Production planning apparatus and method for a crushing plant

Abstract

To provide a production planning device for crushing plants that can automatically create production plans for crushing plants that crush crushed stone and aggregates. [Solution] The crushing plant production planning device 60 of the present invention is characterized by comprising a condition acquisition unit 64 that acquires weather information, crushing order data, crushing inventory quantity, crusher production quantity, crusher wear quantity, worker attendance information, and electricity charges for the crushing plant; a production target setting unit 66 that sets production targets for the production schedule, the production items, production quantity, and production of the crushed material to be produced; a crushing condition estimation unit 68 that estimates production quantity estimation data for the crushing ratio and operating time of the crusher; a maintenance schedule estimation unit 70 that estimates the maintenance timing for the crusher; and a production planning creation unit 72 that creates a production plan based on the production quantity estimation data from the crushing condition estimation unit 68, the maintenance timing for the crusher from the maintenance schedule estimation unit 70, worker attendance information, and electricity charges for the crushing plant.

Description

Technical Field

[0001] The present invention relates to a production planning device and a production planning method for a crushing plant that includes a plurality of crushers and produces crushed stones having a predetermined particle size.

Background Art

[0002] There is a crushing plant that crushes crushing raw materials such as ores and processes them into appropriate particle sizes and shapes. For example, when the crushing raw material is 600 mm to 700 mm, the primary crusher crushes it to a particle size of 100 mm to 300 mm, the secondary crusher crushes it to a particle size of 38 mm, the tertiary crusher crushes it to a particle size of 13.9 mm, and then it is screened to finally produce crushed stones having a particle size of 20 mm to 13 mm, crushed stones having a particle size of 13 mm to 5 mm, crushed stones having a particle size of 5 mm to 2.5 mm, and crushed sand having a particle size of 2.5 mm or less. Such a crushing plant formulates a production plan based on orders, generates various crushed stones and crushed sand in the crushing process, stores them in a stockyard, and ships the products. In recent years, due to labor shortages associated with population decline, automation and labor savings in the manufacturing site have been desired.

[0003] The operating device shown in Patent Document 1 controls the operation to be possible by comparing regional knowledge and case data separately when prioritizing the suppression of the maximum production amount or power consumption of crushed stones. In formulating a production plan, there are various uncertain factors, so it is a very complicated and skill-required task. For example, every time the aggregates and crushed sand to be manufactured are changed, it is necessary to individually change the settings and conditions of various machines of the crushing plant. In addition, since the production plan span of the crushing plant is short, the production plan may be corrected or changed in just one day. Furthermore, it is also affected by the weather (meteorological conditions), and in the case of heavy rain, the crushing plant may have to be stopped.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

[0005] The problem that the present invention aims to solve is, in view of the problems of the prior art described above, to provide a production plan creation device and method for a crushing plant that can automatically create a production plan for crushed stone and aggregates. [Means for solving the problem]

[0006] The present invention, as a first means for solving the above problems, includes a condition acquisition unit that acquires weather information, crushing order data, crushing inventory quantity, crusher production quantity, crusher wear quantity, worker attendance information, and electricity charges for the crushing plant from a storage unit. Based on the aforementioned weather information, crusher order data, and crushed material inventory, a production target setting unit sets the product items to be produced, the production volume, and the production schedule production targets for the crushed material. The aforementioned production target and a crushing condition estimation unit that estimates production data for the set value and operating time of the crusher from the production volume of the crusher, A maintenance schedule estimation unit that estimates the maintenance timing of the crusher based on the amount of wear of the crusher, The objective is to provide a production planning device for a crushing plant, characterized by comprising a production planning unit that creates a production plan based on the production volume estimation data from the crushing condition estimation unit, the maintenance timing of the crusher from the maintenance schedule estimation unit, the attendance information of the workers, and the electricity charges of the crushing plant. According to the first method described above, a highly accurate production plan for a crushing plant can be automatically created, taking into account weather conditions and the maintenance schedule of the crusher.

[0007] As a second means for solving the above problems, the present invention provides an operational performance creation unit for creating operational performance data of a crushing plant in the first means, The objective is to provide a production planning device for a crushing plant, characterized by having a learning unit that uses machine learning to machine-learn the error by comparing the production plan with the actual operational results. According to the second method described above, the discrepancy between operational results and production plans can be efficiently obtained.

[0008] As a third means for solving the above problems, the present invention provides a production planning device for a crushing plant, characterized in that, in the second means, the production planning unit corrects the production plan based on the machine learning error of the learning unit. According to the third method described above, production plans can be efficiently adjusted based on operational results.

[0009] As a fourth means for solving the above problems, the present invention provides a method for creating a production plan for a crushing plant in which a computer creates a production plan for the crushing plant, The computer performs a process of acquiring conditions for obtaining weather information, crushing order data, crushing inventory levels, crusher production volume, crusher wear levels, worker attendance information, and electricity charges for the crushing plant from its storage unit. Based on the aforementioned weather information, crusher order data, and crushed material inventory levels, a production target setting process is performed to set the product items to be produced, the production volume, and the production schedule. The aforementioned production target and the crushing condition estimation process, which estimates production data for the set value and operating time of the crusher from the production volume of the crusher, A maintenance schedule estimation process for estimating the maintenance timing of the crusher based on the amount of wear of the crusher, The objective is to provide a method for creating a production plan for a crushing plant, characterized by having a production plan creation step that creates a production plan based on the production volume estimation data from the crushing condition estimation step, the maintenance timing of the crusher from the maintenance schedule estimation step, the attendance information of the workers, and the electricity charges of the crushing plant. According to the fourth method described above, a highly accurate production plan for a crushing plant can be automatically created, taking into account weather conditions and the maintenance schedule of the crusher.

[0010] As a fifth means for solving the above problems, in the fourth means, an operation result creation step for creating an operation result of the crushing plant, and A method for creating a production plan of a crushing plant is provided, characterized by comprising a learning step of comparing the production plan and the operation result and machine learning the error. According to the above fifth means, an error between the operation result and the production plan can be efficiently obtained.

[0011] As a sixth means for solving the above problems, in the fifth means, the production plan creation step is characterized by correcting the production plan based on the error machine-learned in the learning step, and a method for creating a production plan of a crushing plant is provided. According to the above sixth means, the production plan can be efficiently corrected based on the operation result.

Effect of the Invention

[0012] According to the present invention, an accurate production plan of a crushing plant considering weather conditions and the maintenance schedule of the crusher can be automatically created.

Brief Description of the Drawings

[0013] [Figure 1] It is a schematic configuration diagram of a production plan creation device for a crushing plant of the present invention. [Figure 2] It is a processing flow diagram of a method for creating a production plan of a crushing plant of the present invention. <> [Figure 3] It is a processing flow diagram of production plan creation. [Figure 4] It is an explanatory diagram of a production plan table. [Figure 5] It is an explanatory diagram before and after the change of the production plan table. [Figure 6] It is an explanatory diagram of an operation control device for a crushing plant.

Embodiment for Implementing the Invention

[0014] An embodiment of the production plan creation device for a crushing plant according to the present invention will be described in detail below with reference to the drawings.

[0015] [Production Plan Creation Device 60 for Crushing Plant] FIG. 1 is a schematic configuration diagram of the production plan creation device for a crushing plant according to the present invention. As shown in the figure, the production plan creation device 60 for a crushing plant according to the present invention includes a condition acquisition unit 64 that acquires weather information, order data of crushed materials, inventory quantity of crushed materials, production quantity of crushers, wear quantity of crushers, attendance information of workers, and electricity charges of the plant from a storage unit 62, a production target setting unit 66 that sets production items, production quantity, and production target completion date of the crushed materials to be produced based on the weather information, order data of crushers, and inventory quantity of crushed materials, a crushing condition estimation unit 68 that estimates production quantity estimation data of the crushing ratio and operating time of the crusher from the production target and the production quantity of the crusher, a maintenance schedule estimation unit 70 that estimates the maintenance time of the crusher from the wear quantity of the crusher, and a production plan creation unit 72 that creates a production plan based on the production quantity estimation data of the crushing condition estimation unit 68, the maintenance time of the crusher of the maintenance schedule estimation unit 70, the attendance information of the workers, and the electricity charges of the crushing plant.

[0016] The production plan creation device 60 for a crushing plant can be realized by using a computer system including a communication unit, a storage unit (such as RAM, ROM), an arithmetic processing unit (CPU: Central Processing Unit), an input unit (such as a keyboard, touch panel), a display unit (screen), etc., reading a program from the storage unit, and executing the program.

[0017] Figure 6 is an explanatory diagram of the operation control device of the crushing plant. As shown in the figure, the crushing plant 100 is equipped with primary to tertiary crushers 12A, 12B, and 12C. For example, when the material to be crushed is 600 mm to 700 mm in size, the crushing plant crushes it to a particle size of 100 mm to 300 mm in the primary crusher 12A, to a particle size of 38 mm in the secondary crusher 12B, and to a particle size of 13.9 mm in the tertiary crusher 12C. After sieving, it ultimately produces crushed stone with a particle size of 20 mm to 13 mm, crushed stone with a particle size of 13 mm to 5 mm, crushed stone with a particle size of 5 mm to 2.5 mm, and crushed sand with a particle size of 2.5 mm or less. The primary to tertiary crushers 12A, 12B, and 12C are individually equipped with measuring and control means. The central control unit 40 is electrically connected to the control means (32A, 32B, 32C) of each crusher (12A, 12B, 12C), which include a quantitative control unit, a set adjustment control unit, a liner wear estimation unit, a particle size distribution acquisition unit, and crusher operating data (power value, current value, operating time). With this type of crushing plant operation control system, instead of manually changing the set values ​​of each crusher individually as in the past, it becomes possible to control all crushers in the plant collectively, taking into account the overall balance of each crusher, thereby establishing optimal plant operation (reduced inventory, increased production capacity, and extended durability / lifespan).

[0018] The stockyard 102 has silos set up for each product, and a level meter is placed in each silo to measure the inventory level of each product. The inventory level measurement data is output to the storage unit 62. The memory unit 62 stores weather information, crushing order data, crushing inventory levels, crusher production volume, crusher wear levels, worker attendance information, and electricity costs for the crushing plant. Weather conditions include information such as weather forecasts, temperature, humidity, wind speed, precipitation, and data and predictions regarding various natural phenomena such as typhoons, earthquakes, and tsunamis related to the location where the crushing plant will be installed. Order data for crushed materials includes information such as the order date, customer information, product details (item, quantity, unit price), delivery date, and payment terms. The inventory levels of crushed material represent the inventory levels of each product in each silo at stockyard 102.

[0019] The production volume of a crusher indicates the amount that can be produced based on the type of crusher, the particle size of the discharged material, the type and particle size of the material, etc. The amount of wear on the crusher is estimated based on the power value, current value, operating time, particle size distribution, and level value of each crusher, without relying on experience, intuition, or other manual methods. The product particle size is detected under stable conditions with predetermined power and level values, and the amount of wear is estimated based on the operating data (power value, current value, operating time) and particle size distribution values. Employee attendance information includes details such as arrival time, departure time, number of working days, break time, overtime hours, holiday work, leave information, absences, tardiness, and early departures. The electricity charges for the plant consist of the contracted power capacity of the crushing plant equipment, the amount of electricity used, and the electricity charges.

[0020] The condition acquisition unit 64 acquires weather information, crushing order data, crushing inventory quantity, crusher production quantity, crusher wear amount, operation data and particle size distribution, worker attendance information, and plant electricity charges from the storage unit 62. The production target setting unit 66 calculates production items, production volume, and production schedule (start date, end date) from weather information, order data, and inventory levels. Specifically, the production volume is calculated by subtracting the order volume from the inventory level, and the delivery date is calculated from the production volume and weather conditions. Note that if the silo containing the smallest particle size crushed sand becomes full, the production of other crushed stones will stop, so it is necessary to manufacture other crushed stones before the crushed sand silo becomes full.

[0021] The crushing condition estimation unit 68 estimates the set values, product particle size, speed setting value (feeder speed), and operating time for each crusher based on the production target and the production volume of the crusher. The system acquires estimated wear amounts, particle size distributions, and operating data (power values, current values, and operating time) for each crusher's liner. The operating time is calculated from the data in the production target setting unit 66. The liner life coefficient for each crusher is calculated. Liner wear is calculated using the wear rate and operating time. Liner wear can be calculated as: Wear rate × Operating time × Required power. The liner life coefficient is calculated from the usable liner width, new liner width, and current liner wear. The coefficient at which the liner becomes unusable is set to 1.0: lifespan, and the liner life coefficient is calculated as: Liner life coefficient = (New liner width - Liner wear) / Usable liner width. If the liner life coefficient for each crusher is within the specified value, the crushing ratio for each crusher is calculated using the machine parameters. Simulations are performed using the final product particle size to determine each set value by simulating combinations of crushing ratio, product particle size, and raw material particle size.

[0022] If the liner life coefficient of each crusher is outside the specified value, the crushing ratio of each crusher is changed so that the liner life coefficient of each crusher is 1.1 or less. For crushers with a small liner life coefficient (high liner wear), the crushing ratio is reduced, and the crushing ratios of the other crushers are adjusted to calculate the crushing ratio of each crusher that will yield the desired product particle size. The crushing ratio of each crusher and the particle size before and after each crusher are simulated to calculate the set value, product particle size, and speed setting value (feeder speed) for each crusher. The simulation device is used to calculate the set value, product particle size, and speed setting value (feeder speed) for each crusher. The calculation results are output to the crushing plant 100 after passing through the production plan creation unit 72 and the storage unit 62.

[0023] The maintenance schedule estimation unit 70 estimates the amount of liner wear based on the power value, current value, operating time, particle size distribution, and level value of each crusher, without relying on human judgment such as experience or intuition, and estimates the maintenance timing from this amount of wear. The maintenance schedule estimation unit 70 detects the product particle size under stable conditions with predetermined power and predetermined level values, and estimates the amount of wear based on operating data (power value, current value, operating time) and particle size distribution values. The calculation of the estimated amount of wear involves detecting the product particle size under stable conditions with predetermined power and predetermined level values, and estimating the amount of wear based on the particle size distribution values. A set value is calculated from the current particle size distribution. A simulation is performed to predict the product particle size using the particle size of the raw material, the fracturability (ease of breaking of the raw material), and the set value as input values. The set value can be obtained from the product particle size measured by reverse calculation. The difference between the obtained set value and the current set value is the amount of liner wear. After the start of the wear amount estimation calculation, the change in particle size of the acquired particle size distribution is monitored. Since the amount of change in particle size distribution is proportional to the change in liner wear, the change in particle size is monitored on an hourly basis, and the relationship between the amount of change in particle size distribution and the amount of liner wear is calculated in the simulation, and the amount of liner wear is calculated from the amount of change in particle size distribution. Based on the crushability of the raw material, a difference between the actual set value and the calculated set value is set in advance. The actual set value and the calculated set value are then compared to determine if the difference is large or not. In other words, the actual set value of the crusher is compared with the set value estimated from the particle size distribution. If the set value estimated from the particle size distribution is larger, it is determined that wear is occurring. An arbitrary change in particle size distribution is set from the change in particle size distribution, and it is determined whether the change is greater than the target value. The estimated liner wear amount is updated. By removing exceptions such as disturbance elements and cases where the liner wear amount is negative, a stable liner wear amount that is robust against disturbance factors can be calculated.

[0024] The production planning department 72 creates a production plan for one month. Figure 4 is an explanatory diagram of the production plan. As shown in the diagram, the production plan is a list in which the horizontal column shows the working days for one month, and the vertical column shows the weather forecast, operating hours, maintenance hours, inventory before production starts, inventory after production ends, time schedule, operation manager, personnel composition, and employees on leave. The production plan for one month is created by referring to past performance data and comparing it with various acquired conditions. Specifically, the production items, production volume, production schedule (start date, end date), and operating hours for each day are calculated. This is compared with employee attendance schedules and electricity costs. The amount of work to be done at each time of day is determined. All of this information is accumulated for one month. The operational performance data creation unit 74 creates operational performance data for the crushing plant from the actual production volume, production time, completion date, resource usage (number of workers, equipment), and delay information (shortages, machine malfunctions, etc.) of each crusher.

[0025] The learning unit 76 integrates production plans and operational results to create planned and actual pairs for corresponding production items. It then generates feature points such as the difference between planned and actual (quantity difference, time difference), the efficiency of crusher and personnel usage, variable factors including the history of past production plans and actuals, and external factors (raw materials, weather conditions, crusher wear). Next, it models the relationship between production plans and operational results using machine learning algorithms (regression analysis, time series analysis, clustering, classification models). It evaluates whether the machine learning model can accurately predict production results. Furthermore, since production results and production plans are updated daily, continuous feedback is provided to periodically retrain the model.

[0026] [How to create a production plan for a crushing plant] The production plan creation method for the crushing plant of the present invention using the above configuration will be described below. Figure 2 is a processing flow diagram of the production plan creation device for the crushing plant of the present invention. Figure 3 is a processing flow diagram of the production plan creation method for the crushing plant of the present invention. (Step 1) The system acquires various conditions necessary for the production plan of the crushing plant. The condition acquisition unit 64 acquires weather information, crushing order data, crushing inventory levels, crusher production volume, crusher wear levels, worker attendance information, and electricity costs for the crushing plant from the storage unit 62. (Step 2) Production targets are set. The production target setting unit 66 calculates production items, production volume, and production schedule (start date, end date) from weather information, order data, and inventory levels.

[0027] (Step 3) The crushing conditions are estimated. The crushing condition estimation unit 68 estimates the set values, product particle size, speed setting value (feeder speed), and operating time for each crusher based on the production target and the production volume of the crusher. (Step 4) The maintenance schedule is estimated. The maintenance schedule estimation unit 70 estimates the amount of liner wear based on the power value, current value, operating time, particle size distribution, and level value of each crusher, without relying on human experience or intuition, and estimates the maintenance timing from this amount of wear. (Step 5) Start creating the production plan (see Figure 4). (Step 6) Refer to past performance.

[0028] (Step 7) Compare past performance with specified conditions. (Step 8) A production plan is created. The production plan creation unit 72 creates a one-month production plan. By referring to past performance data and comparing it with various acquired conditions, a one-month production plan is created. (Step 20) Calculate the daily production items and operating hours. (Step 21) Compare employee attendance schedules with electricity costs. (Step 22) We will decide how much and at what time each day we will operate. (Step 23) The calculation is based on an estimated monthly amount.

[0029] (Step 24) Based on the predicted wear and tear on the crusher, maintenance is scheduled on a convenient day. (Step 9) Monitor whether there are any changes to the production plan. (Step 10) Determine whether or not to change the production plan. (Step 11) We will change the production plan. The main reason for changes in the production plan is often the shutdown of the crushing plant due to bad weather. For example, when changing the production plan due to bad weather, the data from the condition acquisition unit is compared with the plan and presented to the operators in order of priority, from "low cost" to "labor saving (eliminating weekend work)." Normally, nighttime operations are performed, but daytime operations are performed instead. Weekend operations are performed without holidays. Maintenance schedules are shifted to the extent that production volume can be maintained. Figure 5 is an explanatory diagram of the situation before and after changes to the production plan. As shown in the figure, if five working days are shut down due to bad weather, maintenance days are shifted (1), operations are performed on weekends (2), and operations are performed during the daytime (3).

[0030] (Step 12) The crushing plant will be operated according to the production plan. (Step 13) Operational performance data is created. The operational performance data creation unit 74 creates operational performance data for the crushing plant from the actual production volume, production time, completion date, resource usage (number of workers, equipment), and delay information (shortages, machine failures, etc.) of each crusher. (Step 14) Machine learning is employed. The learning unit 76 integrates production plans and operational results to create planned and actual pairs for corresponding production items. It then generates feature points such as the difference between plan and actual (quantity difference, time difference), the efficiency of crusher and personnel usage, variable factors including the history of past production plans and actuals, and external factors (raw materials, weather conditions, crusher wear). Next, machine learning algorithms (regression analysis, time series analysis, clustering, classification models) are used to model the relationship between production plans and operational results. The machine learning model is evaluated to see if it can accurately predict production results. Furthermore, since production results and production plans are updated daily, continuous feedback is provided to periodically retrain the model. Furthermore, the system learns from operator adjustments made during production plan changes. Specifically, when the production planning unit automatically outputs changes and adjustments to the production plan, and the operator adopts the outputted changes and adjustments, the system learns from that. In addition, if the operator modifies the outputted changes and adjustments, the system learns from those modifications as well. Furthermore, when changing or correcting the production plan, multiple correction options may be created and compared with the condition acquisition unit 64. The correction options may then be presented to the operator in order of lowest cost and lowest labor consumption.

[0031] According to this invention, it is possible to automatically create an accurate production plan for a crushing plant that takes into account weather conditions and the maintenance schedule of the crusher. The crusher operation control device related to the present invention can be linked with the production planning device 60 of the crushing plant. The production planning device consists of an order receiving process (receiving orders online without manual intervention), a production planning process (automatically creating a production plan from inventory levels and order levels, and planning personnel allocation), a product stocking process (continuing operation at optimal production efficiency according to the production plan in conjunction with the operation control device. Automatically changing the crusher's set values ​​according to the product or liner wear status. Optimal crusher operation is carried out not individually but in coordination with multiple (primary to tertiary) crushers), a product shipping process (automatically managing truck entry and exit, pickup, loading volume, and shipping), an invoice issuance process (automatically issuing invoices to customers after shipping confirmation), and a collection process (online payment management. For customers with outstanding payments, reminders are sent via email, etc., and an outstanding payment list is created and distributed to relevant parties). The crusher operation control device linked to the production planning device can feed back production volume data for each classified crushed material (aggregate) to the production planning system. The central control unit can provide feedback to the production planning system regarding the operating status of each crusher and the wear status of the liners.

[0032] The production planning device may automatically generate a production plan for the crushing plant by comparing the ordered amount of aggregate with the amount of aggregate in stock using AI. Alternatively, the production planning device may automatically acquire pre-approved employee leave schedules, maintenance plans for each crusher, and weekly weather forecasts to create the production plan. Furthermore, the production planning device may record electricity cost data and include a function to automatically operate each crusher in the crushing plant by selecting nighttime operation to minimize electricity costs. In a production planning device, the created production plan may be viewable on a mobile device or the like, and if there are any sudden changes to the production plan, the relevant parties may be automatically notified of the changes. Furthermore, the production planning device may automatically share operating data of the crushing plant with partner companies.

[0033] The production planning device may estimate the replacement timing of consumables such as liners for each crusher from the operating data of the crushing plant, and automatically place orders for the relevant parts so that they can be delivered at an appropriate time. Preferred embodiments of the present invention have been described above. However, the present invention is not limited in any way to the above embodiments, and various modifications are possible without departing from the spirit of the invention. Furthermore, the present invention is not limited to the combinations shown in the embodiments, but can be implemented using various combinations. [Explanation of symbols]

[0034] 12A Primary Crusher 12B Secondary Crusher 12C 3rd generation crusher 32A Primary crusher control means 32B Secondary crusher control means 32C Third-stage crusher control means 40 Central Control Unit 60 Production planning equipment for crushing plants 62 Memory section 64 Condition Acquisition Unit 66 Production Target Setting Department 68 Crushing Condition Estimation Unit 70 Maintenance Schedule Estimation Unit 72 Production Planning Department 74. Department for Creating Driving Records 76 Learning Department 100 Crushing Plants 102 Stockyard

Claims

1. A memory unit retrieves weather information, crushing order data, crushing inventory levels, crusher production volume, crusher wear levels, worker attendance information, and conditions for obtaining plant electricity charges. Based on the aforementioned weather information, crusher order data, and crushed material inventory, a production target setting unit sets the product items to be produced, the production volume, and the production schedule production targets for the crushed material. The aforementioned production target and a crushing condition estimation unit that estimates production data for the set value and operating time of the crusher from the production volume of the crusher, A maintenance schedule estimation unit that estimates the maintenance timing of the crusher based on the amount of wear of the crusher, A production planning device for a crushing plant, characterized by comprising a production planning unit that creates a production plan based on the production volume estimation data of the crushing condition estimation unit, the maintenance timing of the crusher of the maintenance schedule estimation unit, the attendance information of the workers, and the electricity charges of the crushing plant.

2. A production planning device for a crushing plant as described in claim 1, The Operational Record Creation Department creates operational records for the crushing plant, A production planning device for a crushing plant, characterized by comprising a learning unit that uses machine learning to analyze errors by comparing the production plan with the actual operational results.

3. A production planning device for a crushing plant as described in claim 2, The production planning unit is characterized by correcting the production plan based on the machine learning error of the learning unit, thereby providing a production planning device for a crushing plant.

4. A method for creating a production plan for a crushing plant, in which a computer creates a production plan for the crushing plant, The computer performs a process of acquiring conditions for obtaining weather information, crushing order data, crushing inventory levels, crusher production volume, crusher wear levels, worker attendance information, and electricity charges for the crushing plant from its storage unit. Based on the aforementioned weather information, crusher order data, and crushed material inventory levels, a production target setting process is performed to set the product items to be produced, the production volume, and the production schedule. The aforementioned production target and the crushing condition estimation process, which estimates production data for the set value and operating time of the crusher from the production volume of the crusher, A maintenance schedule estimation process for estimating the maintenance timing of the crusher based on the amount of wear of the crusher, A method for creating a production plan for a crushing plant, characterized by having a production plan creation step that creates a production plan based on the production volume estimation data from the crushing condition estimation step, the maintenance timing of the crusher from the maintenance schedule estimation step, the attendance information of the workers, and the electricity charges of the crushing plant.

5. A method for creating a production plan for a crushing plant as described in claim 4, The operational record creation process for creating operational records for the crushing plant, A method for creating a production plan for a crushing plant, characterized by comprising a learning process that uses machine learning to analyze the error by comparing the production plan with the actual operational results.

6. A method for creating a production plan for a crushing plant as described in claim 5, A method for creating a production plan for a crushing plant, characterized in that the production plan creation step corrects the production plan based on the error learned by machine learning in the learning step.

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