A cable operation status monitoring system and method based on big data

Abstract

The present invention relates to the field of cable operation monitoring, and specifically to a cable operation status monitoring system and method based on big data, comprising a data entry storage module, a monitoring cycle division module, a construction feature analysis module, an early warning construction feature determination module and an early warning response transmission module; the data entry storage module is used to enter monitoring equipment data within a preset area where the cable laying position is located into a cable monitoring system; the monitoring cycle division module is used to divide a target monitoring cycle and a first control monitoring cycle; the construction feature analysis module is used to extract the cable operation data recorded within the target monitoring period to determine the construction features of the corresponding construction events with an influence relationship; the early warning construction feature determination module is used to analyze the early warning construction features that can be stored in response to the cable operation; the early warning response transmission module is used to construct an associated early warning model when the early warning construction features are not unique, and to judge the early warning response.

Description

Technical Field

[0001] The present invention relates to the technical field of cable operation monitoring, and in particular to a cable operation status monitoring system and method based on big data. Background Art

[0002] In order to meet the needs of large-capacity power transmission in cities, high-voltage and ultra-high-voltage cables with large conductor cross-sections and large current carrying capacity have become the main force of urban power transmission. Large cities in China are planning, building or using high-voltage transmission cable networks. Therefore, the safe, stable and reliable operation of transmission cables in power systems is very important. In the operation and management of power grid equipment, it is necessary to focus on monitoring the conditions of cables and their channels. To ensure the safe and stable operation of cables, it is necessary to improve the level of online monitoring of cable operation status. The prior art has a complete set of monitoring systems for parameter changes during the operation of cables. When the corresponding fluctuation warning threshold is met, an early warning response will be generated. However, this type of monitoring causes data fluctuations through abnormal occurrences, and cannot effectively make reasonable judgments before data abnormalities. In addition, when there are construction events around some cable laying areas, although protective measures will be laid around the cables and will not directly damage the cables, the presence of construction may increase the speed of cable abnormalities or have a certain impact on the safety life of the cables. Summary of the invention

[0003] The purpose of the present invention is to provide a cable operation status monitoring system and method based on big data to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a cable operation status monitoring method based on big data, comprising the following analysis steps:

[0005] Step S100: input the monitoring equipment data within the preset area of ​​the cable laying position into the cable monitoring system, and store the monitoring equipment data and the cable operation data in the same monitoring period; several monitoring periods constitute a monitoring cycle; extract the monitoring period corresponding to the construction event recorded by the monitoring equipment data and the cable operation data meeting the operation standard as the target monitoring period, and the other monitoring periods in which the cable operation meets the standard as the first control monitoring period;

[0006] Step S200: Based on the cable operation data recorded in the first comparison monitoring period, mark the monitoring period in which the monitoring equipment data records the construction event in the target monitoring period as the target monitoring period, extract the cable operation data recorded in the target monitoring period to determine the construction characteristics of the construction event corresponding to the influencing relationship;

[0007] Step S300: extract the warning events stored in the monitoring system, compare the cable operation data corresponding to the target monitoring period when the construction feature is recorded with the cable operation data recorded in the warning event, and analyze the warning construction features that can respond to the storage when the cable is running;

[0008] Step S400: Perform real-time monitoring based on the warning construction features combined with monitoring equipment data, and build a related warning model when the warning construction features are not unique to determine the warning response.

[0009] Further, step S200 includes the following:

[0010] Step S210: Construction event records construction personnel and construction tools; cable operation data refers to the state parameters obtained by the cable monitoring device based on the digital signal analysis method; extract the j-th state parameter value Q in the i-th first monitoring cycle of the record ij , using the formula:

[0011] P ij =(1 / m ij )∑[(Q ij 1 -Q ij 0 ) / T ij ]

[0012] Calculate the average fluctuation index P of the j-th type of state parameter value in the i-th first monitoring period ij , m ij It represents the total number of changes of the j-th state parameter value within the continuous monitoring time in the i-th first monitoring cycle, Q ij 1 represents the j-th state parameter value before each change in the adjacent monitoring time in the i-th first monitoring period, Q ij 0 represents the j-th state parameter value after each change in the adjacent monitoring time in the i-th first monitoring period; T ij Indicates the interval length corresponding to each change of the j-th type of state parameter value in the i-th first monitoring period;

[0013] Step S220: extract N first monitoring periods and calculate the period average fluctuation index P of the j-th type of state parameter value 0j , P 0j =(1 / N)∑P ij ,∑P ij It represents the sum of the average fluctuation index of the j-th type of state parameter values ​​in N first monitoring periods;

[0014] Step S230: Mark the cable operation data recorded in the target monitoring period as the operation data to be analyzed, extract the various state parameter values ​​to be analyzed recorded in the operation data to be analyzed, and calculate the average fluctuation index p of the jth state parameter value to be analyzed corresponding to the target monitoring period j ; The average volatility index p j The periodic average fluctuation index P of the j-th type state parameter value in the first monitoring period 0j Compare the same type and get the difference K j , K j =p j -P 0j ; if K j >K0, K0 is the difference threshold, K0>0; then the state parameter to be analyzed of the corresponding type is marked as a fluctuation parameter;

[0015] Step S240: obtaining the number of types marked as fluctuation parameters in the target monitoring period, and when the number of types is greater than half of the total number of state parameter types to be analyzed, outputting the construction events in the target monitoring period that have an impact on the cable operation data;

[0016] Step S250: extracting the construction tools of the construction event records having an impact relationship, and the location distance and construction duration recorded in the monitoring equipment data as construction features.

[0017] Analyzing the impact of construction events on cable operation data can effectively assist cable monitoring personnel in realizing multi-dimensional safety monitoring of the cable's operating status.

[0018] Furthermore, step S300 includes the following specific processes:

[0019] Step S310: The warning event refers to an event corresponding to an abnormality in the cable operation state and a state parameter value not within the parameter threshold range; extracting the cable operation data of the previous monitoring period corresponding to the warning parameter of the warning event as the control operation data; the warning parameter refers to the state parameter not within the parameter threshold range;

[0020] Step S320: based on the control operation data, a state curve diagram of each warning parameter value changing with time is drawn; a target curve diagram of the cable operation data recorded during the target monitoring period is drawn with the same horizontal and vertical coordinate interval scales as the state curve diagram; the target curve diagram of the state parameter of the same type as the warning parameter is compared, and the distance d between the state curve diagram corresponding to the same type of warning parameter and the target curve diagram corresponding to the state parameter is calculated using a dynamic time warping algorithm, and a distance threshold d0 is set;

[0021] Step S330: When there is d < d0 between the target curve graph corresponding to any type of status parameter and the status curve graph corresponding to the same warning parameter, mark the construction features recorded during the target monitoring period corresponding to the type of status parameter as warning construction features.

[0022] For the target monitoring period with fluctuating data, further confirmation is carried out to narrow down the scope of events that may lead to warning events, so as to extract effective construction features as the basis for warnings, and improve the comprehensiveness and efficiency of real-time cable safety monitoring.

[0023] Further, step S400 includes the following steps:

[0024] When the monitoring device data response records a construction event, extract the construction distance and construction tools recorded in the real-time construction features. The construction distance is calculated from the spatial horizontal distance L0 between the monitoring device and the cable laying position and the shortest distance L3 of the image after the monitoring device data records the monitoring screen is converted. The conversion process is as follows:

[0025] Obtain the focal length f and imaging size w*h of the monitoring device, calculate the horizontal field of view angle a1 of the monitoring device, a1 = 2arctan(w / 2f), obtain the minimum screen distance L1 of the construction target in the monitoring screen at the horizontal field of view angle a1 after the construction event response, and the length L2 of the monitoring screen;

[0026] Use the formula: a2 = (a1 * L1) / L2 to calculate the estimated angle a2 of the construction event at the monitoring device.

[0027] Based on the estimated angle a2 and the spatial vertical distance H1 between the monitoring device and the cable laying position, use the formula: L3 = H1 * tana2 to calculate the shortest distance L3 of the converted image.

[0028] When the warning construction features are the same as those recorded in the real-time construction features, transmit a judgment warning response.

[0029] When the warning construction features are not the same as those recorded in the real-time construction features and the warning construction feature records are not unique, construct a feature function relationship model.

[0030] Further, constructing the feature function relationship model includes the following steps:

[0031] Step S410: Construct a feature function relationship model for each type of status parameter recorded in the warning event; use the construction distance L4 and construction duration T recorded in the warning construction features corresponding to each type of status parameter for the construction event as input quantities, and the distance d affecting the cable operation data of the corresponding construction event as the output quantity, and construct a feature function relationship model D for each status parameter with the same warning parameter.

[0032] D = k1*L4+k2*T+ε;

[0033] Substitute the input and output of the warning construction feature records in different construction events corresponding to the same type of state parameters to solve the corresponding reference coefficients k1, k2 and error term ε; d is the output of the characteristic function relationship model D;

[0034] Step S420: obtaining the real-time construction features for which there is no record of the early warning construction features, substituting the construction distance of the real-time construction features and the real-time construction duration of each unit time interval into the characteristic function relationship model D of each type of state parameter, calculating the real-time distance d1 of the corresponding construction event affecting the cable operation data, and obtaining the maximum distance dmax of each state parameter record of the same type as the early warning parameter;

[0035] Step S430: If d1≤dmax exists when the real-time construction event is in progress, the response satisfies the time corresponding to d1≤dmax as the cutoff time, and the judgment warning response is transmitted at the cutoff time and the state parameter type of the corresponding model is determined; if d1 calculated in the entire process until the end of the real-time construction event is greater than dmax, no warning is given; the judgment warning response refers to the transmission to the cable status monitoring system that there is a risk of affecting the cable operation in the corresponding area of ​​the real-time construction event.

[0036] By analyzing the function model of each type of status parameter, effective predictions can be made for construction events under different circumstances. By using the dynamic time warping algorithm to analyze the similarity of the curve images as the output, the fluctuation relationship between the current construction events affecting the cable operation data and the abnormal event data can be quickly analyzed, and the estimated conclusions can be given directly and effectively, providing safety guidance for cable status monitoring managers, thereby enhancing the comprehensiveness of system monitoring and the safety of cable status monitoring.

[0037] A cable operation status monitoring system based on big data, comprising a data entry storage module, a monitoring cycle division module, a construction feature analysis module, an early warning construction feature determination module and an early warning response transmission module;

[0038] The data entry storage module is used to enter the monitoring equipment data within the preset area of ​​the cable laying position into the cable monitoring system, and store the monitoring equipment data and the cable operation data in the same monitoring period;

[0039] The monitoring cycle division module is used to divide the target monitoring cycle and the first control monitoring cycle;

[0040] The construction feature analysis module is used to extract the cable operation data recorded during the target monitoring period to determine the construction features of the corresponding construction events with influencing relationships;

[0041] The early warning construction feature determination module is used to analyze the early warning construction features that can be stored in response to the operation of the cable;

[0042] The early warning response transmission module is used to conduct real-time monitoring based on the early warning construction characteristics combined with the monitoring equipment data, and to build an associated early warning model to judge the early warning response when the early warning construction characteristics are not unique.

[0043] Further, the construction feature analysis module includes an average fluctuation index calculation unit, a fluctuation parameter analysis unit, an influence relationship determination unit and a construction feature output unit;

[0044] The average fluctuation index calculation unit is used to calculate the average fluctuation index of the state parameter value in the first monitoring period;

[0045] The fluctuation parameter analysis unit is used to mark the corresponding type of state parameter to be analyzed as a fluctuation parameter;

[0046] The influence relationship determination unit is used to output that the construction events in the target monitoring period have an influence relationship on the cable operation data when the number of types is greater than half of the total number of types of state parameters to be analyzed;

[0047] The construction feature output unit is used to extract the construction tools of the construction event records with an impact relationship, as well as the location distance and construction duration recorded by the monitoring equipment data as construction features.

[0048] Furthermore, the early warning construction feature determination module includes a curve graph drawing unit and a curve distance calculation unit;

[0049] The curve drawing unit is used to draw a state curve graph of each warning parameter value changing over time based on the control operation data; and to draw a target curve graph of the cable operation data recorded during the target monitoring period corresponding to the same horizontal and vertical coordinate interval scales as the state curve graph;

[0050] The curve distance calculation unit is used to calculate the curve distance using a dynamic time warping algorithm and output construction features that meet preset requirements as early warning construction features.

[0051] Further, the early warning response transmission module includes a construction distance analysis unit and a function construction unit;

[0052] The construction distance analysis unit is used to analyze and estimate the construction distance in combination with monitoring equipment;

[0053] The function construction unit is used to construct characteristic function relationship models respectively with all types of status parameters recorded in the warning event.

[0054] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention is based on the monitoring equipment in the cable laying area, and the monitoring data recorded by the monitoring equipment is associated with the operation data of the cable in the same monitoring period, so that when a construction event occurs, the cable operation data in the corresponding area can be effectively and quickly extracted, so as to determine whether the monitored cable operation data will be affected; and the warning events recorded by the cable monitoring system are used to analyze whether the state parameters when the construction event occurs are associated with the state parameters before the warning event occurs; and when construction features that cannot be matched from historical data appear in real time, a functional relationship model is constructed to estimate the possible abnormal construction features for early warning; the estimated conclusion is given directly and effectively, and safety guidance is provided for cable status monitoring managers, thereby enhancing the comprehensiveness of system monitoring and the safety of cable status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0056] Figure 1 It is a structural schematic diagram of a cable operation status monitoring system based on big data of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 The present invention provides a technical solution: a cable operation status monitoring method based on big data, comprising the following analysis steps:

[0059] Step S100: input the monitoring equipment data within the preset area of ​​the cable laying position into the cable monitoring system, and store the monitoring equipment data and the cable operation data in the same monitoring period; several monitoring periods constitute a monitoring cycle; extract the monitoring period corresponding to the construction event recorded by the monitoring equipment data and the cable operation data meeting the operation standard as the target monitoring period, and the other monitoring periods in which the cable operation meets the standard as the first control monitoring period;

[0060] Compliance with the standard means that the parameters monitored by the cable are within the parameter threshold range. For example, the partial discharge online monitoring system monitors the partial discharge high-frequency pulse current of the cable in real time, separates and proposes the partial discharge pulse signal through digital signal analysis methods, and analyzes the amplitude and frequency of the partial discharge pulse, sets the warning threshold; the real-time measurement results are compared with the reference value to obtain the relative deviation, and the deviation is compared with the warning threshold. When the degree of cable insulation aging reflected by partial discharge exceeds the set warning threshold, the device gives a warning; therefore, it complies with the standard without giving the above warning.

[0061] Step S200: Based on the cable operation data recorded in the first comparison monitoring period, mark the monitoring period in which the monitoring equipment data records the construction event in the target monitoring period as the target monitoring period, extract the cable operation data recorded in the target monitoring period to determine the construction characteristics of the construction event corresponding to the influencing relationship;

[0062] Step S300: extract the warning events stored in the monitoring system, compare the cable operation data corresponding to the target monitoring period when the construction feature is recorded with the cable operation data recorded in the warning event, and analyze the warning construction features that can respond to the storage when the cable is running;

[0063] Step S400: Perform real-time monitoring based on the warning construction features combined with monitoring equipment data, and build a related warning model when the warning construction features are not unique to determine the warning response.

[0064] Step S200 includes the following:

[0065] Step S210: Construction events record construction personnel and construction tools; monitoring equipment data records construction events based on monitoring equipment response to target construction personnel and construction tools, pre-training the monitoring equipment with data for personnel positioning and construction tool positioning, and identifying and locating targets that meet the response in the monitoring area; cable operation data refers to the state parameters obtained by the cable monitoring device based on the digital signal analysis method; extract the j-th state parameter value Q in the i-th first monitoring period of the record ij , using the formula:

[0066] P ij =(1 / m ij )∑[(Q ij 1 -Q ij 0 ) / T ij ]

[0067] Calculate the average fluctuation index P of the j-th type of state parameter value in the i-th first monitoring period ij , m ijIt represents the total number of changes of the j-th state parameter value within the continuous monitoring time in the i-th first monitoring cycle, Q ij 1 represents the j-th state parameter value before each change in the adjacent monitoring time in the i-th first monitoring period, Q ij 0 represents the j-th state parameter value after each change in the adjacent monitoring time in the i-th first monitoring period; T ij Indicates the interval length corresponding to each change of the j-th type of state parameter value in the i-th first monitoring period;

[0068] Step S220: extract N first monitoring periods and calculate the period average fluctuation index P of the j-th type of state parameter value 0j , P 0j =(1 / N)∑P ij ,∑P ij It represents the sum of the average fluctuation index of the j-th type of state parameter values ​​in N first monitoring periods;

[0069] Step S230: Mark the cable operation data recorded in the target monitoring period as the operation data to be analyzed, extract the various state parameter values ​​to be analyzed recorded in the operation data to be analyzed, and calculate the average fluctuation index p of the jth state parameter value to be analyzed corresponding to the target monitoring period j ; The average volatility index p j The periodic average fluctuation index P of the j-th type state parameter value in the first monitoring period 0j Compare the same type and get the difference K j , K j =p j -P 0j ; if K j >K0, K0 is the difference threshold, K0>0; then the state parameter to be analyzed of the corresponding type is marked as a fluctuation parameter;

[0070] Step S240: obtaining the number of types marked as fluctuation parameters in the target monitoring period, and when the number of types is greater than half of the total number of state parameter types to be analyzed, outputting the construction events in the target monitoring period that have an impact on the cable operation data;

[0071] Step S250: extracting the construction tools of the construction event records having an impact relationship, and the location distance and construction duration recorded in the monitoring equipment data as construction features.

[0072] Analyzing the impact of construction events on cable operation data can effectively assist cable monitoring personnel in realizing multi-dimensional safety monitoring of the cable's operating status.

[0073] Step S300 includes the following specific processes:

[0074] Step S310: An early warning event refers to an event corresponding to an abnormal cable operation state where the state parameter value is not within the parameter threshold range; extract the cable operation data of the previous monitoring period corresponding to the early warning parameter response of the early warning event as the reference operation data; the early warning parameter refers to the state parameter that is not within the parameter threshold range.

[0075] Step S320: Draw a state curve graph showing the change of each early warning parameter value over time based on the reference operation data; draw a target curve graph recording the cable operation data of the target monitoring period with the same horizontal and vertical coordinate interval scales as the state curve graph; the same horizontal and vertical coordinate interval scales mean that the horizontal coordinate is in time units, with an interval scale of 1 min, and the vertical coordinate is in the corresponding unit of the parameter value. When the parameter value is 5 dB, it forms a coordinate system with an interval scale; compare the target curve graph of the state parameter of the same type as the early warning parameter, and use the dynamic time warping algorithm to calculate the distance d between the state curve graph corresponding to the same type of early warning parameter and the target curve graph corresponding to the state parameter, and set the distance threshold d0.

[0076] When using the dynamic time warping algorithm, use the numerical value of the ordinate corresponding to the curve graph as the input sequence.

[0077] Step S330: When there is a situation where d < d0 between the target curve graph corresponding to any type of state parameter and the state curve graph corresponding to the same early warning parameter, mark the construction characteristics recorded in the target monitoring period corresponding to the type of state parameter as the early warning construction characteristics.

[0078] For the target monitoring period with fluctuating data, further confirmation is carried out to narrow the range of events that may cause early warning events, so as to extract effective construction characteristics as the basis for early warning, and improve the universality and efficiency of real-time cable safety monitoring.

[0079] Step S400 includes the following steps:

[0080] When the monitoring device data response records a construction event, extract the construction distance and construction tools recorded by the real-time construction characteristics. The construction distance is calculated from the spatial horizontal distance L0 between the monitoring device and the cable laying position and the shortest distance L3 of the image after the monitoring device data records the monitoring screen are converted; the conversion process is as follows:

[0081] Obtain the focal length f and imaging size w*h of the monitoring device, calculate the horizontal field of view angle a1 of the monitoring device, a1 = 2arctan(w / 2f), obtain the minimum screen distance L1 of the construction target in the monitoring screen at the horizontal field of view angle a1 after the response construction event, and the length L2 of the monitoring screen;

[0082] Use the formula: a2 = (a1 * L1) / L2 to calculate the estimated angle a2 of the construction event at the monitoring device.

[0083] Based on the estimated angle a2 and the spatial vertical distance H1 between the monitoring device and the cable layout position, the formula: L3=H1*tana2 is used to calculate the shortest distance L3 of the converted image;

[0084] When the warning construction feature exists and the real-time construction feature record is the same, the transmission judgment warning response;

[0085] When the early warning construction feature does not exist and is identical to the real-time construction feature record, and the early warning construction feature record is not unique, a feature function relationship model is constructed.

[0086] The coordinate system is constructed with the monitoring device as the origin. When the cable laying direction and the construction direction are on the same side of the vertical coordinate of the coordinate system, the construction distance is L4=|L0-L3|. If they are not on the same side, the construction distance is L4=L0+L3. The positive and negative units affected by the coordinate system are not considered during calculation, and L0 and L3 are both positive numbers.

[0087] Non-uniqueness means that the construction events corresponding to the warning construction characteristic marks are not the same event.

[0088] Building a feature function relationship model includes the following steps:

[0089] Step S410: construct characteristic function relationship models for all types of state parameters recorded in the warning event; take the construction distance L4 and construction duration T recorded in the warning construction characteristics of the construction event corresponding to each type of state parameter as input, and the distance d corresponding to the construction event affecting the cable operation data as output, and construct a characteristic function relationship model D for each state parameter that is the same as the warning parameter.

[0090] D = k1*L4+k2*T+ε;

[0091] Substitute the input and output of the warning construction feature records in different construction events corresponding to the same type of state parameters to solve the corresponding reference coefficients k1, k2 and error term ε; d is the output of the characteristic function relationship model D;

[0092] Step S420: obtaining the real-time construction features for which there is no record of the early warning construction features, substituting the construction distance of the real-time construction features and the real-time construction duration of each unit time interval into the characteristic function relationship model D of each type of state parameter, calculating the real-time distance d1 of the corresponding construction event affecting the cable operation data, and obtaining the maximum distance dmax of each state parameter record of the same type as the early warning parameter;

[0093] Step S430: If d1≤dmax exists when the real-time construction event is in progress, the response satisfies the time corresponding to d1≤dmax as the cutoff time, and the judgment warning response is transmitted at the cutoff time and the state parameter type of the corresponding model is determined; if d1 calculated in the entire process until the end of the real-time construction event is greater than dmax, no warning is given; the judgment warning response refers to the transmission to the cable status monitoring system that there is a risk of affecting the cable operation in the corresponding area of ​​the real-time construction event.

[0094] By analyzing the function model of each type of status parameter, effective predictions can be made for construction events under different circumstances. By using the dynamic time warping algorithm to analyze the similarity of the curve images as the output, the fluctuation relationship between the current construction events affecting the cable operation data and the abnormal event data can be quickly analyzed, and the estimated conclusions can be given directly and effectively, providing safety guidance for cable status monitoring managers, thereby enhancing the comprehensiveness of system monitoring and the safety of cable status monitoring.

[0095] A cable operation status monitoring system based on big data, comprising a data entry storage module, a monitoring cycle division module, a construction feature analysis module, an early warning construction feature determination module and an early warning response transmission module;

[0096] The data entry storage module is used to enter the monitoring equipment data within the preset area of ​​the cable laying position into the cable monitoring system, and store the monitoring equipment data and the cable operation data in the same monitoring period;

[0097] The monitoring cycle division module is used to divide the target monitoring cycle and the first control monitoring cycle;

[0098] The construction feature analysis module is used to extract the cable operation data recorded during the target monitoring period to determine the construction features of the corresponding construction events with influencing relationships;

[0099] The early warning construction feature determination module is used to analyze the early warning construction features that can be stored in response to the operation of the cable;

[0100] The early warning response transmission module is used to conduct real-time monitoring based on the early warning construction characteristics combined with the monitoring equipment data, and to build an associated early warning model to judge the early warning response when the early warning construction characteristics are not unique.

[0101] The construction feature analysis module includes an average fluctuation index calculation unit, a fluctuation parameter analysis unit, an influence relationship determination unit and a construction feature output unit;

[0102] The average fluctuation index calculation unit is used to calculate the average fluctuation index of the state parameter value in the first monitoring period;

[0103] The fluctuation parameter analysis unit is used to mark the corresponding type of state parameter to be analyzed as a fluctuation parameter;

[0104] The influence relationship determination unit is used to output that the construction events in the target monitoring period have an influence relationship on the cable operation data when the number of types is greater than half of the total number of types of state parameters to be analyzed;

[0105] The construction feature output unit is used to extract the construction tools of the construction event records with an impact relationship, as well as the location distance and construction duration recorded by the monitoring equipment data as construction features.

[0106] The early warning construction feature determination module includes a curve drawing unit and a curve distance calculation unit;

[0107] The curve drawing unit is used to draw a state curve graph of each warning parameter value changing over time based on the control operation data; and to draw a target curve graph of the cable operation data recorded during the target monitoring period corresponding to the same horizontal and vertical coordinate interval scales as the state curve graph;

[0108] The curve distance calculation unit is used to calculate the curve distance using a dynamic time warping algorithm and output construction features that meet preset requirements as early warning construction features.

[0109] The early warning response transmission module includes a construction distance analysis unit and a function construction unit;

[0110] The construction distance analysis unit is used to analyze and estimate the construction distance in combination with monitoring equipment;

[0111] The function construction unit is used to construct characteristic function relationship models respectively with all types of status parameters recorded in the warning event.

[0112] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0113] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A cable operation status monitoring method based on big data, characterized in that: Including the following analysis steps: Step S100: Enter the data of the monitoring devices within the preset area where the cable is laid into the cable monitoring system, and store the monitoring device data and the cable operation data corresponding to the same monitoring period; Several monitoring periods constitute a monitoring cycle; Extract the monitoring cycles in which the monitoring device data records construction events and the cable operation data meets the operation standards as the target monitoring cycles, and the remaining monitoring cycles in which the cable operation meets the standards as the first control monitoring cycles; Step S200: Based on the cable operation data recorded in the first control monitoring cycle, mark the monitoring period in which the monitoring device data records construction events during the target monitoring cycle as the target monitoring period, and extract the cable operation data recorded during the target monitoring period to determine the construction characteristics of the construction events corresponding to the influence relationship; The step S200 includes the following: Step S210: The construction event records the construction personnel and construction tools; the cable operation data refers to the state parameters obtained by the cable monitoring device based on the digital signal analysis method; extract the j-th state parameter value Q in the i-th first monitoring cycle of the record ij , using the formula: P ij =(1 / m ij )∑[(Q ij 1 -Q ij 0 ) / T ij ] Calculate the average fluctuation index P of the j-th type of state parameter value in the i-th first monitoring period ij , m ij It represents the total number of changes of the j-th state parameter value within the continuous monitoring time in the i-th first monitoring cycle, Q ij 1 represents the j-th state parameter value before each change in the adjacent monitoring time in the i-th first monitoring period, Q ij 0 represents the j-th state parameter value after each change in the adjacent monitoring time in the i-th first monitoring period; T ij Indicates the interval length corresponding to each change of the j-th type of state parameter value in the i-th first monitoring period; Step S220: extract N first monitoring periods and calculate the period average fluctuation index P of the j-th type of state parameter value 0j , P 0j =(1 / N)∑P ij ,∑P ij It represents the sum of the average fluctuation index of the j-th type of state parameter values ​​in N first monitoring periods; Step S230: Mark the cable operation data recorded in the target monitoring period as the operation data to be analyzed, extract the various state parameter values ​​to be analyzed recorded in the operation data to be analyzed, and calculate the average fluctuation index p of the jth state parameter value to be analyzed corresponding to the target monitoring period j ; The average volatility index p j The periodic average fluctuation index P of the j-th type state parameter value in the first monitoring period 0j Compare the same type and get the difference K j , K j =p j -P 0j If K j >K0, K0 is the difference threshold, K0>0; then the state parameter to be analyzed of the corresponding type is marked as a fluctuation parameter; Step S240: Obtain the number of types of fluctuation parameters marked during the target monitoring period. When the number of types is greater than half of the total number of state parameter types to be analyzed, output that the construction events during the target monitoring period have an influence relationship on the cable operation data; Step S250: Extract the construction tools recorded in the construction events with an influence relationship, as well as the position distance and construction duration recorded in the monitoring device data as construction characteristics; Step S300: Extract the warning events stored in the monitoring system, compare the cable operation data corresponding to the target monitoring period when the construction characteristics are recorded with the cable operation data recorded in the warning events, and analyze the warning construction characteristics that can be responded and stored during cable operation; Step S400: Based on the warning construction characteristics, combine with the monitoring device data for real-time monitoring, and when the warning construction characteristics are not unique, construct an associated warning model to make a judgment on warning response.

2. The cable operation status monitoring method based on big data according to claim 1 is characterized in that: The step S300 includes the following specific processes: Step S310: The warning event refers to an event corresponding to an abnormal cable operation state and the state parameter value not being within the parameter threshold range; extract the cable operation data of the previous monitoring period corresponding to the warning parameter response of the warning event as the control operation data; the warning parameter refers to the state parameter not within the parameter threshold range; Step S320: Based on the control operation data, draw a state curve graph showing the change of each warning parameter value over time; Draw a target curve graph of the cable operation data recorded during the target monitoring period with the same horizontal and vertical coordinate interval scales corresponding to the state curve graph; Compare the target curve graphs of the state parameters of the same type as the warning parameters, use the dynamic time warping algorithm to calculate the distance d between the state curve graph corresponding to the same type of warning parameter and the target curve graph corresponding to the state parameter, and set a distance threshold d0; Step S330: When there is a situation where d < d0 between the target curve graph corresponding to any type of state parameter and the state curve graph corresponding to the same warning parameter, mark the construction characteristics recorded during the target monitoring period corresponding to the type of state parameter as the warning construction characteristics.

3. The cable operation status monitoring method based on big data according to claim 2 is characterized in that: The step S400 includes the following steps: When the monitoring device data responds to record a construction event, the construction distance and construction tools recorded by the real-time construction feature are extracted. The construction distance is calculated by the spatial horizontal distance L0 between the monitoring device and the cable laying position and the shortest image distance L3 after the monitoring device data records the monitoring screen after conversion; the conversion process is as follows: Obtain the focal length f and imaging size w*h of the monitoring device, calculate the horizontal field of view angle a1 of the monitoring device, a1=2arctan(w / 2f), and obtain the minimum screen distance L1 and monitoring screen length L2 of the monitoring screen where the construction target is located at the horizontal field of view angle a1 after responding to the construction event; Use the formula: a2=(a1*L1) / L2 to calculate the estimated angle a2 of the monitoring device where the construction event is located. Based on the estimated angle a2 and the spatial vertical distance H1 between the monitoring device and the cable layout position, the formula: L3=H1*tana2 is used to calculate the shortest distance L3 of the converted image; When the warning construction feature exists and the real-time construction feature record is the same, the transmission judgment warning response; When the early warning construction feature does not exist and is identical to the real-time construction feature record, and the early warning construction feature record is not unique, a feature function relationship model is constructed.

4. The cable operation status monitoring method based on big data according to claim 3 is characterized in that: The construction of the characteristic function relationship model comprises the following steps: Step S410: construct characteristic function relationship models for all types of state parameters recorded in the warning event; take the construction distance L4 and construction duration T recorded in the warning construction characteristics of the construction event corresponding to each type of state parameter as input, and the distance d corresponding to the construction event affecting the cable operation data as output, and construct a characteristic function relationship model D for each state parameter that is the same as the warning parameter. D = k1*L4+k2*T+ε; Substitute the input and output of the warning construction feature records in different construction events corresponding to the same type of state parameters to solve the corresponding reference coefficients k1, k2 and error term ε; d is the output of the characteristic function relationship model D; Step S420: obtaining the real-time construction features for which there is no record of the early warning construction features, substituting the construction distance of the real-time construction features and the real-time construction duration of each unit time interval into the characteristic function relationship model D of each type of state parameter, calculating the real-time distance d1 of the corresponding construction event affecting the cable operation data, and obtaining the maximum distance dmax of each state parameter record of the same type as the early warning parameter; Step S430: If d1≤dmax exists when the real-time construction event is in progress, the response satisfies the time corresponding to d1≤dmax as the cutoff time, and the judgment and early warning response is transmitted at the cutoff time and the state parameter type of the corresponding model is determined; if d1 calculated in the entire process until the end of the real-time construction event is greater than dmax, no early warning is given; the judgment and early warning response refers to transmitting to the cable status monitoring system that there is a risk of affecting the cable operation in the corresponding area of ​​the real-time construction event.

5. A cable operation status monitoring system based on big data, such as a cable operation status monitoring method based on big data as described in any one of claims 1 to 4, characterized in that: It includes a data entry storage module, a monitoring period division module, a construction feature analysis module, an early warning construction feature determination module and an early warning response transmission module; The data entry storage module is used to enter the monitoring equipment data within the preset area of ​​the cable laying position into the cable monitoring system, and store the monitoring equipment data and the cable operation data in the same monitoring period in correspondence; The monitoring period division module is used to divide the target monitoring period and the first control monitoring period; The construction feature analysis module is used to extract the cable operation data recorded during the target monitoring period to determine the construction features of the construction events corresponding to the influencing relationship; The early warning construction feature determination module is used to analyze the early warning construction features that can be stored in response to the operation of the cable; The early warning response transmission module is used to perform real-time monitoring based on the early warning construction characteristics combined with the monitoring equipment data, and to build an associated early warning model when the early warning construction characteristics are not unique, so as to judge the early warning response.

6. The cable operation status monitoring system based on big data according to claim 5 is characterized by: The construction feature analysis module includes an average fluctuation index calculation unit, a fluctuation parameter analysis unit, an influence relationship determination unit and a construction feature output unit; The average fluctuation index calculation unit is used to calculate the average fluctuation index of the state parameter value in the first monitoring period; The fluctuation parameter analysis unit is used to mark the corresponding type of state parameter to be analyzed as a fluctuation parameter; The influence relationship determination unit is used to output that the construction event in the target monitoring period has an influence relationship on the cable operation data when the number of types is greater than half of the total number of types of state parameters to be analyzed; The construction feature output unit is used to extract the construction tools of the construction event records having an impact relationship, and the location distance and construction duration recorded in the monitoring equipment data as construction features.

7. The cable operation status monitoring system based on big data according to claim 6 is characterized by: The early warning construction feature determination module includes a curve drawing unit and a curve distance calculation unit; The curve drawing unit is used to draw a state curve diagram of each warning parameter value changing over time based on the control operation data; and to draw a target curve diagram of the cable operation data recorded during the target monitoring period corresponding to the same horizontal and vertical coordinate interval scales as the state curve diagram; The curve distance calculation unit is used to calculate the curve distance using a dynamic time warping algorithm and output a construction feature that meets preset requirements as a warning construction feature.

8. The cable operation status monitoring system based on big data according to claim 7 is characterized by: The early warning response transmission module includes a construction distance analysis unit and a function construction unit; The construction distance analysis unit is used to analyze and estimate the construction distance in combination with the monitoring equipment; The function construction unit is used to construct characteristic function relationship models respectively with all types of status parameters recorded in the warning event.

Images