CN111126825A - Intelligent charge-discharge energy-saving management system for visual battery and control method thereof - Google Patents

Abstract

The invention provides a visual intelligent battery charging and discharging energy-saving management system and a control method thereof, which relate to the technical field of charging and discharging energy saving and comprise the following steps: acquiring original data in the charging and discharging process of a production field on line; centralizing original data, and converting the original data into identifiable data for computer identification; 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 visualizing the index data and the optimal energy-saving operation data, and issuing an energy-saving operation instruction according to the optimal energy-saving operation data. According to the intelligent charging and discharging energy-saving management system, the deep learning is utilized to process the related data acquired in the charging and discharging process, the optimal energy-saving operation data of the system is obtained, and an operator is guided to adjust the power distribution parameters and the battery operation parameters, so that the alternating current electric energy is transmitted to the battery at the maximum efficiency, and the purpose of intelligent charging and discharging energy-saving management is achieved.

Description

Intelligent charge-discharge energy-saving management system for visual battery and control method thereof

Technical Field

The invention relates to the technical field of battery charging and discharging energy conservation, in particular to a visual battery intelligent charging and discharging energy conservation management system and a control method thereof.

Background

With the rapid development of economy, the construction in all aspects of China has drawn remarkable achievements, but the problem of serious resource waste is accompanied, so that the contradiction between economic development and 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, but the current enterprises are often serious disaster areas of resource waste of China, and the energy-saving work of the enterprises is made to be a key step for building a resource-saving society of China, so that the enterprises are mainly monitored by related functional departments of China.

According to statistics, 8% of energy loss of the industrial enterprise per year is caused by the lack of an energy monitoring and maintenance plan, and 12% of energy loss of the industrial enterprise per year is caused by the lack of an energy management and control system. Currently, more and more enterprises adopt energy feedback type charging and discharging equipment to charge and discharge batteries required by industrial sites, so energy-saving management of battery charging and discharging is the most important factor for energy-saving work of enterprises. The existing energy-saving management of charging and discharging mainly focuses 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. In addition, the existing energy-saving management of charging and discharging also continuously tries parameter adjustment in various charging and discharging processes through a senior engineer for a long time, and finally selects optimal operation parameters through comparison of a large amount of test data. This process is time consuming and requires a great deal of experience to achieve a good result.

In summary, the conventional charging and discharging energy-saving management method mainly depends on equipment replacement and a means of improving the control process according to experience to achieve the purpose of energy saving. On the one hand, however, the existing energy-saving management method needs huge investment of manpower, material resources and time to improve the energy-saving effect, and the cost investment is too large; on the other hand, the existing energy-saving management method cannot accurately locate the energy waste point, the energy-saving effect needs to be verified through additional means, real-time analysis is difficult, and measures are taken in real time, so that the energy-saving requirement of an enterprise cannot be fully met.

Disclosure of Invention

In view of the above, the present invention is directed to solving, at least to some extent, the technical problems in the related art. In order to achieve the above object, the present invention provides an intelligent charging and discharging energy-saving management system for a visual battery, which includes 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 is used for acquiring original data of the rechargeable battery in the charging and discharging process on the production field 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 issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment on the production site, and 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 field, converts the original data, and carries out comprehensive and intelligent analysis to obtain the related index data, thereby directly measuring the overall energy-saving effect. And deep learning is utilized to extract features, an energy-saving space is excavated, an energy-saving strategy is formulated, the optimal operation data of the whole equipment is obtained and visualized, an operator is guided to adjust the operation parameters of the equipment according to the optimal operation data, and the operation mode of the equipment is optimized. In conclusion, the invention intelligently analyzes the acquired data of the whole operation site, simply and quickly obtains the optimal energy-saving operation data through deep learning, so that the operator can perform quick and real-time energy-saving operation according to the optimal energy-saving operation data, and the user can intuitively realize the energy-saving effect through related data by utilizing the visualization technology, thereby realizing visualization, real-time supervision and operation of the energy-saving effect and fully meeting the energy-saving requirements of enterprises.

Further, the raw data comprises field energy consumption data, characteristic data and bar code data, the field data acquisition module comprises a charging and discharging equipment unit, an imaging unit and a bar code 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 acquiring the field energy consumption data; the imaging unit is used for thermally imaging the rechargeable battery and acquiring 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-shaped scanning unit, and the field energy consumption data, the characteristic data and the bar-shaped code data in the charging process are collected, so that the field energy consumption data, the characteristic data and the bar-shaped code data are analyzed 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 centralizing unit and a network switching unit, wherein the data centralizing unit is used for performing centralized processing on the original data transmitted by the field data acquisition module and transmitting the processed original data to the network switching unit; the network switching unit is used for converting the centrally processed original data into the identifiable data.

Therefore, original data are converted into identifiable data through the data concentration unit and the network switching unit so as to be identified by a computer, and then the data are analyzed and processed, so that the operation speed of the system 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; and 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, data storage is completed through the database unit, intelligent processing and analysis of the data are completed through the intelligent terminal unit, the obtained index data are used as indexes for measuring the energy-saving effect, the obtained optimal energy-saving operation data provide operation basis for operators, and 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; and the operation unit is used for selecting and issuing the energy-saving operation instruction according to the optimal energy-saving operation data.

Therefore, through the processing of the visualization unit, an operator can visually recognize the energy consumption condition, the energy-saving effect and the optimal energy-saving operation data, and meanwhile, through the operation unit, the operator can carry out real-time operation, and the purpose of controlling energy conservation in real time and in time is achieved according to the operation of the optimal energy-saving operation data.

In order to achieve the above object, a second object of the present invention is to provide a control method for a visual battery intelligent charging and discharging energy-saving management system, for controlling the visual battery intelligent charging and discharging energy-saving management system, including:

acquiring original data of a rechargeable battery in a charging and discharging process on a production site on line;

centralizing the raw data, and converting the raw 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, issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment on 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, and the control method efficiently finishes the control of the energy-saving management system through the steps of data acquisition, data conversion, intelligent analysis and processing of data and visual data, provides an energy-saving strategy for the intelligence of operators, enables the operators to know the on-site charge-discharge conditions in real time, and effectively saves energy. In conclusion, the control method provided by the invention intelligently analyzes the acquired data of the whole operation field, obtains the optimal operation data simply and quickly through deep learning, helps operators to determine the relevant energy-saving operation, enables users to intuitively recognize the energy-saving effect through the relevant data by utilizing the visualization technology, realizes the visualization of the energy-saving effect, and fully meets the energy-saving requirements of enterprises.

Further, the raw data comprises field energy consumption data, characteristic data and barcode data, and the online acquisition of the raw data in the charging and discharging process of the rechargeable battery in the production field comprises:

acquiring the field energy consumption data, wherein the field energy consumption data is acquired from a charging and discharging equipment unit of a field data acquisition module;

and collecting the characteristic data and the barcode data, wherein the characteristic data is collected from an imaging unit of the field data collection module, and the barcode data is collected from a barcode unit of the field data collection module.

Therefore, relevant data of on-site charging and discharging energy consumption and battery conditions are comprehensively obtained through acquisition of on-site energy consumption data, characteristic data and bar code data, and further analysis and processing are carried out on the basis of the data, so that an analysis result is more comprehensive, objective and reliable.

Further, the centralizing the raw data, converting the raw data into recognizable data for computer recognition, includes:

performing 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 to obtain identifiable data which can be identified by a computer.

Therefore, the data is integrated and analyzed through the data set, and then the related data conversion is carried out so as to facilitate the analysis processing of the computer, thereby ensuring the effective transmission of the data.

Further, the index data includes the whole data of the charge and discharge system, the charge and discharge loop data, the state data of a single battery, the data of a single charge and discharge device, the statistical data of energy information and the alarm information data.

Therefore, the data is integrated and analyzed through the data set, and then the related data conversion is carried out, so that the analysis and the processing of a computer are facilitated, and the effective transmission of the data is ensured.

Further, the inputting the recognizable data into a pre-trained deep learning model to determine 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-discharge time data, charge-discharge waveform data and shelving time data;

and inputting 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 resting 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 which enable the optimal energy-saving operation of a production site.

Therefore, battery voltage data, battery current data, battery internal resistance data, battery capacity data, charging and discharging time data, charging and discharging waveform data and shelving time data are extracted from the identifiable data, data redundancy is avoided, the data serve as characteristics of the whole charging and discharging operation process, the characteristics are learned through deep learning, the optimal energy-saving operation data enabling the whole charging and discharging operation process are obtained, accordingly, the data are analyzed rapidly in real time through a deep learning model, the optimal energy-saving operation data are obtained, 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 charging and discharging energy-saving management system according to an embodiment of the present invention;

fig. 2 is a schematic diagram of a system framework of a visual battery intelligent charging and discharging energy-saving management system according to an embodiment of the present invention;

FIG. 3 is a schematic structural 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 present invention;

fig. 5 is a schematic structural diagram of an energy 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 flowchart illustrating a control method of a visual battery intelligent charging/discharging energy-saving management system according to an embodiment of the present invention;

FIG. 8 is a schematic flow chart illustrating the collection of raw data according to an embodiment of the present invention;

FIG. 9 is a flow chart illustrating a process of collectively converting raw data according to an embodiment of the present invention;

FIG. 10 is a schematic flow chart of deep learning model training according to an embodiment of the present invention;

fig. 11 is a flowchart illustrating implementation of an energy saving management policy according to an embodiment of the present invention.

Detailed Description

Embodiments in accordance with the present invention will now be described in detail with reference to the drawings, wherein like reference numerals refer to the same or similar elements throughout the different views unless otherwise specified. It is to be noted that the embodiments described in the following exemplary embodiments do not represent all embodiments of the present invention. They are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the claims, and the scope of the present disclosure is not limited in these respects. Features of the various embodiments of the invention may be combined with each other without departing from the scope of the invention.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.

The existing charging and discharging energy-saving management method mainly depends on the means of replacing equipment and improving a control process according to experience to achieve the aim of saving energy. Firstly, the cost investment of the existing energy-saving management method is overlarge, the implementation process is complex, huge manpower, material resources and time are required to be invested to improve the energy-saving effect, and the lifting space has uncertainty; secondly, the energy waste point cannot be accurately positioned by the existing energy-saving management method, so that the excavation of an energy-saving space cannot be started, the energy-saving effect cannot be seen, the energy-saving effect cannot be measured or analyzed, 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 rapidly taken in real time, and the energy-saving requirements of enterprises cannot be fully met.

The embodiment of the first aspect of the invention provides a visual intelligent battery charging and discharging energy-saving management system. Fig. 1 is a schematic structural diagram of a first visual battery intelligent charging and discharging 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 visualization platform module 104, where:

and the field data acquisition module 101 is used for acquiring original data of the rechargeable battery in the charging and discharging process on the production field on line. In the embodiment of the present 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 connected in a bidirectional communication manner, so as to ensure the bidirectional transmission of data between the field data acquisition module 101 and the data conversion module 102. In the embodiment of the invention, the original data comprises field energy consumption data, characteristic data and bar code data, wherein the field energy consumption data comprises voltage information, current information, power, discharge electric energy statistical information and charging electric energy statistical information; the characteristic data includes, for example, battery internal resistance information, battery temperature information, fault record information, alarm information; the bar code data comprises bar code information of each battery to correspond to characteristic information, so that all-dimensional information of a site is collected, the system can conveniently analyze the data in all directions, accurate index data and optimal energy-saving operation data can be obtained, and real-time monitoring with high accuracy is realized.

And the data conversion module 102 is used for centralizing the original data and converting the original data into identifiable data which can be identified by a computer. The original data collected on site are converted into recognizable data, so that the computer can recognize 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 the identifiable data to the energy saving management module 103, and the data conversion module 102 is in bidirectional communication with the energy saving management module 103 to ensure bidirectional transmission of data between the data conversion module 102 and the energy saving management module 103.

And the energy-saving management module 103 is used for storing the identifiable data, analyzing the identifiable data to obtain corresponding index data, using the identifiable data as the input of a pre-trained deep learning model, and determining the optimal energy-saving operation data through the deep learning model. In the embodiment of the invention, the energy-saving management module 103 is used for carrying out comprehensive intelligent analysis on the acquired data to obtain related index data, and the overall energy-saving effect is directly measured through the index data. Meanwhile, the preprocessed identifiable data are used as the input of a deep learning model, and the optimal energy-saving operation data are obtained through the deep learning model trained in advance, wherein the optimal energy-saving operation data comprise equipment parameters, power distribution data and start-stop data of each charging and discharging device on a working site, so that an energy-saving space is excavated, the optimal operation data of the whole device are obtained in real time, and efficient real-time energy saving is realized. The invention realizes the monitoring of the production site through index data, timely reflects the site charging condition, such as voltage, current, electric energy and other information to operators, and utilizes the deep learning model to carry out autonomous learning and intelligent analysis on identifiable data to obtain optimal energy-saving operation data, thereby facilitating the operators to carry out intelligent management strategy according to the optimal operation data, and timely issue operation instructions to adjust the equipment parameter data, power distribution data and start-stop data of the whole system, thereby realizing optimal energy saving. In the embodiment of the present invention, the energy-saving management module 103 transmits the index data and the optimal energy-saving operation data to the energy-saving visualization platform module 104, and the energy-saving management module 103 is in bidirectional communication with the energy-saving visualization platform module 104, so as to ensure bidirectional transmission of data between the energy-saving management module 103 and the energy-saving visualization platform module 104.

And the energy-saving visualization platform module 104 is used for visualizing the index data and the optimal energy-saving operation data and issuing an energy-saving operation instruction, wherein the energy-saving operation instruction is selected by a user according to the optimal energy-saving operation data. The method comprises the steps of visualizing index data through visual operation, directly displaying information such as voltage, current and electric energy of a production site through charts or data, enabling an operator to visually perceive energy consumption conditions and energy-saving effects, enabling optimal energy-saving operation data to be visualized, guiding the operator to implement an intelligent management strategy according to the displayed optimal operation data, issuing instructions, adjusting a system, adjusting equipment parameters of the whole system, ensuring that the equipment can charge alternating current electric energy into a battery with maximum efficiency, adjusting distribution data, ensuring that the power supply of the whole alternating current power grid can be utilized to the maximum degree, adjusting start-stop data, preventing the waste of the power supply of working equipment in a shelving state or a loading-unloading standby state, and stopping charging and discharging equipment for charging and discharging the working equipment in time, so as to save energy. In conclusion, the energy-saving visualization platform module 104 is used for facilitating the issuing of instructions by operators, optimizing the operation mode of the equipment and ensuring real-time monitoring and real-time operation.

Fig. 2 is a schematic diagram of a system framework of a visual battery intelligent charging and discharging 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 visualization platform module 104, an alternating current grid module 105, a battery module 106, and exemplary specific components of each module. Through the mutual cooperation of all the modules, the whole system achieves the effects of high efficiency and real-time energy saving. In the embodiment of the present invention, the ac power grid module 105 is configured to provide an ac power source, during the charging process, the battery module 106 is configured to charge the electric energy of the ac power grid module 105, and the battery module 106 is configured to receive the electric energy provided by the ac power grid and provide the electric energy for the working equipment at the working site. The intelligent charge-discharge energy-saving management system for the visual battery 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 a 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 the embodiment of the present invention. The field data acquisition module 101 includes a charging and discharging device unit 1011, an imaging unit 1012, and a bar scan unit 1013, wherein the charging and discharging 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 present invention is better explained in conjunction 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 and discharging device unit 1011 may be used to rectify the AC power provided by the AC power grid into a suitable DC bus voltage, invert the DC bus voltage source into AC power, and acquire energy consumption data on the AC side of the site. The bidirectional DC/DC converter 10111 in the charging and discharging device unit 1011 may be used to convert the voltage of the common DC bus 10112 into an accurate DC voltage source or DC current source required by the battery, and may also be used to collect energy consumption data of the battery side in the field. Charging and discharging equipment unit the charging and discharging equipment unit is used for supporting the voltage of the system common direct current bus 10112, and reducing the energy consumption of the bidirectional DC/DC converter 10111 from the bidirectional AC/DC converter 10110. Thus, during charging, the bidirectional AC/DC converter 10110 rectifies the alternating current, and then charges the rechargeable battery through the bidirectional DC/DC converter 10111; during discharging, the bidirectional AC/DC converter 10110 actively inverts the direct current into alternating current, so as to transfer the battery energy to an alternating current power grid, and the charging and discharging process is completed. In addition, the charging and discharging equipment unit 1011 contains process information of converting direct current into alternating current and converting alternating current into direct 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 equipment unit 1011, namely the field energy consumption data, can clearly reflect the energy consumption situation of the field charging and discharging process, and is beneficial to further analysis of the system. In the embodiment of the present invention, the field data collecting module 101 includes a plurality of charging and discharging equipment units 1011, wherein each of the charging and discharging equipment units 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 equipments, such as BT1 to BTn. In another embodiment of the present invention, the charging and discharging equipment 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 a battery, and ensure the effectiveness of the charging and discharging.

And an imaging unit 1012 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 and the thermal characteristics of the battery are collected in the charging and discharging process of the battery, the thermal characteristics of the battery can clearly reflect the energy consumption state of the battery, the characteristic data can sufficiently reflect the battery state, the further analysis of the system is facilitated, and the optimal energy-saving effect can be obtained by utilizing complete and comprehensive data. In an embodiment of the present invention, imaging unit 1012 includes multiple imaging devices to facilitate the simultaneous acquisition of characterization data for multiple battery devices.

The barcode scanning unit 1013 is configured to scan barcode data of the rechargeable battery, and acquire the barcode data to correspond to the feature data. Therefore, the number of the rechargeable batteries on site is generally multiple, so that each battery barcode is scanned, and the battery barcodes are collected so as to correspond to the thermal characteristics of the same battery, thereby ensuring the accuracy of data collection and being beneficial to further analysis of a system. In the embodiment of the present invention, the barcode scanning unit 1013 includes a plurality of barcode scanning devices, so as to facilitate simultaneous acquisition of barcode data of a plurality of battery devices.

In the embodiment of the present invention, the charging and discharging device unit 1011 is in bidirectional communication with the data conversion module 102, so as to transmit the field energy consumption data collected by the charging and discharging device unit 1011 to the data conversion module 102 for centralized conversion, which is convenient for the system to transmit and process the field energy consumption data. In the embodiment of the present invention, the imaging unit 1012 and the barcode scanning unit 1013 are respectively in bidirectional communication connection with the data conversion module 102, so as to transmit the feature data collected by the imaging unit 1012 and the barcode data collected by the barcode scanning unit 1013 to the data conversion module 102 for centralized conversion, thereby facilitating the transmission and processing of the whole feature data and barcode data by the system.

In the embodiment of the present invention, as shown in fig. 2 and fig. 3, the field data collection module 101 is connected to the ac power grid module 105 and the battery module 106 to complete data collection during charging and discharging. The specific access mode is that the ac power grid module 105 is electrically connected to the charging and discharging device unit 1011, the charging and discharging device unit 1011 is electrically connected to the battery module 106, and the battery module 106 is respectively connected to the imaging unit 1012 and the barcode scanning unit 1013. Therefore, the ac power grid module 105 provides an ac power source, and is rectified into a dc power by 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 keep data acquisition on the battery module 106 in the process, so as to upload data in real time.

In particular, the present invention is better explained in conjunction with fig. 2 and 4. Fig. 4 is a schematic structural diagram of the data conversion module 102 according to an embodiment of the present invention, which includes a data concentration unit 1021 and a network transit unit 1022, wherein:

the data centralizing unit 1021 is configured to perform centralized processing on the raw data transmitted by the field data collecting 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, for example, the field energy consumption data, the characteristic data and the bar code data are concentrated, the data are conveniently processed by a system, and the effectiveness of data transmission is guaranteed.

The network switching unit 1022 is configured to convert the centrally processed raw data into identifiable data. The raw data generally includes real-time data such as current amount and voltage amount collected on site, and thus cannot be identified by a computer, so that it needs to be converted into identifiable data by the network switching unit 1022, which facilitates processing and transmission of the data by the system.

In the embodiment of the present invention, the data concentration unit 1021 is in bidirectional communication with the network forwarding unit 1022, which ensures bidirectional data transmission between the data concentration unit 1021 and the network forwarding unit 1022, so as to facilitate the processing step of data concentration and conversion.

In the embodiment of the present invention, as seen in fig. 2 and 4, the data concentrating unit 1021 is respectively connected to 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 as to perform centralized processing on the field energy consumption data transmitted by the charging and discharging device unit 1011, the feature data transmitted by the imaging unit 1012 and the barcode data transmitted by the barcode scanning unit 1013, which facilitates further data transmission and ensures efficient data transmission.

In particular, the present invention is better explained in conjunction with fig. 2 and 5. Fig. 5 is a schematic structural diagram of the energy-saving management module 103 according to the embodiment of the present invention, which includes a database unit 1031 and an intelligent terminal unit 1032, where:

and a database unit 1031 for storing the identifiable data transmitted by the data conversion module 102. The overall storage of data is accomplished by the database unit 1031.

And the intelligent terminal unit 1032 stores the recognizable data, determines corresponding index data according to the recognizable data, and determines the optimal energy-saving operation data according to the preprocessed recognizable data through a pre-trained deep learning model. In the embodiment of the invention, the index data comprises the whole data of the charging and discharging system, the charging and discharging loop data, the state data of a single battery, the data of a single charging and discharging device, the statistical data of energy information and the alarm information data, which are obtained from the identifiable data mainly by different statistical analysis methods, and the intelligent analysis is carried out on the collected data by the intelligent terminal unit 1032, so that the obtained index data is used as the index for measuring the energy-saving effect. In the embodiment of the invention, the recognizable data is preprocessed to obtain battery voltage data, battery current data, battery internal resistance data, battery capacity data, charging and discharging time data, charging and discharging waveform data and shelving time data, the preprocessed data is input into a deep learning model which is trained in advance to obtain optimal energy-saving operation data, and the optimal energy-saving operation data comprises equipment parameters, power distribution data and start-stop data of each charging and discharging device on a working site, so that the optimal energy-saving operation data provides an operation basis for an operator, the operator can rapidly implement an intelligent energy-saving strategy, and the effect of intelligent energy-saving management is achieved.

In the embodiment of the present invention, the database unit 1031 is in bidirectional communication connection with the intelligent terminal unit 1032, which ensures bidirectional transmission of data between the database unit 1031 and the intelligent terminal unit 1032, so as to implement a processing step of first storage and then intelligent analysis of data.

In the embodiment of the present invention, as shown in fig. 2 and fig. 5, the database unit 1031 is in bidirectional communication connection with the network forwarding unit 1022, so as to ensure bidirectional transmission of data between the database unit 1031 and the intelligent terminal unit 1032, so that the database unit 1031 stores identifiable data transmitted by the network forwarding unit 1022.

In the embodiment of the present invention, as seen in 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 the 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 field energy consumption data, which includes, for example, voltage information, current information, power, statistical information of discharged electric energy, and statistical information of charged electric energy transmitted to the charging and discharging device unit 1011 by the ac power grid module 105; the battery information database 10312 stores characteristic data and barcode data, the characteristic data including, for example, battery internal resistance information, battery temperature information, fault record information, and alarm information; the production database stores operation records and generates process files. Therefore, the integrity and the reliability of the system analysis data are ensured by classified storage of the data.

In particular, the present invention is better explained in conjunction with fig. 2 and 6. Fig. 6 is a schematic structural diagram of the 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:

and 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 condition, 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 the charging and discharging system, the charging and discharging loop data, the single battery state data, the single charging and discharging equipment data, the energy information statistical data and the alarm information data, the displayed optimal energy-saving operation data comprises the equipment parameters, the power distribution data and the start-stop data of each charging and discharging equipment in a working site, and the battery which can transmit the electric energy in the alternating current power supply to the working equipment with the maximum efficiency is operated according to the optimal energy-saving operation data.

The operation unit 1042 is configured to issue an energy saving operation instruction according to the optimal energy saving operation data to transmit the energy saving operation instruction to the production site. Through the operation unit 1042, the operator can perform real-time operation, and the purpose of real-time control and energy saving can be achieved according to the optimal energy-saving operation data. The optimal energy-saving operation data comprises 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 with maximum efficiency, the power distribution data are adjusted, the power of the whole alternating current power grid can be guaranteed to be utilized to the maximum, the start-stop data are adjusted, the power of working equipment in a shelving state or a charging and discharging standby state is prevented from being wasted, and the work of 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 present invention, the visualized content of the visualization unit 1041 includes basic communication, management display, application display, and comprehensive display, and the displayed content is a storage service condition, a bus service condition, a real-time service condition, and a network system condition through the basic communication module; the method comprises the steps that through a management display module, display contents are state management service, equipment management service, task management service, data management service, intelligent alarm service and log management service; the display contents comprise online monitoring, production management, equipment management and intelligent warning through an application display module; the comprehensive display module is used for displaying the content of the data which is index data and optimal energy-saving operation data, wherein the data comprises the whole data of a charge-discharge system, the data of a charge-discharge loop, the state data of a single battery, the data of single charge-discharge equipment, the statistical data of energy information and the data of alarm information, and the optimal energy-saving operation data comprises the operation data of equipment, the distribution data and the start-stop data.

The embodiment of the first aspect of the invention provides a visual intelligent battery charging and discharging energy-saving management system, which is used for acquiring original data in a charging and discharging process of a production field, converting the original data, carrying out comprehensive and intelligent analysis to obtain related index data and directly measuring the overall energy-saving effect. And deep learning is utilized to extract features, an energy-saving space is excavated, an energy-saving strategy is formulated, the optimal operation data of the whole equipment is obtained and visualized, an operator is guided to adjust the operation parameters of the equipment according to the optimal operation data, the operation mode of the equipment is optimized, real-time supervision and real-time operation are realized, and the energy-saving requirements of enterprises are fully met.

The embodiment of the second aspect of the invention provides a control method of a visual battery intelligent charging and discharging energy-saving management system, which is used for controlling the visual battery intelligent charging and discharging energy-saving management system.

Fig. 7 is a flowchart illustrating a control method of the visual battery intelligent charging/discharging energy-saving management system according to the embodiment of the present invention, including steps S1 to S4.

In step S1, raw data of the rechargeable battery during charging and discharging at the production site is collected online. By acquiring the field data in real time, the transmission and processing of subsequent data are facilitated, and operators can know the field charging and discharging conditions in real time.

In step S2, the raw data is collected and converted into recognizable data for recognition by the computer. By converting the data in a centralized way, the data is convenient to be recognized and processed by a computer.

In step S3, the recognizable data are stored, the corresponding index data are determined according to the recognizable data, the preprocessed recognizable data are input to the pre-trained deep learning model, the optimal energy-saving operation data are determined, and the index data and the optimal energy-saving operation data are transmitted to the energy-saving visualization platform module. Data are intelligently analyzed and processed, optimal operation data are simply and quickly obtained, operators are helped to determine relevant energy-saving operation, and efficient energy conservation is facilitated.

In step S4, the visual index data and the optimal energy-saving operation data are used to issue an energy-saving operation command to transmit the energy-saving operation command to the working equipment on the production site, wherein the energy-saving operation command is determined by the optimal energy-saving operation data. Through visual processing, relevant operators can visually recognize the energy-saving effect through relevant data, the energy-saving effect is visual, and the energy-saving requirements of enterprises are fully met.

Fig. 8 is a schematic flow chart illustrating the process of collecting raw data according to the embodiment of the present invention, which includes steps S11 to S12.

In step S11, the field energy consumption data is collected, and the field energy consumption data is collected from the charge and discharge equipment unit 1011. Therefore, the charging and discharging equipment unit 1011 includes the bidirectional AC/DC converter 10110, the bidirectional DC/DC converter 10111 and the common DC bus 10112, and therefore includes the voltage information, the current information, the power, the statistical information of the discharging power and the statistical information of the charging power transmitted from the AC power grid module 105 to the charging and discharging equipment unit 1011, the above information is comprehensively collected and called as field energy consumption data, and various data are collected through the bidirectional AC/DC converter 10110 and the bidirectional DC/DC converter 10111 in the charging and discharging equipment unit 1011, so as to facilitate the comprehensive analysis of the field power consumption situation by the system.

In step S12, feature data and barcode data are collected, the feature data being collected from the imaging unit 1012, and the barcode data being collected from the barcode unit 1013. In the embodiment of the present invention, the characteristic data includes, for example, battery internal resistance information, battery temperature information, fault record information, and alarm information, and various data are collected from the imaging unit 1012, so as to facilitate the system to comprehensively analyze the rechargeable battery, and bar code data is collected, so as to facilitate transmission corresponding to the characteristic data, thereby ensuring the accuracy of the data.

Fig. 9 is a flowchart illustrating a process of collectively converting original data according to an embodiment of the present invention, which includes steps S21 to S22.

In step S21, the field energy consumption data, the characteristic data, and the barcode data are collectively processed. Through the data set for subsequent integrated analysis of the data.

In step S22, the collected field energy consumption data, characteristic data, and barcode data are converted into recognizable data for recognition by a computer. The data is converted by the relevant data so as to facilitate the analysis processing of the computer, thereby ensuring the effective transmission of the data.

In the embodiment of the present invention, obtaining the optimal energy saving operation data according to the recognizable data through the deep learning model includes steps 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 charging and discharging time data, the charging and discharging waveform data and the resting time data are obtained by analyzing and counting the converted field energy consumption data, the characteristic data and the bar code data. Therefore, the battery voltage data, the battery current data, the battery internal resistance data, the battery capacity data, the charging and discharging time data, the charging and discharging waveform data and the resting time data can be extracted from the identifiable data, data redundancy is avoided, and the data are used as the charging and discharging characteristics of the whole charging and discharging operation process. It is understood that, in other embodiments of the present invention, the data extracted after the preprocessing may be adjusted according to system requirements, and 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 resting time data are input to the deep learning model to obtain the optimal energy-saving operation data, and the operation is performed according to the optimal energy-saving operation data so as to transfer the ac power obtained from the ac power grid to the battery with the maximum efficiency. Therefore, the characteristics are learned by deep learning to obtain the optimal energy-saving operation data of the whole charging and discharging operation process, so that the data are analyzed quickly in real time by using a deep learning model 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 and effective energy-saving operation is guaranteed.

Fig. 10 is a schematic flowchart of deep learning model training according to an embodiment of the present invention, which includes steps S51 to S53.

In step S51, sample set data is acquired, and a sample set is created. In an embodiment of the invention, the sample set data is derived from field energy consumption data, characterization data, and barcode data collected from the production field.

In step S52, feature extraction is performed on the sample set data, resulting in overall feature data. In the embodiment of the present invention, the overall characteristic data includes 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 is understood that the above general characteristic 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) self-training is performed on the feature data, model parameters are obtained through unsupervised learning, and the trained model is saved. In the embodiment of the invention, the best energy-saving operation data of the whole equipment is effectively extracted through RBM self-training. It is understood that, in the embodiment of the present invention, the learning model may also be trained in other effective manners, so that the learning model can perform feature extraction on the key parameters required by the system. The method comprises the steps of inputting battery voltage data, battery current data, battery internal resistance data, battery capacity data, charging and discharging time data, charging and discharging waveform data and shelving time data which are obtained by preprocessing identifiable data into a trained model to obtain optimal energy-saving operation data, wherein the optimal energy-saving operation data comprise equipment parameter data, power distribution data and start-stop data which enable the whole system to be charged and discharged at the maximum efficiency, and adjusting the system according to the equipment parameter data, the power distribution data and the start-stop data in the optimal energy-saving operation data to realize efficient and rapid adjustment of the system, enable alternating current to be converted and used at the maximum efficiency and realize intelligent and efficient energy conservation.

Fig. 11 is a flowchart illustrating implementation of an energy saving management policy according to an embodiment of the present invention, including steps S61 to S62.

In step S61, the operator issues an energy saving operation command 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 work site charging/discharging device unit 1011 are adjusted according to the energy saving operation command, so that the devices in the production site operate according to the optimal energy saving operation data. Therefore, the parameters of the field equipment are adjusted according to the optimal energy-saving operation data, so that the alternating current electric energy of the on-site alternating current power grid is transmitted to the battery at the maximum efficiency, and the charging efficiency is ensured. In the embodiment of the present invention, the maximum efficient power conversion is ensured by adjusting the device parameter data, the power distribution data, and the start-stop data included in the charge/discharge device unit 1011.

The embodiment of the second aspect of the invention provides a control method of a visual battery intelligent charging and discharging energy-saving management system, based on the visual battery intelligent charging and discharging energy-saving management system, the control method efficiently completes control on the energy-saving management system through the steps of data acquisition, data conversion, intelligent analysis and processing of data and visual data, provides an energy-saving strategy for operators intelligently, enables the operators to know the field charging and discharging conditions in real time, and effectively saves energy. In conclusion, the control method provided by the invention intelligently analyzes the acquired data of the whole operation field, obtains the optimal operation data simply and quickly through deep learning, helps operators to determine the relevant energy-saving operation, enables users to intuitively recognize the energy-saving effect through the relevant data by utilizing the visualization technology, realizes the visualization of the energy-saving effect, and fully meets the energy-saving requirements of enterprises.

Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present disclosure, and such changes and modifications will fall within the scope of the present invention.

Claims (10)

1. The utility model provides a visual battery intelligence charge-discharge energy-saving management system which characterized in that, includes field data acquisition module, data conversion module, energy-conserving management module and energy-conserving visual platform module, wherein:

the field data acquisition module is used for acquiring original data of the rechargeable battery in the charging and discharging process on the production field 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 issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment on the production site, and the energy-saving operation instruction is determined according to the optimal energy-saving operation data.

2. The intelligent charging and discharging energy-saving management system for the visual battery according to claim 1, wherein the raw data comprises field energy consumption data, characteristic data and bar code data, the field data acquisition module comprises a charging and discharging equipment unit, an imaging unit and a bar code scanning unit, and the intelligent charging and discharging energy-saving management system comprises:

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 acquiring the field energy consumption data;

the imaging unit is used for thermally imaging the rechargeable battery and acquiring 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.

3. The intelligent charging and discharging energy-saving management system for visual batteries according to claim 1, wherein the data conversion module comprises a data centralizing unit and a network switching unit, wherein:

the data centralizing unit is used for centralizing and processing the original data transmitted by the field data acquisition module and transmitting the processed original data to the network switching unit;

the network switching unit is used for converting the centrally processed original data into the identifiable data.

4. The intelligent charging and discharging energy-saving management system for visual batteries according to 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;

and the intelligent terminal unit is used for analyzing the identifiable data by using 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.

5. The intelligent charging and discharging energy-saving management system for visual batteries according to claim 1, wherein the energy-saving visual platform module comprises a visual 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;

and the operation unit is used for selecting and issuing the energy-saving operation instruction according to the optimal energy-saving operation data.

6. A control method of a visual battery intelligent charging and discharging energy-saving management system is used for controlling the visual battery intelligent charging and discharging energy-saving management system as claimed in any one of claims 1-5, and is characterized by comprising the following steps:

acquiring original data of a rechargeable battery in a charging and discharging process on a production site on line;

centralizing the raw data, and converting the raw 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, issuing an energy-saving operation instruction to transmit the energy-saving operation instruction to the working equipment on the production site, wherein the energy-saving operation instruction is determined according to the optimal energy-saving operation data.

7. The control method of the intelligent charging and discharging energy-saving management system for the visual battery according to claim 6, wherein the raw data comprises field energy consumption data, characteristic data and barcode data, and the online collection of the raw data in the charging and discharging process of the rechargeable battery at the production field comprises:

acquiring the field energy consumption data, wherein the field energy consumption data is acquired from a charging and discharging equipment unit of a field data acquisition module;

and collecting the characteristic data and the barcode data, wherein the characteristic data is collected from an imaging unit of the field data collection module, and the barcode data is collected from a barcode unit of the field data collection module.

8. The control method of the intelligent charging and discharging energy-saving management system for the visual battery according to claim 7, wherein the centralizing the original data and converting the original data into recognizable data for the computer to recognize comprises the following steps:

performing 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 to convert the data into the identifiable data which can be identified by a computer.

9. The control method of the intelligent charging and discharging energy-saving management system for the visual battery according to claim 8, wherein the index data comprises charging and discharging system overall data, charging and discharging loop data, single battery state data, single charging and discharging equipment data, energy information statistical data and alarm information data.

10. The control method of the visual battery intelligent charging and discharging energy-saving management system according to any one of claims 6 to 9, wherein the step of inputting the identifiable data into a pre-trained deep learning model to determine optimal energy-saving operation data comprises the following steps:

preprocessing the identifiable data, and extracting battery voltage data, battery current data, battery internal resistance data, battery capacity data, charge-discharge time data, charge-discharge waveform data and shelving time data;

and inputting 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 resting 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 which enable the optimal energy-saving operation of a production site.

Images