CN116317096A - Drawer type sampling control cabinet system based on artificial intelligence - Google Patents
Drawer type sampling control cabinet system based on artificial intelligence Download PDFInfo
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- H02J13/00007—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using the power network as support for the transmission
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- H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
- H02J13/00016—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using a wired telecommunication network or a data transmission bus
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Abstract
The invention provides a drawer type acquisition and control cabinet system based on artificial intelligence, which comprises an acquisition and control array and an acquisition and control platform based on artificial intelligence, wherein the acquisition and control array is formed by connecting opposite units serving as units in a communication way in the acquisition and control cabinet-feeder line cabinet, and the acquisition and control cabinet and the feeder line cabinet in the acquisition and control array are in drawer cabinet form so as to realize the physical formation mode of the array and become drawer type acquisition and control cabinet and drawer type feeder line column; the system comprises a front-end display module, a service module, a general module, a data system and a data receiving module, wherein a mining control-feeder line communication business adopts a discrete time-staggered and discrete zero line physical scheme, an LSTM artificial intelligent power distribution function in the service module can analyze and process time sequence power carrier signals sent by a mining control array according to the data system to obtain the prediction of power consumption of users at all moments, and the prediction is compared with a real-time power consumption result to determine power distribution, so that electric energy is utilized in an overall efficient and energy-saving manner.
Description
Technical Field
The invention relates to a drawer type acquisition and control cabinet system, in particular to a drawer type acquisition and control cabinet system based on artificial intelligence, and belongs to the field of artificial intelligence electric power acquisition and control.
Background
The electric power system adopts the adoption control cabinet as a necessary means for realizing electric power use and electric power data statistics of urban users, can monitor the electric power use condition of each empty user in real time, and provides an electric power data report for the users. But because the electricity consumption of each user has the characteristics of the user, including the time distribution characteristic, the total electric quantity characteristic and the data of the intelligent home sensor. Thereby meeting the individual power consumption of different users.
However, although the bus power supply scheme can meet the requirements of the electricity load of most users, due to the existence of the individuality of the users, the electricity consumption is saved or the energy consumption utilization rate is much better than that of the traditional optimistic for the modern household electricity, especially the intelligent household electricity, so that different grading measures are adopted on the aspect of power distribution to adapt to the individuality.
On the other hand, the user's personality may also be fine-tuned over time and thus not constant. Therefore, how to dynamically allocate power supply is a technical problem to be solved.
Disclosure of Invention
To solve the above problem, we use the following general design scheme: in the first hardware aspect, a mining control array consisting of a drawer type mining control cabinet and a feeder cabinet is adopted to integrate load data acquisition, intelligent home data acquisition and ammeter data acquisition of a user; and secondly, considering the form of an outgoing circuit carrier signal, and realizing total acquisition, total processing and total analysis and total customization of big data at the upper service terminal. According to the analyzed power distribution characteristics of each user and the result of the total electric quantity, the personalized customization of the users is embodied, and the intelligent distribution of the electric power is realized; thirdly, taking the aspects of electricity safety and abrupt change of electricity demand into consideration, mainly realizing temperature measurement of electrical equipment in a control cabinet, calculating whether possible load is abnormal or not, and timely adjusting allocation by abrupt change of electricity habit or mode of a user.
Based on the above consideration, the present invention provides an artificial intelligence based drawer type sampling control cabinet system, which is characterized by comprising: a mining control array and a mining control platform based on artificial intelligence, wherein,
the acquisition and control array is an array formed by connecting a plurality of units in parallel as units in communication with the interior of the acquisition and control cabinet-feeder cabinet,
preferably, a plurality of feeder cabinets in the array are electrically connected in parallel to realize power data acquisition of a plurality of areas, and each feeder cabinet in the array is electrically connected with a lower-level distribution electric box and a point load to realize power data acquisition of a corresponding user.
Preferably, the electrical connection employs a power carrier wave to transmit the power data,
the acquisition control cabinet and the feeder line cabinet in the acquisition control array are in drawer cabinet form so as to realize the physical formation mode of the array and form drawer-type acquisition control cabinet and drawer-type feeder line column, wherein the drawer-type feeder line column is applicable to GCS/GCK/NMS/equal drawer-type feeder line cabinet
Preferably, the drawer cabinet is a dilatable drawer frame array with multiple cabinet layers, the drawer cabinet can be increased or decreased according to different acquisition and control and the number requirement of feeder lines, and the sizes of the drawers pushed into the cabinet layers and the cabinet layers are adjustable so as to be suitable for accommodating devices in the drawers without occupying the ground.
It will be appreciated that each control cabinet corresponds to one or more feeder columns to enable the collection of power data for a single or multiple users, with different drawer levels representing different areas (e.g., different cells, or different buildings of the same cell).
Each drawer of the drawer type acquisition and control cabinet comprises a 220V three-phase alternating current switch, a concentrator, a wireless temperature measuring device, an environment collector, an acquisition and control device, an ammeter collector and a 24V direct current power supply device, wherein the concentrator comprises a power end and a network line end, the network line end is used for being connected to the exchanger through a network line so as to realize the transmission of power carrier signals for power data acquisition, processing, analysis and real-time power distribution, the ABC three-phase end and the zero line end of the 220V three-phase alternating current switch are connected with the power end in the concentrator, and the phase end and the zero line end of the wireless temperature measuring device, the acquisition and control device and the 24V direct current power supply device in the drawer are respectively connected with the UC and the zero line end N of the 220V three-phase alternating current switch in parallel; the zero line ends of the environment collector and the acquisition controller are separately connected to the power end of the concentrator and are respectively used for collecting necessary analysis data for real-time power distribution and sensing the power data of each load of a user in real time;
preferably, the wireless thermometry and the ammeter acquisition are performed discretely and at staggered times.
Each drawer of the feed line array comprises an ammeter, an electric operation device and an electric control controller, wherein the ammeter 485 protocol is connected with an A end and a B end of an ammeter collector, the electric operation device is electrically connected with the electric control controller, the electric control controller is connected with an anode and a cathode of a 24V direct current power supply device and the A end and the B end of the collector, and power carrier signals generated by different loads in the electric control controller are transmitted to the collector through the A end and the B end in 485 protocol and are input into the power end of the concentrator through a zero line branch; the A and B end 485 protocol of the environment collector is connected with a temperature and humidity sensor, a carbon dioxide sensor, a water logging sensor and a door entering face recognition sensor for real-time distribution of electric power of a power distribution room and/or a user room, signals of the sensors are also input into the electric power end of the concentrator through a zero line branch,
when conditions such as temperature and humidity, carbon dioxide and water immersion of a power distribution room and/or a user house occur, safety problems (such as excessive power application, rain and snow leakage, open fire generation and water flooding) of power use can be caused, so that the power department or a user can know the field conditions through the transmission of power carrier signals; after at least one user enters the door to face recognition, the environment collector knows that the user enters the door, and electricity consumption needs can be increased except for common electric appliances, so that the power department can change the power distribution scheme in real time by transmitting power carrier signals through the environment collector, and the overall power distribution efficiency is improved, so that the waste of power resources is avoided. For example, the power consumption of offices may be increased when a person in charge enters, the power consumption of power supply instruments or equipment which needs to be started for a period of time may be increased when engineers enter, the power consumption of very open electric appliances in a place may be developed, or the power consumption of power supply instruments or equipment which needs to be started for a period of time may be customized by the engineers.
The acquisition control platform based on artificial intelligence comprises a front end display module, a service module, a general module, a data system and a data receiving module, wherein the front end display module comprises a digital large screen, a management page, a user terminal app and other addable applets;
the business module comprises an early warning and alarming processing module, a report management module, an operation and maintenance management module and a data analysis module;
the universal module comprises a login module, an authorization module, an information maintenance module, a template management module and an equipment management module;
the data system comprises: a relational database, a time series database;
the data receiving module comprises a data processing console, wherein
The data processing console performs classification receiving processing on the power data output by the concentrator network cable end and is used as a data base on which the follow-up analysis of the data and the real-time distribution of the power are based;
the time sequence database is used for respectively associating different mining control cabinets, feeder line cabinets and users, and the time sequence database stores the change condition of power data of the power consumption of the users along with time;
the login and authorization module is used for operating the platform by an operator to obtain the control right, the information maintenance module is used for maintaining the information and the operation information of the user and the operator, and the template management and the equipment management are used for respectively managing the platform function division and the templates set by the user for setting the function use preference and managing the acquisition control cabinets and the feeder cabinets;
in the service module, the early warning and alarming processing module is responsible for early warning information management, alarming information management and self-defined safe remote operation, and the operation and maintenance management module comprises equipment running state management; the data analysis module is used for performing data analysis functions such as electric load measurement, energy consumption analysis, electric load analysis, energy consumption comparison analysis and the like by using time sequence data in the time sequence database, and the report management module is used for providing a device operation and maintenance report, an early warning alarm report and an energy consumption analysis report; wherein,,
the custom secure remote operation includes: the user remotely performs remote switching-on operation on the idle opening of each load loop at any time, performs switching-on switching-off operation on the idle opening according to the time period defined by the user, and performs power distribution adjustment through the artificial intelligent power distribution function of the data analysis module.
The artificial intelligence power distribution is realized by the following steps:
s1, collecting power data based on energy consumption analysis and power load analysis, and corresponding time sequences;
s2, establishing an LSTM artificial intelligent network, and predicting power data of users on each time unit by taking each moment as an LSTM time unit;
s3, inputting the power data of the user into the corresponding time unit in real time, predicting the power data at any time after the power data, continuously comparing the power data predicted by the next time unit with the current real power data from the input time, if the difference value of the power data predicted by the next time unit and the current real power data is within a preset range, not performing power distribution adjustment, and if the difference value of the power data is out of the preset range, performing power real-time distribution, and improving or reducing the power distribution power allowed at the current time, wherein the preset range is 100-500W.
The method for establishing the LSTM artificial intelligent network in the step S2 comprises the following steps:
s2-1, dividing the power data into a training set and a verification set on each time node of time sequence, wherein the proportion is 5-3:1-2;
s2-2, sequencing the training set according to time sequence to form and establish a prediction truth value vector
The first time unit vector h (0) Using zero vector activation, forming a first predictor via the output weight U>
And a first true value p 1 The first loss function L is obtained through cross entropy loss function calculation 1 ；
S2-3 adding the first predicted value to
Inputting the first input end, and obtaining a first input vector E in a first input layer through inputting the weight E (1) ，e (1) By assigning weights W e Input to the second time unit, and hidden layer h at the first time (0) By propagation of vector W h Propagating to obtain a second time unit vector h (1) ；
S2-4 second time cell vector h (1) Forming a second predicted value through the output weight U
And a second true value p 2 The first loss function L is obtained through cross entropy loss function calculation 2 ；
S2-5 re-comparing the second predicted value
Inputting the second input end, repeating the steps S2-3-S2-4 to obtain a loss function L at the nth moment n Adding the loss functions obtained at all times to obtain a total loss function l=l 1 +L 2 +…L n And adjusting each weight through back propagation, optimizing the LSTM until the L value tends to be stable, and ending the training with the maximum prediction accuracy.
The scheme of the invention has the following beneficial effects:
1. the system adopts an array cabinet structure of drawer type acquisition and control and feeder line communication, so that physical and chemical device integration is realized by monitoring each power utilization partition, 2, the device is configured to realize the transmission of power carrier code-free decoding by adopting a combination technology of discrete time-staggered and discrete zero lines according to the characteristics of acquisition, 3, the power utilization quantity of each time point of a user is predicted by an LSTM artificial intelligent algorithm of an acquisition and control platform, the power utilization habit of the user is identified, and the power is intelligently distributed by monitoring the change of the power utilization quantity in real time, so that a high-efficiency energy-saving power distribution scheme is realized.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
The above, as well as additional objectives, advantages, and features of the present invention will become apparent to those skilled in the art from the following detailed description of a specific embodiment of the present invention when read in conjunction with the accompanying drawings.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
figure 1 is a schematic structural diagram of the acquisition control array of embodiment 1 of the present invention,
figure 2 is a schematic diagram of the configuration and communication connection modes of the internal devices of the drawer type acquisition and control cabinet and the drawer type feeder line,
figure 3 an artificial intelligence based mining control platform construction logic diagram of embodiment 2 of the present invention,
FIG. 4 is a schematic diagram of the LSTM artificial intelligent network in accordance with the embodiment 3 of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Example 1
The embodiment provides a drawer type adopts accuse cabinet system based on artificial intelligence, its characterized in that includes: a mining control array and a mining control platform based on artificial intelligence, wherein,
as shown in fig. 1, the acquisition and control array is an array formed by connecting a plurality of units in parallel as units in communication with the interior of the acquisition and control cabinet-feeder cabinet. The concrete control cabinet is composed of a plurality of cabinet layers with adjustable sizes, and the feeder cabinet is also composed of a plurality of cabinet layers with adjustable sizes, so that the expandable drawer frame arrays are respectively formed. Each cabinet layer of the two drawers is pushed in to form the drawer type acquisition and control cabinet and the drawer type feeder line.
In fig. 1, a drawer of the mining control cabinet is pushed into one of the cabinet layers, and one of the cabinet layers of the feeder cabinet is also pushed into the drawer, and a pair of units of the mining control array for communication are formed between the two units, so that power consumption of a user in a cell is monitored to realize intelligent power distribution.
The distribution and communication modes of devices in the drawer are shown in fig. 2, and each drawer of the drawer type acquisition and control cabinet comprises a 220V three-phase (ABC) alternating current switch, a concentrator (serving as a crop networking gateway), a wireless temperature measuring device in the acquisition and control cabinet, an environment collector and acquisition and control device of a community, an ammeter collector of one cabinet layer and a 24V direct current power supply device.
The concentrator comprises a power end and a network cable end, wherein the network cable end is used for being connected to a switch through a network cable so as to realize the transmission of power carrier signals for power data acquisition, processing, analysis and power real-time distribution, the UA (A phase line) UB (B phase line) UC (C phase line) and the zero line end N of the 220V three-phase alternating current electric brake are connected with the power end in the concentrator, and the phase cable end and the zero line end of the wireless temperature measuring device, the acquisition controller and the 24V direct current power supply device in the drawer are respectively connected with the UC and the zero line end N of the 220V three-phase alternating current electric brake in parallel; the zero line end of the environment collector and the collector controller is also divided into branches, and the branches are separately connected into the power end of the concentrator, and are respectively used for distributing and collecting necessary analysis data for real-time power and sensing the power data of each load of a user in real time;
each drawer of the feed line array comprises an ammeter, an electric operation device and an electric control controller, wherein the ammeter 485 protocol is connected with an A end and a B end of an ammeter collector, the electric operation device is electrically connected with the electric control controller, the electric control controller is connected with an anode and a cathode of a 24V direct current power supply device and the A end and the B end of the collector, and power carrier signals generated by different loads in the electric control controller are transmitted to the collector through the A end and the B end in 485 protocol and are input into the power end of the concentrator through a zero line branch; the A and B ends 485 protocol of the environment collector are connected to temperature and humidity, carbon dioxide and water sensors of a power distribution room and/or a user room and entrance face recognition sensors for real-time power distribution, and signals of the sensors are input to the power end of the concentrator through a zero line branch.
The 220V three-phase alternating current switch is used for respectively supplying power to the wireless temperature measuring device, the ammeter collector, the environment collector, the acquisition controller and the 24V direct current power supply device in the drawer and the operation of the electric operation controller, and the direct current output of the 24V direct current power supply device is used for supplying power to the electric operation part for the operation of the electric operation controller.
The ammeter collector collects the data of the ammeter and inputs the data into the power end of the concentrator through the zero line, the detection result of the wireless temperature measuring device is also used for loading a power carrier signal through the zero line end and inputting the detection data into the power end through the zero line, and the time of wireless temperature measurement and the time of ammeter collection are obviously different in rules, so that the time point can always be staggered without monitoring (time discrete monitoring), and the time point can not cause unexpected hysteresis of temperature measurement and ammeter data. For example, the staggering may be 1 second or less, so even if the meter data monitoring is that the accident occurs in the previous second or less, the monitoring temperature is not affected after the accident occurs, and the temperature abnormality is found, so that the taking of measures still belonging to the timeliness is not hindered; the data acquisition of the ammeter collector can not acquire the total electric quantity in a period of time because of the delay time of the transmitted temperature-measuring power carrier data. And it is considered that anomalies can still be analyzed based on the data from the start and end of the delay (e.g., one second or less before the accident is still normal, and anomalies are then detected after the accident). The same zero line does not produce a phenomenon of data superposition due to such misalignment. And the acquisition controller and the environment collector need to be separated into zero lines due to the need of synchronous continuous monitoring.
It is also considered that different power carrier codes can be adopted to load on carrier signals of different collectors, and the power carrier signals of the different collectors are identified through platform decoding, so that the parallel acquisition of the common zero line of the power carrier data of the different collectors is realized. However, this mathematical means of encoding-decoding requires additional equipment and maintenance costs, and the decoding algorithm is very complex for the multiple devices with the zero line, where we use the physical means of acquisition controller and environmental collector to separate the zero line and temperature measurement and ammeter acquisition time-staggered and discrete, the faults of the zero line and the faults of the acquisition logic are considered to be very low, so that the power ends of the power carrier signals input to the concentrator without affecting each other are realized at low cost. Because the discrete and continuous monitoring signals have time rules, the environment collector and the acquisition controller as well as the ammeter collector and the power carrier signals of the wireless temperature measuring device can be easily identified, and the data can be received on the subsequent acquisition control platform by only one output network cable to distinguish different carrier signals.
Example 2
As shown in fig. 3, the artificial intelligence based acquisition and control platform comprises a front end display module, a service module, a general module, a data system and a data receiving module, wherein the front end display module comprises a digital large screen, a management page, a user terminal app, other addable applets, such as a weather forecast applet, a peripheral traffic condition applet, an electric charge payment management platform connected with the user terminal app and the like;
the business module comprises an early warning and alarming processing module, a report management module, an operation and maintenance management module and a data analysis module;
the universal module comprises a login module, an authorization module, an information maintenance module, a template management module and an equipment management module;
the data system comprises: a relational database, a time series database;
the data receiving module comprises a data processing console, wherein
The data processing console performs classification receiving processing on the power data output by the concentrator network cable end and is used as a data base on which the follow-up analysis of the data and the real-time distribution of the power are based;
the time sequence database is used for respectively associating different mining control cabinets, feeder line cabinets and users, and the time sequence database stores the change condition of power data of the power consumption of the users along with time;
the login and authorization module is used for operating the platform by an operator to obtain the control right, the information maintenance module is used for maintaining the information and the operation information of the user and the operator, and the template management and the equipment management are used for respectively managing the platform function division and the templates set by the user for setting the function use preference and managing the acquisition control cabinets and the feeder cabinets;
in the service module, the early warning and alarming processing module is responsible for early warning information management, alarming information management and self-defined safe remote operation, and the operation and maintenance management module comprises equipment running state management; the data analysis module is used for performing data analysis functions such as electric load measurement (for example, real-time measurement of electric loads of different residential buildings), energy consumption analysis (for example, real-time distribution of electric loads in different buildings), electric load analysis (for example, real-time distribution of electric loads in different buildings), energy consumption comparison analysis (including time-dependent change comparison analysis of the energy consumption of each building and month-dependent energy consumption comparison analysis of a user) and the like by using time sequence data in the time sequence database, and the artificial intelligent power distribution function, and the report management module is used for providing equipment operation and maintenance reports, early warning alarm reports and energy consumption analysis reports; wherein,,
the custom secure remote operation includes: the user remotely performs remote switching-on operation on the idle opening of each load loop at any time, performs switching-on switching-off operation on the idle opening according to the time period defined by the user, and performs power distribution adjustment through the artificial intelligent power distribution function of the data analysis module. The adjusting includes reducing or increasing the power ration. For example, when the power consumption of the user is low, the maximum power allowed by the user is reduced, the total power of the electric appliances which the user must use is maintained at 105-110%, and the maximum power allowed is appropriately increased to the user who is using the peak, for example, 105-110% of the average electric power used throughout the year of the corresponding period of the building in the cell. The habit of using electricity by the user is identified through intelligent calculation, so that corresponding electricity is distributed. When the habit of the user changes, the habit of the user is found in the intelligent calculation result, so that the power distribution is changed in real time.
Example 3
Specifically, artificial intelligence power distribution is achieved by:
s1, collecting power data based on energy consumption analysis and power load analysis, and corresponding time sequences;
s2, establishing an LSTM artificial intelligent network, and predicting power data of users on each time unit by taking each moment as an LSTM time unit;
s3, inputting the power data of the user into the corresponding time unit in real time, predicting the power data at any time after the power data, continuously comparing the power data predicted by the next time unit with the current real power data from the input time, if the difference value of the power data predicted by the next time unit and the current real power data is within a preset range, not performing power distribution adjustment, and if the difference value of the power data is out of the preset range, performing power real-time distribution, and improving or reducing the power distribution power allowed at the current time, wherein the preset range is 300W.
As shown in fig. 4, the method for establishing the LSTM artificial intelligent network in step S2 is as follows:
s2-1, dividing the power data into a training set and a verification set on each time node of time sequence, wherein the proportion is 3:1, a step of;
s2-2, sequencing the training set according to time sequence to form and establish a prediction truth value vector
The first time unit vector h (0) Using zero vector activation, forming a first predictor via the output weight U>
And a first true value p 1 The first loss function L is obtained through cross entropy loss function calculation 1 ；
S2-3 adding the first predicted value to
Inputting the first input end, and obtaining a first input vector E in a first input layer through inputting the weight E (1) ，e (1) By assigning weights W e Input to the second time unit, and hidden layer h at the first time (0) By propagation of vector W h Propagating to obtain a second time unit vector h (1) ；
S2-4 second time cell vector h (1) Forming a second predicted value through the output weight U
And a second true value p 2 Obtaining a second loss function L through cross entropy loss function calculation 2 ；
S2-5 re-comparing the second predicted value
Inputting the second input terminal, repeating steps S2-3-S2-4, and obtaining a second input vector E in the second input layer by inputting the weight E as shown in FIG. 4 (2) ，e (2) By assigning weights W e Input to the third time unit and the hidden layer h at the second time (1) By propagation of vector W h Propagating to obtain a third time unit vector h (2) Third time cell vector h (2) Forming a third predicted value +.>
And a third true value p 3 The third loss function L is obtained through cross entropy loss function calculation 3 The loss function L at the nth time is finally obtained through the circulation n Adding the loss functions obtained at all times to obtain a total loss function l=l 1 +L 2 +…L n And adjusting each weight through back propagation, optimizing the LSTM until the L value tends to be stable, and ending the training with the maximum prediction accuracy.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all technical features thereof can be replaced by others within the spirit and principle of the present invention; such modifications and substitutions do not depart from the scope of the invention.
Claims (10)
1. Drawer type adopts accuse cabinet system based on artificial intelligence, its characterized in that includes: a mining control array and a mining control platform based on artificial intelligence, wherein,
the acquisition and control array is an array formed by connecting a plurality of units in parallel as units in communication with the interior of the acquisition and control cabinet-feeder cabinet,
the picking control cabinet and the feeder line cabinet in the picking control array are in drawer cabinet form, so that the physical forming mode of the array is realized, and the drawer type picking control cabinet and the drawer type feeder line column are formed;
each drawer of the drawer type acquisition and control cabinet comprises a 220V three-phase alternating current switch, a concentrator, a wireless temperature measuring device, an environment collector, an acquisition and control device, an ammeter collector and a 24V direct current power supply device, wherein the concentrator comprises a power end and a network line end, the network line end is used for being connected to the exchanger through a network line so as to realize the transmission of power carrier signals for power data acquisition, processing, analysis and real-time power distribution, the ABC three-phase end and the zero line end of the 220V three-phase alternating current switch are connected with the power end in the concentrator, and the phase end and the zero line end of the wireless temperature measuring device, the acquisition and control device and the 24V direct current power supply device in the drawer are respectively connected with the UC and the zero line end N of the 220V three-phase alternating current switch in parallel; the zero line ends of the environment collector and the acquisition controller are separately connected to the power end of the concentrator and are respectively used for collecting necessary analysis data for real-time power distribution and sensing the power data of each load of a user in real time;
each drawer of the feed line array comprises an ammeter, an electric operation device and an electric control controller, wherein the ammeter 485 protocol is connected with an A end and a B end of an ammeter collector, the electric operation device is electrically connected with the electric control controller, the electric control controller is connected with an anode and a cathode of a 24V direct current power supply device and the A end and the B end of the collector, and power carrier signals generated by different loads in the electric control controller are transmitted to the collector through the A end and the B end in 485 protocol and are input into the power end of the concentrator through a zero line branch; the A end 485 protocol and the B end 485 protocol of the environment collector are connected to temperature and humidity, carbon dioxide and water sensors of a power distribution room and/or a user room and a door entering face recognition sensor for real-time power distribution, and signals of the sensors are also input into the power end of the concentrator through a zero line branch;
the acquisition control platform based on artificial intelligence comprises a front end display module, a service module, a general module, a data system and a data receiving module, wherein the front end display module comprises a digital large screen, a management page, a user terminal app and other addable applets;
the business module comprises an early warning and alarming processing module, a report management module, an operation and maintenance management module and a data analysis module;
the universal module comprises a login module, an authorization module, an information maintenance module, a template management module and an equipment management module;
the data system comprises: a relational database, a time series database;
the data receiving module comprises a data processing console, wherein
The data processing console performs classification receiving processing on the power data output by the concentrator network cable end and is used as a data base on which the follow-up analysis of the data and the real-time distribution of the power are based;
the time sequence database is used for respectively associating different mining control cabinets, feeder line cabinets and users, and the time sequence database stores the change condition of power data of the power consumption of the users along with time;
the login and authorization module is used for operating the platform by an operator to obtain the control right, the information maintenance module is used for maintaining the information and the operation information of the user and the operator, and the template management and the equipment management are used for respectively managing the platform function division and the templates set by the user for setting the function use preference and managing the acquisition control cabinets and the feeder cabinets;
in the service module, the early warning and alarming processing module is responsible for early warning information management, alarming information management and self-defined safe remote operation, and the operation and maintenance management module comprises equipment running state management; the data analysis module is used for performing data analysis functions such as electric load measurement, energy consumption analysis, electric load analysis, energy consumption comparison analysis and the like by using time sequence data in the time sequence database, and the report management module is used for providing a device operation and maintenance report, an early warning alarm report and an energy consumption analysis report; wherein,,
the custom secure remote operation includes: the user remotely performs remote switching-on operation on the idle opening of each load loop at any time, performs switching-on switching-off operation on the idle opening according to the time period defined by the user, and performs power distribution adjustment through the artificial intelligent power distribution function of the data analysis module.
2. The system of claim 1, wherein the artificial intelligence power distribution is achieved by:
s1, collecting power data based on energy consumption analysis and power load analysis, and corresponding time sequences;
s2, establishing an LSTM artificial intelligent network, and predicting power data of users on each time unit by taking each moment as an LSTM time unit;
s3, inputting the power data of the user into the corresponding time unit in real time, predicting the power data at any time after the power data, continuously comparing the power data predicted by the next time unit with the current real power data from the input time, if the difference value of the power data predicted by the next time unit and the current real power data is within a preset range, not performing power distribution adjustment, and if the difference value of the power data is out of the preset range, performing power real-time distribution, and improving or reducing the power distribution power allowed at the current time, wherein the preset range is 100-500W.
3. The system of claim 2, wherein a plurality of feeder cabinets in the array are electrically connected in parallel to enable power data collection in a plurality of areas, and each feeder cabinet in the array is electrically connected with a lower distribution box and a point load to enable power data collection for a corresponding user.
4. The system of claim 3, wherein the electrical connection employs a power carrier to transmit the power data.
5. The system of claim 2, wherein the establishing LSTM artificial intelligence network in step S2 is established by the following method:
s2-1, dividing the power data into a training set and a verification set on a moment node of each time sequence;
s2-2, sequencing the training set according to time sequence to form and establish a prediction truth value vector
The first time unit vector h (0) Using zero vector activation, forming a first predictor via the output weight U>
And a first true value p 1 The first loss function L is obtained through cross entropy loss function calculation 1 ；
S2-3 adding the first predicted value to
Inputting the first input end, and obtaining a first input vector E in a first input layer through inputting the weight E (1) ，e (1) By assigning weights W e Input to the second time unit, and hidden layer h at the first time (0) By propagation of vector W h Propagating to obtain a second time unit vector h (1) ；
S2-4 second time cell vector h (1) Forming a second predicted value through the output weight U
And second (b)True value p 2 The first loss function L is obtained through cross entropy loss function calculation 2 ；
S2-5 re-comparing the second predicted value
Inputting the second input end, repeating the steps S2-3-S2-4 to obtain a loss function L at the nth moment n Adding the loss functions obtained at all times to obtain a total loss function l=l 1 +L 2 +…L n And adjusting each weight through back propagation, optimizing the LSTM until the L value tends to be stable, and ending the training with the maximum prediction accuracy.
6. The system of claim 5, wherein the ratio of training set to validation set is 5-3:1-2.
7. The system of any of claims 1-6, wherein the drawer cabinet is a scalable drawer rack array having multiple cabinet levels, increasing or decreasing according to the number of different pick-and-place and feed line columns requirements, and the size of the drawers pushed into the cabinet levels, as well as the cabinet levels themselves, are adjustable to accommodate the devices in the drawers.
8. The system of claim 7, wherein each cabinet corresponds to one or more feeder columns to enable collection of power data for a single or multiple users, different drawer levels representing different areas.
9. The system of any one of claims 1-6, wherein wireless thermometry and ammeter acquisition are performed discretely at time intervals.
10. The system of any of claims 1-6, wherein the drawer feeder column is adapted for use with a GCS/GCK/NMS drawer feeder cabinet.
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