CN116846892B - Intelligent energy consumption supervision system and method applying edge computing - Google Patents
Intelligent energy consumption supervision system and method applying edge computing Download PDFInfo
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- 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
- G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
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Abstract
The invention relates to the field of edge calculation, in particular to an intelligent energy consumption supervision system and an intelligent energy consumption supervision method applying edge calculation, wherein the system comprises a data preprocessing module, an energy consumption monitoring and influence analysis module, an early warning condition value setting module and an edge calculation module, wherein the energy consumption monitoring and influence analysis module is used for analyzing the influence of the battery energy consumption condition of mobile equipment on the angle sensitivity of a steering engine of the equipment and the influence of the heating of a motor on the integral performance of the mobile equipment when the high-speed equipment runs for a long time in combination with historical data so as to construct a comprehensive influence analysis model.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to an intelligent energy consumption supervision system and method applying edge computing.
Background
Edge computing, namely, on one side close to an object or a data source, an open platform integrating network, computing, storage and application core capabilities is adopted, nearest service is provided nearby, an application program of the edge computing is initiated on the edge side, a faster network service response is generated, the basic requirements of industry in the aspects of real-time service, application intelligence, security, privacy protection and the like are met, the edge computing is located between a physical entity and industrial connection or at the top end of the physical entity, along with the development of intelligent manufacturing and industrial Internet of things, the edge computing starts to play an increasingly important role, the energy consumption monitoring of intelligent equipment is also remarkable, and at present, the following problems exist for energy consumption monitoring and equipment operation and maintenance:
1. The energy consumption data cannot be uniformly managed and analyzed, and energy consumption changes and hidden dangers cannot be found out in time and measures can be taken;
2. When the intelligent equipment is used in operation, the whole performance of the intelligent equipment is reduced due to the change of the energy supply device, so that the accuracy of data testing is greatly affected.
Disclosure of Invention
The invention aims to provide an intelligent energy consumption monitoring system and method for edge computing, which are used for solving the problems in the background technology, and the invention provides the following technical scheme:
An intelligent energy consumption supervision method for application edge computing, the method comprising the following steps:
S1, acquiring a moving track diagram of a moving device in an area to be monitored through historical data, and preprocessing the moving track diagram;
S2, analyzing the influence of the battery energy consumption condition of the mobile device on the steering engine angle sensitivity of the device by combining the historical data, and constructing a comprehensive influence analysis model according to the influence of the heating of the motor on the overall performance of the mobile device when the high-speed device runs for a long time;
S3, setting early warning condition values according to analysis results of the comprehensive influence analysis model in the S2, and sending out early warning signals of corresponding grades according to different early warning condition values;
S4, acquiring the analysis result in the S3, acquiring early warning signals through an edge calculation module, and adopting corresponding processing schemes to perform the elimination report processing according to the early warning signals of different grades.
Further, the method of S1 includes the following steps:
Step 1001, obtaining a moving track diagram of an a-th mobile device in a region to be monitored, extracting road condition information in the moving track diagram, sequencing corresponding road condition information according to a route sequence of the mobile device, and marking the sequenced road condition information as a set A, wherein the moving track diagram is a moving track route which is required to be circularly executed by the current mobile device;
Wherein the method comprises the steps of The method comprises the steps that n-th road condition information conditions of a path in the running process of an a-th mobile device in a region to be monitored are represented, n represents the total number of the road condition information conditions of the path in the running process of the mobile device, and the road condition information comprises curves, rings and intersections;
step 1002, extracting road parameter information in each road condition information, binding the corresponding road parameter information with the corresponding road condition information one by one, and marking the binding as a set B;
Wherein the method comprises the steps of And the road parameter information corresponding to the nth road condition information of the path in the running process of the a-th mobile equipment in the area to be monitored is represented, and the road parameter information comprises a road width value.
According to the method, the planned moving track route of the corresponding mobile equipment in the area to be monitored is obtained, the road condition information in the moving track route is listed, the road parameter information related to the corresponding road condition is extracted, and data reference is provided for the follow-up analysis of the influence of the battery energy consumption change on the intelligent mobile equipment when the mobile equipment operates.
Further, the method of S2 includes the following steps:
Step 2001, acquiring the operation time length of an a-th mobile device in a region to be monitored through historical data, and dividing the corresponding operation time length into b self-checking periods on average, wherein each self-checking period comprises c time nodes, and c is a database preset value;
2002, constructing a first plane rectangular coordinate system by taking a point o as an origin, taking a time node as an x-axis and battery energy consumption as a y-axis, acquiring a monitoring result of battery energy consumption in a b-th self-checking period, acquiring a battery energy consumption coordinate point corresponding to the time node in the first plane rectangular coordinate system, and sequentially connecting adjacent coordinate points to generate a fitting curve N (x);
Step 2003, combining a fitting curve N (x), taking a point o1 as an origin, taking battery energy consumption corresponding to each time node as an x1 axis, taking the successful steering angle forming condition of the steering engine corresponding to road condition information when the mobile equipment runs as a y1 axis, constructing a second plane rectangular coordinate system, acquiring coordinate points of the successful steering angle forming condition of the steering engine corresponding to the battery energy consumption in the second plane rectangular coordinate system, sequentially counting steering angle forming failure times corresponding to each battery energy consumption, recording a table M, wherein the steering engine angle forming information is identified through a camera before the mobile equipment runs corresponding to the road condition information, generating a feedback signal, sending the feedback signal to a main control unit, sending an angle forming instruction after the main control unit receives the feedback signal, executing steering angle forming operation of the mobile equipment, wherein the successful steering angle forming condition of the steering engine only has two conditions of success and failure, successfully marking as 1, and failure marking as 0, and when the steering angle forming failure of the high-speed equipment mobile equipment is failed, resetting the mobile equipment to the position where the main control unit receives the feedback signal, repeatedly executing the steering angle forming operation until the mobile equipment passes through the current road condition;
Step 2004, based on the analysis results of step 2002 and step 2003, marking the influence of the battery energy consumption condition of the mobile device in the b-th self-test period on the steering engine angle sensitivity of the device as
Wherein alpha i represents a weight value, the weight value is queried through a preset form, wherein the corresponding weight value is set for the battery energy consumption corresponding to the duration of the steering engine angle failure of the mobile device in the preset form,
F represents the total number of steering engine angle making failures, D F represents the interval battery energy consumption value from the beginning time node of the F-th steering engine angle making failure of the mobile equipment to the successful time node of the steering engine angle making, Z b represents the total battery energy consumption value in the b-th self-test period, wherein the interval battery energy consumption value from the beginning time node of the F-th steering engine angle making failure of the mobile equipment to the successful time node of the steering engine angle making is obtained by analyzing the battery energy consumption section corresponding to the continuous failure of the steering engine angle making in the second plane rectangular coordinate system, and then the difference value operation is carried out on the battery energy consumption value corresponding to the ending point in the interval battery energy consumption value and the battery energy consumption value corresponding to the initial point in the first plane rectangular coordinate system to obtain the corresponding interval battery energy consumption value, and the total battery energy consumption value represents the difference value between the battery energy consumption value corresponding to the last time node in the b-th self-test period and the battery energy consumption value corresponding to the first time node;
step 2005, inquiring the total number of intermittent angle forming failures of the mobile equipment in the b-th self-checking period through a table M, marking as Ff, and calculating the influence condition of the heating change on the overall performance of the mobile equipment when the motor runs for a long time according to the total number of intermittent angle forming failures of the mobile equipment, marking as
Wherein gamma represents a proportionality coefficient which is a database preset value;
Step 2006, combining the analysis results of step 2004 and step 2005, constructing a comprehensive influence analysis model, denoted as Y ∩,
Wherein beta represents a proportionality coefficient, which is a database preset value.
According to the invention, through analyzing the relation between battery energy consumption in any self-checking period in historical data and analyzing the real-time running state of the mobile equipment, the debugging times of the high-speed running equipment under the abnormal steering engine angle condition are obtained, the influence condition of the motor on the overall performance of the mobile equipment during long-time running is recorded in a table, the influence of battery energy consumption change on the overall performance of the mobile equipment and the running states of the mobile equipment under different road conditions under different battery energy consumption conditions are comprehensively considered, and data reference is provided for subsequent abnormal value calibration in combination with the running state of the current mobile equipment.
Further, the method of S3 includes the following steps:
Step 3001, obtaining a comprehensive influence analysis model constructed in step 2006;
Step 3002, obtaining the analysis result of step 3001, setting the early warning condition value in combination with the analysis result,
If ω 1<Y∩≤ω2 indicates that the current battery power consumption is insufficient to cause the mobile device to operate in a standard state, indicating that the mobile device is operating at a nominal movement speed, a secondary warning signal is issued, wherein ω 1 and ω 2 are database preset values,
If Y ∩>ω2, the current battery energy consumption is abnormal, a first-level early warning signal is sent,
If Y ∩≤ω1 is not less than 0, the current battery energy consumption is normal, and no early warning signal is sent.
Further, the method of S4 includes the following steps:
step 4001, acquiring early warning signal grades in real time through an edge calculation module;
Step 4002, in combination with the early warning signal level obtained in step 4001, carrying out emergency scheme adjustment on the mobile equipment,
If the early warning signal grade obtained by the edge calculation module is one level, sending a request to related staff, overhauling each module of the mobile equipment, replacing a battery of the mobile equipment under the condition that each module element is not damaged,
If the early warning signal grade obtained by the edge calculation module is the second grade, the angle forming calibration of the steering engine of the mobile equipment is carried out in combination with the road parameter information corresponding to the road condition information,
A steering engine of the mobile equipment is taken as a reference point o2, a third plane rectangular coordinate system is constructed by taking the reference point o2 as an origin, a road on which the mobile equipment is running is mapped into the third plane rectangular coordinate system, wherein the positions of left and right boundary lines of the road are marked in the third plane rectangular coordinate system, and the intersection points of the left and right boundary lines and y2=0 are obtained and marked as (x left, 0) and (x right, 0)
Carrying out steering engine angle calibration by combining the road condition information identified by the current mobile equipment, and marking as J b,
Where |x left | represents the distance from the current location of the steering engine of the mobile device to the left boundary, |x right | represents the distance from the current location of the steering engine of the mobile device to the right boundary, D g represents the angle value of the steering engine of the mobile device,And inquiring the weight value through a database preset form, wherein the database preset form combines a deviation operation result of the steering engine position of the mobile device relative to the central line position of the road with the corresponding weight value.
An intelligent energy consumption monitoring system for application edge computing, the system comprising the steps of:
and a data preprocessing module: the data preprocessing module is used for acquiring a track diagram of the mobile equipment in the area to be monitored through historical data and preprocessing the track diagram;
And the energy consumption monitoring and impact analysis module is as follows: the energy consumption monitoring and impact analysis module is used for analyzing the impact of the battery energy consumption condition of the mobile device on the steering engine angle sensitivity of the device and the impact of the heating of the motor on the overall performance of the mobile device when the high-speed device runs for a long time by combining the historical data, so as to construct a comprehensive impact analysis model;
The early warning condition value setting module: the early warning condition value setting unit is used for setting early warning condition values by combining the analysis results of the energy consumption monitoring and influence analysis module and sending out early warning signals of corresponding grades according to different early warning condition values;
and an edge calculation module: the edge calculation module is used for combining the early warning signals sent by the early warning condition value setting module, and adopting corresponding emergency treatment schemes to eliminate the alarm signals according to the early warning signals of different grades.
Further, the data preprocessing module comprises a moving track acquisition unit and a road condition information analysis unit:
the mobile track acquisition unit is used for acquiring a work task of the mobile equipment in the area to be monitored and acquiring a simulation route of the mobile equipment in combination with the work task;
The road condition information analysis unit is used for acquiring special road sections in the simulated route in the moving track acquisition unit, wherein the special road sections comprise curves, rings and intersections.
Further, the energy consumption monitoring and influence analysis module comprises a battery energy consumption monitoring unit, an equipment sensitivity analysis unit, a motor state monitoring unit, an equipment performance monitoring unit and a comprehensive influence analysis model construction unit:
the battery energy consumption monitoring unit is used for monitoring the relation between battery energy consumption and operation time when the mobile equipment operates in real time;
The equipment sensitivity analysis unit is used for judging the inversion sensitivity degree of the equipment aiming at special road condition processing according to the analysis result of the battery energy consumption monitoring unit;
The motor state monitoring unit is used for monitoring the operation heating condition of the motor in real time by combining the analysis result of the battery energy consumption monitoring unit;
The equipment performance monitoring unit is used for monitoring the states of various working indexes of the mobile equipment in real time by combining the analysis results of the motor state monitoring unit;
The comprehensive influence analysis model construction unit is used for comprehensively analyzing the battery energy consumption monitoring unit, the equipment sensitivity analysis unit, the motor state monitoring unit and the equipment performance monitoring unit and constructing a comprehensive influence analysis model by combining an analysis structure.
Further, the early warning condition value setting module comprises an early warning signal analysis unit and an early warning grade setting unit:
the early warning signal analysis unit is used for setting an early warning signal condition value according to the analysis result of the influence analysis model construction unit;
The early warning level setting unit is used for setting early warning levels by combining different early warning signal condition values in the early warning signal analysis unit.
Further, the edge calculation module comprises an edge calculation monitoring unit and a steering engine angle calibration unit:
the edge calculation monitoring unit is used for receiving the analysis result of the early warning condition value setting module in real time;
the steering engine angle calibration unit is used for calibrating the angle of the mobile equipment in real time according to the analysis result of the upper computer monitoring unit.
According to the method, the influence of the real-time battery energy consumption change trend on the running state of the mobile equipment is utilized, the data is uploaded to the edge calculation module in real time for analysis and processing, and the steering engine angle value of the mobile equipment is calibrated in real time by combining the analysis result, so that the processing efficiency of the high-speed running equipment on road condition information under different energy consumption states is improved, the automatic data calibration efficiency is improved, and a great amount of time spent on manually calibrating the data is avoided.
Drawings
FIG. 1 is a schematic flow chart of an intelligent energy consumption monitoring method for edge computing;
fig. 2 is a schematic block diagram of an intelligent energy consumption monitoring system using edge computing according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, in this embodiment:
the method for intelligently supervising the energy consumption of the application edge calculation is realized, and comprises the following steps:
S1, acquiring a moving track diagram of a moving device in an area to be monitored through historical data, and preprocessing the moving track diagram;
the method of S1 comprises the following steps:
Step 1001, obtaining a moving track diagram of an a-th mobile device in a region to be monitored, extracting road condition information conditions in the moving track diagram, and sequencing corresponding road condition information according to a passing sequence of the mobile device, and marking the sequenced road condition information as a set A;
Wherein the method comprises the steps of The method comprises the steps that n-th road condition information conditions of a path in the running process of an a-th mobile device in a region to be monitored are represented, and n represents the total number of the road condition information conditions of the path in the running process of the mobile device;
step 1002, extracting road parameter information in each road condition information, binding the corresponding road parameter information with the corresponding road condition information one by one, and marking the binding as a set B;
Wherein the method comprises the steps of And the road parameter information corresponding to the nth road condition information of the path in the running process of the a-th mobile equipment in the area to be monitored is represented.
S2, analyzing the influence of the battery energy consumption condition of the mobile device on the steering engine angle sensitivity of the device by combining the historical data, and constructing a comprehensive influence analysis model according to the influence of the heating of the motor on the overall performance of the mobile device when the high-speed device runs for a long time;
The method of S2 comprises the following steps:
Step 2001, acquiring the operation time length of an a-th mobile device in a region to be monitored through historical data, and dividing the corresponding operation time length into b self-checking periods on average, wherein each self-checking period comprises c time nodes, and c is a database preset value;
2002, constructing a first plane rectangular coordinate system by taking a point o as an origin, taking a time node as an x-axis and battery energy consumption as a y-axis, acquiring a monitoring result of battery energy consumption in a b-th self-checking period, acquiring a battery energy consumption coordinate point corresponding to the time node in the first plane rectangular coordinate system, and sequentially connecting adjacent coordinate points to generate a fitting curve N (x);
step 2003, combining a fitting curve N (x), taking a point o1 as an origin, taking battery energy consumption corresponding to each time node as an x1 axis, taking the successful steering engine angle forming condition corresponding to road condition information in the running process of the mobile equipment as a y1 axis, constructing a second plane rectangular coordinate system, acquiring corresponding steering engine angle forming condition coordinate points corresponding to battery energy consumption in the second plane rectangular coordinate system, sequentially counting steering engine angle forming failure times corresponding to each battery energy consumption, and recording a table M;
Step 2004, based on the analysis results of step 2002 and step 2003, marking the influence of the battery energy consumption condition of the mobile device in the b-th self-test period on the steering engine angle sensitivity of the device as
Wherein alpha i represents a weight value, the weight value is queried through a preset form, wherein the corresponding weight value is set for the battery energy consumption corresponding to the duration of the steering engine angle failure of the mobile device in the preset form,
F represents the total number of steering engine angle making failures, D F represents the battery energy consumption value between the time node of starting the steering engine angle making failure of the F-th time and the time node of successful steering engine angle making of the mobile equipment, Z b represents the total battery energy consumption value in the b-th self-checking middle period;
Step 2005, inquiring the total number of intermittent angle making failures of the mobile equipment in the b-th self-checking period through a table M, marking as F f, and calculating the influence condition of the heating change on the overall performance of the mobile equipment when the motor runs for a long time by combining the total number of intermittent angle making failures of the mobile equipment, marking as
Wherein gamma represents a proportionality coefficient which is a database preset value;
Step 2006, combining the analysis results of step 2004 and step 2005, constructing a comprehensive influence analysis model, denoted as Y ∩,
Wherein beta represents a proportionality coefficient, which is a database preset value.
S3, setting early warning condition values according to analysis results of the comprehensive influence analysis model in the S2, and sending out early warning signals of corresponding grades according to different early warning condition values;
the method of S3 comprises the following steps:
Step 3001, obtaining a comprehensive influence analysis model constructed in step 2006;
Step 3002, obtaining the analysis result of step 3001, setting the early warning condition value in combination with the analysis result,
If omega 1<Y∩≤ω2, the current battery energy consumption condition is insufficient to enable the mobile device to work in a standard state, a secondary early warning signal is sent out, wherein omega 1 and omega 2 are preset values for a database,
If Y ∩>ω2, the current battery energy consumption is abnormal, a first-level early warning signal is sent,
If Y ∩≤ω1 is not less than 0, the current battery energy consumption is normal, and no early warning signal is sent.
S4, acquiring the analysis result in the S3, acquiring early warning signals through an edge calculation module, and adopting corresponding processing schemes to perform the elimination report processing according to the early warning signals of different grades.
The method of S4 comprises the following steps:
step 4001, acquiring early warning signal grades in real time through an edge calculation module;
Step 4002, in combination with the early warning signal level obtained in step 4001, carrying out emergency scheme adjustment on the mobile equipment,
If the early warning signal grade obtained by the edge calculation module is one level, sending a request to related staff, overhauling each module of the mobile equipment, replacing a battery of the mobile equipment under the condition that each module element is not damaged,
If the early warning signal grade obtained by the edge calculation module is the second grade, the angle forming calibration of the steering engine of the mobile equipment is carried out in combination with the road parameter information corresponding to the road condition information,
A steering engine of the mobile equipment is taken as a reference point o2, a third plane rectangular coordinate system is constructed by taking the reference point o2 as an origin, a road on which the mobile equipment is running is mapped into the third plane rectangular coordinate system, wherein the positions of left and right boundary lines of the road are marked in the third plane rectangular coordinate system, and the intersection points of the left and right boundary lines and y2=0 are obtained and marked as (x left, 0) and (x right, 0)
Carrying out steering engine angle calibration by combining the road condition information identified by the current mobile equipment, and marking as J b,
Where |x left | represents the distance from the current location of the steering engine of the mobile device to the left boundary, |x right | represents the distance from the current location of the steering engine of the mobile device to the right boundary, D g represents the angle value of the steering engine of the mobile device,And inquiring the weight value through a database preset form, wherein the database preset form combines a deviation operation result of the steering engine position of the mobile device relative to the central line position of the road with the corresponding weight value.
In this embodiment:
an intelligent energy consumption supervision system (shown in fig. 2) for applying edge computing is disclosed, and the system is used for realizing specific scheme content of a method.
Example 2: the road conditions required to be executed by the current mobile equipment are as follows: straight line-left turn-straight line-left turn,
Setting the current mobile equipment to adopt a new battery for test, setting the mobile equipment to move 10 times according to the complete track according to the route requirement, combining the comprehensive influence analysis model to obtain the corresponding comprehensive influence condition when the 8 th movement is carried out, marking as Y 8,
Comparing Y 8 with the early warning condition value to obtain omega 1<Y8≤ω2, indicating that the battery energy consumption of the current mobile equipment is insufficient to enable the mobile equipment to work in a standard state, sending out a secondary early warning signal, calibrating the steering engine angle of the current mobile equipment,
The position of the steering engine of the mobile device relative to the road center is obtained, the left boundary distance between the steering engine of the mobile device and the road is calculated to be d left, the right boundary distance is d right, wherein d left-dright is more than 0, the required angle value is J b after the current mobile device identifies the road condition information,
D left-dright >0 indicates that the position of the steering engine of the mobile device is close to the right boundary, the angle value of the steering engine of the current mobile device is reduced, the steering engine of the current mobile device can successfully pass through the current road section, the energy consumption of a battery is monitored in real time, when the early warning signal level acquired by the edge calculation module is one level, a request is sent to related staff, each module of the mobile device is overhauled, and the battery of the mobile device is replaced under the condition that each module element is not damaged.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. An intelligent energy consumption supervision method for application edge calculation is characterized by comprising the following steps:
S1, acquiring a moving track diagram of a moving device in an area to be monitored through historical data, and preprocessing the moving track diagram;
S2, analyzing the influence of the battery energy consumption condition of the mobile device on the steering engine angle sensitivity of the device by combining the historical data, and constructing a comprehensive influence analysis model according to the influence of the heating of the motor on the overall performance of the mobile device when the high-speed device runs for a long time;
S3, setting early warning condition values according to analysis results of the comprehensive influence analysis model in the S2, and sending out early warning signals of corresponding grades according to different early warning condition values;
S4, acquiring an analysis result in the S3, acquiring early warning signals through an edge calculation module, and adopting corresponding processing schemes to perform reporting elimination according to the early warning signals of different grades;
The method of S2 comprises the following steps:
Step 2001, acquiring the operation time length of an a-th mobile device in a region to be monitored through historical data, and dividing the corresponding operation time length into b self-checking periods on average, wherein each self-checking period comprises c time nodes, and c is a database preset value;
2002, constructing a first plane rectangular coordinate system by taking a point o as an origin, taking a time node as an x-axis and battery energy consumption as a y-axis, acquiring a monitoring result of battery energy consumption in a b-th self-checking period, acquiring a battery energy consumption coordinate point corresponding to the time node in the first plane rectangular coordinate system, and sequentially connecting adjacent coordinate points to generate a fitting curve N (x);
step 2003, combining a fitting curve N (x), taking a point o1 as an origin, taking battery energy consumption corresponding to each time node as an x1 axis, taking the successful steering engine angle forming condition corresponding to road condition information in the running process of the mobile equipment as a y1 axis, constructing a second plane rectangular coordinate system, acquiring corresponding steering engine angle forming condition coordinate points corresponding to battery energy consumption in the second plane rectangular coordinate system, sequentially counting steering engine angle forming failure times corresponding to each battery energy consumption, and recording a table M;
Step 2004, based on the analysis results of step 2002 and step 2003, marking the influence of the battery energy consumption condition of the mobile device in the b-th self-test period on the steering engine angle sensitivity of the device as
Wherein alpha i represents a weight value, the weight value is queried through a preset form, wherein the corresponding weight value is set for the battery energy consumption corresponding to the duration of the steering engine angle failure of the mobile device in the preset form,
F represents the total number of steering engine angle making failures, D F represents the battery energy consumption value between the time node of starting the steering engine angle making failure of the F-th time and the time node of successful steering engine angle making of the mobile equipment, Z b represents the total battery energy consumption value in the b-th self-checking middle period;
Step 2005, inquiring the total number of intermittent angle making failures of the mobile equipment in the b-th self-checking period through a table M, marking as F f, and calculating the influence condition of the heating change on the overall performance of the mobile equipment when the motor runs for a long time by combining the total number of intermittent angle making failures of the mobile equipment, marking as
Wherein gamma represents a proportionality coefficient which is a database preset value;
Step 2006, combining the analysis results of step 2004 and step 2005, constructing a comprehensive influence analysis model, denoted as Y ∩,
Wherein beta represents a proportionality coefficient, which is a database preset value.
2. The method for intelligently supervising the energy consumption of the application edge calculation according to claim 1, wherein the method of S1 comprises the following steps:
Step 1001, obtaining a moving track diagram of an a-th mobile device in a region to be monitored, extracting road condition information conditions in the moving track diagram, and sequencing corresponding road condition information according to a passing sequence of the mobile device, and marking the sequenced road condition information as a set A;
Wherein the method comprises the steps of The method comprises the steps that n-th road condition information conditions of a path in the running process of an a-th mobile device in a region to be monitored are represented, and n represents the total number of the road condition information conditions of the path in the running process of the mobile device;
step 1002, extracting road parameter information in each road condition information, binding the corresponding road parameter information with the corresponding road condition information one by one, and marking the binding as a set B;
Wherein the method comprises the steps of And the road parameter information corresponding to the nth road condition information of the path in the running process of the a-th mobile equipment in the area to be monitored is represented.
3. An intelligent supervision method for energy consumption of application edge computing according to claim 1, wherein the method of S3 comprises the following steps:
Step 3001, obtaining a comprehensive influence analysis model constructed in step 2006;
Step 3002, obtaining the analysis result of step 3001, setting the early warning condition value in combination with the analysis result,
If omega 1<Y∩≤ω2, the current battery energy consumption condition is insufficient to enable the mobile device to work in a standard state, a secondary early warning signal is sent out, wherein omega 1 and omega 2 are preset values for a database,
If Y ∩>ω2, the current battery energy consumption is abnormal, a first-level early warning signal is sent,
If Y ∩≤ω1 is not less than 0, the current battery energy consumption is normal, and no early warning signal is sent.
4. An intelligent supervision method for energy consumption of application edge computing according to claim 1, wherein the method of S4 comprises the following steps:
step 4001, acquiring early warning signal grades in real time through an edge calculation module;
Step 4002, in combination with the early warning signal level obtained in step 4001, carrying out emergency scheme adjustment on the mobile equipment,
If the early warning signal grade obtained by the edge calculation module is one level, sending a request to related staff, overhauling each module of the mobile equipment, replacing a battery of the mobile equipment under the condition that each module element is not damaged,
If the early warning signal grade obtained by the edge calculation module is the second grade, the angle forming calibration of the steering engine of the mobile equipment is carried out in combination with the road parameter information corresponding to the road condition information,
A steering engine of the mobile equipment is taken as a reference point o2, a third plane rectangular coordinate system is constructed by taking the reference point o2 as an origin, a road on which the mobile equipment is running is mapped into the third plane rectangular coordinate system, wherein the positions of left and right boundary lines of the road are marked in the third plane rectangular coordinate system, and the intersection points of the left and right boundary lines and y2=0 are obtained and marked as (x left, 0) and (x right, 0)
Carrying out steering engine angle calibration by combining the road condition information identified by the current mobile equipment, and marking as J b,
Jb＝θ*(|xleft|-|xright|)+Dg，
The method comprises the steps of (1) determining a weight value, wherein |x left | represents the distance from the position of a current mobile device steering engine to a left boundary, |x right | represents the distance from the position of the current mobile device steering engine to a right boundary, D g represents the angle value of the current mobile device steering engine, and theta is the weight value, wherein the weight value is queried through a database preset form, and the deviation operation result of the position of the mobile device steering engine relative to the position of a road center line is combined with the corresponding weight value in the database preset form.
5. An intelligent monitoring system for energy consumption of application edge calculation, applied to an intelligent monitoring method for energy consumption of application edge calculation according to any one of claims 1 to 4, characterized in that the system comprises:
and a data preprocessing module: the data preprocessing module is used for acquiring a track diagram of the mobile equipment in the area to be monitored through historical data and preprocessing the track diagram;
And the energy consumption monitoring and impact analysis module is as follows: the energy consumption monitoring and impact analysis module is used for analyzing the impact of the battery energy consumption condition of the mobile device on the steering engine angle sensitivity of the device and the impact of the heating of the motor on the overall performance of the mobile device when the high-speed device runs for a long time by combining the historical data, so as to construct a comprehensive impact analysis model;
The early warning condition value setting module: the early warning condition value setting unit is used for setting early warning condition values by combining the analysis results of the energy consumption monitoring and influence analysis module and sending out early warning signals of corresponding grades according to different early warning condition values;
And an edge calculation module: the edge calculation module is used for combining the early warning signals sent by the early warning condition value setting module, and adopting corresponding emergency treatment schemes to eliminate the alarm signals according to the early warning signals of different grades;
the energy consumption monitoring and influence analysis module comprises a battery energy consumption monitoring unit, a device sensitivity analysis unit, a motor state monitoring unit, a device performance monitoring unit and a comprehensive influence analysis model construction unit:
the battery energy consumption monitoring unit is used for monitoring the relation between battery energy consumption and operation time when the mobile equipment operates in real time;
The equipment sensitivity analysis unit is used for judging the inversion sensitivity degree of the equipment aiming at special road condition processing according to the analysis result of the battery energy consumption monitoring unit;
The motor state monitoring unit is used for monitoring the operation heating condition of the motor in real time by combining the analysis result of the battery energy consumption monitoring unit;
The equipment performance monitoring unit is used for monitoring the states of various working indexes of the mobile equipment in real time by combining the analysis results of the motor state monitoring unit;
The comprehensive influence analysis model construction unit is used for comprehensively analyzing the battery energy consumption monitoring unit, the equipment sensitivity analysis unit, the motor state monitoring unit and the equipment performance monitoring unit and constructing a comprehensive influence analysis model by combining an analysis structure.
6. The intelligent monitoring system for energy consumption based on application edge computing according to claim 5, wherein the data preprocessing module comprises a movement track acquisition unit and a road condition information analysis unit:
the mobile track acquisition unit is used for acquiring a work task of the mobile equipment in the area to be monitored and acquiring a simulation route of the mobile equipment in combination with the work task;
The road condition information analysis unit is used for acquiring special road sections in the simulated route in the moving track acquisition unit, wherein the special road sections comprise curves, rings and intersections.
7. The intelligent monitoring system for energy consumption by using edge calculation according to claim 5, wherein the early warning condition value setting module comprises an early warning signal analysis unit and an early warning level setting unit:
the early warning signal analysis unit is used for setting an early warning signal condition value according to the analysis result of the influence analysis model construction unit;
The early warning level setting unit is used for setting early warning levels by combining different early warning signal condition values in the early warning signal analysis unit.
8. The intelligent monitoring system for energy consumption by applying edge calculation according to claim 5, wherein the edge calculation module comprises an edge calculation monitoring unit and a steering engine angle calibration unit:
the edge calculation monitoring unit is used for receiving the analysis result of the early warning condition value setting module in real time;
the steering engine angle calibration unit is used for calibrating the angle of the mobile equipment in real time according to the analysis result of the upper computer monitoring unit.
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| CN1913217A (en) * | 2005-08-08 | 2007-02-14 | 丰田自动车株式会社 | Powertrain battery life predicting and warning apparatuses |
| CN114331000A (en) * | 2021-11-25 | 2022-04-12 | 中通服和信科技有限公司 | Wisdom garden energy consumption management system based on artificial intelligence |
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