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

Abstract

To improve prediction accuracy when predicting quality by a prediction model.SOLUTION: A quality prediction method of ready-mixed concrete comprises: an acquisition step of acquiring input information including first information concerning a power load value of a mixer producing ready-mixed concrete by kneading concrete material and second information concerning a production period of the ready-mixed concrete by the mixer; and a prediction step of predicting quality of the ready-mixed concrete on the basis of a prediction model constructed in advance by machine learning in such a way as to output quality information representing the quality of the ready-mixed concrete in response to input of the input information and the input information acquired in the acquisition step.SELECTED DRAWING: Figure 6

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a method for predicting the quality of ready-mixed 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 ready-mixed concrete quality prediction method, a quality prediction program, and a ready-mixed concrete quality prediction device 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 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.

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

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

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

[0009] [5] The method for predicting the 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 the quality of ready-mixed concrete according to 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 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 period, a mean value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, kurtosis, and skewness.

[0011] [7] A quality prediction program that causes a computer to execute the method for predicting the quality of ready-mixed 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 the power load value of a mixer that mixes concrete materials to produce ready-mixed concrete and second information related to the time when the ready-mixed concrete is produced by the mixer; a prediction model that is pre-constructed by machine learning to output quality information that indicates the quality of the ready-mixed concrete in response to the 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. [Effects of the Invention]

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

[0014] [Figure 1] FIG. 1 is a schematic diagram showing an example of a ready-mix concrete manufacturing system. [Figure 2] FIG. 2 is a block diagram illustrating an example of a functional configuration of the control device. [Figure 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. [Figure 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 processing flow in the learning phase, and Fig. 6(b) is a diagram showing an example of a processing flow in the evaluation phase. [Figure 7] 7(a) and 7(b) are schematic diagrams showing an example of the calculation process using a prediction model, and FIG. 7(c) is a schematic diagram showing an example of the calculation process using a neural network. [Figure 8] FIG. 8 is a graph showing an example of the 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 INVENTION

[0015] An embodiment will be described below with reference to the drawings. In the description, identical elements or elements having identical functions are given the same reference numerals, and duplicated explanations will be omitted. Figure 1 shows a schematic diagram of a ready-mixed concrete manufacturing system equipped with a quality prediction device according to one 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 mixes concrete materials to manufacture ready-mix concrete.

[0017] The concrete materials used in the manufacturing system 1 include cement, admixtures, coarse aggregate, fine aggregate, water, and admixtures. Examples of coarse aggregate include gravel, crushed stone, slag coarse aggregate, lightweight coarse aggregate, recycled coarse aggregate, recovered aggregate, and coarse aggregates made from a mixture of these. Examples of gravel include mountain gravel, land gravel, river gravel, and sea gravel. Examples of slag coarse aggregate include blast furnace slag aggregate, ferronickel slag aggregate, electric arc furnace oxidizing slag aggregate, and coal gasification slag aggregate. Examples of lightweight coarse aggregate include natural lightweight aggregate, by-product lightweight aggregate, and artificial lightweight aggregate. Examples of coarse aggregate include crushed rock or crushed limestone.

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

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

[0020] The manufacturing system 1 loads the manufactured ready-mixed concrete onto the transport vehicle 200. After the ready-mixed concrete has been loaded onto the transport vehicle 200, the transport vehicle 200 transports the ready-mixed concrete to the site where the ready-mixed concrete will be used (for example, a construction site). Examples of the transport vehicle 200 include an agitator vehicle (mixer vehicle) or a dump truck. The manufacturing system 1 may manufacture ready-mixed 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 operating 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 is controlled (inspected, etc.) before it is shipped so that the target quality of the ready-mixed concrete when it is used, set for each site, is met. 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 shipping, 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 in-house standards, etc., set 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 condition (quality) of the materials used during production, 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 the in-house standards 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-mixed concrete based on operation instructions from the control device 10. The manufacturing apparatus 100 manufactures ready-mixed 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 storage yard 101 is a place where concrete materials are stored. The material storage 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 concrete materials stored in the multiple silos 102 to the storage bins 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 bins 111.

[0025] The storage bottles 111 temporarily store various types of concrete materials. The various types of concrete materials are transported (conveyed) to the storage bottles 111 from the material storage area 101 by the transport device 104. The storage bottles 111 are configured to individually store various types of concrete materials. Hereinafter, "concrete materials" may be simply referred to as "materials." The various materials stored in the storage bottles 111 are supplied to the measuring bottles 112 as needed.

[0026] The measuring bottle 112 is disposed below the storage bottle 111. The measuring bottle 112 operates based on operational instructions from the control device 10, and individually measures various materials. When the measuring bottle 112 detects the 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 the 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 necessarily have to be equipped with the collecting hopper 113, and the 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, etc. 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 agitating members 114a are members that agitate the various materials supplied to the mixer 114. The two agitating members 114a are arranged side by side inside the main body (container portion) of the mixer 114 and are rotatable. Each of the two agitating members 114a includes a rotation shaft that extends horizontally in one direction. The mixer driving unit 114b rotates the rotation shaft of each of the two agitating 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 applies driving force to the agitating members 114a. An opening and closing port is provided at the bottom of the main body of the mixer 114 for discharging the produced ready-mixed 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-mixed concrete manufacturing apparatus, and the ready-mixed concrete manufacturing apparatus may be configured in any manner as long as it is capable of mixing concrete materials using a mixer and manufacturing ready-mixed 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 processing." The number of times a batch processing has been executed since a certain reference point in time (cumulative number of executions) 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 processing executed the kth time in a day (k is an integer greater than or equal to 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 sequence). Multiple batch processes according to the same production conditions may be carried out consecutively in sequence during a certain period of the day.

[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 in accordance with set operating conditions. At least some 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 a worker or the like to the control device 10. The input device 12 may be any device that can input desired information, and may be a keyboard (keypad), an operation panel, or a mouse. The monitor 14 is a device that displays information from the control device 10 to a worker or the like. The monitor 14 may be any device that can display graphics, and may be a liquid crystal display. The input device 12 and the monitor 14 may be integrated, such as a touch panel. The control device 10, the input device 12, and the monitor 14 may be integrated, such as a tablet computer (tablet terminal).

[0035] In addition to controlling the manufacturing apparatus 100, the control device 10 may have a function of predicting the quality of ready-mixed concrete manufactured by the manufacturing apparatus 100. In this case, the control device 10 constitutes a quality prediction device that predicts the quality of ready-mixed concrete (ready-mixed concrete quality prediction device). 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 content. 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 content. The quality of fresh concrete to be predicted by the control device 10 may be one quality of slump, slump flow, and air content, or may be two or more qualities of slump, slump flow, and air content.

[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 time of production 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 the input information acquired in the acquisition step and a prediction model that has been constructed in advance by machine learning so as to output quality information indicating the quality of the ready-mixed concrete in response to the input of the input information.

[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 processing executed by these functional blocks corresponds to the processing executed by the control device 10.

[0039] The operation control unit 22 controls the manufacturing apparatus 100 to manufacture ready-mixed concrete in accordance with predetermined operating conditions. At least some 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 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 operations when producing ready-mixed concrete. 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 hydraulic 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 schematically illustrates time-series data relating to the power load values ​​of the mixer 114 during processing of one batch. The time-series data relating to the power load values ​​is, for example, data obtained by repeatedly measuring the power (kW) supplied to the mixer at a predetermined sampling period. The operation information acquisition unit 24 may calculate (acquire) as statistical data at least one selected from the group consisting of a fluctuation range (difference between the maximum value and the minimum value), a decline range (difference between the maximum value and the final 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, kurtosis, and skewness. The operation information acquisition unit 24 may calculate at least one of a minimum value and a maximum value as statistical data in addition to at least one selected from the above group. The statistical data acquired by the operation information acquisition unit 24 may include statistical values ​​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 the 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 terminating mixing by the mixer 114 is met. For example, when a predetermined time has elapsed since the start of driving the agitating member 114a, the operation control unit 22 determines that the above condition is met and stops driving the agitating member 114a.

[0043] The timing information acquisition unit 26 acquires information related to the time when ready-mixed concrete was produced by the mixer 114 (hereinafter referred to as "second information"). The time when ready-mixed concrete was produced by the mixer 114 refers to the timing when the mixer 114 mixed the 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 performed mixing. In the second information, the date when the mixer 114 performed mixing is expressed as a specific day when the mixer 114 produced ready-mixed concrete, using a combination of year, month, and day, or a combination of month and day.

[0044] In the second information, the time when mixer 114 performs 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 the time when the produced ready-mixed concrete 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 agitating member 114a in mixer 114 is started, or the time when drive of agitating member 114a is stopped.

[0045] In the second information, the date and time may each be specified using trigonometric functions. For example, in the second information, the month and day are each specified using trigonometric functions, and the hour, minute, and second are each specified using trigonometric functions. First, with reference to FIG. 4, a case will be described in which the "hour" of time is specified (expressed) using trigonometric functions. Normally, the "hour" of a day is specified (expressed) using 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, even though there is only a one-hour difference between 0:00 and 23:00 without taking the date into consideration, there is a large numerical difference. 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) are represented by a combination of cos{2π×(h / 24)} and sin{2π×(h / 24)} as shown in Figure 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 each 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 information relating to the production time, may further include information representing the cumulative number of batches mixed by the mixer 114 in one day. The information representing the cumulative number of batches in one day is information specifying which batch process was carried out on that day (which batch process produced the ready-mixed concrete). The timing of production can be indicated by the batch process number in one day in which the ready-mixed concrete of interest was produced, so the information representing 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 input from an operator such as a worker via the input device 12. Instead of or in addition to 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 operation information acquiring unit 24 and the timing information acquiring unit 26 described above function as input information acquiring units that acquire 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 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 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 content. The prediction model M may be constructed to output, as 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 is a technique in which a machine (computer) autonomously finds laws or rules by repeatedly learning based on given information. A prediction model M can be constructed using an algorithm and a data structure. A 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 and nerves. There are no particular limitations on the specific algorithm of machine learning used when constructing a prediction model M. A 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, thereby improving prediction accuracy.

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

[0053] The stage of autonomously constructing the prediction model M corresponds to the 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 storage unit 32 stores 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 storage unit 32 may store 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 into 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 about the unknown quality of the 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-mixed concrete (manufactured ready-mixed concrete) that is the target of prediction.

[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 to and from 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 reference pulses at a fixed interval. The circuit 50 is not necessarily limited to one in which each function is configured by a program. For example, the circuit 50 may have at least some of its functions configured by a dedicated logic circuit or an ASIC (Application Specific Integrated Circuit) that integrates such dedicated logic circuits. The quality prediction program may be provided by being permanently 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] [Ready-mix concrete manufacturing method] Next, an example of a method for producing ready-mixed concrete executed in the production system 1 will be described. The method for producing ready-mixed concrete includes a production process and a quality prediction process. The production process is a process for producing ready-mixed concrete. The quality prediction process is a process for predicting the quality of the ready-mixed concrete produced in the production process. The quality prediction process may be executed during 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 transporting device 104, and the various materials are individually supplied to the storage bottles 111. In the weighing process, the various materials are individually supplied from the storage bottles 111 to measuring bottles 112, and the various materials are weighed in the measuring bottles 112. In the weighing process, when the measured amount of each material reaches a predetermined set amount, the material is discharged into a collecting hopper 113. In the feeding process, after all types of materials have been collected in the collecting hopper 113, the materials in the collecting hopper 113 are fed (supplied) into 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 in accordance with 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 in the mixer 114 is completed, the ready-mixed concrete is discharged from the mixer 114 into 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 step. This model construction step is executed before the above-mentioned manufacturing step is executed in the manufacturing apparatus 100, or at an early stage after the above-mentioned manufacturing step has started. In this model construction step, for example, ready-mixed concrete actually manufactured in the manufacturing apparatus 100 is used as ready-mixed concrete for learning.

[0065] In the model building process, step S11 is first executed. In step S11, for example, an operator such as a worker prepares learning data for machine learning. The learning data is made up 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 produced, and a correct 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 portion of the ready-mixed concrete is extracted by a worker or the like. The slump, slump flow, air content, etc. of the extracted ready-mixed concrete are then measured by the worker or the like, and at least a portion of these actual measured values ​​are used as the correct values ​​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 constructs a prediction model M by performing machine learning using the learning data (plurality of data sets) prepared in step S11. The model construction unit 30 may construct the prediction model M by machine learning using a neural network.

[0068] 7(a) and 7(b) each schematically show 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 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 information. In the present disclosure, machine learning performed so that the prediction model M outputs a single quality prediction value (a prediction value of only one type of quality) is referred to as "single-task learning." Furthermore, 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 from among a prediction model M that outputs a predicted value of slump, a prediction model M that outputs a predicted value of slump flow, and a prediction model M that outputs a predicted value of 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 for each of two or more qualities of slump, slump flow, and air volume. The model construction unit 30 may construct a prediction model M (one prediction model M) that outputs a ratio of slump flow to slump in addition to one or more qualities of slump, slump flow, and 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, for example, as in the following formulas (1) and (2).

number

number

[0071] In equation (2), Y represents the output value of quality, and in a prediction model M constructed by single-task learning, it is the output value of a single quality. In a prediction model M constructed by multi-task learning, Y is the output value of one of two or more types of quality, and equations (1) and (2) are calculated for each type of quality. N is an integer equal to or greater than two 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-mentioned 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 (e.g., date and time) by mixer 114 included in the second information.

[0072] wi is a weight (coefficient), and b is a bias term (coefficient). f(U) represents the activation function. The activation function can be 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, or ACON function.

[0073] The model construction unit 30 may use training data to repeatedly evaluate the error (loss) between Y (predicted value) obtained by equation (2) and the correct quality value, and determine the weights wi and bias term b in equation (1) so as to minimize the error. 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 in constructing the prediction model M) obtained by equation (2) and the correct quality value. The role of the loss function is to input the predicted value and the correct value into the loss function and output a loss value L(a) based on the error a between the predicted value and the correct value. The weights wi are then calculated using the loss value L(a). The model construction unit 30 may use, for example, one loss function selected from the group consisting of the Huber loss function (HuberLoss), the mean absolute error (MAE), and the ε-insensitive loss function (ε-insensitiveloss). From the viewpoint of improving prediction accuracy, it is preferable to use a loss function that ensures 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 as a function that ensures that the loss value L(a) is always equal to or less than the absolute value of the error a. Alternatively, by setting δ of the Huber loss function shown in the following formula (3) to 1.0 or less, it is also possible to use a function that ensures that the loss value L(a) is always equal to or less than the absolute value of the error a.

number

[0074] The model construction unit 30 may repeatedly update the weights wi using a 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 referred to as 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 delete some connections in the fully connected layer. The operation to normalize the input information refers to 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 dropping some connections in the fully connected layer means that, when performing machine learning using a neural network, learning is performed while deactivating an arbitrary proportion of nodes (dropout). Figure 7(c) shows a schematic diagram of the calculation process of the prediction model M when dropping some connections in the fully connected layer (when performing dropout). Note that 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 drop 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 constructed in step S12. This completes the model construction 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 the evaluation timing arrives, which is the timing for evaluating 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 the timing of executing a certain number of batch processes in a day. The evaluation timing may also be the timing when an instruction to perform 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 using trigonometric functions as described above. The input information acquired in step S22 is input information for evaluation whose 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-mixed 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 into the prediction model M, and acquires a predicted value of one or more quality types among 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 the series of processes of 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 the series of processes of steps S21 to S24 each time multiple batches of ready-mixed concrete are produced (each time multiple batch processes are 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 that of the example described above. Steps different from those of the example described above may be executed instead of or in addition to at least some of the steps of the series of processes described above.

[0085] The first information may include at least one of the initial value, minimum value, maximum value, and 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 used as 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 amount of concrete mixed for one batch, the mixing time, the time at which the power load value reaches its maximum value, and the time from when the power load value reaches its maximum value until the fresh concrete is discharged from the mixer 114. The input information used as 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 used as 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 time when the ready-mixed concrete is produced 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 produced 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 mixer 114 starts driving the agitating member 114a, or the time when the mixer 114 stops driving the agitating member 114a. 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 executed mixing.

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

[0089] In addition to the prediction of the quality of ready-mixed concrete by the control device 10, the quality of ready-mixed concrete may be measured periodically (for example, 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 (for example, information related to the power load value) when the actual measurement value was obtained. In the prediction step, the quality of the ready-mixed concrete may be predicted using the updated prediction model M. Note that even when the updated prediction model M is used, the step 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 of the various examples described above, at least some 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 dataset in which the correct values ​​are 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 mixer 114, was verified. In this verification, 2,765 training datasets were prepared to construct a predictive model using machine learning, and 789 evaluation datasets, in which the correct values ​​were known, were prepared for evaluation. In the training datasets and evaluation datasets, various input values ​​included in the input information are associated with the correct 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 evaluation datasets.

[0093] The second information in the input information for the training dataset and the evaluation dataset was information representing the manufacturing year, manufacturing month, manufacturing date, hour, minute, second, and cumulative batch count per day. The information representing the manufacturing month, manufacturing date, hour, minute, and second was modified to an expression format using trigonometric functions as described above. Using 2,765 training datasets, a prediction model M was constructed using input information including the first information and the second information. The prediction model M was constructed using multi-task learning to output slump, slump flow, and air volume.

[0094] To verify the impact of the second information on prediction accuracy, a comparison model was constructed using the same 2,765 training datasets, excluding the second information from the input information. The impact of the second information on prediction accuracy was evaluated for slump, slump flow, and air volume. Using 789 evaluation datasets, the prediction results of the model M and the comparison model were compared with the correct values ​​in the evaluation dataset. The model prediction results were defined as correct when they fell within the range obtained by adding a specified tolerance to the correct value in the evaluation dataset, and the accuracy rate was used as the evaluation index. Figure 8 shows a graph illustrating the results of verifying the impact of the second information.

[0095] Regarding the verification results for slump in Figure 8, when the tolerance is "±0.5 cm," datasets whose prediction results (predicted values) by the model are within a range of ±0.5 cm of the correct value are considered correct, and the percentage of datasets determined to be correct is shown as the accuracy rate (%). Accuracy rates (%) are also calculated in the same way 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 second information (information related to the manufacturing date) in its input information, and "No" indicates the evaluation result using the comparison model, which does not include the second information in its input information.

[0096] Regarding the slump flow verification results in Figure 8, the tolerance was changed to ±2.5 cm, ±3.0 cm, ±5.0 cm, and ±7.5 cm, as in the slump verification. The accuracy rate (%) was calculated for each tolerance. Regarding the air volume verification results in Figure 8, the tolerance was changed to ±0.3%, ±0.5%, ±1.0%, and ±1.5%, as in the slump verification. Overall, the verification results shown in Figure 8 indicate that the accuracy rate (%) for quality predictions using prediction model M (second information) tends to be higher than for quality predictions using the comparison model. Similar results were obtained when using statistics derived from the moving average of time-series data related to power load values.

[0097] (Trends in power load values) FIG. 9 shows, as a graph, the transition of the measurement results of the power load value when ready-mixed concrete was actually produced. The initial value in the time-series data related to the power load value is measured as the power load value. Batch processing is performed continuously under the same operating conditions (production conditions) from around 9:00 to around 11:30, and batch processing is performed continuously under the same operating conditions (production conditions) from around 12:00 to around 14:30. At time t1 shown in FIG. 9, the interior of the mixer 114 is cleaned. The operating conditions (production conditions) in the previous stage before time t1 and the operating conditions (production conditions) in the subsequent stage after time t1 are the same. FIG. 9 also plots the results of measuring slump by sampling from multiple batch processings performed continuously.

[0098] The graph shown in Fig. 9 shows 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 similar between multiple batch processes in the earlier stages before time t1 and multiple batch processes in the later stages after time t1. The graph shown in Fig. 9 shows that the power load value also varies depending on the time of production of ready-mixed concrete by mixer 114, and therefore 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 the power load value of a mixer (114) that mixes concrete materials to produce ready-mixed concrete and second information related to the time when the mixer (114) produces ready-mixed concrete, and a prediction step of predicting the quality of the ready-mixed concrete based on a prediction model (M) previously 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 the input information acquired in the acquisition step.

[0100] Studies have been conducted to predict the quality of ready-mixed concrete, such as its hardness, from the magnitude of the power load value. However, as a result of studies by the present inventors, it has been discovered that the power load value itself can vary depending on the time when the ready-mixed concrete was produced by the mixer (114), even if the operating conditions (production conditions) are the same. In the quality prediction method described above, information related to the time when the ready-mixed concrete was produced by the mixer (114) is used as one of the input information for the prediction model (M) constructed by machine learning. Therefore, when performing machine learning, the prediction model (M) is constructed so that a correct value can be obtained by taking into account not only the power load value but also the production time. Therefore, the quality prediction method described above is useful for improving the prediction accuracy when predicting quality using a prediction model.

[0101] In the above-described method for predicting the quality of ready-mixed concrete, the second information may include information indicating the 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 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, the accuracy of prediction by the trained model for predicting quality can be improved.

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

[0103] In the above-described method for predicting the quality of ready-mixed concrete, the second information may further include information indicating the cumulative number of batches mixed 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 the cumulative number of batches. Therefore, the above-described method can construct a prediction model (M) for predicting quality by taking into account the fluctuation in the power load value due to the cumulative number of batches in one day. This can further improve the accuracy of predictions made by the trained model for predicting quality.

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

[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 and minimum values, a decline range representing the difference between the maximum and final value, a total value within an arbitrarily set time period, a mean value, a standard deviation, a coefficient of variation, a median, a first quartile, a third quartile, kurtosis, and skewness. In this case, using statistics related to the power load values ​​can reduce the influence of disturbances that may be contained in each value in the time-series data of the power load values. This is therefore even more useful for improving prediction accuracy.

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

[0107] The above-described ready-mixed concrete quality prediction device (10) 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 the ready-mixed concrete by the mixer (114), a prediction model (M) 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 (28) that predicts the quality of the ready-mixed concrete based on the input information acquired by the input information acquisition unit (24, 26). Similar to the above-described quality prediction method, this quality prediction device (10) is useful for improving the prediction accuracy when predicting quality using a prediction model. [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 step of acquiring 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 step of predicting the quality of the ready-mixed concrete based on a prediction model 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 in the acquisition step; A method for predicting the quality of ready-mix concrete, including:

2. The second information includes information indicating a date and time when the mixer performs mixing. The method for predicting the quality of ready-mixed concrete according to claim 1.

3. In the second information, each of the date and the time is specified using a trigonometric function. The method for predicting the quality of ready-mixed concrete according to claim 2.

4. The second information further includes information indicating a cumulative number of batches mixed by the mixer in one day. The method for predicting the quality of ready-mixed concrete according to claim 2.

5. The second information includes information representing the time from an arbitrarily set reference time point to the time point when the mixer executes mixing, or information representing the cumulative number of batches mixed by the mixer from the reference time point. The method for predicting the quality of ready-mixed concrete according to claim 1.

6. the first information includes statistics obtained from time series data of the power load value when the mixer performs one mixing operation, 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 a final 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, kurtosis, and skewness. The method for predicting the quality of ready-mixed concrete according to any one of claims 1 to 5.

7. A quality prediction program that causes a computer to execute the ready-mixed concrete quality prediction method according to any one of claims 1 to 5.

8. an input information acquisition unit that acquires input information including first information related to an electric 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 that indicates 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; A ready-mixed concrete quality prediction device comprising:

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