CN114358555A - Rail transit wisdom energy management system - Google Patents
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Abstract
The invention provides a rail transit intelligent energy management system, which comprises a communication interface platform, a control platform and a control platform, wherein the communication interface platform is used for accessing data of an external system into the intelligent energy management system; the data management platform is used for counting and storing external data accessed through the communication interface platform; the data analysis platform is used for carrying out energy consumption analysis on external data characteristics stored by the data management platform; and the expert decision platform predicts the consumption of the subsequent energy consumption equipment according to the equipment energy consumption model established by the data analysis platform and parameters of equipment operation, and provides a decision for reducing the energy consumption of the energy consumption equipment of the rail transit station according to the historical experience of equipment operation. The rail transit intelligent energy management system solves the problems of energy-saving potential analysis and decision, reduces the load of a server on the basis of meeting basic data requirements, establishes a unified interface with a PSCADA system and a BAS system, and is improved on the basis of the original energy management system to meet the requirements of various systems.
Description
Technical Field
The invention belongs to the technical field of energy management systems, and particularly relates to a rail transit intelligent energy management system.
Background
With the development of scientific technology, the development of rail transit intelligent stations is more mature, application scenes are closer to practical requirements, the development of the intelligent stations is greatly improved, meanwhile, requirements for subordinate systems are more diversified, original functions in subsystems are broken through, data interaction and data analysis functions of the subsystems are increased, and higher requirements are provided for data timeliness, effectiveness and system stability. The energy management system is used as an important subsystem in the field of rail transit and undertakes the work of energy consumption data access, energy consumption data analysis, energy consumption data storage and report making. In the face of new requirements brought forward by smart station development, the energy management system is not only connected with original energy consumption data such as electricity consumption, water consumption and gas consumption data, but also connected with sensor data such as passenger flow, temperature, humidity, carbon dioxide concentration and the like. A large amount of data are generated by the short-time sensor, accumulated for a certain time and converged into massive data, and the massive data are not uploaded to an energy management system or a smart station system platform. The existing requirements are that various energy consumption data and sensor data are further processed at the side of an energy management system, a potential optimization scheme in the energy consumption data is obtained by means of big data and machine learning, the energy consumption utilization rate of equipment is improved, energy conservation and emission reduction are further realized, and the power is contributed to the carbon reduction career.
Disclosure of Invention
In view of this, the present invention provides a rail transit intelligent energy management system, which solves the problem of energy saving potential analysis and decision, reduces the server load on the basis of meeting basic data requirements, establishes a unified interface with a PSCADA system and a BAS system, and performs modification and upgrade on the basis of an original energy management system to meet various system requirements.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a rail transit intelligent energy management system comprises: the communication interface platform is used for accessing data of an external system into the intelligent energy management system and simultaneously is responsible for forwarding analysis and decision data of the intelligent energy management system to the external system;
the data management platform is used for counting and storing external data accessed through the communication interface platform;
the data analysis platform is used for carrying out energy consumption analysis on the external data characteristics stored by the data management platform, establishing an equipment energy consumption model and predicting the consumption condition of subsequent energy consumption equipment by using various parameters of equipment operation;
the data analysis platform comprises a big data analysis module, and the big data analysis module comprises:
the station energy consumption plan and assessment function submodule is used for automatically generating energy consumption statistical data according to a formulated energy consumption plan taking a station as a unit and specific decomposition indexes and automatically assessing various types of energy consumption according to station energy consumption item assessment indexes;
and the expert decision platform is used for predicting the consumption of the subsequent energy consumption equipment according to the equipment energy consumption model established by the data analysis platform and parameters of equipment operation, and providing a decision for reducing the energy consumption of the energy consumption equipment of the rail transit station according to the historical experience of equipment operation.
Further, the communication interface platform comprises a peer system acquisition module and a superior system forwarding module, wherein the peer system acquisition module is used for acquiring data of the statistical system, and the superior system forwarding module is used for forwarding the data of the superior system to the peer system acquisition module;
the peer system comprises a station PSCADA system, a BAS system, an ISCS comprehensive monitoring system and an edge computing system;
the station PSCADA system, the BAS system and the ISCS integrated monitoring system are used for monitoring the power utilization condition of each node in the power grid in real time and forwarding the power utilization condition to the intelligent energy management system and acquiring a control instruction of the intelligent energy management system.
Furthermore, the edge computing system comprises an intelligent network shutdown device, subordinate intelligent meter metering equipment, PLC equipment and a sensor;
the intelligent network shutdown device, the subordinate intelligent meter device and the sensor are all connected with the PLC device;
the subordinate intelligent meter equipment comprises an electric meter, a water meter, a gas meter and a heat meter;
the sensor comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor and a brightness sensor.
Furthermore, the intelligent network shutdown comprises a communication module, an edge calculation module, a storage module and a control module;
the communication module is used for regularly summarizing and forwarding data and states of subordinate intelligent meter equipment to a peer data acquisition module in the intelligent energy management system, and is also used for receiving a control command sent by the peer data acquisition module of the intelligent energy management system to the subordinate intelligent meter equipment;
the edge calculation module is a small computer system with calculation and storage capacity and is used for calculating edge points; and the communication module acquires the energy consumption data and the electric energy quality data of the subordinate intelligent meter equipment and carries out energy consumption data statistics according to 6 time types of minutes, hours, days, months, seasons and years; generating an alarm event according to the emergency of the intelligent network shutdown device and the subordinate intelligent marking equipment; the intelligent energy management system has a data cleaning function, removes invalid data, issues a recruitment command for the invalid data and the missing data, and ensures the validity and the real-time performance of the data uploaded to the intelligent energy management system;
the control module is used for adjusting and controlling the operation modes of the lighting equipment, the air conditioning equipment and the fan equipment according to the record in the storage module and the passenger flow, the temperature, the humidity and the illumination conditions detected by the time period and the detection sensor; the communication module acquires a control command sent by an external system in real time and performs corresponding operation on the equipment;
the storage module is used for storing collected and statistical data in subordinate equipment, performing data recruitment operation after the intelligent energy management system loses data abnormally, and storing simple monitoring and control strategies of the subordinate equipment issued by the intelligent energy management system.
Further, the big data analysis module further comprises:
1) the station energy consumption equipment is with ability knowledge base function submodule: energy consumption data, load data and environment data are collected regularly; forming a knowledge base through the accumulated energy consumption of various working conditions and equipment operation parameters, analyzing energy-saving potential, forming an equipment energy consumption model, finding equipment energy-saving key points, predicting loads in advance according to the knowledge base, and optimizing the operation mode of each system;
2) the station energy consumption comprehensive analysis function submodule is as follows: generating various energy consumption indexes by combining station passenger flow data, driving data, station ticket business income data and various data of station building area to form energy consumption comprehensive analysis data;
3) the station available load prediction function submodule comprises: according to different energy utilization devices, one algorithm of linear regression, logistic regression, random forest, support vector machine or time sequence is adopted to carry out energy consumption modeling to predict each energy utilization load, an energy consumption trend graph in a specified period is given, and early warning information and suggestions are given;
4) the station energy-saving analysis function submodule is as follows: and monitoring electromechanical equipment of the environmental control system by utilizing the environmental parameters acquired by the ISCS according to the characteristics of the environmental control equipment, the change of seasonal temperature and the change of passenger flow, and combining a time schedule and mode control.
Furthermore, the data analysis platform also comprises a Web display module which is used for analyzing and displaying the data characteristics.
An assessment method of a rail transit intelligent energy management system comprises the following steps:
s1, acquiring a corresponding operation object ID according to an operation object needing to be inquired;
s2, inquiring and acquiring record items Records and record number Size corresponding to the operation object ID according to the inquiry time set by the assessment index;
s3, obtaining a historical assessment index record Kt which is set by assessment indexes and has the latest query time, and a set operated index Value2 of the index record;
s4, judging that the number of records Size is greater than 0;
s5, if the judgment structure is negative, acquiring the total energy consumption En within the specified time, performing assessment calculation on the total energy consumption En by using an assessment calculation method, generating an assessment evaluation record, and finishing the assessment evaluation calculation;
s6, if the judgment result is yes, obtaining an assessment index Record; simultaneously acquiring the energy consumption Es before the modification operation time UpdateTime within the specified time; carrying out assessment calculation by an assessment calculation method;
s7, replacing historical assessment index Records Kt which are recorded in Records in Records before the modification operation time UpdateTime and are corresponding to the latest query time set by the assessment indexes one by one;
s8, judging whether the next record exists in the Records before the update operation time UpdateTime,
s9, if the next record exists, acquiring the next record of the record item Records before the update operation time UpdateTime, and continuously executing the steps S6-S8;
and S10, if no next record exists, ending the assessment and evaluation calculation and generating assessment records.
Further, the assessment calculation in any one of the steps S5 or S6 includes the following steps:
the post-operation index Value2 set using the index record acquired in step S3 is compared with the total energy consumption amount:
if En is more than 0.8 Value2, the assessment is of a reminding type;
if En > Value2, the assessment is evaluated as alarm type.
Compared with the prior art, the rail transit intelligent energy management system has the following beneficial effects:
(1) the rail transit intelligent energy management system provided by the invention has the advantages that the energy-saving potential is analyzed and decided, the server load is reduced on the basis of meeting basic data requirements, a unified interface is established with a PSCADA system and a BAS system, the system is modified and upgraded on the basis of an original energy management system, and the requirements of various systems are met.
(2) The assessment evaluation method of the rail transit intelligent energy management system can flexibly set and modify assessment indexes aiming at various energy consumption intelligent meter devices, can generate corresponding energy consumption assessment records in real time through query, does not need other modules to carry out configuration assessment index recalculation operation, and further assists in energy-saving potential analysis.
(3) According to the expert decision platform of the rail transit intelligent energy management system, the original energy consumption data and the passenger flow, the temperature, the humidity, the brightness and the carbon dioxide concentration in the sensor data are combined to further adjust the operation mode of energy consumption equipment, so that manual operation is reduced, and the energy consumption utilization rate is further improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a structural diagram of a rail transit intelligent energy management system according to an embodiment of the invention;
fig. 2 is a structural diagram of an intelligent gateway system according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating an exemplary embodiment of energy consumption index data;
fig. 4 is a flowchart illustrating energy consumption assessment, evaluation, query and calculation of the rail transit intelligent energy management system according to the embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first", "second", etc. 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," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a rail transit intelligent energy management system includes a communication interface platform, a data management platform, a data analysis platform, and an expert decision platform;
the communication interface platform is mainly used for accessing data of an external system into the intelligent energy management system and simultaneously is responsible for forwarding analysis and decision data of the intelligent energy management system to the external system and is divided into a peer system acquisition module and a superior system forwarding module; the external system also comprises a peer system and a superior system; the peer system comprises a PSCADA system, a BAS system, an ISCS comprehensive monitoring system and an edge computing system; the superior system comprises a smart station system, a network level energy management system and the like.
The station PSCADA system, the BAS system and the ISCS integrated monitoring system are used for monitoring the electricity utilization condition of each node in the power grid in real time, forwarding the electricity utilization condition to the energy management system and acquiring a control instruction of the intelligent energy management system.
The intelligent energy management system adopts different communication protocols according to the relation with each system. The basic data communication mode that wisdom energy management system gathered mainly adopts the form of stipulation, supports including standard protocols such as national grid 2009 stipulations, DLT645 stipulations, Modbus-RTU, Modbus-TCP, 104 stipulations and 102 stipulations, also supports self-defined stipulations, like the self-defined stipulations of XML form, the self-defined stipulations of Json form.
The intelligent energy management system and the peer system are mainly communicated by adopting a standard protocol, and the mode has large data transmission quantity and high transmission efficiency and is suitable for interaction of a large amount of basic data. Because the superior system is accessed to various systems, if basic data of all the systems are uploaded, great difficulty is caused to the operation and maintenance of the superior system, the data analyzed by each system is generally uploaded to the superior system in a self-defined protocol form, the mass data storage and analysis work of the superior system is reduced, the response speed and the operation efficiency of the superior system are improved, and the effects of data display and equipment control are absorbed.
The intelligent energy management system and the superior system are mainly communicated in a self-defined protocol mode, and are mainly suitable for complex and various data types, data types with large changes and a small amount of analyzed data required by the display of the superior system, such as data with different types, energy consumption efficiency data and control commands issued by the superior system to equipment.
As shown in fig. 2, the edge computing system is composed of an intelligent network shutdown device, subordinate meters, PLC equipment and sensor equipment, wherein the intelligent meters include an electric meter, a water meter, a gas meter and a heat meter, and the sensors include a temperature sensor, a humidity sensor, a carbon dioxide sensor and a brightness sensor.
As shown in fig. 2, the intelligent network shutdown device includes a communication module, an edge calculation module, a storage module and a control module.
The communication module supports the protocol communication requirement of the intelligent energy management system, can collect and forward the data and state of subordinate intelligent meter equipment to a peer system acquisition module (namely a peer system acquisition module) in the intelligent energy management system at regular time, and simultaneously receives a control command, such as a command of disconnecting a line switch, issued by the peer system acquisition of the intelligent energy management system to the subordinate intelligent meter equipment; meanwhile, the module supports multipoint connection and can simultaneously communicate with different system machines through different communication protocols.
The edge calculation module of the intelligent network shutdown is a small computer system with calculation and storage capacity and is used for calculating edge points; therefore, the traditional data sink node equipment is transformed into equipment with a certain analysis and calculation function, and the intelligent network shutdown is matched with the calculation requirements of the intelligent energy management system on the edge nodes.
Generating an alarm event according to the emergency of the intelligent network shutdown device and the subordinate intelligent marking equipment;
generating a specific alarm event, wherein if the voltage of a meter exceeds the rated voltage, an edge module in the intelligent network shutdown machine can judge out the alarm and generate a voltage over-rated value alarm; for example, the network of the meter device is interrupted, so that the intelligent network shutdown acquisition module cannot acquire the data in the corresponding meter device, the intelligent network shutdown can judge that the data in the meter device is not acquired in a certain time range through the statistical data, and the meter offline alarm can be generated.
The intelligent energy management system also has the functions of cleaning the collected data, removing invalid data, supporting the recruitment function of the invalid data and missing data and ensuring the validity and the real-time property of the data uploaded to the intelligent energy management system; meanwhile, the intelligent network shutdown can also carry out energy consumption data statistics on data of energy consumption related equipment according to 6 time types of minutes, hours, days, months, seasons and years respectively, then upload the statistical data to the intelligent energy management system, reduce the operation load of a central server of the intelligent energy management system, improve the operation efficiency of the server, monitor emergencies in the operation process of subordinate equipment in real time, and timely and actively upload the emergencies to the intelligent energy management system.
The storage module is used for storing collected and statistical data in subordinate equipment in a certain time period, performing data recruitment operation after the intelligent energy management system loses data abnormally, and storing simple monitoring and control strategies, such as start-stop strategies of a fan according to the time period, of the subordinate equipment, which are issued by the intelligent energy management system.
The intelligent network shutdown device is provided with a real-time library and a historical library, the real-time library stores data acquired and analyzed by subordinate intelligent meter equipment through protocols, the historical library stores data acquired and analyzed by protocols at different time points, the data acquisition requirements of different data of the intelligent energy management system are met by the real-time library and the historical library, the historical library is still maintained after power is off, data loss is prevented, the historical library adopts a circular coverage mode, and storage space in edge computing nodes is utilized to the maximum extent. The data frequency of on-site intelligent network shutdown collection is 15 times of the data frequency of the intelligent energy management system in the intelligent network shutdown collection, so that a large amount of data is reduced to be uploaded to the intelligent energy management system, and when the intelligent energy management system finds out the abnormal-energy equipment, the intelligent network shutdown can pertinently upload fine-grained data for further analysis of the intelligent energy management system.
The data management platform mainly comprises a basic data statistics module and a storage module, wherein the basic data statistics module is used for performing energy consumption data statistics on collected data of energy consumption related equipment outside the edge computing system according to 6 time types of minutes, hours, days, months, seasons and years, mainly comprises power consumption statistics, water consumption statistics, natural gas statistics, heat statistics, passenger flow statistics, air conditioning refrigeration capacity, fan air volume and other data, and also performs statistics on electric energy quality data, including daily, monthly and yearly maximum, minimum voltage, current, power, frequency, harmonic wave, demand, power factor and the like, and also includes statistics on data of a sensor, such as station hall temperature and humidity, water pump liquid level, indoor carbon dioxide concentration, indoor lighting time and brightness, accumulated running time and horizontal/vertical vibration of a ring control tunnel fan and a ring control hot air exhaust fan, the running time of the refrigerating pump and the cooling tower, the water inlet and outlet temperature of cooling water and chilled water, the flow of the water pipe and the accumulated running time of the fan. The storage module is mainly used for maintaining a large amount of data, ensuring that the data are sequentially and orderly written into the database in time for storage, and providing interfaces for other platforms of the intelligent energy management system to acquire internal data.
The Web display module provides energy data visualization and a human-computer interaction interface, has the functions of displaying the energy utilization conditions of objects and equipment at all levels in the rail transit energy management system in the modes of curves, bar charts, pie charts, tables and the like, provides comparison, and has the functions of inquiring and displaying the same-ratio and ring-ratio, and has the functions of monitoring the energy efficiency of the equipment and optimizing the operation.
The data analysis platform comprises a big data analysis module and a Web display module, mainly analyzes data characteristics, and further analyzes data in the basic data acquisition and basic data statistics module in the intelligent energy management system according to the data characteristics. The big data analysis module comprises the following sub-modules:
(1) and the energy consumption equipment of the station is an energy knowledge base functional submodule. The energy management system collects energy consumption data, load data and environment data at regular time. And forming a constantly evolving knowledge base through the accumulated energy consumption of various working conditions and equipment operation parameters, analyzing the energy-saving potential, forming an equipment energy consumption model, finding the energy-saving key points of the equipment, predicting the load in advance according to the knowledge base, and optimizing the operation mode of each system.
(2) And a station energy consumption comprehensive analysis functional submodule. And generating various energy consumption indexes by combining station passenger flow data, driving data, station ticket business income data, station building area and other data to form an energy consumption comprehensive analysis data basis.
(3) And a station energy consumption plan and evaluation functional submodule. According to the formulated energy consumption plan taking the station as a unit and the specific decomposition index, energy consumption statistical data are automatically generated, and various types of energy consumption are automatically evaluated according to the station energy consumption item assessment and evaluation index. The assessment and evaluation are not limited to energy consumption data assessment, and meanwhile, the electric energy quality data can be assessed, such as parameters of current, voltage, harmonic wave, power factor and the like. The energy consumption data assessment is divided into energy consumption types such as electricity consumption, water consumption, gas consumption and the like, and can be performed in time span types such as day, week, ten days, month, quarter, year and the like. The assessment indexes based on assessment evaluation are respectively set for energy-using equipment, stations, lines and other assessment objects according to time span types, assessment calculation is automatically carried out in each time period according to the set assessment indexes in the subsequent time, the set assessment indexes can be updated and modified at any time in the subsequent time, the system can automatically switch new indexes to assess the energy consumption data in the time period, but the historical assessment evaluation is not modified, and only the energy consumption data is effective after the assessment indexes are modified for time. The assessment evaluation is generated through the assessment indexes, and the assessment evaluation is mainly generated at regular time and query time.
As shown in fig. 3, the assessment index database stores a data structure. The operation object ID refers to an assessment object such as energy utilization equipment, a station, a line and the like. And displaying a new or modified mark of the assessment index in the operation type Oper. The assessment time Ktime is the time for assessing and evaluating the energy consumption in the time period, and the operation time UpdateTime is the time point of the new establishment or modification of the assessment index. The energy consumption index type energy type refers to energy consumption types such as electricity consumption, water consumption, gas consumption and the like. The time type TimeType refers to different time types such as day and week, ten days, month, quarter, year and the like which are increased according to the time span and correspond to the Ktime time composition form. The pre-operation index Value1 index Value modifies the pre-operation Value. The Value of the post-operation index Value2 is the modified Value.
In order to save system computing resources and system space, the system does not generate all energy consumption assessment evaluation, the regular generation is to calculate relatively important assessment indexes which are fixed and unchanged in the system, the assessment evaluation accounts for a small part of the assessment evaluation, energy consumption data in the assessment evaluation exceed a specified assessment index threshold value, alarm data can be correspondingly generated, the assessment evaluation is divided into a first-level energy consumption alarm and a second-level energy consumption alarm according to the level, and the first-level energy consumption alarm and the second-level energy consumption alarm are transmitted to a specified module to be further processed. And query calculation, namely generating a calculation assessment result in real time according to the energy consumption type, the time span type and the real-time combined query of the assessment object queried by the system.
(4) And the energy load prediction function submodule for the station. The method comprises the steps of adopting a neural network, linear regression, logistic regression, random forest, a support vector machine, a time sequence and the like, adopting the proper algorithm to carry out energy consumption modeling according to the running characteristics of different energy consumption equipment, predicting each energy consumption load, giving an energy consumption trend map of a certain period in the future, and giving related early warning information and suggestions to prompt a user to take related precautionary measures.
(5) And a station energy-saving analysis functional submodule. The energy management system utilizes the environmental parameters acquired by the ISCS, monitors electromechanical equipment of the environmental control system according to the characteristics of the environmental control equipment, the change of seasonal temperature and the change of passenger flow rate by combining a time schedule and mode control, automatically closes some equipment, switches, control valves and the like which exceed energy consumption indexes, or automatically adjusts the opening degree of related valves, controls the flow rate, and further performs statistics and data analysis on the whole-line energy consumption.
The expert decision platform is composed of a decision control module, and mainly provides a decision for reducing the energy consumption of the energy consumption equipment of the rail transit station according to an equipment energy consumption model established by the data analysis platform and the usage condition of the subsequent energy consumption equipment predicted by each parameter of equipment operation and the historical experience of equipment operation. Firstly, the analysis, examination and evaluation functions of the electric energy data are utilized to find the electric load with larger energy-saving space, and the equipment energy consumption model and the energy consumption prediction mechanism established by the data analysis platform prompt the adoption of more efficient energy-saving measures, and the operation of the equipment is controlled by adopting a fuzzy PID algorithm. And the data analysis platform provides support for evaluating the energy-saving measures and the actual effect of the energy-saving equipment. Meanwhile, a new system operation mode and a new management mode can be explored through the energy-saving management assistant decision-making system. Secondly, subway line loss monitoring is carried out, and a subway reactive compensation strategy is provided. The energy management system monitors the power quality of each monitoring point of each station line, further analyzes the characteristics of the station power load rule through the data analysis platform, coordinates the mode operation of reactive power compensation equipment, forms a reactive power optimization strategy and reduces the reactive power loss of the line.
As shown in fig. 3 and 4, an assessment method for a rail transit intelligent energy management system includes the following steps:
s1, acquiring a corresponding operation object ID according to an operation object needing to be inquired;
s2, inquiring and acquiring record items Records and record number Size corresponding to the operation object ID according to the inquiry time set by the assessment index;
s3, obtaining a historical assessment index record Kt which is set by assessment indexes and has the latest query time, and a set operated index Value2 of the index record;
s4, judging that the number of records Size is greater than 0;
s5, if the judgment structure is negative, acquiring the total energy consumption En within the specified time, performing assessment calculation on the total energy consumption En by using an assessment calculation method, generating an assessment evaluation record, and finishing the assessment evaluation calculation;
if the TimeType is month, the total energy consumption En of the operation object ID in the SearchTime current month is acquired.
S6, if the judgment result is yes, obtaining an assessment index Record; simultaneously acquiring the energy consumption Es before the modification operation time UpdateTime within the specified time; carrying out assessment calculation by an assessment calculation method;
if the TimeType is month, the unit smaller than the month is day, and in this step, the operation object ID daily accumulated energy consumption Es between the query time SearchTime from the current month 1 to the modification operation time UpdateTime set by the assessment index is counted. The following were used:
wherein i is the starting number of 1, n is the number of next-level time type components in the Timetype, if the TimeType is month, the next-level time type is day, Ktime is 2021-12, UpdateTime is 2021-12-09, then n is 9, EiThe data amount of energy used per day is summarized, and Es represents the sum of energy consumption per day in the period from 2021-12-01 to 2021-12-09.
S7, replacing historical assessment index Records Kt which are recorded in Records in Records before the modification operation time UpdateTime and are corresponding to the latest query time set by the assessment indexes one by one;
s8, judging whether the next record exists in the Records before the update operation time UpdateTime,
s9, if the next record exists, acquiring the next record of the record item Records before the update operation time UpdateTime, and continuously executing the steps S6-S8;
and S10, if no next record exists, ending the assessment and evaluation calculation and generating assessment records.
Further, the assessment calculation in any one of the steps S5 or S6 includes the following steps:
the post-operation index Value2 set using the index record acquired in step S3 is compared with the total energy consumption amount:
if En is more than 0.8 Value2 and En is total energy consumption, the assessment is of a reminding type;
if En > Value2, the assessment is evaluated as alarm type.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A rail transit wisdom energy management system which characterized in that includes: the communication interface platform is used for accessing data of an external system into the intelligent energy management system and simultaneously is responsible for forwarding analysis and decision data of the intelligent energy management system to the external system;
the data management platform is used for counting and storing external data accessed through the communication interface platform;
the data analysis platform is used for carrying out energy consumption analysis on the external data characteristics stored by the data management platform, establishing an equipment energy consumption model and predicting the consumption condition of subsequent energy consumption equipment by using various parameters of equipment operation;
the data analysis platform comprises a big data analysis module, and the big data analysis module comprises:
the station energy consumption plan and assessment function submodule is used for automatically generating energy consumption statistical data according to a formulated energy consumption plan taking a station as a unit and specific decomposition indexes and automatically assessing various types of energy consumption according to station energy consumption item assessment indexes;
and the expert decision platform is used for predicting the consumption of the subsequent energy consumption equipment according to the equipment energy consumption model established by the data analysis platform and parameters of equipment operation, and providing a decision for reducing the energy consumption of the energy consumption equipment of the rail transit station according to the historical experience of equipment operation.
2. The rail transit intelligent energy management system of claim 1, wherein: the communication interface platform comprises a peer system acquisition module and a superior system forwarding module, wherein the peer system acquisition module is used for acquiring data of a statistical system, and the superior system forwarding module is used for forwarding the data of the superior system to the peer system acquisition module;
the peer system comprises a station PSCADA system, a BAS system, an ISCS comprehensive monitoring system and an edge computing system;
the station PSCADA system, the BAS system and the ISCS integrated monitoring system are used for monitoring the power utilization condition of each node in the power grid in real time and forwarding the power utilization condition to the intelligent energy management system and acquiring a control instruction of the intelligent energy management system.
3. The rail transit intelligent energy management system of claim 1, wherein: the edge computing system comprises an intelligent network shutdown device, subordinate intelligent meter metering equipment, a PLC (programmable logic controller) device and a sensor;
the intelligent network shutdown device, the subordinate intelligent meter device and the sensor are all connected with the PLC device;
the subordinate intelligent meter equipment comprises an electric meter, a water meter, a gas meter and a heat meter;
the sensor comprises a temperature sensor, a humidity sensor, a carbon dioxide sensor and a brightness sensor.
4. The rail transit intelligent energy management system according to claim 3, wherein: the intelligent network shutdown comprises a communication module, an edge calculation module, a storage module and a control module;
the communication module is used for regularly summarizing and forwarding data and states of subordinate intelligent meter equipment to a peer data acquisition module in the intelligent energy management system, and is also used for receiving a control command sent by the peer data acquisition module of the intelligent energy management system to the subordinate intelligent meter equipment;
the edge calculation module is a small computer system with calculation and storage capacity and is used for calculating edge points; and the communication module acquires the energy consumption data and the electric energy quality data of the subordinate intelligent meter equipment and carries out energy consumption data statistics according to 6 time types of minutes, hours, days, months, seasons and years; generating an alarm event according to the emergency of the intelligent network shutdown device and the subordinate intelligent marking equipment; the intelligent energy management system has a data cleaning function, removes invalid data, issues a recruitment command for the invalid data and the missing data, and ensures the validity and the real-time performance of the data uploaded to the intelligent energy management system;
the control module is used for adjusting and controlling the operation modes of the lighting equipment, the air conditioning equipment and the fan equipment according to the record in the storage module and the passenger flow, the temperature, the humidity and the illumination conditions detected by the time period and the detection sensor; the communication module acquires a control command sent by an external system in real time and performs corresponding operation on the equipment;
the storage module is used for storing collected and statistical data in subordinate equipment, performing data recruitment operation after the intelligent energy management system loses data abnormally, and storing simple monitoring and control strategies of the subordinate equipment issued by the intelligent energy management system.
5. The rail transit intelligent energy management system of claim 1, wherein: big data analysis module still includes:
1) the station energy consumption equipment is with ability knowledge base function submodule: energy consumption data, load data and environment data are collected regularly; forming a knowledge base through the accumulated energy consumption of various working conditions and equipment operation parameters, analyzing energy-saving potential, forming an equipment energy consumption model, finding equipment energy-saving key points, predicting loads in advance according to the knowledge base, and optimizing the operation mode of each system;
2) the station energy consumption comprehensive analysis function submodule is as follows: generating various energy consumption indexes by combining station passenger flow data, driving data, station ticket business income data and various data of station building area to form energy consumption comprehensive analysis data;
3) the station available load prediction function submodule comprises: according to different energy utilization devices, one algorithm of linear regression, logistic regression, random forest, support vector machine or time sequence is adopted to carry out energy consumption modeling to predict each energy utilization load, an energy consumption trend graph in a specified period is given, and early warning information and suggestions are given;
4) the station energy-saving analysis function submodule is as follows: and monitoring electromechanical equipment of the environmental control system by utilizing the environmental parameters acquired by the ISCS according to the characteristics of the environmental control equipment, the change of seasonal temperature and the change of passenger flow, and combining a time schedule and mode control.
6. The rail transit intelligent energy management system of claim 1, wherein: the data analysis platform also comprises a Web display module which is used for analyzing and displaying the data characteristics.
7. An assessment and evaluation method of a rail transit intelligent energy management system, which is used for the rail transit intelligent energy management system as claimed in any one of claims 1 to 6, and is characterized by comprising the following steps:
s1, acquiring a corresponding operation object ID according to an operation object needing to be inquired;
s2, inquiring and acquiring record items Records and record number Size corresponding to the operation object ID according to the inquiry time set by the assessment index;
s3, obtaining a historical assessment index record Kt which is set by assessment indexes and has the latest query time, and a set operated index Value2 of the index record;
s4, judging that the number of records Size is greater than 0;
s5, if the judgment structure is negative, acquiring the total energy consumption En within the specified time, performing assessment calculation on the total energy consumption En by using an assessment calculation method, generating an assessment evaluation record, and finishing the assessment evaluation calculation;
s6, if the judgment result is yes, obtaining an assessment index Record; simultaneously acquiring the energy consumption Es before the modification operation time UpdateTime within the specified time; carrying out assessment calculation by an assessment calculation method;
s7, replacing historical assessment index Records Kt which are recorded in Records in Records before the modification operation time UpdateTime and are corresponding to the latest query time set by the assessment indexes one by one;
s8, judging whether the next record exists in the Records before the update operation time UpdateTime,
s9, if the next record exists, acquiring the next record of the record item Records before the update operation time UpdateTime, and continuously executing the steps S6-S8;
and S10, if no next record exists, ending the assessment and evaluation calculation and generating assessment records.
8. The assessment and evaluation method of the intelligent energy management system for rail transit according to claim 7, wherein: the assessment calculation of any one of the steps S5 or S6 includes the steps of:
the post-operation index Value2 set using the index record acquired in step S3 is compared with the total energy consumption amount:
if En is more than 0.8 Value2, the assessment is of a reminding type;
if En > Value2, the assessment is evaluated as alarm type.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115292393A (en) * | 2022-10-10 | 2022-11-04 | 宁波高盛电气有限公司 | Data management system for intelligent gateway |
| CN116700071A (en) * | 2023-05-18 | 2023-09-05 | 长沙穗城轨道交通有限公司 | Intelligent energy-saving control platform, method, electronic equipment and storage medium |
| CN117236507A (en) * | 2023-09-25 | 2023-12-15 | 广州汇锦能效科技有限公司 | Urban public transportation green intelligent energy management system, method and storage medium |
| CN116700071B (en) * | 2023-05-18 | 2024-04-26 | 长沙穗城轨道交通有限公司 | Intelligent energy-saving control platform, method, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115292393A (en) * | 2022-10-10 | 2022-11-04 | 宁波高盛电气有限公司 | Data management system for intelligent gateway |
| CN116700071A (en) * | 2023-05-18 | 2023-09-05 | 长沙穗城轨道交通有限公司 | Intelligent energy-saving control platform, method, electronic equipment and storage medium |
| CN116700071B (en) * | 2023-05-18 | 2024-04-26 | 长沙穗城轨道交通有限公司 | Intelligent energy-saving control platform, method, electronic equipment and storage medium |
| CN117236507A (en) * | 2023-09-25 | 2023-12-15 | 广州汇锦能效科技有限公司 | Urban public transportation green intelligent energy management system, method and storage medium |
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