CN111126825B - Intelligent charge-discharge energy-saving management system for visual battery and control method thereof - Google Patents
Intelligent charge-discharge energy-saving management system for visual battery and control method thereof Download PDFInfo
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Abstract
The invention provides an intelligent charge-discharge energy-saving management system of a visual battery and a control method thereof, relating to the technical field of charge-discharge energy conservation and comprising the following steps: collecting original data in the charging and discharging process of a production site on line; centralizing the original data, and converting the original data into identifiable data which can be identified by a computer; storing identifiable data, analyzing the identifiable data to obtain corresponding index data, and obtaining optimal energy-saving operation data according to the identifiable data through a deep learning model; and the energy-saving operation instruction is issued according to the optimal energy-saving operation data. The invention utilizes deep learning to process the related data collected in the charging and discharging process to obtain the optimal energy-saving operation data of the system, and guides operators to adjust the power distribution parameters and the battery operation parameters so as to transfer the alternating current energy to the battery at maximum efficiency, thereby realizing the purpose of intelligent charging and discharging energy-saving management.
Description
Technical Field
The invention relates to the technical field of battery charge and discharge energy conservation, in particular to an intelligent charge and discharge energy conservation management system for a visual battery and a control method thereof.
Background
Along with the rapid development of economy, the construction of various aspects of China has achieved attention, but the problem of serious resource waste is accompanied, so that the contradiction between the economic development and the environmental safety is increasingly prominent. In order to solve the problem of increasingly exhausted resources, energy conservation and emission reduction are important propositions in the development of China. Enterprises are important components of the economy of China, the current enterprises are often the re-disaster areas with the waste of the resources of China, and the energy-saving work of the enterprises is a key step for constructing the resource-saving society of China, so the enterprises are monitored by the key of the related functional departments of the China.
Statistics indicate that 8% of the industrial enterprise's annual energy consumption results from the lack of energy monitoring and maintenance programs, and 12% of the industrial enterprise's annual energy consumption results from the lack of energy management and control systems. Currently, more and more enterprises adopt energy feedback type charging and discharging equipment to charge and discharge batteries required by industrial sites, so that energy-saving management of charging and discharging the batteries is a great weight of energy-saving work of the enterprises. The existing energy-saving management of charging and discharging is mainly focused on the transformation of charging and discharging equipment, so that the investment cost is high, and the energy-saving space which can be achieved is smaller and smaller. Besides, the existing energy-saving management of charging and discharging is to continuously try to adjust parameters in various charging and discharging processes for a long time through a senior engineer, and finally compare through a large amount of test data to select the optimal operation parameters. This process is time consuming and requires a lot of experience to achieve a good result.
In summary, the existing charge-discharge energy-saving management method mainly relies on equipment replacement and means for improving control process according to experience to achieve the energy-saving purpose. However, on one hand, the existing energy-saving management method needs to input huge manpower, material resources and time to improve the energy-saving effect, and the cost input is excessive; on the other hand, the existing energy-saving management method cannot accurately determine potential energy waste points, the energy-saving effect needs to be verified through additional means, real-time analysis is difficult, measures are taken in real time, and therefore the energy-saving requirement of enterprises cannot be fully met.
Disclosure of Invention
In view of this, the present invention aims to solve the technical problems in the related art at least to some extent. In order to achieve the above purpose, the invention provides an intelligent charge-discharge energy-saving management system for a visual battery, which comprises a field data acquisition module, a data conversion module, an energy-saving management module and an energy-saving visual platform module, wherein:
The on-site data acquisition module is used for acquiring original data in the charging and discharging process of the rechargeable battery on the production site on line;
The data conversion module is used for centralizing the original data and converting the original data into identifiable data which can be identified by a computer;
the energy-saving management module is used for storing the identifiable data; determining corresponding index data according to the identifiable data; inputting the identifiable data into a pre-trained deep learning model, and determining optimal energy-saving operation data;
The energy-saving visualization platform module is used for visualizing the index data and the optimal energy-saving operation data, and is also used for issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment of the production site, wherein the energy-saving operation instruction is determined according to the optimal energy-saving operation data.
Therefore, the invention acquires the original data in the charging and discharging process of the production site, converts the original data, performs comprehensive and intelligent analysis to obtain the related index data, and directly measures the overall energy saving effect. And deep learning is utilized to extract characteristics, excavate energy-saving space, formulate energy-saving strategy, obtain the best operation data of the whole equipment, visualize the best operation data, guide operators to adjust equipment operation parameters according to the best operation data, and optimize equipment operation modes. In summary, the intelligent analysis is performed on the collected data of the whole operation site, the optimal energy-saving operation data is simply and quickly obtained through deep learning, an operator can perform rapid and real-time energy-saving operation according to the optimal energy-saving operation data, a user intuitively recognizes the energy-saving effect through relevant data by utilizing a visualization technology, the energy-saving effect is visualized, the real-time supervision and operation are performed, and the energy-saving requirement of an enterprise is fully met.
Further, the raw data includes field energy consumption data, characteristic data and bar code data, and the field data acquisition module includes a charging and discharging equipment unit, an imaging unit and a bar scanning unit, wherein: the charging and discharging equipment unit is used for rectifying alternating current provided by an alternating current power grid into direct current required by the rechargeable battery, inverting the direct current into the alternating current and collecting the field energy consumption data; the imaging unit is used for thermally imaging the rechargeable battery and collecting the characteristic data of the rechargeable battery; the bar scanning unit is used for scanning the bar code data of the rechargeable battery and collecting the bar code data to correspond to the characteristic data.
Therefore, the charging process is completed by arranging the charging and discharging equipment unit, the imaging unit and the bar scanning unit, and field energy consumption data, characteristic data and bar code data in the charging process are collected so as to analyze the field energy consumption data, the characteristic data and the bar code data to obtain the power consumption condition of the whole working field, and the operation reliability of the whole system is improved.
Further, the data conversion module comprises a data centralization unit and a network switching unit, wherein the data centralization unit is used for centralizing the original data transmitted by the field data acquisition module and transmitting the data to the network switching unit; the network switching unit is used for converting the raw data after centralized processing into the identifiable data.
Therefore, the original data are converted into identifiable data through the data concentration unit and the network switching unit so as to be conveniently identified by a computer, and then the data are analyzed and processed, so that the system operation speed can be effectively improved, and the processing efficiency is improved.
Further, the energy-saving management module comprises a database unit and an intelligent terminal unit, wherein the database unit is used for storing the identifiable data transmitted by the data conversion module; the intelligent terminal unit is used for analyzing the identifiable data by using various statistical modes to obtain the index data, inputting the identifiable data into the pre-trained deep learning model and determining the optimal energy-saving operation data.
Therefore, the data is stored through the database unit, the intelligent processing analysis of the data is completed through the intelligent terminal unit, the obtained index data is used as an index for measuring the energy-saving effect, and the obtained optimal energy-saving operation data provides operation basis for operators, so that the effect of intelligent energy-saving management is achieved.
Further, the energy-saving visualization platform module comprises a visualization unit and an operation unit, wherein: the visualization unit is used for visualizing the index data and the optimal energy-saving operation data transmitted by the energy-saving management module; the operation unit is used for selectively issuing the energy-saving operation instruction according to the optimal energy-saving operation data.
Therefore, through the processing of the visualization unit, operators can intuitively recognize the energy consumption condition, the energy saving effect and the optimal energy saving operation data, and simultaneously, through the operation unit, the operators can perform real-time operation according to the optimal energy saving operation data, so that the purpose of controlling energy saving in real time is achieved.
In order to achieve the above object, a second object of the present invention is to provide a control method of an intelligent charge-discharge energy-saving management system for a visual battery, for controlling the intelligent charge-discharge energy-saving management system for a visual battery, which includes:
collecting original data in the charging and discharging process of the rechargeable battery on the production site on line;
centralizing the original data, and converting the original data into identifiable data which can be identified by a computer;
storing the identifiable data, determining corresponding index data according to the identifiable data, inputting the identifiable data into a pre-trained deep learning model, and determining optimal energy-saving operation data;
And visualizing the index data and the optimal energy-saving operation data, and issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment of the production site, wherein the energy-saving operation instruction is determined according to the optimal energy-saving operation data.
The invention provides a control method of the intelligent charge-discharge energy-saving management system of the visual battery based on the intelligent charge-discharge energy-saving management system of the visual battery, which efficiently controls the energy-saving management system by the steps of collecting data, converting the data, intelligently analyzing and processing the data and visualizing the data, intelligently provides an energy-saving strategy for operators, enables the operators to know the charge-discharge condition on site in real time, and effectively saves energy. In summary, the control method provided by the invention intelligently analyzes the collected data of the whole operation site, simply and quickly obtains the optimal operation data through deep learning, helps operators to determine related energy-saving operation, and utilizes a visualization technology to enable users to intuitively recognize the energy-saving effect through the related data, so that the energy-saving effect visualization is achieved, and the energy-saving requirement of enterprises is fully met.
Further, the raw data includes field energy consumption data, feature data and barcode data, and the raw data in the process of charging and discharging the rechargeable battery in the production field is collected online, including:
the field energy consumption data are collected, wherein the field energy consumption data are collected from a charging and discharging equipment unit of a field data collection module;
And acquiring the characteristic data and the bar code data, wherein the characteristic data is acquired from an imaging unit of the field data acquisition module, and the bar code data is acquired from a bar code unit of the field data acquisition module.
Therefore, through the collection of the field energy consumption data, the characteristic data and the barcode data, the relevant data of the field charge and discharge energy consumption and the battery condition are comprehensively obtained, and further analysis processing is further carried out on the basis of the data, so that the analysis result is more comprehensive, objective and reliable.
Further, the gathering the raw data, converting the raw data into identifiable data for computer recognition, includes:
carrying out data centralized processing on the field energy consumption data, the characteristic data and the barcode data;
and carrying out data conversion on the concentrated field energy consumption data, the characteristic data and the barcode data, and converting the field energy consumption data, the characteristic data and the barcode data into identifiable data which can be identified by a computer.
Therefore, the data is integrated and analyzed through the data set, and relevant data conversion is carried out for analysis and processing of a computer, so that effective transmission of the data is ensured.
Further, the index data comprise overall data of a charging and discharging system, charging and discharging loop data, single battery state data, single charging and discharging equipment data, energy information statistical data and alarm information data.
Therefore, the data is integrated and analyzed through the data set, and relevant data conversion is carried out, so that analysis and processing of a computer are carried out, and effective transmission of the data is ensured.
Further, the inputting the identifiable data into the pre-trained deep learning model, determining optimal energy-saving operation data, comprises:
preprocessing the identifiable data, and extracting battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge and discharge time data, charge and discharge waveform data and shelf time data;
And inputting the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge and discharge time data, the charge and discharge waveform data and the shelf time data into the pre-trained deep learning model to obtain the optimal energy-saving operation data, wherein the optimal energy-saving operation data comprises equipment parameter data, power distribution data and start-stop data for enabling a production site to operate in an optimal energy-saving mode.
Therefore, battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge-discharge time data, charge-discharge waveform data and shelf time data are extracted from the identifiable data, data redundancy is avoided, the data are used as characteristics of the whole charge-discharge operation process, deep learning is used for learning the characteristics to obtain optimal energy-saving operation data of the whole charge-discharge operation process, a deep learning model is used for rapidly analyzing the data in real time to obtain the optimal energy-saving operation data, an operator can conveniently adjust equipment parameters according to the optimal energy-saving operation data, and real-time effective energy-saving operation is guaranteed.
Drawings
Fig. 1 is a schematic structural diagram of a visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the present invention;
Fig. 2 is a schematic diagram of a system frame of a visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a field data acquisition module according to an embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a data conversion module according to an embodiment of the invention;
FIG. 5 is a schematic diagram of a power saving management module according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an energy-saving visualization platform module according to an embodiment of the present invention;
fig. 7 is a schematic flow chart of a control method of the visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the invention;
FIG. 8 is a flow chart of the method for collecting raw data according to an embodiment of the invention;
FIG. 9 is a schematic flow chart of the centralized conversion of the original data according to the embodiment of the present invention;
FIG. 10 is a schematic flow chart of training a deep learning model according to an embodiment of the present invention;
fig. 11 is a schematic flow chart of implementation of the energy saving management strategy according to the embodiment of the invention.
Detailed Description
Embodiments according to the present invention will be described in detail below with reference to the drawings, and when the description refers to the drawings, the same reference numerals in different drawings denote the same or similar elements unless otherwise indicated. It is noted that the implementations described in the following exemplary examples do not represent all implementations of the invention. They are merely examples of apparatus and methods consistent with aspects of the present disclosure as detailed in the claims and the scope of the invention is not limited thereto. Features of the various embodiments of the invention may be combined with each other without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
The existing charge-discharge energy-saving management method mainly relies on equipment replacement and a means of improving a control process according to experience to achieve the energy-saving purpose. Firstly, the existing energy-saving management method has the defects of overlarge cost investment, complex implementation process, huge manpower, material resources and time investment for improving the energy-saving effect, and uncertainty of the improvement space; secondly, the existing energy-saving management method cannot accurately determine potential energy waste points, so that the energy-saving space is excavated, the energy-saving effect is invisible, the energy-saving effect is poor, metering is impossible, analysis is impossible, and the energy-saving effect needs to be verified by an additional means; finally, the existing energy-saving management method is difficult to analyze in real time, measures are taken rapidly in real time, and the energy-saving requirement of enterprises cannot be fully met.
The embodiment of the first aspect of the invention provides an intelligent charge-discharge energy-saving management system for a visual battery. Fig. 1 is a schematic structural diagram of a visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the present invention, which includes a field data acquisition module 101, a data conversion module 102, an energy-saving management module 103, and an energy-saving visual platform module 104, wherein:
The field data acquisition module 101 is used for acquiring original data in the process of charging and discharging the rechargeable battery on the production field on line. In the embodiment of the invention, the field data acquisition module 101 transmits the original data to the data conversion module 102, and the field data acquisition module 101 and the data conversion module 102 are in bidirectional communication connection, so that the bidirectional transmission of the data between the field data acquisition module 101 and the data conversion module 102 is ensured. In the embodiment of the invention, the original data comprises site energy consumption data, characteristic data and barcode data, and the site energy consumption data comprises voltage information, current information, power, discharge electric energy statistical information and charge electric energy statistical information; the feature data includes, for example, battery internal resistance information, battery temperature information, fault record information, and alarm information; the bar code data comprises bar code information of each battery to correspond to the characteristic information, so that the on-site full-scale information is collected, the full-scale analysis of the data by the system is facilitated, accurate index data and optimal energy-saving operation data are obtained, and high-accuracy real-time monitoring is realized.
The data conversion module 102 is configured to collect the original data, and convert the original data into identifiable data that can be identified by a computer. The original data collected on site is converted into identifiable data, so that the computer can identify and process the collected data, the operation speed of the system is improved, and the overall operation efficiency is improved. In the embodiment of the present invention, the data conversion module 102 further transmits identifiable data to the energy-saving management module 103, and the data conversion module 102 is in bidirectional communication connection with the energy-saving management module 103, so as to ensure bidirectional transmission of data between the data conversion module 102 and the energy-saving management module 103.
The energy-saving management module 103 is configured to store identifiable data, analyze the identifiable data to obtain corresponding index data, use the identifiable data as input of a pre-trained deep learning model, and determine optimal energy-saving operation data through the deep learning model. In the embodiment of the invention, the collected data is subjected to comprehensive intelligent analysis through the energy-saving management module 103 to obtain related index data, and the overall energy-saving effect is directly measured through the index data. And meanwhile, the preprocessed identifiable data is used as the input of a deep learning model, and the optimal energy-saving operation data is obtained through the pre-trained deep learning model, wherein the optimal energy-saving operation data comprises equipment parameters, power distribution data and start-stop data of each charge and discharge equipment at a working site, so that an energy-saving space is excavated, the optimal operation data of the whole equipment is obtained in real time, and efficient and real-time energy saving is realized. According to the invention, the monitoring of the production site is realized through the index data, the site charging condition, such as voltage, current, electric energy and other information, is timely reflected to operators, and the identifiable data is subjected to autonomous learning and intelligent analysis by utilizing the deep learning model, so that the optimal energy-saving operation data is obtained, the operators can conveniently perform intelligent management strategies according to the optimal operation data, and the equipment parameter data, the power distribution data and the start-stop data of the whole system are regulated by issuing operation instructions, so that the optimal energy saving is realized. In the embodiment of the invention, the energy-saving management module 103 transmits index data and optimal energy-saving operation data to the energy-saving visual platform module 104, and the energy-saving management module 103 and the energy-saving visual platform module 104 are in bidirectional communication connection, so that bidirectional transmission of data between the energy-saving management module 103 and the energy-saving visual platform module 104 is ensured.
The energy-saving visualization platform module 104 is configured to visualize the index data and the optimal energy-saving operation data, and further configured to issue an energy-saving operation instruction, where the energy-saving operation instruction is selected by a user according to the optimal energy-saving operation data. Through visual operation, index data are visualized, information such as voltage, current, electric energy and the like of a production site is directly displayed through charts or data, an operator perceives energy consumption conditions and energy saving effects intuitively, optimal energy saving operation data are visualized, the operator is guided to implement an intelligent management strategy according to the displayed optimal operation data, an instruction is issued, a system is regulated, such as regulating equipment parameters of the whole system, the equipment can be guaranteed to charge the battery with alternating current electric energy most efficiently, distribution data are regulated, the power supply of the whole alternating current power grid can be guaranteed to be utilized most, start-stop data are regulated, the power supply is prevented from being wasted by working equipment in a resting state or a loading and unloading standby state, and charging and discharging equipment for the working equipment is stopped in time, so that energy is saved. In summary, the energy-saving visualization platform module 104 is used for facilitating the operator to issue instructions, optimizing the running mode of the equipment and ensuring real-time monitoring and real-time operation.
Fig. 2 is a schematic diagram of a system frame of a visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the present invention, which includes a field data acquisition module 101, a data conversion module 102, an energy-saving management module 103, an energy-saving visual platform module 104, an ac power grid module 105, and a battery module 106, and further includes exemplary specific components of each module. Through the mutual cooperation of the modules, the whole system achieves the efficient and real-time energy-saving effect. In the embodiment of the present invention, the ac power grid module 105 is configured to provide an ac power source, and during a charging process, the battery module 106 is configured to receive the electrical energy provided by the ac power grid, and provide the electrical energy for the working equipment on the working site. The visual battery intelligent charge-discharge energy-saving management system provided by the embodiment of the invention is connected with the alternating current power grid module 105 and the battery module 106 through the field data acquisition module 101, so that the real-time supervision of the charge-discharge process of the production field is realized, and the implementation of efficient charge-discharge energy-saving operation is ensured.
Specifically, fig. 3 is a schematic structural diagram of the field data acquisition module 101 according to an embodiment of the present invention. The field data acquisition module 101 includes a charge and discharge device unit 1011, an imaging unit 1012, and a bar scan unit 1013, wherein the charge and discharge device unit 1011 includes a bidirectional AC/DC converter unit 10110, a bidirectional DC/DC converter 10111, and a common DC bus 10112.
In particular, the invention is better explained in connection with fig. 2 and 3. The field data acquisition module 101 specifically includes a bidirectional AC/DC converter unit 10110, a bidirectional DC/DC converter 10111, a common DC bus 10112, an imaging unit 1012, and a bar scan unit 1013, wherein:
The bidirectional AC/DC converter 10110 in the charging/discharging device unit 1011 can be used for rectifying the AC power supplied by the AC power grid into a suitable DC bus voltage, and can also be used for inverting the DC bus voltage source into the AC power, and can also be used for collecting the energy consumption data on the AC side on site. The bidirectional DC/DC converter 10111 in the charging/discharging device unit 1011 can be used to secondarily convert the voltage of the common DC bus 10112 into an accurate DC voltage source or DC current source required by the battery, and can also be used to collect field battery side energy consumption data. The charge-discharge device unit is used for supporting the voltage of the system common DC bus 10112, and reducing the consumption of energy from the bidirectional AC/DC converter 10110 by the bidirectional DC/DC converter 10111. Thus, during charging, the bi-directional AC/DC converter 10110 rectifies the alternating current and charges the rechargeable battery through the bi-directional DC/DC converter 10111; during discharging, the bidirectional AC/DC converter 10110 actively inverts the DC power into AC power, thereby transferring the battery energy to the AC power grid, and completing the charging and discharging process. In addition, the charging and discharging device unit 1011 contains process information of converting direct current into alternating current, and the charging and discharging data of the bidirectional AC/DC converter 10110 and the bidirectional DC/DC converter 10111 in the charging and discharging device unit 1011, namely, field energy consumption data, are collected in real time, so that the energy consumption condition of the field charging and discharging process can be clearly reflected, and further analysis of the system is facilitated. In an embodiment of the present invention, the field data acquisition module 101 includes a plurality of charging and discharging device units 1011, wherein each charging and discharging device unit 1011 includes at least one bidirectional AC/DC converter 10110, at least one bidirectional DC/DC converter 10111, and a common DC bus 10112, thereby facilitating simultaneous charging and discharging of a plurality of battery devices, such as BT1 to BTn. In still another embodiment of the present invention, the charging and discharging device unit 1011 includes a plurality of bidirectional AC/DC converters 10110 and a plurality of bidirectional DC/DC converters 10111, and further includes a common DC bus 10112, wherein each bidirectional AC/DC converter 10110 and each bidirectional DC/DC converter 10111 cooperate with each other to complete a charging and discharging process of one battery, and ensure effectiveness of charging and discharging.
And the imaging unit 1012 is used for performing thermal imaging on the rechargeable battery and collecting characteristic data of the rechargeable battery. Therefore, the imaging unit 1012 can ensure that the battery is thermally imaged in the charging and discharging process of the battery, collect the thermal characteristics of the battery, and the thermal characteristics of the battery can clearly reflect the energy consumption state of the battery, are characteristic data fully reflecting the state of the battery, are beneficial to further analysis of a system, and ensure that the best energy-saving effect is obtained by utilizing complete and comprehensive data. In an embodiment of the present invention, the imaging unit 1012 includes a plurality of imaging devices, so that feature data of a plurality of battery devices can be collected at the same time.
And a bar code scanning unit 1013 for scanning bar code data of the rechargeable battery and collecting the bar code data to correspond to the characteristic data. Therefore, the number of the rechargeable batteries on site is generally multiple, each battery bar code is scanned, and the battery bar codes are collected so as to correspond to the thermal characteristics of the same battery, so that the accuracy of data collection is ensured, and the further analysis of the system is facilitated. In an embodiment of the present invention, the barcode scanning unit 1013 includes a plurality of barcode scanning devices, so that barcode data of a plurality of battery devices is collected at the same time.
In the embodiment of the present invention, the charging and discharging device unit 1011 is in bidirectional communication connection with the data conversion module 102, so as to transmit the site energy consumption data collected by the charging and discharging device unit 1011 to the data conversion module 102 for centralized conversion, so that the system is convenient for transferring and processing the site energy consumption data. In the embodiment of the invention, the imaging unit 1012 and the barcode scanning unit 1013 are respectively connected with the data conversion module 102 in a two-way communication manner, so that the characteristic data acquired by the imaging unit 1012 and the barcode data acquired by the barcode scanning unit 1013 are transmitted to the data conversion module 102 for centralized conversion, and the transmission and the processing of the whole characteristic data and the barcode data by the system are facilitated.
In the embodiment of the present invention, as seen in fig. 2 and 3, the field data acquisition module 101 is connected to the ac power grid module 105 and the battery module 106, so as to complete data acquisition in the charging and discharging process. The specific access mode is that the alternating current power grid module 105 is electrically connected with the charging and discharging equipment unit 1011, the charging and discharging equipment unit 1011 is electrically connected with the battery module 106, and the battery module 106 is respectively connected with the imaging unit 1012 and the bar code scanning unit 1013. Thus, the ac power grid module 105 provides ac power, converts the ac power into dc power through the charging and discharging device unit 1011, and transmits the dc power to the battery module 106, and the imaging unit 1012 and the barcode scanning unit 1013 maintain data acquisition on the battery module 106 during the process, so as to perform real-time data uploading.
In particular, the invention is better explained in connection with fig. 2 and 4. Fig. 4 is a schematic structural diagram of a data conversion module 102 according to an embodiment of the present invention, including a data collection unit 1021 and a network switching unit 1022, where:
The data centralizing unit 1021 is configured to centralize and process the raw data transmitted by the field data collection module 101, and transmit the raw data to the network switching unit 1022. Therefore, different data collected in the field charging and discharging process are concentrated, such as the field energy consumption data, the characteristic data and the bar code data are concentrated, the system is convenient to process the data, and the effectiveness of data transmission is ensured.
The network switching unit 1022 is configured to convert the raw data after centralized processing into identifiable data. The raw data generally comprises real-time data such as current amount and voltage amount collected in the field, and cannot be identified by a computer, so that the raw data needs to be converted into identifiable data through the network switching unit 1022, and the system is convenient for processing and transmitting the data.
In the embodiment of the present invention, the data concentration unit 1021 is in bidirectional communication connection with the network switching unit 1022, so that bidirectional transmission of data between the data concentration unit 1021 and the network switching unit 1022 is ensured, so that processing steps of data concentration and then conversion are performed.
In the embodiment of the present invention, as seen in fig. 2 and 4, the data centralized unit 1021 is respectively connected with the charging and discharging device unit 1011 (including the bidirectional AC/DC converter 10110, the bidirectional DC/DC converter 10111 and the common DC bus 10112), the imaging unit 1012 and the barcode scanning unit 1013 in a bidirectional communication manner, so that the field energy consumption data transmitted by the charging and discharging device unit 1011, the characteristic data transmitted by the imaging unit 1012 and the barcode data transmitted by the barcode scanning unit 1013 are centralized, thereby facilitating the further transmission of the data and ensuring the efficient data transmission.
In particular, the invention is better explained in connection with fig. 2 and 5. Fig. 5 is a schematic structural diagram of an energy saving management module 103 according to an embodiment of the present invention, including a database unit 1031 and an intelligent terminal unit 1032, where:
the database unit 1031 is configured to store the identifiable data transmitted by the data conversion module 102. The full storage of data is accomplished by the database unit 1031.
The intelligent terminal unit 1032 stores the identifiable data, determines corresponding index data according to the identifiable data, and determines the optimal energy-saving operation data by passing the preprocessed identifiable data through a pre-trained deep learning model. In the embodiment of the invention, the index data comprises whole data of a charging and discharging system, charging and discharging loop data, single battery state data, single charging and discharging equipment data, energy information statistical data and alarm information data, and is mainly obtained from identifiable data through different statistical analysis methods, and the acquired data is intelligently analyzed through an intelligent terminal unit 1032, so that the obtained index data is used as an index for measuring the energy saving effect. In the embodiment of the invention, the identifiable data is preprocessed to obtain the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge-discharge time data, the charge-discharge waveform data and the rest time data, the preprocessed data is input into a deep learning model trained in advance to obtain the optimal energy-saving operation data, and the optimal energy-saving operation data comprises equipment parameters, power distribution data and start-stop data of each charge-discharge equipment at a working site, for example, so that an operation basis is provided for an operator, and the operator can quickly implement an intelligent energy-saving strategy to achieve the effect of intelligent energy-saving management.
In the embodiment of the present invention, the database unit 1031 is connected with the intelligent terminal unit 1032 in a bi-directional communication manner, so as to ensure the bi-directional transmission of data between the database unit 1031 and the intelligent terminal unit 1032, so as to implement the processing steps of storing data before intelligent analysis.
In the embodiment of the present invention, as seen in fig. 2 and 5, the database unit 1031 is connected to the network switching unit 1022 in a bi-directional communication manner, so that bi-directional data transmission between the database unit 1031 and the intelligent terminal unit 1032 is ensured, so that the database unit 1031 stores the identifiable data transmitted by the network switching unit 1022.
In an embodiment of the present invention, as seen in connection with fig. 2 and 5, the database unit 1031 includes an energy consumption information database 10311, a battery information database 10312, and a production database 10313. By arranging a plurality of databases, the data can be conveniently classified and stored, and the data can be conveniently analyzed. The energy consumption information database 10311 stores site energy consumption data including, for example, voltage information, current information, power, discharge electric energy statistics, charge electric energy statistics transmitted to the charge and discharge device unit 1011 by the ac power grid module 105; the battery information database 10312 stores feature data including, for example, battery internal resistance information, battery temperature information, fault record information, and alarm information, and barcode data; the production database stores operational records and production process files. Therefore, the data is classified and stored, so that the perfection and reliability of the system analysis data are ensured.
In particular, the invention is better explained in connection with fig. 2 and 6. Fig. 6 is a schematic structural diagram of an energy-saving visualization platform module 104 according to an embodiment of the present invention, including a visualization unit 1041 and an operation unit 1042, where:
A visualization unit 1041, configured to visualize the index data and the optimal energy-saving operation data transmitted by the energy-saving management module 103. Through the processing of the visualization unit 1041, an operator can intuitively recognize the energy consumption situation, the energy saving effect and the optimal energy saving operation data. In the embodiment of the invention, the displayed index data comprises the whole data of a charging and discharging system, the data of a charging and discharging loop, the data of a single battery state, the data of a single charging and discharging device, the statistical data of energy information and the data of alarm information, the displayed optimal energy-saving operation data comprises the device parameters, the power distribution data and the start-stop data of each charging and discharging device of a working site, and the battery which can enable the electric energy from an alternating-current power supply to be transmitted to the working device at maximum efficiency according to the optimal energy-saving operation data.
The operation unit 1042 is used for issuing an energy-saving operation instruction according to the optimal energy-saving operation data so as to transmit the energy-saving operation instruction to the production site. Through the operation unit 1042, an operator can perform real-time operation according to the optimal energy-saving operation data, so as to achieve the purpose of real-time control of energy saving. The optimal energy-saving operation data comprise equipment operation data, power distribution data and start-stop data, equipment parameters of the whole system are adjusted, the equipment can be guaranteed to charge the alternating current power into the battery at maximum efficiency, the power distribution data are adjusted, the power source of the whole alternating current power grid can be utilized to the maximum extent, the start-stop data are adjusted, the working equipment in a resting state or a loading and unloading standby state is prevented from wasting power, and the work of the charging and discharging equipment for charging and discharging the working equipment is stopped in time, so that energy is saved.
In the embodiment of the invention, the visualized content of the visualization unit 1041 comprises basic communication, management display, application display and comprehensive display, and the display content is storage service condition, bus service condition, real-time service condition and network system condition through a basic communication module; the management display module is used for displaying the content as a state management service, a device management service, a task management service, a data management service, an intelligent alarm service and a log management service; the display content is online monitoring, production management, equipment management and intelligent alarm through an application display module; the comprehensive display module is used for displaying the content as index data and optimal energy-saving operation data, wherein the optimal energy-saving operation data comprises overall data of a charging and discharging system, charging and discharging loop data, single battery state data, single charging and discharging equipment data, energy information statistical data and alarm information data, and the optimal energy-saving operation data comprises equipment operation data, power distribution data and start-stop data.
The embodiment of the first aspect of the invention provides an intelligent charge-discharge energy-saving management system for a visual battery, which is used for acquiring raw data in the charge-discharge process of a production site, converting the raw data, comprehensively and intelligently analyzing the raw data to obtain related index data and directly measuring the overall energy-saving effect. And deep learning is utilized to extract characteristics, excavate energy-saving space, formulate energy-saving strategy, obtain the best operation data of the whole equipment, visualize the best operation data, guide operators to adjust equipment operation parameters according to the best operation data, optimize equipment operation modes, realize real-time supervision and real-time operation, and fully meet the energy-saving requirements of enterprises.
The embodiment of the second aspect of the invention provides a control method of an intelligent charge-discharge energy-saving management system of a visual battery, which is used for controlling the intelligent charge-discharge energy-saving management system of the visual battery.
Fig. 7 is a flow chart of a control method of the visual battery intelligent charge-discharge energy-saving management system according to an embodiment of the invention, including steps S1 to S4.
In step S1, raw data during charging and discharging of the rechargeable battery in the production site is collected online. The on-site data is collected in real time, so that the subsequent data transmission and processing are facilitated, and operators can know on-site charge and discharge conditions in real time.
In step S2, the raw data is collected and converted into identifiable data that can be identified by a computer. The data is conveniently identified and processed by a computer through centralized conversion.
In step S3, identifiable data is stored, corresponding index data is determined according to the identifiable data, the preprocessed identifiable data is input to a pre-trained deep learning model, optimal energy-saving operation data is determined, and the index data and the optimal energy-saving operation data are transmitted to the energy-saving visualization platform module. The data is analyzed and processed intelligently, so that the optimal operation data is obtained simply and quickly, an operator is helped to determine relevant energy-saving operation, and efficient energy saving is facilitated.
In step S4, the index data and the optimal energy-saving operation data are visualized, and an energy-saving operation instruction is issued to transmit the energy-saving operation instruction to the working equipment of the production site, the energy-saving operation instruction being determined by the optimal energy-saving operation data. Through the visualization processing, relevant operators can intuitively recognize the energy-saving effect through relevant data, the energy-saving effect visualization is achieved, and the energy-saving requirement of enterprises is fully met.
Fig. 8 is a schematic flow chart of the process of collecting the original data according to the embodiment of the invention, including steps S11 to S12.
In step S11, field energy consumption data is collected from the charging and discharging device unit 1011. Therefore, the charging and discharging device unit 1011 includes the bidirectional AC/DC converter 10110, the bidirectional DC/DC converter 10111 and the common DC bus 10112, so that the charging and discharging device unit 1011 includes the voltage information, the current information, the power, the discharging power statistics information and the charging power statistics information transmitted to the charging and discharging device unit 1011 by the AC power grid module 105, and the above information is comprehensively collected and called as the field energy consumption data, and the bidirectional AC/DC converter 10110 and the bidirectional DC/DC converter 10111 in the charging and discharging device unit 1011 collect various data, so as to facilitate the comprehensive analysis of the field power consumption condition by the system.
In step S12, feature data and barcode data are acquired, the feature data is acquired from the imaging unit 1012, and the barcode data is acquired from the barcode unit 1013. In the embodiment of the invention, the characteristic data comprise, for example, internal resistance information of the battery, temperature information of the battery, fault record information and alarm information, and various data are collected from the imaging unit 1012, so that the system can conveniently and comprehensively analyze the rechargeable battery, and barcode data are collected, so that the transmission corresponding to the characteristic data is convenient, and the accuracy of the data is ensured.
Fig. 9 is a schematic flow chart of centralized conversion of original data according to an embodiment of the present invention, including steps S21 to S22.
In step S21, the field energy consumption data, the feature data, and the barcode data are subjected to data centralized processing. Through the dataset for subsequent integrated analysis of the data.
In step S22, the collected site energy consumption data, feature data and barcode data are subjected to data conversion to be converted into identifiable data for computer identification. And the related data conversion is carried out so as to facilitate the analysis and the processing of a computer, thereby ensuring the effective transmission of the data.
In the embodiment of the invention, the best energy-saving operation data is obtained according to the identifiable data through the deep learning model, and the method comprises the steps of S411 to S412:
In step S411, the identifiable data is preprocessed to obtain battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge-discharge time data, charge-discharge waveform data, and shelf time data. The battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge and discharge time data, the charge and discharge waveform data and the shelf time data are obtained by analyzing and counting the converted field energy consumption data, characteristic data and barcode data. Therefore, the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge and discharge time data, the charge and discharge waveform data and the rest time data can be extracted from the identifiable data, so that the data redundancy is avoided, and the data are used as the charge and discharge characteristics of the whole charge and discharge operation process. It is to be understood that, in other embodiments of the present invention, the data extracted after the preprocessing may be adjusted according to the system requirement, which is not limited thereto.
In step S412, the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge-discharge time data, the charge-discharge waveform data, and the rest time data are input to the deep learning model, so as to obtain the optimal energy-saving operation data, and the optimal energy-saving operation data is used to transfer the maximum efficiency of the ac power obtained from the ac power grid to the battery. Therefore, the characteristics are learned by deep learning, so that the optimal energy-saving operation data of the whole charge and discharge operation process is obtained, the data is rapidly analyzed in real time by using a deep learning model, the optimal energy-saving operation data is obtained, an operator can conveniently adjust equipment parameters according to the optimal energy-saving operation data, and real-time effective energy-saving operation is ensured.
Fig. 10 is a schematic flow chart of training a deep learning model according to an embodiment of the present invention, including steps S51 to S53.
In step S51, sample set data is collected, and a sample set is created. In an embodiment of the invention, the sample set data is derived from field energy consumption data, feature data and barcode data collected from the production site.
In step S52, feature extraction is performed on the sample set data, resulting in overall feature data. In the embodiment of the invention, the overall characteristic data comprises battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge and discharge time data, charge and discharge waveform data and shelf time data. It can be understood that the above general feature data can be adjusted according to the actual application requirements of the system, and is not limited thereto.
In step S53, RBM (RESTRICTED BOLTZMANN MACHINE, limited boltzmann machine) self-training is performed on the feature data, model parameters are obtained by unsupervised learning, and the trained model is stored. In the embodiment of the invention, the RBM self-training is used for effectively extracting the optimal energy-saving operation data of the whole equipment. It will be appreciated that in embodiments of the present invention, other effective ways of training the learning model may be used to enable feature extraction of key parameters required by the system. And inputting battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge-discharge time data, charge-discharge waveform data and shelf time data which are obtained by preprocessing the identifiable data into a trained model to obtain optimal energy-saving operation data, wherein the optimal energy-saving operation data comprises equipment parameter data, power distribution data and start-stop data which enable the whole system to perform charge-discharge operation at maximum efficiency, and the system is adjusted according to the equipment parameter data, the power distribution data and the start-stop data in the optimal energy-saving operation data, so that the system is efficiently and quickly adjusted, alternating current is converted and used at maximum efficiency, and intelligent and efficient energy saving is realized.
Fig. 11 is a schematic flow chart of implementation of the energy saving management strategy according to the embodiment of the present invention, including steps S61 to S62.
In step S61, the operator issues an energy-saving operation instruction through the energy-saving visualization platform module 104 according to the optimal energy-saving operation data.
In step S62, the device parameter data, the power distribution data, and the start-stop data of the on-site charging/discharging device unit 1011 are adjusted according to the energy-saving operation instruction, so that the devices on the production site operate according to the optimal energy-saving operation data. Therefore, parameters of the field device are adjusted according to the optimal energy-saving operation data, so that alternating current energy of an alternating current power grid on site is transmitted to the battery with maximum efficiency, and the charging efficiency is ensured. In the embodiment of the invention, the maximum efficiency of electric energy conversion is ensured by adjusting the equipment parameter data, the power distribution data and the start-stop data contained in the charge-discharge equipment unit 1011.
The embodiment of the second aspect of the invention provides a control method of the intelligent charge-discharge energy-saving management system of the visual battery, which is based on the intelligent charge-discharge energy-saving management system of the visual battery, and the control method is used for efficiently completing the control of the energy-saving management system by the steps of collecting data, converting the data, intelligently analyzing and processing the data and visualizing the data, providing an energy-saving strategy for an operator intelligently, enabling the operator to know the on-site charge-discharge condition in real time and effectively saving energy. In summary, the control method provided by the invention intelligently analyzes the collected data of the whole operation site, simply and quickly obtains the optimal operation data through deep learning, helps operators to determine related energy-saving operation, and utilizes a visualization technology to enable users to intuitively recognize the energy-saving effect through the related data, so that the energy-saving effect visualization is achieved, and the energy-saving requirement of enterprises is fully met.
Although the present disclosure is described above, the scope of protection of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and these changes and modifications will fall within the scope of the invention.
Claims (9)
1. The visual battery intelligent charging and discharging energy-saving management system is characterized by comprising a field data acquisition module, a data conversion module, an energy-saving management module and an energy-saving visual platform module, wherein:
the field data acquisition module comprises a charging and discharging equipment unit, an imaging unit and a bar scanning unit, and is used for acquiring original data in the charging and discharging process of the rechargeable battery on the production field on line, wherein the original data comprises field energy consumption data, characteristic data and bar code data;
The charging and discharging equipment unit comprises a bidirectional AC/DC converter, a bidirectional DC/DC converter and a public DC bus, and is used for rectifying alternating current provided by an alternating current power grid into direct current required by the rechargeable battery, inverting the direct current into the alternating current, and collecting on-site energy consumption data, wherein the on-site energy consumption data comprises voltage information, current information, power, discharge electric energy statistical information and charging electric energy statistical information;
the imaging unit is used for thermally imaging the rechargeable battery and collecting the characteristic data of the rechargeable battery, wherein the characteristic data comprises battery internal resistance information, battery temperature information, fault record information and alarm information;
the bar scanning unit is used for scanning the bar code data of the rechargeable battery and collecting the bar code data to correspond to the characteristic data;
The data conversion module is used for centralizing the original data and converting the original data into identifiable data which can be identified by a computer;
The energy-saving management module is used for storing and preprocessing the identifiable data; determining corresponding index data according to the identifiable data; inputting the preprocessed identifiable data into a pre-trained deep learning model, and determining optimal energy-saving operation data, wherein the preprocessed identifiable data comprises battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge-discharge time data, charge-discharge waveform data and shelf time data, the index data comprises charge-discharge system integral data, charge-discharge loop data, single battery state data, single charge-discharge equipment data, energy information statistical data and alarm information data, and the optimal energy-saving operation data comprises equipment parameters, power distribution data and start-stop data of charge-discharge equipment;
the energy-saving visualization platform module is used for visualizing the index data and the optimal energy-saving operation data, and is also used for issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to working equipment on the production site, adjusting the equipment parameters of the working equipment according to the optimal energy-saving operation data so as to enable alternating current electric energy acquired from the alternating current power grid to be transmitted to the rechargeable battery at maximum efficiency, adjusting the power distribution data and the start-stop data, and determining the energy-saving operation instruction according to the optimal energy-saving operation data.
2. The visual battery intelligent charge-discharge energy saving management system of claim 1, wherein the data conversion module comprises a data centralization unit and a network switching unit, wherein:
The data centralization unit is used for centralizing the original data transmitted by the field data acquisition module and transmitting the data to the network switching unit;
The network switching unit is used for converting the raw data after centralized processing into the identifiable data.
3. The visual battery intelligent charge and discharge energy saving management system of claim 1, wherein the energy saving management module comprises a database unit and an intelligent terminal unit, wherein:
the database unit is used for storing the identifiable data transmitted by the data conversion module;
the intelligent terminal unit is used for analyzing the identifiable data in a statistical mode to obtain the index data, inputting the identifiable data into the pre-trained deep learning model and determining the optimal energy-saving operation data.
4. The visualization battery intelligent charge and discharge energy saving management system of claim 1, wherein the energy saving visualization platform module comprises a visualization unit and an operation unit, wherein:
the visualization unit is used for visualizing the index data and the optimal energy-saving operation data transmitted by the energy-saving management module;
The operation unit is used for selectively issuing the energy-saving operation instruction according to the optimal energy-saving operation data.
5. A control method of a visual battery intelligent charge-discharge energy-saving management system for controlling the visual battery intelligent charge-discharge energy-saving management system according to any one of claims 1 to 4, comprising:
collecting original data in the charging and discharging process of the rechargeable battery on the production site on line;
centralizing the original data, and converting the original data into identifiable data which can be identified by a computer;
storing the identifiable data, determining corresponding index data according to the identifiable data, inputting the identifiable data into a pre-trained deep learning model, and determining optimal energy-saving operation data;
And visualizing the index data and the optimal energy-saving operation data, and issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment of the production site, wherein the energy-saving operation instruction is determined according to the optimal energy-saving operation data.
6. The method for controlling the intelligent charge-discharge energy-saving management system for the visual battery according to claim 5, wherein the raw data comprises field energy consumption data, characteristic data and bar code data, and the on-line acquisition of the raw data in the process of charging and discharging the rechargeable battery at the production site comprises the following steps:
the field energy consumption data are collected, wherein the field energy consumption data are collected from a charging and discharging equipment unit of a field data collection module;
And acquiring the characteristic data and the bar code data, wherein the characteristic data is acquired from an imaging unit of the field data acquisition module, and the bar code data is acquired from a bar code unit of the field data acquisition module.
7. The method for controlling a visual battery intelligent charge-discharge energy-saving management system according to claim 6, wherein the concentrating the raw data, converting the raw data into identifiable data recognizable by a computer, comprises:
carrying out data centralized processing on the field energy consumption data, the characteristic data and the bar code data;
and carrying out data conversion on the concentrated field energy consumption data, the characteristic data and the bar code data, and converting the concentrated field energy consumption data, the characteristic data and the bar code data into the identifiable data which can be identified by a computer.
8. The method for controlling the intelligent charge-discharge energy-saving management system for the visual battery according to claim 7, wherein the index data comprises overall charge-discharge system data, charge-discharge loop data, single battery state data, single charge-discharge equipment data, energy information statistical data and alarm information data.
9. The control method of the visual battery intelligent charge-discharge energy saving management system according to any one of claims 5 to 8, wherein the inputting the identifiable data into the pre-trained deep learning model, determining the optimal energy saving operation data, comprises:
preprocessing the identifiable data, and extracting battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge and discharge time data, charge and discharge waveform data and shelf time data;
And inputting the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charge and discharge time data, the charge and discharge waveform data and the shelf time data into the pre-trained deep learning model to obtain the optimal energy-saving operation data, wherein the optimal energy-saving operation data comprises equipment parameter data, power distribution data and start-stop data for enabling a production site to operate in an optimal energy-saving mode.
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