CN109828894B - Equipment state data acquisition method and device, storage medium and electronic equipment - Google Patents
Equipment state data acquisition method and device, storage medium and electronic equipment Download PDFInfo
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- CN109828894B CN109828894B CN201811613069.6A CN201811613069A CN109828894B CN 109828894 B CN109828894 B CN 109828894B CN 201811613069 A CN201811613069 A CN 201811613069A CN 109828894 B CN109828894 B CN 109828894B
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Abstract
The disclosure relates to a method and a device for acquiring equipment state data, a storage medium and electronic equipment, wherein the method comprises the following steps: deleting the original state data of the equipment through a polygon approximation algorithm to obtain approximate state data of the equipment; and correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment. The method can reduce the data volume of storage and display while ensuring the accuracy of the equipment state data statistics, save network transmission resources and computing resources of a client, and reduce the resource loss in the operation and maintenance process of the equipment.
Description
Technical Field
The present disclosure relates to the field of device operation and maintenance, and in particular, to a method and an apparatus for acquiring device status data, a storage medium, and an electronic device.
Background
In an internet of things system integrated with large-scale equipment, equipment state data needs to be collected in real time, the collected equipment state data is transmitted to a background to be stored, and then the equipment state data is displayed at a later stage. And the operation and maintenance personnel can analyze and judge the operation condition of the equipment through the displayed curve or specific data value of the equipment state data. In the related art, state data acquisition units for different indexes are usually arranged for equipment, the equipment state data acquired by the acquisition units are continuously transmitted to a background during the operation of the equipment, and the received equipment state data are output to operation and maintenance personnel to assist the maintenance of the equipment. However, with the increasing number of devices in the application scene of the modern internet of things, the time spent on acquiring the device state data by the existing data acquisition method is longer and longer, and the frequency of uploading the data is higher and higher. The extremely large data volume and the extremely high data transmission frequency occupy a large amount of storage space, network resources and client computing resources, and increase the resource loss in the operation and maintenance process of the equipment.
Disclosure of Invention
To overcome the problems in the related art, an object of the present disclosure is to provide a method, an apparatus, a storage medium, and an electronic device for acquiring device status data.
In order to achieve the above object, according to a first aspect of embodiments of the present disclosure, there is provided a method for acquiring device status data, the method including:
deleting original state data of the equipment through a polygon approximation algorithm to obtain approximate state data of the equipment;
correcting a curve-integrated area error of the approximate state data by adding a plurality of target data points to the approximate state data to obtain target state data of the device.
Optionally, the obtaining the target state data of the device by adding a plurality of target data points to the approximate state data and correcting a curve integral area of the approximate state data includes:
determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data;
when determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data, determining a first data point in the original state data, wherein the first data point is a middle point of the ith data point and the (i + 1) th data point in a data curve of the original state data;
determining the target data point according to the target abscissa of the first data point, the ith data point and the (i + 1) th data point;
adding the target data point to the approximate state data to correct a curve-integrated area error between the ith data point and the (i + 1) th data point;
and if i is equal to i +1, circularly executing the step of determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data or not, adding the target data point into the approximate state data to correct the curve integral area error between the ith data point and the (i + 1) th data point until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed to acquire the target state data consisting of the approximate state data and a plurality of target data points.
Optionally, the determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data includes:
determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data;
determining a first curve integral area between the ith data point and the (i + 1) th data point;
determining a second curve integration area between the second data point and the third data point;
and when the first curve integral area and the second curve integral area are determined to be different, determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
Optionally, the determining the target data point according to the target abscissa of the first data point, the ith data point, and the (i + 1) th data point includes:
taking the second curve integral area, the target abscissa, the abscissa and the ordinate of the ith data point and the abscissa and the ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; wherein the area equivalence formula is as follows:
wherein S1 is the second curve integral area，x3Is the target abscissa, x1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate, y, of the i +1 th data point3Is the target ordinate;
and taking the data point determined by the target ordinate and the target abscissa as the target data point.
According to a second aspect of the embodiments of the present disclosure, there is provided an apparatus for acquiring device status data, the apparatus including:
the data deleting module is used for deleting the original state data of the equipment through a polygon approximation algorithm so as to obtain the approximate state data of the equipment;
an error correction module for correcting a curve-integrated area error of the approximate state data by adding a plurality of target data points to the approximate state data to obtain target state data of the device.
Optionally, the raw state data includes a plurality of data points sequentially arranged in a time sequence, and the error correction module includes:
an error determination submodule, configured to determine whether a curve integral area error exists between an ith data point and an (i + 1) th data point in the approximate state data;
a first data point determining submodule, configured to determine a first data point in the raw state data when it is determined that a curve integral area error exists between an i-th data point and an i + 1-th data point in the approximate state data, where the first data point is a middle point of the i-th data point and the i + 1-th data point in a data curve of the raw state data;
a second data point determination submodule, configured to determine the target data point according to the target abscissa of the first data point, the ith data point, and the (i + 1) th data point;
a data point adding submodule for adding the target data point to the approximate state data to correct a curve integral area error between the ith data point and the (i + 1) th data point;
and a loop execution sub-module, configured to make i equal to i +1, and loop execution from the step of determining whether a curve integral area error exists between an i-th data point and an i + 1-th data point in the approximate state data to the step of adding the target data point to the approximate state data to correct the curve integral area error between the i-th data point and the i + 1-th data point until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed, so as to obtain the target state data composed of the approximate state data and a plurality of target data points.
Optionally, the error determination sub-module is configured to:
determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data;
determining a first curve integral area between the ith data point and the (i + 1) th data point;
determining a second curve integration area between the second data point and the third data point;
and when the first curve integral area and the second curve integral area are determined to be different, determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
Optionally, the second data point determining submodule is configured to:
taking the second curve integral area, the target abscissa, the abscissa and the ordinate of the ith data point and the abscissa and the ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; wherein the area equivalence formula is as follows:
wherein S1 is the second curve integral area, x3Is the target abscissa, x1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate, y, of the i +1 th data point3Is the target ordinate;
and taking the data point determined by the target ordinate and the target abscissa as the target data point.
According to a third aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for acquiring device status data provided by the first aspect of the embodiments of the present disclosure.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a memory having a computer program stored thereon;
a processor, configured to execute the computer program in the memory, so as to implement the steps of the method for acquiring device status data provided in the first aspect of the embodiment of the present disclosure.
By the technical scheme, the original state data of the equipment can be deleted through the polygon approximation algorithm so as to obtain the approximate state data of the equipment; and correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment. The method can reduce the data volume of storage and display while ensuring the accuracy of the equipment state data statistics, save network transmission resources and computing resources of a client, and reduce the resource loss in the operation and maintenance process of the equipment.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
FIG. 1 is a flow chart illustrating a method of collecting device status data in accordance with an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating the execution of a polygon approximation algorithm in accordance with an exemplary embodiment;
FIG. 3 is a flow chart illustrating a method of error correction according to the embodiment shown in FIG. 1;
FIG. 4 is a schematic diagram illustrating the execution of a data point addition method in accordance with an exemplary embodiment;
FIG. 5 is a block diagram illustrating an apparatus for acquiring device status data in accordance with an exemplary embodiment;
FIG. 6 is a block diagram of an error correction module according to the embodiment shown in FIG. 5;
FIG. 7 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Fig. 1 is a flow chart illustrating a method of collecting device status data according to an exemplary embodiment, as shown in fig. 1, the method comprising:
Illustratively, the raw state data is operation state data of the device collected within a preset time period. The device status data (e.g., the power consumption of the generator or the mileage of the vehicle) of the device may be collected every predetermined time period (e.g., 1 second) within the predetermined time period, and one value collected every 1 second is a data point. The raw state data includes a plurality of data points. The plurality of data points in the raw state data may be stored in chronological order for ease of subsequent observation and analysis. Thus, the data curve of the raw state data can be obtained by using the time as an x-axis coordinate (abscissa) and using the value corresponding to the data point as a y-axis coordinate (ordinate).
For example, the polygon approximation algorithm, also called Douglas-Peucker algorithm (Douglas-Peucker algorithm), is an algorithm that approximates a curve as a series of points and reduces the number of points. In step 101, the data points in the raw state data may be pruned by existing polygon approximation algorithms. Specifically, this step 101 includes: in step 1011, a straight line segment is connected between the head and the tail of the data curve of the raw state data, and the straight line segment can be called as a chord of the data curve; in step 1012, obtaining a data point X on the data curve with the largest distance from the straight line segment, and calculating a vertical distance between the data point X and the straight line segment; in step 1013, the vertical distance is compared with a preset threshold; when the vertical distance is less than the threshold, in step 1014, determining that the straight-line segment is an approximation of the data curve, i.e. the straight-line segment is a data curve of the approximate state data; alternatively, when the vertical distance is greater than the threshold, in step 1015, the data point X is retained as a dividing point, the data curve is divided into two segments (from the starting point to the data point X and from the data point X to the end point), and the above steps 1011 to 1013 are performed on the two segments of the data curve respectively until the vertical distances between all the retained data points and the corresponding chords are less than the threshold. Finally, after all the data curves are processed, the starting point, all the division points and the end point (the polygonal line formed by the division points) are sequentially acquired as the approximate state data of the original state data. It will be appreciated that the plurality of data points in the approximate state data are also stored in chronological order.
And step 102, correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment.
For example, the approximate state data is an approximation to the original state data, and may be used in a device operation and maintenance scenario where the accuracy requirement for the device state data display is not high. However, in an equipment operation and maintenance scene where the redisplayed equipment state data needs to be further calculated, the approximate state data may cause the final calculation result not to be attached to the actual condition of the equipment, thereby misleading the judgment of the operation and maintenance personnel. Therefore, the approximate state data needs to be corrected in step 102. Specifically, the error is determined based on the area of the curve integral formed by every two adjacent data points in the approximate state data and the area of the curve integral formed by the same two data points in the original state data. When the integral areas of the two curves are the same, the two data points can be considered to have no error, and then the two data points are reserved; or, when the two curve integral areas are different, an error exists between the two data points, and the curve integral area different from the two data points is filled up by adding one data point between the two data points. After all data points in the approximate state data are traversed through the steps, all the added data points and the approximate state data are reserved as finally output target state data of the equipment.
In summary, the present disclosure can delete the original state data of the device through the polygon approximation algorithm to obtain the approximate state data of the device; and correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment. The method can reduce the data volume of storage and display while ensuring the accuracy of the equipment state data statistics, save network transmission resources and computing resources of a client, and reduce the resource loss in the operation and maintenance process of the equipment.
FIG. 2 is a schematic diagram illustrating an implementation of a polygon approximation algorithm according to the embodiment shown in FIG. 1, wherein FIG. 2 (a) shows raw state data of a device collected within a predetermined time period, the raw state data including data points 1-8. As shown in FIG. 2, the process of obtaining the approximate state data of the data points 1-8 by the above-mentioned polygon approximation algorithm may include: first, a straight line segment 18 is connected between data point 1 and data point 8; secondly, obtaining a data point 4 with the maximum vertical distance between the data curve and the straight line segment 18, and calculating the vertical distance between the data point 4 and the straight line segment 18; then, comparing the vertical distance with a preset threshold, if it is determined that the vertical distance is greater than the threshold, the data point 4 is retained as a segmentation point, the data curve is divided into two segments (data point 1 to data point 4 and data point 4 to data point 8), and the processing steps are performed on the two segments of data curves respectively. In fig. 2 (b), the vertical distances between data point 1 and data point 4, data point 2 and data point 3, and straight line segment 14 are both smaller than the above threshold, and therefore, only data point 1 and data point 4 remain, thereby completing the data approximation between data point 1 and data point 4. Meanwhile, between data point 4 and data point 8, data point 6 having the largest vertical distance from straight line segment 48 is determined, and data point 6 is retained. Then, the data curves between the data points 4 and 6 and between the data points 6 and 8 are approximated in fig. 2c, and finally the data curve shown in fig. 2d is obtained, and the corresponding approximate state data includes: data points 1, 4, 6, 7 and 8.
Fig. 3 is a flow chart illustrating an error correction method according to an exemplary embodiment, where the raw state data includes a plurality of data points arranged in time sequence, as shown in fig. 3, and the step 102 may include:
at step 1021, it is determined whether there is a curve integral area error between the ith data point and the (i + 1) th data point in the approximate state data.
Illustratively, this step 1021 may comprise: determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data; determining a first curve integral area between the ith data point and the (i + 1) th data point; a second curve integrated area between the second data point and the third data point is determined. It is understood that the second data point and the ith data point, and the third data point and the (i + 1) th data point are actually the same data point, but in different data sets, the two sets of data points correspond to different curve integral areas. When the first curve integral area and the second curve integral area are determined to be different, curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
At step 1022, when it is determined that there is a curve integral area error between the ith data point and the (i + 1) th data point in the approximate state data, a first data point is determined in the raw state data.
Wherein, the first data point is the middle point of the ith data point and the (i + 1) th data point in the data curve of the original state data.
Exemplarily, in the data curve of the raw state data, the ith data point and the (i + 1) th data point sequentially include: data point X, data point Y, and data point Z, then data point Y is determined to be the intermediate point. When an even number of data points are included between the ith data point and the (i + 1) th data point, any one of the two data points located in the middle may be taken as the first data point.
At step 1023, the target data point is determined according to the target abscissa of the first data point, the ith data point, and the (i + 1) th data point.
Illustratively, this step 1023 may include: taking the second curve integral area, the target abscissa, the abscissa and ordinate of the ith data point and the abscissa and ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; and taking the data point determined by the target ordinate and the target abscissa as the target data point.
Wherein, the area equivalence formula can be expressed as formula (1):
wherein S1 is the second curve integral area, x3For the known abscissa, x, of the object1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate, y, of the i +1 th data point3For the solution of equation (1), this solution may have both positive and negative results, where only the positive result is taken as the target ordinate. The S1 can be calculated by integrating every two data points in the data curve of the raw state data,
and
actually, it is a calculation formula of trapezoidal (or triangular in special case) area. The meaning of the actual expression of the formula (1) is that data points (x) are added3，y3) Then, the data point (x)3，y3) Respectively associated with data points (x)1，y1) And data points (x)2，y2) The curve integral area of the two trapezoids is equal to the second curve integral area.
For example, adding the target data point to the i-th data point and the i + 1-th data point can change the curve integral area therebetween, i.e., correct the curve integral error.
And step 1025, making i equal to i +1, circularly executing the step of determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data or not, adding the target data point to the approximate state data so as to correct the curve integral area error between the ith data point and the (i + 1) th data point until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed so as to acquire the target state data consisting of the approximate state data and a plurality of target data points.
Fig. 4 is a schematic diagram illustrating the implementation process of a data point adding method according to an exemplary embodiment, wherein fig. 4a is a data curve corresponding to the approximate state data, and when the curve integral error of the approximate state data is corrected based on steps 1021 and 1025 as described above, first, for a data point 1 and a data point 4, data points between the two data points in the original state curve, that is, a data point 2 and a data point 3 as shown in fig. 4B, are obtained, and the data point 2 is the intermediate point as described above, and then the abscissa B of the data point 2 is obtained. Next, a data point h having the target abscissa B is set in fig. 4c, the sum of the areas of the trapezoid 1ABh and the trapezoid hBD4 is set to be equal to the sum of the areas of the trapezoids 1AB2, 2BC3 and 3CD4 in fig. 4B, and the target ordinate is calculated from the above formula (1). Finally, a target data point h defined by the abscissa B and the target ordinate is added between the data point 1 and the data point 4 of the approximate state data. Thus, the data point 1, the target data point h, and the data point 4 are target state data obtained by correcting the curve integral area error between the data point 1 and the data point 4. Thereafter, the curve integral areas between the data points 4 and 6, the data points 6 and 7, and the data points 7 and 8 are detected and corrected by the same steps, and all the target data points and the original approximate state data added in the above process are used as the final output target state data.
In summary, the present disclosure can delete the original state data of the device through the polygon approximation algorithm to obtain the approximate state data of the device; and correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment. The method can reduce the data volume of storage and display while ensuring the accuracy of the equipment state data statistics, save network transmission resources and computing resources of a client, and reduce the resource loss in the operation and maintenance process of the equipment.
Fig. 5 is a block diagram illustrating an apparatus for acquiring device status data according to an exemplary embodiment, and as shown in fig. 5, the apparatus 500 includes:
a data deleting module 510, configured to delete the original state data of the device through a polygon approximation algorithm to obtain approximate state data of the device;
an error correction module 520, configured to correct the curve-integrated area error of the approximate state data by adding a plurality of target data points to the approximate state data, so as to obtain target state data of the device.
Fig. 6 is a block diagram of an error correction module according to the embodiment shown in fig. 5, where the raw state data includes a plurality of data points arranged in time sequence, as shown in fig. 6, and the error correction module 520 includes:
an error determination submodule 521, configured to determine whether a curve integral area error exists between an i-th data point and an i + 1-th data point in the approximate state data;
a first data point determining submodule 522 for determining a first data point in the raw state data when it is determined that a curve integrated area error exists between an ith data point and an (i + 1) th data point in the approximate state data, the first data point being a middle point of the ith data point and the (i + 1) th data point in a data curve of the raw state data;
a second data point determining submodule 523 configured to determine the target data point according to the target abscissa of the first data point, the ith data point, and the (i + 1) th data point;
a data point adding sub-module 524, configured to add the target data point to the approximate state data to correct a curve-integrated area error between the ith data point and the (i + 1) th data point;
a loop execution submodule 525 configured to set i to i +1, and loop execution of the step of determining whether a curve integral area error exists between an i-th data point and an i + 1-th data point in the approximate state data, adding the target data point to the approximate state data, so as to correct the curve integral area error between the i-th data point and the i + 1-th data point, until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed, so as to obtain the target state data composed of the approximate state data and a plurality of target data points.
Optionally, the error determining submodule 521 is configured to:
determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data;
determining a first curve integral area between the ith data point and the (i + 1) th data point;
determining a second curve integration area between the second data point and the third data point;
when the first curve integral area and the second curve integral area are determined to be different, curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
Optionally, the second data point determining submodule 523 is configured to:
taking the second curve integral area, the target abscissa, the abscissa and ordinate of the ith data point and the abscissa and ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; wherein the area equivalence formula is:
wherein S1 is the second curve integral area, x3Is thatTarget abscissa, x1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate, y, of the i +1 th data point3Is the target ordinate;
and taking the data point determined by the target ordinate and the target abscissa as the target data point.
In summary, the present disclosure can delete the original state data of the device through the polygon approximation algorithm to obtain the approximate state data of the device; and correcting the curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data so as to obtain target state data of the equipment. The method can reduce the data volume of storage and display while ensuring the accuracy of the equipment state data statistics, save network transmission resources and computing resources of a client, and reduce the resource loss in the operation and maintenance process of the equipment.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Fig. 7 is a block diagram illustrating an electronic device 700 in accordance with an example embodiment. As shown in fig. 7, the electronic device 700 may include: a processor 701, a memory 702, multimedia components 703, input/output (I/O) interfaces 704, and communication components 705.
The processor 701 is configured to control the overall operation of the electronic device 700, so as to complete all or part of the steps in the above-mentioned method for acquiring device status data. The memory 702 is used to store various types of data to support operation at the electronic device 700, such as instructions for any application or method operating on the electronic device 700 and application-related data, such as contact data, transmitted and received messages, pictures, audio, video, and the like. The Memory 702 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia components 703 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 702 or transmitted through the communication component 705. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 704 provides an interface between the processor 701 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 705 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the electronic Device 700 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing the above-mentioned method for collecting Device status data.
In another exemplary embodiment, a computer readable storage medium comprising program instructions, such as the memory 702 comprising program instructions, which are executable by the processor 701 of the electronic device 700 to perform the above-described method of acquiring device status data is also provided.
Although the preferred embodiments of the present disclosure have been described in detail with reference to the accompanying drawings, the present disclosure is not limited to the specific details of the embodiments, and other embodiments of the present disclosure can be easily conceived by those skilled in the art within the technical scope of the present disclosure after considering the description and practicing the present disclosure, and all fall within the protection scope of the present disclosure.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. Meanwhile, any combination can be made between various different embodiments of the disclosure, and the disclosure should be regarded as the disclosure of the disclosure as long as the combination does not depart from the idea of the disclosure. The present disclosure is not limited to the precise structures that have been described above, and the scope of the present disclosure is limited only by the appended claims.
Claims (8)
1. A method for collecting device status data, the method comprising:
deleting original state data of the equipment through a polygon approximation algorithm to obtain approximate state data of the equipment;
correcting a curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data to obtain target state data of the device;
the raw state data includes a plurality of data points arranged in time sequence, and the obtaining of the target state data of the device by adding a plurality of target data points to the approximate state data and correcting a curve integral area of the approximate state data includes:
determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data;
when determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data, determining a first data point in the original state data, wherein the first data point is a middle point of the ith data point and the (i + 1) th data point in a data curve of the original state data;
determining the target data point according to the target abscissa of the first data point, the ith data point and the (i + 1) th data point;
adding the target data point to the approximate state data to correct a curve-integrated area error between the ith data point and the (i + 1) th data point;
and if i is equal to i +1, circularly executing the step of determining whether a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data or not, adding the target data point into the approximate state data to correct the curve integral area error between the ith data point and the (i + 1) th data point until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed to acquire the target state data consisting of the approximate state data and a plurality of target data points.
2. The method of claim 1, wherein determining whether a curve-integrated area error exists between an ith data point and an i +1 th data point in the approximation state data comprises:
determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data;
determining a first curve integral area between the ith data point and the (i + 1) th data point;
determining a second curve integration area between the second data point and the third data point;
and when the first curve integral area and the second curve integral area are determined to be different, determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
3. The method of claim 2, wherein said determining the target data point from the target abscissa of the first data point, the ith data point, and the (i + 1) th data point comprises:
taking the second curve integral area, the target abscissa, the abscissa and the ordinate of the ith data point and the abscissa and the ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; wherein the area equivalence formula is:
wherein S1 is the second curve integral area, x3Is the target abscissa, x1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate, y, of the i +1 th data point3Is the target ordinate;
and taking the data point determined by the target ordinate and the target abscissa as the target data point.
4. An apparatus for collecting device data, the apparatus comprising:
the data deleting module is used for deleting the original state data of the equipment through a polygon approximation algorithm so as to obtain the approximate state data of the equipment;
an error correction module, configured to correct a curve integral area error of the approximate state data by adding a plurality of target data points to the approximate state data, so as to obtain target state data of the device;
the raw state data includes a plurality of data points arranged in time sequence, and the error correction module includes:
an error determination submodule, configured to determine whether a curve integral area error exists between an ith data point and an (i + 1) th data point in the approximate state data;
a first data point determining submodule, configured to determine a first data point in the raw state data when it is determined that a curve integral area error exists between an ith data point and an (i + 1) th data point in the approximate state data, where the first data point is a middle point of the ith data point and the (i + 1) th data point in a data curve of the raw state data;
a second data point determining submodule, configured to determine the target data point according to the target abscissa of the first data point, the ith data point, and the (i + 1) th data point;
a data point adding submodule for adding the target data point to the approximate state data to correct a curve integral area error between the ith data point and the (i + 1) th data point;
and a loop execution sub-module, configured to make i equal to i +1, and loop execution from the step of determining whether a curve integral area error exists between an i-th data point and an i + 1-th data point in the approximate state data to the step of adding the target data point to the approximate state data to correct the curve integral area error between the i-th data point and the i + 1-th data point until the correction of the curve integral area error between every two adjacent data points in the approximate state data is completed, so as to obtain the target state data composed of the approximate state data and a plurality of target data points.
5. The apparatus of claim 4, wherein the error determination submodule is configured to:
determining a second data point and a third data point, wherein the second data point is a data point corresponding to the ith data point in the original state data, and the third data point is a data point corresponding to the (i + 1) th data point in the original state data;
determining a first curve integral area between the ith data point and the (i + 1) th data point;
determining a second curve integration area between the second data point and the third data point;
and when the first curve integral area and the second curve integral area are determined to be different, determining that a curve integral area error exists between the ith data point and the (i + 1) th data point in the approximate state data.
6. The apparatus of claim 5, wherein the second data point determination submodule is configured to:
taking the second curve integral area, the target abscissa, the abscissa and the ordinate of the ith data point and the abscissa and the ordinate of the (i + 1) th data point as the input of an area equivalent formula to obtain the target ordinate output by the area equivalent formula; wherein the area equivalence formula is as follows:
wherein S1 is the second curve integral area, x3Is the target abscissa, x1And y1Respectively the abscissa and ordinate, x, of the ith data point2And y2Respectively the abscissa and ordinate of the i +1 th data point, y3Is the target ordinate;
and taking the data point determined by the target ordinate and the target abscissa as the target data point.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 3.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for executing the computer program in the memory to carry out the steps of the method of any one of claims 1 to 3.
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| CN103309964A (en) * | 2013-06-03 | 2013-09-18 | 广州市香港科大霍英东研究院 | High-efficiency visible monitoring analysis system for large-scale traffic data |
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