CN116937756B - Internet-based remote monitoring system and monitoring method for precious charging cabinet - Google Patents
Internet-based remote monitoring system and monitoring method for precious charging cabinet Download PDFInfo
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- CN116937756B CN116937756B CN202311208779.1A CN202311208779A CN116937756B CN 116937756 B CN116937756 B CN 116937756B CN 202311208779 A CN202311208779 A CN 202311208779A CN 116937756 B CN116937756 B CN 116937756B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000005192 partition Methods 0.000 claims abstract description 37
- 230000008569 process Effects 0.000 claims abstract description 11
- 230000006870 function Effects 0.000 claims description 32
- 238000005070 sampling Methods 0.000 claims description 20
- 238000012795 verification Methods 0.000 claims description 20
- 238000012545 processing Methods 0.000 claims description 18
- 238000005457 optimization Methods 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 6
- 238000000638 solvent extraction Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 4
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
- H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- General Physics & Mathematics (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The invention belongs to the technical field of monitoring of charger cabinet, and particularly relates to a remote monitoring system and a monitoring method for a charger cabinet based on the Internet. The invention can determine the fluctuation interval of the output power of the charger cabinet by utilizing the partition model, and further can pertinently adjust the required output power of the charger cabinet, so that the charger cabinet does not need to work according to the rated output power at any time, the loss of the charger cabinet in the use process is effectively reduced, meanwhile, the excessive loss critical point of the charger cabinet can be predicted, the output power of the charger cabinet can not reach the requirement of the charger, the charger cabinet can be ensured to be maintained in a safe environment, the excessive loss critical point is offset, the early warning node is determined, the overload operation of the charger cabinet can be avoided, and the charger cabinet can be ensured to supply power to the charger in the safe environment all the time.
Description
Technical Field
The invention belongs to the technical field of monitoring of charger cabinet, and particularly relates to a remote monitoring system and a monitoring method for a charger cabinet based on the Internet.
Background
Along with popularization of sharing economy, more and more shared products appear in people's daily life, for example sharing bicycle, sharing treasured that charges and sharing car etc. it can let idle resource by not being used to can be fully utilized, the emergence of sharing treasured that charges just can effectually solve the problem that mobile phone that people face in daily trip in-process can not guarantee normal continuation of journey, it generally distributes in the region that crowds such as super, convenience store and restaurant gather, so that people take, and in order to guarantee the continuation of journey ability of treasured that charges, all can set up the treasured rack that charges of adaptation at its distribution point, thereby after the user returns treasured that charges to the distribution point, treasured that charges can charge to treasured, guarantee that its continuation of journey ability can not receive the influence.
In the prior art, when monitoring treasured rack charges, often need the staff to maintain treasured rack charges at regular fixed point, and this kind of mode is although can realize monitoring operation, but often needs a large amount of manpowers to assist, and monitoring period overlength in time can't in time discover whether treasured rack charges overload operation, this will definitely lead to treasured rack charges the risk when treasured charges, based on this, this scheme provides a remote monitoring method of early warning in advance before treasured rack charges excessive loss.
Disclosure of Invention
The invention aims to provide a remote monitoring system and a monitoring method for a precious charging cabinet based on the Internet, which can early warn in advance before the precious charging cabinet is excessively worn, so that overload operation of the precious charging cabinet is avoided.
The technical scheme adopted by the invention is as follows:
a remote monitoring method for a charger cabinet based on the Internet comprises the following steps:
acquiring operation parameters of a charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
acquiring the quantity of the charging treasures in the charging treasures cabinet, and counting the electric quantity parameters of each charging treasure;
inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet;
constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
inputting the parameters to be evaluated into a partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
obtaining rated output power of the charger cabinet, calibrating a reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
acquiring an allowable deviation threshold and comparing the allowable deviation threshold with the deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the tolerance deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
obtaining deviation parameters from the reference data set, and inputting the deviation parameters into a trend analysis model to obtain a loss trend value of the charger cabinet;
and inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet, predicting an excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
In a preferred embodiment, the step of inputting the electric quantity parameter into a measurement model to obtain the required output power of the charger cabinet includes:
numbering is added to the charger in the charger cabinet, and electric quantity parameters of the online charger are obtained in real time;
comparing the charging threshold value of the online charging bank with the electric quantity parameter of the online charging bank;
if the electric quantity parameter of the online charging bank is larger than the charging threshold, the online charging bank is marked as the to-be-taken charging bank;
if the electric quantity parameter of the online charging bank is smaller than or equal to the charging threshold, the online charging bank is marked as the to-be-charged charging bank;
invoking a measurement function from the measurement model;
and acquiring the required charging power of the to-be-charged charger baby, inputting the required charging power into an measuring and calculating function, and determining the output result as required output power.
In a preferred embodiment, the step of inputting the parameter to be evaluated into a partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet includes:
acquiring the occurrence node of the parameter to be evaluated, and calibrating the occurrence node as the node to be evaluated;
acquiring interval time length of adjacent nodes to be evaluated, and calibrating the interval time length as the time length to be evaluated;
calling a classification threshold from the partition model, and comparing the classification threshold with the duration to be evaluated;
if the time length to be evaluated is greater than or equal to a classification threshold, indicating that the node to be evaluated corresponding to the time length to be evaluated is a partition node, and arranging the partition node according to the sequence of the occurrence time;
if the time length to be evaluated is smaller than the classification threshold value, indicating that the node to be evaluated corresponding to the time length to be evaluated is a non-partition node;
and determining the initial occurrence node of the parameter to be evaluated as a partition initial node, and constructing a plurality of fluctuation intervals by taking each two nodes as a group according to the arrangement sequence of the partition nodes.
In a preferred embodiment, after the fluctuation interval is determined, it is input into a verification model, and the verification process is as follows:
acquiring a plurality of fluctuation intervals in the monitoring period, and respectively calibrating head and tail nodes of each fluctuation interval as a starting point to be compared and a ending point to be compared;
calculating the difference values between all the starting points to be compared and the ending points to be compared, and calibrating the difference values as parameters to be checked;
calling a check function from the check model, inputting the parameter to be checked into the check function, and calibrating an output result of the parameter to be checked into the parameter to be compared;
the parameters to be compared are rounded upwards to obtain optimized parameters, and the optimized parameters are arranged according to the sequence from big to small;
acquiring a verification threshold value and the occurrence frequency of each optimization parameter, and comparing the occurrence frequency of the optimization parameters with the verification threshold value;
if the occurrence frequency of the optimized parameters is larger than a verification threshold value, determining a corresponding fluctuation interval as a normal fluctuation interval;
if the occurrence frequency of the optimized parameters is smaller than or equal to a verification threshold value, the corresponding fluctuation interval is marked as an instantaneous fluctuation interval, and the instantaneous fluctuation interval is marked.
In a preferred scheme, after the normal fluctuation interval is determined, the maximum required power in each normal fluctuation interval is counted, and the output power of the charger cabinet is determined according to the maximum required power.
In a preferred embodiment, the step of obtaining the loss trend value of the charger cabinet by obtaining the deviation parameter from the reference data set and inputting the deviation parameter into a trend analysis model includes:
acquiring the deviation parameter;
calling a trend analysis function from the trend analysis model;
and inputting all the deviation parameters into a trend analysis function, and calibrating the output result as a loss trend value of the charger cabinet.
In a preferred embodiment, the step of inputting the loss trend value into a prediction model to obtain a safe operation duration of the charger cabinet, and predicting an excessive loss critical point based on the safe operation duration includes:
acquiring an actual loss value, an excessive loss critical value and a loss trend value under the current node;
calling a prediction function from the prediction model;
and inputting the actual loss value, the excessive loss critical value and the loss trend value under the current node into a prediction function, and calibrating the output result as the excessive loss critical point.
In a preferred embodiment, the step of performing the offset processing on the excessive loss critical point and determining an offset result as an early warning node includes:
acquiring the excessive loss critical point;
acquiring rated offset time length, and performing reverse offset by taking the excessive loss critical point as a reference node to obtain an early warning node;
and after the charger cabinet runs to the early warning node, an early warning signal is synchronously sent out.
The invention also provides a remote monitoring system for the internet-based charger cabinet, which is applied to the remote monitoring method for the internet-based charger cabinet, and comprises the following steps:
the first acquisition module is used for acquiring the operation parameters of the charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
the second acquisition module is used for acquiring the quantity of the charging treasures in the charging treasures cabinet and counting the electric quantity parameters of each charging treasures;
the measuring and calculating module is used for inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet;
the sampling module is used for constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
the partitioning module is used for inputting the parameters to be evaluated into a partitioning model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
the third acquisition module is used for acquiring rated output power of the charger cabinet, calibrating a reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
the alarm module is used for acquiring an allowable deviation threshold and comparing the allowable deviation threshold with the deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the tolerance deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
the trend analysis module is used for acquiring deviation parameters from the reference data set and inputting the deviation parameters into a trend analysis model to obtain a loss trend value of the charger cabinet;
the prediction module is used for inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet, predicting an excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
And, a precious for rack remote monitoring terminal charges based on internet includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the remote monitoring method for the internet-based charger cabinet.
The invention has the technical effects that:
the invention can determine the fluctuation interval of the output power of the charger cabinet by utilizing the partition model, and further can pertinently adjust the required output power of the charger cabinet, so that the charger cabinet does not need to work according to the rated output power at any time, the loss of the charger cabinet in the use process is effectively reduced, meanwhile, the excessive loss critical point of the charger cabinet can be predicted, the output power of the charger cabinet can not reach the requirement of the charger, the charger cabinet can be ensured to be maintained in a safe environment, the excessive loss critical point is offset, the early warning node is determined, the overload operation of the charger cabinet can be avoided, and the charger cabinet can be ensured to supply power to the charger in the safe environment all the time.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
fig. 2 is a block diagram of a system provided by the present invention.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one preferred embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1 and 2, the invention provides a remote monitoring method for a charger cabinet based on internet, which comprises the following steps:
s1, acquiring operation parameters of a charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
s2, acquiring the quantity of the charging treasures in the charging treasures cabinet, and counting the electric quantity parameters of each charging treasures;
s3, inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet;
s4, constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
s5, inputting parameters to be evaluated into the partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
s6, obtaining rated output power of the charger cabinet, calibrating a reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
s7, acquiring an allowable deviation threshold value and comparing the allowable deviation threshold value with a deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the allowable deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
s8, obtaining deviation parameters from the reference data set, and inputting the deviation parameters into a trend analysis model to obtain a loss trend value of the charger cabinet;
s9, inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet, predicting an excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
As described in the above steps S1-S9, with popularization of sharing economy, more and more shared products appear in daily life of people, such as sharing bicycles, sharing charging treasures, sharing automobiles, etc., which can make full use of idle resources from unused, and the appearance of sharing charging treasures can effectively solve the problem that mobile phones cannot guarantee normal cruising faced by people in daily traveling process, and generally are distributed in areas where people gather such as commercial, convenience stores and restaurants, etc., so as to facilitate people taking, and in order to guarantee cruising ability of charging treasures, adaptive charging cabinets are arranged at distribution points of the charging treasures, so that after users return the charging treasures to distribution points, the charging treasures can be charged, and cruising ability of the charging treasures is guaranteed not to be affected, the embodiment provides a method for remotely monitoring a precious charging cabinet, firstly, the operation parameters of the precious charging cabinet and the quantity of precious charging in the precious charging cabinet are required to be obtained, and the electric quantity parameters of the precious charging are also required to be obtained in real time, then the electric quantity parameters are input into a measuring and calculating model, the required output power of the precious charging cabinet can be obtained, the precious charging cabinet is inevitably worn out to a certain extent in the long-time operation process, the actual output power of the precious charging cabinet is also affected, in order to avoid the phenomenon, the embodiment obtains the required output power of the precious charging cabinet under each sampling node by constructing a monitoring period and constructing a plurality of sampling nodes in the monitoring period, then the required output power is calibrated as a parameter to be evaluated, and then the fluctuation interval of the output power of the precious charging cabinet is determined by utilizing a partition model according to the parameter to be evaluated, the method comprises the steps of obtaining rated output power of a charger cabinet, calibrating the rated output power as a reference parameter, collecting parameters to be evaluated to determine a difference value between required output power and rated output power, determining the difference value as a deviation parameter, comparing the deviation parameter with a deviation threshold value, judging whether the charger cabinet is excessively worn, summarizing the corresponding deviation parameter as a reference data set when the charger cabinet is not excessively worn, and predicting an excessive loss critical point of the charger cabinet by combining a trend analysis model and a prediction model to avoid that the output power of the charger cabinet cannot meet the requirement of a charger, ensuring that the charger cabinet can be maintained in a safe environment, performing offset processing on the charger cabinet after the excessive loss critical point is determined, determining an offset result as an early warning node, and reminding a worker to timely maintain the charger cabinet to ensure that the charger cabinet can supply power to the charger in the safe environment.
In a preferred embodiment, the step of inputting the electric quantity parameter into the measurement model to obtain the required output power of the charger cabinet includes:
s301, adding numbers to the charger in the charger cabinet, and acquiring electric quantity parameters of the online charger in real time;
s302, comparing the charging threshold value of the online charging bank with the electric quantity parameter of the online charging bank;
if the electric quantity parameter of the online charging bank is larger than the charging threshold, the online charging bank is marked as the to-be-taken charging bank;
if the electric quantity parameter of the online charging bank is smaller than or equal to the charging threshold value, the online charging bank is marked as the charging bank to be charged;
s303, calling a measuring function from the measuring model;
s304, the required charging power of the to-be-charged charger baby is obtained and is input into the measuring and calculating function, and the output result is determined to be the required output power.
As described in steps S301-S304, a plurality of charging devices are placed in the charging device cabinet, in order to facilitate distinguishing, this embodiment numbers each charging device, so that the terminal can clearly distinguish the electric quantity parameters of each charging device, and the charging device may be used only briefly after being used by a user, so that the influence on the cruising ability of the charging device is small, the internal storage electric quantity consumption is low, the subsequent use can be completely satisfied, and the charging device charging frequency increases, the loss of the charging device can be deepened, the embodiment judges whether the returned charging device needs to be charged by setting a charging threshold, so that the charging device in the charging device cabinet is classified into a charging device to be charged and a charging device to be taken, and = the rated taking priority of the charging device to be taken is higher than the taking priority of the charging device to be charged, and the charging device is only charged when the electric quantity parameters of the charging device are lower than the charging threshold, until the charging device is stopped after being full, in general, the charging device in the same charging device can completely satisfy the subsequent use, and the charging device charging requirement in the same as the measuring and calculating function can be combined with the visual function, wherein the output device in the measuring and calculating device has the visual requirement function, and the measuring and calculating device has the visual requirement.Wherein->Representing the required output power, +.>Indicates the number of charging treasures to be charged, +.>Indicating the required charging power of the treasured to be charged, < +.>Representing power reserve, ensuring that the required output power is greater than the required charge power, based on whichTo confirm the precious rack that charges to the demand output who charges precious safety, the overload phenomenon of precious rack takes place to effectual avoiding charging.
In a preferred embodiment, the step of inputting the parameter to be evaluated into the partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet includes:
s501, acquiring an occurrence node of a parameter to be evaluated, and calibrating the occurrence node as the node to be evaluated;
s502, acquiring interval time of adjacent nodes to be evaluated, and calibrating the interval time as the time to be evaluated;
s503, calling a classification threshold value from the partition model, and comparing the classification threshold value with the duration to be evaluated;
if the time length to be evaluated is greater than or equal to the classification threshold value, indicating that the node to be evaluated corresponding to the time length to be evaluated is a partition node, and arranging the partition node according to the sequence of the occurrence time;
if the time length to be evaluated is smaller than the classification threshold value, indicating that the node to be evaluated corresponding to the time length to be evaluated is a non-partition node;
s504, determining an initial occurrence node of the parameter to be evaluated as a partition initial node, and constructing a plurality of fluctuation intervals by taking each two as a group according to the arrangement sequence of the partition nodes.
As described in the above steps S501-S504, after the parameters to be evaluated are determined, the generating nodes are synchronously acquired, and in this embodiment, the generating nodes are calibrated as the nodes to be evaluated, and the interval duration between the adjacent nodes to be evaluated is used as the basis of the time duration to be evaluated and the classification threshold value to be compared, so as to determine whether each node to be evaluated is a partition node, and then the initial generating node of the parameters to be evaluated is used as the partition initial node to determine the fluctuation interval, i.e. each two adjacent partition nodes form a fluctuation interval, for example, the first partition node forms a fluctuation interval with the partition initial node, the second partition node forms a fluctuation interval with the third partition node, and so on, a plurality of fluctuation intervals can be determined one by one, and the last partition node forms a fluctuation interval with the partition initial node, so that the circulating fluctuation interval can be obtained in the monitoring period.
In a preferred embodiment, after the fluctuation interval is determined, the fluctuation interval is input into a verification model, and the verification process is as follows:
stp1, acquiring fluctuation intervals in a plurality of monitoring periods, and respectively calibrating head and tail nodes of each fluctuation interval as a starting point to be compared and a comparison ending point to be compared;
stp2, measuring and calculating the difference between all the starting points to be compared and the comparison ending points to be compared, and calibrating the difference as a parameter to be checked;
stp3, calling a checking function from the checking model, inputting the parameter to be checked into the checking function, and calibrating the output result as the parameter to be compared;
stp4, rounding up the parameters to be compared to obtain optimized parameters, and arranging the optimized parameters in a sequence from big to small;
stp5, acquiring a check threshold value and the occurrence frequency of each optimization parameter, and comparing the occurrence frequency of the optimization parameter with the check threshold value;
if the occurrence frequency of the optimization parameters is larger than the verification threshold, determining the corresponding fluctuation interval as a normal fluctuation interval;
if the occurrence frequency of the optimization parameters is smaller than or equal to the verification threshold value, the corresponding fluctuation interval is marked as an instantaneous fluctuation interval, and the instantaneous fluctuation interval is marked.
As described in the above steps Stp1-Stp5, after the fluctuation intervals are determined, a verification process is performed on each fluctuation interval, so as to plan the required output power in each fluctuation interval, first, the head and tail nodes of each fluctuation interval are respectively calibrated as a starting point to be compared and an ending point to be compared, and based on the starting point to be compared and the ending point to be compared, a parameter to be verified is determined, and then the verification parameter is input into a verification function, so as to obtain the parameter to be compared, wherein the expression of the verification function is:wherein->Representing waitingAlignment parameters (I)>And->Representing adjacent starting points to be aligned, +.>And->Representing adjacent comparison result points, and after the comparison parameters are determined, rounding the comparison result points upwards to ensure that the comparison result points are valued as integers, the embodiment calibrates the comparison result points as optimization parameters, and can determine a normal fluctuation interval and an instantaneous fluctuation interval by counting occurrence frequencies of all the optimization parameters, and after the normal fluctuation interval is determined, respectively counting the maximum required power in all the normal fluctuation intervals, and determining the output power of the charger cabinet according to the maximum required power.
In a preferred embodiment, the step of obtaining the loss trend value of the charger cabinet by obtaining the deviation parameter from the reference data set and inputting the deviation parameter into the trend analysis model includes:
s801, obtaining deviation parameters;
s802, calling a trend analysis function from the trend analysis model;
s803, inputting all deviation parameters into a trend analysis function, and calibrating an output result of the deviation parameters into a loss trend value of the charger cabinet.
As described in the above steps S801-S803, after the deviation parameter is determined, the deviation parameter is compared with a deviation threshold value, so as to determine whether the charger cabinet can normally supply power to the charger therein, and send an alarm signal when the charger cabinet fails to normally supply power, at this time, the operation of the charger cabinet should be stopped, staff is arranged to perform maintenance processing, and when two groups of deviation parameters are greater than the tolerance deviation threshold value, a trend analysis function is called from a trend analysis model, where the expression of the trend analysis function is:wherein->Loss trend value of the charger cabinet is represented, +.>Representing the duration of the fluctuation interval of the corresponding deviation parameter, < >>Number indicating deviation parameter>And->And representing adjacent deviation parameters, and determining the loss trend value of the charger cabinet based on the adjacent deviation parameters, so as to provide corresponding data support for the follow-up prediction of the excessive loss critical point of the charger.
In a preferred embodiment, the step of inputting the loss trend value into the prediction model to obtain the safe operation duration of the charger cabinet and predicting the excessive loss critical point based on the safe operation duration includes:
s901, acquiring an actual loss value, an excessive loss critical value and a loss trend value under a current node;
s902, calling a prediction function from a prediction model;
s903, inputting the actual loss value, the excessive loss critical value and the loss trend value under the current node into the prediction function, and calibrating the output result as the excessive loss critical point.
As described in the above steps S901-S903, after the loss trend value is determined, the loss trend value is input into a prediction function together with an actual loss value and an excessive loss critical value under the current node, where the expression of the prediction function is:wherein->Indicating the safe operation duration>Represents an excessive loss threshold value +_>The actual loss value under the current node is represented, based on the formula, the safe operation time length after the current node can be obtained, the current node is taken as a reference node, the safe operation time length is combined for shifting, the excessive loss critical point can be obtained, and the early warning node can be determined based on the excessive loss critical point.
In a preferred embodiment, the step of performing the offset processing on the excessive loss critical point and determining the offset result as the early warning node includes:
s904, acquiring an excessive loss critical point;
s905, acquiring rated offset time length, and performing reverse offset by taking an excessive loss critical point as a reference node to obtain an early warning node;
after the charger cabinet operates to the early warning node, an early warning signal is sent out synchronously.
As described in the above steps S904-S905, after the determination of the excessive loss critical point, the rated offset time is determined by combining the maintenance time of the worker on the charger cabinet, so as to provide an offset reference for the excessive loss critical point, after the offset of the excessive loss critical point is completed, the early warning node can be obtained, and after the charger cabinet operates to the early warning node, the early warning signal is synchronously sent, so as to remind the worker to timely maintain the charger cabinet, and ensure that the charger cabinet does not operate in an overload state.
The invention also provides a remote monitoring system for the internet-based charger cabinet, which is applied to the remote monitoring method for the internet-based charger cabinet, and comprises the following steps:
the first acquisition module is used for acquiring the operation parameters of the charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
the second acquisition module is used for acquiring the quantity of the charging treasures in the charging treasures cabinet and counting the electric quantity parameters of each charging treasures;
the measuring and calculating module is used for inputting the electric quantity parameters into the measuring and calculating model to obtain the required output power of the charger cabinet;
the sampling module is used for constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
the partitioning module is used for inputting parameters to be evaluated into the partitioning model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
the third acquisition module is used for acquiring rated output power of the charger cabinet, calibrating the reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
the alarm module is used for acquiring an allowable deviation threshold and comparing the allowable deviation threshold with a deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the allowable deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
the trend analysis module is used for acquiring deviation parameters from the reference data set and inputting the deviation parameters into the trend analysis model to obtain a loss trend value of the charger cabinet;
the prediction module is used for inputting the loss trend value into the prediction model to obtain the safe operation time length of the charger cabinet, predicting the excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
When the monitoring system is executed, the operation parameters of the charging treasured cabinet are firstly acquired through the first acquisition module, the quantity of the charging treasures and the electric quantity parameters in the charging treasured cabinet are acquired through the second acquisition module, the required output power of the charging treasured cabinet is calculated by utilizing the measuring and calculating module based on the electric quantity parameters of the charging treasures, so that the charging treasures can supplement the electric quantity in a safe environment, after the charging treasured cabinet operates for a period of time, a monitoring period is constructed through the sampling module, a plurality of sampling nodes are arranged in the monitoring period, so that the required output power under each sampling node is acquired in real time, then the required output power is calibrated into a parameter to be evaluated, then a fluctuation interval corresponding to the output power of the charging treasured cabinet can be determined according to the generation node of the parameter to be evaluated through the action of the partition module, the deviation parameter is acquired through the third acquisition module, and comparing the deviation parameter with a deviation threshold value in the alarm module so as to judge whether the precious cabinet excessively loses, sending an alarm signal under the condition of excessive loss of the precious cabinet, prompting a worker to maintain, inputting the corresponding deviation parameter into the trend analysis module when the precious cabinet can normally supply power to the precious, calculating the loss trend value of the precious cabinet, finally combining the effect of the prediction module to calculate an excessive loss critical point, ensuring that the precious cabinet can be maintained in a safe environment, carrying out offset processing on the precious cabinet after the excessive loss critical point is determined, and determining an offset result as an early warning node so as to remind the worker to timely maintain the precious cabinet, and ensuring that the precious cabinet can supply power to the precious under the safe environment.
And, a precious for rack remote monitoring terminal charges based on internet includes:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program executable by the at least one processor, so that the at least one processor can execute the remote monitoring method for the internet-based charger cabinet.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention. Structures, devices and methods of operation not specifically described and illustrated herein, unless otherwise indicated and limited, are implemented according to conventional means in the art.
Claims (10)
1. The utility model provides a precious rack of charging is with remote monitoring method based on internet which characterized in that: comprising the following steps:
acquiring operation parameters of a charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
acquiring the quantity of the charging treasures in the charging treasures cabinet, and counting the electric quantity parameters of each charging treasure;
inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet;
constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
inputting the parameters to be evaluated into a partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
obtaining rated output power of the charger cabinet, calibrating a reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
acquiring an allowable deviation threshold and comparing the allowable deviation threshold with the deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the tolerance deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
obtaining deviation parameters from the reference data set, and inputting the deviation parameters into a trend analysis model to obtain a loss trend value of the charger cabinet;
and inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet, predicting an excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
2. The internet-based remote monitoring method for a charger cabinet according to claim 1, wherein the method comprises the following steps: the step of inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet comprises the following steps:
numbering is added to the charger in the charger cabinet, and electric quantity parameters of the online charger are obtained in real time;
comparing the charging threshold value of the online charging bank with the electric quantity parameter of the online charging bank;
if the electric quantity parameter of the online charging bank is larger than the charging threshold, the online charging bank is marked as the to-be-taken charging bank;
if the electric quantity parameter of the online charging bank is smaller than or equal to the charging threshold, the online charging bank is marked as the to-be-charged charging bank;
invoking a measurement function from the measurement model;
and acquiring the required charging power of the to-be-charged charger baby, inputting the required charging power into an measuring and calculating function, and determining the output result as required output power.
3. The internet-based remote monitoring method for a charger cabinet according to claim 1, wherein the method comprises the following steps: the step of inputting the parameter to be evaluated into a partition model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet comprises the following steps:
acquiring the occurrence node of the parameter to be evaluated, and calibrating the occurrence node as the node to be evaluated;
acquiring interval time length of adjacent nodes to be evaluated, and calibrating the interval time length as the time length to be evaluated;
calling a classification threshold from the partition model, and comparing the classification threshold with the duration to be evaluated;
if the time length to be evaluated is greater than or equal to a classification threshold, indicating that the node to be evaluated corresponding to the time length to be evaluated is a partition node, and arranging the partition node according to the sequence of the occurrence time;
if the time length to be evaluated is smaller than the classification threshold value, indicating that the node to be evaluated corresponding to the time length to be evaluated is a non-partition node;
and determining the initial occurrence node of the parameter to be evaluated as a partition initial node, and constructing a plurality of fluctuation intervals by taking each two nodes as a group according to the arrangement sequence of the partition nodes.
4. The remote monitoring method for the internet-based charger cabinet according to claim 3, wherein the method comprises the following steps: after the fluctuation interval is determined, the fluctuation interval is input into a verification model, and the verification process is as follows:
acquiring a plurality of fluctuation intervals in the monitoring period, and respectively calibrating head and tail nodes of each fluctuation interval as a starting point to be compared and a ending point to be compared;
calculating the difference values between all the starting points to be compared and the ending points to be compared, and calibrating the difference values as parameters to be checked;
calling a check function from the check model, inputting the parameter to be checked into the check function, and calibrating an output result of the parameter to be checked into the parameter to be compared;
the parameters to be compared are rounded upwards to obtain optimized parameters, and the optimized parameters are arranged according to the sequence from big to small;
acquiring a verification threshold value and the occurrence frequency of each optimization parameter, and comparing the occurrence frequency of the optimization parameters with the verification threshold value;
if the occurrence frequency of the optimized parameters is larger than a verification threshold value, determining a corresponding fluctuation interval as a normal fluctuation interval;
if the occurrence frequency of the optimized parameters is smaller than or equal to a verification threshold value, the corresponding fluctuation interval is marked as an instantaneous fluctuation interval, and the instantaneous fluctuation interval is marked.
5. The remote monitoring method for the internet-based charger cabinet of claim 4, wherein the method comprises the following steps: and after the normal fluctuation interval is determined, respectively counting the maximum required power in each normal fluctuation interval, and determining the output power of the charger cabinet according to the maximum required power.
6. The internet-based remote monitoring method for a charger cabinet according to claim 1, wherein the method comprises the following steps: the step of obtaining the deviation parameter from the reference data set and inputting the deviation parameter into a trend analysis model to obtain the loss trend value of the charger cabinet comprises the following steps:
acquiring the deviation parameter;
calling a trend analysis function from the trend analysis model;
and inputting all the deviation parameters into a trend analysis function, and calibrating the output result as a loss trend value of the charger cabinet.
7. The remote monitoring method for the internet-based charger cabinet of claim 6, wherein the method comprises the following steps: the step of inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet and predicting the excessive loss critical point based on the safe operation time length comprises the following steps:
acquiring an actual loss value, an excessive loss critical value and a loss trend value under the current node;
calling a prediction function from the prediction model;
and inputting the actual loss value, the excessive loss critical value and the loss trend value under the current node into a prediction function, and calibrating the output result as the excessive loss critical point.
8. The internet-based remote monitoring method for a charger cabinet according to claim 1, wherein the method comprises the following steps: the step of performing offset processing on the excessive loss critical point and determining an offset result as an early warning node comprises the following steps:
acquiring the excessive loss critical point;
acquiring rated offset time length, and performing reverse offset by taking the excessive loss critical point as a reference node to obtain an early warning node;
and after the charger cabinet runs to the early warning node, an early warning signal is synchronously sent out.
9. The internet-based remote monitoring system for the charger cabinet is applied to the internet-based remote monitoring method for the charger cabinet, which is characterized in that: comprising the following steps:
the first acquisition module is used for acquiring the operation parameters of the charger cabinet, wherein the operation parameters of the charger cabinet comprise voltage parameters, current parameters and power parameters;
the second acquisition module is used for acquiring the quantity of the charging treasures in the charging treasures cabinet and counting the electric quantity parameters of each charging treasures;
the measuring and calculating module is used for inputting the electric quantity parameters into a measuring and calculating model to obtain the required output power of the charger cabinet;
the sampling module is used for constructing a monitoring period, constructing a plurality of sampling nodes in the monitoring period, acquiring the required output power of each sampling node in real time, and calibrating the required output power as a parameter to be evaluated;
the partitioning module is used for inputting the parameters to be evaluated into a partitioning model to obtain a plurality of fluctuation intervals corresponding to the output power of the charger cabinet;
the third acquisition module is used for acquiring rated output power of the charger cabinet, calibrating a reference parameter, and performing difference processing on the reference parameter and the parameter to be evaluated to obtain a deviation parameter;
the alarm module is used for acquiring an allowable deviation threshold and comparing the allowable deviation threshold with the deviation parameter;
if the deviation parameter is larger than the allowable deviation threshold, the charging device cabinet is indicated to be capable of normally supplying power to the charging device, and the corresponding deviation parameter is summarized into a reference data set;
if the deviation parameter is smaller than or equal to the tolerance deviation threshold, the excessive loss of the charging bank cabinet is indicated, the power supply to the charging bank is stopped, and an alarm signal is synchronously sent;
the trend analysis module is used for acquiring deviation parameters from the reference data set and inputting the deviation parameters into a trend analysis model to obtain a loss trend value of the charger cabinet;
the prediction module is used for inputting the loss trend value into a prediction model to obtain the safe operation time length of the charger cabinet, predicting an excessive loss critical point based on the safe operation time length, performing offset processing on the excessive loss critical point, and determining an offset result as an early warning node.
10. Internet-based remote monitoring terminal for precious rack charges, its characterized in that: comprising the following steps:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the internet-based remote monitoring method for a charger cabinet of any one of claims 1 to 8.
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