Cable state monitoring method and system for smart power plant

Abstract

The invention relates to the technical field of intelligent power plants, and discloses a cable state monitoring method and system for an intelligent power plant, and the method comprises the steps: obtaining the state data of a target monitoring cable at all collection moments within a preset time period, and enabling the state data to comprise cable surface temperature data and insulating layer partial discharge amount data; respectively determining temperature fluctuation factors of the cable surface temperature data at the acquisition moments within all preset durations; calculating a state monitoring coefficient of the target monitoring cable according to all the temperature fluctuation factors and the corresponding insulating layer partial discharge quantity data; the method comprises the following steps: presetting a preset state monitoring coefficient, judging whether a target monitoring cable has state abnormity according to a relationship between the state monitoring coefficient and the preset state monitoring coefficient, and performing variability analysis on state data, thereby ensuring the state monitoring precision and efficiency of the intelligent power plant cable, and judging whether abnormity exists. Therefore, the cable can be overhauled in advance, and further damage can be avoided.

Description

Technical Field

[0001] This invention relates to the field of smart power plant technology, and more specifically, to a method and system for monitoring the status of cables in a smart power plant. Background Technology

[0002] A smart power plant refers to a modern power plant that utilizes advanced technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) to achieve intelligent monitoring, optimized operation, and autonomous decision-making of power generation equipment. Cables are conductors used in power systems to transmit electrical energy or signals, typically composed of a conductive core, insulation layer, shielding layer, and sheath. In power plants, they perform functions such as power transmission, equipment connection, and data transmission. The intelligent operation of a smart power plant relies on a highly reliable cable network, and real-time monitoring of cable status is a key link in achieving equipment health management. Through intelligent monitoring technology, cable faults can be predicted in advance, ensuring the safe and stable operation of the power plant.

[0003] Existing monitoring technologies mostly rely on periodic inspections or threshold monitoring. Periodic inspections struggle to capture instantaneous changes in cable operating status in a timely manner, resulting in poor real-time performance and accuracy. Threshold monitoring often depends on a single parameter for judgment, lacking the ability to collaboratively analyze multi-source data, leading to problems such as long processing times and low accuracy. Summary of the Invention

[0004] This invention provides a method and system for monitoring the status of cables in a smart power plant. This invention can perform variability analysis on the status data of cables in a smart power plant, ensuring the accuracy and efficiency of the status monitoring of cables in a smart power plant, thereby determining whether there are any abnormalities, and carrying out early inspection and maintenance of the cables to avoid further damage.

[0005] To achieve the above objectives, the present invention provides a cable status monitoring method for smart power plants, comprising: Acquire the status data of the target monitoring cable at all acquisition times within a preset time period, wherein the status data includes cable surface temperature data and insulation layer partial discharge data; Determine the temperature fluctuation factor of the cable surface temperature data at each of the preset time intervals. The condition monitoring coefficient of the target monitoring cable is calculated based on all temperature fluctuation factors and the corresponding partial discharge data of the insulation layer. A preset state monitoring coefficient is set in advance, and the relationship between the state monitoring coefficient and the preset state monitoring coefficient is used to determine whether the target monitoring cable has an abnormal state.

[0006] Furthermore, before determining the temperature fluctuation factor of the cable surface temperature data at each of the preset time intervals, the following steps are also included: A pre-analysis is performed on all cable surface temperature data and insulation partial discharge data, wherein the pre-analysis includes deleting duplicate data and deleting erroneous data; The temperature fluctuation factor is determined based on the pre-analyzed cable surface temperature data.

[0007] Furthermore, when determining the temperature fluctuation factor of the cable surface temperature data at each acquisition time within all preset durations, the following is included: Randomly select a collection time as the feature collection time, and determine the corresponding feature cable surface temperature data; Determine the feature cable surface temperature data group corresponding to the feature cable surface temperature data; The temperature fluctuation factor of the surface temperature data of the characteristic cable is calculated based on the surface temperature data set of the characteristic cable.

[0008] Further, in determining the feature cable surface temperature data group corresponding to the feature cable surface temperature data, the following steps are included: A first preset quantity and a second preset quantity are preset, and the surface temperature data of the left neighboring cable corresponding to the surface temperature data of the feature cable is determined based on the first preset quantity; Based on the second preset quantity, determine the surface temperature data of the right neighboring cable corresponding to the surface temperature data of the feature cable; The surface temperature data group of the characteristic cable is determined based on the surface temperature data of the characteristic cable, the surface temperature data of the left neighboring cable, and the surface temperature data of the right neighboring cable.

[0009] Furthermore, when calculating the temperature fluctuation factor of the surface temperature data of the characteristic cable based on the characteristic cable surface temperature data set, the following steps are included: Using the surface temperature data of the characteristic cable as the cluster center, determine the cluster distance between the surface temperature data of the cable in the characteristic cable surface temperature data group and the surface temperature data of the characteristic cable. Sort all cluster distances from smallest to largest, and select the cable surface temperature data corresponding to the top q1 cluster distances as trend data, wherein the trend data includes the surface temperature data of the feature cable. Polynomial fitting is performed on all trend data to obtain the trend cable surface temperature data curve, wherein the horizontal axis of the cable surface temperature data curve is the acquisition time, and the vertical axis is the corresponding trend cable surface temperature data. Determine the trend slope of the surface temperature data curve of the trend cable; Identify all extreme points of the trend cable surface temperature data curve, perform linear fitting on the trend cable surface temperature data corresponding to each extreme point, and determine the corresponding linear fitting slope. The ratio of the trend slope to the linear fitting slope is used as the temperature fluctuation factor of the characteristic cable surface temperature data.

[0010] Furthermore, when calculating the condition monitoring coefficient of the target monitoring cable based on all temperature fluctuation factors and the corresponding partial discharge data of the insulation layer, the following steps are included: Based on the acquisition time corresponding to each temperature fluctuation factor, the corresponding partial discharge data of the insulation layer is determined; Each temperature fluctuation factor is mapped one-to-one with the corresponding partial discharge data of the insulation layer; A temperature fluctuation factor is randomly extracted as the first temperature fluctuation factor, and the corresponding partial discharge data of the first insulating layer is extracted. A temperature fluctuation factor is randomly extracted as the second temperature fluctuation factor, and the corresponding partial discharge data of the second insulating layer is extracted. Calculate the absolute value of the first difference between the first temperature fluctuation factor and the second temperature fluctuation factor; Determine the maximum and minimum temperature fluctuation factors from all the first temperature fluctuation factors, and calculate the extreme factor difference between the maximum and minimum temperature fluctuation factors. The ratio of the absolute value of the extreme factor difference to the absolute value of the first difference is determined as the first calculation coefficient; Calculate the absolute value of the second difference between the partial discharge data of the first insulating layer and the partial discharge data of the first insulating layer; The maximum and minimum partial discharge data of the first insulating layer are determined from all the partial discharge data of the first insulating layer, and the extreme data difference between the maximum and minimum partial discharge data of the insulating layer is calculated. The ratio of the absolute value of the extreme data difference to the absolute value of the second difference is determined as the second calculation coefficient; The first and second calculated coefficients are weighted and summed to obtain the comprehensive calculated coefficients. The remaining temperature fluctuation factor and the corresponding partial discharge data of the insulation layer are extracted to obtain the corresponding comprehensive calculation coefficient; The status monitoring coefficient of the target monitoring cable is calculated based on all the comprehensive calculation coefficients.

[0011] Furthermore, when calculating the status monitoring coefficient of the target monitoring cable based on all the comprehensive calculation coefficients, the following are included: The condition monitoring coefficient of the target monitoring cable is calculated according to the following formula: ; Where s is the status monitoring coefficient of the target monitoring cable, m is the number of comprehensive calculation coefficients, and k i Let k be the i-th comprehensive calculation coefficient. i+1 Let be the (i+1)th comprehensive calculation coefficient, and j be the mean of all comprehensive calculation coefficients.

[0012] Furthermore, when determining whether the target monitoring cable has an abnormal state based on the relationship between the state monitoring coefficient and the preset state monitoring coefficient, the method includes: When the status monitoring coefficient is less than the preset status monitoring coefficient, it is determined that the target monitoring cable does not have a status abnormality. When the status monitoring coefficient is greater than or equal to the preset status monitoring coefficient, it is determined that the target monitoring cable has an abnormal status.

[0013] To achieve the above objectives, the present invention also provides a cable status monitoring system for smart power plants, comprising: The data acquisition module is used to acquire the status data of the target monitoring cable at all acquisition times within a preset time period, wherein the status data includes cable surface temperature data and insulation layer partial discharge data. The factor determination module is used to determine the temperature fluctuation factor of the cable surface temperature data at all preset time intervals. The coefficient calculation module is used to calculate the status monitoring coefficient of the target monitoring cable based on all temperature fluctuation factors and the corresponding partial discharge data of the insulation layer; The status monitoring module is used to pre-set a preset status monitoring coefficient and determine whether the target monitoring cable has an abnormal status based on the relationship between the status monitoring coefficient and the preset status monitoring coefficient.

[0014] Furthermore, it also includes: The data preprocessing module is used to pre-analyze all cable surface temperature data and insulation partial discharge data, wherein the pre-analysis includes deleting duplicate data and deleting erroneous data; The temperature fluctuation factor is determined based on the pre-analyzed cable surface temperature data.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention discloses a cable condition monitoring method and system for smart power plants. The method acquires the condition data of a target monitoring cable at all acquisition times within a preset time period. The condition data includes cable surface temperature data and insulation partial discharge data. The method determines the temperature fluctuation factor of the cable surface temperature data at each acquisition time within the preset time period. Based on all temperature fluctuation factors and the corresponding insulation partial discharge data, the method calculates the condition monitoring coefficient of the target monitoring cable. A preset condition monitoring coefficient is pre-set. Based on the relationship between the condition monitoring coefficient and the preset condition monitoring coefficient, the method determines whether the target monitoring cable has any condition anomalies. The method performs variability analysis on the condition data, ensuring the accuracy and efficiency of cable condition monitoring in smart power plants, identifying anomalies, and enabling early maintenance of the cables to prevent further damage. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a cable status monitoring method for a smart power plant according to an embodiment of the present invention is shown. Figure 2 A schematic diagram of a cable status monitoring system for a smart power plant according to an embodiment of the present invention is shown. Detailed Implementation

[0017] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0018] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] The following is a description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0022] like Figure 1 As shown, an embodiment of the present invention discloses a cable status monitoring method for a smart power plant, comprising: S110: Obtain the status data of the target monitoring cable at all acquisition times within a preset time period, wherein the status data includes cable surface temperature data and insulation layer partial discharge data. In this embodiment, the preset duration is a specific time period, such as from the 1st second to the 120th second, and the collection time refers to a specific time point, such as the 1st second, the 2nd second, the 4th second, etc., which are not shown one by one here.

[0023] In this embodiment, each acquisition moment corresponds to a cable surface temperature data and an insulation layer partial discharge data.

[0024] S120: Determine the temperature fluctuation factor of the cable surface temperature data at each time point within the preset time period; In some embodiments of this application, before determining the temperature fluctuation factor of the cable surface temperature data collected at all preset time intervals, the method further includes: A pre-analysis is performed on all cable surface temperature data and insulation partial discharge data, wherein the pre-analysis includes deleting duplicate data and deleting erroneous data; The temperature fluctuation factor is determined based on the pre-analyzed cable surface temperature data.

[0025] The beneficial effects of the above technical solution are: the present invention deletes duplicate data and erroneous data, which can ensure the accuracy of the collected data and provide a reliable data foundation for cable status monitoring.

[0026] In some embodiments of this application, determining the temperature fluctuation factor of the cable surface temperature data collected at all preset time intervals includes: Randomly select a collection time as the feature collection time, and determine the corresponding feature cable surface temperature data; Determine the feature cable surface temperature data group corresponding to the feature cable surface temperature data; The temperature fluctuation factor of the surface temperature data of the characteristic cable is calculated based on the surface temperature data set of the characteristic cable.

[0027] In some embodiments of this application, determining the feature cable surface temperature data group corresponding to the feature cable surface temperature data includes: A first preset quantity and a second preset quantity are preset, and the surface temperature data of the left neighboring cable corresponding to the surface temperature data of the feature cable is determined based on the first preset quantity; Based on the second preset quantity, determine the surface temperature data of the right neighboring cable corresponding to the surface temperature data of the feature cable; The surface temperature data group of the characteristic cable is determined based on the surface temperature data of the characteristic cable, the surface temperature data of the left neighboring cable, and the surface temperature data of the right neighboring cable.

[0028] In this embodiment, the first preset quantity is preferably 8, and the second preset quantity is preferably 10. If the number of surface temperature data of the left neighboring cable or the right neighboring cable is less than the preset quantity, the actual quantity shall prevail.

[0029] The beneficial effects of the above technical solution are: the present invention determines the characteristic cable surface temperature data group based on the characteristic cable surface temperature data, the left neighbor cable surface temperature data, and the right neighbor cable surface temperature data, and determines the characteristic cable surface temperature data group based on the characteristic cable surface temperature data and the characteristic cable surface temperature data in the field, laying the foundation for the analysis of changes in cable surface temperature data and ensuring the accuracy of the analysis of changes in cable surface temperature data.

[0030] In some embodiments of this application, calculating the temperature fluctuation factor of the surface temperature data of the characteristic cable based on the characteristic cable surface temperature data set includes: Using the surface temperature data of the characteristic cable as the cluster center, determine the cluster distance between the surface temperature data of the cable in the characteristic cable surface temperature data group and the surface temperature data of the characteristic cable. Sort all cluster distances from smallest to largest, and select the cable surface temperature data corresponding to the top q1 cluster distances as trend data, wherein the trend data includes the surface temperature data of the feature cable. Polynomial fitting is performed on all trend data to obtain the trend cable surface temperature data curve, wherein the horizontal axis of the cable surface temperature data curve is the acquisition time, and the vertical axis is the corresponding trend cable surface temperature data. Determine the trend slope of the surface temperature data curve of the trend cable; Identify all extreme points of the trend cable surface temperature data curve, perform linear fitting on the trend cable surface temperature data corresponding to each extreme point, and determine the corresponding linear fitting slope. The ratio of the trend slope to the linear fitting slope is used as the temperature fluctuation factor of the characteristic cable surface temperature data.

[0031] In this embodiment, the method for determining the cluster distance will not be described in detail here.

[0032] In this embodiment, q1 is preferably 10.

[0033] In this embodiment, extreme points refer to the peak or valley values ​​reached within a local range of data.

[0034] In this embodiment, each extreme point corresponds to a trend cable surface temperature data.

[0035] The beneficial effects of the above technical solution are: the present invention uses the ratio of the trend slope to the linear fitting slope as the temperature fluctuation factor of the characteristic cable surface temperature data, and the temperature fluctuation of the characteristic cable surface temperature data is fed back through the temperature fluctuation factor, thus ensuring the accuracy of cable status monitoring.

[0036] S130: Calculate the condition monitoring coefficient of the target monitoring cable based on all temperature fluctuation factors and the corresponding partial discharge data of the insulation layer; In some embodiments of this application, the calculation of the condition monitoring coefficient of the target monitoring cable based on all temperature fluctuation factors and corresponding partial discharge data of the insulation layer includes: Based on the acquisition time corresponding to each temperature fluctuation factor, the corresponding partial discharge data of the insulation layer is determined; Each temperature fluctuation factor is mapped one-to-one with the corresponding partial discharge data of the insulation layer; A temperature fluctuation factor is randomly extracted as the first temperature fluctuation factor, and the corresponding partial discharge data of the first insulating layer is extracted. A temperature fluctuation factor is randomly extracted as the second temperature fluctuation factor, and the corresponding partial discharge data of the second insulating layer is extracted. Calculate the absolute value of the first difference between the first temperature fluctuation factor and the second temperature fluctuation factor; Determine the maximum and minimum temperature fluctuation factors from all the first temperature fluctuation factors, and calculate the extreme factor difference between the maximum and minimum temperature fluctuation factors. The ratio of the absolute value of the extreme factor difference to the absolute value of the first difference is determined as the first calculation coefficient; Calculate the absolute value of the second difference between the partial discharge data of the first insulating layer and the partial discharge data of the first insulating layer; The maximum and minimum partial discharge data of the first insulating layer are determined from all the partial discharge data of the first insulating layer, and the extreme data difference between the maximum and minimum partial discharge data of the insulating layer is calculated. The ratio of the absolute value of the extreme data difference to the absolute value of the second difference is determined as the second calculation coefficient; The first and second calculated coefficients are weighted and summed to obtain the comprehensive calculated coefficients. The remaining temperature fluctuation factor and the corresponding partial discharge data of the insulation layer are extracted to obtain the corresponding comprehensive calculation coefficient; The status monitoring coefficient of the target monitoring cable is calculated based on all the comprehensive calculation coefficients.

[0037] In this embodiment, each temperature fluctuation factor corresponds to a partial discharge quantity data of the insulating layer.

[0038] In this embodiment, a first weight is configured for the first calculation coefficient, and a second weight is configured for the second calculation coefficient. The weight configuration method includes subjective weighting and objective weighting, which can be selected according to the actual situation. Here, the first weight is preferably 0.4, and the second weight is preferably 0.6.

[0039] In this embodiment, if there is a separate temperature fluctuation factor and the corresponding partial discharge data of the insulation layer, it can be deleted and not included in the calculation.

[0040] In this embodiment, multiple comprehensive calculation coefficients can be obtained according to the above steps.

[0041] The beneficial effects of the above technical solution are as follows: The present invention performs a weighted summation of the first calculation coefficient and the second calculation coefficient to obtain a comprehensive calculation coefficient, which integrates temperature and power generation, comprehensively considers the degree of influence of temperature on power generation, extracts the remaining temperature fluctuation factor and the corresponding partial discharge data of the insulation layer, and obtains the corresponding comprehensive calculation coefficient. The degree of influence is fed back through the comprehensive calculation coefficient, thereby realizing the cable status monitoring of the smart power plant.

[0042] In some embodiments of this application, the calculation of the status monitoring coefficient of the target monitoring cable based on all comprehensive calculation coefficients includes: The condition monitoring coefficient of the target monitoring cable is calculated according to the following formula: ; Where s is the status monitoring coefficient of the target monitoring cable, m is the number of comprehensive calculation coefficients, and k iLet k be the i-th comprehensive calculation coefficient. i+1 Let be the (i+1)th comprehensive calculation coefficient, and j be the mean of all comprehensive calculation coefficients.

[0043] S140: Pre-set a preset status monitoring coefficient, and determine whether the target monitoring cable has an abnormal status based on the relationship between the status monitoring coefficient and the preset status monitoring coefficient.

[0044] In some embodiments of this application, determining whether the target monitoring cable has an abnormal state based on the relationship between the state monitoring coefficient and the preset state monitoring coefficient includes: When the status monitoring coefficient is less than the preset status monitoring coefficient, it is determined that the target monitoring cable does not have a status abnormality. When the status monitoring coefficient is greater than or equal to the preset status monitoring coefficient, it is determined that the target monitoring cable has an abnormal status.

[0045] In this embodiment, the preset state monitoring coefficient is preferably 6, but it can be adjusted adaptively according to the actual situation.

[0046] The beneficial effects of the above technical solution are: the present invention determines whether the target monitoring cable has an abnormal state based on the relationship between the state monitoring coefficient and the preset state monitoring coefficient, ensuring the intuitiveness and accuracy of the judgment of the abnormal state, avoiding the errors and subjectivity of manual judgment, improving the accuracy and efficiency of the state monitoring of cables in smart power plants, and thus carrying out early inspection and maintenance of the cables to avoid further damage.

[0047] To further illustrate the technical concept of this invention, the technical solution of this invention will now be described in conjunction with specific application scenarios.

[0048] Correspondingly, such as Figure 2 As shown, this application also provides a cable status monitoring system for a smart power plant, including: The data acquisition module is used to acquire the status data of the target monitoring cable at all acquisition times within a preset time period, wherein the status data includes cable surface temperature data and insulation layer partial discharge data. The factor determination module is used to determine the temperature fluctuation factor of the cable surface temperature data at all preset time intervals. The coefficient calculation module is used to calculate the status monitoring coefficient of the target monitoring cable based on all temperature fluctuation factors and the corresponding partial discharge data of the insulation layer; The status monitoring module is used to pre-set a preset status monitoring coefficient and determine whether the target monitoring cable has an abnormal status based on the relationship between the status monitoring coefficient and the preset status monitoring coefficient.

[0049] In some embodiments of this application, it also includes: The data preprocessing module is used to pre-analyze all cable surface temperature data and insulation partial discharge data, wherein the pre-analysis includes deleting duplicate data and deleting erroneous data; The temperature fluctuation factor is determined based on the pre-analyzed cable surface temperature data.

[0050] In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0051] Although the invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the embodiments disclosed in this invention can be combined with each other in any way. The fact that not all of these combinations are described in this specification is merely for the sake of brevity and resource conservation.

[0052] It will be understood by those skilled in the art that the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable state monitoring method of a smart power plant, characterized by, The method comprises the following steps: acquiring state data of the target monitoring cable at all collection time points within a preset time period, wherein the state data comprises cable surface temperature data and insulation layer partial discharge quantity data; determining a temperature fluctuation factor of the cable surface temperature data at all collection time points within the preset time period respectively; calculating a state monitoring coefficient of the target monitoring cable according to all temperature fluctuation factors and corresponding insulation layer partial discharge quantity data; pre-setting a preset state monitoring coefficient, and determining whether the target monitoring cable has a state abnormality according to a relationship between the state monitoring coefficient and the preset state monitoring coefficient. 2.The cable condition monitoring method of a smart power plant according to claim 1, wherein, Before determining the temperature fluctuation factor of the cable surface temperature data at all collection time points within the preset time period respectively, the method further comprises the following steps: pre-analyzing all cable surface temperature data and insulation layer partial discharge quantity data, wherein the pre-analysis comprises deleting repeated data and deleting error data; determining the temperature fluctuation factor according to the pre-analyzed cable surface temperature data. 3.The cable condition monitoring method of the smart power plant according to claim 1, wherein, When determining the temperature fluctuation factor of the cable surface temperature data at all collection time points within the preset time period respectively, the method comprises the following steps: randomly extracting a collection time point as a feature collection time point, and determining corresponding feature cable surface temperature data; determining a feature cable surface temperature data group corresponding to the feature cable surface temperature data; calculating the temperature fluctuation factor of the feature cable surface temperature data based on the feature cable surface temperature data group. 4.The cable condition monitoring method of the smart power plant according to claim 3, wherein, When determining the feature cable surface temperature data group corresponding to the feature cable surface temperature data, the method comprises the following steps: pre-setting a first preset number and a second preset number, determining left-neighbor cable surface temperature data corresponding to the feature cable surface temperature data based on the first preset number; determining right-neighbor cable surface temperature data corresponding to the feature cable surface temperature data based on the second preset number; determining the feature cable surface temperature data group according to the feature cable surface temperature data, the left-neighbor cable surface temperature data and the right-neighbor cable surface temperature data. 5.The cable condition monitoring method of the smart power plant according to claim 3, wherein, When calculating the temperature fluctuation factor of the feature cable surface temperature data based on the feature cable surface temperature data group, the method comprises the following steps: determining clustering distances between cable surface temperature data in the feature cable surface temperature data group and the feature cable surface temperature data, taking the feature cable surface temperature data as a clustering center; sorting all clustering distances from small to large, and selecting q1 cable surface temperature data corresponding to the first q1 clustering distances as trend data, wherein the trend data comprises the feature cable surface temperature data; performing polynomial fitting on all trend data to obtain a trend cable surface temperature data curve, wherein the horizontal coordinate of the cable surface temperature data curve is a collection time point, and the vertical coordinate is corresponding trend cable surface temperature data; determining a trend slope of the trend cable surface temperature data curve; identifying all extreme points of the trend cable surface temperature data curve, performing linear fitting on trend cable surface temperature data corresponding to each extreme point to determine a corresponding linear fitting slope; A ratio of the trend slope to the linear fitting slope is taken as a temperature fluctuation factor of the characteristic cable surface temperature data. 6.The cable condition monitoring method of the smart power plant according to claim 5, wherein, In the calculation of the state monitoring coefficient of the target monitoring cable according to all the temperature fluctuation factors and corresponding insulation layer partial discharge quantity data, comprising: According to the acquisition time corresponding to each temperature fluctuation factor, the corresponding insulation layer partial discharge quantity data is determined; Each temperature fluctuation factor is one-to-one corresponding to the corresponding insulation layer partial discharge quantity data; Randomly extract a temperature fluctuation factor as a first temperature fluctuation factor, and extract the corresponding first insulation layer partial discharge quantity data; Randomly extract a temperature fluctuation factor as a second temperature fluctuation factor, and extract the corresponding second insulation layer partial discharge quantity data; Calculate the first difference absolute value of the first temperature fluctuation factor and the second temperature fluctuation factor; Determine the maximum temperature fluctuation factor and the minimum temperature fluctuation factor from all the first temperature fluctuation factors, and calculate the extreme factor difference of the maximum temperature fluctuation factor and the minimum temperature fluctuation factor; Determine the ratio of the extreme factor difference to the first difference absolute value as a first calculation coefficient; Calculate the second difference absolute value of the first insulation layer partial discharge quantity data and the first insulation layer partial discharge quantity data; Determine the maximum insulation layer partial discharge quantity data and the minimum insulation layer partial discharge quantity data from all the first insulation layer partial discharge quantity data, and calculate the extreme data difference of the maximum insulation layer partial discharge quantity data and the minimum insulation layer partial discharge quantity data; Determine the ratio of the extreme data difference to the second difference absolute value as a second calculation coefficient; The first calculation coefficient and the second calculation coefficient are weighted and summed to obtain a comprehensive calculation coefficient; The remaining temperature fluctuation factors and corresponding insulation layer partial discharge quantity data are extracted to obtain corresponding comprehensive calculation coefficients; According to all the comprehensive calculation coefficients, the state monitoring coefficient of the target monitoring cable is calculated. 7.The cable condition monitoring method of the smart power plant according to claim 6, wherein, In the calculation of the state monitoring coefficient of the target monitoring cable according to all the comprehensive calculation coefficients, comprising: The state monitoring coefficient of the target monitoring cable is calculated according to the following formula: ； Wherein, s is the state monitoring coefficient of the target monitoring cable, m is the number of comprehensive calculation coefficients, k i is the i-th comprehensive calculation coefficient, k i+1 is the i+1-th comprehensive calculation coefficient, and j is the mean value of all comprehensive calculation coefficients. 8.The cable condition monitoring method of the smart power plant according to claim 1, wherein, In the judgment of whether the target monitoring cable has state abnormalities according to the relationship between the state monitoring coefficient and the preset state monitoring coefficient, comprising: When the state monitoring coefficient is less than the preset state monitoring coefficient, it is judged that the target monitoring cable does not have state abnormalities; When the state monitoring coefficient is greater than or equal to the preset state monitoring coefficient, it is judged that the target monitoring cable has state abnormalities.

9. A cable state monitoring system of a smart power plant, applied to the cable state monitoring method of the smart power plant according to any one of claims 1-8, characterized in that, Comprising: The data acquisition module is used for acquiring the state data of the target monitoring cable at all acquisition times within a preset time length, wherein the state data includes cable surface temperature data and insulation layer partial discharge quantity data; The factor determination module is used for determining the temperature fluctuation factor of the cable surface temperature data at all acquisition times within the preset time length respectively; The coefficient calculation module is used for calculating the state monitoring coefficient of the target monitoring cable according to all the temperature fluctuation factors and corresponding insulation layer partial discharge quantity data; The coefficient calculation module is used for calculating the state monitoring coefficient of the target monitoring cable according to all the temperature fluctuation factors and corresponding insulation layer partial discharge quantity data; The state monitoring module is configured to preset a preset state monitoring coefficient, and determine whether the target monitoring cable has a state abnormality according to a relationship between the state monitoring coefficient and the preset state monitoring coefficient. 10.The cable condition monitoring system of the smart power plant of claim 9, wherein, Further comprising: The data preprocessing module is configured to pre-analyze all cable surface temperature data and insulation layer partial discharge data, wherein the pre-analysis includes deleting repeated data and deleting error data. A temperature fluctuation factor is determined according to the pre-analyzed cable surface temperature data.

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