阅读说明：本技术 一种太阳能充电桩智能管理系统 (Intelligent management system for solar charging piles ) 是由 郭开华 于 2020-07-10 设计创作，主要内容包括：一种太阳能充电桩智能管理系统,包括参数检测模块、图像检测模块、信息传输模块和智能管理终端,所述参数检测模块用于采集太阳能充电桩表面的温度数据,所述图像检测模块用于采集太阳能充电桩的图像,所述信息传输模块用于将采集的温度数据和图像传输至智能管理终端,所述智能管理终端用于对接收到的温度数据进行处理和判断,并对接收到的图像进行处理和显示。本发明的有益效果：实现了对太阳能充电桩安全运营的有效管理。(The utility model provides a solar charging stake intelligent management system, includes parameter detection module, image detection module, information transmission module and intelligent management terminal, parameter detection module is used for gathering the temperature data on solar charging stake surface, image detection module is used for gathering the image of solar charging stake, information transmission module is used for transmitting the temperature data and the image transmission of gathering to intelligent management terminal, intelligent management terminal is used for handling and judging received temperature data to handle and show received image. The invention has the beneficial effects that: the effective management of the safe operation of the solar charging pile is realized.)

1. An intelligent management system for solar charging piles is characterized by comprising a parameter detection module, an image acquisition module, an information transmission module and an intelligent management terminal, wherein the parameter detection module adopts a sensor node to acquire temperature data of the surface of a solar charging pile and transmits the acquired temperature data to the intelligent management terminal through the information transmission module, the image acquisition module comprises an acquisition control unit and an image acquisition unit, the acquisition control unit is used for controlling the image acquisition unit to acquire images of the solar charging pile according to received acquisition instructions, the image acquisition unit transmits the acquired images of the solar charging pile to the intelligent management terminal through the information transmission module, the intelligent management terminal comprises a danger analysis unit, an image optimization unit and an image display unit, and the danger analysis unit is used for processing the received temperature data, and comparing the processed temperature data with a preset safety threshold, giving an early warning when the temperature data exceeds the preset safety threshold, sending an acquisition instruction to an acquisition control unit through an information transmission module, and processing the received image by the image optimization unit and displaying the processed image on an image display unit.

2. The intelligent management system for the solar charging piles as claimed in claim 1, wherein the information transmission module adopts a GPRS communication mode for information transmission.

3. The intelligent management system for the solar charging pile as claimed in claim 2, wherein the image acquisition unit adopts a camera to acquire the image of the solar charging pile.

4. The intelligent management system for the solar charging pile as claimed in claim 3, wherein the danger analysis unit is configured to process the received temperature data, let t (i) denote the received ith temperature data, and given a detection threshold τ (t), when the temperature data t (i) satisfies | t (i) -t (i-1) | ≦ τ (t), determine that the temperature data t (i) is normal data, wherein t (i-1) denotes the received (i-1) th temperature data;

when temperature data t (i) satisfies | t (i) -t (i-1) | > tau (t), marking the temperature data received before and after the temperature data t (i), setting t (l) to represent the first temperature data received, when the temperature data t (l) satisfies | t (l) -t (l-1) | ≦ tau (t), marking the temperature data t (l) as 0, when the temperature data t (l) satisfies | t (l) -t (l) | > tau (t), marking the temperature data t (l) as 1, wherein t (l-1) represents the (l-1) th temperature data received; is provided withRepresents temperature data received before and closest to temperature data t (i) and labeled 1, wherein a represents temperature data

let μ (b) denote temperature dataCorresponding attribute judgment coefficients, | (b) representing temperature data

(1) when μ (b) is 1, then the expression for h (i) is:

wherein the content of the first and second substances,

(2) When μ (b) is 0, then the expression for h (i) is:

wherein μ (c) represents temperature dataCorresponding attribute judgment coefficients, l (c) representing temperature dataWhen l (c) < c₁When μ (c) is 0, when l (c) > c₂When d is greater than 1, d is greater than 1₁≤l(c)≤c₂When mu (c) is equal to-1, theta₁(μ (c)) represents a first value function, and

(3) When μ (b) ═ 1 and (μ (c) ═ 0 or μ (c) ═ 1), then the expression of h (i) is:

when μ (b) ═ 1 and μ (c) ═ 1, then the expression for h (i) is:

wherein the content of the first and second substances,indicating temperature dataAnd temperature data

When the temperature data t (i) is in c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) 1, the temperature data t (i) is normal data, and if the temperature data t (i) is at c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) ═ 1, the temperature data t (i) is noise data;

when the temperature data t (i) is judged as the noise data, the temperature data t (i) is corrected, and t' (i) represents a value obtained by correcting the temperature data t (i), and

5. the intelligent management system for the solar charging piles as claimed in claim 4, wherein the image optimization unit is used for denoising the acquired images.

Technical Field

The invention relates to the field of safety monitoring, in particular to an intelligent management system for solar charging piles.

Background

The development of new energy automobiles in China is a need for meeting important challenges of energy conservation and emission reduction, and is also a need for the cross-over development of the automobile industry and the improvement of international competitiveness. The popularization of the electric automobile is bound to be based on the construction of the charging pile, and the solar charging pile becomes an important way for the electric automobile to obtain electric energy by virtue of the advantages of environmental protection, convenience in use and easiness in installation. In order to improve the convenience of charging of the electric automobile, the solar charging piles are generally installed in a scattered manner. In order to solve the problem that the solar charging piles which are installed dispersedly are difficult to maintain and inefficient to manage, the application provides an intelligent management system for the solar charging piles, and effective management of safe operation of the solar charging piles can be achieved.

Disclosure of Invention

In view of the above problems, the present invention aims to provide an intelligent management system for solar charging piles.

The purpose of the invention is realized by the following technical scheme:

an intelligent management system for solar charging piles comprises a parameter detection module, an image acquisition module, an information transmission module and an intelligent management terminal, wherein the parameter detection module adopts a sensor node to acquire temperature data of the surface of a solar charging pile and transmits the acquired temperature data to the intelligent management terminal through the information transmission module, the image acquisition module comprises an acquisition control unit and an image acquisition unit, the acquisition control unit is used for controlling the image acquisition unit to acquire images of the solar charging pile according to received acquisition instructions, the image acquisition unit transmits the acquired images of the solar charging pile to the intelligent management terminal through the information transmission module, the intelligent management terminal comprises a danger analysis unit, an image optimization unit and an image display unit, the danger analysis unit is used for processing the received temperature data, and comparing the processed temperature data with a preset safety threshold, giving an early warning when the temperature data exceeds the preset safety threshold, sending an acquisition instruction to an acquisition control unit through an information transmission module, and processing the received image by the image optimization unit and displaying the processed image on an image display unit.

Preferably, the information transmission module transmits information in a GPRS communication mode.

Preferably, the image acquisition unit adopts a camera to acquire the image of the solar charging pile.

This preferred embodiment provides a solar charging stake intelligent management system, judges through the temperature data of gathering solar charging stake surface whether the safe operation of solar charging stake, when judging that the solar charging stake appears dangerously in the operation in-process, gather the image of solar charging stake and show, can more audio-visually know the operation condition of solar charging stake.

Preferably, the danger analyzing unit is used for processing the received temperature data, let t (i) denote the ith received temperature data, and given a detection threshold value τ (t), the value of τ (t) can be 5 ℃, and when the temperature data t (i) meets | t (i) | t (i-1) | ≦ τ (t), the temperature data t (i) is judged to be normal data, wherein t (i-1) denotes the (i-1) th received temperature data;

when temperature data t (i) satisfies | t (i) -t (i-1) | > tau (t), marking the temperature data received before and after the temperature data t (i), setting t (l) to represent the first temperature data received, when the temperature data t (l) satisfies | t (l) -t (l-1) | ≦ tau (t), marking the temperature data t (l) as 0, when the temperature data t (l) satisfies | t (l) -t (l) | > tau (t), marking the temperature data t (l) as 1, wherein t (l-1) represents the (l-1) th temperature data received; is provided withRepresents temperature data received before and closest to temperature data t (i) and labeled 1, wherein a represents temperature dataFor the received a-th temperature data,represents the temperature data received after the temperature data t (i) and marked as 1 nearest to the temperature data t (i), wherein b represents the temperature data

For the received b-th temperature data,denotes temperature data received after temperature data t (i) and marked 1 next to temperature data t (i), wherein c denotes temperature dataFor the received c-th temperature data,

represents the temperature data received after the temperature data t (i) and marked as 1, third nearest to the temperature data t (i), wherein d represents the temperature dataFor the received d-th temperature data, l (i) represents the truncation distance of the temperature data t (i), and l (i) b-i, a first truncation threshold c is given₁And a second truncation threshold c₂Wherein c is₁And c₂Is a positive integer, and c₂＞c₁，c₁May take the value of 5, c₂Can take the value of 10 when l (i) > c₂If so, the temperature data t (i) is judged to be normal data, and when l (i) < c₁When it is, the temperature data c is determined₁Is noise data; when c is going to₁≤l(i)≤c₂Then, the temperature data t (i) is judged in the following way:

let μ (b) denote temperature dataCorresponding attribute judgment coefficients, | (b) representing temperature data

When l (b) < c₁When μ (b) is 0, when l (b) > c₂When d is greater than 1, then d (b) is greater than 1₁≤l(b)≤c₂When μ (b) ═ 1, definition h (i) indicates temperature data t (i) at c₁≤l(i)≤c₂In the case of corresponding detection coefficients, h (i) is calculated using the following formula:

(1) when μ (b) is 1, then the expression for h (i) is:

wherein the content of the first and second substances,

represents the mean of the temperature data t (i) and the temperature data within its cutoff distance, and

indicating temperature data

And the mean of the temperature data within its cutoff distance, and

indicating temperature data

And the mean of the temperature data within its cutoff distance, andwherein t (j) represents the j-th temperature data received,indicating temperature data

And temperature data

Corresponding judgment function whenThen

When in use

When it is, then

Indicating temperature dataAnd temperature data

Corresponding comparison function whenWhen it is, thenWhen in use

When it is, then

Indicating temperature dataAnd temperature data

Corresponding comparison function, blueWhen it is, then

When in useWhen it is, then

Representing a judgment function

Corresponding statistical coefficient when

When it is, then

When in use Then

(2) When μ (b) is 0, then the expression for h (i) is:

wherein μ (c) represents temperature data

Corresponding attribute judgment coefficients, l (c) representing temperature data

When l (c) < c₁When μ (c) is 0, when l (c) > c₂When d is greater than 1, d is greater than 1₁≤l(c)≤c₂When mu (c) is equal to-1, theta₁(μ (c)) represents a first value function, and

indicating temperature dataAnd the mean of the temperature data within its cutoff distance, and

indicating temperature dataAnd temperature data

Corresponding judgment function whenWhen it is, thenWhen in use

When it is, then Indicating temperature dataAnd temperature dataCorresponding comparison function whenWhen it is, then

When in useWhen it is, thenθ₂(μ (c)) represents a second value function, and representing a judgment function

Corresponding statistical coefficient, blueWhen it is, thenWhen in useWhen it is, then

(3) When μ (b) ═ 1 and (μ (c) ═ 0 or μ (c) ═ 1), then the expression of h (i) is:

when μ (b) ═ 1 and μ (c) ═ 1, then the expression of h (i) is

Wherein the content of the first and second substances,indicating temperature dataAnd temperature data

Corresponding comparison function when

When it is, thenWhen in use

When it is, then

As a function of value when

When the temperature of the water is higher than the set temperature,

otherwise

When the temperature data t (i) is in c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) 1, the temperature data t (i) is normal data, and if the temperature data t (i) is at c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) ═ 1, the temperature data t (i) is noise data;

when the temperature data t (i) is judged as the noise data, the temperature data t (i) is corrected, and t' (i) represents a value obtained by correcting the temperature data t (i), and

preferably, the image optimization unit is configured to perform denoising processing on the acquired image.

The beneficial effects created by the invention are as follows:

(1) the utility model provides a solar charging stake intelligent management system, the temperature data through gathering solar charging stake surface judges whether safe operation is gone into to the solar charging stake, when judging that the solar charging stake appears dangerously in the operation process, gathers the image of solar charging stake and shows, can more audio-visual know the operation condition of current solar charging stake.

(2) The invention carries out denoising processing on the received temperature data in sequence, compares the processed temperature data with a preset safety threshold value, judges whether the solar charging pile is operated safely or not, ensures that the safe operation of the solar charging pile can be judged accurately, avoids the misjudgment phenomenon caused by the noise data to the safe operation of the solar charging pile, carries out preliminary judgment on the temperature data to be detected through the given detection threshold value when carrying out noise detection on the temperature data, judges the temperature data to be normal data when the difference value between the temperature data to be detected and the temperature data received before the temperature data to be detected is in the given detection threshold value range, carries out further judgment on the temperature data to be detected when the difference value between the temperature data to be detected and the temperature data received before the temperature data to be detected is out of the given detection threshold value range, marking the temperature data received before and after the temperature data to be detected, in the marking process, comparing the temperature data to be marked with the temperature data received before the temperature data to be marked, marking the temperature data with smaller change as 0, marking the temperature data with larger change as 1, selecting the temperature data which is received after the temperature data to be detected and is closest to the temperature data to be detected and marked as 1, calculating the truncation distance of the temperature data to be detected, when the truncation distance of the temperature data to be detected is smaller than a given first truncation threshold, indicating that the temperature data to be detected is the isolated temperature data with larger change, namely indicating that the temperature data to be detected is the noise data, and when the truncation distance of the temperature data to be detected is larger than a given second truncation threshold, indicating that the change of the temperature data to be detected is the data change caused by the normal operation process of the charging pile, at the moment, judging that the temperature data to be detected is normal data, when the truncation distance of the temperature data to be detected is between a given first truncation threshold and a given second truncation threshold, introducing a plurality of temperature data marked as 1 which are closer to the temperature data to be detected to detect the temperature data to be detected, selecting the temperature data marked as 1 which is received after the temperature data to be detected and is closest to the temperature data to be detected as reference data, defining a corresponding detection coefficient for the temperature data to be detected according to the attribute judgment coefficient value of the reference data, when the attribute judgment coefficient value of the reference data is equal to 1, indicating that the reference data is normal data, and comparing the mean value of the reference data and the temperature data within the truncation distance thereof with the mean value of the temperature data which is received before the temperature data to be detected and is marked as 1 and the temperature data within the truncation distance thereof, when the comparison result is within the detection threshold range, the change of the temperature data to be detected is not in accordance with the actual temperature change trend in the charging pile operation process, the temperature data to be detected is judged to be noise data, when the comparison result is outside the detection threshold range, the detection coefficient is respectively measured by introducing a comparison function to the change trend of the temperature data to be detected and the change trend of the reference data, when the change trend of the temperature data to be detected is the same as the change trend of the reference data, the change of the temperature data to be detected is in accordance with the actual temperature change trend in the charging pile operation process, namely the temperature data to be detected is judged to be normal data, and when the change trend of the temperature to be detected is different from the change trend of the reference data, the change of the temperature data to be detected is not in accordance with the actual temperature change trend, namely the temperature data to be detected is noise data; when the attribute judgment coefficient of the reference data is 0, namely, the temperature data which is received after the temperature data to be detected and is the second nearest to the temperature data to be detected and marked as 1 is introduced into the detection coefficient of the temperature data to be detected, when the newly introduced temperature data is also noise data or the attribute judgment coefficient is-1, the temperature data to be detected is judged to be noise data, when the newly introduced temperature data is normal data, the average value of the newly introduced temperature data and the temperature data within the truncation distance thereof is compared with the temperature data to be detected and the temperature data within the truncation distance thereof, when the comparison result is within the detection threshold range, the temperature data to be detected is judged to be normal data, when the comparison result is outside the detection threshold range, the detection coefficient measures the change trend of the temperature data to be detected and the newly introduced temperature data by introducing a comparison function, when the variation trends of the temperature data to be detected and the newly introduced temperature data are the same, judging that the temperature data to be detected is normal data, and when the variation trends of the temperature data to be detected and the reference data are different, judging that the temperature data to be detected is noise data; when the attribute judgment coefficient of the reference data is-1, temperature data which is received after the temperature data to be detected and is the second nearest to the temperature data to be detected and marked as 1 is introduced into the detection coefficient of the temperature data to be detected for detection, when the attribute judgment coefficient of the newly introduced temperature data is 0 or 1, the defined detection coefficient is the same as the detection coefficient defined when the attribute judgment coefficient of the reference data is 0, when the attribute judgment coefficient of the newly introduced temperature data is-1, the defined detection coefficient is measured by the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data, when the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data is the same, the temperature data to be detected is judged to be normal data, and when the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data is different, the temperature data to be detected is judged as noise data.

Drawings

The invention is further described with the aid of the accompanying drawings, in which, however, the embodiments do not constitute any limitation to the invention, and for a person skilled in the art, without inventive effort, further drawings may be derived from the following figures.

FIG. 1 is a schematic diagram of the present invention.

Detailed Description

The invention is further described with reference to the following examples.

Referring to fig. 1, the intelligent management system for the solar charging pile of the embodiment includes a parameter detection module, an image collection module, an information transmission module and an intelligent management terminal, wherein the parameter detection module collects temperature data of the surface of the solar charging pile by using a sensor node and transmits the collected temperature data to the intelligent management terminal through the information transmission module, the image collection module includes a collection control unit and an image collection unit, the collection control unit is configured to control the image collection unit to collect an image of the solar charging pile according to a received collection instruction, the image collection unit transmits the collected image of the solar charging pile to the intelligent management terminal through the information transmission module, the intelligent management terminal includes a risk analysis unit, an image optimization unit and an image display unit, the risk analysis unit is configured to perform denoising processing on the received temperature data, and comparing the denoised temperature data with a preset safety threshold, warning when the temperature data exceeds the preset safety threshold, sending an acquisition instruction to an acquisition control unit through an information transmission module, and displaying the denoised image on an image display unit by the image optimization unit.

Preferably, the information transmission module transmits information in a GPRS communication mode.

Preferably, the image acquisition unit adopts a camera to acquire the image of the solar charging pile.

This preferred embodiment provides a solar charging stake intelligent management system, judges through the temperature data of gathering solar charging stake surface whether the safe operation of solar charging stake, when judging that the solar charging stake appears dangerously in the operation in-process, gather the image of solar charging stake and show, can more audio-visually know the operation condition of solar charging stake.

Preferably, the danger analyzing unit is used for processing the received temperature data, let t (i) denote the ith received temperature data, and given a detection threshold value τ (t), the value of τ (t) can be 5 ℃, and when the temperature data t (i) meets | t (i) | t (i-1) | ≦ τ (t), the temperature data t (i) is judged to be normal data, wherein t (i-1) denotes the (i-1) th received temperature data;

when temperature data t (i) satisfies | t (i) -t (i-1) | > tau (t), marking the temperature data received before and after the temperature data t (i), setting t (l) to represent the first temperature data received, when the temperature data t (l) satisfies | t (l) -t (l-1) | ≦ tau (t), marking the temperature data t (l) as 0, when the temperature data t (l) satisfies | t (l) -t (l) | > tau (t), marking the temperature data t (l) as 1, wherein t (l-1) represents the (l-1) th temperature data received; is provided withRepresents temperature data received before and closest to temperature data t (i) and labeled 1, wherein a represents temperature dataFor the received a-th temperature data,

represents the temperature data received after the temperature data t (i) and marked as 1 nearest to the temperature data t (i), wherein b represents the temperature dataFor the number of the received b temperatureAccording to the above-mentioned technical scheme,

denotes temperature data received after temperature data t (i) and marked 1 next to temperature data t (i), wherein c denotes temperature data

For the received c-th temperature data,

represents the temperature data received after the temperature data t (i) and marked as 1, third nearest to the temperature data t (i), wherein d represents the temperature data

For the received d-th temperature data, l (i) represents the truncation distance of the temperature data t (i), and l (i) b-i, a first truncation threshold c is given₁And a second truncation threshold c₂Wherein c is₁And c₂Is a positive integer, and c₂＞c₁，c₁May take the value of 5, c₂Can take the value of 10 when l (i) > c₂If so, the temperature data t (i) is judged to be normal data, and when l (i) < c₁When it is, the temperature data c is determined₁Is noise data; when c is going to₁≤l(i)≤c₂Then, the temperature data t (i) is judged in the following way:

let μ (b) denote temperature dataCorresponding attribute judgment coefficients, | (b) representing temperature dataWhen l (b) < c₁When μ (b) is 0, when l (b) > c₂When d is greater than 1, then d (b) is greater than 1₁≤l(b)≤c₂When μ (b) ═ 1, definition h (i) indicates temperature data t (i) at c₁≤l(i)≤c₂Detection of correspondence in case of occurrenceAnd measuring coefficients, and then h (i) is calculated by adopting the following formula:

(1) when μ (b) is 1, then the expression for h (i) is:

wherein the content of the first and second substances,represents the mean of the temperature data t (i) and the temperature data within its cutoff distance, and

indicating temperature dataAnd the mean of the temperature data within its cutoff distance, and

indicating temperature data

And the mean of the temperature data within its cutoff distance, andwherein t (j) represents the j-th temperature data received,

indicating temperature dataAnd temperature dataCorresponding judgment function when

ThenWhen in useWhen it is, then

Indicating temperature dataAnd temperature data

Corresponding comparison function whenWhen it is, then

When in use

When it is, then

Indicating temperature dataAnd temperature data

Corresponding comparison functionNumber whenWhen it is, then

When in useWhen it is, then

Representing a judgment function

Corresponding statistical coefficient, blue

When it is, thenWhen in use

Then

(2) When μ (b) is 0, then the expression for h (i) is:

wherein μ (c) represents temperature data

Corresponding attribute judgment coefficients, l (c) representing temperature data

When l (c) < c₁When μ (c) is 0, when l (c) > c₂When d is greater than 1, d is greater than 1₁≤l(c)≤c₂When mu (c) is equal to-1, theta₁(μ (c)) represents a first value function, and

indicating temperature dataAnd the mean of the temperature data within its cutoff distance, and indicating temperature dataAnd temperature dataCorresponding judgment function whenWhen it is, thenWhen in useWhen it is, then

Indicating temperature dataAnd temperature dataCorresponding comparison function when

When it is, then

When in use

When it is, then

θ₂(μ (c)) represents a second value function, and

representing a judgment function

Corresponding statistical coefficient whenWhen it is, then

When in useWhen it is, then

(3) When μ (b) ═ 1 and (μ (c) ═ 0 or μ (c) ═ 1), then the expression of h (i) is:

when μ (b) ═ 1 and μ (c) ═ 1, then the expression of h (i) is

Wherein the content of the first and second substances,indicating temperature data

And temperature dataCorresponding comparison function when

When it is, then

When in use

When it is, then As a function of value when

When the temperature of the water is higher than the set temperature,

otherwise

When the temperature data t (i) is in c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) 1, the temperature data t (i) is normal data, and if the temperature data t (i) is at c₁≤l(i)≤c₂If the corresponding detection coefficient h (i) satisfies h (i) ═ 1, the temperature data t (i) is noise data;

when the temperature data t (i) is judged as the noise data, the temperature data t (i) is corrected, and t' (i) represents a value obtained by correcting the temperature data t (i), and

the preferred embodiment is used for sequentially carrying out denoising processing on the received temperature data, comparing the processed temperature data with a preset safety threshold value, judging whether the solar charging pile is safely operated or not, ensuring that the safe operation of the solar charging pile can be accurately judged, avoiding the phenomenon of misjudgment caused by the noise data on the safe operation of the solar charging pile, firstly carrying out preliminary judgment on the temperature data to be detected through the given detection threshold value when carrying out noise detection on the temperature data, judging the temperature data to be normal data when the difference value between the temperature data to be detected and the temperature data received before the temperature data to be detected is within the given detection threshold value range, and carrying out further judgment on the temperature data to be detected when the difference value between the temperature data to be detected and the temperature data received before the temperature data to be detected is outside the given detection threshold value range, marking the temperature data received before and after the temperature data to be detected, in the marking process, comparing the temperature data to be marked with the temperature data received before the temperature data to be marked, marking the temperature data with smaller change as 0, marking the temperature data with larger change as 1, selecting the temperature data which is received after the temperature data to be detected and is closest to the temperature data to be detected and marked as 1, calculating the truncation distance of the temperature data to be detected, when the truncation distance of the temperature data to be detected is smaller than a given first truncation threshold, indicating that the temperature data to be detected is the isolated temperature data with larger change, namely indicating that the temperature data to be detected is the noise data, and when the truncation distance of the temperature data to be detected is larger than a given second truncation threshold, indicating that the change of the temperature data to be detected is the data change caused by the normal operation process of the charging pile, at the moment, judging that the temperature data to be detected is normal data, when the truncation distance of the temperature data to be detected is between a given first truncation threshold and a given second truncation threshold, introducing a plurality of temperature data marked as 1 which are closer to the temperature data to be detected to detect the temperature data to be detected, selecting the temperature data marked as 1 which is received after the temperature data to be detected and is closest to the temperature data to be detected as reference data, defining a corresponding detection coefficient for the temperature data to be detected according to the attribute judgment coefficient value of the reference data, when the attribute judgment coefficient value of the reference data is equal to 1, indicating that the reference data is normal data, and comparing the mean value of the reference data and the temperature data within the truncation distance thereof with the mean value of the temperature data which is received before the temperature data to be detected and is marked as 1 and the temperature data within the truncation distance thereof, when the comparison result is within the detection threshold range, the change of the temperature data to be detected is not in accordance with the actual temperature change trend in the charging pile operation process, the temperature data to be detected is judged to be noise data, when the comparison result is outside the detection threshold range, the detection coefficient is respectively measured by introducing a comparison function to the change trend of the temperature data to be detected and the change trend of the reference data, when the change trend of the temperature data to be detected is the same as the change trend of the reference data, the change of the temperature data to be detected is in accordance with the actual temperature change trend in the charging pile operation process, namely the temperature data to be detected is judged to be normal data, and when the change trend of the temperature to be detected is different from the change trend of the reference data, the change of the temperature data to be detected is not in accordance with the actual temperature change trend, namely the temperature data to be detected is noise data; when the attribute judgment coefficient of the reference data is 0, namely, the temperature data which is received after the temperature data to be detected and is the second nearest to the temperature data to be detected and marked as 1 is introduced into the detection coefficient of the temperature data to be detected, when the newly introduced temperature data is also noise data or the attribute judgment coefficient is-1, the temperature data to be detected is judged to be noise data, when the newly introduced temperature data is normal data, the average value of the newly introduced temperature data and the temperature data within the truncation distance thereof is compared with the temperature data to be detected and the temperature data within the truncation distance thereof, when the comparison result is within the detection threshold range, the temperature data to be detected is judged to be normal data, when the comparison result is outside the detection threshold range, the detection coefficient measures the change trend of the temperature data to be detected and the newly introduced temperature data by introducing a comparison function, when the variation trends of the temperature data to be detected and the newly introduced temperature data are the same, judging that the temperature data to be detected is normal data, and when the variation trends of the temperature data to be detected and the reference data are different, judging that the temperature data to be detected is noise data; when the attribute judgment coefficient of the reference data is-1, temperature data which is received after the temperature data to be detected and is the second nearest to the temperature data to be detected and marked as 1 is introduced into the detection coefficient of the temperature data to be detected for detection, when the attribute judgment coefficient of the newly introduced temperature data is 0 or 1, the defined detection coefficient is the same as the detection coefficient defined when the attribute judgment coefficient of the reference data is 0, when the attribute judgment coefficient of the newly introduced temperature data is-1, the defined detection coefficient is measured by the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data, when the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data is the same, the temperature data to be detected is judged to be normal data, and when the variation trend of the temperature data to be detected, the reference data and the newly introduced temperature data is different, the temperature data to be detected is judged as noise data.

Preferably, the image optimization unit is configured to perform denoising processing on the acquired image.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.