Product energy consumption prediction method and system based on accurate calculation of hot-rolled product energy consumption

Abstract

The present invention provides a product specification energy consumption prediction method and system based on accurate calculation of hot-rolled product energy consumption, which relates to the field of hot rolling technology. The method includes: obtaining the energy consumption data of materials at each moment; taking materials as the calculation object, establishing a material energy consumption allocation model by coupling the process position of the materials with the consumption of various energy meters, and realizing accurate calculation of material energy consumption; according to the material energy consumption calculation result, grouping the energy data of materials of different steel grades and specifications according to the grouping rules, and establishing a material energy consumption sample database; according to the normal distribution mathematical model, calculating the mean and variance of the energy consumption data of the same steel grade and the same specification after grouping, obtaining the characteristic value of the energy consumption of the product specification sample as the standard data of the energy consumption of the product specification material; matching the energy consumption sample database with the planned material specifications, and predicting the planned material production energy consumption; dynamically adjusting the product specification energy consumption sample database according to the actual situation of the unit, and improving the accuracy of product energy consumption prediction.

Description

Technical Field

[0001] The present invention relates to the field of hot rolling technology, and in particular to a method and system for predicting product energy consumption based on accurate calculation of energy consumption of hot-rolled products. Background Art

[0002] The hot rolling production process is often accompanied by a large amount of energy medium consumption. At the same time, due to the different types and specifications of product steel, energy consumption, especially electricity consumption, fluctuates greatly, which has a greater impact on the energy medium pipeline network and requires higher dynamic balance and scheduling capabilities of various energy media. Therefore, establishing an energy prediction model that conforms to the actual production of steel rolling enterprises has become an important part of the energy management system. Predicting, statistics and analyzing energy production and consumption data can provide theoretical data support for the dynamic balance allocation of energy media and energy optimization scheduling, so as to achieve the purpose of optimizing energy use and saving energy.

[0003] The patent with the publication number CN 115114847 A was searched and disclosed a method, device and related equipment for industrial energy consumption prediction. The method is based on a grey prediction model with a power time term. By transforming the original data sequence of industrial energy consumption, the smoothness of the data sequence of industrial energy consumption prediction modeling is improved, the robustness of the model for irregular fluctuation data is enhanced, and the initial conditions of the model are determined by the principle of new information priority, thereby improving the accuracy of industrial energy consumption prediction. However, the application did not really touch on how to realize the energy consumption prediction of planned products based on the statistical analysis of the historical data of hot-rolled product production energy consumption.

[0004] After searching, the patent with publication number CN 111160598 A disclosed an energy prediction and energy consumption control method and system based on dynamic energy consumption benchmark. This method is based on the energy consumption benchmark library under different influencing factors, taking into account the impact of different factors on the energy consumption benchmark, and makes a relatively accurate prediction of the normal energy consumption of the current working conditions. However, the application did not really touch on the impact of steel grades and specifications on product energy consumption.

[0005] In summary, the existing steel rolling energy consumption prediction methods, on the one hand, predict energy consumption by constructing a gray model of industrial energy consumption data; on the other hand, based on energy consumption benchmark calculations, the energy consumption benchmark calculation results are corrected after considering influencing factors such as processing plans, device load, seasons, and operating conditions, thereby realizing energy consumption prediction. However, how to conduct big data analysis on historical data on product production energy consumption to obtain product energy consumption prediction data is not involved. Summary of the invention

[0006] The present invention provides a product energy consumption prediction method and system based on accurate calculation of hot-rolled product energy consumption. Due to the different steel grades and specifications of products, energy consumption, especially electricity consumption, fluctuates greatly, which has a greater impact on the energy medium pipeline network and also places higher requirements on the dynamic balance and scheduling capabilities of various energy media.

[0007] In order to solve the above-mentioned invention object, the technical solution provided by the present invention is as follows: a method for predicting the energy consumption of a product specification based on accurate calculation of the energy consumption of hot-rolled products, characterized in that the steps include:

[0008] S1: Based on the production line material tracking system and energy consumption measurement and monitoring system, obtain the energy consumption data of materials at every moment;

[0009] S2: Taking materials as accounting objects, by coupling the process location of materials with the consumption of various energy meters, a material energy consumption allocation model is established to calculate material energy consumption and obtain energy consumption accounting results;

[0010] S3: According to the energy consumption calculation results, the energy data of materials of different steel grades and specifications are grouped according to grouping rules to establish a material energy consumption sample database;

[0011] S4: According to the normal distribution mathematical model, the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping are calculated to obtain the characteristic value of the energy consumption of the sample of the specification; the characteristic value is used as the standard data of energy consumption of the material of the specification;

[0012] S5: Match the energy consumption sample database according to the planned material specifications to obtain the planned material predicted production energy consumption data;

[0013] S6: Dynamically adjust the energy consumption sample database of product specifications according to the actual situation of the unit, and complete the energy consumption forecast of product specifications based on the accurate calculation of the energy consumption of hot-rolled products.

[0014] Preferably, in step S1, based on the production line material tracking system and the energy consumption metering and monitoring system, the energy consumption data of the material at each moment is obtained, including:

[0015] Based on the events of MES and Kafka material tracking system, the residence time of materials in each process is obtained; based on the energy consumption metering and monitoring system, the data of each energy medium consumption instrument at each moment is recorded; and abnormal data is eliminated through the box plot method.

[0016] Preferably, in step S2, materials are taken as accounting objects, and a material energy consumption allocation model is established by coupling the process position of the materials with the consumption of various energy meters, and material energy consumption is calculated to obtain energy consumption calculation results, including:

[0017] Based on the production line material tracking system, the position of the material on the production line at each moment is obtained; the material energy consumption is calculated by coupling the consumption of various energy meters in the area with the position of the material; based on the situation of multiple devices sharing meters in the area, a material energy consumption allocation mechanism is established according to the preset material energy consumption theoretical model, material energy consumption is calculated, and energy consumption calculation results are obtained.

[0018] Preferably, in step S3, according to the energy consumption calculation result, the energy data of materials of different steel grades and specifications are grouped according to grouping rules to establish a material energy consumption sample database, including:

[0019] Group material energy consumption data by different steel grades;

[0020] The material energy consumption data is grouped according to different material specifications, wherein the material specifications include: the width, thickness and length of the slab;

[0021] Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

[0022] Preferably, in step S5, the energy consumption sample database is matched according to the planned material specifications to obtain the planned material predicted production energy consumption data, including:

[0023] Obtain steel types and specifications of production plan materials; match the energy consumption sample database based on the obtained steel types and specifications of production plan materials to obtain planned material production energy consumption forecast data.

[0024] Preferably, in step S6, the product specification energy consumption sample database is dynamically adjusted according to the actual situation of the unit to complete the product specification energy consumption prediction based on the accurate calculation of the energy consumption of hot-rolled products, including:

[0025] Repeat steps S1-S3 to collect and accumulate material production energy consumption data, dynamically adjust the product energy consumption sample database, and complete the product energy consumption forecast based on the accurate calculation of hot-rolled product energy consumption.

[0026] A product specification energy consumption prediction system based on accurate calculation of hot-rolled product energy consumption, the system is used for the product specification energy consumption prediction method based on accurate calculation of hot-rolled product energy consumption, the system comprises:

[0027] Data collection module, used to obtain the energy consumption data of materials at every moment based on the production line material tracking system and energy consumption measurement and monitoring system;

[0028] The energy consumption accounting module is used to take materials as the accounting object, establish a material energy consumption allocation model by coupling the material's process location with the consumption of various energy meters, calculate the material energy consumption, and obtain the energy consumption accounting results;

[0029] A database establishment module is used to group the energy data of materials of different steel grades and specifications according to the energy consumption calculation results according to the grouping rules, and establish a material energy consumption sample database;

[0030] The standard data establishment module is used to calculate the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping according to the normal distribution mathematical model, and obtain the characteristic value of the energy consumption of the sample of the specification; and use the characteristic value as the standard data of energy consumption of the material of the specification;

[0031] Energy consumption forecasting module, used to match energy consumption sample database according to planned material specifications and obtain planned material forecast production energy consumption data;

[0032] The precise accounting module is used to dynamically adjust the product energy consumption sample database according to the actual situation of the unit, and complete the product energy consumption forecast based on the precise calculation of the energy consumption of hot-rolled products.

[0033] Preferably, the data acquisition module is further used to obtain the residence time of materials in each process based on the events of MES and Kafka material tracking system; based on the energy consumption metering monitoring system, record the data of each energy medium consumption instrument at each moment; and eliminate abnormal data through the box plot method.

[0034] Preferably, the energy consumption accounting module is further used to: obtain the production line position of the material at each moment based on the production line material tracking system; calculate the material energy consumption by coupling the consumption of various energy medium meters in the area with the position of the material; based on the situation of multiple devices sharing meters in the area, establish a material energy consumption allocation mechanism according to the preset material energy consumption theoretical model, perform material energy consumption accounting, and obtain energy consumption accounting results.

[0035] Preferably, the database establishment module is further used to group the material energy consumption data according to different steel grades;

[0036] The material energy consumption data is grouped according to different material specifications, wherein the material specifications include: the width, thickness and length of the slab;

[0037] Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

[0038] On the one hand, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the above-mentioned method for predicting product energy consumption based on accurate calculation of hot-rolled product energy consumption.

[0039] On the one hand, a computer-readable storage medium is provided, in which at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the above-mentioned method for predicting product energy consumption based on accurate calculation of hot-rolled product energy consumption.

[0040] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0041] The above scheme, the invention establishes a product energy consumption sample database through statistical analysis of product energy consumption data of different steel grades and specifications in the hot rolling production process. In the subsequent production process, by matching the specifications of the planned products with the product energy consumption sample database, the energy consumption prediction standard data of the product specifications is obtained, thereby realizing accurate prediction of product production energy consumption data. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A schematic flow chart of a method for predicting product energy consumption based on accurate calculation of hot-rolled product energy consumption provided by an embodiment of the present invention;

[0044] Figure 2 It is a data preprocessing box plot of a product specification energy consumption prediction method based on accurate calculation of energy consumption of hot-rolled products provided by an embodiment of the present invention;

[0045] Figure 3 It is a probability distribution diagram after data preprocessing of a product specification energy consumption prediction method based on accurate calculation of energy consumption of hot-rolled products provided in an embodiment of the present invention;

[0046] Figure 4 It is a normal distribution curve diagram of gas consumption energy of a product specification energy consumption prediction method based on accurate calculation of hot-rolled product energy consumption provided by an embodiment of the present invention;

[0047] Figure 5 It is an energy consumption cost sample library diagram of a product specification energy consumption prediction method based on accurate calculation of energy consumption of hot-rolled products provided by an embodiment of the present invention;

[0048] Figure 6 It is a system block diagram of a product specification energy consumption prediction system based on accurate calculation of energy consumption of hot-rolled products provided by an embodiment of the present invention;

[0049] Figure 7 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, an embodiment of the present invention provides a method for predicting energy consumption of hot-rolled products based on accurate calculation of energy consumption. The method can be implemented by an electronic device, which can be a terminal or a server. The method steps include:

[0052] S101: Based on the production line material tracking system and energy consumption measurement and monitoring system, obtain the energy consumption data of materials at every moment;

[0053] In a feasible implementation manner, the present invention obtains the residence time of materials in each process based on the events of MES and Kafka material tracking systems; and records the data of energy medium consumption instruments at each moment based on the energy consumption metering monitoring system.

[0054] In a feasible implementation, in order to avoid abnormal data affecting energy consumption prediction results, abnormal data is eliminated through a box plot method.

[0055] In a feasible implementation, in order to avoid the influence of abnormal data on energy consumption prediction results, the box plot method is used to eliminate abnormal data. We use a priori experience judgment and the median method to clean the energy consumption data in the database. First, according to prior experience, the upper and lower limits are set. For data with energy consumption values ​​outside the upper and lower limits, they are directly judged as outliers and cleaned. Then, the median, upper and lower quartiles of the remaining data are calculated, so as to calculate the upper and lower bounds of the data. Data outside the upper and lower bounds are treated as outliers and cleaned:

[0056] The formula for calculating the lower quartile is:

[0057]

[0058] Median calculation formula:

[0059]

[0060] The upper quartile calculation formula is:

[0061]

[0062] The formula for calculating the full range value is:

[0063] IQR=Q 上 -Q 下

[0064] The formula for calculating the lower bound is:

[0065] Q 下界 =Q 下 -1.5 IQR

[0066] The upper limit calculation formula is:

[0067] Q 上界 =Q 上 +1.5 IQR

[0068] like Figure 2 As shown in the box plot of the steel billet gas consumption after data preprocessing, the lower limit value is about 100 and the upper limit value is about 320. The scattered points outside the upper and lower limits are outliers. Figure 3 As shown, this is the probability distribution diagram after data preprocessing.

[0069] S102: Taking materials as accounting objects, by coupling the process location of the materials with the consumption of various energy meters, a material energy consumption allocation model is established, material energy consumption is calculated, and energy consumption calculation results are obtained;

[0070] In a feasible implementation method, based on the production line material tracking system, the position of the material on the production line at each moment is obtained; the material energy consumption is calculated by coupling the consumption of various energy medium meters in the area with the position of the material; based on the situation of multiple devices sharing meters in the area, a material energy consumption allocation mechanism is established according to a preset material energy consumption theoretical model, material energy consumption is calculated, and energy consumption calculation results are obtained.

[0071] In a feasible implementation method, material energy consumption accounting relies on the production line material tracking system to accurately obtain the position of the material on the production line at each moment. The material energy consumption is calculated by coupling the consumption of various energy meters in the area with the location. At the same time, the characteristics of the process are taken into consideration. If there are multiple devices in the area that share meters, a material energy consumption allocation mechanism is established based on the material energy consumption theoretical model to realize material energy consumption accounting. (For example, if multiple racks share an electric meter, the rolling theoretical model is used to allocate the electricity consumption to each piece of material). For areas that do not have a material tracking system, the corresponding theoretical model is adopted to scientifically allocate the regional energy consumption to each piece of material.

[0072] In a feasible implementation, 1) energy distribution:

[0073] Public energy: including lighting, driving energy, electricity and gas energy in electrical rooms, office buildings, canteens, service areas, etc.

[0074] Auxiliary energy that cannot be separated by material tracking: including public hydraulic stations, lubrication stations, environmental protection fans, substation electricity, etc.;

[0075] Energy used for production preparation: including energy used in raw material warehouses, maintenance facilities and equipment, and during maintenance periods, etc.

[0076] 2) Accurately track accounting:

[0077] Real-time monitoring of material location information of the material tracking system. When the material enters the designated area division point, the consumption of all energy meters in the area will be calculated until the material leaves the designated area. Calculation by mechanism model: For areas without material tracking, different mechanism models should be used according to the characteristics of the processes in the area to apportion the energy consumption in the area for a period of time to a single piece of material. For shared meters,

[0078] Apply the mechanism model to allocate the metered energy consumption to each material.

[0079] Taking rolling power allocation as an example, the rolling power mechanism model is as follows:

[0080]

[0081] M p =2Pl c ψZ1Z2g

[0082]

[0083]

[0084]

[0085] Among them, M p is the rolling torque; R is the roller radius; v is the roller linear velocity ψ is the arm coefficient, determined by the Denton-Crane formula; R is the roller radius; H is the entrance thickness; h is the exit thickness; Zg is the geometric mean aspect ratio; P is the rolling force; Z1 and Z2 are the long-term and short-term self-learning coefficients of the rolling torque, respectively; g is the gravitational acceleration constant.

[0086] Through the above mechanism model, the theoretical power consumption value of each rack can be calculated without a separate instrument, and it can be used as a weight ratio to apportion the shared meter power consumption to each material.

[0087] S103: According to the energy consumption calculation result, the energy data of materials of different steel grades and specifications are grouped according to the grouping rules, and a material energy consumption sample database is established;

[0088] In a feasible implementation, the material energy consumption data is grouped according to different steel grades;

[0089] The material energy consumption data is grouped according to different material specifications, wherein the material specifications include: the width, thickness and length of the slab;

[0090] Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

[0091] In a feasible implementation, based on the accurate calculation data of material energy consumption, a product specification electric energy consumption database is established according to different steel grades and specifications as shown in Table 1:

[0092] Table 1 Product specification power consumption database table

[0093]

[0094] S104: According to the normal distribution mathematical model, the mean and variance of the energy consumption data of the same steel grade and the same specification of materials after grouping are calculated to obtain the characteristic value of the energy consumption of the sample of the specification; and the characteristic value is used as the standard data of energy consumption of the material of the specification.

[0095] In a feasible implementation, Figure 4 As shown in the figure, taking the coal gas consumption data per ton of steel as an example, after data screening, the data is approximately normally distributed as a whole, and as the number of data samples increases, its normal distribution characteristics become more typical. After obtaining the standard sample, the mean and variance of the standard sample can be calculated according to the mathematical model of normal distribution to obtain the characteristic value of the sample.

[0096] S105: Matching the energy consumption sample database according to the planned material specifications to obtain the planned material predicted production energy consumption data;

[0097] In a feasible implementation, the steel type and specification of the production plan material are obtained; according to the obtained steel type and specification of the production plan material, the energy consumption sample database is matched to obtain the planned material production energy consumption forecast data.

[0098] In a feasible implementation, Figure 5 The energy consumption cost sample library entered in this embodiment is shown. By matching the steel type and specification of the planned material with the steel type and specification of the product specification electric energy consumption database, the energy consumption forecast data of the planned material is obtained.

[0099] In a feasible implementation manner, the power consumption forecast data of the planned product is shown in Table 2.

[0100] Table 2 Electricity consumption forecast data table of planned products

[0101]

[0102] S106: Dynamically adjust the product energy consumption sample database according to the actual situation of the unit, and complete the product energy consumption forecast based on the accurate calculation of the energy consumption of hot-rolled products.

[0103] In a feasible implementation, steps S101-S103 are repeated to collect and accumulate material production energy consumption data, dynamically adjust the product energy consumption sample database, and complete product energy consumption prediction based on accurate calculation of hot-rolled product energy consumption.

[0104] In a feasible implementation, material production energy consumption data is continuously collected to enrich the material energy consumption sample database, and the characteristic values ​​of the energy consumption database samples are optimized and adjusted according to the mathematical model of normal distribution to ensure the accuracy of energy consumption prediction.

[0105] In the embodiment of the present invention, based on the accurate calculation of product energy consumption, a standard sample library of product energy consumption is established through statistical analysis of a large amount of product energy consumption data in the actual production process, and accurate prediction of energy consumption is achieved in combination with the production plan, providing theoretical data support for the dynamic balance allocation of enterprise energy media and energy optimization scheduling, so as to achieve the purpose of optimizing energy use and saving energy.

[0106] Figure 6 1 is a system block diagram of a product specification energy consumption prediction system based on accurate calculation of hot-rolled product energy consumption provided by an embodiment of the present invention. The system is applicable to the product specification energy consumption prediction method based on accurate calculation of hot-rolled product energy consumption. The system 200 includes:

[0107] The data collection module 210 is used to obtain the energy consumption data of the material at each moment based on the production line material tracking system and the energy consumption measurement and monitoring system;

[0108] The energy consumption accounting module 220 is used to take materials as accounting objects, establish a material energy consumption allocation model by coupling the process position of the materials with the consumption of various energy meters, perform material energy consumption accounting, and obtain energy consumption accounting results;

[0109] A database establishment module 230 is used to group the energy data of materials of different steel grades and specifications according to the energy consumption calculation results according to the grouping rules, and establish a material energy consumption sample database;

[0110] The standard data establishment module 240 is used to calculate the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping according to the normal distribution mathematical model, and obtain the characteristic value of the energy consumption of the sample of the specification; and use the characteristic value as the standard data of energy consumption of the material of the specification;

[0111] Energy consumption prediction module 250, used to match the energy consumption sample database according to the planned material specifications and obtain the planned material predicted production energy consumption data;

[0112] The precise calculation module 260 is used to dynamically adjust the product energy consumption sample database according to the actual situation of the unit, and complete the product energy consumption prediction based on the precise calculation of the energy consumption of hot-rolled products.

[0113] Preferably, the data acquisition module 210 is further used to obtain the residence time of materials in each process based on the events of MES and Kafka material tracking system; based on the energy consumption metering monitoring system, record the data of each energy medium consumption instrument at each moment; and eliminate abnormal data through the box plot method.

[0114] Preferably, the energy consumption accounting module 220 is further used to: obtain the production line position of the material at each moment based on the production line material tracking system; calculate the material energy consumption by coupling the consumption of various energy medium meters in the area with the position of the material; based on the situation of multiple devices sharing meters in the area, establish a material energy consumption allocation mechanism according to the preset material energy consumption theoretical model, perform material energy consumption accounting, and obtain energy consumption accounting results.

[0115] Preferably, the database establishment module 230 is further used to group the material energy consumption data according to different steel grades;

[0116] The material energy consumption data are grouped according to different material specifications, wherein the material specifications include: width, thickness and length of the slab.

[0117] Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

[0118] Preferably, the energy consumption prediction module 250 is further used to obtain the steel type and specification of the production plan material; according to the obtained steel type and specification of the production plan material, the energy consumption sample database is matched to obtain the planned material production energy consumption prediction data.

[0119] Preferably, the precise accounting module 260 is further used to repeatedly collect and accumulate material production energy consumption data, dynamically adjust the product energy consumption sample database, and complete the product energy consumption forecast based on the precise accounting of hot-rolled product energy consumption.

[0120] In the embodiment of the present invention, based on the accurate calculation of product energy consumption, a standard sample library of product energy consumption is established through statistical analysis of a large amount of product energy consumption data in the actual production process, and accurate prediction of energy consumption is achieved in combination with the production plan, providing theoretical data support for the dynamic balance allocation of enterprise energy media and energy optimization scheduling, so as to achieve the purpose of optimizing energy use and saving energy.

[0121] Figure 73 is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present invention. The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 301 and one or more memories 302, wherein the memory 302 stores at least one instruction, and the at least one instruction is loaded and executed by the processor 301 to implement the following steps of the product specification energy consumption prediction method based on accurate calculation of hot-rolled product energy consumption:

[0122] S1: Based on the production line material tracking system and energy consumption measurement and monitoring system, obtain the energy consumption data of materials at every moment;

[0123] S2: Taking materials as accounting objects, by coupling the process location of materials with the consumption of various energy meters, a material energy consumption allocation model is established to calculate material energy consumption and obtain energy consumption accounting results;

[0124] S3: According to the energy consumption calculation results, the energy data of materials of different steel grades and specifications are grouped according to grouping rules to establish a material energy consumption sample database;

[0125] S4: According to the normal distribution mathematical model, the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping are calculated to obtain the characteristic value of the energy consumption of the sample of the specification; the characteristic value is used as the standard data of energy consumption of the material of the specification;

[0126] S5: Match the energy consumption sample database according to the planned material specifications to obtain the planned material predicted production energy consumption data;

[0127] S6: Dynamically adjust the energy consumption sample database of product specifications according to the actual situation of the unit, and complete the energy consumption forecast of product specifications based on the accurate calculation of the energy consumption of hot-rolled products.

[0128] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including instructions, which can be executed by a processor in a terminal to complete the above-mentioned method for predicting the energy consumption of a product specification based on the accurate calculation of the energy consumption of hot-rolled products. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0129] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0130] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting product energy consumption based on accurate calculation of hot-rolled product energy consumption, characterized in that the steps include: S1: Based on the production line material tracking system and energy consumption measurement and monitoring system, obtain the energy consumption data of materials at every moment; S2: Taking materials as accounting objects, by coupling the process location of materials with the consumption of various energy meters, a material energy consumption allocation model is established to calculate material energy consumption and obtain energy consumption accounting results; In step S2, materials are taken as accounting objects, and a material energy consumption allocation model is established by coupling the process position of the materials with the consumption of various energy meters, and material energy consumption is calculated to obtain energy consumption calculation results, including: Based on the production line material tracking system, obtain the location of the material on the production line at every moment; The material energy consumption is calculated by coupling the location of the material with the consumption of various energy meters in the area; based on the shared meters of multiple devices in the area, a material energy consumption allocation mechanism is established according to a preset material energy consumption theoretical model, material energy consumption is calculated, and energy consumption calculation results are obtained; S3: According to the energy consumption calculation results, the energy data of materials of different steel grades and specifications are grouped according to grouping rules to establish a material energy consumption sample database; S4: According to the normal distribution mathematical model, the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping are calculated to obtain the characteristic value of the energy consumption of the sample of the specification; the characteristic value is used as the standard data of energy consumption of the material of the specification; S5: Match the energy consumption sample database according to the planned material specifications to obtain the planned material predicted production energy consumption data; S6: Dynamically adjust the energy consumption sample database of product specifications according to the actual situation of the unit, and complete the energy consumption forecast of product specifications based on the accurate calculation of the energy consumption of hot-rolled products.

2. The method according to claim 1, characterized in that In step S1, based on the production line material tracking system and the energy consumption measurement and monitoring system, the energy consumption data of the material at each moment is obtained, including: Based on the events of MES and Kafka material tracking system, the residence time of materials in each process is obtained; based on the energy consumption metering and monitoring system, the data of each energy medium consumption instrument at each moment is recorded; and abnormal data is eliminated through the box plot method.

3. The method according to claim 2, characterized in that In step S3, according to the energy consumption calculation result, the energy data of materials of different steel grades and specifications are grouped according to the grouping rules to establish a material energy consumption sample database, including: Group material energy consumption data by different steel grades; The material energy consumption data is grouped according to different material specifications, wherein the material specifications include: the width, thickness and length of the slab; Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

4. The method according to claim 1, characterized in that: In step S5, the energy consumption sample database is matched according to the planned material specifications to obtain the planned material production forecast energy consumption data, including: Obtain steel types and specifications of production plan materials; match the energy consumption sample database based on the obtained steel types and specifications of production plan materials to obtain planned material production energy consumption forecast data.

5. The method according to claim 1, characterized in that In step S6, the product specification energy consumption sample database is dynamically adjusted according to the actual situation of the unit to complete the product specification energy consumption prediction based on the accurate calculation of the energy consumption of hot-rolled products, including: Repeat steps S1-S3 to collect and accumulate material production energy consumption data, dynamically adjust the product energy consumption sample database, and complete product energy consumption prediction based on accurate calculation of hot-rolled product energy consumption.

6. A product energy consumption prediction system based on accurate calculation of hot-rolled product energy consumption, characterized in that: The system is applied to the method according to any one of claims 1 to 5, and the system comprises: Data collection module, used to obtain the energy consumption data of materials at every moment based on the production line material tracking system and energy consumption measurement and monitoring system; The energy consumption accounting module is used to take materials as the accounting object, establish a material energy consumption allocation model by coupling the material's process location with the consumption of various energy meters, calculate the material energy consumption, and obtain the energy consumption accounting results; The energy consumption accounting module is further used to: obtain the position of the material on the production line at each moment based on the production line material tracking system; calculate the material energy consumption by coupling the position of the material with the consumption of various energy medium meters in the area; establish a material energy consumption allocation mechanism based on the shared meter situation of multiple devices in the area and according to the preset material energy consumption theoretical model, perform material energy consumption accounting, and obtain energy consumption accounting results; A database establishment module is used to group the energy data of materials of different steel grades and specifications according to the energy consumption calculation results according to the grouping rules, and establish a material energy consumption sample database; The standard data establishment module is used to calculate the mean and variance of the energy consumption data of the same steel grade and the same specification materials after grouping according to the normal distribution mathematical model, and obtain the characteristic value of the energy consumption of the sample of the specification; and use the characteristic value as the standard data of energy consumption of the material of the specification; Energy consumption forecasting module, used to match energy consumption sample database according to planned material specifications and obtain planned material forecast production energy consumption data; The precise accounting module is used to dynamically adjust the product energy consumption sample database according to the actual situation of the unit, and complete the product energy consumption forecast based on the precise calculation of the energy consumption of hot-rolled products.

7. The system according to claim 6, characterized in that The data acquisition module is further used to obtain the residence time of materials in each process based on events of MES and Kafka material tracking system; Based on the energy consumption metering and monitoring system, the data of each energy medium consumption instrument at each moment is recorded; abnormal data is eliminated through the box plot method.

8. The system according to claim 7, characterized in that The database establishment module is further used to group material energy consumption data according to different steel grades; The material energy consumption data is grouped according to different material specifications, wherein the material specifications include: the width, thickness and length of the slab; Based on the results of grouping by steel type and material specification, a material energy consumption sample database is established.

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