A method for line temperature diagnosis for smart signs and smart signs

Abstract

This invention provides a method for diagnosing line temperature of a smart sign and a smart sign, belonging to the field of anomaly diagnosis technology. Specifically, it includes: using a smart sign to perform temperature measurement processing on the transmission line to obtain temperature data at different times; combining this data with ambient temperature data and transmission power in the adjacent time period; determining when the temperature anomaly of the transmission line does not meet requirements; transmitting the temperature measurement data of the transmission line within a preset time period to a monitoring platform; using other transmission lines in the monitoring platform and the temperature measurement data of the transmission line at different times; determining when the line temperature deviation coefficient of the transmission line is not abnormal; and determining the temperature anomaly diagnosis method of the transmission line based on the line temperature deviation coefficient of the transmission line after different number of identifications in the monitoring platform, thereby improving the accuracy of the temperature anomaly diagnosis results of the transmission line.

Description

Technical Field

[0001] This invention belongs to the field of abnormal diagnosis technology, and in particular relates to a line temperature diagnosis method for smart signage and a smart signage. Background Technology

[0002] Transmission lines inevitably generate heat during operation. Monitoring the temperature of transmission lines is crucial for detecting abnormal conditions and real-time monitoring of line losses. Therefore, how to detect and manage line temperature has become a pressing technical problem.

[0003] To achieve the detection and processing of transmission line temperature, the invention patent application CN201410595301.3, "Online Detection System and Method for Transmission Line Conductor Temperature," connects to a temperature monitoring terminal via a second wireless communication module, thereby solving the power supply problem for long-term operation in the field and realizing unmanned remote measurement of conductor temperature. However, the following technical problems exist:

[0004] When performing line temperature diagnostics, the line temperature of a transmission line is affected by many factors such as the load on the transmission line and the ambient temperature. Therefore, if a fixed line temperature threshold is used, it will be impossible to accurately assess the actual operating status of the transmission line.

[0005] To address the aforementioned technical issues, this application specifically provides a method for line temperature diagnosis of smart signs and a smart sign. Summary of the Invention

[0006] To achieve the objectives of this invention, the following technical solution is adopted:

[0007] According to one aspect of the present invention, a method for line temperature diagnosis of smart signs is provided.

[0008] A method for diagnosing line temperature for smart signs, specifically including:

[0009] S1 uses the analysis results of the temperature measurement data of the smart sign to determine the variation of the transmission power of the transmission line. Based on the variation of the transmission power of the transmission line, if the power variation of the transmission line meets the requirements, proceed to the next step.

[0010] S2 uses the intelligent sign to perform temperature measurement processing on the transmission line to obtain temperature measurement data at different times, and combines it with the ambient temperature data and transmission power in the adjacent time period of the time to determine if the temperature abnormality of the transmission line does not meet the requirements, and then proceeds to the next step.

[0011] S3 transmits the temperature measurement data of the transmission line within a preset time period to the monitoring platform. Using the temperature measurement data of other transmission lines in the monitoring platform and the transmission line at different times, it is determined that the line temperature deviation coefficient of the transmission line is not abnormal. Based on the line temperature deviation coefficient of the transmission line in the monitoring platform with different identification times, the temperature anomaly diagnosis method of the transmission line is determined.

[0012] The beneficial effects of this invention are as follows:

[0013] By utilizing the monitoring platform and the temperature measurement data of other transmission lines at different times, it is possible to determine whether there are any abnormalities in the line temperature deviation coefficient of the transmission line. This enables the assessment of abnormal conditions of the transmission line based on the deviation between the temperature measurement data of other transmission lines and the temperature measurement data of the transmission line itself. This avoids the technical problem that the accuracy of judging temperature anomalies caused by solely considering the temperature measurement data of the transmission line is insufficient, and ensures the accuracy of the assessment and screening of transmission lines with abnormal line temperatures.

[0014] Based on the line temperature deviation coefficient of the transmission line according to the different number of identifications in the monitoring platform, the method for diagnosing temperature anomalies of the transmission line is determined. This avoids the technical problem of inaccurate diagnosis results of line temperature anomalies using smart signs. By combining the number of identifications, the error diagnosis number of transmission lines using smart signs is determined, thereby improving the accuracy of the line temperature anomaly diagnosis results.

[0015] A further technical solution is that the variation of the transmission power of the transmission line includes the time of power variation of the transmission line in different time periods and the amount of power variation at each time of power variation.

[0016] A further technical solution is that the power change time is the time when the deviation of the power from the adjacent time is not within a preset range.

[0017] A further technical solution is that the power variation is the power deviation between the power variation time and the adjacent time.

[0018] A further technical solution involves determining that the power fluctuation of the transmission line meets the requirements, specifically including:

[0019] Based on the variation of the transmission power of the transmission line, determine the timing of the power variation of the transmission line in different time periods;

[0020] The power fluctuation periods within the time period are determined by the number of power fluctuation moments.

[0021] The power fluctuation of the transmission line is determined based on the number of power fluctuation periods to determine whether the power fluctuation meets the requirements.

[0022] A further technical solution is that the power fluctuation period is a period in which the number of power fluctuation moments is greater than the preset number of moments.

[0023] A further technical solution is that when the number of power fluctuation periods is not within the preset range of time periods, it is determined that the power fluctuation of the transmission line does not meet the requirements.

[0024] A further technical solution involves using a monitoring platform to diagnose and handle temperature anomalies when the power fluctuation of the transmission line does not meet the requirements.

[0025] A further technical solution is that the method for determining the temperature anomaly diagnosis method of the transmission line is as follows:

[0026] The number of times the transmission line is identified on the monitoring platform is used to determine the number of times the transmission line is identified within a preset time period;

[0027] After normalizing the line temperature deviation coefficient for different recognition counts, the recognition error weighting coefficients for different recognition counts are obtained.

[0028] The error coefficient sum is determined by summing the recognition error weighting coefficients of the number of times the transmission line is identified within a preset time period, and the temperature anomaly diagnosis method of the transmission line is determined by using the error coefficient sum.

[0029] A further technical solution involves utilizing the aforementioned deviation coefficient and determining a method for diagnosing temperature anomalies in the transmission line, specifically including:

[0030] Based on the error coefficients, a preset diagnostic method corresponding to the error coefficients is determined, and the preset diagnostic method is used to determine the temperature anomaly diagnostic method for the transmission line.

[0031] A further technical solution is that the abnormality diagnosis method involves using the monitoring platform to perform temperature abnormality diagnosis and processing, and using smart signage to perform temperature abnormality diagnosis and processing.

[0032] Secondly, the present invention provides a smart signboard, applied to the aforementioned method for line temperature diagnosis of smart signboards, specifically including:

[0033] Temperature measurement module, line temperature diagnostic module, data transmission module;

[0034] The temperature measurement module is responsible for performing temperature measurement processing on the transmission line to obtain temperature data at different times.

[0035] The line temperature diagnostic module is responsible for using other power transmission lines in the monitoring platform and the temperature measurement data of the power transmission lines at different times to determine whether there is any abnormality in the line temperature deviation coefficient of the power transmission lines.

[0036] The data transmission module is responsible for data transmission processing between the monitoring platform and the monitoring platform.

[0037] Other features and advantages will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0039] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0040] Figure 1 This is a flowchart of a line temperature diagnostic method for smart signage;

[0041] Figure 2 It is a flowchart for determining whether the power variation of the transmission line meets the requirements;

[0042] Figure 3 This is a flowchart for determining whether abnormal temperature conditions in transmission lines do not meet requirements;

[0043] Figure 4 This is a flowchart illustrating the method for determining the diagnostic approach for temperature anomalies in power transmission lines. Detailed Implementation

[0044] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0045] For ease of understanding, the following will describe the scheme through Examples 1 and 2. Example

[0046] To solve the above problems, according to one aspect of the present invention, such as Figure 1As shown, according to one aspect of the present invention, a method for line temperature diagnosis of smart signs is provided, specifically including:

[0047] S1 uses the analysis results of the temperature measurement data of the smart sign to determine the variation of the transmission power of the transmission line. Based on the variation of the transmission power of the transmission line, if the power variation of the transmission line meets the requirements, proceed to the next step.

[0048] Furthermore, the variation in the transmission power of the transmission line includes the timing of the power variation and the amount of power variation at different times.

[0049] Specifically, the power change time is the time when the deviation of the power from the adjacent time is not within a preset range.

[0050] It is understood that the power variation is the power deviation between the time of power variation and the adjacent time.

[0051] It should be noted that, as Figure 2 As shown, determining that the power fluctuation of the transmission line meets the requirements specifically includes:

[0052] Based on the variation of the transmission power of the transmission line, determine the timing of the power variation of the transmission line in different time periods;

[0053] The power fluctuation periods within the time period are determined by the number of power fluctuation moments.

[0054] The power fluctuation of the transmission line is determined based on the number of power fluctuation periods to determine whether the power fluctuation meets the requirements.

[0055] Furthermore, the power fluctuation period is a period in which the number of power fluctuation moments is greater than the preset number of moments.

[0056] It is understood that when the number of power fluctuation periods is not within the preset range, the power fluctuation of the transmission line is determined to be unsatisfactory.

[0057] Specifically, when the power fluctuation of the transmission line does not meet the requirements, the monitoring platform is used to diagnose and handle temperature anomalies.

[0058] In another embodiment, determining that the power fluctuation of the transmission line meets the requirements specifically includes:

[0059] Based on the variation of the transmission power of the transmission line, the power variation time of the transmission line in different time periods is determined. If the number of power variation time periods does not meet the requirements, it is determined that the power variation of the transmission line does not meet the requirements.

[0060] When the number of power change moments is not within the preset range of change moment numbers:

[0061] If, based on the number of power fluctuation moments in different time periods, it is determined that there are no power fluctuation moments in the time period, then the power fluctuation of the transmission line meets the requirements.

[0062] When the number of power fluctuation moments is within a preset range of fluctuation moments or when there is a power fluctuation period:

[0063] The number of power fluctuation periods is obtained. If the number of power fluctuation periods is not within the preset range of the number of periods, it is determined that the power fluctuation of the transmission line does not meet the requirements.

[0064] When the number of power fluctuation periods is within a preset range:

[0065] The number of power fluctuation periods on different dates is determined based on the distribution data of power fluctuation periods. When there are dates where the number of power fluctuation periods exceeds a preset threshold:

[0066] Dates with a number of power fluctuation periods exceeding a preset threshold are defined as power fluctuation dates. If the number of power fluctuation dates does not meet the requirements, the power fluctuation of the transmission line is determined to be unsatisfactory.

[0067] When the number of power change dates meets the requirement:

[0068] The date variation coefficient for different power variation dates is determined by the proportion of power variation times in different power variation dates. If the sum of the date variation coefficients for different power variation dates does not meet the requirements, then the power variation of the transmission line is determined to be unsatisfactory.

[0069] When there are no days with a number of power fluctuation periods greater than the preset threshold, or when the sum of the date fluctuation coefficients of different power fluctuation dates meets the requirements:

[0070] The date variation coefficient for different dates is determined by the proportion of power variation moments on different dates, and the power variation of the transmission line is determined to meet the requirements based on the average of the date variation coefficients for different dates.

[0071] S2 uses the intelligent sign to perform temperature measurement processing on the transmission line to obtain temperature measurement data at different times, and combines it with the ambient temperature data and transmission power in the adjacent time period of the time to determine if the temperature abnormality of the transmission line does not meet the requirements, and then proceeds to the next step.

[0072] Specifically, such as Figure 3 As shown, the temperature anomaly of the transmission line is determined to be unacceptable, specifically including:

[0073] Based on the ambient temperature data and transmission power at different times within the adjacent time period, the temperature threshold at different times is determined using a preset mapping function;

[0074] The line temperature at different times is determined based on the temperature measurement data at different times, and the abnormal temperature moments are determined by combining the temperature thresholds corresponding to different times.

[0075] The number of abnormal temperature events within a preset time period determines whether the temperature anomaly of the transmission line meets the requirements.

[0076] It should be noted that the value range of the adjacent time period is between 30 minutes and 1 hour.

[0077] Furthermore, the abnormal temperature moment is the moment when the line temperature exceeds the temperature threshold.

[0078] Specifically, if the number of abnormal temperature moments within a preset time period is greater than the preset number of abnormal moments, then the temperature abnormality of the transmission line is determined to be unacceptable.

[0079] It is understandable that when the temperature anomaly of the transmission line meets the requirements, the intelligent signboard is used for temperature anomaly diagnosis and processing.

[0080] In another embodiment, determining that the temperature anomaly of the transmission line does not meet the requirements specifically includes:

[0081] S21 uses ambient temperature data and transmission power at different times within a short period as a basis, and employs a preset mapping function to determine the temperature threshold at different times;

[0082] S22 determines the line temperature at different times based on the temperature measurement data at different times, and determines the time of temperature abnormality in the time by combining the temperature threshold corresponding to different times.

[0083] Optionally, step S22 above includes the following:

[0084] S221 determines the line temperature at different times based on the temperature measurement data at different times, and determines that there are no abnormal temperature times in the time when the temperature is determined in combination with the temperature threshold corresponding to different times. If there are abnormal temperature times, the temperature abnormality of the transmission line meets the requirements. If there are abnormal temperature times, proceed to step S222.

[0085] S222 Obtain the number of abnormal temperature moments. When the number of abnormal temperature moments is greater than the preset abnormal number threshold, it is determined that the temperature abnormality of the transmission line does not meet the requirements. When the number of abnormal temperature moments is not greater than the preset abnormal number threshold, proceed to step S223.

[0086] S223 uses the distribution data of different temperature anomaly times to determine the interval data between different temperature anomaly times, and uses the interval data to determine the clustered anomaly times in the temperature anomaly times. When the number of clustered anomaly times does not meet the requirements, it is determined that the temperature anomaly of the transmission line does not meet the requirements. When the number of clustered anomaly times meets the requirements, it proceeds to step S224.

[0087] S224 obtains the percentage of the number of abnormal temperature moments. When the percentage of the number of abnormal temperature moments is within a preset percentage range, proceed to step S23. When the percentage of the number of abnormal temperature moments is not within the preset percentage range, it is determined that the temperature abnormality of the transmission line meets the requirements.

[0088] S23 determines the abnormal weight coefficient for different temperature abnormal times based on the deviation between the line temperature and the temperature threshold at different temperature abnormal times using a preset mapping function, determines the weight coefficient sum by summing the abnormal weight coefficients for different temperature abnormal times within a preset time period, and determines whether the temperature abnormality of the transmission line meets the requirements based on the weight coefficient sum.

[0089] Optionally, step S23 above includes the following:

[0090] S231 Based on the deviation between the line temperature and the temperature threshold at different abnormal temperature times, the abnormal weight coefficient for different abnormal temperature times is determined using a preset mapping function. When there is an abnormal temperature time with an abnormal weight coefficient greater than the preset weight coefficient threshold, the process proceeds to step S232. When there is no abnormal temperature time with an abnormal weight coefficient greater than the preset weight coefficient threshold, the process proceeds to step S233.

[0091] S232 defines temperature anomalies with an abnormal weight coefficient greater than a preset weight coefficient threshold as severe anomalies. If the number of severe anomalies does not meet the requirements, the temperature anomaly of the transmission line is determined to be unacceptable. If the number of severe anomalies meets the requirements, proceed to step S233.

[0092] S233 determines the weighting coefficient sum by summing the abnormal weighting coefficients of different temperature abnormal times within a preset time period, and determines whether the temperature abnormality of the transmission line meets the requirements based on the weighting coefficient sum.

[0093] S3 transmits the temperature measurement data of the transmission line within a preset time period to the monitoring platform. Using the temperature measurement data of other transmission lines in the monitoring platform and the transmission line at different times, it is determined that the line temperature deviation coefficient of the transmission line is not abnormal. Based on the line temperature deviation coefficient of the transmission line in the monitoring platform with different identification times, the temperature anomaly diagnosis method of the transmission line is determined.

[0094] Furthermore, determining that the line temperature deviation coefficient of the transmission line is not abnormal specifically includes:

[0095] Based on the transmission power and ambient temperature data of other transmission lines at different times, similar transmission lines among the other transmission lines are identified.

[0096] Based on the temperature measurement data of the similar transmission line and the transmission line at different times, the deviation at different times is determined, and the deviation time of different similar transmission lines is determined using the deviation.

[0097] The deviation coefficient of different similar transmission lines is determined by the proportion of deviation times of different similar transmission lines, and the line temperature deviation coefficient of the transmission line is determined by the average value of the deviation coefficients of different similar transmission lines. The abnormality is then determined by combining the preset temperature deviation coefficient threshold.

[0098] It is understood that the similar transmission lines refer to other transmission lines whose transmission power and ambient temperature data at different times are all within 5%.

[0099] Furthermore, the deviation time is the moment when the deviation amount does not meet the requirements.

[0100] Specifically, when the line temperature deviation coefficient of different similar transmission lines is greater than the preset temperature deviation coefficient threshold, it is determined that the line temperature of the transmission line is abnormal.

[0101] It is understandable that when the temperature of the transmission line is abnormal, a temperature abnormality signal will be directly issued, and the monitoring platform will be used for temperature abnormality diagnosis and processing.

[0102] Specifically, such as Figure 4 As shown, the method for determining the temperature anomaly diagnosis method for the transmission line is as follows:

[0103] The number of times the transmission line is identified on the monitoring platform is used to determine the number of times the transmission line is identified within a preset time period;

[0104] After normalizing the line temperature deviation coefficient for different recognition counts, the recognition error weighting coefficients for different recognition counts are obtained.

[0105] The error coefficient sum is determined by summing the recognition error weighting coefficients of the number of times the transmission line is identified within a preset time period, and the temperature anomaly diagnosis method of the transmission line is determined by using the error coefficient sum.

[0106] Furthermore, the method for diagnosing temperature anomalies in the transmission line, using the aforementioned deviation coefficient, specifically includes:

[0107] Based on the error coefficients, a preset diagnostic method corresponding to the error coefficients is determined, and the preset diagnostic method is used to determine the temperature anomaly diagnostic method for the transmission line.

[0108] It should also be noted that the abnormality diagnosis methods include using the monitoring platform to diagnose and process temperature anomalies, and using smart signage to diagnose and process temperature anomalies.

[0109] In another embodiment, the method for determining the temperature anomaly diagnosis method of the transmission line is as follows:

[0110] S41 determines the number of times the transmission line is identified within a preset time period based on the number of times the transmission line is identified on the monitoring platform;

[0111] Optionally, step S41 above includes the following:

[0112] S411 determines the number of times the transmission line is identified within a preset time period based on the number of times the transmission line is identified on the monitoring platform. If the number of identifications within the preset time period is greater than the preset identification threshold, it is determined that the monitoring platform will be used for temperature anomaly diagnosis. If the number of identifications within the preset time period is not greater than the preset identification threshold, proceed to step S412.

[0113] S412 If it is determined that there are no recognition counts with an interval duration shorter than the preset interval duration based on the interval duration between recognition counts within the preset time duration, proceed to step S42; if there are recognition counts with an interval duration shorter than the preset interval duration, proceed to step S413.

[0114] S413 takes the number of times the interval is less than the preset interval as the number of times of cluster recognition. When the number of times of cluster recognition is greater than the preset number of times of cluster recognition, it is determined that the monitoring platform will be used for temperature anomaly diagnosis. When the number of times of cluster recognition is not greater than the preset number of times of cluster recognition, the process proceeds to step S42.

[0115] S42 uses the line temperature deviation coefficient of different recognition times, performs normalization processing to obtain the recognition error weight coefficient of different recognition times, and calculates the recognition deviation number of the smart sign in the recognition times based on the recognition error weight coefficient.

[0116] Optionally, step S42 above includes the following:

[0117] S421 uses the line temperature deviation coefficient of different recognition times, performs normalization processing to obtain the recognition error weight coefficient of different recognition times. When the average value of the recognition error weight coefficient of different recognition times meets the requirements, then proceed to step S422. When the average value of the recognition error weight coefficient of different recognition times does not meet the requirements, then determine to use the monitoring platform to perform temperature anomaly diagnosis processing.

[0118] S422 If there are no identification errors with a weight coefficient greater than the preset error coefficient threshold, then determine to use the smart sign for temperature anomaly diagnosis. If there are identification errors with a weight coefficient greater than the preset error coefficient threshold, proceed to step S423.

[0119] S423 calculates the number of identification deviations of the smart sign in the number of identification attempts based on the identification error weighting coefficient. If the number of identification deviations does not meet the requirements, it is determined to use the monitoring platform for temperature anomaly diagnosis. If the number of identification deviations meets the requirements, proceed to step S43.

[0120] S43 determines the recognition error coefficient of the smart sign based on the recognition error weighting coefficient of different recognition deviation numbers, and uses the recognition error coefficient to determine the temperature anomaly diagnosis method of the transmission line.

[0121] Furthermore, the number of recognition deviations of the smart signboard in the recognition count is the number of recognitions where the recognition error weighting coefficient is greater than a preset coefficient threshold.

[0122] Specifically, the recognition error coefficient is determined based on the sum of the recognition error weighting coefficients for different recognition deviation counts. Example

[0123] Secondly, the present invention provides a smart signboard, applied to the aforementioned method for line temperature diagnosis of smart signboards, specifically including:

[0124] Temperature measurement module, line temperature diagnostic module, data transmission module;

[0125] The temperature measurement module is responsible for performing temperature measurement processing on the transmission line to obtain temperature data at different times.

[0126] The line temperature diagnostic module is responsible for using other power transmission lines in the monitoring platform and the temperature measurement data of the power transmission lines at different times to determine whether there is any abnormality in the line temperature deviation coefficient of the power transmission lines.

[0127] The data transmission module is responsible for data transmission processing between the monitoring platform and the monitoring platform.

[0128] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0129] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0130] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for diagnosing line temperature for intelligent signage, characterized in that, Specifically, it includes: Based on the analysis results of the temperature measurement data of the smart sign, the variation of the transmission power of the transmission line is determined. Based on the variation of the transmission power of the transmission line, if the power variation of the transmission line meets the requirements, proceed to the next step. The temperature measurement data of the transmission line at different times is obtained by using the smart sign. Combined with the ambient temperature data and transmission power in the adjacent time period, if the temperature abnormality of the transmission line does not meet the requirements, proceed to the next step. The temperature measurement data of the transmission line within a preset time period is transmitted to the monitoring platform. Using the temperature measurement data of other transmission lines in the monitoring platform and the transmission line at different times, it is determined that the line temperature deviation coefficient of the transmission line is not abnormal. Based on the line temperature deviation coefficient of the transmission line in the monitoring platform with different identification times, the temperature abnormality diagnosis method of the transmission line is determined. The deviation coefficient of different similar transmission lines is determined by the proportion of deviation times of different similar transmission lines, and the line temperature deviation coefficient of the transmission line is determined by the average value of the deviation coefficients of different similar transmission lines. The abnormality is then determined by combining the preset temperature deviation coefficient threshold.

2. The method for line temperature diagnosis of intelligent signage as described in claim 1, characterized in that, The variation in the transmission power of the transmission line includes the time of power variation and the amount of power variation at different times.

3. The method for line temperature diagnosis of intelligent signage as described in claim 2, characterized in that, The power change moment is the moment when the deviation of the power from the adjacent moment is not within a preset range.

4. The method for line temperature diagnosis of intelligent signage as described in claim 2, characterized in that, The power variation is the power deviation between the time of power variation and the adjacent time.

5. The method for line temperature diagnosis of intelligent signage as described in claim 1, characterized in that, Determining that the power fluctuation of the transmission line meets the requirements specifically includes: Based on the variation of the transmission power of the transmission line, determine the timing of the power variation of the transmission line in different time periods; The power fluctuation periods within the time period are determined by the number of power fluctuation moments. The power fluctuation of the transmission line is determined based on the number of power fluctuation periods to determine whether the power fluctuation meets the requirements.

6. The method for line temperature diagnosis of intelligent signage as described in claim 5, characterized in that, The power fluctuation period is a period in which the number of power fluctuation moments is greater than the preset number of moments.

7. The method for line temperature diagnosis of intelligent signage as described in claim 1, characterized in that, When the power fluctuation of the transmission line does not meet the requirements, the monitoring platform is used to diagnose and handle temperature anomalies.

8. The method for line temperature diagnosis of intelligent signage as described in claim 1, characterized in that, The method for determining the temperature anomaly diagnosis method for the transmission line is as follows: The number of times the transmission line is identified on the monitoring platform is used to determine the number of times the transmission line is identified within a preset time period; After normalizing the line temperature deviation coefficient for different recognition counts, the recognition error weighting coefficients for different recognition counts are obtained. The error coefficient sum is determined by summing the recognition error weighting coefficients of the number of times the transmission line is identified within a preset time period, and the temperature anomaly diagnosis method of the transmission line is determined by using the error coefficient sum.

9. A method for line temperature diagnosis of intelligent signage as described in claim 8, characterized in that, The method for diagnosing temperature anomalies in the transmission line, using the aforementioned deviation coefficient, specifically includes: Based on the error coefficients, a preset diagnostic method corresponding to the error coefficients is determined, and the preset diagnostic method is used to determine the temperature anomaly diagnostic method for the transmission line.

10. A smart signboard, applied to the line temperature diagnostic method for smart signboards as described in any one of claims 1-9, characterized in that, Specifically, it includes: Temperature measurement module, line temperature diagnostic module, data transmission module; The temperature measurement module is responsible for performing temperature measurement processing on the transmission line to obtain temperature data at different times. The line temperature diagnostic module is responsible for using other power transmission lines in the monitoring platform and the temperature measurement data of the power transmission lines at different times to determine whether there is any abnormality in the line temperature deviation coefficient of the power transmission lines. The data transmission module is responsible for data transmission processing between the monitoring platform and the monitoring platform.

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