CN116010114B - Equipment energy efficiency management and control system based on edge calculation - Google Patents
Equipment energy efficiency management and control system based on edge calculation Download PDFInfo
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Abstract
The equipment energy efficiency management and control system based on edge calculation adopts an end layer, an edge layer and a cloud layer framework, wherein energy consumption equipment and energy consumption meters are uniformly distributed on the end layer, a central server is arranged on the cloud layer, and the edge layer comprises an energy data acquisition module, an energy consumption edge calculation module and an equipment management and control module. The equipment management and control module of the edge layer comprises an operation control sub-module, a fault management sub-module and an equipment ledger sub-module; the energy data acquisition module acquires energy consumption data of the energy consumption instrument; the energy consumption edge calculation module is used for carrying out energy consumption edge calculation according to the energy consumption data, and the calculation result is sent to the central server; the central server performs statistical analysis on the calculation result, performs energy efficiency control processing, establishes control description and corresponding control parameters of the energy consumption equipment, and maps the control description and corresponding control parameters to the operation control sub-module. The invention provides data computing capability at the edge layer, and ensures the instantaneity of energy efficiency management and control.
Description
Technical Field
The invention belongs to the technical field of energy efficiency management and control of industrial equipment, and is used for managing and controlling energy consumption of water, electricity, gas and the like of the industrial equipment, and is an equipment energy efficiency management and control system based on edge calculation.
Background
The energy efficiency management and control of the industrial equipment mainly analyzes the energy consumption by collecting various types of energy data such as electricity, water, natural gas, industrial gas, cold and heat and the like, gives out corresponding technical means, eliminates energy consumption blind areas and reduces the cost.
In the prior art, the main method for controlling the energy efficiency of a plurality of industrial devices is generally to collect energy utilization data of a certain workshop or a certain factory in a local area network, the data is directly uploaded to a monitoring center through a data acquisition gateway, the data is stored in an energy efficiency control center server of the monitoring center, the server manages, analyzes and displays the data, and the energy utilization devices are controlled by manual intervention of experienced technicians according to observation data.
Obviously, the energy efficiency management and control system realized based on the method has the following problems:
(1) The data acquisition is single, the data of the main energy consumption equipment is not acquired and analyzed, and the data is collected and acquired widely aiming at the workshop or the factory. (2) The energy consumption meter data are all uploaded in real time, the data volume and the bandwidth consumption are large, and the cost of the energy efficiency management and control center server memory Chu Yunwei is high. (3) The collected energy efficiency data analysis work is less, the data value is insufficient for guiding and controlling main energy consumption equipment, and the analysis work is seriously dependent on the self experience of technicians. (4) The data of various instruments are directly transmitted to a monitoring center, the expansibility is poor, and the communication debugging is relatively complex. (5) The control program and description for the main energy consumption equipment are generally based on the self-contained specific brand controller, the interface is relatively closed, the program adjustment or optimization is relatively difficult, and once the self-contained controller is stopped or replaced after a long time, the operation and maintenance of the equipment are difficult to carry out.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide an equipment energy efficiency management and control system based on edge calculation, which can provide complex data calculation capability at the near equipment end of an edge layer, ensure the instantaneity of an energy efficiency management and control internet of things network, so as to improve the flexibility and expansibility of arrangement and reduce the load of a server; and further expands the data acquisition range so that the prediction is more accurate.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the equipment energy efficiency management and control system based on edge calculation adopts an architecture of an end layer, an edge layer and a cloud layer, wherein energy consumption equipment and energy consumption meters are uniformly distributed on the end layer, a central server is arranged on the cloud layer, and the edge layer comprises an energy data acquisition module, an energy consumption edge calculation module and an equipment management and control module; the equipment management and control module comprises an operation control sub-module, a fault management sub-module and an equipment ledger sub-module;
the energy data acquisition module acquires energy consumption data of the energy consumption instrument;
the energy consumption edge calculation module is used for carrying out energy consumption edge calculation according to the energy consumption data, and a calculation result is sent to the central server; the central server performs statistical analysis on the calculation result, performs energy efficiency control processing, establishes control description and corresponding control parameters of the energy consumption equipment, and maps the control description and corresponding control parameters to the operation control sub-module;
the operation control sub-module collects parameter information of the energy consumption equipment, sends the parameter information to the fault management sub-module, sends fault alarm information triggered according to fault setting conditions to the equipment ledger sub-module, and gathers the parameter information and sends the information to the central server through the equipment ledger sub-module; and the operation control sub-module controls the energy consumption equipment to execute the control parameters.
In one embodiment, the energy data acquisition module comprises a data acquisition sub-module, a preprocessing sub-module and a data storage sub-module; the energy consumption meter comprises a water meter, an ammeter and a gas meter;
the data acquisition sub-module acquires original energy consumption data of the energy consumption instrument, constructs a water consumption energy consumption data column vector, an electricity consumption energy consumption data column vector and a gas consumption energy consumption data column vector, wherein each column vector represents energy consumption in 1 time period and is divided into a plurality of time periods every day;
the pretreatment sub-module respectively carries out data cleaning, data transformation and data updating on the water consumption data column vector, the electricity consumption data column vector and the gas consumption data column vector;
the data storage sub-module is used for storing the preprocessing result of the preprocessing sub-module.
In one embodiment, the data cleaning is performed, whether the data in each period is an abnormal value is analyzed, and if the data is the abnormal value, the abnormal value is replaced by using an average value of adjacent bits; the data transformation transforms the original column vector into a sequence to be processed for energy consumption prediction calculation; and when the data updating is carried out and the energy consumption on-line prediction is carried out, each energy consumption value of each period of the (i+1) th day is assigned to each energy consumption value of each period of the (i) th day.
In one embodiment, the energy consumption edge calculation module comprises an energy consumption multi-scale analysis sub-module, an energy consumption alarm sub-module and an energy consumption prediction sub-module;
the energy consumption multi-scale analysis sub-module performs energy consumption analysis in two dimensions of a space scale and a time scale;
the energy consumption alarm submodule sets a threshold percentage according to the water consumption according to the time scale, and if the water consumption exceeds the threshold compared with the water consumption of the previous time scale, an alarm is triggered; setting energy consumption data acquisition frequency aiming at gas consumption and electricity consumption, and triggering an alarm when energy consumption fluctuation exceeds a set threshold value;
the energy consumption prediction sub-module predicts water consumption and gas consumption of each energy consumption device in one time scale in the future by using a moving average method, and predicts the electricity consumption of the time scale in the future by using a long-period and short-period memory network prediction model.
In one embodiment, the central server trains the long-term and short-term memory network prediction model by using energy consumption data, and issues model parameters to the energy consumption prediction sub-module.
In one embodiment, the central server comprises an energy consumption configuration monitoring sub-module, an energy consumption prediction calculation sub-module and an energy efficiency control processing sub-module;
the energy consumption configuration monitoring submodule establishes an overall arrangement display interface of each energy consumption device in an industrial configuration mode according to a process flow so as to monitor energy consumption data of each energy consumption device;
the energy consumption prediction calculation sub-module trains the long-period memory network by using the collected energy consumption data to obtain model parameters, and sends the model parameters to the energy consumption prediction sub-module according to updating conditions to perform online energy consumption prediction at an edge layer;
the energy efficiency control processing sub-module is used for controlling and describing energy consumption equipment by using a control programming language, introducing production process parameters, process constraint and an energy consumption predicted value of a future time scale, taking the lowest energy consumption cost as an optimization target, and executing calculation processing to obtain corresponding control parameters.
In one embodiment, the energy efficiency control processing sub-module uses the energy consumption device as a control object, uses a top-down object-oriented programming method, establishes an energy consumption device control description corresponding to the energy consumption data by using the composite function blocks, uses a configuration function to connect each composite function block, forms a control description based on a composite function block network, and maps the control description to the operation control sub-module.
In one embodiment, the control description of the energy consumption device is a mapping relation and an implementation code of input and output established by the parameter information of the energy consumption device, the mechanism of the energy consumption device and the control constraint acquired by the end layer; the operation control sub-module provides the resource environment for the operation of the control description at the edge layer for the virtual container engine resource.
In one embodiment, the edge layer includes a plurality of edge control devices, each including the energy data acquisition module, the energy consumption edge calculation module, and the equipment management module.
Compared with the prior art, the invention has the beneficial effects that:
according to the energy efficiency management and control system based on the cloud computing and the edge computing, the advantages of the cloud computing and the edge computing are utilized, and the energy efficiency management and control system based on the end-edge cloud structure is provided, so that the energy efficiency management and control system is easy to arrange and convenient to expand. Various energy consumption meters are used as data acquisition sensing objects, main energy consumption equipment is used as a controlled object, complex data computing capacity can be provided at an edge layer through edge computing processing, the requirement on uplink data bandwidth is greatly reduced, the instantaneity of an energy efficiency management and control Internet of things network is guaranteed, the storage requirement of a central server is greatly reduced, and the investment and operation and maintenance cost of cloud layers are reduced.
Drawings
FIG. 1 is a schematic diagram of an energy efficiency management and control system according to the present invention.
FIG. 2 is a flow chart of an energy efficiency management and control method according to the present invention.
FIG. 3 is a schematic diagram showing the layout association of a plurality of end layers, a plurality of edge layers and cloud layers according to the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, the present invention provides an edge computing-based device energy efficiency management and control system, which adopts an architecture of an end layer, an edge layer and a cloud layer. The cloud layer is provided with a central server, and the edge layer comprises an energy data acquisition module, an energy consumption edge calculation module and an equipment management and control module; the equipment management and control module comprises a fault management sub-module, an equipment ledger sub-module and an operation control sub-module. Wherein:
the energy consumption data acquisition module is in communication connection with an on-site energy consumption instrument and is used for acquiring energy consumption data of the energy consumption instrument;
the energy consumption edge calculation module is used for carrying out energy consumption edge calculation according to the energy consumption data and sending a calculation result to the central server; the central server performs statistical analysis on the calculation result, performs energy efficiency control decision processing, establishes control description and corresponding control parameters of the energy consumption equipment, maps the control description and the corresponding control parameters to the operation control sub-module, and controls the operation control sub-module to control the energy consumption equipment to execute the control parameters.
The operation control sub-module collects parameter information of the energy consumption equipment, sends the parameter information to the fault management sub-module, sends fault alarm information triggered according to fault setting conditions to the equipment ledger sub-module, and gathers the parameter information and sends the fault alarm information to the central server through the equipment ledger sub-module.
According to the invention, energy consumption prediction calculation and treatment are carried out on cloud layers based on historical energy consumption data, calculated parameter information is issued to edge layers, meanwhile, the parameter information is updated according to certain conditions, the energy consumption data are collected at the edge layers by utilizing the computing capacity of the edge layers, pretreatment and online energy consumption prediction are carried out, online prediction is realized at the near-equipment side of the edge layers, and equipment management and control are carried out on main energy consumption equipment of the end layers; corresponding control description is established on the cloud layer aiming at main energy consumption equipment, equipment in the end layer is arranged at different positions according to the field condition, and all the layers are connected in different modes. The energy efficiency management and control structure based on edge calculation can provide complex data calculation capability and ensure the instantaneity of an energy efficiency management and control Internet of things network.
In the invention, the energy consumption equipment is arranged on the end layer, and the energy consumption meter mainly comprises a water meter, an electric meter and a gas meter and is also arranged on the end layer. According to the device energy efficiency management and control system based on the cloud layer-edge layer-end layer Internet of things structure, the energy efficiency management and control method comprises the following steps: and collecting energy consumption data of water meters, electric meters, gas meters and the like of the terminal layer, sending the energy consumption data to the edge layer, and sending the energy consumption data to a cloud layer central server after energy consumption calculation, analysis and treatment. Referring to fig. 2, the specific flow is as follows:
and (3) energy consumption data acquisition: and collecting energy consumption data of the water meter, the electric meter, the gas meter and the like, including data collection, preprocessing and data storage, and sending the energy consumption data to an energy consumption edge calculation module.
Energy consumption edge calculation: and performing multi-scale analysis, energy consumption alarm, energy consumption prediction and other processing by using the collected energy consumption data, and sending an analysis result to a central server.
And (3) equipment management and control: parameter information of main energy consumption equipment is collected, the main energy consumption equipment enters an operation control environment function to execute control monitoring, the main energy consumption equipment is transmitted to a fault management sub-module, fault alarm information triggered according to fault setting conditions is transmitted to an equipment ledger sub-module, and the equipment ledger sub-module gathers and transmits the fault alarm information and the ledger information to a central server.
In some embodiments of the present invention, to achieve the above-mentioned energy consumption data acquisition, the energy data acquisition module includes a data acquisition sub-module, a preprocessing sub-module, and a data storage sub-module. The data acquisition sub-module is in communication connection with an on-site energy consumption instrument, acquires original energy consumption data of the energy consumption instrument, and constructs a water consumption energy consumption data column vector W (k), an electricity consumption energy consumption data column vector E (k) and an air consumption energy consumption data column vector G (k), which can be expressed as:
W(k) = [W(1,1), …, W(1,24), W(2,1), …, W(2,24), …, W(N,1), …, W(N,24)] T
E(k) = [E(1,1), …, E(1,24), E(2,1), …, E(2,24), …, E(N,1), …, E(N,24)] T
G(k) = [G(1,1), …, G(1,24), G(2,1), …, G(2,24), …, G(N,1), …, G(N,24)] T
in each column vector, each term represents the energy consumption in 1 period, for example, W (1, 1) represents the water consumption in 1 period on day 1, W (N, 1) represents the water consumption in 1 period on day N, W (N, 24) represents the water consumption in 24 period on day N, N represents the number of days on which collection is performed, divided into 24 periods per day. Similarly, E (N, 1) represents the electricity consumption amount in the 1 st period on the nth day, and E (N, 24) represents the electricity consumption amount in the 24 th period on the nth day; g (N, 1) represents the gas consumption amount in the 1 st period on the nth day, and G (N, 24) represents the gas consumption amount in the 24 th period on the nth day.
The invention can analyze the data of the energy consumption instrument in a protocol to collect the data of the energy consumption instrument with different protocols, and the collection sensing support comprises Modbus TCP, modbus RTU, OPC and other protocols.
And the preprocessing sub-module respectively performs data cleaning, data transformation and data updating on the water consumption data column vector W (k), the electricity consumption data column vector E (k) and the gas consumption data column vector G (k). The preprocessed data can be sent to the cloud layer for reporting, and the requirement on uplink data bandwidth is greatly reduced.
In practical application, if the fault conditions such as abnormal communication sensitivity and communication signal interference occur, the problem of data acquisition is caused, and the accuracy of subsequent data calculation is further affected. In this embodiment, the data is cleaned, whether the data in each period is an outlier is analyzed by using a method such as a rada rule, and if the data is an outlier, the outlier is replaced by using an average value of adjacent bits.
The data transformation transforms the original column vector into a sequence to be processed for energy consumption prediction calculation, namely:
W’(k) = [W(1), W(2), …, W(N)] T
E’(k) = [E(1), E(2), …, E(N)] T
G’(k) = [G(1), G(2), …, G(N)] T
here, the sequence of W (1, 1), …, W (1, 24) is represented by W (1), and the sequence of W (N, 1), …, W (N, 24) is represented by W (N); similarly, the sequences of E (1, 1), …, E (1, 24) are denoted by E (1), and the sequences of E (N, 1), …, E (N, 24) are denoted by E (N); g (1, 1), …, G (1, 24) sequences are denoted by G (1), and G (N, 1), …, G (N, 24) sequences are denoted by G (N).
The data updating is to continuously update an input data sequence when carrying out energy consumption online prediction, and specifically, each energy consumption value of each period of the i+1th day is assigned to each energy consumption value of each period of the i th day.
The data storage sub-module is used for storing the preprocessing result of the preprocessing sub-module.
In an embodiment of the invention, the energy consumption edge calculation module comprises an energy consumption multi-scale analysis sub-module, an energy consumption alarm sub-module and an energy consumption prediction sub-module.
The energy consumption multi-scale analysis sub-module performs energy consumption analysis in two dimensions of a space scale and a time scale, wherein the space scale and the time scale are customized as required. Where the spatial scale is generally defined as four levels of a production line, plant, factory building, and campus, and the temporal scale is generally four levels by day, month, quarter, and year. And selecting physical space and different time scales for analysis and comparison aiming at different energy media.
Typically, for water consumption, monthly and annual statistical analyses are performed according to the plant; and carrying out monthly and quarterly statistical analysis on the gas consumption and the electricity consumption according to four space scales of a production line, a workshop, a factory building and a park.
The energy consumption alarm can be performed on the basis of energy consumption multi-scale analysis, and historical energy consumption alarm and real-time energy consumption alarm are performed aiming at energy consumption data of different spatial scales and time scales. On the basis of 24 hours before the production line is subjected to statistical analysis, monitoring the real-time fluctuation of gas and water consumption of the production line, setting the fluctuation percentage of energy consumption in a time threshold, and triggering energy consumption alarm based on the threshold.
Specifically, the energy consumption alarm sub-module sets a threshold percentage according to the water consumption according to the time scale water consumption, such as the month and the year water consumption, if the water consumption exceeds the threshold compared with the water consumption of the previous time scale, an alarm is triggered, so that operation and maintenance personnel are reminded to pay attention to and pertinently analyze the energy consumption change possibility, the auxiliary energy consumption is optimized and improved, and the typical threshold percentage is 10%. Obviously, the water consumption early warning belongs to historical energy consumption warning.
Aiming at the gas consumption and the electricity consumption, setting energy consumption data acquisition frequency, and triggering an alarm when the energy consumption fluctuation exceeds a set threshold value. Obviously, the gas consumption and the electricity consumption belong to real-time alarm, in the actual energy consumption monitoring of a park, the energy consumption data acquisition frequency can be set to be 30 seconds, the alarm is triggered when the energy consumption fluctuation exceeds a certain threshold, and the energy consumption fluctuation threshold is generally set to be 15%. And similarly, the energy consumption alarm information is stored, analysis reasons are recorded, and decision basis is provided for subsequent alarm analysis and optimization.
The energy consumption prediction submodule predicts water consumption and gas consumption of each energy consumption device in a future time scale by using a moving average method, namely, an average value of observed values of the past K moments is used as a prediction for the future moment. And predicting the electricity consumption of a time scale by using a long-short-term memory network prediction model.
Illustratively, long-term memory (LSTM) networks employ a single hidden layer network structure, including an input layer, LSTM layers having 10 LSTM cells, fitted to the model using a random gradient descent effective Adam version, using a sigmoid activation function, and optimized using a mean square error loss function. In one embodiment, the input is the power consumption of the past 30 days, the output is the power consumption of the predicted next day, a sliding window mode is adopted to generate a training sequence, if the power consumption of the next day is predicted by using 20 days of power consumption data, the training window size is set to 20, the tag window size is 1, after 1 data after each 20 days of data are predicted, the window moves one bit to the right, the next 1 data are predicted by using the next 20 data, and the whole sample data are traversed. 60% of the sequences, i.e. 6, were set as training sets, the remainder as test sets. Setting 1 batch, and iterating 5 rounds of training; and carrying out single-step prediction on the trained model, carrying out inverse normalization on the prediction result, and outputting the predicted next-day power consumption.
In this embodiment, the long-term memory network prediction model may be trained by the central server using the energy consumption data, i.e. the model training process is performed in cloud layers and model parameters are issued to the energy consumption prediction sub-module. For example, model parameters of the cloud layer are issued to the edge layer through a standard MQTT (Message Queuing Telemetry Transport, message queue telemetry transport) protocol, and operation processing is performed in the edge layer, so that the function of online prediction at the near-device end is realized.
The device management and control module is used for packing and mapping the established control description of the main energy consumption device to an operation control environment in which a Docker container is arranged according to a corresponding composite function block code, wherein the Docker container is an open-source application container engine. The established control description of the main energy consumption equipment refers to the input and output mapping relation and implementation codes established through parameter information of the main energy consumption equipment acquired by the end layer, mechanism of the equipment, control constraint and the like.
Specifically, in the equipment ledger submodule, equipment names, model specifications, purchase dates, service lives, depreciation ages, asset numbers, service departments, service conditions and the like are recorded and stored, and data can be synchronized to cloud layer storage. The data synchronization period of the ledger information can be freely set, and is generally 1 year.
In the fault management sub-module, equipment fault information is stored, fault types are classified, statistical analysis is carried out, and meanwhile, specific troubleshooting and solving schemes aiming at typical fault matching are provided, so that operation and maintenance personnel are guided to remove faults. As new faults occur, stored, classified and corresponding solutions are entered, there is a complete fault knowledge base for the different primary energy consuming devices. And providing fault alarm signals for three faults with highest occurrence frequency of different energy consumption devices.
The operation control sub-module provides an energy efficiency control description of the resource environment, typically virtual container engine resources, that can be operated at the edge layer.
The cloud layer mainly realizes the functions of energy consumption configuration monitoring, energy consumption prediction calculation, energy efficiency control processing and the like. The energy consumption configuration monitoring utilizes different types of components of cloud layers, and can monitor, overview, data query and energy flow graph construction of water, electricity, gas energy sources and self-defined energy consumption equipment in the whole park and each factory workshop. The energy efficiency management and control decision process is based on control description of IEC61499 standard main energy consumption equipment, object-oriented programming and event driving to form a composite function block code.
In some embodiments of the present invention, the central server includes an energy consumption configuration monitoring sub-module, an energy consumption prediction calculation sub-module, and an energy efficiency control processing sub-module.
The energy consumption configuration monitoring submodule establishes an overall arrangement display interface of each energy consumption device in an industrial configuration mode according to a process flow so as to monitor energy consumption data of each energy consumption device;
and the energy consumption prediction calculation sub-module trains the long-period memory network by using the collected energy consumption data to obtain model parameters, and sends the model parameters to the energy consumption prediction sub-module according to updating conditions to perform online energy consumption prediction at an edge layer.
The energy efficiency control processing sub-module is used for controlling and describing energy consumption equipment by using a control programming language, introducing production process parameters, process constraint and an energy consumption predicted value of a future time scale, taking the lowest energy consumption cost as an optimization target, and executing calculation processing to obtain corresponding control parameters. And the equipment management and control module performs control on parameters of main energy consumption equipment of the terminal layer according to the optimization target.
The control description of the main energy consumption equipment built in the cloud layer is based on IEC61499 standard, object-oriented programming and event driving, and a composite function block code is formed. The control description of the energy consumption equipment established by the cloud layer is packed and mapped to the equipment management and control module, and the connected terminal layer main energy consumption equipment is controlled; the energy consumption data collection and energy consumption edge calculation are operated in the form of application programs.
Specifically, the energy efficiency control processing sub-module uses energy consumption equipment as a control object, uses a top-down object-oriented programming method conforming to IEC61499, establishes energy consumption equipment control description corresponding to the energy consumption data by using the composite functional blocks, uses a configuration function to connect each composite functional block, forms control description based on a composite functional block network, and maps the control description to the operation control sub-module.
The composite function block is in accordance with a modularized design paradigm, and function block instances in the IEC61499 standard can be combined according to certain logic to form a function block network with specific functions, and the composite function block types which can be multiplexed are formed through encapsulation. The present invention may use code blocks established to satisfy device control logic and execution relationships including structured text, ladder diagrams, continuous function flow chart languages, and the like. The control description established for the main energy consumption equipment is issued in the form of a composite functional block through wired connection and through the self-mapping function of the integrated development environment software. In this embodiment, a control description of the primary energy consumption device is built in the cloud layer, and the control description is issued to the application container engine resource of the edge layer.
In some embodiments of the invention, a campus-level overall distributed control system description is established, a single main energy consumption device is taken as a controlled object, each main energy consumption device for collecting data from an end layer is included, and a second type device model is used for establishing a control description of the main energy consumption device for collecting data from the end layer based on IEC61499 standard. Control description of cloud deployment an integrated development environment uses 4diac to install Eclipse 4diac IDE software at a cloud center server. The Eclipse 4diac is an open source project of an IEC61499 distributed control system and mainly comprises a development environment IDE and a runtime Forte, wherein the runtime Forte software is installed in an edge control device. Development environment IDE is typically disposed at an edge server or cloud server. The control description has the advantages that the control description of each main energy consumption device of the end layer is formed based on standard IEC61499 standard language, and can be reused later without depending on a single brand.
The equipment management and control module maps the control description and corresponding control parameters of the energy consumption equipment established by the cloud layer into an operation control sub-module of the edge layer, and the energy consumption data acquisition and energy consumption edge calculation module operates in the edge layer in an application program mode.
In the embodiment of the invention, the edge layer comprises a plurality of edge control devices, and each edge control device comprises the energy data acquisition module, the energy consumption edge calculation module and the equipment management and control module, as shown in fig. 3. And collecting energy consumption data of the water, electricity, gas and other energy consumption data at the edge layer, calculating the energy consumption edge, and controlling main energy consumption equipment at the edge layer.
In the prior art, data acquisition is directly from equipment to a central server, an edge layer is not arranged in the middle, and the data acquisition is required to be in a total service center during debugging, so that the data acquisition is not beneficial to expansion. In this embodiment, each edge control device independently includes an energy data acquisition module, an energy consumption edge calculation module, and an equipment management and control module, and its hardware is the hardware portion of each edge layer. For effective collection, an edge control device may be arranged in each vehicle or in each group of energy consuming devices, and preferably in the vehicle in a vicinity of the energy consuming devices and energy consuming meters, for example in an existing control cabinet or in a separately assigned electrical cabinet. The cloud layer of the embodiment, the hardware of which comprises a central server, can select a public central server or a private central server, and can be generally arranged in a central control room of a park.
The devices in each layer of the embodiment are arranged at different positions according to the field condition, and the layers are connected in different modes to realize communication interaction. The system implementation comprises the following steps:
step 1: arranging hardware of an end layer, an edge layer and a cloud layer; the end layer, the hardware includes energy consumption instrument and energy consumption equipment of each vehicle or each group of energy consumption equipment; the edge layer is mainly a plurality of edge control devices; at least one edge control device is arranged on each vehicle or each group of energy consumption equipment; and the cloud layer and the hardware comprise a central server. The grouping of energy consuming devices is generally based on physical distance.
Step 1.1, installing various energy consumption meters of an end layer and ensuring that each meter has a Modbus RTU or Modbus TCP protocol port; and identifying and definitely focusing on the communication interface type of the energy consumption equipment.
In step 1.2, the edge control device is installed nearby the main energy consumption equipment, and the electric control cabinet installed on the main energy consumption equipment or the cabinet is independently installed beside the main energy consumption equipment is suggested. An edge control device is arranged in a workshop, and particularly, the edge control device is respectively and independently arranged for equipment groups such as boilers, air conditioning units, air compressors and the like.
In step 1.3, at the central server of the cloud layer, private clouds may be arranged or public cloud servers may be directly rented, which must have a public network IP.
Step 2: connecting and communicating and debugging the hardware of the end layer, the edge layer and the cloud layer;
the communication debugging is to connect the edge control device with hardware of an end layer through Modbus RTU communication or by using a LoRa networking mode; and connecting the edge control device with the cloud layer through any one form of 4G or 5G connection, wiFi connection and wired connection.
Step 3: testing the data flow of communication between each layer;
the data flow test of the communication is respectively tested:
the communication from the data of each energy consumption instrument at the end layer to the edge layer is normal and stable;
the communication from the parameters of each energy consumption device of the terminal layer to the edge layer is normal and stable;
and the data of the energy consumption instrument and the parameters of the energy consumption equipment are normally and stably communicated from the edge layer to the cloud layer. And setting a packet loss rate, wherein the packet loss rate meets a set value, namely the test is successful, and otherwise, checking is needed.
Step 4: configuring the edge layer; the configuration includes: according to working conditions and acquisition requirements, configuring the parameter types and acquisition frequencies of the energy consumption equipment to be acquired and the data types and acquisition frequencies of the energy consumption meters to be acquired to the edge control device;
the above configuration work is set by the debugging tool of the edge control device. After the configuration, the data of the energy consumption instrument are collected, preprocessed and stored; the equipment management and control module sends the parameters of the energy consumption equipment to the operation control submodule for operation and sets management fault information, and further processes the equipment account information.
Step 5: and at a central server of the cloud layer, performing energy consumption prediction calculation by utilizing data uploaded by an edge layer, training a prediction model, and performing energy efficiency control processing on main energy consumption equipment.
Step 6: based on the set updating conditions, model parameters obtained by cloud layer training are issued to an edge layer, control description and optimized control parameters after cloud layer energy efficiency control processing are issued to the edge layer, and meanwhile, the control description is processed in an operation control sub-module.
The updating conditions of the energy consumption prediction calculation processing result are as follows: 1) Initializing, i.e. powering up the device again. 2) Based on the time threshold T, T is typically set to 1 month, which is dynamically adjustable. And meeting any one of the conditions, namely meeting the updating condition, and sending the energy consumption prediction model parameters to the edge layer by the cloud layer. Likewise, the corresponding control parameter update conditions are: 1) Initializing, i.e. powering up the device again. 2) The process constraints change. 3) The controlled device is replaced. The corresponding control parameters can be updated to the edge layer when any of the above conditions are met.
Further, in the embodiment of the present invention, to better implement device energy efficiency management and control, the following configuration is also performed:
firstly, an operation control environment is configured at an edge layer, and a control description for main energy consumption equipment in an end layer is constructed on the basis of the operation control environment and mapped to an edge control device. The method specifically comprises the following steps:
s1, for main energy consumption equipment such as an air compressor, boiler equipment and the like, an IEC61499 functional block model is taken as a sample plate, and the logical relationship is packaged into a reusable functional block type. The writing implementation of the logic relation can use five languages of traditional sequential function charts, ladder diagrams, function block diagrams, instruction lists and structured texts, and the structured texts are selected preferentially.
S2, connecting corresponding function block examples according to event streams and data streams specified by an application model, and constructing application of the whole distributed control system description of the whole park level in the form of a function block network, wherein the application does not contain any hardware configuration information and is focused on functional design and verification of the system control description.
S3, under the framework of development environment IDE system mode, firstly configuring and connecting the edge control device, and then configuring and operating the functional blocks in the control application on one edge control device through a self-contained mapping mechanism.
In an exemplary embodiment, a certain brand of air compressor in an air compressor workshop is used as a controlled object, a control description of the air compressor is written and established in a development environment IDE by using a structured text, and on the basis, the control description of other air compressors is modified, adjusted and established and respectively packaged into reusable functional blocks; according to the actual arrangement network structure of the air-pressure workshop, connecting the functional blocks corresponding to the event stream and the data stream to form a functional block network, establishing the integral distributed control description of the air-pressure workshop, and mapping the integral distributed control description to an edge control device arranged in the air-pressure workshop for corresponding control operation. The other main workshops build control descriptions similar to the air compressor workshops, ultimately forming a campus-level overall distributed control system description. In this example, the constructed control description is stored in a central server.
And secondly, setting a fault management function at the edge layer. The method specifically comprises the following steps:
s1, defining faults of main energy consumption equipment such as an air compressor and a boiler, setting and classifying the faults in multiple dimensions, and generally classifying the faults according to alarm frequency, influence of an alarm on production and the like.
S2, setting fault alarm triggering conditions for faults after different equipment are classified, and setting an alarm information pushing mode.
S3, processing the equipment after pushing the fault information, and storing the fault information and establishing a fault knowledge base.
Finally, the equipment ledger information is input and stored. Firstly, establishing original information of equipment, equipment names, model specifications, purchase dates, service lives, depreciation ages, asset numbers, service conditions of use departments and the like, and then dynamically updating and storing the equipment information, wherein the original information of the equipment is updated every year, and for equipment fault information, once equipment fails, the fault information and corresponding treatments are stored to form a historical fault library of each equipment, so that the equipment is convenient for subsequent staff to maintain.
After the edge layer is processed by the energy consumption data acquisition module, the data flow enters the energy consumption edge calculation module for processing, the data is transmitted to the cloud layer by the MQTT protocol, the energy consumption prediction calculation and processing are carried out by using the calculation resources of the cloud layer, and the calculated parameters are downloaded to the edge layer to realize online energy consumption prediction. In one embodiment of the invention, parameters after the energy consumption prediction calculation of the cloud layer are issued to the edge layer edge control device through the standard MQTT protocol, so that the function of online prediction at the near-equipment end is realized.
According to the scheme, the edge cloud structure is adopted, the edge control device is installed and arranged at the position close to main energy consumption equipment in a workshop, the workshop water and electricity information and parameters of the main energy consumption equipment are collected through the edge control device, the edge control device has three functions of energy consumption data collection, energy consumption edge calculation and equipment management and control, the edge control device is communicated with the monitoring center server, the processed data are uploaded to the center server of the cloud layer, meanwhile, the center server utilizes sufficient calculation resources to conduct energy consumption prediction calculation and control processing, and the parameters, control description and the like after the prediction calculation are issued to the edge control device to control parameters of the issuing equipment such as the main energy consumption equipment.
Claims (7)
1. The equipment energy efficiency management and control system based on edge calculation adopts an architecture of an end layer, an edge layer and a cloud layer, wherein energy consumption equipment and energy consumption meters are uniformly distributed on the end layer, a central server is arranged on the cloud layer, and the edge layer comprises an energy data acquisition module, an energy consumption edge calculation module and an equipment management and control module; the equipment management and control module comprises an equipment ledger sub-module, a fault management sub-module and an operation control sub-module;
the energy data acquisition module acquires energy consumption data of the energy consumption instrument;
the energy consumption edge calculation module is used for carrying out energy consumption edge calculation according to the energy consumption data, and a calculation result is sent to the central server; the central server performs statistical analysis on the calculation result, performs energy efficiency control processing, establishes control description and corresponding control parameters of the energy consumption equipment, and maps the control description and corresponding control parameters to the operation control sub-module;
the operation control sub-module collects parameter information of the energy consumption equipment, sends the parameter information to the fault management sub-module, sends fault alarm information triggered according to fault setting conditions to the equipment ledger sub-module, and gathers the parameter information and sends the information to the central server through the equipment ledger sub-module;
the energy data acquisition module comprises a data acquisition sub-module, a preprocessing sub-module and a data storage sub-module; the energy consumption meter comprises a water meter, an ammeter and a gas meter;
the data acquisition sub-module acquires original energy consumption data of the energy consumption instrument, constructs a water consumption energy consumption data column vector, an electricity consumption energy consumption data column vector and a gas consumption energy consumption data column vector, wherein each column vector represents energy consumption in 1 time period and is divided into a plurality of time periods every day;
the pretreatment sub-module respectively carries out data cleaning, data transformation and data updating on the water consumption data column vector, the electricity consumption data column vector and the gas consumption data column vector;
the data storage sub-module is used for storing the preprocessing result of the preprocessing sub-module;
the method is characterized in that the data are cleaned, whether the data in each period are abnormal values is analyzed, and if the data are abnormal values, the abnormal values are replaced by using the average value of adjacent bits; the data transformation transforms the original column vector into a sequence to be processed for energy consumption prediction calculation; and when the data updating is carried out and the energy consumption on-line prediction is carried out, each energy consumption value of each period of the (i+1) th day is assigned to each energy consumption value of each period of the (i) th day.
2. The edge computing-based device energy efficiency management and control system of claim 1, wherein the energy consumption edge computing module comprises an energy consumption multi-scale analysis sub-module, an energy consumption alarm sub-module, and an energy consumption prediction sub-module;
the energy consumption multi-scale analysis sub-module performs energy consumption analysis in two dimensions of a space scale and a time scale;
the energy consumption alarm submodule sets a threshold percentage according to the water consumption according to the time scale, and if the water consumption exceeds the threshold compared with the water consumption of the previous time scale, an alarm is triggered; setting energy consumption data acquisition frequency aiming at gas consumption and electricity consumption, and triggering an alarm when energy consumption fluctuation exceeds a set threshold value;
the energy consumption prediction sub-module predicts water consumption and gas consumption of each energy consumption device in one time scale in the future by using a moving average method, and predicts the electricity consumption of the time scale in the future by using a long-period and short-period memory network prediction model.
3. The edge computing-based device energy efficiency management and control system of claim 2, wherein the central server trains the long-short term memory network prediction model with energy consumption data and issues model parameters to the energy consumption prediction sub-module.
4. The edge computing-based device energy efficiency management and control system of claim 2 or 3, wherein the central server comprises an energy consumption configuration monitoring sub-module, an energy consumption prediction computing sub-module, and an energy efficiency control processing sub-module;
the energy consumption configuration monitoring submodule establishes an overall arrangement display interface of each energy consumption device in an industrial configuration mode according to a process flow so as to monitor energy consumption data of each energy consumption device;
the energy consumption prediction calculation sub-module trains the long-period memory network by using the collected energy consumption data to obtain model parameters, and sends the model parameters to the energy consumption prediction sub-module according to updating conditions to perform online energy consumption prediction at an edge layer;
the energy efficiency control processing sub-module is used for controlling and describing energy consumption equipment by using a control programming language, introducing production process parameters, process constraint and an energy consumption predicted value of a future time scale, taking the lowest energy consumption cost as an optimization target, and executing calculation processing to obtain corresponding control parameters.
5. The system according to claim 4, wherein the energy efficiency control processing sub-module uses the energy consumption device as a control object, uses a top-down object-oriented programming method, uses the composite function blocks to build the corresponding energy consumption device control description of the energy consumption data, uses the configuration function to connect the composite function blocks, and forms a control description based on the composite function block network, and maps the control description to the operation control sub-module.
6. The edge computing-based device energy efficiency management and control system according to claim 1, wherein the control description of the energy consumption device is a mapping relation and an implementation code of input and output established through parameter information of the energy consumption device collected by an end layer, a mechanism of the energy consumption device and control constraint; the operation control sub-module provides the resource environment for the operation of the control description at the edge layer for the virtual container engine resource.
7. The edge computing-based equipment energy efficiency management and control system of claim 1, wherein the edge layer comprises a plurality of edge control devices, each edge control device comprising the energy data acquisition module, an energy consumption edge computing module, and an equipment management and control module.
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