Method for predicting quality of ready-mixed concrete, quality prediction program, method for manufacturing ready-mixed concrete, and device for predicting quality of ready-mixed concrete

Abstract

Improve the accuracy of quality predictions using predictive models. [Solution] A method for predicting the quality of ready-mixed concrete, comprising: an acquisition step of acquiring input information including first information relating to the power load value of a mixer that mixes concrete materials to produce ready-mixed concrete, and second information relating to the time when the ready-mixed concrete is produced by the mixer; and a prediction step of predicting the quality of the ready-mixed concrete based on a prediction model previously constructed by machine learning to output quality information indicating the quality of the ready-mixed concrete in response to the input of the input information, and the input information acquired in the acquisition step.

Description

[Technical field]

[0001] The present disclosure relates to a quality prediction method for ready-mix concrete, a quality prediction program, and a quality prediction device for ready-mix concrete. [Background technology]

[0002] Patent Document 1 discloses a method for predicting the quality of ready-mix concrete using a prediction model. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-124304 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a quality prediction method for ready-mix concrete, a quality prediction program, and a quality prediction device for ready-mix concrete that are useful for improving prediction accuracy when predicting quality using a prediction model. [Means for solving the problem]

[0005] [1] A method for predicting the quality of ready-mixed concrete, comprising: an acquisition step of acquiring input information including first information relating to a power load value of a mixer that mixes concrete materials to produce ready-mixed concrete, and second information relating to a time when the ready-mixed concrete was produced by the mixer; and a prediction step of predicting the quality of the ready-mixed concrete based on a prediction model previously constructed by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information, and the input information acquired in the acquisition step.

[0006] [2] The method for predicting quality of ready-mixed concrete described in [1] above, wherein the second information includes information representing the date and time when the mixer performs mixing.

[0007] [3] The method for predicting quality of ready-mixed concrete described in [2] above, wherein in the second information, the date and the time are each identified using a trigonometric function.

[0008] [4] The method for predicting quality of ready-mixed concrete according to the above [2] or [3], wherein the second information further includes information representing a cumulative number of batches mixed by the mixer in one day.

[0009] [5] The method for predicting quality of ready-mixed concrete described in [1] above, wherein the second information includes information representing the time from an arbitrarily set reference point to the point at which the mixer performs mixing, or information representing the cumulative number of batches mixed by the mixer from the reference point.

[0010] [6] The method for predicting quality of ready-mixed concrete described in any one of [1] to [5] above, wherein the first information includes a statistical quantity obtained from time series data of the power load value when the mixer performs one mixing cycle, and the statistical quantity is at least one value selected from the group consisting of a fluctuation range representing the difference between a maximum value and a minimum value, a decline range representing the difference between a maximum value and an end value, a total value within an arbitrarily set time period, an average value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, a kurtosis, and a skewness.

[0011] [7] A quality prediction program for causing a computer to execute the quality prediction method for ready-mix concrete according to any one of [1] to [6] above.

[0012] [8] A quality prediction device for ready-mixed concrete, comprising: an input information acquisition unit that acquires input information including first information related to a power load value of a mixer that mixes concrete materials to produce ready-mixed concrete and second information related to a production time of the ready-mixed concrete by the mixer; a prediction model that is pre-constructed by machine learning to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information; and a prediction calculation unit that predicts the quality of the ready-mixed concrete based on the input information acquired by the input information acquisition unit. Effect of the Invention

[0013] According to the present disclosure, there are provided a quality prediction method for ready-mixed concrete, a quality prediction program, and a quality prediction device for ready-mixed concrete, which are useful for improving prediction accuracy when predicting quality using a prediction model. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a schematic diagram showing an example of a ready-mix concrete manufacturing system. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a functional configuration of the control device. [Diagram 3] FIG. 3 is a diagram illustrating data relating to power load values. [Figure 4] FIG. 4 is a diagram for explaining the format of expressing dates and times. [Diagram 5] FIG. 5 is a block diagram illustrating an example of a hardware configuration of the control device. [Figure 6] Fig. 6(a) is a diagram showing an example of a process flow in the learning phase, and Fig. 6(b) is a diagram showing an example of a process flow in the evaluation phase. [Figure 7] 7(a) and 7(b) are schematic diagrams showing an example of a calculation process using a prediction model, and Fig. 7(c) is a schematic diagram showing an example of a calculation process using a neural network. [Figure 8] FIG. 8 is a graph showing an example of a comparison result between the predicted value and the correct value. [Figure 9] FIG. 9 is a graph illustrating the transition of the measurement results of the power load value. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment will be described with reference to the drawings. In the description, the same elements or elements having the same functions are given the same reference numerals, and duplicated descriptions will be omitted. Fig. 1 shows a schematic diagram of a ready-mix concrete manufacturing system equipped with a quality prediction device according to an embodiment.

[0016] [Ready-mix concrete manufacturing system] First, an overview of a ready-mix concrete manufacturing system will be described. The manufacturing system 1 shown in Fig. 1 is a system for manufacturing ready-mix concrete. The manufacturing system 1 kneads concrete materials to manufacture ready-mix concrete.

[0017] The concrete materials used in the manufacturing system 1 include cement, admixtures, coarse aggregates, fine aggregates, water, and admixtures. Examples of the coarse aggregates include gravel, crushed stone, slag coarse aggregates, lightweight coarse aggregates, recycled coarse aggregates, recovered aggregates, and coarse aggregates made by mixing these. The gravel includes mountain gravel, land gravel, river gravel, and sea gravel. The slag coarse aggregates include blast furnace slag aggregates, ferro-nickel slag aggregates, electric furnace oxidized slag aggregates, and coal gasification slag aggregates. The lightweight coarse aggregates include natural lightweight aggregates, by-product lightweight aggregates, and artificial lightweight aggregates. The coarse aggregates may include crushed rock, or limestone crushed stone.

[0018] Examples of fine aggregates include sand, crushed sand, slag fine aggregate, lightweight fine aggregate, recycled fine aggregate, recovered aggregate, and fine aggregates made by mixing these. Sand includes mountain sand, land sand, river sand, sea sand, etc. Slag fine aggregates include blast furnace slag aggregate, ferro-nickel slag aggregate, copper slag aggregate, electric furnace oxidized slag aggregate, coal gasification slag aggregate, etc. Lightweight fine aggregates include natural lightweight aggregate, by-product lightweight aggregate, artificial lightweight aggregate, etc.

[0019] Rock types of the crushed stone and crushed sand include, for example, igneous rocks, sedimentary rocks, metamorphic rocks, quartzite, limestone, domalite, or peridotite. Igneous rocks include granite, diorite, gabbro, porphyrite, diabase, rhyolite, andesite, basalt, or serpentinite. Sedimentary rocks include conglomerate, sandstone, shale, slate, or tuff. Metamorphic rocks include gneiss or schist.

[0020] The manufacturing system 1 loads the manufactured ready-mix concrete onto the transport vehicle 200. After the ready-mix concrete is loaded onto the transport vehicle 200, the transport vehicle 200 transports the ready-mix concrete to a site (e.g., a construction site) where the ready-mix concrete is to be used. Examples of the transport vehicle 200 include an agitator vehicle (mixer vehicle) or a dump truck. The manufacturing system 1 may manufacture ready-mix concrete from concrete materials so as to satisfy a target quality (required quality) set for each site. For example, an operator of the manufacturing system 1 determines the mix of concrete materials so as to satisfy the target quality, and inputs operation instructions to the manufacturing system 1.

[0021] The target quality set for each site is different from the quality of the ready-mixed concrete produced by the production system 1 before it is shipped (at the time of shipment). For example, in the production system 1, ready-mixed concrete is produced by the production system 1, and the quality of the ready-mixed concrete before it is shipped is controlled (inspected, etc.) so that the target quality of the ready-mixed concrete when it is used set for each site is satisfied. The time when the ready-mixed concrete is used corresponds to the time when the ready-mixed concrete is received at the site. In order to control the quality of the ready-mixed concrete before it is shipped, the target quality of the ready-mixed concrete when it is shipped may be determined based on the target quality of the ready-mixed concrete when it is used. The target quality of the ready-mixed concrete when it is shipped may be set according to an in-house standard or the like determined for each factory. In setting the target quality of the ready-mixed concrete when it is shipped, at least one of the following information may be taken into consideration: the state (quality) of the materials used when it is produced, the season (temperature), the type of concrete, the target quality when it is used, and the transportation time. The target quality of the ready-mixed concrete when it is shipped is set, for example, by adding a value determined by an in-house standard to the target quality of the ready-mixed concrete when it is used.

[0022] The manufacturing system 1 includes, for example, a manufacturing apparatus 100 and a control device 10. The manufacturing apparatus 100 is an apparatus that manufactures ready-mix concrete based on operation instructions from the control device 10. The manufacturing apparatus 100 manufactures ready-mix concrete by mixing concrete materials. The manufacturing apparatus 100 includes, for example, a material storage area 101, a transport device 104, a storage bottle 111, a measuring bottle 112, a collection hopper 113, a mixer 114, and a loading hopper 115.

[0023] The material yard 101 is a place where concrete materials are stored. The material yard 101 includes a plurality of silos 102. The plurality of silos 102 are containers that store at least a portion of the concrete materials by material type. The plurality of silos 102 include, for example, a silo 102 that stores coarse aggregate, a silo 102 that stores fine aggregate, and a silo 102 that stores cement.

[0024] The transporting device 104 is a device that transports the concrete materials stored in the multiple silos 102 to the storage bottle 111. The transporting device 104 includes, for example, a belt conveyor that transports the concrete materials. The transporting device 104 may transport the concrete materials by type at different times. In one example, based on an operation instruction from the control device 10, a specific material from among the various concrete materials is transferred to the transporting device 104 and transported to the storage bottle 111.

[0025] Storage bottle 111 temporarily stores various types of concrete materials. Various types of concrete materials are transported (conveyed) to storage bottle 111 from material storage yard 101 by transport device 104. Storage bottle 111 is configured to store various types of concrete materials individually. Hereinafter, "concrete material" may be simply referred to as "material". Various materials stored in storage bottle 111 are supplied to measuring bottle 112 as necessary.

[0026] The measuring bottle 112 is disposed below the storage bottle 111. The measuring bottle 112 operates based on an operation instruction from the control device 10, and individually measures various materials. When the measuring bottle 112 detects a target amount of material instructed by the control device 10, it supplies the material to the collecting hopper 113. When water is supplied to the measuring bottle 112, an admixture may be mixed into the water. The collecting hopper 113 is disposed below the measuring bottle 112. The collecting hopper 113 collects various materials discharged from the measuring bottle 112, and supplies the collected various materials to the mixer 114. Note that the manufacturing apparatus 100 does not need to be provided with the collecting hopper 113, and various materials may be supplied to the mixer 114 from the measuring bottle 112.

[0027] The mixer 114 is disposed below the collecting hopper 113. The mixer 114 is a device that mixes concrete materials. The mixer 114 produces ready-mixed concrete by mixing (kneading) aggregate, cement, water, admixtures, and the like. The ready-mixed concrete is discharged from the bottom of the mixer 114 into a loading hopper 115. The mixer 114 may be a tilting mixer, a horizontal single-shaft mixer, a horizontal twin-shaft mixer, or a pan-type mixer. The mixer 114 includes, for example, two stirring members 114a and a mixer drive unit 114b.

[0028] The stirring member 114a is a member that stirs various materials supplied to the mixer 114. The two stirring members 114a are arranged side by side inside the main body (container) of the mixer 114 and are provided so as to be freely rotatable. Each of the two stirring members 114a includes a rotating shaft that extends in one horizontal direction. The mixer driving unit 114b rotates the rotating shafts of the two stirring members 114a based on an operation instruction from the control device 10. The mixer driving unit 114b includes, for example, a driving source such as a motor that imparts driving force to the stirring members 114a. An opening and closing port is provided at the bottom of the main body of the mixer 114 for discharging the manufactured ready-mix concrete into the loading hopper 115.

[0029] The loading hopper 115 is disposed below the mixer 114 and temporarily stores the ready-mixed concrete. The loading hopper 115 supplies the temporarily stored ready-mixed concrete to the transport vehicle 200.

[0030] The manufacturing apparatus 100 described above is an example of a ready-mix concrete manufacturing apparatus, and the ready-mix concrete manufacturing apparatus may be configured in any manner as long as it is capable of mixing concrete materials using a mixer and producing ready-mix concrete.

[0031] In this disclosure, a unit of ready-mixed concrete produced by one mixing in the mixer 114 and loaded onto the transport vehicle 200 is defined as "one batch." The process executed by the manufacturing system 1 to produce one batch of ready-mixed concrete is defined as "batch process." The number of times a batch process has been executed (the cumulative number of executions) since a certain reference point in time is referred to as the "cumulative batch number." At the reference point in time, the cumulative batch number is reset to zero. For example, the cumulative batch number is reset to zero each day when the manufacturing apparatus 100 starts operating. In this case, the cumulative batch number for the batch process executed the kth time in a day (k is an integer equal to or greater than 1) is k.

[0032] One to three batches of ready mixed concrete may be loaded onto one transport vehicle 200. For example, when two batches of ready mixed concrete are loaded onto one transport vehicle 200, two batch processes according to the same production conditions are carried out at different times (in different orders). During a certain period of a day, multiple batch processes according to the same production conditions may be carried out consecutively in different orders.

[0033] <Control device (quality prediction device)> The control device 10 is a device that controls the manufacturing equipment 100. The control device 10 is configured with one or more computers. When the control device 10 is configured with multiple computers, these computers are connected to each other so that they can communicate with each other. The control device 10 controls the manufacturing equipment 100 according to set operating conditions. At least a part of the operating conditions may be determined by instructions from an operator such as a worker.

[0034] An input device 12 and a monitor 14 may be connected to the control device 10. The input device 12 is a device that inputs information indicating instructions from an operator or the like to the control device 10. The input device 12 may be any device capable of inputting desired information, and may be a keyboard (keypad), an operation panel, or a mouse. The monitor 14 is a device for displaying information from the control device 10 to an operator or the like. The monitor 14 may be any device capable of displaying graphics, and may be a liquid crystal display. The input device 12 and the monitor 14 may be integrated together, such as a touch panel. The control device 10, the input device 12, and the monitor 14 may be integrated together, such as a tablet computer (tablet terminal).

[0035] The control device 10 may have a function of predicting the quality of ready-mixed concrete manufactured by the manufacturing apparatus 100, in addition to controlling the manufacturing apparatus 100. In this case, the control device 10 constitutes a quality prediction device that predicts the quality of ready-mixed concrete (a device for predicting quality of ready-mixed concrete). In the following description, the quality of ready-mixed concrete predicted by the control device 10 is the quality after it has been manufactured by the manufacturing apparatus 100 and before it is shipped to the site (i.e., the quality of the ready-mixed concrete at the time of shipment).

[0036] The quality of fresh concrete to be predicted by the control device 10 may include one or more qualities of slump, slump flow, and air volume. The quality of fresh concrete to be predicted by the control device 10 may include two or more qualities of slump, slump flow, and air volume. The quality of fresh concrete to be predicted by the control device 10 may be one quality of slump, slump flow, and air volume, or may be two or more qualities of slump, slump flow, and air volume.

[0037] The control device 10 is configured to execute at least an acquisition step and a prediction step. The acquisition step is a step of acquiring input information including information related to the power load value of the mixer 114 and information related to the production time of ready-mixed concrete by the mixer 114. The prediction step is a step of predicting the quality of the ready-mixed concrete to be predicted based on a prediction model previously constructed by machine learning so as to output quality information indicating the quality of ready-mixed concrete in response to the input of the input information, and the input information acquired in the acquisition step.

[0038] 2 shows an example of functional components (hereinafter referred to as "functional blocks") included in the control device 10. The control device 10 has, for example, as functional blocks, an operation control unit 22, an operation information acquisition unit 24, a timing information acquisition unit 26, a model construction unit 30, a model holding unit 32, a prediction calculation unit 28, and a display output unit 34. The processes executed by these functional blocks correspond to the processes executed by the control device 10.

[0039] The operation control unit 22 controls the manufacturing apparatus 100 to manufacture ready-mixed concrete according to predetermined operating conditions. At least a part of the operating conditions may be determined by an operator such as a worker each time ready-mixed concrete is manufactured. The operation control unit 22 may control the mixer driving unit 114b of the mixer 114 so that the rotation speed of the mixer driving unit 114b of the mixer 114 follows a target rotation speed defined in the operating conditions. When controlling the mixer driving unit 114b, the operation control unit 22 may adjust the power (e.g., current value) supplied to the mixer driving unit 114b. If the ready-mixed concrete to be manufactured is hard, the power load value tends to be large, and if the ready-mixed concrete to be manufactured is soft, the power load value tends to be small.

[0040] The operation information acquisition unit 24 acquires information related to the operation when the ready-mixed concrete is manufactured. The operation information acquisition unit 24 acquires information related to the power load value of the mixer 114 (hereinafter referred to as "first information"). The power load value of the mixer 114 may be a value indicating the power (kW) itself supplied to the mixer 114, or may be a value indicating the current value (A) supplied to the mixer 114. Alternatively, the power load value of the mixer 114 may be replaced with a value indicating the load oil pressure (MPa). The information related to the power load value of the mixer 114 may be continuous time series data while the mixer 114 is operating during the processing of one batch, or may be statistical data obtained from the time series data.

[0041] FIG. 3 shows a schematic diagram of time series data related to the power load value of the mixer 114 in processing one batch. The time series data related to the power load value is data obtained by repeatedly measuring the power (kW) supplied to the mixer at a predetermined sampling period, for example. The operation information acquisition unit 24 may calculate (acquire) at least one selected from the group consisting of a fluctuation range (difference between the maximum value and the minimum value), a fall width (difference between the maximum value and the final value), a total value within an arbitrarily set time, an average value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, kurtosis, and skewness as the statistical data. The operation information acquisition unit 24 may calculate at least one of a minimum value and a maximum value as well as at least one selected from the above group as the statistical data. The statistical data acquired by the operation information acquisition unit 24 may include a statistical value obtained by calculation using two or more power load values ​​included in the time series data.

[0042] In the graph shown in FIG. 3, "P1" represents an initial value in the time series data. The initial value P1 is the power load value at the start of mixing in the time series data. "P2" represents the minimum value in the time series data. The minimum value P2 is the minimum power load value after the time when the initial value P1 is obtained. Note that the power load value at the start of mixing (initial value P1) may also be the minimum. "P3" represents the maximum value in the time series data. The maximum value P3 is the maximum power load value after the time when the initial value P1 is obtained. "P4" represents the final value in the time series data. The final value P4 is the power load value at the time when the operation control unit 22 determines that the condition for ending mixing by the mixer 114 is satisfied. For example, when a predetermined time has elapsed since the start of driving the stirring member 114a, the operation control unit 22 determines that the above condition is satisfied and stops driving the stirring member 114a.

[0043] The timing information acquisition unit 26 acquires information related to the production time of ready-mixed concrete by the mixer 114 (hereinafter referred to as "second information"). The production time of ready-mixed concrete by the mixer 114 means the timing at which the mixer 114 mixes concrete materials to produce ready-mixed concrete. The second information, which is information related to the production time (production timing), includes, for example, information indicating the date and time when the mixer 114 performs mixing. In the second information, the date when the mixer 114 performs mixing is expressed as a specific day when the mixer 114 produced ready-mixed concrete, as a combination of year, month, and day, or a combination of month and day.

[0044] In the second information, the time when mixer 114 executes mixing is expressed as a specific point in time when mixer 114 produces ready-mixed concrete, expressed as a combination of hours, minutes, and seconds, or a combination of hours and minutes. The time in the second information (the specific point in time when mixer 114 produces ready-mixed concrete) may be the time when concrete materials are supplied to mixer 114, or may be the time when ready-mixed concrete after production is discharged from mixer 114. The time in the second information (the specific point in time when mixer 114 produces ready-mixed concrete) may be the time when drive of agitation member 114a in mixer 114 is started, or may be the time when drive of agitation member 114a is stopped.

[0045] In the second information, the date and time may be specified using trigonometric functions. For example, in the second information, the month and day are specified using trigonometric functions, and the hours, minutes, and seconds are specified using trigonometric functions. First, with reference to FIG. 4, a case where the "hour" of time is specified (expressed) using trigonometric functions will be described. Usually, the "hour" of a day is specified (expressed) by 24 numerical values ​​from 0:00 to 23:00, assuming that it returns to 0:00 at midnight. When specified using such normal numerical values, for example, between 0:00 and 23:00, although there is only a one-hour difference without considering the date, the numerical values ​​are far apart. Therefore, the "hour" is specified (expressed) to have periodicity using trigonometric functions.

[0046] In one example, h hours (h is any integer between 0 and 23) is represented by a combination of cos{2π×(h / 24)} and sin{2π×(h / 24)} as shown in Fig. 4. In this case, midnight is (1,0), 6 o'clock is (0,1), 12 o'clock is (-1,0), and 18 o'clock is (0,-1). m minutes (m is any integer between 0 and 59) and s seconds (s is any integer between 0 and 59) may also be represented by the following combinations. ·m minutes:cos{2π×(m / 60)},sin{2π×(m / 60)} ·s seconds: cos{2π×(s / 60)}, sin{2π×(s / 60)}

[0047] If the "month" of a date is month M (where M is any integer between 1 and 12) and the "day" is day D (where D is any integer between 1 and Dm), month M and day D may be expressed by the following combinations. Dm is determined for each month and is one of the integers 28, 29, 30, and 31. ·M month: cos[2π×{(M-1) / 12)], sin[2π×((M-1) / 12)] ·D day: cos[2π×{(D-1) / Dm)], sin[2π×((M-1) / Dm)]

[0048] The second information, which is the information relating to the production time, may further include information indicating the cumulative number of batches mixed by the mixer 114 in one day. The information indicating the cumulative number of batches in one day is information specifying which batch process was performed on that day (the batch process in which the ready-mixed concrete was produced). The timing at which the ready-mixed concrete of interest was produced can be indicated depending on which batch process in one day it was produced by. Therefore, the information indicating the cumulative number of batches in one day is also information relating to the production time.

[0049] The timing information acquiring unit 26 may acquire the second information based on an input from an operator such as a worker via the input device 12. Instead of or in addition to an input from an operator such as a worker, the timing information acquiring unit 26 (the control device 10 itself) may acquire at least a part of the second information based on the timing at which the operation control unit 22 controls the mixer 114, etc. The above-mentioned operation information acquiring unit 24 and timing information acquiring unit 26 function as an input information acquiring unit that acquires input information including the first information and the second information.

[0050] The model construction unit 30 constructs a model for predicting the quality of ready-mixed concrete (hereinafter, referred to as "prediction model M"). The prediction model M is a model that outputs quality information (quality value) indicating the quality of ready-mixed concrete in response to input of input information including the first information and the second information. The model construction unit 30 constructs the prediction model M by machine learning based on the input information and the correct quality value associated with the input information. The prediction model M may be constructed to output, as the quality information, one or more predicted values ​​of a predicted value of slump, a predicted value of slump flow, and a predicted value of air volume. The prediction model M may be constructed to output, as the quality information, a predicted value of the ratio of slump flow to slump (slump flow / slump) instead of or in addition to the one or more predicted values.

[0051] Machine learning refers to a method in which a machine (computer) autonomously finds laws or rules by repeatedly learning based on given information. The prediction model M can be constructed using an algorithm and a data structure. The prediction model M is realized, for example, using a neural network, which is an information processing model that mimics the mechanism of the human brain. There is no particular limitation on the specific algorithm of the machine learning performed when constructing the prediction model M. The neural network has, for example, an input layer, one or more intermediate layers, and an output layer. By including one or more intermediate layers, a more complex prediction model M can be constructed, and prediction accuracy can be improved.

[0052] The model construction unit 30 may autonomously construct a prediction model M for predicting the quality of ready-mix concrete by performing machine learning using data provided as input for machine learning and correct answer data (correct answer values ​​for slump, etc.) that are the output of the machine learning. The input for the machine learning is various data sets of input information including the first information and the second information. The output of the machine learning is data (numerical values) that indicate the quality of the ready-mix concrete. The model construction unit 30 iteratively learns a model that outputs predicted values ​​for slump, etc., using multiple combinations of the data sets of input information and correct answer values ​​for slump, etc.

[0053] The stage of autonomously constructing the prediction model M corresponds to a learning phase. The learning phase may be performed before the production phase in which ready-mix concrete is manufactured, or may be performed at an early stage of the production phase. The model holding unit 32 holds the prediction model M constructed by the model construction unit 30. The prediction model M, which is a trained model, may be transferable between computers. Therefore, the prediction model M constructed in the control device 10 may be used in another manufacturing system different from the manufacturing system 1, and the model holding unit 32 may hold the prediction model M constructed in the other manufacturing system.

[0054] In the evaluation phase, the prediction calculation unit 28 predicts the quality of the ready-mixed concrete to be predicted, based on the input information acquired by the operation information acquisition unit 24 and the timing information acquisition unit 26 (input information acquisition unit) and the prediction model M. The prediction calculation unit 28 inputs the acquired input information to the prediction model M and acquires a predicted value output from the prediction model M. The input information acquired in the evaluation phase (input information for evaluation) is information regarding the unknown quality of ready-mixed concrete.

[0055] The display output unit 34 outputs information (quality value) indicating the quality predicted by the prediction calculation unit 28 to the monitor 14. As a result, the predicted value of quality is displayed on the monitor 14, and an operator such as a worker can grasp the predicted value of the quality of the ready-mix concrete (manufactured ready-mix concrete) that is the prediction target.

[0056] As shown in Fig. 5, the control device 10 includes a circuit 50. The circuit 50 includes a processor 51, a memory 52, a storage 53, and an input / output port 54. The storage 53 is configured with one or more non-volatile memory devices such as a flash memory or a hard disk. The storage 53 stores at least a quality prediction program that causes a computer to execute the acquisition step and the prediction step. The storage 53 stores a quality prediction program for configuring each functional block of the control device 10.

[0057] The memory 52 is composed of one or more volatile memory devices such as a random access memory. The memory 52 temporarily stores a quality prediction program loaded from the storage 53. The processor 51 is composed of one or more arithmetic devices such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit). The processor 51 configures each functional block of the control device 10 by executing the quality prediction program loaded into the memory 52. ​​The calculation results by the processor 51 are temporarily stored in the memory 52. ​​The input / output port 54 inputs and outputs information between the input device 12, the monitor 14, the mixer 114, etc. in response to a request from the processor 51.

[0058] The timer 55 measures the elapsed time by, for example, counting a reference pulse at a constant period. The circuit 50 is not necessarily limited to one in which each function is configured by a program. For example, the circuit 50 may be configured with at least some of its functions by a dedicated logic circuit or an ASIC (Application Specific Integrated Circuit) that integrates the dedicated logic circuit. The quality prediction program may be provided in a state in which it is fixedly recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the quality prediction program may be provided via a communication network as a data signal superimposed on a carrier wave.

[0059] [Method of manufacturing ready-mix concrete] Next, an example of a method for producing ready-mix concrete executed in the production system 1 will be described. The method for producing ready-mix concrete includes a production process and a quality prediction process. The production process is a process for producing ready-mix concrete. The quality prediction process is a process for predicting the quality of the ready-mix concrete produced in the production process. The quality prediction process may be executed in a period that overlaps with at least a portion of the period during which the production process is repeatedly executed.

[0060] The manufacturing process includes, for example, a transporting process, a weighing process, a feeding process, a mixing process, a discharging process, and a loading process. In the transporting process, various types of concrete materials are transported to storage bottles 111 by a transport device 104, and the various materials are individually supplied to the storage bottles 111. In the weighing process, various materials are individually supplied from the storage bottles 111 to weighing bottles 112, and the various materials are weighed in the weighing bottles 112. In the weighing process, when the measured amount of each material reaches a predetermined set amount, the material is discharged to a collecting hopper 113. In the feeding process, after all types of materials are collected in the collecting hopper 113, the materials in the collecting hopper 113 are fed (supplied) to a mixer 114.

[0061] In the mixing process, multiple types of concrete materials are mixed in the mixer 114. In the mixing process, the control device 10 may control the mixer driving unit 114b according to predetermined operating conditions. In the mixing process, the power supplied from the control device 10 to the mixer driving unit 114b may be adjusted so that the rotation speed of the mixer driving unit 114b follows a target rotation speed.

[0062] In the discharging process, after mixing of the concrete materials is completed in the mixer 114, the ready-mixed concrete is discharged from the mixer 114 to the loading hopper 115. In the loading process, the ready-mixed concrete discharged into the loading hopper 115 is loaded onto the transport vehicle 200.

[0063] The quality prediction process (quality prediction method) includes a model construction process in a learning phase and a quality evaluation process in an evaluation phase. In the quality prediction process, the model construction process is executed before the quality evaluation process. An example of the model construction process and an example of the quality evaluation process will be described below.

[0064] (Model building process) 6(a) is a flowchart showing an example of a series of processes executed in the model construction process. This model construction process is executed before the above-mentioned manufacturing process is executed in the manufacturing apparatus 100, or at an early stage when the above-mentioned manufacturing process is started. In this model construction process, for example, ready-mix concrete actually manufactured in the manufacturing apparatus 100 is used as ready-mix concrete for learning.

[0065] In the model construction process, step S11 is executed first. In step S11, for example, an operator such as a worker prepares learning data for machine learning. The learning data is composed of a plurality of data sets. Each of the plurality of data sets includes input information (learning input information) including the first information and the second information obtained when the learning ready mixed concrete is manufactured, and a correct answer value of quality information (for example, slump) associated with the input information.

[0066] The correct value of the quality information may be a value obtained by actually measuring the quality of the ready-mixed concrete for learning. In one example, after the ready-mixed concrete for learning is loaded onto the transport vehicle 200, a part of the ready-mixed concrete is extracted by a worker or the like. Then, the slump, slump flow, air content, and the like of the extracted ready-mixed concrete are measured by the worker or the like, and at least a part of these actual measured values ​​is used as the correct value in the learning data.

[0067] Next, step S12 is executed. In step S12, for example, the model construction unit 30 of the control device 10 performs machine learning using the learning data (multiple data sets) prepared in step S11 to construct a prediction model M. The model construction unit 30 may construct the prediction model M by machine learning using a neural network.

[0068] Each of Fig. 7(a) and Fig. 7(b) shows a schematic diagram of a prediction model M constructed by the model construction unit 30. The prediction model M shown in Fig. 7(a) is a model that outputs a single quality prediction value in response to input of input information. The prediction model M shown in Fig. 7(b) is a model that outputs two or more types of quality prediction values ​​in response to input of input information. In the present disclosure, machine learning performed so that the prediction model M outputs a single quality prediction value (only a prediction value of one type of quality) is referred to as "single-task learning". Moreover, machine learning performed so that the prediction model M outputs two or more types of quality prediction values ​​is referred to as "multi-task learning".

[0069] The model construction unit 30 may construct the prediction model M by performing single-task learning. In this case, the model construction unit 30 may construct a prediction model M for each type of quality. For example, the model construction unit 30 constructs two or more models among a prediction model M that outputs a predicted value of a slump, a prediction model M that outputs a predicted value of a slump flow, and a prediction model M that outputs a predicted value of an air volume. The model construction unit 30 may construct the prediction model M by performing multi-task learning. For example, the model construction unit 30 constructs a prediction model M (one prediction model M) that outputs a predicted value of each of two or more qualities among a slump, a slump flow, and an air volume. The model construction unit 30 may construct a prediction model M (one prediction model M) that outputs a ratio of a slump flow to a slump in addition to one or more qualities among a slump, a slump flow, and an air volume.

[0070] An example of the prediction model M will be described below using simplified formulas for ease of understanding. The prediction model M constructed by the model construction unit 30 can be simply expressed as, for example, the following formulas (1) and (2).

number

number

[0071] In formula (2), Y represents an output value of quality, and in a prediction model M constructed by single-task learning, it is an output value of a single quality. In a prediction model M constructed by multi-task learning, Y is an output value of any one of two or more types of quality, and formulas (1) and (2) are calculated for each type of quality. N is an integer of 2 or more and represents the number of input data. x represents various input values ​​included in the input data, and the input data corresponds to the above input information, and the input data includes at least a value related to the power load value included in the first information (e.g., two or more statistics) and a value indicating the manufacturing time by mixer 114 included in the second information (e.g., date and time).

[0072] wi is a weight (coefficient), and b is a bias term (coefficient). f(U) represents the activation function. The activation function is a linear function (identity function) or a nonlinear function such as a polynomial, absolute value, step function, sigmoid function, hardsigmoid function, logsigmoid function, softmax function, logsoftmax function, softmin function, softplus function, softsign function, tanh function, tanhShrink function, hardtanh function, tanhexp function, ReLU function, ReLU6 function, Leaky-ReLU function, PReLU function, ELU function, SELU function, CELU function, Swith function, Mish function, ACON function, etc.

[0073] The model construction unit 30 may determine the weight wi and bias term b in formula (1) so as to minimize the error (loss) by repeatedly evaluating the error (loss) between Y (predicted value) obtained by formula (2) and the correct value of quality using data for learning. The model construction unit 30 may use any type of loss function as a function for evaluating the error between Y (output value from an intermediate model at an intermediate stage of constructing the prediction model M) obtained by formula (2) and the correct value of quality. The role of the loss function is to input the predicted value and the correct value to the loss function and output a loss value L(a) from the error a between the predicted value and the correct value. Then, the weight wi is calculated using the loss value L(a). For example, the model construction unit 30 uses one selected from the group consisting of the Huber loss function (HuberLoss), the mean absolute error (MAE: Mean Absolute Error), and the ε-tolerable loss function (ε-insensitiveloss) as the loss function. From the viewpoint of improving prediction accuracy, it is preferable to use a function as the loss function such that the loss value L(a) is always equal to or less than the absolute value of the error a. As the function such that the loss value L(a) is always equal to or less than the absolute value of the error a, the mean absolute error or the ε-allowable loss function may be used, or it is also possible to use a function such that the loss value L(a) is always equal to or less than the absolute value of the error a by setting δ of the Huber loss function shown in the following formula (3) to 1.0 or less.

number

[0074] The model construction unit 30 may repeatedly update the weights wi by the gradient method so as to minimize the error evaluated by the loss function. The model construction unit 30 may use any type of update formula (weight update formula) when updating the weights wi. For example, the model construction unit 30 uses one selected from the group consisting of Adam, AdamBelief, Adamax, AdaBound, Adagrad, AMSGRAD, AMSBound, RMSprop, SgdW, Momentum, and Nesterov as the weight update formula. The weight update formula is also called an optimization algorithm or an optimization method.

[0075] The model construction unit 30 may construct the prediction model M by machine learning using a neural network that does not perform an operation to normalize the input information and an operation to cause some connections in the fully connected layer to be missing. The operation to normalize the input information means an operation (batch normalization) that normalizes (standardizes) each value included in the input information that is input to the prediction model M so that the average is 0 and the standard deviation is 1.

[0076] The operation of missing some connections in the fully connected layer means that learning is performed while deactivating an arbitrary proportion of nodes (Dropout) when performing machine learning using a neural network. The operation process of the prediction model M when some connections in the fully connected layer are missing (when Dropout is performed) is shown in FIG. 7(c). The model construction unit 30 may construct the prediction model M by machine learning using a neural network that performs at least one of an operation to normalize input information and an operation to missing some connections in the fully connected layer.

[0077] 6(a), after step S12 is executed, step S13 is executed. In step S13, for example, the model holding unit 32 stores the prediction model M built in step S12. This completes the model building process.

[0078] (Quality evaluation process) 6(b) is a flowchart showing an example of a series of processes executed in the quality evaluation process. This quality evaluation process is executed, for example, during a period overlapping at least a part of the period during which the manufacturing process is executed by the manufacturing apparatus 100.

[0079] In the quality evaluation process, first, the control device 10 executes step S21. In step S21, for example, the control device 10 waits until it is time to evaluate the quality of the ready-mixed concrete to be evaluated. The ready-mixed concrete to be evaluated is also the ready-mixed concrete to be manufactured by the manufacturing apparatus 100. The evaluation timing may be predetermined to a certain time period in a day, or may be predetermined to a timing to execute a certain number of batch processes in a day. The evaluation timing may be a timing at which an instruction to execute the evaluation is received from an operator such as a worker.

[0080] Next, the control device 10 executes step S22. In step S22, for example, the operation information acquisition unit 24 and the timing information acquisition unit 26 acquire input information including the first information (information related to the power load value) and the second information (information related to the production time) when the ready-mixed concrete to be evaluated was produced. When the timing information acquisition unit 26 acquires information representing date and time as the second information in a normal expression format, the timing information acquisition unit 26 may convert it into a format expressed by trigonometric functions as described above. The input information acquired in step S22 is input information for evaluation in which quality information (for example, slump) is unknown.

[0081] Next, the control device 10 executes step S23. In step S23, for example, the prediction calculation unit 28 predicts quality information of the ready-mix concrete to be evaluated based on the input information acquired in step S22 and the prediction model M held in the model holding unit 32. In one example, the prediction calculation unit 28 inputs the input information acquired in step S22 to the prediction model M, and acquires a predicted value of one or more qualities of slump, slump flow, and air content output from the prediction model M.

[0082] Next, the control device 10 executes step S24. In step S24, for example, the display output unit 34 displays the predicted value of quality acquired in step S23 on the monitor 14. This allows an operator such as a worker to check the predicted value of quality.

[0083] This completes the quality evaluation process. The control device 10 may execute a series of processes from steps S21 to S24 each time one batch of ready-mixed concrete is produced (each time a batch process is performed). The control device 10 may execute a series of processes from steps S21 to S24 each time multiple batches of ready-mixed concrete are produced (each time a multiple batch process is performed).

[0084] [Variations] The series of processes shown in Figures 6(a) and 6(b) are examples and can be modified as appropriate. In the series of processes, one step and the next step may be executed in parallel, or some steps may be executed in an order different from the example described above. Instead of at least some of the steps of the series of processes, or in addition to the series of processes, steps with contents different from the example described above may be executed.

[0085] The first information may include at least one of the initial value, the minimum value, the maximum value, and the final value in the time series data related to the power load value. The input information acquired by the control device 10 (input information acquisition unit) and input to the prediction model M may include information related to mixing by the mixer 114 (information other than the power load value). Examples of the information related to mixing include the mixing amount of one batch of concrete, the mixing time, the time when the power load value reaches the maximum value, and the time from when the power load value reaches the maximum value to when the fresh concrete is discharged from the mixer 114. The input information input to the prediction model M may include the nominal strength, information indicating the type of cement, information indicating the type of admixture, information indicating the type of fine aggregate, information indicating the type of coarse aggregate, and information indicating the amount of additive added. The input information input to the prediction model M may include information related to an image of the fresh concrete being produced by the mixer 114 or immediately after production, or an image of the fresh concrete after being discharged from the mixer 114. The input information to be input to the prediction model M may include at least one of the target quality of the ready-mixed concrete when it is used and the target quality of the ready-mixed concrete when it is shipped. The input information to be input to the prediction model M needs to include at least information related to the power load value of the mixer and information related to the production time of the ready-mixed concrete by the mixer, and may include other types of information instead of or in addition to the various types of information exemplified above.

[0086] The second information regarding the time when the mixer 114 produced ready-mixed concrete may include information indicating the time from an arbitrarily set reference time point to the time when the mixer 114 performed mixing. The reference time point may be set by an operator such as a worker. For example, the reference time point may be set to a time point after the interior of the mixer 114 is cleaned during maintenance of the manufacturing apparatus 100 and before production by the mixer 114 is resumed. The time between the reference time point and the time when the mixer 114 performed mixing (production of ready-mixed concrete) may be expressed in minutes or seconds.

[0087] The time when the mixer 114 executes mixing (production of ready-mixed concrete) may be the time when concrete materials are supplied to the mixer 114, or the time when the manufactured ready-mixed concrete is discharged from the mixer 114. The time when the mixer 114 executes mixing (production of ready-mixed concrete) may be the time when the drive of the agitating member 114a in the mixer 114 is started, or the time when the drive of the agitating member 114a in the mixer 114 is stopped. The timing information acquisition unit 26 (the control device 10 itself) may measure information indicating the time from a reference time point to the time when the mixer 114 executes mixing.

[0088] The second information on the time when the mixer 114 produced ready-mixed concrete may include information indicating the cumulative number of batches since the reference time point, instead of information indicating the time from the reference time point to the time when the mixer 114 performed mixing. The cumulative number of batches since the reference time point is information specifying the number of batch processes to be executed from the reference time point (the number of batch processes from the reference time point in which the ready-mixed concrete was produced). With regard to the cumulative number of batches since the reference time point, the cumulative number of batches is reset to 0 at the reference time point.

[0089] In addition to the prediction of the quality of the ready-mixed concrete by the control device 10, the quality of the ready-mixed concrete may be actually measured periodically (e.g., several times a day) in the manufacturing apparatus 100. In this case, the prediction model M may be updated based on the actual measurement value of the quality of the ready-mixed concrete and input information (e.g., information related to the power load value) at the time when the actual measurement value was obtained. In the prediction step, the quality of the ready-mixed concrete may be predicted by the updated prediction model M. Note that even when the updated prediction model M is used, the process of predicting the quality of the ready-mixed concrete is still performed based on the prediction model M and the input information acquired in the acquisition step.

[0090] The manufacturing system 1 may include, in addition to the control device 10, a quality prediction device (ready-mixed concrete quality prediction device) having at least an operation information acquisition unit 24, a timing information acquisition unit 26, a prediction calculation unit 28, and a model storage unit 32 as functional blocks. A computer constituting the quality prediction device may be communicably connected to the control device 10. In one example among the various examples described above, at least a part of the matters described in the other examples may be combined.

[0091] [Verification of prediction results using the prediction model] Next, we will explain the results of verifying the prediction results of quality information using a prediction model M that inputs input information including the first information and the second information, using a data set in which the correct answer value is known.

[0092] (Influence of information related to manufacturing date) The influence of the second information, which is information related to the production time of ready-mixed concrete by the mixer 114, was verified. In this verification, 2765 training datasets were prepared for constructing a prediction model by machine learning, and 789 evaluation datasets, in which the correct answer values ​​are known, were prepared for evaluation. In the training datasets and the evaluation datasets, various input values ​​included in the input information are associated with the correct answer values ​​of the quality information. Statistics obtained from time series data related to power load values ​​are used as the first information of the input information in the training datasets and the evaluation datasets.

[0093] As the second information of the input information in the learning dataset and the evaluation dataset, information representing the manufacturing year, manufacturing month, manufacturing date, hours, minutes, seconds, and cumulative batch number per day is used. The information representing the manufacturing month, manufacturing date, hours, minutes, and seconds was modified to an expression format using trigonometric functions as described above. Using 2765 learning datasets, a prediction model M was constructed using input information including the first information and the second information. The prediction model M was constructed by multitask learning to output slump, slump flow, and air volume.

[0094] In order to verify the influence of the second information on the prediction accuracy, a comparative model was constructed using the same 2765 learning datasets after removing the second information from the input information. The influence of the second information on the prediction accuracy was evaluated for each of slump, slump flow, and air volume. Using 789 evaluation datasets, the prediction results of the model and the correct answer value in the evaluation dataset were compared for each of the prediction model M and the comparative model. The prediction result of the model was defined as correct when it was within a range obtained by adding a predetermined tolerance to the correct answer value in the evaluation dataset, and the accuracy rate was used as the evaluation index. Figure 8 shows a graph showing the results of verifying the influence of the second information.

[0095] Regarding the verification results for slump in FIG. 8, when the tolerance is "±0.5 cm", the data set in which the prediction result (predicted value) by the model is within a range of ±0.5 cm of the correct value is regarded as the correct answer, and the percentage of data sets judged to be correct is shown as the accuracy rate (%). The accuracy rates (%) are also calculated in the same manner when the tolerance is "±1.0 cm", "±1.5 cm", "±2.0 cm", and "±2.5 cm". "Yes" indicates the evaluation result using the prediction model M which includes the second information (information related to the manufacturing time) in the input information, and "No" indicates the evaluation result using the comparison model which does not include the second information in the input information.

[0096] Regarding the verification results of the slump flow in FIG. 8, the results of obtaining the accuracy rate (%) for each tolerance are shown by changing the tolerance to "±2.5 cm", "±3.0 cm", "±5.0 cm", and "±7.5 cm" in the same manner as in the verification of the slump. Regarding the verification results of the air volume in FIG. 8, the results of obtaining the accuracy rate (%) for each tolerance are shown by changing the tolerance to "±0.3%", "±0.5%", "±1.0%", and "±1.5%" in the same manner as in the verification of the slump. When observing the entire verification results shown in FIG. 8, it can be seen that the accuracy rate (%) when the quality is predicted using the prediction model M (second information) tends to be higher than when the quality is predicted using the comparison model. Note that similar verification results were obtained even when a verification was performed using statistics obtained from the moving average value of the time series data related to the power load value.

[0097] (Changes in power load values) FIG. 9 shows a graph of the transition of the measurement results of the power load value when the production of ready-mix concrete was actually performed. As the power load value, an initial value in the time series data related to the power load value is measured. From about 9:00 to about 11:30, the batch processing is performed continuously under the same operating conditions (production conditions), and from about 12:00 to about 14:30, the batch processing is performed continuously under the same operating conditions (production conditions). At time t1 shown in FIG. 9, the inside of the mixer 114 is cleaned. The operating conditions (production conditions) in the front stage before time t1 and the operating conditions (production conditions) in the rear stage after time t1 are the same. In FIG. 9, the results of measuring the slump by sampling from multiple batch processings performed continuously are also plotted.

[0098] From the graph shown in Fig. 9, it can be seen that in multiple consecutive batch processes, the initial value of the power load value gradually decreases as the number of batch processes increases, even under the same operating conditions. It can be seen that the power load values ​​at the beginning (when the cumulative number of batches is small) are approximately the same between multiple batch processes in the previous stage before time t1 and multiple batch processes in the later stage after time t1. From the graph shown in Fig. 9, it can be seen that since the power load value varies depending on the production time of ready-mixed concrete by the mixer 114, it is useful to perform machine learning so that both information related to the production time and information related to the power load value are input to the prediction model.

[0099] [Summary of this disclosure] The above-described method for predicting the quality of ready-mixed concrete includes an acquisition step of acquiring input information including first information related to a power load value of a mixer (114) that mixes concrete materials to produce ready-mixed concrete and second information related to a production time of the ready-mixed concrete by the mixer (114), and a prediction step of predicting the quality of the ready-mixed concrete based on a prediction model (M) previously constructed by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired in the acquisition step.

[0100] Studies have been conducted in the past on predicting the quality of fresh concrete, which indicates the hardness, etc., of the fresh concrete, from the magnitude of the power load value. However, as a result of the studies by the present inventors, it was found that the power load value itself may vary depending on the time when the fresh concrete was produced by the mixer (114) even if the operating conditions (production conditions) are the same. In the above quality prediction method, information related to the time when the fresh concrete was produced by the mixer (114) is used as one of the input information of the prediction model (M) constructed by machine learning. Therefore, when machine learning is performed, the prediction model (M) is constructed so that a correct answer value can be obtained by taking into account not only the power load value but also the production time. Therefore, the above quality prediction method is useful for improving the prediction accuracy when predicting quality by a prediction model.

[0101] In the above-described method for predicting quality of ready-mixed concrete, the second information may include information indicating a date and time when the mixer (114) performs mixing. The power load value may vary depending on the date and time when the mixer (114) performs mixing. Therefore, in the above-described method, a prediction model (M) for predicting quality can be constructed taking into account the variation in the power load value depending on the date and time. Therefore, it is possible to improve the accuracy of prediction by the trained model for predicting quality.

[0102] In the above-described method for predicting the quality of ready-mixed concrete, the date and time in the second information may be specified using trigonometric functions. In this case, the date and time can be expressed to have periodicity. Therefore, the periodically repeating date and time can be expressed in a format close to reality, and machine learning can be performed.

[0103] In the above-described method for predicting quality of ready-mixed concrete, the second information may further include information indicating a cumulative number of batches of mixing by the mixer (114) in one day. The cumulative number of batches in one day indicates the order in which mixing (the above-described batch processing) by the mixer (114) is performed in one day. The power load value may also vary depending on such a cumulative number of batches. Therefore, in the above-described method, a prediction model (M) for predicting quality can be constructed taking into account the fluctuation in the power load value due to the cumulative number of batches in one day. This makes it possible to further improve the accuracy of prediction by the trained model for predicting quality.

[0104] In the above-described method for predicting quality of ready-mixed concrete, the second information may include information indicating the time from an arbitrarily set reference time point to the time point when the mixer (114) executes mixing, or information indicating the cumulative number of batches mixed by the mixer (114) since the reference time point. The power load value may vary depending on the time from a certain reference time point to the time point when the mixer (114) executes mixing. The power load value may also vary depending on the cumulative number of batches mixed by the mixer (114) since the certain reference time point. Therefore, in the above-described method, a prediction model (M) for predicting quality can be constructed by taking into account the time from a certain reference time point to the time when mixing is executed, or the cumulative number of batches since the certain reference time point. Therefore, the accuracy of prediction by the trained model for predicting quality can be improved.

[0105] In the above-described method for predicting the quality of ready-mixed concrete, the first information may include statistics obtained from time-series data of power load values ​​when the mixer (114) performs one mixing cycle. The statistics may be at least one value selected from the group consisting of a fluctuation range representing the difference between the maximum value and the minimum value, a decline range representing the difference between the maximum value and the final value, a total value within an arbitrarily set time, an average value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, kurtosis, and skewness. In this case, by using the statistics related to the power load values, the influence of disturbances that may be included in each value in the time-series data of the power load values ​​can be reduced. Therefore, it is further useful for improving the prediction accuracy.

[0106] The quality prediction program described above is a program for causing a computer to execute the above-mentioned quality prediction method for ready-mix concrete. This quality prediction program executes the above-mentioned quality prediction method, and is therefore useful for improving prediction accuracy when predicting quality using a prediction model.

[0107] The quality prediction device (10) for ready-mixed concrete described above includes an input information acquisition unit (24, 26) that acquires input information including first information related to a power load value of a mixer (114) that mixes concrete materials to produce ready-mixed concrete and second information related to a production time of ready-mixed concrete by the mixer (114), a prediction model (M) that is constructed in advance by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information, and a prediction calculation unit (28) that predicts the quality of the ready-mixed concrete based on the input information acquired by the input information acquisition unit (24, 26). This quality prediction device (10) is useful for improving prediction accuracy when predicting quality using a prediction model, similar to the above-mentioned quality prediction method. [Explanation of symbols]

[0108] 1... manufacturing system, 10... control device, 24... operation information acquisition unit, 26... timing information acquisition unit, 28... prediction calculation unit, 30... model construction unit, M... prediction model, 100... manufacturing apparatus, 114... mixer, 114a... stirring member, 114b... mixer drive unit.

Claims

1. An acquisition process for acquiring input information including first information relating to a power load value of a mixer that mixes concrete materials to produce ready-mix concrete, and second information relating to a production time of ready-mix concrete by the mixer; A prediction process of predicting the quality of the ready-mixed concrete based on a prediction model previously constructed by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired in the acquisition process; Including, The second information includes information indicating a date and a time when the mixer performs mixing. Methods for predicting the quality of ready mixed concrete.

2. An acquisition process for acquiring input information including first information relating to a power load value of a mixer that mixes concrete materials to produce ready-mix concrete, and second information relating to a production time of ready-mix concrete by the mixer; A prediction process of predicting the quality of the ready-mixed concrete based on a prediction model previously constructed by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired in the acquisition process; Including, The second information includes information representing a time from an arbitrarily set reference time point to a time point when the mixer executes mixing. Methods for predicting the quality of ready mixed concrete.

3. The reference time is set to a time after the mixer is cleaned and before production by the mixer is resumed. The method for predicting quality of ready-mix concrete according to claim 2.

4. A quality prediction program that causes a computer to execute the quality prediction method of ready-mixed concrete according to any one of claims 1 to 3.

5. A manufacturing process for producing ready-mix concrete; A quality prediction step of predicting the quality of the ready-mixed concrete produced in the production step by the quality prediction method of ready-mixed concrete according to any one of claims 1 to 3. A method for producing ready-mix concrete.

6. An input information acquisition unit that acquires input information including first information related to a power load value of a mixer that mixes concrete materials to produce ready-mixed concrete and second information related to a production time of the ready-mixed concrete by the mixer; A prediction calculation unit that predicts the quality of the ready-mixed concrete based on a prediction model that is constructed in advance by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired by the input information acquisition unit; Equipped with The second information includes information indicating a date and time when the mixer performs mixing.

7. An input information acquisition unit that acquires input information including first information related to a power load value of a mixer that mixes concrete materials to produce ready-mixed concrete and second information related to a production time of the ready-mixed concrete by the mixer; A prediction calculation unit that predicts the quality of the ready-mixed concrete based on a prediction model that is constructed in advance by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to input of the input information and the input information acquired by the input information acquisition unit; Equipped with The second information includes information representing a time from an arbitrarily set reference time point to a time point when the mixer executes mixing. Ready-mix concrete quality prediction device.

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