset according to the construction flow sequence of the pouring location to obtain the basic demand list of the target process.

[0025] Specifically, the system directly reads the electronic document of the construction design file, traverses each graphic unit in the file through preset component identification rules, and extracts the units marked as structural components from the graphic units.

[0026] Specifically, the system generates a unique internal classification identifier for each parsed structural component, and binds the internal classification identifier to the design quantity value of the structural component read from the design file as a key-value pair.

[0027] Specifically, the system reads a pre-set sequence list of structural component pouring from the construction flow sequence configuration file. This list clearly specifies the order in which each structural component is constructed according to the direction of the pouring process.

[0028] Furthermore, the design volume values ​​marked in the unit attributes are read simultaneously, and all the extracted structural components and their corresponding design volume values ​​are gathered together in their original order to form a complete record set of engineering structural data and corresponding design volumes constituting the casting part.

[0029] Furthermore, all key-value pairs corresponding to structural components are stored in the same data table. Each row of the data table contains the name of the structural component, its internal classification identifier, and the design quantity value of the structural component. This data table is the basic dataset of the target process.

[0030] Furthermore, the system rearranges the structural components in the basic dataset according to the order specified in the list and writes them sequentially into a new list document. Each record in the list document contains a sorting number, the name of the structural component, and its corresponding design quantity value. This list document is the basic requirements list of the target process.

[0031] In summary, by directly reading construction design documents and automatically extracting structural components and their design quantities according to preset rules, omissions and errors that may occur during manual review of drawings and manual data entry are eliminated. This ensures the accuracy and completeness of engineering structural data and design quantities at the source, providing a reliable data foundation for all subsequent prediction calculations.

[0032] In summary, by establishing a unique identifier for each structural component and mapping it one-to-one with the design quantities, the originally scattered graphical and numerical information is integrated into a structured data table. This enables the system to accurately identify the material requirements of each independent component, avoiding data confusion and misalignment, and providing clear data support for subsequent hierarchical comparison and dynamic allocation.

[0033] In summary, by introducing the construction flow sequence to sort the structural components, the order of the basic requirements list is made completely consistent with the actual pouring operation sequence. When comparing volumes and locking the current component, the system can proceed step by step according to the construction sequence, avoiding component identification errors caused by disordered sequence, and enabling subsequent material scheduling to be accurately matched with the on-site construction rhythm.

[0034] In this embodiment of the invention, the step of constructing a progress sequence based on the cumulative pouring volume in the target process, and marking the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain the pouring progress set of the target process, is specifically used for: obtaining the cumulative pumping stroke count and the corresponding single-stroke displacement coefficient of the pumping equipment in the target process at fixed time intervals; taking the product of the cumulative pumping stroke count and the single-stroke displacement coefficient as the cumulative pouring volume of the target process; recording the sampling timestamp of the cumulative pouring volume in chronological order, and binding the sampling timestamp with the corresponding cumulative pouring volume to obtain the progress sequence of the target process; taking the unloading start time and unloading end time of the vehicle in the target process as the unloading time, aligning it with the time point position of the progress sequence to obtain the pouring progress set of the target process.

[0035] Specifically, the system establishes a real-time communication connection with the controller of the pumping equipment through a data acquisition interface, and periodically sends read commands to the controller at preset time intervals. After receiving the command, the controller returns the cumulative pumping stroke count at the current moment.

[0036] Specifically, the system multiplies the cumulative number of pumping strokes and the single-stroke displacement coefficient in memory each time. The result is the total amount of concrete pumped by the pumping equipment from the start of pouring to the sampling time. The system records this value as the cumulative pouring volume at the corresponding sampling time.

[0037] Specifically, after each calculation of the cumulative pouring volume is completed, the system immediately obtains the current precise time from the system clock as a sampling timestamp. The sampling timestamp and the calculated cumulative pouring volume value are stored in a data list arranged in chronological order as key-value pairs. Each record in the list contains two fields: sampling timestamp and cumulative pouring volume. This data list is the progress sequence of the target process.

[0038] Specifically, the system obtains the exact time when each concrete transport vehicle begins unloading and the exact time when it finishes unloading at the construction site through a vehicle positioning terminal or entry identification device, and marks these two times as the unloading start time and unloading end time, respectively.

[0039] Furthermore, the system simultaneously reads the pre-stored single-stroke displacement coefficient value of the pumping equipment from the equipment parameter configuration table. This coefficient is determined by the equipment model and the pump cylinder diameter and remains constant. The system obtains both values ​​at each time interval.

[0040] Furthermore, the time point closest to the start time of unloading is found in the progress sequence according to the timestamp, and a start unloading mark is added to the record corresponding to the position. Then, the time point closest to the end time of unloading is found in the same way, and an end unloading mark is added to the record corresponding to the position. The progress sequence after all unloading time marks are completed is the pouring progress set of the target process.

[0041] In summary, by establishing real-time communication with the pump controller and actively reading data periodically, fully automatic acquisition of pouring volume data was achieved. This completely eliminated reading errors, recording delays, and data omissions that may occur during manual instrument reading and recording. It ensured the synchronization and real-time nature of the two core raw data, the cumulative pumping stroke count and the single-stroke displacement coefficient, providing a stable and reliable data source for the accurate calculation of the cumulative pouring volume.

[0042] In summary, by multiplying the number of mechanical actions of the pumping equipment with the inherent displacement parameters of the equipment, the actual volume of pumped concrete can be directly calculated. This conversion method avoids the zero-point drift and installation disturbance problems that may exist in weighing sensors or flow meters, and ensures that the cumulative pouring volume is strictly consistent with the actual output of the pumping equipment, providing an accurate quantitative basis for subsequent progress comparison and component locking.

[0043] In summary, by attaching a precise sampling timestamp to each cumulative pouring volume and organizing and storing it in chronological order, a complete pouring progress timeline is constructed, enabling the system to trace the completed volume at any given moment. This also provides a traceable time-series data foundation for subsequent calculations of the average pouring rate, analysis of changes in pouring rhythm, and identification of pouring anomalies.

[0044] In summary, by matching and annotating the unloading time of vehicles with the time position in the progress sequence, the spatiotemporal alignment of material supply events and pouring progress events is achieved. This enables the system to accurately identify when each truckload of concrete begins to affect the accumulation of pouring volume and when the supply is interrupted. It provides a comprehensive data carrier that integrates supply and consumption information for subsequent judgment of current structural components, analysis of the matching relationship between supply rhythm and consumption rate, and generation of precise truck dispatch instructions.

[0045] In this embodiment of the invention, the step of comparing the real-time pouring volume of the pouring schedule set with the theoretical demand of structural components in the basic demand list step by step to lock the current structural component of the target process is specifically used as follows: accumulating the corresponding theoretical demand according to the arrangement order of structural components in the basic demand list to obtain the upper limit of the cumulative volume of structural components in the target process; comparing the real-time pouring volume of the pouring schedule set with the upper limit of the cumulative volume: if the real-time pouring volume is less than or equal to the upper limit of the cumulative volume of the first structural component in the target process, then the first structural component is determined as the target. The current structural component of the process; if the real-time pouring volume is greater than the upper limit of the cumulative volume of the first structural component, then the real-time pouring volume is compared sequentially with the upper limit of the cumulative volume of subsequent structural components in the target process: when the real-time pouring volume is greater than the upper limit of the cumulative volume of the preceding structural component in the target process and less than or equal to the upper limit of the cumulative volume of the structural component to be inspected in the target process, the structural component to be inspected is determined as the current structural component of the target process; if the real-time pouring volume is greater than the upper limit of the cumulative volume of all structural components in the target process, then the pouring task of the target process is determined to be completed.

[0046] Specifically, the system reads the theoretical demand value of the first structural component from the first record of the basic demand list, stores the value in an accumulation variable, and records the accumulation result as the upper limit of the cumulative volume of the first structural component in memory. Then, the system moves to the second record to read the theoretical demand value of the second structural component, adds the value to the current value in the accumulation variable, updates the accumulation variable, and records the updated accumulation result as the upper limit of the cumulative volume of the second structural component in memory.

[0047] Specifically, the system reads the cumulative pouring volume value from the latest record in the pouring progress set as the real-time pouring volume, and at the same time reads the upper limit value of the cumulative volume of the first structural component from memory. The system performs a comparison operation in memory to determine whether the real-time pouring volume is less than or equal to the upper limit value of the cumulative volume of the first structural component. If the comparison result is true, the system sets the name of the first structural component and the corresponding component information as the value of the current structural component, and the determination process is completed.

[0048] Specifically, after determining that the real-time pouring volume is greater than the upper limit of the cumulative volume of the first structural component, the system starts the sequential traversal process. The system reads the upper limit of the cumulative volume of each structural component starting from the second structural component. For the structural component to be inspected in the current traversal, the system first obtains the upper limit of the cumulative volume of the previous structural component of the structural component to be inspected, and then executes two judgment conditions at the same time.

[0049] Specifically, after the system completes the sequential traversal of the cumulative volume upper limit of all structural components, it finds that the real-time pouring volume is greater than the cumulative volume upper limit of each structural component. At this point, the system no longer executes the determination operation for the current structural component, but marks the pouring task status as completed and terminates all subsequent calculation processes related to the current structural component.

[0050] Furthermore, the system processes each structural component in the basic requirements list sequentially in this manner until the last structural component is processed, ultimately obtaining the upper limit value of the cumulative volume corresponding to each structural component.

[0051] Furthermore, the first judgment condition is whether the real-time pouring volume is greater than the upper limit of the cumulative volume of the preceding structural component, and the second judgment condition is whether the real-time pouring volume is less than or equal to the upper limit of the cumulative volume of the structural component to be inspected. When both conditions are met, the system stops traversing and determines the structural component to be inspected as the current structural component of the target process.

[0052] In summary, by accumulating the theoretical demand of discrete structural components according to the construction flow sequence, the volume of the originally independent individual components is transformed into a continuous cumulative volume range. This establishes a unique quantitative reference system for the accurate mapping of real-time pouring volume and component position, enabling the system to directly determine the component position of the current pouring operation through volume values ​​without relying on complex geometric positioning.

[0053] In summary, the ability to quickly lock the current component with just one comparison operation significantly reduces computational overhead when the pouring operation has just started or when it is in the first component stage, while providing a clear basis for component ownership for predicting the material requirements of the first component.

[0054] In summary, by traversing sequentially to locate the current component level by level, the system can accurately identify the casting object at any time based solely on the volume value without relying on position sensors. At the same time, the continuous characteristics of the cumulative volume interval ensure the accurate identification of the component switching boundary, avoiding misjudgment of components due to volume fluctuations.

[0055] In summary, the termination status of a pouring task can be automatically identified through a single comparison condition, enabling the system to promptly stop subsequent material demand forecasting and vehicle scheduling calculations. This avoids generating invalid scheduling instructions after the task is completed and provides a clear end signal for the entire forecasting process.

[0056] In this embodiment of the invention, when the theoretical demand of subsequent structural components in the basic demand list is dynamically allocated based on the pouring strength correction coefficient corresponding to the current structural component as a constraint to obtain the dynamic consumption rate index of the target process, the specific steps are as follows: interpolating and encrypting the time point and real-time pouring volume corresponding to the current structural component to obtain the time sequence of the current structural component; calculating the average pouring rate of the current structural component over the time interval based on the volume difference and time difference between adjacent points in the time sequence, and associating the average pouring rate with the midpoint of the time interval to obtain the speed-corresponding point of the current structural component; and connecting the speed pairs in chronological order. The actual casting rate curve of the current structural component is obtained. The ratio of the peak rate to the average rate of the actual casting rate curve is determined as the peak rate ratio, and the ratio of the standard deviation to the average value of the instantaneous rate values ​​on the actual casting rate curve is used as the rate fluctuation index of the current structural component. The peak rate ratio and the rate fluctuation index are combined to obtain the casting strength correction coefficient of the current structural component. Based on the correspondence between the physical property parameters and process property parameters of subsequent structural components in the target process, the baseline consumption rate of the subsequent structural components is calculated. The product of the baseline consumption rate and the casting strength correction coefficient is used as the dynamic consumption index of the target process.

[0057] Specifically, the system extracts all sampling records belonging to the current structural component's pouring time period from the pouring progress set. Each record contains a sampling timestamp and a cumulative pouring volume value. The system arranges these records in chronological order, then takes out every two adjacent records in turn, calculates the time interval between these two records, and divides the time interval into several equal parts.

[0058] Specifically, the system sequentially reads each two adjacent records from the time sequence, subtracts the cumulative pouring volume of the previous record from the cumulative pouring volume of the next record to obtain the volume difference value, subtracts the timestamp of the previous record from the timestamp of the next record to obtain the time difference value, and divides the volume difference value by the time difference value to obtain the average pouring rate value within the time interval.

[0059] Specifically, the system arranges all the points corresponding to the time speed in the coordinate plane according to the order of the midpoint time. The horizontal axis of the coordinate plane represents time, and the vertical axis represents the average pouring rate. The system connects each point with the next point with a straight line segment to form a continuous broken line. This broken line fully shows all the details of the pouring rate change with time during the pouring process of the current structural component. This broken line is the actual pouring rate curve of the current structural component.

[0060] Specifically, the system iterates through the vertical coordinate values ​​of all points on the actual pouring rate curve, finds the maximum value as the peak rate value, calculates the arithmetic mean of all vertical coordinate values ​​as the average rate value, and divides the peak rate value by the average rate value to obtain the rate-to-peak ratio value.

[0061] Specifically, the system calculates the peak rate ratio and the rate fluctuation index by weighting and summing them according to a pre-set weight ratio. The weighting coefficients of the peak rate ratio and the rate fluctuation index are both pre-set and fixed by the system based on the characteristics of concrete pouring process. The value obtained after weighted summation is the pouring strength correction coefficient of the current structural component. This coefficient is used to characterize the comprehensive amplification of the actual strength of the current pouring operation relative to the standard strength.

[0062] Specifically, the system reads the volume, surface area, and reinforcement ratio of the subsequent structural components from the basic requirements list, and reads the concrete slump, layer pouring thickness, and vibration influence coefficient of the subsequent structural components from the construction process parameter configuration table. The system divides the volume value by the unit time reference value to obtain the foundation speed.

[0063] Specifically, the system performs a multiplication operation in memory between the calculated baseline consumption rate of the subsequent structural components and the pouring strength correction coefficient of the current structural components. The calculated value is the dynamic consumption index of the target process, which represents the predicted volume of concrete that the subsequent structural components need to consume per unit time under the current pouring strength conditions.

[0064] Furthermore, at each division point, the corresponding cumulative pouring volume is calculated according to a linear relationship. The time at each division point and the calculated cumulative pouring volume are combined into a new record. After merging all the original records and the newly generated records and sorting them by time, the resulting dense set of records with one-to-one correspondence between time and volume is the time sequence of the current structural component.

[0065] Furthermore, the midpoint time value of the time interval is obtained by adding the timestamp of the previous record to the timestamp of the next record and dividing by two. The system binds the calculated average pouring rate value to the midpoint time value as a point. After all time intervals are processed in this way, a series of points are generated, which are the speed corresponding points of the current structural component.

[0066] Furthermore, the system then calculates the square of the difference between each vertical axis value and the average rate value, sums all the squared values ​​and divides them by the total number of vertical axes to obtain the variance value, takes the square root of the variance value to obtain the standard deviation value, and divides the standard deviation value by the average rate value to obtain the rate fluctuation index value.

[0067] Furthermore, the foundation rate is then increased based on the surface area value, decreased based on the reinforcement ratio value, adjusted a second time based on the deviation of the concrete slump value from the slump reference value, and adjusted a third time based on the deviation of the layer pouring thickness value from the layer thickness reference value. Finally, the vibration influence coefficient value is multiplied by the foundation rate after all adjustments to obtain the reference consumption rate of the subsequent structural components.

[0068] In summary, by equally dividing the time intervals between the original sampling points and interpolating, the originally sparse time series is transformed into a dense and uniform time series, eliminating the data loss problem caused by uneven sampling intervals or insufficient sampling frequency. This provides a continuous and equally spaced data foundation for subsequent calculation of the average pouring rate within the time interval, significantly improving the accuracy and stability of rate calculation.

[0069] In summary, by binding the average pouring rate within each time interval to its corresponding midpoint, the discrete rate values ​​are accurately time-located, avoiding the phase deviation caused by incorrectly attributing the interval rate to the endpoint time. This lays a precise data foundation for constructing an actual pouring rate curve that accurately reflects the rate change trend.

[0070] In summary, by connecting discrete points corresponding to the time velocity sequentially with line segments to form a continuous and complete actual pouring rate curve, the system can intuitively identify the changing trend of the pouring rate over time, the location of the peak, and the overall shape of the rate fluctuation, providing a visualized data carrier for subsequent extraction of peak rate, average rate, and rate fluctuation characteristics.

[0071] In summary, the extreme amplification of the pouring intensity is quantified by the ratio of peak rate to average rate, and the stability of the pouring process is quantified by the ratio of standard deviation to average. These two indicators mathematically describe the actual pouring behavior from the two dimensions of upper limit of strength and fluctuation range, respectively, transforming the abstract process characteristics into specific values ​​that can participate in subsequent calculations.

[0072] In summary, by weighting and fusing the peak rate ratio, which reflects extreme intensity, with the rate fluctuation index, which reflects stability, the overall strength characteristics of the current structural component casting operation are comprehensively characterized. This allows the correction coefficient to simultaneously reflect the intensity and fluctuation characteristics of the casting process, providing a comprehensive and practical basis for adjusting the dynamic consumption rate in the future.

[0073] In summary, by comprehensively considering multiple dimensions such as the volume, surface area, reinforcement ratio, concrete slump, layer pouring thickness, and vibration influence coefficient of subsequent structural components, the inherent properties and construction process parameters of structural components are uniformly quantified into the unit time consumption volume under standard conditions. This allows the system to predict the theoretical consumption rate of the component in advance before pouring begins, providing a forward-looking quantitative basis for material scheduling.

[0074] In summary, by multiplying the baseline consumption rate calculated based on theoretical parameters with the pouring strength correction coefficient extracted from the actual pouring process, the theoretical prediction and the actual situation are organically integrated. The resulting dynamic consumption index reflects both the inherent consumption characteristics of subsequent structural components and the actual strength level of the current pouring operation, providing an accurate rate basis that is synchronized with the on-site working conditions for the calculation of the subsequent safety stock threshold and the generation of train dispatch instructions.

[0075] In this embodiment of the invention, the formula for calculating the baseline consumption rate includes: in, The baseline consumption rate, The volume of the subsequent structural component. As a unit time base value, The surface area influence coefficient. The external surface area of ​​the subsequent structural component. It is a natural constant. The coefficient representing the influence of reinforcement ratio. The reinforcement ratio of the subsequent structural members. This refers to the concrete slump. The slump reference value, The slump influence index, As the reference value for layer thickness, For the thickness of the layered pouring, The effect index is determined by the layer thickness. This represents the vibration influence coefficient.

[0076] Specifically, the volume of the subsequent structural component is derived from the design volume value of the structural component in the basic requirements list. This value is directly marked in the construction design documents and stored in the basic dataset after parsing. The unit time reference value is derived from the system's preset process parameter configuration table, which is set to a fixed time unit according to industry standards for concrete pouring processes. The surface area influence coefficient is derived from the system's preset material property parameter library. This coefficient is predetermined and stored in a fixed manner based on the degree of influence of the contact area between concrete and formwork on the pouring rate. The outer surface area of ​​the subsequent structural component is derived from the geometric parameters of the structural component in the construction design documents. The system calculates its total outer surface area by parsing the three-dimensional dimensional data of the structural component. The reinforcement ratio influence coefficient is derived from the system's preset rebar density correction parameter table. This coefficient is predetermined based on the strength of the resistance effect of rebar arrangement on concrete flow. The reinforcement ratio of the subsequent structural component is derived from the rebar arrangement data of the structural component in the construction design documents. The system obtains this value by reading the ratio of the total cross-sectional area of ​​the rebar to the cross-sectional area of ​​the component. The concrete slump is derived from on-site test reports. The system stores the slump values ​​of the concrete mixture used in the structural component into the process parameter database via a data input interface. The slump reference value is derived from a pre-set concrete workability standard parameter table, which is preset based on the basic requirements for fluidity in concrete pouring. The slump influence index is derived from a pre-set fluidity correction coefficient table, which is preset based on the degree of influence on the pouring rate when the slump deviates from the reference value. The layer thickness reference value is derived from a pre-set layered pouring process standard parameter table, which is preset based on the effective depth of concrete vibration and construction specifications. The layered pouring thickness is derived from on-site construction plan configuration data. The system obtains this value by reading the layered thickness setting value in the structural component's pouring process file. The layered thickness influence index is derived from a pre-set layered correction coefficient table, which is preset based on the degree of influence on the pouring rate when the layered thickness deviates from the reference value. The vibration influence coefficient is derived from a pre-set vibration process parameter table, which is preset based on the degree of influence of the performance parameters of the vibration equipment and the vibration method on the concrete compaction efficiency.

[0077] Furthermore, the formula for the baseline consumption rate signifies that it quantifies the physical and technological properties of subsequent structural components into a predicted value of concrete consumption per unit time, enabling the system to perform material scheduling calculations based on this prediction. The system first divides the volume by the baseline value per unit time to obtain a base rate, which represents the volume of concrete that can be poured per unit time under standard conditions. The system then divides the external surface area by the volume, multiplies it by the surface area influence coefficient, adds the product to one, and multiplies the result by the base rate to complete the first correction for the influence of the exposed surface size of structural components. A larger external surface area means more time needs to be allocated for work around the formwork, resulting in a correspondingly higher consumption rate. The system uses the natural constant as the base, multiplies the negative reinforcement ratio influence coefficient by the reinforcement ratio as the exponent, calculates the exponential function value, and multiplies it by the result of the previous step to complete the second correction for the influence of the reinforcement density. A higher reinforcement ratio indicates a stronger resistance to concrete flow, resulting in a correspondingly lower consumption rate. The system divides the concrete slump by the baseline slump value, then takes the slump influence exponent of the resulting ratio. This result is multiplied by the result from the previous step to complete the third correction for the impact on concrete fluidity. A higher slump indicates better concrete fluidity and a correspondingly higher consumption rate. The system then divides the layer thickness baseline value by the layer pouring thickness, takes the layer thickness influence exponent of the resulting ratio, and multiplies this result by the result from the previous step to complete the fourth correction for the impact on layer thickness. A higher layer thickness indicates a greater volume of material requiring vibration per layer, resulting in a correspondingly lower consumption rate per unit time. Finally, the system multiplies the vibration influence coefficient by all the correction results from the previous step to complete the final correction for the impact of vibration technology. The final value obtained is the baseline consumption rate.

[0078] In general, the baseline consumption rate increases as the volume of the subsequent structural member increases, indicating that larger volume members require a higher consumption rate per unit time. The baseline consumption rate also increases as the external surface area of ​​the subsequent structural member relative to its volume increases, because a larger surface area increases the contact between the concrete and the external environment, requiring faster pouring to ensure quality. The baseline consumption rate decreases as the reinforcement ratio of the subsequent structural member increases, because dense reinforcement hinders concrete flow and filling, reducing pouring efficiency. The baseline consumption rate increases as the concrete slump increases, because more fluid concrete is easier to pump and spread, accelerating the pouring speed. The baseline consumption rate also increases as the thickness of each layer increases, as thicker layers reduce the interval between layers, increasing the consumption per unit time. A higher vibration influence coefficient corresponds to a higher baseline consumption rate, indicating that more thorough vibration improves concrete density and pouring continuity, thereby increasing the consumption rate.

[0079] In this embodiment of the invention, the step of calculating the safety stock threshold of the target process based on the dynamic consumption rate index and the arrival time of the next vehicle in the target process, and determining the next vehicle dispatch instruction for the target process based on the comparison result of the safety stock threshold and the real-time inventory in the target process, is specifically used as follows: the product of the dynamic consumption rate index and the arrival time of the next vehicle in the target process is used as the safety stock threshold of the target process; when the real-time inventory in the target process is lower than the safety stock threshold, the status information of subsequent vehicles in the target process is assembled with the current location information to obtain the next vehicle dispatch instruction for the target process.

[0080] Specifically, the system reads the value of the dynamic consumption rate index from memory, which represents the amount of concrete consumed per unit time. At the same time, it reads the value of the next truck arrival time of the target process from the system configuration parameters, which represents the time required from issuing the dispatch instruction to the arrival of the transport vehicle on site. The system multiplies the value of the dynamic consumption rate index and the value of the next truck arrival time in memory. The result is the safety stock threshold of the target process, which represents the minimum amount of concrete reserves that need to be maintained on site within the next truck arrival time.

[0081] Specifically, the system collects the real-time inventory value at the site during the target process through on-site inventory sensors, and compares the value with the calculated safety stock threshold value. When the real-time inventory value at the site is less than the safety stock threshold value, the system filters out all subsequent vehicle records in the vehicle management database that are in the state of pending departure or have departed but have not yet arrived at the site.

[0082] Furthermore, for each record, the vehicle identifier, vehicle status, current latitude and longitude coordinates, and estimated arrival time fields are extracted. These fields are then concatenated into a complete string according to a pre-set instruction format. This string contains key information about all subsequent vehicles, and it is the vehicle dispatch instruction for the target process.

[0083] In summary, by multiplying the unit time consumption rate by the time required for transport vehicles to arrive at the site from the issuance of the instruction, a safety stock threshold that dynamically matches the actual consumption rate on site is calculated. This threshold accurately reflects the minimum amount of concrete that needs to be maintained on site within the time of the next vehicle's arrival. This avoids the problems caused by using a fixed stock threshold, such as issuing dispatch instructions too early and causing vehicle backlog, or issuing dispatch instructions too late and causing pouring interruption. This enables precise synchronization between stock warning and supply response.

[0084] In summary, by continuously comparing real-time inventory monitoring with safety stock thresholds, the system automatically triggers dispatch instructions as soon as inventory drops to the critical point, eliminating response delays that exist in manual inspections and manual calls. At the same time, by assembling the status information of subsequent vehicles with the current location information into structured dispatch instructions, the instruction recipient can directly obtain the real-time location and availability of vehicles without the need for secondary queries and confirmations, significantly shortening the response time from triggering conditions to vehicle departure.

[0085] In this embodiment of the invention, when updating the number of material trucks and the estimated arrival time required for the remaining pouring tasks in the target process based on the basic demand list, the pouring schedule set, and the truck dispatching instructions to obtain the dynamic demand list for the target process, the specific steps are as follows: The difference between the theoretical demand for the remaining structural components in the basic demand list and the accumulated pouring volume in the pouring schedule set is used as the remaining total quantity for the target process; the current supply rhythm level of the target process is determined according to the dynamic consumption rate index of the pouring schedule set, and the remaining total quantity is matched with the current supply rhythm level to obtain the total number of trucks for the target process; the vehicle identifiers in the truck dispatching instructions are sorted according to departure time to obtain the vehicle entry sequence for the target process; the estimated arrival time slots are allocated to the vehicles in the target process according to the location information of the vehicle entry sequence and the current supply rhythm level; and the total number of trucks and the estimated arrival time slots are summarized to obtain the dynamic demand list for the target process.

[0086] Specifically, the system reads the theoretical demand values ​​of all uncast structural components from the basic demand list, sums these values ​​to obtain the total theoretical demand of the remaining structural components, and reads the cumulative pouring volume value from the latest record in the pouring progress set as the cumulative pouring volume. The system subtracts the cumulative pouring volume value from the total theoretical demand of the remaining structural components in memory, and the difference is the remaining total of the target process. This value represents the volume of concrete that still needs to be poured from the current moment until the pouring task is completed.

[0087] Specifically, the system reads the dynamic consumption rate index value from memory and compares it with the dynamic consumption rate index value of the previous time period in the pouring progress set. If the current value is greater than the previous value, it is determined to be an acceleration rhythm; if the current value is equal to the previous value, it is determined to be a constant speed rhythm; if the current value is less than the previous value, it is determined to be a deceleration rhythm. The system stores the determination result as the current supply rhythm level.

[0088] Specifically, the system parses the vehicle identifiers and corresponding departure time fields of all subsequent vehicles from the vehicle dispatching instruction, arranges these records in order of departure time from morning to evening to form an ordered vehicle list. Each record in the list contains a sorting number, a vehicle identifier, and a departure time. This ordered list is the vehicle entry sequence of the target process.

[0089] Specifically, the system reads the current location coordinates of each vehicle in the vehicle entry sequence, calculates the travel time from that location to the construction site through the map path planning interface, and adds the travel time to the vehicle's departure time to obtain the estimated arrival time.

[0090] Specifically, the system writes the total number of trips into the summary row of the list, pairs the vehicle identifier of each vehicle in the vehicle entry sequence with its corresponding expected arrival time, and writes it into the list details line by line according to the sorting order of the vehicle entry sequence. Each line of the list contains two fields: vehicle identifier and expected arrival time. The list ends with a total number of trips field. This complete list document is the dynamic requirement list for the target process.

[0091] Furthermore, the standard loading capacity of each transport vehicle is then read from the vehicle configuration parameters. The remaining total is divided by the standard loading capacity of each vehicle, and the quotient is rounded up to obtain the total departure frequency. This total departure frequency value is associated with the current supply rhythm level and stored. This total departure frequency value is the total number of trips in the target process.

[0092] Furthermore, the system determines the arrival time interval between each vehicle and the vehicle before it based on the current supply rhythm level. If the current supply rhythm level is an accelerating rhythm, the arrival time interval gradually shortens; if it is a constant rhythm, the arrival time interval remains unchanged; and if it is a decelerating rhythm, the arrival time interval gradually lengthens. The system assigns a specific expected arrival time period to each vehicle in the sequence according to this rule. This time period starts from the expected arrival time and has an arrival time interval as its length.

[0093] In summary, by subtracting the volume of concrete already poured from the total theoretical demand of all structural components that have not yet been poured, the volume of concrete still to be completed from the current moment until the end of all pouring tasks can be accurately calculated. This remaining total is strictly linked to the actual progress on site, avoiding the problem of excess or shortage caused by relying solely on the theoretical design total for material preparation and ignoring the completed work. This provides an accurate basis for calculating the work volume for subsequent trips.

[0094] In summary, by comparing the dynamic consumption rate index with historical trends, the current pouring operation can be accurately identified as being in a state of acceleration, uniformity, or deceleration. This rhythm level is then used as a constraint to match the remaining total quantity. This ensures that the determination of the total number of trips not only considers the size of the project but also fully takes into account the changing trend of the current supply rhythm, thus avoiding the supply and consumption disconnect caused by using a fixed departure frequency.

[0095] In summary, by arranging the dispatched vehicles in order of departure time, a clear and orderly vehicle arrival queue is established, enabling the system to determine the expected order of each vehicle. This provides an accurate basis for the subsequent allocation of expected arrival time slots, while avoiding the order chaos and arrival conflicts caused by dispatching multiple vehicles simultaneously.

[0096] In summary, by combining the current location of each vehicle to calculate the travel time and using the current supply rhythm level as the basis for adjusting the arrival time interval, a differentiated expected arrival time period is assigned to each vehicle in the sequence. This keeps the vehicle arrival rhythm synchronized with the changing trend of the on-site consumption rate, avoiding the problems of excessively dense or sparse arrivals under a uniform speed rhythm and misalignment between the arrival rhythm and the consumption rhythm under a variable speed rhythm.

[0097] In summary, by integrating the calculated total number of truck trips with the estimated arrival time of each truck into a complete list document, a material requirements execution plan is formed that can be directly used by site managers and the mixing plant. This list includes both the total demand and the arrival times of each batch, enabling the site to arrange unloading channels and pumping equipment according to plan, and enabling the mixing plant to organize production and dispatch in sequence, thus achieving seamless connection between the material supply side and the construction consumption side.

[0098] In this embodiment of the invention, the step of determining the current supply rhythm level of the target process based on the dynamic consumption rate index of the pouring progress set, and matching the remaining total quantity with the current supply rhythm level to obtain the total number of trains for the target process, specifically involves: comparing the dynamic consumption rate index with the dynamic consumption rate index of the historical period of the pouring progress set; dividing the current supply rhythm into acceleration rhythm, constant speed rhythm, and deceleration rhythm based on the trend comparison result; dividing the remaining total quantity into portions using the standard loading volume per train of the train dispatching instruction in the target process as the modulus to obtain the total departure frequency of the target process; allocating the total departure frequency to corresponding departure intervals according to the requirements of the acceleration rhythm, the constant speed rhythm, and the deceleration rhythm to obtain the subsequent departure time sequence of the target process; and determining the number of departures in the subsequent departure time sequence as the total number of trains for the target process.

[0099] Specifically, the system extracts the dynamic consumption rate index value within the most recent complete time window from the pouring progress set as the historical time period value, and reads the dynamic consumption rate index value at the current moment from memory as the current value.

[0100] Specifically, the system parses the standard loading volume of a single vehicle from the vehicle dispatch instruction. This value represents the volume of concrete that each transport vehicle can carry in one trip. The system divides the remaining total value by the standard loading volume of a single vehicle. If the remaining total value is divisible by the standard loading volume of a single vehicle, the quotient is the total departure frequency value. If the remaining total value is not divisible by the standard loading volume of a single vehicle, the integer part of the quotient is incremented by one to obtain the total departure frequency value, which represents the total number of vehicle departures required to complete the remaining total volume.

[0101] Specifically, the system reads the current supply rhythm level. If it is a constant speed rhythm, it sets the departure interval between all adjacent trains to a fixed duration value. If it is an accelerating rhythm, it sets the first departure interval to an initial duration value. Each subsequent departure interval is shortened by a fixed step value compared to the previous departure interval until all departure intervals are allocated.

[0102] Furthermore, the system performs a numerical comparison operation in memory. If the current value is greater than the historical value, it is determined to be an accelerated rhythm; if the current value is equal to the historical value, it is determined to be a constant rhythm; if the current value is less than the historical value, it is determined to be a decelerated rhythm. The system stores the determination result as the current supply rhythm level.

[0103] Furthermore, if the deceleration rhythm is adopted, the first departure interval is set as the initial duration value. Each subsequent departure interval is extended by a fixed step value compared to the previous departure interval until all departure intervals are allocated. Starting from the current time, the system sequentially adds each departure interval to the departure time of the previous train to obtain the departure time of each train. All departure times are arranged in order to form a subsequent departure time sequence. The system counts the number of departure times contained in the sequence, and this number is the total number of trains in the target process.

[0104] In summary, by directly comparing the current consumption rate with the historical consumption rate, the system can accurately identify the actual rate change trend of the pouring operation, transforming the abstract dynamic consumption index into a specific supply rhythm level. This provides a rhythm control basis for the subsequent allocation of departure intervals that matches the on-site consumption change trend, avoiding the problem of oversupply or undersupply caused by using a fixed rhythm.

[0105] In summary, by dividing the remaining total volume into integer multiples of the standard loading capacity of a single vehicle, the calculated total frequency of departures is made to perfectly match the actual loading capacity of the transport vehicles. This avoids the problem of insufficient or overloaded vehicles due to discrepancies between theoretical calculations and transport capacity. At the same time, by dividing the data into portions, it is ensured that each portion is within an integer multiple of the standard loading capacity of a single vehicle, making the total frequency value directly executable.

[0106] In summary, by assigning differentiated departure intervals to each departure in the total departure frequency according to different supply rhythm levels, the departure density of subsequent departure time sequences is synchronized with the changing trend of on-site consumption rate. Under an accelerating rhythm, the departure interval is gradually shortened to match the accelerated consumption rate; under a decelerating rhythm, the departure interval is gradually lengthened to adapt to the slowed consumption rate; and under a uniform rhythm, the departure interval remains constant to maintain a stable supply rhythm. Ultimately, the number of departures in subsequent departure time sequences is strictly consistent with the total departure frequency, ensuring that the timing of material supply is precisely matched with the actual on-site needs.

[0107] Compared with the prior art, the present invention has the following beneficial effects:

[0108] 1. This technology acquires the cumulative pumping strokes and unloading time of the pumping equipment in real time, constructs a pouring progress set that is completely synchronized with the actual pouring progress on site, and compares the real-time pouring volume with the upper limit of the cumulative volume of the structural components step by step to accurately locate the structural components currently being poured, so that the starting point of the material demand prediction is precisely aligned with the actual construction position, eliminating the positional deviation between the prediction and the site.

[0109] 2. This technology uses the pouring strength correction coefficient exhibited by the current structural components during actual pouring as a constraint to dynamically allocate the theoretical demand for subsequent structural components. This allows the dynamic consumption rate index to be adjusted in real time according to changes in the on-site pouring rhythm. Simultaneously, a safety stock threshold is calculated based on the product of the dynamic consumption rate index and the arrival time of each material truck. When the real-time on-site inventory falls below this threshold, a dispatch instruction for each truck is automatically generated. Finally, a dynamic demand list is formed by updating the number of material trucks and the estimated arrival time required for the remaining pouring tasks. This creates a closed-loop linkage between the rhythm and quantity of material supply and the progress and intensity of on-site pouring, ensuring synchronous matching between material replenishment and on-site consumption.

[0110] Figure 2 shows a functional module diagram of an intelligent prediction system for material demand at a concrete pouring site provided in an embodiment of the present invention.

[0111] The intelligent material demand prediction system 100 for concrete pouring sites described in this invention can be installed in an electronic device. Depending on the functions implemented, the intelligent material demand prediction system 100 for concrete pouring sites may include a basic demand list module 101, a pouring progress set module 102, a current structural component module 103, a dynamic consumption rate index module 104, a train dispatch instruction module 105, and a dynamic demand list module 106. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0112] In this embodiment, the functions of each module / unit are as follows: The basic requirements list module acquires the engineering structural data and corresponding design volume of the pouring location during the target process to obtain the basic requirements list for the target process; the pouring progress set module constructs a progress sequence based on the cumulative pouring volume during the target process, and marks the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain the pouring progress set for the target process; the current structural component module compares the real-time pouring volume of the pouring progress set with the theoretical requirements of the structural components in the basic requirements list level by level to lock the current structural component of the target process; the dynamic consumption rate index module uses the pouring strength correction coefficient corresponding to the current structural component as a constraint to adjust the basic requirements list... The theoretical demand for subsequent structural components in the single process is dynamically allocated to obtain the dynamic consumption rate index of the target process; the next-vehicle dispatching instruction module calculates the safety stock threshold of the target process based on the dynamic consumption rate index and the next-vehicle arrival time of the target process, and determines the next-vehicle dispatching instruction of the target process based on the comparison result of the safety stock threshold and the real-time inventory on site in the target process; the dynamic demand list module updates the number of material trucks and the expected arrival time required for the remaining pouring tasks in the target process based on the basic demand list, the pouring schedule set and the next-vehicle dispatching instruction, to obtain the dynamic demand list of the target process. In the several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules is merely a logical functional division; in actual implementation, there may be other division methods.

[0113] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0115] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0116] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent prediction of material demand at concrete pouring sites, characterized in that, The method includes: acquiring the engineering structural data and corresponding design volume of the pouring location in the target process to obtain a basic requirement list for the target process; constructing a progress sequence based on the cumulative pouring volume in the target process, and marking the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain a pouring progress set for the target process; comparing the real-time pouring volume of the pouring progress set with the theoretical requirement of the structural components in the basic requirement list step by step to lock the current structural component of the target process; and using the pouring strength correction coefficient corresponding to the current structural component as a constraint to adjust the basic requirements list... The theoretical demand for subsequent structural components in the basic demand list is dynamically allocated to obtain the dynamic consumption rate index of the target process. Based on the dynamic consumption rate index and the arrival time of each vehicle in the target process, a safety stock threshold is calculated. Based on a comparison between the safety stock threshold and the real-time inventory on-site during the target process, a vehicle dispatch instruction for the target process is determined. Based on the basic demand list, the pouring schedule set, and the vehicle dispatch instruction, the number of material vehicles and their estimated arrival time for the remaining pouring tasks in the target process are updated to obtain the dynamic demand list for the target process.

2. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The process of obtaining the engineering structural data and corresponding design volumes of the pouring sections in the target process to obtain the basic requirements list of the target process includes: parsing the engineering structural data and corresponding design volumes of the pouring sections from the construction design documents of the pouring sections in the target process; associating and pairing the engineering structural data with the design volumes to obtain the basic dataset of the target process; and sorting the structural components of the basic dataset according to the construction flow order of the pouring sections to obtain the basic requirements list of the target process.

3. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The step of constructing a progress sequence based on the cumulative pouring volume in the target process, and marking the unloading time on the time axis of the progress sequence according to the unloading rhythm of the target process, to obtain the pouring progress set of the target process, includes: obtaining the cumulative pumping stroke count and the corresponding single-stroke displacement coefficient of the pumping equipment in the target process at fixed time intervals; taking the product of the cumulative pumping stroke count and the single-stroke displacement coefficient as the cumulative pouring volume of the target process; recording the sampling timestamp of the cumulative pouring volume in chronological order, and binding the sampling timestamp with the corresponding cumulative pouring volume to obtain the progress sequence of the target process; taking the unloading start time and unloading end time of the vehicle in the target process as the unloading time, aligning it with the time point position of the progress sequence, to obtain the pouring progress set of the target process.

4. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The step of comparing the real-time pouring volume of the pouring schedule set with the theoretical demand of structural components in the basic demand list to lock the current structural component in the target process includes: accumulating the corresponding theoretical demand according to the arrangement order of structural components in the basic demand list to obtain the upper limit of the cumulative volume of structural components in the target process; comparing the real-time pouring volume of the pouring schedule set with the upper limit of the cumulative volume: if the real-time pouring volume is less than or equal to the upper limit of the cumulative volume of the first structural component in the target process, then the first structural component is determined as the current structural component in the target process. If the real-time pouring volume is greater than the upper limit of the cumulative volume of the first structural component, then the real-time pouring volume is compared sequentially with the upper limit of the cumulative volume of subsequent structural components in the target process: when the real-time pouring volume is greater than the upper limit of the cumulative volume of the preceding structural component in the target process and less than or equal to the upper limit of the cumulative volume of the structural component to be inspected in the target process, the structural component to be inspected is determined as the current structural component of the target process; if the real-time pouring volume is greater than the upper limit of the cumulative volume of all structural components in the target process, then the pouring task of the target process is determined to be completed.

5. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The method of dynamically allocating the theoretical demand of subsequent structural components in the basic demand list, constrained by the pouring strength correction coefficient corresponding to the current structural component, to obtain the dynamic consumption rate index of the target process, includes: interpolating and encrypting the time point and real-time pouring volume corresponding to the current structural component to obtain the time sequence of the current structural component; calculating the average pouring rate of the current structural component within the time interval based on the volume difference and time difference between adjacent points in the time sequence, and associating the average pouring rate with the midpoint of the time interval to obtain the time speed corresponding point of the current structural component; connecting the time speed corresponding points in chronological order to obtain the... The actual casting rate curve of the current structural component is described; the ratio of the peak rate to the average rate of the actual casting rate curve is determined as the peak rate ratio, and the ratio of the standard deviation to the average value of the instantaneous rate values ​​on the actual casting rate curve is used as the rate fluctuation index of the current structural component; the peak rate ratio and the rate fluctuation index are fused to obtain the casting strength correction coefficient of the current structural component; the baseline consumption rate of the subsequent structural components is calculated based on the correspondence between the physical property parameters and the process property parameters of the subsequent structural components in the target process; the product of the baseline consumption rate and the casting strength correction coefficient is used as the dynamic consumption index of the target process.

6. The intelligent prediction method for material demand at concrete pouring sites as described in claim 5, characterized in that, The formula for calculating the baseline consumption rate includes: in, The baseline consumption rate, The volume of the subsequent structural component. As a unit time base value, The surface area influence coefficient. The external surface area of ​​the subsequent structural component. It is a natural constant. The coefficient representing the influence of reinforcement ratio. The reinforcement ratio of the subsequent structural members. This refers to the concrete slump. The slump reference value, The slump influence index, As the reference value for layer thickness, For the thickness of the layered pouring, The effect index is determined by the layer thickness. This represents the vibration influence coefficient.

7. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The step of calculating the safety stock threshold of the target process based on the dynamic consumption rate index and the arrival time of the next vehicle in the target process, and determining the next vehicle dispatch instruction for the target process based on the comparison result of the safety stock threshold and the real-time inventory in the target process, includes: using the product of the dynamic consumption rate index and the arrival time of the next vehicle in the target process as the safety stock threshold of the target process; when the real-time inventory in the target process is lower than the safety stock threshold, assembling the status information of subsequent vehicles in the target process with the current location information to obtain the next vehicle dispatch instruction for the target process.

8. The intelligent prediction method for material demand at concrete pouring sites as described in claim 1, characterized in that, The process of updating the number of material trucks and estimated arrival times required for the remaining pouring tasks in the target process based on the basic demand list, the pouring schedule set, and the truck dispatch instructions to obtain the dynamic demand list for the target process includes: taking the difference between the theoretical demand for the remaining structural components in the basic demand list and the accumulated pouring volume in the pouring schedule set as the remaining total quantity for the target process; determining the current supply rhythm level of the target process according to the dynamic consumption rate index of the pouring schedule set, and matching the remaining total quantity with the current supply rhythm level to obtain the total number of trucks for the target process; sorting the vehicle identifiers of the truck dispatch instructions according to the departure time to obtain the vehicle entry sequence for the target process; allocating estimated arrival time slots to the vehicles in the target process according to the location information of the vehicle entry sequence and the current supply rhythm level; and summarizing the total number of trucks and the estimated arrival time slots to obtain the dynamic demand list for the target process.

9. The intelligent prediction method for material demand at concrete pouring sites as described in claim 8, characterized in that, The step of determining the current supply rhythm level of the target process based on the dynamic consumption rate index of the pouring progress set, and matching the remaining total quantity with the current supply rhythm level to obtain the total number of trains for the target process, includes: comparing the dynamic consumption rate index with the dynamic consumption rate index of the historical period of the pouring progress set; dividing the current supply rhythm into acceleration rhythm, constant speed rhythm, and deceleration rhythm based on the trend comparison result; dividing the remaining total quantity into portions using the standard loading volume per train of the train dispatching instruction in the target process as the modulus to obtain the total departure frequency of the target process; allocating the total departure frequency to corresponding departure intervals according to the requirements of the acceleration rhythm, the constant speed rhythm, and the deceleration rhythm to obtain the subsequent departure time sequence of the target process; and determining the number of departures in the subsequent departure time sequence as the total number of trains for the target process.

10. A smart prediction system for material demand at a concrete pouring site, used to implement the smart prediction method for material demand at a concrete pouring site as described in any one of claims 1-9, characterized in that, The system includes: a basic requirements list module, which acquires the engineering structural data and corresponding design volumes of the pouring locations during the target process to obtain a basic requirements list for the target process; a pouring progress set module, which constructs a progress sequence based on the cumulative pouring volumes during the target process and marks the unloading times on the time axis of the progress sequence according to the unloading rhythm of the target process to obtain a pouring progress set for the target process; a current structural component module, which compares the real-time pouring volumes of the pouring progress set with the theoretical requirements of the structural components in the basic requirements list step by step to lock the current structural component of the target process; and a dynamic consumption rate index module, which adjusts the pouring intensity corresponding to the current structural component. Using coefficients as constraints, the theoretical demand for subsequent structural components in the basic demand list is dynamically allocated to obtain the dynamic consumption rate index of the target process; the next-vehicle dispatching instruction module calculates the safety stock threshold of the target process based on the dynamic consumption rate index and the next-vehicle arrival time of the target process, and determines the next-vehicle dispatching instruction of the target process based on the comparison result between the safety stock threshold and the real-time inventory on site in the target process; the dynamic demand list module updates the number of material trucks and the expected arrival time required for the remaining pouring tasks in the target process based on the basic demand list, the pouring schedule set and the next-vehicle dispatching instruction, to obtain the dynamic demand list of the target process.

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