WO2023028842A1 - Procédé et appareil de prédiction d'exploitation d'usine, et support de stockage lisible par ordinateur - Google Patents

Procédé et appareil de prédiction d'exploitation d'usine, et support de stockage lisible par ordinateur Download PDF

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WO2023028842A1
WO2023028842A1 PCT/CN2021/115691 CN2021115691W WO2023028842A1 WO 2023028842 A1 WO2023028842 A1 WO 2023028842A1 CN 2021115691 W CN2021115691 W CN 2021115691W WO 2023028842 A1 WO2023028842 A1 WO 2023028842A1
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production process
energy consumption
consumption data
electric energy
data
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PCT/CN2021/115691
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English (en)
Chinese (zh)
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徐四清
夏雨
赵爽
王德慧
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西门子股份公司
西门子（中国）有限公司
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Priority to PCT/CN2021/115691 priority Critical patent/WO2023028842A1/fr
Priority to CN202180101142.5A priority patent/CN117751373A/zh
Publication of WO2023028842A1 publication Critical patent/WO2023028842A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

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  • the invention relates to the technical field of factory data processing, in particular to a method, device and computer-readable storage medium for predicting factory operations.
  • the construction of smart industrial park is an important research field that has attracted much attention in the operation and management of modern industrial parks, and its technology is expanding and maturing day by day. With the development of technology, there is an increasing demand for developing smart industrial parks in various places, so as to provide better services for the enterprises in the parks.
  • the construction of a smart park includes energy supply, transportation and logistics, security, environmental protection, and industrial collaboration.
  • the intelligence of these sectors usually requires the support of the production status and production plan data of each enterprise. However, in reality, companies usually do not provide data such as production status and production forecasts for commercial confidentiality or other purposes.
  • Embodiments of the present invention provide a method, device, and computer-readable storage medium for predicting factory operations.
  • a method of forecasting plant operations comprising:
  • the electric energy consumption data of the workshop in the future production process is predicted based on the electric energy consumption prediction model.
  • the power consumption data in the future production process can be predicted based on the power consumption data in the production process, so as to facilitate the provision of energy services.
  • the separating the electric energy consumption data of the workshop in the production process from the electric energy consumption data of the workshop in the factory includes:
  • the electric energy consumption data of the workshop in the production process is separated from the electric energy consumption data of the workshop in the factory;
  • the power consumption data of the workshop in the factory during the production process is separated from the power consumption data of the workshop.
  • the second threshold value is greater than the first threshold value.
  • the working status of the production process can be determined based on the electric energy consumption data of the production process, so as to facilitate understanding of the working status.
  • the output of the production process is Qk (t); the logistics demand of the production process is Cargo k (t); Cap is the transportation capacity of each vehicle; P k (t) is the electric energy in the production process Consumption data; Q krated is the rated production capacity of the production process; P krated is the rated electric power of the generation process; t is the time parameter.
  • the output and logistics requirements can be determined to facilitate the provision of logistics services.
  • the determining an electric energy consumption prediction model adapted to predict the electric energy consumption data in the production process based on the electric energy consumption data in the production process includes:
  • the electric energy consumption data in the production process, the steam consumption in the production process and the model input data are jointly used as training data to train the neural network model to obtain the electric energy consumption prediction model; wherein the model input data includes the following At least one of: time stamp; weather; temperature; sewage production; sewage assay results; work status; holidays.
  • an electric energy consumption prediction model can be established based on artificial intelligence technology.
  • the trained neural network model is more accurate by using the determined working conditions as model input data.
  • the prediction of the electric energy consumption data of the workshop in the future production process based on the electric energy consumption prediction model includes:
  • the method also includes:
  • At least one of the following is determined based on the electrical energy consumption data in the future production process: predicted output of the future production process; predicted logistics requirements of the future production process; predicted storage requirements of the future production process.
  • predicting the power consumption data in the future production process based on the power consumption prediction model is convenient for planning the logistics services and storage services that need to be provided in the future. Moreover, based on the predicted steam consumption data in the future production process, it is convenient to plan the steam service that needs to be provided in the future.
  • a plant operation forecasting device comprising:
  • the separation module is used to separate the electric energy consumption data of the workshop in the production process from the electric energy consumption data of the workshop in the factory;
  • a determining module configured to determine an electric energy consumption prediction model adapted to predict the electric energy consumption data in the production process based on the electric energy consumption data in the production process;
  • a prediction module configured to predict the electric energy consumption data of the workshop in the future production process based on the electric energy consumption prediction model.
  • the power consumption data in the future production process can be predicted based on the power consumption data in the production process, so as to facilitate the provision of energy services.
  • the separation module is used to separate the power consumption data of the workshop in the production process from the power consumption data of the workshop in the factory based on the energy decomposition method of the non-parametric factor hidden Markov model or, based on the percentage determined by the historical power distribution data, separating the power consumption data of the workshop in the production process from the power consumption data of the workshop in the factory.
  • the determination module is further configured to determine the working status of the production process based on the power consumption data in the production process, wherein: when the power consumption data is less than a preset first threshold value, it is determined that the working condition of the production process is a stop condition; when the power consumption data is greater than the first threshold value and less than a preset second threshold value, it is determined that the working condition of the production process It is a waiting state; when the power consumption data is greater than the second threshold value, it is determined that the working state of the production process is an active running state; wherein the second threshold value is greater than the first threshold value.
  • the working status of the production process can be determined based on the electric energy consumption data of the production process, so as to facilitate understanding of the working status.
  • the determination module is further configured to determine the output of the production process and the logistics demand of the production process based on the electric energy consumption data in the production process;
  • the output of the production process is Q k (t); the logistics demand of the production process is Cargo k (t); Cap is the transportation capacity of each vehicle; P k (t) is the Electric energy consumption data; Q krated is the rated production capacity of the production process; P krated is the rated electric power of the generation process; t is a time parameter.
  • the output and logistics requirements can be determined to facilitate the provision of logistics services.
  • the determination module is configured to use the electric energy consumption data in the production process, the steam consumption in the production process and model input data together as training data to train the neural network model to obtain the Electric energy consumption prediction model; wherein the model input data includes at least one of the following: time stamp; weather; temperature; sewage production; sewage test results; holidays.
  • an electric energy consumption prediction model can be established based on artificial intelligence technology.
  • the trained neural network model is more accurate by using the determined working conditions as model input data.
  • the prediction module is configured to input the model input data in the future production process into the electric energy consumption prediction model, so as to predict the electric energy consumption data in the future production process and the steam in the future production process Consumption data; determining at least one of the following based on the electrical energy consumption data in the future production process: predicted output of the future production process; predicted logistics requirements of the future production process; predicted storage requirements of the future production process.
  • predicting the power consumption data in the future production process based on the power consumption prediction model is convenient for planning the logistics services and storage services that need to be provided in the future. Moreover, based on the predicted steam consumption data in the future production process, it is convenient to plan the steam service that needs to be provided in the future.
  • a plant operation forecasting device comprising a processor and a memory
  • An application program that can be executed by the processor is stored in the memory, which is used to make the processor execute any method for predicting plant operation as described above.
  • a computer-readable storage medium in which computer-readable instructions are stored, and the computer-readable instructions are used to execute any one of the methods for predicting plant operation as described above.
  • FIG. 1 is a flowchart of a method for predicting plant operation according to an embodiment of the present invention.
  • Fig. 2 is an exemplary schematic diagram of a method for predicting plant operation according to an embodiment of the present invention.
  • FIG. 3 is an exemplary schematic diagram of a non-parametric factorial hidden Markov model (NFHMM) process according to an embodiment of the present invention.
  • NFHMM non-parametric factorial hidden Markov model
  • FIG. 4 is an exemplary structure diagram of a recurrent neural network (RNN) according to an embodiment of the present invention.
  • Fig. 5 is a configuration diagram of a plant operation prediction device according to an embodiment of the present invention.
  • FIG. 6 is a structural diagram of a plant operation forecasting device with a processor-memory architecture according to an embodiment of the present invention.
  • the embodiment of the present invention proposes a method to estimate the production status of the enterprise based on the power consumption of the key production process of the key workshop in the factory based on the decomposition of energy consumption, and apply it to the smart industry
  • the construction of the park solves the problem of lack of key data faced by energy services and logistics services in the construction of smart industrial parks.
  • FIG. 1 is a flowchart of a method for predicting plant operation according to an embodiment of the present invention.
  • the method 100 includes:
  • Step 101 Separate the power consumption data of the workshop in the production process from the power consumption data of the workshop in the factory.
  • the industrial park often only measures the power consumption of each production workshop (or section), but does not have detailed power consumption data of each equipment in each enterprise workshop.
  • the power consumption of each production workshop there may also be a situation where two or more workshops share one measurement point.
  • step 101 based on the professional knowledge of the factory's production characteristics, it is determined that the production workshop with key functions and that can effectively collect energy consumption data is the "workshop" described in step 101.
  • the power consumption data of the workshop mentioned in step 101 usually also includes parts that do not directly respond to production, such as lighting, air conditioning, and maintenance. Therefore, it is necessary to separate the power consumption data of the parts that do not directly respond to production in step 101 .
  • the power consumption data of the workshop in the production process is separated from the power consumption data of the workshop in the factory.
  • Hidden Markov model is a kind of Markov chain. Its state cannot be observed directly, but it can be observed through the observation vector sequence. Each observation vector is expressed as various states through certain probability density distributions. Each An observation vector is generated from a sequence of states with a corresponding probability density distribution. So, the Hidden Markov Model is a double stochastic process with a hidden Markov chain with a certain number of states and a set of explicit random functions.
  • FIG. 3 is an exemplary schematic diagram of NFHMM processing according to an embodiment of the present invention.
  • the K equipment includes the equipment of the generation process, and also includes the equipment that does not directly reflect the production such as lighting, air conditioning, and maintenance.
  • no prior knowledge 50 about the number of devices is assumed, ie K is unknown.
  • Z be a T*K matrix, and the z t corresponding to the (t, k) item, k represents the state of device k at time t.
  • NFHMM is described as follows:
  • the state z t of the k-th device at time t, k follows Put the prior distribution 50 on ⁇ k ⁇ Beta( ⁇ /K, 1), b k ⁇ Beta( ⁇ , ⁇ ).
  • Hyperparameters 51 include ⁇ , ⁇ , ⁇ , and H.
  • the device status 53 includes the status of each device.
  • the device energy consumption feature 54 is ⁇ , where the kth entry of the vector represents the Gaussian distributed power of the kth device.
  • the power consumption data of the workshop in the factory during the production process is separated from the power consumption data of the workshop.
  • the working status of the production process is determined based on the electric energy consumption data in the production process, wherein: when the electric energy consumption data is less than a preset first threshold value, the production process is determined to be The working state of the production process is a stop state; when the power consumption data is greater than the first threshold value and less than the preset second threshold value, it is determined that the working state of the production process is a waiting state; when the electric energy When the consumption data is greater than the second threshold value, it is determined that the working state of the production process is an active running state; wherein the second threshold value is greater than the first threshold value.
  • C k (t) Condition_map(P k (t));
  • C k (t) is the working condition.
  • An exemplary matching method is as follows, and other matching methods can also be used: if P k ⁇ P 1 , the workshop is in shutdown; if P 1 ⁇ P k ⁇ P 2 , the workshop is on standby; if P 2 ⁇ P k , the workshop in processing.
  • the production conditions of each workshop, logistics, etc. can be reasonably planned.
  • the output of the enterprise can be calculated.
  • the power consumption can be matched to the operating mode of the equipment.
  • the production law of the enterprise can be obtained.
  • the method further includes: determining the output of the production process and the logistics demand of the production process based on the electric energy consumption data in the production process;
  • the output of the production process is Q k (t); the logistics demand of the production process is Cargo k (t); Cap is the transportation capacity of each vehicle; P k (t) is the Electric energy consumption data; Q krated is the rated production capacity of the production process; t is the time parameter.
  • Step 102 Determine an electric energy consumption prediction model adapted to predict the electric energy consumption data in the production process based on the electric energy consumption data.
  • the electric energy consumption data in the production process, the steam consumption in the production process and the model input data are jointly used as training data to train the neural network model to obtain the electric energy consumption prediction model; wherein the model input data includes the following At least one of: time stamp; weather; temperature; effluent production; effluent assay results; work status; holidays.
  • the working status as the input data of the model may be determined based on the electric energy consumption data in the production process.
  • the future working conditions, output and energy consumption are predicted. Simultaneously calculate inventory, logistics demand, etc.
  • the neural network model can be trained to obtain the electric energy consumption prediction model of the production process, and then the future prediction data can be obtained according to the input.
  • the neural network model can be implemented as: recurrent neural network (RNN), feedforward neural network model, radial basis neural network model, long short-term memory (LSTM) network model, echo state network (ESN), gate recurrent unit ( GRU) network model or deep residual network model, etc.
  • RNN recurrent neural network
  • LSTM long short-term memory
  • ESN echo state network
  • GRU gate recurrent unit
  • the neural network model is implemented as an LSTM network model.
  • the factory will regularly report production information, and the power consumption prediction model can be updated based on actual data.
  • Fig. 4 is an exemplary structural diagram of an RNN in an embodiment of the present invention.
  • the power load forecasting model based on RNN is established by multi-dimensional input.
  • Multidimensional inputs include historical power load data, time stamps, weather data, temperature data, steam consumption data, wastewater production data, and holiday data.
  • the recurrent neural network has a memory function that other types of neural networks do not have through its own feedback mechanism.
  • x 1 , x 2 , ..., x t is the input of each step
  • s 1 , s 2 , ..., s t is the hidden layer state of each step
  • y 1 , y 2 , ... , yt is the output of each step.
  • A, B, and C in Figure 4 are all matrices, which are transformation parameters from input to hidden layer state, hidden layer state to output, and current state to next state
  • b h and b y are bias items. They are what are to be learned in training.
  • the forward process of RNN can be expressed as:
  • x t (time stamp, date, temperature t , weather t , waste_water_generation t , holiday, etc.);
  • time stamp is the timestamp; date is the date; temperature t is the temperature; weather t is the weather; waste_water_generation t is the amount of wastewater generated; holiday is the holiday.
  • Step 103 Predict the electric energy consumption data of the workshop in the future production process based on the electric energy consumption prediction model.
  • FQ k (t) represents the predicted future output of workshop k
  • Steam(t) represents the amount of steam produced
  • S k represents the storage space required by workshop k
  • S k_c represents the processing capacity of workshop k
  • FSteam(t) represents the steam demand for future production
  • FP k (t) represents the predicted power demand of workshop k
  • FCargo k (t) represents the predicted logistics demand of workshop k.
  • the embodiment of the present invention mainly includes:
  • Preprocessing includes supplementary processing of missing data, correction of data that is incorrect or exceeds the allowable range, and provides the processed data to the load forecasting model. .
  • the power consumption data of the production workshop also includes lighting, air conditioning, maintenance and other parts that do not directly reflect production. Therefore, in order to analyze the key production process, it is necessary to decompose the key circuit power consumption data.
  • key information such as production status, output, and energy efficiency can be calculated.
  • Data such as steam consumption and natural gas consumption often have fewer measurement points than electricity consumption data, so the granularity is larger. They reflect production somewhat, but not as accurately as electricity consumption data.
  • information such as the amount of waste water produced and test sheets can be used together with energy consumption data such as steam and electricity to provide a basis for judging production status, such as judging abnormal production.
  • the embodiments of the present invention can also predict energy consumption, output, logistics demand, inventory, energy consumption demand, etc., and further provide energy data extension services.
  • Fig. 2 is an exemplary schematic diagram of a method for predicting plant operation according to an embodiment of the present invention.
  • the identification process 20 of the production process includes obtaining the power consumption data 211 of the workshop at the first historical time, the power consumption data 221 of the workshop at the second historical time, and the power consumption data of the workshop at the mth historical time Data 2m1.
  • the number of m is a positive integer greater than 2.
  • a separation process 212 is performed on the consumption data 211, a production assessment 213 is performed based on the separated data (including determining operating conditions and power consumption data), a separation process 222 is performed on the consumption data 221, a production assessment 223 is performed based on the separated data ( Including determining working conditions and power consumption data), performing a separation process 2m2 on consumption data 2m1, performing production evaluation 2m3 based on the separated data (including determining working conditions and power consumption data). Then, based on the model input functions such as steam consumption, sewage production, sewage test results and the results of production evaluation 213, 223 and 2m3, the electric energy consumption prediction model 40 is trained. Then, the planning strategy of the service 41 of the future generation process can be predicted by using the electric energy consumption prediction model 40 . The service 41 of the current production process can also be determined using the results of the production evaluation 213, 223 and 2m3.
  • Fig. 5 is a configuration diagram of a plant operation prediction device according to an embodiment of the present invention.
  • the plant operation forecasting device 500 includes:
  • the separation module 501 is used to separate the power consumption data of the workshop in the production process from the power consumption data of the workshop in the factory; the determination module 502 is used to determine the power consumption data suitable for predicting the production process based on the power consumption data.
  • the separation module 501 is used to separate the power consumption data of the workshop in the production process from the power consumption data of the workshop in the factory based on the energy decomposition method of the non-parametric factor hidden Markov model; or, Based on the percentage determined from the historical power distribution data, the power consumption data of the workshop in the production process is separated from the power consumption data of the workshop in the factory.
  • the determining module 502 is further configured to determine the working status of the production process based on the power consumption data in the production process, wherein: when the power consumption data is less than the preset first threshold value, determine the operating status of the production process The working state is a stop state; when the power consumption data is greater than the first threshold value and less than the preset second threshold value, it is determined that the working state of the production process is a waiting state; when the power consumption data is greater than the second threshold value , determining that the working state of the production process is an active running state; wherein the second threshold value is greater than the first threshold value.
  • the determination module 502 is also used to determine the output of the production process and the logistics demand of the production process based on the electric energy consumption data in the production process;
  • the output of the production process is Q k (t); the logistics demand of the production process is Cargo k (t); Cap is the transportation capacity of each vehicle; P k (t) is the power consumption data in the production process; Q krated is the rated production capacity of the production process; P krated is the rated electric power of the generation process; t is the time parameter.
  • the determination module 502 is used to use the data of electric energy consumption in the production process, the steam consumption in the production process, and the input data of the model together as training data to train the neural network model, so as to obtain the prediction model of electric energy consumption;
  • the input data includes at least one of the following: time stamp; weather; temperature; sewage production; sewage test results; holidays.
  • the prediction module 503 is used to input the input data in the future production process into the electric energy consumption prediction model, so as to predict the electric energy consumption data in the future production process and the steam consumption data in the future production process;
  • the electrical energy consumption data in the process determines at least one of: predicted production capacity of the future production process; predicted logistics requirements of the future production process; predicted storage needs of the future production process; predicted steam demand of the future production process.
  • the data analysis method proposed in the embodiment of the present invention adopts a recurrent neural network between multi-dimensional input and output when analyzing energy data, and comprehensively considers power consumption, steam consumption, waste water production, weather data, Data with different characteristics such as holidays, deep learning between various production processes, between the time series of the process itself, and between production and time, weather, and holidays, etc., to comprehensively and effectively predict future energy loads.
  • the model is adjusted to ensure the accuracy of the forecast results.
  • the output forecast data provides key data support for energy consumption management, energy consumption, logistics coordination, metering station usage plan, parking lot usage plan, storage plan, etc., improves the accuracy and economy of all aspects of the park's scheduling, and achieves the construction of a smart industrial park the goal of.
  • FIG. 6 is a structural diagram of a plant operation forecasting device with a processor-memory architecture according to an embodiment of the present invention.
  • the plant operation forecasting device 600 includes a processor 601, a memory 602, and a computer program stored on the memory 602 and operable on the processor 601.
  • the computer program is executed by the processor 501, any of the above A forecasting method for plant operations.
  • the memory 602 can be specifically implemented as various storage media such as electrically erasable programmable read-only memory (EEPROM), flash memory (Flash memory), and programmable program read-only memory (PROM).
  • the processor 601 may be implemented to include one or more central processing units or one or more field programmable gate arrays, wherein the field programmable gate arrays integrate one or more central processing unit cores.
  • the central processing unit or central processing unit core may be implemented as a CPU or MCU or DSP, and so on.
  • the hardware modules in the various embodiments may be implemented mechanically or electronically.
  • a hardware module may include specially designed permanent circuits or logic devices (such as special-purpose processors, such as FPGAs or ASICs) to perform specific operations.
  • Hardware modules may also include programmable logic devices or circuits (eg, including general-purpose processors or other programmable processors) temporarily configured by software to perform particular operations.
  • programmable logic devices or circuits eg, including general-purpose processors or other programmable processors

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Abstract

Les modes de réalisation de la présente invention concernent un procédé et un appareil de prédiction de l'exploitation d'une usine, ainsi qu'un support de stockage lisible par ordinateur. Le procédé comporte les étapes consistant à: séparer, à partir de données de consommation d'énergie électrique d'un atelier dans une usine, des données de consommation d'énergie électrique de l'atelier au cours d'un processus de production; d'après les données de consommation d'énergie électrique au cours du processus de production, déterminer un modèle de prédiction de consommation d'énergie électrique qui concorde avec la prédiction de données de consommation d'énergie électrique au cours d'un processus de production; et d'après le modèle de prédiction de consommation d'énergie électrique, prédire des données de consommation d'énergie électrique de l'atelier au cours d'un futur processus de production. Pour une usine où des données, comme une situation de production et une prédiction de production, ne sont pas fournies, la situation d'exploitation de l'usine peut être prédite d'après des données de consommation d'énergie électrique au cours d'un processus de production, ce qui facilite la fourniture d'une pluralité de services tels qu'un service d'énergie ou un service de logistique.
PCT/CN2021/115691 2021-08-31 2021-08-31 Procédé et appareil de prédiction d'exploitation d'usine, et support de stockage lisible par ordinateur WO2023028842A1 (fr)

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CN202180101142.5A CN117751373A (zh) 2021-08-31 2021-08-31 工厂运营的预测方法、装置及计算机可读存储介质

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CN116755343A (zh) * 2023-08-18 2023-09-15 兆和能源(威海)有限公司 一种基于自学习模糊控制节电器

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