CN117644794B - Intelligent period control system based on charging pile - Google Patents
Intelligent period control system based on charging pile Download PDFInfo
- Publication number
- CN117644794B CN117644794B CN202410112881.XA CN202410112881A CN117644794B CN 117644794 B CN117644794 B CN 117644794B CN 202410112881 A CN202410112881 A CN 202410112881A CN 117644794 B CN117644794 B CN 117644794B
- Authority
- CN
- China
- Prior art keywords
- communication
- charging pile
- time
- scheduling
- real
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000004891 communication Methods 0.000 claims abstract description 307
- 230000005540 biological transmission Effects 0.000 claims abstract description 120
- 238000004458 analytical method Methods 0.000 claims abstract description 28
- 238000012544 monitoring process Methods 0.000 claims abstract description 26
- 238000007405 data analysis Methods 0.000 claims abstract description 13
- 230000007246 mechanism Effects 0.000 claims abstract description 11
- 238000007726 management method Methods 0.000 claims description 57
- 238000007667 floating Methods 0.000 claims description 39
- 238000012546 transfer Methods 0.000 claims description 34
- 230000005856 abnormality Effects 0.000 claims description 13
- 238000010223 real-time analysis Methods 0.000 claims description 5
- 238000012384 transportation and delivery Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 abstract description 4
- 238000004422 calculation algorithm Methods 0.000 description 17
- 238000000034 method Methods 0.000 description 10
- 230000002159 abnormal effect Effects 0.000 description 8
- 230000005611 electricity Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000006399 behavior Effects 0.000 description 4
- 230000008859 change Effects 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- 230000002787 reinforcement Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 101001069691 Homo sapiens Protogenin Proteins 0.000 description 1
- 102100033834 Protogenin Human genes 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000008303 genetic mechanism Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000013515 script Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Abstract
The invention discloses an intelligent time period control system based on a charging pile, which relates to the technical field of controlling the charging pile and comprises a data acquisition module, a real-time data analysis module, a scheduling strategy making module, a scheduling instruction communication transmission module, a real-time monitoring module, a hidden danger type comprehensive analysis module and an early warning module; and the data acquisition module is used for acquiring related data for intelligent scheduling by the charging pile management system. According to the invention, by introducing a communication anomaly monitoring mechanism, the system can sense the anomaly hidden danger in the communication transmission process of the scheduling strategy in real time, so that the anomaly hidden danger in the communication transmission process can be timely sensed, and secondly, by setting different hidden danger signal levels, the anomaly hidden danger can be effectively classified, and an alarm prompt of a corresponding level is sent out, so that an operator can quickly respond when the anomaly occurs, and corresponding maintenance and management measures are adopted, so that the timely transmission of the scheduling instruction is ensured, and the stability and reliability of the system are improved.
Description
Technical Field
The invention relates to the technical field of charging pile control, in particular to an intelligent period control system based on a charging pile.
Background
The intelligent period control of the charging pile refers to optimizing and managing the charging period of the charging pile of the electric automobile through advanced technology and intelligent algorithm, and is characterized in that the resources of the electric power system are utilized to the greatest extent so as to realize charging in the period with high power demand and lower electricity price, thereby improving efficiency, reducing cost and lightening the burden of the electric power network. Firstly, the system provides more comprehensive energy consumption data for users by acquiring information of an electric power market in real time, including electricity prices and supply and demand conditions. And secondly, the system can intelligently plan and adjust the charging time period by combining with the charging pile management system through the prediction of the user demand, so that the charging is finished in the time period with the highest user demand and the lowest electricity price, the user demand is met to the greatest extent, and the charging cost is reduced.
In the intelligent period control system of the charging pile, one of key components is a charging pile management system. The system is responsible for monitoring and managing the state of the charging pile, ensuring the normal operation of the charging pile and operating according to a charging plan formulated by an intelligent algorithm. The method relates to functions of remote monitoring, fault diagnosis, charging pile state reporting and the like, so as to ensure the reliability and stability of the charging pile. The user interface is also an important component in the system, and provides a convenient operation interface for the user, so that the user can set and adjust the personalized charging plan. Through the intelligent scheduling algorithm, the system dynamically optimizes the charging plan according to the power market information and the user demand, and maximally utilizes the power system resources. The realization of the system not only improves the charging efficiency and reduces the charging cost, but also is beneficial to smoothing the load curve of the power system, promotes better integration of renewable energy sources, and provides powerful support for sustainable development of electric automobiles.
The charging pile management system plays a vital role in an intelligent period control system based on charging piles, and charging pile scheduling is performed through the system. The charging pile management system makes an optimized charging plan by using an intelligent scheduling algorithm through acquiring data such as power market information, user charging demands, system loads and the like in real time. Through cooperation with the internal communication module of the charging pile, the system conveys specific charging period, charging power and other instructions to the charging pile, and intelligent scheduling of the charging pile is achieved. The scheduling mechanism aims to maximally utilize the power resources, reduce the charging cost, and complete charging in the time period of peak demand and lowest electricity price of a user, so that the charging efficiency is improved, the load curve of the power system is smoothed, and the intelligent management of the whole electric automobile charging process is realized.
The prior art has the following defects: the prior art cannot know the communication condition of the charging pile management system in real time, the charging pile management system conveys the scheduling strategy of the charging pile through communication, such as charging time period, power control and the like, communication abnormality can cause that the scheduling instructions cannot be timely transmitted to the charging pile, so that the system cannot realize flexible scheduling of the charging pile, flexible charging pile scheduling cannot be implemented, the system cannot effectively manage according to the power grid load condition, the charging pile cannot adjust the charging time period according to the needs during the peak load period of the power system, and the power grid load imbalance is aggravated, and the instability of the power system is possibly caused.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide an intelligent time period control system based on a charging pile, which can sense abnormal hidden dangers in the communication transmission process of a scheduling strategy in real time by introducing a communication abnormal monitoring mechanism, so that the abnormal hidden dangers in the communication transmission process can be sensed in time, and secondly, the abnormal hidden dangers can be effectively classified by setting different hidden danger signal levels and alarm prompts of corresponding levels are sent out, so that operators can quickly respond when the abnormality occurs, corresponding maintenance and management measures are adopted, the timely transmission of scheduling instructions is ensured, and the stability and the reliability of the system are improved, so that the problems in the background technology are solved.
In order to achieve the above object, the present invention provides the following technical solutions: an intelligent period control system based on a charging pile comprises a data acquisition module, a real-time data analysis module, a scheduling strategy making module, a scheduling instruction communication transmission module, a real-time monitoring module, a hidden danger type comprehensive analysis module and an early warning module;
The data acquisition module is used for acquiring related data for intelligent scheduling by the charging pile management system;
the real-time data analysis module is used for carrying out real-time analysis on the collected data related to intelligent scheduling and comprehensively evaluating the scheduling strategy of the charging pile;
the scheduling strategy making module is used for making a scheduling strategy of the charging pile based on the real-time data analysis result;
the scheduling instruction communication module is used for communicating the formulated scheduling strategy to each charging pile through a communication unit of the charging pile management system, so that the charging pile can execute the intelligent period control charging strategy according to the scheduling instruction;
the real-time monitoring module is used for acquiring communication performance information and time domain information during communication transmission of the scheduling strategy, comprehensively analyzing the processed communication performance information and time domain information, establishing a communication abnormality monitoring mechanism and monitoring the communication transmission of the scheduling strategy in real time;
and the hidden danger type comprehensive analysis module is used for further analyzing the hidden danger when sensing that the scheduling strategy communication transmission process has the hidden danger through the communication abnormity monitoring mechanism, and sending different early warning prompts through the early warning module aiming at the analyzed hidden danger type.
Preferably, the communication performance information during communication transmission of the scheduling strategy comprises communication frequency information and bandwidth information, and after the communication frequency information and the bandwidth information are acquired, the communication frequency information and the bandwidth information are respectively processed to generate a communication frequency floating index and a bandwidth instability index; the time domain information during communication transmission of the scheduling strategy comprises clock information of the charging pile management system and clock information of the charging pile, and after the clock information of the charging pile management system and the clock information of the charging pile are obtained, a clock synchronization precision deviation index is generated after the clock information of the charging pile management system and the clock information of the charging pile are processed.
Preferably, the logic for the communication frequency floating index acquisition is as follows:
obtaining the optimal communication frequency range during the communication transmission of the scheduling strategy, and calibrating the optimal communication frequency range asWherein->And->Respectively representing the minimum value and the maximum value of the optimal communication frequency range;
in the time window Q, acquiring the real-time communication frequency of the communication transmission of the scheduling strategy, and using the real-time communication frequency as a functionA representation;
calculating a communication frequency lower floating index and a communication frequency upper floating index, wherein the calculated expression is as follows:，/>wherein->And->Respectively representing a communication frequency lower floating index and a communication frequency upper floating index, < >>Indicating that the real-time communication frequency is lower than +.>Is (are) period of->，/>Indicating that the real-time communication frequency is higher than that in the communication transmission of the scheduling strategyIs (are) period of->；
Calculating a communication frequency floating index, wherein the calculated expression is as follows:wherein->Indicating the floating index of the communication frequency, 0.7 and 0.2 are the floating index of the communication frequency, respectively +.>And a communication frequency floating indexIs a weight factor of (a).
Preferably, the logic for bandwidth instability index acquisition is as follows:
in the time window Q, acquiring the real-time bandwidth of the communication transmission of the scheduling strategy, and calibrating the real-time bandwidth as ，xA number representing the real-time bandwidth acquired within the time window Q at the time of dispatch strategy communication delivery,x=1、2、3、4、……、p，pis a positive integer;
establishing a data set from the acquired real-time bandwidths, sequencing the real-time bandwidths in the data set according to time sequence, and calculating a bandwidth instability index according to the fluctuation condition of two adjacent real-time bandwidths, wherein the calculated expression is as follows:wherein->Representing the bandwidth instability index.
Preferably, the logic for obtaining the clock synchronization accuracy deviation index is as follows:
in the time window Q, the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication is respectively acquired through the charging pile management system and the charging pile, and the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication on the charging pile management system is marked asAnd the transmission time stamp acquired by the same scheduling instruction on the charging pile is marked as +.>，yThe number of the scheduling instruction acquired when the scheduling policy communication is transferred in the time window Q is represented,y=1、2、3、4、……、q，qis a positive integer;
calculating a clock synchronization precision deviation index, wherein the calculated expression is as follows:wherein->Representing the clock synchronization accuracy deviation index.
Preferably, the communication frequency floating index when the communication transmission of the scheduling strategy is obtained Index of bandwidth instabilityClock synchronization accuracy deviation index +.>After that, the communication frequency is floated by index->Bandwidth instability index->Clock synchronization accuracy deviation index +.>Carrying out formulated analysis to generate communication transmission coefficient +.>The formula according to is: />Wherein->、、/>The communication frequency floating index is respectively->Bandwidth instability index->Clock synchronization accuracy deviation index->Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
Preferably, in the time window Q, the communication transmission coefficient generated during communication transmission of the scheduling policy is compared with a preset reference threshold value of the communication transmission coefficient, and the comparison and analysis result is as follows:
if the communication transmission coefficient is greater than or equal to the communication transmission coefficient reference threshold value, generating a hidden danger instruction signal;
if the communication transfer coefficient is smaller than the communication transfer coefficient reference threshold, generating a normal command signal.
Preferably, when the scheduling policy communication transmission process generates the suffering instruction signal, acquiring the communication transmission coefficient corresponding to the current moment and a plurality of subsequently generated communication transmission coefficients to establish an analysis set, and calibrating the analysis set asIThen，fA number representing the communication transfer coefficient within the analysis set, f=1、2、3、4、……、u，uIs a positive integer;
calculating a communication transmission coefficient standard deviation and a communication transmission coefficient average value by analyzing the communication transmission coefficients in the collection, and respectively comparing the communication transmission coefficient standard deviation and the communication transmission coefficient average value with a preset standard deviation reference threshold value and a preset communication transmission coefficient reference threshold value to obtain the following comparison analysis results:
if the average value of the communication transmission coefficients is larger than or equal to the reference threshold value of the communication transmission coefficients, generating a first-level hidden danger signal, and sending out a first-level alarm prompt to the first-level hidden danger signal;
if the average value of the communication transmission coefficients is smaller than the reference threshold value of the communication transmission coefficients and the standard deviation of the communication transmission coefficients is larger than or equal to the reference threshold value of the standard deviation, generating a secondary hidden danger signal, and sending a secondary alarm prompt to the secondary hidden danger signal;
if the average value of the communication transmission coefficients is smaller than the reference threshold value of the communication transmission coefficients and the standard deviation of the communication transmission coefficients is smaller than the reference threshold value of the standard deviation, generating a three-level hidden danger signal, and not giving an alarm prompt to the three-level hidden danger signal.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, the data acquisition module is used for acquiring the related data of the charging pile management system in real time, so that the system comprehensively knows the state and the charging requirement of the charging pile, the real-time data analysis module is used for carrying out real-time analysis on the scheduling strategy of the charging pile, the scheduling strategy formulating module is used for formulating a more intelligent and flexible scheduling strategy of the charging pile based on the comprehensive evaluation result, and the scheduling instruction communication and communication module is used for ensuring that the formulated scheduling strategy can be timely communicated to the charging pile, thereby realizing that the charging pile can control the charging strategy to execute according to the intelligent time.
According to the invention, by introducing a communication anomaly monitoring mechanism, the system can sense the anomaly hidden danger in the communication transmission process of the scheduling strategy in real time, so that the anomaly hidden danger in the communication transmission process can be timely sensed, and secondly, by setting different hidden danger signal levels, the anomaly hidden danger can be effectively classified, and an alarm prompt of a corresponding level is sent out, so that an operator can quickly respond when the anomaly occurs, and corresponding maintenance and management measures are adopted, so that the timely transmission of the scheduling instruction is ensured, and the stability and reliability of the system are improved.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
Fig. 1 is a schematic block diagram of an intelligent period control system based on a charging pile according to the present invention.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides an intelligent time period control system based on a charging pile as shown in fig. 1, which comprises a data acquisition module, a real-time data analysis module, a scheduling strategy making module, a scheduling instruction communication transmission module, a real-time monitoring module, a hidden danger type comprehensive analysis module and an early warning module, wherein the real-time data analysis module is used for analyzing the real-time data of the charging pile;
the data acquisition module is used for acquiring related data for intelligent scheduling by the charging pile management system;
the relevant data for intelligent scheduling includes, but is not limited to, the price of the electric power market, the real-time state of the charging pile, the charging requirement of a user, the load condition of the electric power system and the like, and the obtained data relevant to intelligent scheduling can be selected according to the actual use situation.
The real-time data analysis module is used for carrying out real-time analysis on the collected data related to intelligent scheduling and comprehensively evaluating the scheduling strategy of the charging pile;
evaluating a scheduling policy of a charging stake may involve using complex algorithms and models to optimize a charging plan;
in the prior art, a complex algorithm and model for optimizing a charging pile scheduling strategy cover a plurality of fields, and the main aim is to meet the requirements of electric power market conditions, user requirements and power grid loads to the greatest extent, and simultaneously reduce charging cost and improve energy utilization efficiency. The following are some common algorithms and models:
Dynamic programming algorithm: dynamic programming algorithms are widely used to solve multi-objective optimization problems, such as optimal charging plans at different electricity price time periods, by constructing state transfer equations and value functions to find optimal decision sequences, minimizing overall costs.
Genetic algorithm: the genetic algorithm is an optimization algorithm simulating natural selection and genetic mechanism, and is suitable for complex multi-objective optimization. In the charging pile scheduling, a genetic algorithm can be used for searching a potential optimal charging plan, and factors such as electricity price, user requirements, grid load and the like are considered.
Reinforcement learning: reinforcement learning is a method of obtaining an optimal strategy through interactive learning of an agent and an environment. In the charging pile scheduling, the reinforcement learning algorithm can continuously optimize the charging strategy through trial and error learning so as to adapt to the continuously changing environment and requirements.
Linear programming model: the linear programming model is used for solving the optimization problem under the linear constraint, can be applied to the resource allocation and cost minimization problem in the charging pile scheduling, and can consider a plurality of factors such as the price of an electric power market, the requirement of a user, the load of a power grid and the like.
Neural network model: the neural network model in deep learning can be used to learn and predict complex charging pile scheduling patterns. Neural networks are capable of capturing nonlinear relationships, suitable for processing complex relationships between large-scale data and multivariate variables.
Fuzzy logic control: the fuzzy logic control model can process the ambiguity of input parameters, is suitable for the condition that the uncertainty and the ambiguity exist in the charging pile scheduling, and can make decisions and optimize in a complex environment.
Mixed integer programming: hybrid integer programming combines the characteristics of integer programming and linear programming to accommodate problems involving discrete decision variables, such as scheduling charges during discrete time periods. This helps to schedule the behaviour of the charging pile more finely.
The complex algorithm and model for optimizing the charging pile scheduling strategy are not particularly limited herein, and are selected according to actual requirements.
The scheduling strategy making module is used for making a scheduling strategy of the charging pile based on the real-time data analysis result;
the scheduling strategy of the charging pile comprises determining the charging period, power control and the like, and the aim of the scheduling strategy is to minimize the charging cost, balance the load of the power system and meet the requirements of users, wherein the scheduling strategy can be realized by the dynamic programming algorithm, the linear programming model and the like.
The scheduling instruction communication module is used for communicating the formulated scheduling strategy to each charging pile through a communication unit of the charging pile management system, so that the charging pile can execute the intelligent period control charging strategy according to the scheduling instruction;
The communication unit transmits a scheduling instruction to the charging pile, informs the charging pile of charging in a specific time period, and possibly comprises information such as power control and the like;
the charging stake is according to dispatch instruction execution intelligence period control charging strategy, this perhaps involves starting, stopping the operation such as charging at appointed period, adjustment charging power to the flexible adjustment is carried out according to the requirement of system dispatch.
The real-time monitoring module is used for acquiring communication performance information and time domain information during communication transmission of the scheduling strategy, comprehensively analyzing the processed communication performance information and time domain information, establishing a communication abnormality monitoring mechanism and monitoring the communication transmission of the scheduling strategy in real time;
the communication performance information during communication transmission of the scheduling strategy comprises communication frequency information and bandwidth information, and after the communication frequency information and the bandwidth information are acquired, the communication frequency information and the bandwidth information are respectively processed to generate a communication frequency floating index and a bandwidth instability index;
the communication frequency refers to the frequency of signal oscillation or the number of periodical changes in a signal waveform in a communication system, and in an intelligent period control system based on the charging pile, the communication frequency refers to the frequency of transmission scheduling policy information between the charging pile management system and the charging pile, namely the time interval or frequency of information transmission. The selection of the communication frequency is closely related to the real-time requirement of the system and the communication efficiency. The higher communication frequency can improve the real-time performance of the information, but also increases the burden of the communication system. The setting of the communication frequency needs to give consideration to the effective utilization of the system resources on the premise of meeting the real-time requirement. Bandwidth refers to the maximum frequency range of a signal that can be transmitted or the ability to transmit information in communication. In the intelligent charging pile system, the bandwidth represents the information transmission rate and capacity that can be supported by the communication channel between the charging pile management system and the charging pile. The higher bandwidth means that the system can transfer the scheduling policy information faster, improving the communication efficiency. The choice of bandwidth requires consideration of the complexity of the communication signal and the information transfer requirements to ensure that the system is able to maintain efficient communication while processing large amounts of data. Reasonable allocation of communication frequency and bandwidth is an important factor for the charging pile management system to realize intelligent time period control, and influences the real-time performance, stability and performance of the system.
When the charging pile management system communicates the scheduling policy of the charging pile through communication to realize intelligent period control, the communication frequency is too low or too high, which may cause that the charging pile management system cannot implement flexible charging pile scheduling. The following is a detailed description:
the communication frequency is too low:
scheduling response delay: too low a communication frequency can result in a longer time interval for dispatch instruction delivery, and the system cannot respond to grid load changes, user demands, or other important events in time. This causes a delay in scheduling, so that the charging pile cannot quickly adjust the charging strategy to accommodate the real-time demand.
The system scheduling policy is inflexible: the inability to implement flexible charging stake scheduling means that the system is unable to adjust the charging stake scheduling strategy according to real-time conditions and requirements. The system may adopt a fixed and preset scheduling plan, but cannot make a timely decision according to the current power grid state and the user demand, so that the intelligence and flexibility of the system are reduced.
Grid balancing is difficult: the load condition of the power grid may change at any time, and the charging strategy of the charging pile needs to be timely and flexibly adjusted to maintain the balance of the power grid. The low communication frequency makes the system unable to update the scheduling strategy rapidly when the load of the power grid changes, which makes the balance management of the power grid difficult.
The emergency situation cannot be handled: in case of an emergency situation in the grid, such as a fluctuation in the price of the electricity market or a grid failure, the state of the charging piles needs to be quickly adjusted to accommodate the new situation. The low communication frequency may cause that the system cannot acquire related information in time and issue an emergency dispatch instruction, and the emergency situation cannot be effectively treated.
Communication frequency is too high:
communication congestion: too high a communication frequency may cause the communication system to communicate scheduling instructions too frequently. This can increase the burden on the communication system, potentially resulting in excessive communication overhead and resource consumption, such that the system cannot efficiently process large amounts of information. Too high a communication frequency may cause congestion of a communication channel, resulting in instability of information transfer, and affecting accuracy and timeliness of a scheduling instruction.
The power market is unstable: too high a communication frequency may cause the charging post to frequently receive new scheduling strategies, so that the charging post often changes charging behavior in a short time. This constantly changing schedule may lead to instability in the power market, burdening the grid.
Therefore, the communication frequency during communication transmission of the scheduling strategy is monitored, and the problem that the system cannot effectively manage according to the load condition of the power grid can be solved because the communication frequency is too high or too low, which can cause that a scheduling instruction cannot be transmitted to the charging pile in time, so that the system cannot realize flexible scheduling of the charging pile.
The logic for obtaining the communication frequency floating index is as follows:
obtaining the optimal communication frequency range during the communication transmission of the scheduling strategy, and calibrating the optimal communication frequency range asWherein->And->Respectively representing the minimum value and the maximum value of the optimal communication frequency range;
it should be noted that, firstly, performance analysis is performed on the charging pile management system, including indexes of system response time, communication delay and the like, performance of the system is evaluated by testing and simulating the system under different communication frequencies, a communication frequency range optimizing the performance of the system is found in a time window Q, and secondly, an optimal communication frequency range is determined in consideration of scheduling policy requirements of the charging pile, such as actual workload of the charging pile, change of user requirements and the like, so as to meet the requirements of the scheduling policy, and ensure that the charging pile can flexibly schedule according to a predetermined policy.
In the time window Q, acquiring the real-time communication frequency of the communication transmission of the scheduling strategy, and using the real-time communication frequency as a function A representation;
it should be noted that, using special network monitoring tools, communication data packets can be captured and analyzed in real time, these tools can provide statistical information of communication frequency, for example tcpdump is a command line network packet analysis tool, can capture network data packets in real time and provide detailed protocol information, and can obtain relevant information of communication frequency by performing timestamp analysis on the captured data packets, for example Nagios is an open source network monitoring system, which can be used for monitoring various network services and devices. By configuring proper plug-ins and detection scripts, the monitoring of the communication frequency can be realized;
calculating a communication frequency lower floating index and a communication frequency upper floating index, wherein the calculated expression is as follows:，/>wherein->And->Respectively representing a communication frequency lower floating index and a communication frequency upper floating index, < >>Indicating that the real-time communication frequency is lower than +.>Is (are) period of->，/>Indicating that the real-time communication frequency is higher than +.>Is (are) period of->；
Calculating a communication frequency floating index, wherein the calculated expression is as follows:wherein->Indicating the floating index of the communication frequency, 0.7 and 0.2 are the floating index of the communication frequency, respectively +. >And a communication frequency floating indexWherein the weight factor is used for balancing the duty ratio of each item of data in the formula, thereby promoting the accuracy of the calculation result;
according to the calculation expression of the communication frequency floating index, in the time window Q, the larger the expression value of the communication frequency floating index generated when the scheduling strategy is communicated is, the larger the hidden danger of abnormality is indicated when the charging pile management system communicates the charging pile scheduling strategy through communication, otherwise, the smaller the hidden danger of abnormality is indicated when the charging pile management system communicates the charging pile scheduling strategy through communication.
When the charging pile management system transmits a scheduling strategy of the charging pile through communication to realize intelligent period control, the poor stability of bandwidth may cause that a scheduling instruction cannot be timely transmitted to the charging pile, so that the system cannot realize flexible scheduling of the charging pile. This situation can affect the effective management of the system, especially when dealing with demands such as grid load fluctuations. The following is a detailed description:
delay and packet loss: bandwidth instability can lead to increased communication latency and packet loss. If the dispatching instruction can not reach the charging pile in the time required by the real-time performance, the system can not respond to the load change of the power grid or the user demand in time, so that the realization of flexible dispatching is affected.
Scheduling instruction failure: unstable bandwidth may cause the scheduling instruction to fail or be truncated during the transfer. If the dispatching instruction does not completely reach the charging pile, the system cannot execute the dispatching strategy correctly and cannot charge according to the preset time period and the power requirement, so that the effective management of the system on the charging pile is reduced.
The real-time performance is reduced: the instability of the bandwidth may cause the real-time performance of the communication to be reduced, and the system cannot timely adjust the state of the charging pile under the condition that the load of the power grid is rapidly changed. This affects the flexible perceived and scheduled response of the system to grid load conditions so that the charging piles cannot adjust the charging schedule as required.
The system loses control: in case of unstable bandwidth, the system may lose effective control of the charging pile. The scheduling strategy of the charging pile management system depends on timely communication to realize control and scheduling of the charging pile, and if the communication is unreliable due to unstable bandwidth, the system can lose effective management of the charging pile.
Therefore, the bandwidth during communication transmission of the scheduling strategy is monitored, and the problem that the system cannot effectively manage according to the load condition of the power grid due to the fact that the scheduling instruction cannot be timely transmitted to the charging pile due to the fact that the bandwidth stability is poor can be timely found, so that the system cannot flexibly schedule the charging pile.
The logic for bandwidth instability index acquisition is as follows:
in the time window Q, acquiring the real-time bandwidth of the communication transmission of the scheduling strategy, and calibrating the real-time bandwidth as，xA number representing the real-time bandwidth acquired within the time window Q at the time of dispatch strategy communication delivery,x=1、2、3、4、……、p，pis a positive integer;
it should be noted that, special network traffic analysis software, such as NetFlow Analyzer, PRTG Network Monitor, etc., is used, and these software can monitor network traffic in real time and provide real-time statistics of bandwidth usage;
establishing a data set from the acquired real-time bandwidths, sequencing the real-time bandwidths in the data set according to time sequence, and calculating a bandwidth instability index according to the fluctuation condition of two adjacent real-time bandwidths, wherein the calculated expression is as follows:wherein->Representing a bandwidth instability index;
according to the calculation expression of the bandwidth instability index, in the time window Q, the larger the expression value of the bandwidth instability index generated when the scheduling policy is communicated is, the larger the hidden danger of abnormality is indicated when the charging pile management system communicates the charging pile scheduling policy through communication, otherwise, the smaller the hidden danger of abnormality is indicated when the charging pile management system communicates the charging pile scheduling policy through communication.
The time domain information during communication transmission of the scheduling strategy comprises clock information of the charging pile management system and clock information of the charging pile, and after the clock information of the charging pile management system and the clock information of the charging pile are obtained, a clock synchronization precision deviation index is generated after the clock information of the charging pile management system and the clock information of the charging pile are processed.
The clock synchronization precision refers to the degree of consistency of clocks between the charging pile management system and the charging pile, namely the synchronization accuracy between the internal clock of the system and the charging pile clock, and in intelligent period control, accurate clock synchronization is critical to ensuring the instantaneity and reliability of a scheduling strategy. The high clock synchronization precision means that the system can coordinate the behavior of the charging pile more accurately, and ensure that the charging plan is executed according to expectations, so that the overall efficiency and controllability of the system are improved.
When the charging pile management system transmits a scheduling strategy of the charging pile through communication to realize intelligent time period control, the poor clock synchronization precision may cause that a scheduling instruction cannot be timely transmitted to the charging pile, so that the system cannot realize flexible scheduling of the charging pile. This has a serious impact on the effective management of the system, especially when dealing with grid load fluctuations and like demands, as will be explained in detail below:
Scheduling instruction delays: the poor clock synchronization precision leads to the fact that clocks between the charging pile management system and the charging pile are not synchronous, and scheduling instructions are affected by clock differences in the transmission process, so that delay is caused. The delay of the scheduling instruction means that the charging pile cannot timely obtain the update of the charging plan, and the quick response to the power grid load change is affected.
Scheduling inaccuracy: inaccuracy in clock synchronization may cause the dispatch instructions to deviate in execution. The charging stake may execute the scheduling policy at different times than the timing expected by the system. This inaccuracy makes the system unable to precisely control the behavior of the charging stake, reducing the feasibility of flexible scheduling.
The charging schedule cannot be adjusted in real time: clock synchronization differences can prevent the system from adjusting the charging schedule in real time to accommodate grid load conditions. The system cannot flexibly adjust the charging period and the power of the charging pile according to actual demands, so that effective management of the power grid load cannot be realized.
The system loses control over the charging stake: poor clock synchronization accuracy may cause the system to lose effective control of the charging pile. Inaccurate transmission of the scheduling instruction enables the system to schedule the charging pile according to the expectation, so that the overall management capacity of the system on the charging pile is affected.
Therefore, the bandwidth during communication transmission of the scheduling strategy is monitored, and the problem that the scheduling instruction cannot be timely transmitted to the charging pile due to poor clock synchronization accuracy, so that the system cannot flexibly schedule the charging pile, and the system cannot effectively manage according to the power grid load condition due to the fact that the flexible charging pile scheduling cannot be implemented is solved.
The logic for obtaining the clock synchronization precision deviation index is as follows:
in the time window Q, the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication is respectively acquired through the charging pile management system and the charging pile, and the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication on the charging pile management system is marked asAnd the transmission time stamp acquired by the same scheduling instruction on the charging pile is marked as +.>，yThe number of the scheduling instruction acquired when the scheduling policy communication is transferred in the time window Q is represented,y=1、2、3、4、……、q，qis a positive integer;
calculating a clock synchronization precision deviation index, wherein the calculated expression is as follows:wherein->Representing a clock synchronization accuracy deviation index;
according to the calculation expression of the clock synchronization precision deviation index, in the time window Q, the larger the expression value of the clock synchronization precision deviation index generated during communication transmission of the scheduling strategy is, the larger the hidden danger of abnormality is indicated when the charging pile management system transmits the charging pile scheduling strategy through communication, otherwise, the smaller the hidden danger of abnormality is indicated when the charging pile management system transmits the charging pile scheduling strategy through communication.
Obtaining communication frequency floating index when dispatch strategy communication is transmittedIndex of bandwidth instabilityClock synchronization accuracy deviation index +.>After that, the communication frequency is floated by index->Bandwidth instability index->Clock synchronization accuracy deviation index +.>Carrying out formulated analysis to generate communication transmission coefficient +.>The formula according to is: />Wherein->、、/>The communication frequency floating index is respectively->Bandwidth instability index->Clock synchronization accuracy deviation index->Is a preset proportionality coefficient of>、/>、/>Are all greater than 0;
as can be seen from the calculation formula, in the time window Q, the larger the communication frequency floating index generated during the communication transmission of the scheduling strategy, the larger the bandwidth instability index and the larger the clock synchronization accuracy deviation index are, namely the communication transmission coefficient generated in the time window Q during the communication transmission of the scheduling strategyThe larger the expression value of the charging pile management system is, the larger the hidden danger of abnormality occurs when the charging pile management system transmits the charging pile scheduling strategy through communication, otherwise, the smaller the hidden danger of abnormality occurs when the charging pile management system transmits the charging pile scheduling strategy through communication.
In the time window Q, the communication transmission coefficient generated during communication transmission of the scheduling policy is compared with a preset communication transmission coefficient reference threshold value, and the comparison analysis results are as follows:
If the communication transfer coefficient is larger than or equal to the communication transfer coefficient reference threshold, generating a hidden danger instruction signal, and when the hidden danger instruction signal is generated during the communication transfer of the scheduling strategy, indicating that abnormal hidden danger possibly exists during the communication transfer of the scheduling strategy;
if the communication transfer coefficient is smaller than the communication transfer coefficient reference threshold, generating a normal instruction signal, and when the normal instruction signal is generated during the communication transfer of the scheduling strategy, indicating that the normal scheduling strategy communication can be realized during the communication transfer of the scheduling strategy.
The hidden danger type comprehensive analysis module is used for further analyzing the hidden danger when sensing that the scheduling strategy communication transmission process has the hidden danger through the communication abnormity monitoring mechanism, and sending different early warning prompts through the early warning module aiming at the analyzed hidden danger type;
when a scheduling strategy communication transmission process generates a patient instruction signal, acquiring a communication transmission coefficient corresponding to the current moment and a plurality of communication transmission coefficients generated subsequently to establish an analysis set, and calibrating the analysis set asIThen，fA number representing the communication transfer coefficient within the analysis set,f=1、2、3、4、……、u，uis a positive integer;
calculating a communication transmission coefficient standard deviation and a communication transmission coefficient average value by analyzing the communication transmission coefficients in the collection, and respectively comparing the communication transmission coefficient standard deviation and the communication transmission coefficient average value with a preset standard deviation reference threshold value and a preset communication transmission coefficient reference threshold value to obtain the following comparison analysis results:
If the average value of the communication transfer coefficients is larger than or equal to the reference threshold value of the communication transfer coefficients, a primary hidden danger signal is generated, and a primary alarm prompt is sent to the primary hidden danger signal, when the primary hidden danger signal is generated during the communication transfer of the scheduling strategy, the continuous serious abnormal hidden danger exists during the communication transfer of the scheduling strategy, and the scheduling instruction can not be transferred to the charging pile in time and needs to be maintained and managed in time;
if the average value of the communication transfer coefficients is smaller than the reference threshold value of the communication transfer coefficients and the standard deviation of the communication transfer coefficients is larger than or equal to the reference threshold value of the standard deviation, generating a secondary hidden danger signal, and sending a secondary alarm prompt to the secondary hidden danger signal, when the secondary hidden danger signal is generated during communication transfer of the scheduling strategy, the fact that serious abnormal hidden danger of unstable operation exists during communication transfer of the scheduling strategy is indicated, and the scheduling instruction can not be timely transferred to the charging pile and needs to be timely maintained and managed is also possible;
if the average value of the communication transfer coefficients is smaller than the reference threshold value of the communication transfer coefficients and the standard deviation of the communication transfer coefficients is smaller than the reference threshold value of the standard deviation, three-level hidden danger signals are generated, alarm prompt is not sent to the three-level hidden danger signals, when the three-level hidden danger signals are generated during communication transfer of the scheduling strategy, the fact that sudden and slight abnormal hidden danger possibly occurs during communication transfer of the scheduling strategy is indicated, and when the situation occurs, scheduling instructions cannot be timely transferred to the charging piles.
According to the invention, the data acquisition module is used for acquiring the related data of the charging pile management system in real time, so that the system comprehensively knows the state and the charging requirement of the charging pile, the real-time data analysis module is used for carrying out real-time analysis on the scheduling strategy of the charging pile, the scheduling strategy formulating module is used for formulating a more intelligent and flexible scheduling strategy of the charging pile based on the comprehensive evaluation result, and the scheduling instruction communication and communication module is used for ensuring that the formulated scheduling strategy can be timely communicated to the charging pile, thereby realizing that the charging pile can control the charging strategy to execute according to the intelligent time.
According to the invention, by introducing a communication anomaly monitoring mechanism, the system can sense the anomaly hidden danger in the communication transmission process of the scheduling strategy in real time, so that the anomaly hidden danger in the communication transmission process can be timely sensed, and secondly, by setting different hidden danger signal levels, the anomaly hidden danger can be effectively classified, and an alarm prompt of a corresponding level is sent out, so that an operator can quickly respond when the anomaly occurs, and corresponding maintenance and management measures are adopted, so that the timely transmission of the scheduling instruction is ensured, and the stability and reliability of the system are improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The intelligent time period control system based on the charging pile is characterized by comprising a data acquisition module, a real-time data analysis module, a scheduling strategy making module, a scheduling instruction communication transmission module, a real-time monitoring module, a hidden danger type comprehensive analysis module and an early warning module;
The data acquisition module is used for acquiring related data for intelligent scheduling by the charging pile management system;
the real-time data analysis module is used for carrying out real-time analysis on the collected data related to intelligent scheduling and comprehensively evaluating the scheduling strategy of the charging pile;
the scheduling strategy making module is used for making a scheduling strategy of the charging pile based on the real-time data analysis result;
the scheduling instruction communication module is used for communicating the formulated scheduling strategy to each charging pile through a communication unit of the charging pile management system, so that the charging pile can execute the intelligent period control charging strategy according to the scheduling instruction;
the real-time monitoring module is used for acquiring communication performance information and time domain information during communication transmission of the scheduling strategy, comprehensively analyzing the processed communication performance information and time domain information, establishing a communication abnormality monitoring mechanism and monitoring the communication transmission of the scheduling strategy in real time;
the communication performance information during communication transmission of the scheduling strategy comprises communication frequency information and bandwidth information, and after the communication frequency information and the bandwidth information are acquired, the communication frequency information and the bandwidth information are respectively processed to generate a communication frequency floating index and a bandwidth instability index; the time domain information during communication transmission of the scheduling strategy comprises clock information of the charging pile management system and clock information of the charging pile, and after the clock information of the charging pile management system and the clock information of the charging pile are obtained, a clock synchronization precision deviation index is generated after the clock information of the charging pile management system and the clock information of the charging pile are processed;
And the hidden danger type comprehensive analysis module is used for further analyzing the hidden danger when sensing that the scheduling strategy communication transmission process has the hidden danger through the communication abnormity monitoring mechanism, and sending different early warning prompts through the early warning module aiming at the analyzed hidden danger type.
2. The intelligent period control system based on a charging pile according to claim 1, wherein the logic for obtaining the communication frequency floating index is as follows:
obtaining optimal scheduling policy communication deliveryCommunication frequency range, and calibrating the optimal communication frequency range asWherein->And->Respectively representing the minimum value and the maximum value of the optimal communication frequency range;
in the time window Q, acquiring the real-time communication frequency of the communication transmission of the scheduling strategy, and using the real-time communication frequency as a functionA representation;
calculating a communication frequency lower floating index and a communication frequency upper floating index, wherein the calculated expression is as follows:，/>wherein->Andrespectively representing a communication frequency lower floating index and a communication frequency upper floating index, < >>Indicating that the real-time communication frequency is lower than +.>Is (are) period of->，/>Indicating that the real-time communication frequency is higher than +.>Is (are) period of->；
Calculating a communication frequency floating index, wherein the calculated expression is as follows: Wherein->Indicating the floating index of the communication frequency, 0.7 and 0.2 are the floating index of the communication frequency, respectively +.>And communication frequency up-floating index->Is a weight factor of (a).
3. The intelligent period control system based on charging piles according to claim 2, wherein the logic for obtaining the bandwidth instability index is as follows:
in the time window Q, acquiring the real-time bandwidth of the communication transmission of the scheduling strategy, and calibrating the real-time bandwidth as，xA number representing the real-time bandwidth acquired within the time window Q at the time of dispatch strategy communication delivery,x=1、2、3、4、……、p，pis a positive integer;
establishing a data set from the acquired real-time bandwidths, sequencing the real-time bandwidths in the data set according to time sequence, and passing through adjacent data setsCalculating a bandwidth instability index according to the fluctuation conditions of two real-time bandwidths, wherein the calculated expression is as follows:wherein->Representing the bandwidth instability index.
4. A charging pile based intelligent time period control system according to claim 3, wherein the logic for obtaining the clock synchronization accuracy deviation index is as follows:
in the time window Q, the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication is respectively acquired through the charging pile management system and the charging pile, and the sending time stamp of the scheduling instruction during the transmission of the scheduling policy communication on the charging pile management system is marked as And the transmission time stamp acquired by the same scheduling instruction on the charging pile is marked as +.>，yThe number of the scheduling instruction acquired when the scheduling policy communication is transferred in the time window Q is represented,y=1、2、3、4、……、q，qis a positive integer;
calculating a clock synchronization precision deviation index, wherein the calculated expression is as follows:wherein->Representing the clock synchronization accuracy deviation index.
5. The intelligent period control system based on charging piles of claim 4, wherein the scheduling policy is obtainedCommunication frequency floating index during communication transmissionBandwidth instability index->Clock synchronization accuracy deviation index +.>After that, the communication frequency is floated by index->Bandwidth instability index->Clock synchronization accuracy deviation index +.>Carrying out formulated analysis to generate communication transmission coefficient +.>The formula according to is:wherein->、/>、/>The communication frequency floating index is respectively->Bandwidth instability index->Clock synchronization accuracy deviation index->Is a preset proportionality coefficient of>、/>、/>Are all greater than 0.
6. The intelligent period control system based on the charging pile according to claim 5, wherein in the time window Q, the communication transmission coefficient generated when the communication of the scheduling policy is transmitted is compared with a preset communication transmission coefficient reference threshold value, and the comparison analysis results are as follows:
If the communication transmission coefficient is greater than or equal to the communication transmission coefficient reference threshold value, generating a hidden danger instruction signal;
if the communication transfer coefficient is smaller than the communication transfer coefficient reference threshold, generating a normal command signal.
7. The intelligent time period control system based on the charging pile according to claim 6, wherein when the scheduling policy communication transmission process generates the suffering instruction signal, the communication transmission coefficient corresponding to the current time and the subsequently generated communication transmission coefficients are acquired to build an analysis set, and the analysis set is calibrated asIThen，fA number representing the communication transfer coefficient within the analysis set,f=1、2、3、4、……、u，uis a positive integer;
calculating a communication transmission coefficient standard deviation and a communication transmission coefficient average value by analyzing the communication transmission coefficients in the collection, and respectively comparing the communication transmission coefficient standard deviation and the communication transmission coefficient average value with a preset standard deviation reference threshold value and a preset communication transmission coefficient reference threshold value to obtain the following comparison analysis results:
if the average value of the communication transmission coefficients is larger than or equal to the reference threshold value of the communication transmission coefficients, generating a first-level hidden danger signal, and sending out a first-level alarm prompt to the first-level hidden danger signal;
If the average value of the communication transmission coefficients is smaller than the reference threshold value of the communication transmission coefficients and the standard deviation of the communication transmission coefficients is larger than or equal to the reference threshold value of the standard deviation, generating a secondary hidden danger signal, and sending a secondary alarm prompt to the secondary hidden danger signal;
if the average value of the communication transmission coefficients is smaller than the reference threshold value of the communication transmission coefficients and the standard deviation of the communication transmission coefficients is smaller than the reference threshold value of the standard deviation, generating a three-level hidden danger signal, and not giving an alarm prompt to the three-level hidden danger signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410112881.XA CN117644794B (en) | 2024-01-26 | 2024-01-26 | Intelligent period control system based on charging pile |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410112881.XA CN117644794B (en) | 2024-01-26 | 2024-01-26 | Intelligent period control system based on charging pile |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117644794A CN117644794A (en) | 2024-03-05 |
| CN117644794B true CN117644794B (en) | 2024-04-09 |
Family
ID=90046201
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410112881.XA Active CN117644794B (en) | 2024-01-26 | 2024-01-26 | Intelligent period control system based on charging pile |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117644794B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112234638A (en) * | 2020-09-11 | 2021-01-15 | 国网山东省电力公司济南供电公司 | Power grid peak regulation system and method based on load side intelligent charging pile cluster control |
| CN112801447A (en) * | 2020-12-22 | 2021-05-14 | 北京新能源汽车技术创新中心有限公司 | Intelligent charging network system and electric vehicle charging scheduling method based on same |
| CN113415203A (en) * | 2021-06-29 | 2021-09-21 | 湖南昌茂电能发展有限公司 | Intelligent charging pile management system based on Internet of things |
| TWI741713B (en) * | 2020-07-31 | 2021-10-01 | 拓連科技股份有限公司 | Charging scheduling systems and methods thereof for charging devices |
| CN113949091A (en) * | 2021-12-21 | 2022-01-18 | 北京理工大学 | Intelligent charging electric vehicle energy networking scheduling method and system |
| CN217048320U (en) * | 2022-05-06 | 2022-07-26 | 华北电力大学 | Electric automobile charge-discharge intelligent management system |
| CN117077872A (en) * | 2023-10-17 | 2023-11-17 | 深圳汇能新能源科技有限公司 | Intelligent scheduling management system for new energy electric automobile charging pile |
| CN117148001A (en) * | 2023-08-29 | 2023-12-01 | 合肥掌魅无线信息科技有限公司 | New energy automobile fills electric pile fault prediction system based on artificial intelligence |
-
2024
- 2024-01-26 CN CN202410112881.XA patent/CN117644794B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TWI741713B (en) * | 2020-07-31 | 2021-10-01 | 拓連科技股份有限公司 | Charging scheduling systems and methods thereof for charging devices |
| CN112234638A (en) * | 2020-09-11 | 2021-01-15 | 国网山东省电力公司济南供电公司 | Power grid peak regulation system and method based on load side intelligent charging pile cluster control |
| CN112801447A (en) * | 2020-12-22 | 2021-05-14 | 北京新能源汽车技术创新中心有限公司 | Intelligent charging network system and electric vehicle charging scheduling method based on same |
| CN113415203A (en) * | 2021-06-29 | 2021-09-21 | 湖南昌茂电能发展有限公司 | Intelligent charging pile management system based on Internet of things |
| CN113949091A (en) * | 2021-12-21 | 2022-01-18 | 北京理工大学 | Intelligent charging electric vehicle energy networking scheduling method and system |
| CN217048320U (en) * | 2022-05-06 | 2022-07-26 | 华北电力大学 | Electric automobile charge-discharge intelligent management system |
| CN117148001A (en) * | 2023-08-29 | 2023-12-01 | 合肥掌魅无线信息科技有限公司 | New energy automobile fills electric pile fault prediction system based on artificial intelligence |
| CN117077872A (en) * | 2023-10-17 | 2023-11-17 | 深圳汇能新能源科技有限公司 | Intelligent scheduling management system for new energy electric automobile charging pile |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117644794A (en) | 2024-03-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lin et al. | Power system and communication network co-simulation for smart grid applications | |
| CN109597014A (en) | A kind of electric energy meter error diagnostic method based on artificial intelligence technology | |
| EP3427390B1 (en) | Optimized smart meter reporting schedule | |
| CN111131417B (en) | Metering, monitoring and analyzing system and method for transformer substation | |
| CN103733463A (en) | Method and apparatus for controlling energy services based on market data | |
| CN109116812A (en) | Intelligent power distribution cabinet, energy conserving system and method based on SparkStreaming | |
| CN105515184A (en) | Wireless sensor network-based cooperative monitoring system of multi-sensor and multi-parameter distribution network | |
| CN109190836A (en) | Public building energy resource system intelligently manages cloud | |
| CN111046519A (en) | Application analysis method of artificial intelligence technology in electric energy meter error diagnosis | |
| Nezhad et al. | Joint Peak Clipping and Load Scheduling Based on User Behavior Monitoring in an IoT Platform | |
| CN114095311A (en) | Power Internet of things gateway, power system and method for configuring newly-added power equipment | |
| CN117644794B (en) | Intelligent period control system based on charging pile | |
| CN116526667B (en) | Secondary fusion distribution network feeder terminal system based on current Internet of things feedback mechanism | |
| CN112950001A (en) | Intelligent energy management and control system and method based on cloud edge closed-loop architecture | |
| Yudho | Construction of lora data power sensor from giot and acsip using sql technique | |
| CN101231523B (en) | Control system and method for sensibility charge | |
| CN116362522A (en) | Digital energy nitrogen station data processing method and system based on multipoint distribution model | |
| CN109672227A (en) | A kind of Economical Operation of Power Systems dispatching method | |
| US11854054B2 (en) | Adaptive energy storage operating system for multiple economic services | |
| CN114969010A (en) | Power distribution network monitoring processing method and device | |
| Dobrescu et al. | Using IoT communication infrastructure for improving performance of power microgrids | |
| CN116862036A (en) | Load prediction method and device | |
| CN114414938B (en) | Dynamic response method and system for power distribution network faults | |
| CN112262404A (en) | Global control system for consumption of energy resources based on IOT technology | |
| CN117374978B (en) | Grid-connected scheduling management method and system constructed by combining knowledge graph |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |