Method and system for evaluating operating state of cable branch box

Abstract

The application provides a cable branch box operation state evaluation method and system, and relates to the technical field of data processing.The method comprises the following steps: step 1, collecting electrical parameters and physical state parameters of cable terminal heads, connecting wire ears and insulating sleeves in the cable branch box, and preprocessing the electrical parameters and physical state parameters to obtain a standardized state data sequence; step 2, classifying the standardized state data sequence according to the three components of the cable terminal heads, the connecting wire ears and the insulating sleeves to form three independent state data subsets, and establishing an analysis framework comprising M rows and N columns of analysis units for each state data subset.The application can realize accurate evaluation of the operation states of the cable terminal heads, the connecting wire ears and the insulating sleeves, the core components in the cable branch box, and graded early warning of fault risks, thereby generating differentiated operation and maintenance instructions, and effectively improving operation and maintenance efficiency and equipment operation reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a cable branch box operation state evaluation method and system. BACKGROUND

[0002] In the daily operation and maintenance of urban power distribution networks, operation and maintenance personnel often use infrared thermal imagers to routinely detect outdoor cable branch boxes in order to find early overheating hazards of internal components. However, due to the limited internal space of the cable branch box, the cable terminal and the metal conductor components such as the connecting lug are closely adjacent in physical position. This structural feature may pose certain challenges in actual detection.

[0003] For example, when the connecting lug abnormally heats up due to poor contact, the heat will quickly conduct and radiate to the adjacent cable terminal, resulting in the formation of a heating area on the infrared thermal image that covers multiple components and has a blurred boundary. In this case, the existing method may have difficulty in accurately distinguishing and locating the initial heat source component from this mutually superimposed thermal field effect, which sometimes causes inconvenience to accurate judgment of hazards and subsequent targeted maintenance work, affecting the efficiency of operation and maintenance work. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a cable branch box operation state evaluation method and system that can distinguish the operation states of different components and provide clear basis for hazard location.

[0005] To solve the above technical problems, the technical solutions of the present application are as follows:

[0006] In a first aspect, a cable branch box operation state evaluation method is provided, the method comprising:

[0007] Step 1: Collecting electrical parameters and physical state parameters of cable terminal, connecting lug and insulating sleeve in the cable branch box, and preprocessing the electrical parameters and physical state parameters to obtain a standardized state data sequence;

[0008] Step 2: Classifying the standardized state data sequence according to the three components of the cable terminal, the connecting lug and the insulating sleeve to form three independent state data subsets, and for each state data subset, establishing an analysis framework containing M rows and N columns of analysis units;

[0009] Step 3: Mapping the data points in the state data subset to the corresponding analysis units in the analysis framework, constructing the spatial connection relationship between the analysis units based on the geometric relationship of adjacent points, forming an analysis unit group composed of multiple adjacent analysis units, and generating an initial value of the operation state of the analysis unit group according to the distribution of the data points in each analysis unit group;

[0010] Step 4, calculate the local state correction coefficient of the data points in each analysis unit group, correct the initial value of the running state of the corresponding analysis unit group using the local state correction coefficient, and generate the corrected running state value of the analysis unit group;

[0011] Step 5, integrate the corrected running state values of all analysis unit groups to generate the running state level of the cable terminal head, connection wire lug and insulation sleeve, and based on the running state level, obtain the hierarchical early warning signal through fault evolution analysis;

[0012] Step 6, generate the differentiated operation and maintenance instructions of the cable branch box according to the hierarchical early warning signal.

[0013] The second aspect is a cable branch box running state evaluation system, comprising:

[0014] The acquisition module is configured to acquire electrical parameters and physical state parameters of the cable terminal head, connection wire lug and insulation sleeve in the cable branch box, and pre-process the electrical parameters and physical state parameters to obtain a standardized state data sequence;

[0015] The division module is configured to classify the standardized state data sequence according to the cable terminal head, connection wire lug and insulation sleeve, form three independent state data subsets, and establish an analysis framework comprising M rows and N columns of analysis units for each state data subset;

[0016] The mapping module is configured to map the data points in the state data subset to the corresponding analysis units of the analysis framework, construct a spatial connection relationship between the analysis units based on the geometric relationship of adjacent points, form an analysis unit group composed of multiple adjacent analysis units, and generate an initial value of the running state of the analysis unit group according to the distribution of the data points in each analysis unit group;

[0017] The correction module is configured to calculate the local state correction coefficient of the data points in each analysis unit group, correct the initial value of the running state of the corresponding analysis unit group using the local state correction coefficient, and generate the corrected running state value of the analysis unit group;

[0018] The hierarchical module is configured to integrate the corrected running state values of all analysis unit groups to generate the running state level of the cable terminal head, connection wire lug and insulation sleeve, and based on the running state level, obtain the hierarchical early warning signal through fault evolution analysis;

[0019] The difference module is configured to generate the differentiated operation and maintenance instructions of the cable branch box according to the hierarchical early warning signal.

[0020] The third aspect is a computing device, comprising:

[0021] one or more processors;

[0022] a storage device storing one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method.

[0023] In a fourth aspect, a computer-readable storage medium stores a program, which, when executed by a processor, implements the method.

[0024] The above scheme of the present application at least has the following beneficial effects:

[0025] By splitting the data into independent subsets according to the cable terminal head, the connecting wire lug, and the insulating sleeve, establishing a component-specific analysis framework, and constructing unit connections based on the geometric relationship of adjacent points, the operating state of different components can be distinguished, the heat source confusion is avoided, and clear basis is provided for hazard positioning; by calculating the local state correction coefficient to correct the initial state value, the actual operating state of the analysis unit group can be more accurately reflected, and the evaluation error caused by the difference in data distribution is reduced; the independent operating state level of each component is generated, and the state deterioration degree index and the overall fault development probability are calculated based on the fault evolution analysis, so that potential hazards of the components, such as poor contact of the connecting wire lug and insulation deterioration of the cable terminal head, can be identified early, the hierarchical early warning signals can be used for early intervention, and the hazards can be avoided from developing into serious faults; based on the hierarchical early warning signals, differentiated operation and maintenance instructions are generated, the inspection cycle and the maintenance priority are determined according to the fault type, invalid operation and maintenance actions are reduced, the operation and maintenance pertinence and efficiency are improved, and unnecessary operation and maintenance costs are reduced. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a flowchart of a cable branch box operating state evaluation method provided by an embodiment of the present application.

[0027] Figure 2 is a schematic diagram of a cable branch box operating state evaluation system provided by an embodiment of the present application. DETAILED DESCRIPTION

[0028] Exemplary embodiments of the present disclosure will be described below in greater detail with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0029] As Figure 1 shown, an embodiment of the present application proposes a cable branch box operating state evaluation method, which comprises the following steps:

[0030] Step 1, collecting electrical parameters and physical state parameters of cable terminal heads, connecting wire ears and insulating sleeves in the cable branch box, and preprocessing the electrical parameters and physical state parameters to obtain a standardized state data sequence;

[0031] Step 2, classifying the standardized state data sequence according to the three components of the cable terminal head, the connecting wire ear and the insulating sleeve to form three independent state data subsets, and establishing an analysis framework containing M rows and N columns of analysis units for each state data subset;

[0032] Step 3, mapping the data points in the state data subset to the corresponding analysis units of the analysis framework, constructing the spatial connection relationship between the analysis units based on the geometric relationship of adjacent points, forming an analysis unit group composed of multiple adjacent analysis unit groups, and generating an initial value of the running state of the analysis unit group according to the distribution of the data points in each analysis unit group;

[0033] Step 4, calculating the local state correction coefficient of the data points in each analysis unit group, correcting the initial value of the running state of the corresponding analysis unit group using the local state correction coefficient, and generating a corrected running state value of the analysis unit group;

[0034] Step 5, integrating the corrected running state values of all analysis unit groups to generate the running state level of the cable terminal head, the connecting wire ear and the insulating sleeve, and obtaining a graded early warning signal based on the running state level through fault evolution analysis;

[0035] Step 6, generating a differentiated operation and maintenance instruction for the cable branch box according to the graded early warning signal.

[0036] In the embodiment of the present application, by splitting the data into independent subsets according to the cable terminal head, the connecting wire ear and the insulating sleeve, establishing a component-specific analysis framework, and constructing unit connections combined with the geometric relationship of adjacent points, the running state of different components can be distinguished, and the confusion of heat sources is avoided to provide clear basis for hidden danger positioning. By calculating the local state correction coefficient to correct the initial state value, the actual running state of the analysis unit group can be more accurately reflected, and the evaluation error caused by the difference in data distribution can be reduced. The independent running state level of each component is generated, and the state deterioration degree index and the overall fault development probability are calculated by combining fault evolution analysis, which can early identify potential hidden dangers of components, such as poor connection of connecting wire ear and insulation deterioration of cable terminal head. Through the graded early warning signal, early intervention is realized to avoid the development of hidden dangers into serious faults. Based on the graded early warning signal, a differentiated operation and maintenance instruction is generated, the inspection cycle / repair priority is determined according to the fault type, the invalid operation and maintenance actions are reduced, the operation and maintenance pertinence and efficiency are improved, and the unnecessary operation and maintenance cost is reduced.

[0037] In a preferred embodiment of the present application, the step 1 comprises:

[0038] In step 100, three-phase current, ground insulation resistance and partial discharge quantity electrical parameters of the cable terminal head are collected, contact point temperature physical state parameters of the connecting lug are collected, surface temperature and environmental humidity physical state parameters of the insulation sleeve are collected, and the collection of electrical parameters and physical state parameters of the cable branch box internal cable terminal head, connecting lug and insulation sleeve is carried out, for the cable terminal head, current sensors are installed on the A-phase, B-phase and C-phase lines respectively, and three-phase current is obtained in real time through the sensors; an insulation resistance tester is used, one end of the tester is connected to the conductive part of the cable terminal head, the other end is connected to the grounding end of the branch box, and the ground insulation resistance is measured and obtained through the instrument; the coupling sensor of the partial discharge detector is closely attached to the outer surface of the cable terminal head, and the sensor and the terminal head surface are well coupled to collect the partial discharge quantity; for the connecting lug, a contact temperature sensor is selected, the detection end of the sensor is closely attached to the surface of the contact point of the connecting lug, and the sensor and the contact point are gapless, and the temperature of the contact point is collected through the sensor; for the insulation sleeve, an infrared temperature sensor is used, the sensor is aimed at the outer surface of the insulation sleeve, the distance and angle between the sensor and the sleeve surface are kept stable, and the surface temperature of the sleeve is collected in a non-contact manner; meanwhile, a temperature and humidity sensor is installed at the middle position inside the cable branch box to collect the environmental humidity inside the branch box, and the collection of all parameters is continuously carried out according to the pre-set sampling frequency, for example, data is collected once every 5 minutes, and the values of each parameter collected each time are arranged in time sequence to form the original parameter sequence.

[0039] Step 101, the abnormal data of three-phase current, ground insulation resistance, partial discharge quantity, contact point temperature, surface temperature and environmental humidity parameters are eliminated, and each parameter after the abnormal data elimination processing is normalized to obtain each parameter after the normalization processing, specifically including: for the original parameter sequence of the six parameters of three-phase current, ground insulation resistance, partial discharge quantity, contact point temperature, surface temperature and environmental humidity, each parameter is processed, for the original data of each parameter, the mean and standard deviation of all valid data in the sequence are calculated, wherein the mean is the sum of all data divided by the number of data, and the standard deviation is the square root of the sum of the square of each data and the mean divided by the number of data; the data points whose values exceed the range of [mean-3 standard deviation, mean+3 standard deviation] are determined as abnormal data and eliminated from the sequence; for the data gap position after the abnormal data is eliminated, the arithmetic mean of the two effective data before and after the gap position is calculated, that is, the sum of the two data divided by 2, and the average value is used to fill the gap to ensure the continuity of the data sequence; after the abnormal data elimination is completed, the processed valid data sequence of each parameter is normalized, for each data in each parameter sequence, the normalized value is calculated, through the calculation, the numerical range of all parameters is uniformly converted to the interval [0, 1], and each parameter after the normalization processing is obtained.

[0040] Step 102: Perform environmental compensation processing on the normalized parameters to obtain a standardized state data sequence. Specifically, this includes: For the three-phase current of the cable termination, since ambient temperature affects conductor resistance and thus current measurement results, a temperature compensation coefficient needs to be calculated. This coefficient is calculated as: 1 + Temperature Coefficient of Resistance of the Cable Termination Conductor Material × (Internal Ambient Temperature of the Branch Box - 25℃). The temperature coefficient of resistance of the conductor material is determined based on the actual material used; for example, the temperature coefficient of resistance of copper is approximately 0.004℃. The internal ambient temperature of the branch box is obtained through a temperature and humidity sensor. The compensated current parameter = normalized current × the temperature compensation coefficient calculated above. For the surface temperature of the insulating bushing, considering that ambient temperature affects its measurement value through conduction, the following compensation is performed: The compensated surface temperature equals the normalized insulating bushing surface temperature - ambient temperature influence weight coefficient × the temperature influence factor corresponding to the normalized ambient temperature. The ambient temperature influence weight coefficient is determined in advance based on the material of the insulating bushing and the installation environment. The coefficient ranges from 0 to 1. For example, for porcelain insulating bushings in well-ventilated outdoor environments, this coefficient can be 0.3 to 0.4, while for silicone rubber insulating bushings in enclosed indoor environments, this coefficient can be 0.5 to 0.6. The normalized ambient temperature corresponding to the temperature influence factor is obtained by normalizing the ambient temperature. Its value is determined based on the common ambient temperature range of cable branch boxes. Specifically, when the ambient temperature is -20℃, the factor is 0; when the ambient temperature is 60℃, the factor is 1; when the ambient temperature is between -20℃ and 60℃... The value of this factor increases linearly with the increase of ambient temperature, that is, it is obtained by calculating (actual ambient temperature + 20) ÷ (60 + 20). For example, when the ambient temperature is 20℃, the factor is (20 + 20) ÷ 80 = 0.5, and when the ambient temperature is 40℃, the factor is (40 + 20) ÷ 80 = 0.75. In practical applications, the specific value of this factor varies with the actual measured value of the ambient temperature and the above normalized calculation result. The higher the ambient temperature, the larger the value of this factor after normalization, and vice versa.

[0041] For the ground insulation resistance and the partial discharge amount, the environmental humidity has an influence on them, and therefore a humidity compensation coefficient needs to be calculated, the calculation manner of the humidity compensation coefficient is 1-humidity influence coefficient*normalized environmental humidity, wherein the humidity influence coefficient is determined according to the characteristics of the insulation material, and the value range is between 0 and 1, for example, the humidity influence coefficient of the epoxy resin insulation material can be 0.3 to 0.5, and the humidity influence coefficient of the silicone rubber insulation material can be 0.2 to 0.4; the normalized environmental humidity is the normalized environmental humidity value, and the value range is between 0 and 1, the value is 0 when the environmental humidity is 0%, and the value is 1 when the environmental humidity is 100%, and the actual value is calculated through normalization according to the measured value of the environmental humidity, the compensated insulation resistance = normalized insulation resistance*the humidity compensation coefficient calculated above, and the compensated partial discharge amount = normalized partial discharge amount*the humidity compensation coefficient; for the contact point temperature of the connecting wire lug, the same compensation logic as the surface temperature of the insulation sleeve is adopted, that is, the compensated contact point temperature = normalized contact point temperature-environmental temperature influence weight coefficient*normalized environmental temperature corresponding temperature influence factor, wherein the normalized contact point temperature is the normalized contact point temperature value; the environmental temperature influence weight coefficient is determined in advance according to the material and installation environment of the connecting wire lug, and the value range is between 0 and 1, for example, the weight coefficient of the copper connecting wire lug in a well-ventilated environment can be 0.2 to 0.3, and in a closed environment, the weight coefficient can be 0.4 to 0.5; the normalized environmental temperature corresponding temperature influence factor is obtained by normalizing the environmental temperature, and the value range is between 0 and 1, the factor is 0 when the environmental temperature is-20℃, and the factor is 1 when the environmental temperature is 60℃, the actual value is calculated through normalization according to the measured value of the environmental temperature, and the higher the environmental temperature, the larger the value of the factor; after the environmental compensation processing of all the parameters is completed, the compensation results of the parameters are integrated together to form a standardized state data sequence.

[0042] In this embodiment, the key parameters of the key components are collected and multi-layer preprocessed, and the reliability problem caused by the original data disturbed by the environment and the mixed abnormal values is effectively solved, wherein the multi-dimensional parameter collection covers the electrical performance of the cable terminal head, the contact state of the connecting wire lug and the environmental adaptability of the insulation sleeve; the abnormal data elimination eliminates the distortion data caused by accidental interference, such as sensor instantaneous failure, and guarantees the continuity of the data sequence; the normalization processing unifies the numerical scales of different parameters, and avoids the analysis deviation caused by the dimension difference; and the environmental compensation processing corrects the influence of environmental factors such as temperature and humidity on the parameters, so that the data is more in line with the actual operation state of the components.

[0043] In a preferred embodiment of the present application, the step 2 comprises:

[0044] Step 200, classifying the data points representing three-phase current, ground insulation resistance and partial discharge amount in the standardized state data sequence into a cable terminal head state data subset, classifying the data points representing contact point temperature into a connecting wire ear state data subset, and classifying the data points representing surface temperature and environmental humidity into an insulation sleeve state data subset, specifically comprising: on the basis of the attribution relationship between the parameters and the key components in the cable branch box, classifying the standardized state data sequence after environmental compensation, first, integrating the three-phase current data, ground insulation resistance data and partial discharge amount data after environmental compensation into one whole, which is the cable terminal head state data subset, because these three types of parameters directly reflect the running state of the cable terminal head; then, integrating the contact point temperature data after environmental compensation alone to form the connecting wire ear state data subset, this type of parameter is only related to the contact state of the connecting wire ear; finally, integrating the surface temperature data and environmental humidity data after environmental compensation together to form the insulation sleeve state data subset, these two types of parameters jointly reflect the environmental adaptation and running state of the insulation sleeve. In the above three state data subsets, each data point contains three core information, one is the specific value of the data point, that is, the parameter value after environmental compensation; two is the time information when the data is collected, accurate to the specific collection time (such as a certain year, month, day, hour and minute); three is the spatial position information when the data is collected, for example, the data point belongs to a certain length of A phase of the cable terminal head, a certain contact point of the connecting wire ear, a certain height and angle area of the insulation sleeve, etc., to ensure that the data is accurately corresponding to the actual position of the component.

[0045] Step 201, based on the physical structure characteristics of the cable terminal head, the connection ear and the insulation sleeve, the analysis framework of the cable terminal head is established, the analysis framework of the connection ear is established, and the analysis framework of the insulation sleeve is established, wherein each analysis framework contains M rows and N columns of analysis units, specifically including: for the divided cable terminal head state data subset, connection ear state data subset and insulation sleeve state data subset, the dedicated analysis framework is established based on the physical structure characteristics of the corresponding component, so as to realize the accurate matching of data and component space position; for the cable terminal head, because its physical structure presents the characteristics of independent arrangement of phase A, phase B and phase C, the longitudinal unit of the analysis framework is directly divided according to three phases, that is, the number of longitudinal units M is fixed as 3, and the three longitudinal units correspond to phase A, phase B and phase C respectively; the transverse unit of the analysis framework is divided along the length direction of the cable terminal head according to the principle of uniform interval, and the number of transverse units N needs to be determined according to the actual length of the cable terminal head, for example, when the actual length of the cable terminal head is 50 centimeters, it can be set that every 10 centimeters is an interval to divide the transverse unit, and at this time the number of transverse units N is 5, thereby forming a 3x5 cable terminal head analysis framework, and each analysis unit in the framework corresponds to a specific length section of a certain phase of the cable terminal head, for example, the analysis unit of the first row and the first column corresponds to the 0-10 centimeter section of phase A, the analysis unit of the second row and the second column corresponds to the 10-20 centimeter section of phase B, and so on, so as to ensure that each unit can accurately match the specific space area of the cable terminal head.

[0046] For the connection ear, the core feature of its physical structure is that there are multiple connection nodes connected with the cable terminal head and the busbar, so the longitudinal unit of the analysis framework is divided according to the actual number of connection nodes, that is, the number of longitudinal units M is equal to the total number of connection nodes of the connection ear; the transverse unit of the analysis framework is divided along the extension direction of the connection ear (from the root part connected with the cable terminal head to the end part connected with the busbar), which is divided into three parts of root part, middle part and end part, that is, the number of transverse units N is fixed as 3, thereby forming an Mx3 connection ear analysis framework, and each analysis unit in the framework corresponds to a specific part of a certain connection node of the connection ear, for example, the analysis unit of the first row and the first column corresponds to the root part of the first connection node, and the analysis unit of the third row and the second column corresponds to the middle part of the third connection node, so as to ensure that the unit accurately corresponds to the specific connection part of the connection ear.

[0047] For the insulating sleeve, its physical structure is cylindrical, and the functions and state distributions of the upper and lower ends are different, so the longitudinal units of the analysis framework are divided along the height direction of the insulating sleeve, and are divided into four parts, i.e., top, upper middle, lower middle and bottom, so that the number of longitudinal units M is fixed as 4; the lateral units of the analysis framework are divided along the circumferential direction of the insulating sleeve, and are distributed according to six uniform angle regions, each angle region corresponds to a central angle of 60 degrees (360 degrees ÷ 6 = 60 degrees), so that the number of lateral units N is fixed as 6, thereby forming an analysis framework of the insulating sleeve with 4 rows and 6 columns, each analysis unit in the framework corresponds to an angle region at a certain height of the insulating sleeve, for example, the analysis unit in the first row and the first column corresponds to the region from 0 to 60 degrees at the top, the analysis unit in the second row and the second column corresponds to the region from 60 to 120 degrees at the upper middle, so as to ensure that the units are accurately matched with the cylindrical space region of the insulating sleeve; finally, each analysis unit in each analysis framework can accurately correspond to a specific space region of the component to which it belongs, and the data points in each state data subset are matched into a specific analysis unit.

[0048] In this embodiment, by classifying data according to components and establishing exclusive analysis frameworks, the state feature confusion problem caused by the proximity of the physical positions of components is effectively solved, data classification clearly separates the parameters of different components, avoids the mutual interference of the state data of the cable terminal head, the connecting wire lug and the insulating sleeve, and ensures the independence of the state evaluation of each component; the analysis framework established based on the physical structure characteristics of the components accurately corresponds the data points to the specific space region of the components, converts the abstract parameter data into locatable space state information, and improves the pertinence and positioning accuracy of the state evaluation.

[0049] In a preferred embodiment of the present application, the step 3 comprises:

[0050] Step 300, mapping the data points in the cable terminal head state data subset, the connecting wire lug state data subset and the insulating sleeve state data subset to the corresponding analysis units of the cable terminal head analysis framework, the connecting wire lug analysis framework and the insulating sleeve analysis framework according to the spatial position relationship, specifically including: mapping the data points in the three state data subsets of the cable terminal head, the connecting wire lug and the insulating sleeve into the analysis units of the corresponding analysis framework according to the spatial position information carried by each of them, specifically, for the data points in the cable terminal head state data subset, if the spatial position information is clear as the 10 to 20 centimeter segment of phase A, then in the 3-row 5-column cable terminal head analysis framework, the longitudinal unit corresponding to phase A is the first row, and the transverse unit corresponding to the 10 to 20 centimeter segment is the second column, so the data point is matched into the analysis unit of the first row and the second column; for the data points in the connecting wire lug state data subset, if the spatial position information is the middle part of the second connecting node, in the M-row 3-column connecting wire lug analysis framework, the longitudinal unit corresponding to the second connecting node is the second row, and the transverse unit corresponding to the middle part is the second column, so the data point is matched into the analysis unit of the second row and the second column; for the data points in the insulating sleeve state data subset, if the spatial position information is the 60 to 120 degree region of the upper middle part, in the 4-row 6-column insulating sleeve analysis framework, the longitudinal unit corresponding to the upper middle part is the second row, and the transverse unit corresponding to the 60 to 120 degree region is the second column, so the data point is matched into the analysis unit of the second row and the second column, through such a mapping mode, each data point can be accurately corresponded to the analysis unit of the component analysis framework.

[0051] Step 301, based on the spatial adjacency relationship of the analysis unit, the connection between the analysis units is constructed, and the adjacent analysis units with shared boundaries are connected to form an analysis unit group, specifically including: based on the spatial adjacency relationship between the analysis units, the connection is constructed, and then the analysis unit group is formed. The spatial adjacency relationship here specifically refers to the existence of a shared boundary between two analysis units, including horizontally adjacent (sharing a horizontal boundary) and vertically adjacent (sharing a vertical boundary) in the horizontal direction, but not including only the case of vertex contact with each other, for example, in the cable terminal head analysis framework, the analysis unit in the first row and the first column (corresponding to the 0-10 cm segment of phase A) and the analysis unit in the first row and the second column (corresponding to the 10-20 cm segment of phase A) share a vertical boundary due to vertical adjacency and belong to adjacent units; at the same time, the analysis unit in the first row and the first column and the analysis unit in the second row and the first column (corresponding to the 0-10 cm segment of phase B) share a horizontal boundary due to horizontal adjacency and also belong to adjacent units. These adjacent analysis units will be connected into a whole. In the connection ear analysis framework, the analysis unit in the first row and the first column (corresponding to the root of the first connection node) and the analysis unit in the first row and the second column (corresponding to the middle of the first connection node) are adjacent due to vertical adjacency and share a vertical boundary, and the analysis unit in the second row and the first column (corresponding to the root of the second connection node) is adjacent due to horizontal adjacency and shares a horizontal boundary. These adjacent units will also be connected into a whole. In this way, all adjacent analysis units with shared boundaries will be connected to form multiple independent analysis unit groups.

[0052] Step 302, according to the numerical characteristics of all data points in the analysis unit group, the initial value of the running state of each analysis unit group is generated by weighted average calculation, specifically including: first, each data point in each analysis unit group is assigned a weight, and the size of the weight is determined by the collection time of the data point. The closer the collection time is to the current time, the greater the weight. Specifically, the weight value is calculated according to the time decay law, for example, the weight of the data point that is t minutes away from the current time is wherein, is a natural constant; since the sum of the initial weights may not be 1, the initial weights of all data points need to be normalized, that is, the maximum weight of each data point is equal to the initial weight of the data point divided by the sum of the initial weights of all data points in the group, so as to ensure that the sum of the maximum weights of all data points in the group is 1; then, the product of the value of each data point and the maximum weight of the data point is calculated, and the sum of the products of all data points in the group is added to obtain the weighted sum. Finally, the weighted sum obtained by the above calculation is determined as the initial value of the running state of the analysis unit group, that is, the initial value of the running state is equal to the sum of the products of the values of all data points in the group and the corresponding maximum weight.

[0053] In this embodiment, the state feature confusion problem caused by the physical position proximity of the components is solved by accurately mapping the data points into the units of the component-specific analysis framework, forming analysis unit groups based on the spatial adjacency relationship, and quantifying the initial state by weighted average; the spatial mapping of the data points and the analysis units directly associates the abstract data with the specific area of the components, avoiding the cross interference of data of different components; the analysis unit groups formed based on the shared boundary can reflect the overall state of the local area of the components, reducing the influence of the data fluctuation of a single unit; in the weighted average calculation of the initial value, the influence of the recent data is highlighted by the time weight, making the initial value more consistent with the current running trend of the components, and relieving the state misjudgment problem caused by the thermal field superposition.

[0054] In a preferred embodiment of the present application, the step 4 comprises:

[0055] In step 400, the spatial center coordinates of all data points in each analysis unit group are calculated, specifically including: first, the spatial coordinates of each data point are determined, and the coordinates are set based on the unit position of the analysis framework; for example, in the cable terminal head analysis framework, the longitudinal units are set as 1, 2 and 3 in sequence for phase A, phase B and phase C, and the transverse units are set as 1, 2, 3, 4 and 5 in sequence for 0-10 cm, 10-20 cm, 20-30 cm, 30-40 cm and 40-50 cm; therefore, the data point coordinates of the 0-10 cm section unit of phase A are (1, 1), the data point coordinates of the 10-20 cm section unit of phase A are (1, 2), and so on; then, the arithmetic mean of the x coordinates of all data points is calculated as the x component of the spatial center coordinates, and the arithmetic mean of the y coordinates of all data points is calculated as the y component of the spatial center coordinates, so as to finally obtain the spatial center coordinates of the analysis unit group as (center x, center y).

[0056] In step 401, the spatial coordinates of the data points in the analysis unit group are decomposed to obtain the first characteristic vector direction and the second characteristic vector direction, specifically including: first, the covariance matrix of the spatial coordinates of the data points is calculated, which contains three key elements, the variance of the x coordinates, the variance of the y coordinates and the covariance of x and y, wherein the variance of the x coordinates is the sum of the squares of the differences between the x coordinates of each data point and the center x, divided by the total number of data points n; the variance of the y coordinates is the sum of the squares of the differences between the y coordinates of each data point and the center y, divided by n; the covariance of x and y is the sum of the products of (x coordinate minus center x) and (y coordinate minus center y) of each data point, divided by n;

[0057] After calculating the covariance matrix, the next step is to perform eigenvalue decomposition on this 2x2 covariance matrix. Since the covariance matrix describes the distribution and correlation characteristics of data points along the x and y coordinate directions, the purpose of eigenvalue decomposition is to extract the most dominant distribution direction of the data from this correlation. Specifically, the eigenvalue decomposition process is achieved by solving the characteristic equation. For a 2x2 covariance matrix C, its characteristic equation has the following form: Its form is ,here The variance of the x-coordinate. Let x represent the covariance of x and y; λ be the eigenvalues ​​to be determined; I be a 2x2 identity matrix, in the form of... To solve this characteristic equation, we first need to calculate the matrix. To determine the determinant, the first step is to determine the matrix. The specific form, soon Subtract each element The element at the corresponding position, i.e. ,therefore Second step calculation The determinant of a matrix is ​​the element in the top left corner of the matrix. With the bottom right element Multiply, then multiply by the top right element. With the bottom left element Multiply them, and finally subtract the latter from the former, that is, the result of the determinant is ( ()( )- The third step is to set the result of this determinant to 0, thus obtaining a quadratic equation in λ, namely ( ()( )- =0, expanding and rearranging the equation into standard quadratic form gives: ( + ) +( The fourth step is to solve this quadratic equation in one variable, based on the quadratic equation... Quadratic formula The coefficient of the quadratic term here coefficient of the first term constant term Substituting these values ​​into the quadratic formula yields two unequal real solutions (since the covariance matrix is ​​a symmetric positive definite matrix, its eigenvalues ​​are all positive real numbers). These two solutions are the λ values ​​that satisfy the characteristic equation, which are the two eigenvalues ​​of the covariance matrix.

[0058] After obtaining the two eigenvalues, substitute each eigenvalue into the relation. Solving for eigenvectors wherein is the eigenvector to be solved, in the form of For example, when the first eigenvalue λ1 is substituted into the relationship, the relationship can be converted into an equation group, wherein is the x component of the eigenvector , and is the y component thereof; The vector obtained by solving the equation group is the eigenvector corresponding to λ1. In the same way, when the second eigenvalue λ2 is substituted into the relationship, the corresponding eigenvector can be obtained; For the two eigenvalues calculated, the one with the larger value is called the first eigenvalue, and the vector corresponding thereto is called the first eigenvector. The one with the smaller value is called the second eigenvalue, and the vector corresponding thereto is called the second eigenvector. The directions of the two eigenvectors directly reflect the spatial distribution characteristics of the data points. The direction of the first eigenvector is the direction in which the data points are most dispersed in space. This is because the size of the first eigenvalue represents the dispersion degree of the data in this direction. The larger the eigenvalue, the more obvious the degree of deviation of the data points from the center in this direction, and the wider the distribution range. For example, if the data points mainly spread along the horizontal direction, the first eigenvector will be close to the horizontal direction, and the corresponding first eigenvalue will be significantly larger than the second eigenvalue. The direction of the second eigenvector is the direction in which the data points are less dispersed. Since the second eigenvalue is smaller than the first eigenvalue, the dispersion degree of the data in this direction is weaker than that of the first eigenvector, and the distribution range is more concentrated. Through the directions of the two eigenvectors, the main extension trend of the data points in the analysis unit group in space can be accurately captured.

[0059] In step 402, the expansion angle between the directions of the first eigenvector and the second eigenvector is calculated, and a state evaluation sector is demarcated based on the expansion angle with the spatial center coordinate as the apex. Specifically, first, the components of the two eigenvectors are determined. The first eigenvector includes the x component and the y component , and the second eigenvector also includes the x component and the y component The calculation of the expansion angle is performed in four steps by the vector dot product method. In the first step, the dot product of the two eigenvectors is calculated. The component of the first eigenvector in the x direction is multiplied by the component of the second eigenvector in the x direction to obtain a product. The component of the first eigenvector in the y direction is multiplied by the component of the second eigenvector in the y direction to obtain another product. The two products are added together, and the result is the dot product. In the second step, the modulus of the two eigenvectors is calculated. For the first eigenvector, the component in the x direction is squared, and the component in the y direction is squared. The two squared results are added together and the square root is taken to obtain the modulus of the first eigenvector. The modulus of the second eigenvector is calculated in the same way, i.e., the component in the x direction is squared and the component in the y direction is squared, and the square root is taken. In the third step, the cosine of the angle is calculated. The dot product obtained in the first step is divided by the product of the modulus of the first eigenvector and the modulus of the second eigenvector, and the result is the cosine of the angle between the two eigenvectors. In the fourth step, the expansion angle is calculated by taking the inverse cosine of the cosine obtained in the third step. The angle obtained is the expansion angle between the first eigenvector and the second eigenvector, and the angle range is between 0 degrees and 180 degrees. After the expansion angle is obtained, the spatial center coordinates of the analysis unit group are taken as the vertices, the directions of the first eigenvector and the second eigenvector are taken as the two edges of the sector, and a region containing the expansion angle is formed. During the division process, the coverage of the sector on the data points needs to be checked. If the sector fails to cover most of the data points in the group (more than 90% need to be covered), the angle between the two edges is appropriately expanded (not more than 180 degrees) with the spatial center coordinates as the vertices until the coverage requirement is met. The region finally determined is the state evaluation sector.

[0060] At step 403, the convex hull boundary of the data points in the state evaluation sector is calculated based on the position distribution of the data points in the state evaluation sector, specifically including: first, determining the initial vertices of the convex hull, among all the data points in the sector, first find the data point with the minimum x coordinate, if there are multiple data points with the same x coordinate, select the one with the minimum y coordinate, then find the data point with the maximum x coordinate, if there are multiple data points with the same x coordinate, select the one with the maximum y coordinate, take these two points as the initial vertices of the convex hull, and the line between the two points as the initial edge of the convex hull; second, adding the vertices of the convex hull point by point, from the remaining data points in the sector, select one point at a time, judge whether the point is outside the current polygon composed of the initial vertices, the judgment method is realized by vector cross product, that is, taking one side of the current polygon, such as the side from vertex A to vertex B, constructing two vectors, one is the vector from vertex A to vertex B (the x coordinate of vertex B minus the x coordinate of vertex A, the y coordinate of vertex B minus the y coordinate of vertex A), the other is the vector from vertex A to the point to be judged (the x coordinate of the point to be judged minus the x coordinate of vertex A, the y coordinate of the point to be judged minus the y coordinate of vertex A); calculate the cross product of the two vectors, use the x component of the first vector x the y component of the second vector minus the y component of the first vector x the x component of the second vector, if the cross product result is positive, it means that the point to be judged is outside the current edge, that is, outside the polygon, if the cross product result is less than or equal to 0, it means that the point to be judged is inside the polygon or on the edge, if the point to be judged is outside, add it to the vertex set of the convex hull, and update the edges of the polygon, that is, replace the original edge with the line from the original edge start point to the point, and the line from the point to the original edge end point, to ensure that the newly formed polygon is still convex (without concave); third, verify that all data points are covered, repeat the second step to judge each remaining data point in the sector, until all data points are checked and included in the current convex polygon, if there are points not included, recheck the judgment process, such as whether the cross product calculation of a certain edge is missed, until all data points are included in the polygon; fourth, determine the convex hull boundary, the edges of the final convex polygon, that is, the lines between adjacent vertices, together constitute the convex hull boundary, these edges are composed of continuous line segments, each line segment has two ends connecting two adjacent convex hull vertices, and the entire boundary has no intersection and no concave, and can tightly wrap all data points in the sector.

[0061] At step 404, according to the perimeter and area parameters of the convex hull boundary, the local state correction coefficient of the analysis unit group is calculated, and the initial value of the operating state of the analysis unit group is corrected using the local state correction coefficient to generate the corrected operating state value of the analysis unit group, which specifically includes: first, calculate the perimeter of the convex hull boundary, list all the vertices of the convex hull in clockwise or counterclockwise order (maintain the same order as when constructing the convex hull), and repeat the first vertex as the last vertex (make the convex hull closed); calculate the length of the line segment between each two adjacent vertices. For a line segment from vertex 1 to vertex 2, first calculate the x-coordinate of vertex 2 minus the x-coordinate of vertex 1 to get the difference in the x-direction, and square the difference. Then calculate the y-coordinate of vertex 2 minus the y-coordinate of vertex 1 to get the difference in the y-direction, and square the difference. Add the two square results and take the square root to get the length of the line segment. Add the lengths of all adjacent line segments to get the perimeter of the convex hull boundary. Second, calculate the area of the convex hull boundary. Use the shoelace formula, list all the vertices of the convex hull in clockwise or counterclockwise order (the order should be consistent with the calculation of the perimeter), and repeat the first vertex as the last vertex. Calculate the total sum of the first group of products. For each vertex, multiply the x-coordinate of the vertex by the y-coordinate of the next vertex to get a product. Add all the products corresponding to the vertices. Calculate the total sum of the second group of products. For each vertex, multiply the y-coordinate of the vertex by the x-coordinate of the next vertex to get a product. Add all the products corresponding to the vertices. Subtract the total sum of the second group of products from the total sum of the first group of products, take the absolute value of the difference, and multiply by 0.5 to get the area of the convex hull boundary.5 is a fixed coefficient in the shoelace formula; the third step is to calculate the local state correction coefficient, the local state correction coefficient = the area of the convex hull ÷ (the perimeter of the convex hull + 1); the reason for adding 1 is that when all data points completely coincide, such as stable equipment state and no data fluctuation, the perimeter of the convex hull is 0, at this time the denominator is 0 + 1 = 1, which can avoid the error of the denominator being 0 in the division operation, and at the same time, when the perimeter is small (the data points are densely distributed), adding 1 can prevent the local state correction coefficient from being too large, and ensure that the correction result is reasonable; the fourth step is to correct the initial value of the running state of the analysis unit group; the initial value of the running state of the analysis unit group is calculated by weighted average in step 302, which reflects the basic running state of the group; now, the local state correction coefficient obtained in the third step is multiplied by the initial value of the running state to obtain the corrected running state value; the logic of this correction is that the local state correction coefficient is determined by the area and the perimeter of the convex hull, which can reflect the spatial distribution characteristics of the data points; if the area of the convex hull is large and the perimeter is small, it means that the data points are densely distributed and cover a wide range, which may correspond to the local state anomaly of the equipment, at this time the local state correction coefficient is large, which can make the initial value of the running state closer to the actual abnormal state; if the area of the convex hull is small and the perimeter is small, it means that the data points are densely distributed and cover a small range, and the state of the equipment is stable, at this time the local state correction coefficient is small, which will appropriately lower the initial value of the running state and reduce the misjudgment caused by data fluctuation; through such calculation and correction, the corrected running state value of the analysis unit group can more accurately reflect the actual running state of the unit group.

[0062] In this embodiment, by analyzing the spatial distribution characteristics of the data points, including the center coordinates, the feature vectors, and the convex hull boundary, the local correction coefficient is calculated to correct the initial value, which improves the accuracy of state evaluation; the spatial center coordinates and the feature vectors can capture the distribution trend of the data points and avoid evaluation deviation caused by local data density; the expansion angle and the convex hull boundary can accurately describe the spatial aggregation characteristics of the data and reflect the real state difference of the local area of the component; the correction coefficient based on the perimeter and the area of the convex hull can effectively correct the error caused by uneven data distribution in the initial value, so that the corrected value can better reflect the actual running state of the analysis unit group, which solves the problem of fuzzy heat source positioning caused by the superposition of the hot field in the background art, and provides a more accurate quantitative basis for distinguishing the state anomalies of different components.

[0063] In a preferred embodiment of the present application, the step 5 comprises:

[0064] Step 500, the cable terminal head analysis framework, connection line ear analysis framework and insulation sleeve analysis framework all analysis unit group correction operation state value is weighted fusion calculation, generates cable terminal head operation state level, connection line ear operation state level and insulation sleeve operation state level, specific including: first, determine the weight of each analysis unit group, weight distribution according to the importance of analysis unit group corresponding component area, this importance is determined by two aspects, one is the component physical structure risk assessment, such as whether the area is directly involved in conduction, whether it is easy to be corroded by environment, whether it is a fault prone area, etc.; Two is to combine the influence degree of each area fault on the overall performance in historical operation and maintenance data; Taking the cable terminal head analysis framework as an example, the longitudinal unit is divided into A phase, B phase and C phase, because the three-phase functions are equal in the circuit, so the weight is 1 / 3; The horizontal unit is divided according to the distance from the joint, the 0 to 10 centimeter section near the joint is the area where the electric field is concentrated and easy to overheat, the weight is set to 0.3, the 10 to 20 centimeter section is next (0.25), the 20 to 30 centimeter section is 0.2, the 30 to 40 centimeter section is 0.15, the 40 to 50 centimeter section is 0.1, ensuring that the total weight of the horizontal unit is 1, and the total weight of all unit groups in a single framework is 1; In the connection line ear analysis framework, the node unit connected with the bus directly bears high voltage current, and the fault is easy to cause the whole power failure, so the weight is set to 0.6; The weight of the node unit connected with the terminal head is set to 0.4, because the top and bottom of the insulation sleeve analysis framework have high sealing requirements and are easy to be wet, the weight of each is set to 0.3; The upper and lower middle parts have small sealing pressure, and the weight of each is set to 0.2, ensuring that the total weight is 1; For each component analysis framework, process the analysis unit group in the framework one by one, that is, multiply the correction operation state value of a certain analysis unit group by the weight of the unit group to obtain the weighted contribution value of the unit group; After the weighted contribution values of all unit groups are calculated, add these values to obtain the total weighted fusion value of the component, the calculation formula is total weighted fusion value = first analysis unit group correction operation state value × its weight + second analysis unit group correction operation state value × its weight +…+ last analysis unit group correction operation state value × its weight; Taking the cable terminal head analysis framework as an example, assuming that the framework contains 3 analysis unit groups, the first unit group correction operation state value is 0.75, the weight is 0.4; The second unit group correction operation state value is 0.6, the weight is 0.3; The third unit group correction operation state value is 0.8, the weight is 0.3, first calculate the weighted contribution value of each unit group, the first unit group weighted contribution value = 0.75 × 0.4 = 0.3; The second unit group weighted contribution value = 0.6 × 0.3 = 0.18; The third unit group weighted contribution value = 0.8 × 0.3 = 0.24, then add the three weighted contribution values to obtain the total weighted fusion value of the cable terminal head = 0.3 + 0.18 + 0.24 = 0.72, Finally, the running state level is determined, the corresponding threshold of the total weighted fusion value and the running state level is set in advance according to the industry operation and maintenance standard and historical fault data, when the total weighted fusion value is greater than or equal to 0.8, the state level is excellent, there is no obvious abnormality, and the performance is stable; when 0.6 is less than the total weighted fusion value and is greater than 0.8, the level is good, and slight fluctuation does not affect normal operation; when 0.4 is less than the total weighted fusion value and is greater than 0.6, the level is medium, there is potential abnormality, and attention is needed; when the total weighted fusion value is less than 0.4, the level is poor, the abnormality is obvious, and the operation may be affected; according to the threshold range to which the total weighted fusion value of each component belongs, the running state level of the cable terminal head, the running state level of the connecting wire lug and the running state level of the insulation sleeve are determined respectively.

[0065] Step 501, the cable terminal head state deterioration degree index, the connecting wire lug state deterioration degree index and the insulation sleeve state deterioration degree index are calculated based on the cable terminal head running state level, the connecting wire lug running state level and the insulation sleeve running state level, specifically including: setting the reference value of each running state level, the reference value needs to reflect the ideal state level under the level, and is positively correlated with the state advantage and disadvantage, the higher the level, the greater the reference value, combining the parameter range of the normal operation of the component, setting the reference value 1.0 corresponding to excellent, representing the ideal state without any decline; 0.8 corresponding to good, representing slight decline, performance retention 80%; 0.5 corresponding to medium, representing moderate decline, performance retention 50%; 0.2 corresponding to poor, representing serious decline, performance close to critical value, then, the state deterioration degree index is calculated, the calculation formula of the index is state deterioration degree index = (ideal state reference value-current state reference value) ÷ ideal state reference value, wherein the ideal state is unified as excellent (reference value 1.0), because excellent is the best state of the component operation, which is used as a reference to clearly reflect the decline degree; for example, if the current running state level of the cable terminal head is good (reference value 0.8), then the state deterioration degree index = (1.0-0.8) ÷ 1.0 = 0.2, which indicates that it has declined by 20% compared with the ideal state; if the state level is poor (reference value 0.2), then the index = (1.0-0.2) ÷ 1.0 = 0.8, which indicates that it has declined by 80%, the index value range is 0 to 1, the greater the value, the more serious the deterioration of the component state.

[0066] At step 502, the overall failure development probability is calculated according to the cable terminal head state deterioration degree index, the connecting wire lug state deterioration degree index and the insulating sleeve state deterioration degree index, specifically including: first, assigning the influence weight of each component, the influence weight is determined according to the contribution degree of each component to the overall failure of the cable branch box, which is obtained by fault tree analysis, i.e. analyzing the probability of overall failure caused by single component failure and the severity of failure consequences, the cable terminal head as the core conductive component, its failure is easy to cause short circuit, power failure and other serious consequences, the influence weight is set to 0.4; the connecting wire lug is responsible for current conduction, poor contact is easy to cause overheating and burning, the influence weight is set to 0.3; the insulating sleeve guarantees the insulation performance, failure is easy to cause electric leakage and breakdown, the influence weight is set to 0.3, the sum of the influence weights of the three components is 1; then the overall failure development probability is calculated, first, the state deterioration degree index of each component is multiplied by its corresponding influence weight to obtain the failure contribution probability of each component, then the failure contribution probabilities of the three components are added, finally multiplied by 100% to convert to percentage form, the calculation formula is: overall failure development probability = (cable terminal head state deterioration degree index × 0.4 + connecting wire lug state deterioration degree index × 0.3 + insulating sleeve state deterioration degree index × 0.3) × 100%, for example, if the cable terminal head index is 0.5, the connecting wire lug is 0.3, and the insulating sleeve is 0.2, then the overall failure development probability = (0.5 × 0.4 + 0.3 × 0.3 + 0.2 × 0.3) × 100% = (0.2 + 0.09 + 0.06) × 100% = 35%, which represents the probability of current overall failure development is 35%.

[0067] At step 503, a graded early warning signal containing a warning level and a fault type is generated based on the overall fault development probability and the state deterioration degree indexes of the cable terminal head, the connecting wire lug and the insulation sleeve, specifically including: according to the size of the overall fault development probability, combining the time law of historical fault occurrence, such as when the probability is greater than or equal to 80%, the fault occurs within 24 hours, setting three early warning thresholds, the first early warning corresponds to the overall fault development probability greater than or equal to 80% (the fault risk is extremely high, which needs to be handled urgently); the second early warning corresponds to 50% less than the overall fault development probability less than 80% (the fault risk is higher, which needs to be handled preferentially); the third early warning corresponds to the overall fault development probability less than 50% (the fault risk is lower, which needs to be paid attention to regularly), then the fault type is determined, the state deterioration degree indexes of the cable terminal head, the connecting wire lug and the insulation sleeve are compared, and the component with the maximum index value is determined as the main fault associated component, because the component has the highest deterioration degree and is the main potential source of overall fault. If the index values of two or more components are the same and are the maximum, they are combined into a comprehensive fault type; for example, if the cable terminal head index is 0.7, the connecting wire lug is 0.4, and the insulation sleeve is 0.3, the fault type is the abnormal state of the cable terminal head; if the cable terminal head and the connecting wire lug indexes are both 0.6, and are greater than the insulation sleeve index of 0.4, the fault type is the comprehensive abnormality of the cable terminal head and the connecting wire lug; if the indexes of the three components are the same, the fault type is a multi-component comprehensive abnormality, finally the determined warning level and fault type are integrated to form a structured early warning signal, for example, the first early warning is the abnormal state of the cable terminal head, the second early warning is the abnormal state of the insulation sleeve, and the third early warning is the comprehensive abnormality of the connecting wire lug and the insulation sleeve, etc., to ensure that the signal can reflect the emergency degree of risk and clearly indicate the problem component.

[0068] In this embodiment, the weighted fusion calculation considers the regional importance of each analysis unit group, avoids state misjudgment caused by single data fluctuation, and ensures the objectivity of the operation state level of each component; the state deterioration degree index converts the qualitative level into quantitative degradation degree, clearly reflects the degradation trend of the component state from the ideal level, and provides a quantitative basis for risk assessment; the overall fault development probability integrates the risk contributions of the three core components, avoids focusing on a single component while ignoring overall fault hazards, and improves the comprehensiveness of risk assessment; the graded early warning signal clearly indicates the warning level and the fault type, reduces the blindness of operation and maintenance decision, and reduces the risk of missed judgment and misjudgment.

[0069] In a preferred embodiment of the present application, the step 6 comprises:

[0070] At step 600, the warning level and fault type in the hierarchical early warning signal are analyzed, and the corresponding operation and maintenance response level is matched according to the warning level, wherein the first-level warning matches the emergency response level, the second-level warning matches the important response level, and the third-level warning matches the general response level; the operation and maintenance measure combination is determined according to the fault type, and the operation and maintenance measure combination includes the inspection cycle adjustment measure, the maintenance priority measure and the maintenance operation measure, specifically including: first, matching the operation and maintenance response level, the response level directly corresponds to the warning level to ensure the timeliness of resource allocation, the first-level warning matches the emergency response level, which needs to immediately start the emergency operation and maintenance process, and preferentially allocates emergency teams, backup equipment and other resources to avoid the expansion of the fault; the second-level warning matches the important response level, which needs to start the operation and maintenance process within 24 hours, and preferentially arranges the regular operation and maintenance team to ensure that the processing is completed before the risk increases; the third-level warning matches the general response level, which is executed according to the regular operation and maintenance cycle, and does not need to allocate additional resources, and only needs to pay attention to in the next regular inspection, then, the operation and maintenance measure combination is determined, and the operation and maintenance measure combination includes three types of inspection cycle adjustment measures, maintenance priority measures and maintenance operation measures, the first type is the inspection cycle adjustment measure, and the inspection interval is shortened according to the warning level to more timely monitor the state change. When the first-level warning occurs, the inspection cycle is adjusted from the regular one time per week to one time per day to ensure that the state fluctuation is mastered every day; when the second-level warning occurs, the inspection cycle is adjusted to one time every 3 days to balance the monitoring frequency and resource input; when the third-level warning occurs, the regular cycle of one time per week is maintained; the second type is the maintenance priority measure, and the sorting of the maintenance task is determined according to the warning level to avoid resource conflicts, the maintenance priority of the first-level warning is the highest, other non-emergency operation and maintenance tasks are suspended, and the first-level warning is preferentially processed; the maintenance priority of the second-level warning is the second, and is preferentially arranged on the premise of not affecting the first-level task; the maintenance priority of the third-level warning is sorted according to the regular, and is integrated into the daily operation and maintenance plan; the third type is the maintenance operation measure, and the targeted operation is matched according to the fault type to directly hit the problem source, if the fault type is the cable terminal head abnormality, the measures include cable terminal head insulation resistance detection (to check insulation aging), terminal head sealing performance inspection (to prevent water and damp), and joint fastening state checking (to avoid poor contact); if it is a connection line ear abnormality, the measures include connection point contact resistance measurement (to detect conductivity), contact point temperature re-measurement (to monitor whether it is overheated), and connection point oxide layer polishing (to remove the poor contact caused by oxidation); if it is an insulation sleeve abnormality, the measures include sleeve surface contamination degree cleaning (to prevent pollution flashover), sleeve insulation performance test (to check insulation failure), and sleeve damage condition inspection (to prevent electric leakage).

[0071] Step 601, based on the operation and maintenance response level and the operation and maintenance measure combination, generate the differentiated operation and maintenance instructions of the cable branch box, specifically including: first, clarify the structure of the operation and maintenance instructions, the instructions need to include three parts of core content, response level requirements (clear resource allocation and emergency degree of processing), specific measure content (list the inspection, maintenance and maintenance operations to be performed), execution time limit (specify the time node of starting and completing), ensure that the operation and maintenance personnel do not need additional judgment to directly execute, then, according to the combination of the warning level and the fault type to generate specific instructions, for example, if the warning signal is a first-level warning-cable terminal head state abnormality, the operation and maintenance instruction is an emergency response level, immediately allocate the emergency operation and maintenance team to carry the insulation resistance tester and sealing detection tool to the scene; the specific measures include adjusting the inspection cycle to once a day, and executing from the current day; the maintenance priority is set to the highest, and other non-emergency tasks are suspended; carry out the insulation resistance detection of the cable terminal head, the sealing performance check, and the joint fastening state check; all operations need to be started within 2 hours, and the preliminary troubleshooting and feedback results need to be completed within 4 hours; if the warning signal is a second-level warning-connection line ear state abnormality, the operation and maintenance instruction is an important response level, and the regular operation and maintenance team is arranged to execute within 24 hours; the specific measures include adjusting the inspection cycle to once every 3 days, and executing from the next day; the maintenance priority is set to the second highest, and is higher than the third-level warning task; carry out the connection point contact resistance measurement, temperature re-measurement, and oxidation layer polishing; the operation is started within 24 hours, and the maintenance report is submitted within 12 hours after completion; if the warning signal is a third-level warning-insulation sleeve state abnormality, the operation and maintenance instruction is a general response level, and is executed by the daily operation and maintenance team according to the normal process; the specific measures include maintaining the inspection cycle to once a week; the maintenance priority is sorted according to the normal process; carry out the sleeve surface cleaning and insulation performance test; complete in the next regular operation (within 7 days), and the results are included in the weekly operation and maintenance report.

[0072] In this embodiment, the operation and maintenance response level is matched according to the warning level, which avoids over-investment of operation and maintenance resources for low-risk states, and prevents the expansion of faults due to untimely response for high-risk states, thereby realizing the reasonable allocation of operation and maintenance resources; the operation and maintenance measure combination is designed in combination with the fault type, which solves the abnormal problems of the corresponding components in a targeted manner, thereby improving the effectiveness of the operation and maintenance measures; the differentiated operation and maintenance instructions clarify the response requirements, specific measures and execution time limit, so that the operation and maintenance personnel can directly execute without additional judgment, thereby shortening the operation and maintenance preparation time, improving the operation and maintenance efficiency, and further reducing the possibility of the development of the cable branch box fault.

[0073] As shown in Figure 2 The embodiment of the present application also provides a cable branch box operation state evaluation system, which comprises:

[0074] The acquisition module is used for acquiring the electrical parameters and physical state parameters of the cable terminal head, the connection line ear and the insulation sleeve in the cable branch box, and preprocessing the electrical parameters and physical state parameters to obtain a standardized state data sequence.

[0075] a dividing module configured to classify the standardized state data sequence according to the three components of the cable terminal head, the connecting wire lug and the insulating sleeve, form three independent state data subsets, and establish an analysis framework comprising M rows and N columns of analysis units for each state data subset;

[0076] a mapping module configured to map the data points in the state data subsets into corresponding analysis units of the analysis framework, construct spatial connection relationships between the analysis units based on geometric relationships of adjacent points, form analysis unit groups each comprising a plurality of adjacent analysis units, and generate initial values of operating states of the analysis unit groups according to distributions of the data points in each analysis unit group;

[0077] a correcting module configured to calculate local state correction coefficients of the data points in each analysis unit group, correct the initial values of operating states of the corresponding analysis unit group using the local state correction coefficients, and generate corrected operating state values of the analysis unit groups;

[0078] a grading module configured to integrate the corrected operating state values of all the analysis unit groups, generate operating state grades of the cable terminal head, the connecting wire lug and the insulating sleeve, and obtain a grading early warning signal through fault evolution analysis based on the operating state grades;

[0079] a difference module configured to generate a difference operation and maintenance instruction of the cable branch box according to the grading early warning signal.

[0080] It should be noted that the system corresponds to the above method, and all the implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0081] Embodiments of the present application also provide a computing device, comprising a processor and a memory storing a computer program, wherein the computer program is executed by the processor to perform the above method. All the implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0082] Embodiments of the present application also provide a computer readable storage medium storing instructions, wherein the instructions are executed on a computer to make the computer perform the above method. All the implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0083] The above is the preferred embodiment of the present application. It should be noted that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should also be considered within the scope of protection of the present application.

Claims

1. A method for evaluating the operating status of cable branch boxes, characterized in that, The method includes: Step 1: Collect electrical and physical state parameters of cable terminals, connecting lugs, and insulating sleeves in the cable branch box, and preprocess the electrical and physical state parameters to obtain a standardized state data sequence. Step 2: The standardized state data sequence is classified into three components: cable terminal head, connecting lug, and insulating sleeve, forming three independent state data subsets. For each state data subset, an analysis framework containing M rows and N columns of analysis units is established. Step 3: Map the data points from the cable termination status data subset, connector lug status data subset, and insulating bushing status data subset to the corresponding analysis units in the cable termination analysis framework, connector lug analysis framework, and insulating bushing analysis framework according to their spatial location relationships. The cable termination status data subset includes standardized and normalized data for the three-phase current, insulation resistance to ground, and partial discharge quantity of the cable termination. The connector lug status data subset includes standardized and normalized data for the contact point temperature physical state parameter of the connector lug. The insulating bushing status data subset includes standardized and normalized data for the surface temperature and ambient humidity physical state parameters of the insulating bushing. Based on the spatial adjacency relationship of the analysis units, the connection between analysis units is constructed, and adjacent analysis units with shared boundaries are connected to form analysis unit groups; Based on the numerical characteristics of all data points within the analysis unit group, the initial values ​​of the operating status of each analysis unit group are generated by weighted average calculation. Step 4: For each analysis unit group, calculate the spatial center coordinates of all data points within the analysis unit group; The spatial coordinates of the data points within the analysis unit group are decomposed into features to obtain the directions of the first and second feature vectors. Calculate the extended angle between the directions of the first and second eigenvectors, and delineate the state evaluation sector based on the spatial center coordinates as the vertex and the extended angle. Based on the location distribution of data points within the state assessment sector, calculate the convex hull boundary of the data points in the state assessment sector. Based on the perimeter and area parameters of the convex hull boundary, the local state correction coefficient of the analysis unit group is calculated, and the initial value of the operating state of the analysis unit group is corrected using the local state correction coefficient to generate the corrected operating state value of the analysis unit group. Step 5: Integrate the corrected operating status values ​​of all analysis unit groups to generate the operating status levels of cable terminals, connecting lugs and insulating bushings, and obtain graded early warning signals based on the operating status levels through fault evolution analysis. Step 6: Generate differentiated operation and maintenance instructions for cable branch boxes based on the graded early warning signals.

2. The method for evaluating the operating status of a cable branch box according to claim 1, characterized in that, Step 1 includes: The electrical parameters of the cable terminal head, including three-phase current, insulation resistance to ground, and partial discharge, are collected. The physical parameters of the contact point temperature of the connecting lugs are also collected. The physical parameters of the surface temperature and ambient humidity of the insulating bushing are also collected. Abnormal data removal is performed on parameters such as three-phase current, ground insulation resistance, partial discharge, contact point temperature, surface temperature and ambient humidity. Then, the parameters after abnormal data removal are normalized to obtain the normalized parameters. After normalization, environmental compensation processing is performed on each parameter to obtain a standardized state data sequence.

3. The method for evaluating the operating status of a cable branch box according to claim 2, characterized in that, Step 2 includes: The data points representing three-phase current, insulation resistance to ground, and partial discharge in the standardized state data sequence are classified into the cable terminal head state data subset, the data points representing contact point temperature are classified into the connecting lug state data subset, and the data points representing surface temperature and ambient humidity are classified into the insulating bushing state data subset. An analysis framework is established based on the physical structural characteristics of cable termination heads, an analysis framework is established based on the physical structural characteristics of connecting lugs, and an analysis framework is established based on the physical structural characteristics of insulating sleeves. Each analysis framework contains M rows and N columns of analysis units.

4. The method for evaluating the operating status of a cable branch box according to claim 3, characterized in that, Step 5 includes: The corrected operating status values ​​of all analysis unit groups in the cable terminal head analysis framework, connector lug analysis framework and insulating bushing analysis framework are weighted and fused to generate the operating status levels of cable terminal head, connector lug, and insulating bushing. Based on the operating status level of the cable terminal head, the operating status level of the connecting lug, and the operating status level of the insulating bushing, calculate the deterioration index of the cable terminal head condition, the deterioration index of the connecting lug condition, and the deterioration index of the insulating bushing condition. Calculate the overall fault development probability based on the indicators of cable terminal head condition deterioration, connector lug condition deterioration, and insulating bushing condition deterioration. Based on the overall probability of fault development and indicators of the deterioration of cable terminations, connecting lugs, and insulating bushings, a graded early warning signal is generated, which includes the warning level and fault type.

5. The method for evaluating the operating status of a cable branch box according to claim 4, characterized in that, Step 6 includes: The system analyzes the warning level and fault type in the graded early warning signal, and matches the corresponding operation and maintenance response level according to the warning level. The first-level warning matches the emergency response level, the second-level warning matches the important response level, and the third-level warning matches the general response level. The system determines the operation and maintenance measures combination according to the fault type. The operation and maintenance measures combination includes inspection cycle adjustment measures, maintenance priority measures, and maintenance operation measures. Based on the combination of operation and maintenance response levels and operation and maintenance measures, differentiated operation and maintenance instructions for cable branch boxes are generated.

6. A cable branch box operation status assessment system, wherein the system implements the method as described in any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to acquire electrical and physical state parameters of cable terminals, connecting lugs, and insulating sleeves in the cable branch box, and to preprocess the electrical and physical state parameters to obtain a standardized state data sequence. The segmentation module is used to classify the standardized state data sequence according to three components: cable terminal head, connecting lug, and insulating sleeve, forming three independent state data subsets. For each state data subset, an analysis framework containing M rows and N columns of analysis units is established. The mapping module is used to map data points from the cable terminal head status data subset, connector lug status data subset, and insulating bushing status data subset to corresponding analysis units in the cable terminal head analysis framework, connector lug analysis framework, and insulating bushing analysis framework according to their spatial location relationships. Specifically, the cable terminal head status data subset includes standardized and normalized data of the three-phase current, insulation resistance to ground, and partial discharge quantity of the cable terminal head; the connector lug status data subset includes standardized and normalized data of the contact point temperature physical state parameter of the connector lug; and the insulating bushing status data subset includes standardized and normalized data of the surface temperature and ambient humidity physical state parameters of the insulating bushing. The correction module is used to calculate the spatial center coordinates of all data points within each analysis unit group. The spatial coordinates of the data points within the analysis unit group are decomposed into features to obtain the directions of the first and second feature vectors. Calculate the extended angle between the directions of the first and second eigenvectors, and delineate the state evaluation sector based on the spatial center coordinates as the vertex and the extended angle. Based on the location distribution of data points within the state assessment sector, calculate the convex hull boundary of the data points in the state assessment sector. Based on the perimeter and area parameters of the convex hull boundary, the local state correction coefficient of the analysis unit group is calculated, and the initial value of the operating state of the analysis unit group is corrected using the local state correction coefficient to generate the corrected operating state value of the analysis unit group. The hierarchical module is used to integrate the corrected operating status values ​​of all analysis unit groups, generate the operating status levels of cable terminals, connecting lugs and insulating bushings, and obtain hierarchical early warning signals based on the operating status levels through fault evolution analysis. The differentiation module is used to generate differentiated operation and maintenance instructions for cable branch boxes based on the graded early warning signals.

7. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 5.

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