Power transmission status monitoring method, system and related equipment

Abstract

The present application relates to a power transmission status monitoring method, system and related equipment. The power transmission status monitoring method includes: obtaining a first characteristic parameter state of a power transmission device; performing an item set search on the first characteristic parameter to obtain a plurality of first frequent item sets; obtaining a feature association rule based on a plurality of the first frequent item sets; obtaining a second frequent item set; obtaining an abnormal cause association rule based on the second frequent item set and the abnormal cause of the power transmission device; obtaining abnormal situation information related to the power transmission device based on the abnormal cause association rule and the feature association rule. The power transmission status monitoring method has high monitoring efficiency and high monitoring accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission, and in particular to a power transmission status monitoring method, system and related equipment. Background Art

[0002] With the development of optical device technology, spectroscopy technology, as a non-destructive and rapid detection method, can qualitatively and quantitatively analyze the object to be tested, is suitable for real-time online monitoring, and has been applied in many fields.

[0003] In related technologies, there are two main ways to monitor the health status of transmission towers:

[0004] (1) The dangerousness index of transmission towers is calculated based on the double-factor hierarchical superposition method. By measuring the position relationship between abnormal points and towers, the healthy transmission status monitoring and early warning of transmission towers are realized. However, this algorithm needs to consider the location of abnormal points, which takes a long time to monitor and makes the monitoring efficiency low.

[0005] (2) Fiber optic sensors are used to monitor the abnormal transmission status of transmission towers. Fiber optic sensors are installed at different heights of the towers and the stress and strain of the towers are monitored to monitor the abnormal transmission status. However, this algorithm requires too one-sided data collection, resulting in large deviations in the monitoring results and low monitoring accuracy. Summary of the invention

[0006] Based on this, it is necessary to provide a power transmission status monitoring method, system, computer equipment, computer-readable storage medium and computer program product with high monitoring efficiency and high monitoring accuracy in order to address the above technical problems.

[0007] In a first aspect, the present application provides a power transmission status monitoring method, the power transmission status monitoring method comprising:

[0008] Acquire a first characteristic parameter of a power transmission device; wherein the first characteristic parameter is used to characterize data associated with a power transmission state of the power transmission device, wherein the power transmission state includes a normal state and an abnormal state;

[0009] Performing an item set search on the first feature parameter to obtain a plurality of first frequent item sets; wherein the first frequent item sets are item sets in the first feature parameter whose support is greater than or equal to a preset support threshold;

[0010] Acquire a feature association rule according to the plurality of first frequent item sets; wherein the feature association rule includes at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold;

[0011] Acquire a second frequent item set; wherein the second frequent item set is a data set of the power transmission device in an abnormal state;

[0012] Acquire an abnormal cause association rule according to the second frequent item set and the abnormal cause of the power transmission equipment;

[0013] According to the abnormal cause association rule and the feature association rule, abnormal situation information related to the power transmission device is acquired; wherein the abnormal situation information is used to characterize the power transmission state of the power transmission device.

[0014] In one embodiment, the step of obtaining a first characteristic parameter of a power transmission device includes:

[0015] Acquire the data to be processed of the power transmission device; wherein the data to be processed includes at least one of the temperature, wind speed, wind direction and wind deflection angle of the power transmission device;

[0016] Dividing the data to be processed into multiple groups of feature data sets;

[0017] Data fusion processing is performed according to the plurality of sets of feature data sets to obtain the first feature parameter.

[0018] In one embodiment, the step of performing data fusion processing according to the plurality of sets of feature data sets to obtain the first feature parameter comprises:

[0019] Obtaining each group of feature data sets for calculation, and obtaining feature fusion data of each group of feature data sets;

[0020] Data fusion processing is performed based on the feature fusion data of the plurality of sets of the feature data sets to obtain the first feature parameter.

[0021] In one embodiment, the step of acquiring feature association rules according to the plurality of first frequent item sets includes:

[0022] Obtaining the confidence of the first frequent itemset;

[0023] At least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold is obtained as the feature association rule.

[0024] In one embodiment, the step of obtaining the second frequent item set includes:

[0025] Acquire a second characteristic parameter of the power transmission device; wherein the second characteristic parameter is data associated with an abnormal state of the power transmission device;

[0026] According to the second characteristic parameter, the second frequent item set is obtained; wherein the second frequent item set is an item set in the second characteristic parameter whose support is greater than or equal to a preset support threshold.

[0027] In one embodiment, the step of acquiring an abnormal cause association rule according to the second frequent item set and the abnormal cause of the power transmission equipment includes:

[0028] Acquire multiple first feature subsets based on the second frequent item set; wherein the first feature subset is a non-empty subset in the second frequent item set;

[0029] Acquire multiple first feature subsets and acquire multiple second feature subsets; wherein the second feature frequent subset is a first feature subset whose support is greater than or equal to a preset support threshold;

[0030] respectively calculating the confidence of the association rule between each of the second feature subsets and the abnormal cause;

[0031] Based on the plurality of the second feature subsets, a plurality of third feature subsets are obtained to construct the abnormal cause association rule; wherein each of the third feature subsets is a second feature subset having a confidence greater than or equal to a confidence threshold.

[0032] In a second aspect, a power transmission status monitoring system is also provided, the system comprising:

[0033] A calculation and analysis module is used to obtain a first characteristic parameter of a power transmission device, wherein the first characteristic parameter is used to characterize data associated with a power transmission state of the power transmission device, wherein the power transmission state includes a normal state and an abnormal state; perform an item set search on the first characteristic parameter to obtain a plurality of first frequent item sets, wherein the first frequent item set is an item set in the first characteristic parameter whose support is greater than or equal to a preset support threshold; obtain a feature association rule based on the plurality of first frequent item sets, wherein the feature association rule includes at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold; obtain a second frequent item set, wherein the second frequent item set is a data set of the power transmission device in an abnormal state; obtain an abnormal cause association rule based on the second frequent item set and the abnormal cause of the power transmission device; and,

[0034] The state monitoring module is used to obtain abnormal situation information related to the power transmission equipment according to the abnormal cause association rule and the feature association rule, wherein the abnormal situation information is used to characterize the power transmission state of the power transmission equipment.

[0035] According to a third aspect, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0036] In a fourth aspect, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0037] In a fifth aspect, a computer program product is also provided, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0038] In the above-mentioned transmission status monitoring method, system, computer equipment, storage medium and computer program product, an association rule algorithm is used to participate in the transmission status monitoring of the transmission equipment, which associates various data related to the transmission status of the transmission equipment and establishes association rules between various data. It can quickly detect abnormal situation information of the transmission equipment, which is conducive to avoiding the influence of environmental factors on the accuracy of abnormal monitoring of the transmission equipment, and effectively improves the accuracy of health monitoring of transmission towers. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the conventional technology, the drawings required for use in the embodiments or the conventional technology descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 is a flow chart of a method for monitoring power transmission status in one embodiment;

[0041] Figure 2 It is a schematic flow chart of a first characteristic parameter calculation step in one embodiment;

[0042] Figure 3 A schematic diagram of a flow chart of a data fusion calculation step in an embodiment;

[0043] Figure 4 A flowchart of a feature association rule acquisition step in one embodiment;

[0044] Figure 5 is a flowchart of a second frequent item set acquisition step in one embodiment;

[0045] Figure 6 for Figure 5 A flow chart of step 504 in FIG.

[0046] Figure 7 is a schematic flow chart of a maximum output power calculation step of a photovoltaic array in one embodiment;

[0047] Figure 8 FIG. 1 is a schematic diagram of the structure of a power transmission status monitoring system in an embodiment. DETAILED DESCRIPTION

[0048] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. Embodiments of the present application are provided in the drawings. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0050] It can be understood that the terms "first", "second", etc. used in the present application can be used in this article to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from another element.

[0051] It should be noted that when an element is considered to be "connected" to another element, it can be directly connected to the other element, or connected to the other element through an intermediate element. In addition, the "connection" in the following embodiments should be understood as "electrical connection", "communication connection", etc. if there is transmission of electrical signals or data between the connected objects.

[0052] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0053] Among them, before executing the power transmission state monitoring method, it is necessary to pre-set a preset support threshold and a preset confidence threshold. The preset support threshold actually refers to the minimum support when calculating the association rule, and the preset confidence threshold actually refers to the minimum confidence when calculating the association rule. The specific values ​​of the preset support threshold and the preset confidence threshold will directly affect the monitoring accuracy of the power transmission state detection method. Therefore, the specific values ​​of the preset support threshold and the preset confidence threshold can be set according to the actual monitoring needs. For example, in some embodiments, the preset support threshold can be set to 0.7, and the preset confidence can be set to 0.6.

[0054] In one embodiment, Figure 1 As shown, a power transmission status monitoring method is provided, which is applied to a power transmission status monitoring system. The method comprises:

[0055] Step 102: Acquire a first characteristic parameter of the power transmission equipment.

[0056] Wherein, in step 102, the first characteristic parameter is used to characterize data associated with the power transmission state of the power transmission device, and the power transmission state includes a normal state and an abnormal state.

[0057] Step 104: perform item set search on the first characteristic parameter to obtain a plurality of first frequent item sets.

[0058] In step 104, the support of each item set of the first feature parameter is calculated, and the support of each item set of the first feature parameter is compared with a preset support threshold, and finally the item set in the first feature parameter whose support is greater than or equal to the preset support threshold is taken as the first frequent item set.

[0059] Step 106: Obtain a second frequent itemset.

[0060] Wherein, in step 106, the second frequent item set is a data set of the power transmission equipment in an abnormal state.

[0061] Step 108: Acquire an abnormal cause association rule according to the second frequent item set and the abnormal cause of the power transmission equipment.

[0062] Step 110: Acquire abnormal situation information related to the power transmission equipment according to the abnormal cause association rule and the feature association rule.

[0063] In step 110, the abnormal situation information is used to characterize the power transmission state of the power transmission equipment.

[0064] In the above-mentioned transmission status monitoring method, system, computer equipment, storage medium and computer program product, an association rule algorithm is used to participate in the transmission status monitoring of the transmission equipment, which associates various data related to the transmission status of the transmission equipment and establishes association rules between various data. It can quickly detect abnormal situation information of the transmission equipment, which is conducive to avoiding the influence of environmental factors on the accuracy of abnormal monitoring of the transmission equipment, and effectively improves the accuracy of health monitoring of transmission towers.

[0065] like Figure 2 As shown, in some embodiments, the step of obtaining the first characteristic parameter of the power transmission device includes:

[0066] Step 202: Obtain data to be processed from the power transmission equipment.

[0067] Wherein, in step 202, the data to be processed includes at least one of the temperature, wind speed, wind direction and wind deflection angle of the power transmission equipment.

[0068] Step 204: divide the data to be processed into multiple groups of feature data sets.

[0069] In step 204, for example, the data to be processed by the power transmission device is divided into two sets of feature data sets. The first feature data set is (x 11 , x 12 , ..., x 1r , where r≤4), the second feature data set is (x 21 , x 22 , ..., x 2r , where r≤4).

[0070] Step 206: Perform data fusion processing according to the multiple sets of feature data sets to obtain a first feature parameter.

[0071] In step 206, for example, the first feature data set and the second feature data set are fused to obtain a first feature parameter.

[0072] The above steps 204-206 implement the batch estimation algorithm to achieve data fusion of the data to be processed. This data processing method can effectively avoid the adverse effects caused by the stable collection of the data to be processed, and can better ensure the accuracy of subsequent state detection.

[0073] Further, such as Figure 3 As shown, in some embodiments, the step of performing data fusion processing according to multiple sets of feature data sets to obtain a first feature parameter includes:

[0074] Step 302: Obtain each set of feature data sets for calculation, and obtain feature fusion data of each set of feature data sets.

[0075] In step 302, taking two sets of feature data sets as an example, the specific calculation process is as follows:

[0076] First, calculate the first feature data set (x 11 , x 12 , ..., x 1r , where r≤4) and the second feature dataset (x 21 , x 22 , ..., x 2r , where r≤4) The calculation formulas are shown in the following equations (1) and (2):

[0077]

[0078]

[0079] Then, based on the average value of the first feature data set and the average value of the second feature data set Calculate the standard deviation φ1 of the first feature data set respectively 2 and the standard deviation φ2 of the second feature data set 2 , the calculation formula is shown in the following formulas (3) and (4):

[0080]

[0081]

[0082] The average value of the first feature data set and standard deviation φ1 2 , and the average value of the second feature data set and standard deviation φ2 2 As feature fusion data.

[0083] Step 304: Perform data fusion processing based on the feature fusion data of the multiple sets of feature data sets to obtain a first feature parameter.

[0084] In step 304, taking two sets of feature data sets as an example, the specific calculation process is as follows:

[0085] The first feature parameter X is calculated based on the feature fusion data, and the calculation formula is shown in the following formula (5):

[0086]

[0087] like Figure 4 As shown, in some embodiments, the step of obtaining feature association rules according to multiple first frequent item sets includes:

[0088] Step 402: Obtain the confidence of the first frequent itemset.

[0089] Wherein, in step 402, if there is a first frequent item set, all non-empty subsets of the first frequent item set P are detected respectively, then an association rule is obtained, and the confidence of the association rule is obtained.

[0090] Step 404: obtaining at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold as a feature association rule.

[0091] In step 404, association rules with confidence greater than or equal to a preset confidence threshold are retained as feature association rules, and other association rules that do not meet the conditions are deleted.

[0092] like Figure 5 As shown, in some embodiments, the step of obtaining the second frequent item set includes:

[0093] Step 502: Acquire a second characteristic parameter of the power transmission device.

[0094] Among them, in step 502, the second characteristic parameter is data associated with the abnormal state of the power transmission equipment; it should be noted that the second characteristic parameter can be obtained after data fusion processing is performed based on at least one data of temperature, wind speed, wind direction and wind deviation angle collected by the power transmission equipment in the abnormal state. The calculation principle of the second characteristic parameter is the same as the calculation principle of the first characteristic parameter, and it will not be elaborated here.

[0095] Step 504: Obtain a second frequent itemset according to the second characteristic parameter.

[0096] In step 504, the second frequent item set is an item set in the second characteristic parameter whose support is greater than or equal to a preset support threshold. Figure 6 As shown in Figure 2, the process of obtaining the second frequent itemset is as follows:

[0097] Step 1: Let Q be the data set of the original transmission tower in abnormal state (i.e., the second characteristic parameter), and the maximum number of data items extracted is n, which can be obtained by searching the i-th record Q i (i=1), assign n to the maximum number of data items a, that is, n=a. Establish the candidate set C1, C2, ..., C n , count all alternatives as 1.

[0098] Step 2, Q i+1 For the second step of searching the records, the number n of such data items is extracted.

[0099] If n>a does not hold, the original alternative item set needs to be replaced, the original data is increased by 1, and the data item set that does not exist in the original data is directly added to the corresponding alternative item set, and the count is 1.

[0100] If n>a holds, establish C a+1 , C a+2 , …, C n , count all candidate items to 1; assign n to the maximum number of data items a, that is, n = a; at the same time, the initial candidate set needs to be changed and counted.

[0101] Step 3: After each record of steps 1 and 2 is scanned, it is necessary to analyze whether the record is the last record of the abnormal state data of the power transmission equipment. If so, the scan ends and the frequent item set that meets the minimum requirements for the abnormal state of the power transmission equipment is output; if not, re-execute step 2.

[0102] Through the above steps 502-504, each data set recorded when scanning the power transmission device in an abnormal state (i.e., the item set in the second characteristic parameter) is realized, and then all candidate item sets are updated and created until the frequent item set of the power transmission device in the abnormal state (i.e., the second frequent item set) is found and the scanning is stopped.

[0103] like Figure 7 As shown, in some embodiments, the step of obtaining an abnormal cause association rule according to the second frequent item set and the abnormal cause of the power transmission equipment includes:

[0104] Step 702: Obtain a plurality of first feature subsets based on the second frequent item set.

[0105] Wherein, in step 702, the first feature subset is a non-empty subset in the second frequent item set.

[0106] Step 704: Acquire multiple first feature subsets and acquire multiple second feature subsets.

[0107] In step 704, the support of multiple first feature subsets is calculated, and the support of each first feature subset is compared with a preset support threshold, and finally the first feature subsets whose support is greater than or equal to the preset support threshold are used as second feature subsets.

[0108] Step 706, respectively calculating the confidence of the association rule between each second feature subset and the abnormal cause.

[0109] In step 706, each second feature subset X is calculated i and abnormal reason R j The association rules between Confidence The confidence The second frequent item set contains X i and R i The number of data records is the same as the number of data records in the second frequent item set that only contains X i The ratio between the number of data records is as shown in the following formula (6):

[0110]

[0111] Step 708: Acquire multiple third feature subsets based on the multiple second feature subsets to construct abnormal cause association rules.

[0112] In step 708, the plurality of second feature subsets X i The confidences of the second feature subsets are compared with the preset confidence thresholds, and the second feature subsets whose confidences are greater than or equal to the confidence thresholds are used as the third feature subsets. The abnormal cause association rules are constructed according to the multiple second feature subsets.

[0113] After step 708, the plurality of third feature subsets in the abnormal cause association rule are arranged in descending order of confidence. The arrangement order of the plurality of third feature subsets in the abnormal cause association rule is used as a criterion for judging the order of the feature association rules in the abnormal cause association rule.

[0114] It should be understood that although Figure 1-Figure 7 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 7 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0115] It should also be noted that in order to facilitate understanding of the calculation principle of confidence and support in the association rule algorithm, the following is an expanded explanation:

[0116] (I) Items and Itemsets

[0117] The values ​​of each field in the data set are inconsistent. Assume that each value is assigned to an item. A set of k items is described by a k-itemset.

[0118] Let item set I = {I1, I2, I3, ... I k}Because the itemset includes k items, I is called a k-itemset.

[0119] (II) Confidence

[0120] Confidence is a characteristic of association rules. The confidence level describes that the database may contain both X and Y, which can be expressed as As shown in the following formula (7):

[0121]

[0122] Therefore, the user sets the minimum confidence (i.e., the preset confidence threshold), which is expressed as:

[0123] (III) Number of supports and degree of support

[0124] The support number represents the frequency of item set X in the transaction database, λ xRepresentative. Support is the number of support items in the set λ x The ratio of the total number of transaction databases is expressed as Support(X). The formula for calculating support is shown in the following formula (8):

[0125] Support(X)=λ x / |H|, (8);

[0126] In the formula, the total number of thing databases is represented by |H|, and the minimum support is represented by min-sup.

[0127] (IV) Association rules

[0128] Association rules describe The implied formula of , where and This rule is used to describe the implicit relationship between databases. The antecedent is X and the consequent is Y.

[0129] The rules that meet the following requirements are called strong association rules, otherwise, they are called weak association rules.

[0130] a. Confidence Not less than the specified minimum confidence That is, the database has When something includes X, it also includes Y.

[0131] b. The support Support(X∪Y) is not less than the specified minimum support min-sup, that is, the number of things in the thing database with Support(X∪Y)*|H| contains both X and Y.

[0132] In one embodiment, Figure 8 As shown, a power transmission status monitoring system 800 is provided, which includes a calculation and analysis module 810 and a status monitoring module 820 connected to the calculation and analysis module 810, wherein:

[0133] The calculation and analysis module 810 is used to obtain a first characteristic parameter of the power transmission device 900, wherein the first characteristic parameter is used to characterize data associated with the power transmission state of the power transmission device 900, and the power transmission state includes a normal state and an abnormal state; perform an item set search on the first characteristic parameter to obtain a plurality of first frequent item sets, wherein the first frequent item set is an item set in the first characteristic parameter whose support is greater than or equal to a preset support threshold; obtain a feature association rule based on the plurality of first frequent item sets, wherein the feature association rule includes at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold; obtain a second frequent item set, wherein the second frequent item set is a data set of the power transmission device in an abnormal state; and obtain an abnormal cause association rule based on the second frequent item set and the abnormal cause of the power transmission device.

[0134] The state monitoring module 820 is used to obtain abnormal situation information related to the power transmission device 900 according to the abnormal cause association rule and the feature association rule, wherein the abnormal situation information is used to characterize the power transmission state of the power transmission device 900.

[0135] In one embodiment, the computing and analysis module 810 is further used to obtain data to be processed of the power transmission equipment, wherein the data to be processed includes at least one of the temperature, wind speed, wind direction and wind deviation angle of the power transmission equipment; divide the data to be processed into multiple groups of feature data sets; and perform data fusion processing based on the multiple groups of feature data sets to obtain a first feature parameter.

[0136] In one embodiment, the calculation and analysis module 810 is further used to obtain each group of feature data sets for calculation, and obtain feature fusion data of each group of feature data sets; perform data fusion processing based on the feature fusion data of multiple groups of feature data sets to obtain a first feature parameter.

[0137] In one embodiment, the calculation and analysis module 810 is further used to obtain the confidence of the first frequent item set; and obtain at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold as a feature association rule.

[0138] In one embodiment, the calculation and analysis module 810 is further used to obtain a second characteristic parameter of the power transmission equipment; wherein the second characteristic parameter is data associated with an abnormal state of the power transmission equipment; and based on the second characteristic parameter, a second frequent item set is obtained, wherein the second frequent item set is an item set in the second characteristic parameter whose support is greater than or equal to a preset support threshold.

[0139] In one embodiment, the computing and analysis module 810 is further used to obtain multiple first feature subsets based on the second frequent item set, wherein the first feature subset is a non-empty subset in the second frequent item set; obtain multiple first feature subsets, obtain multiple second feature subsets, wherein the second feature frequent subsets are first feature subsets whose support is greater than or equal to a preset support threshold; respectively calculate the confidence of the association rule between each second feature subset and the abnormal cause; based on the multiple second feature subsets, obtain multiple third feature subsets to construct an abnormal cause association rule, wherein each third feature subset is a second feature subset whose confidence is greater than or equal to the confidence threshold.

[0140] For the specific definition of the power transmission status monitoring system, please refer to the definition of the power transmission status monitoring method above, which will not be repeated here. Each module in the above-mentioned power transmission status monitoring system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. It should be noted that the division of modules in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0141] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned power transmission status monitoring method when executing the computer program.

[0142] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned power transmission status monitoring method are implemented.

[0143] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the above-mentioned power transmission status monitoring method when executed by a processor.

[0144] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0145] In the description of this specification, the description with reference to the terms "some embodiments", "other embodiments", "ideal embodiments", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic descriptions of the above terms do not necessarily refer to the same embodiment or example.

[0146] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring power transmission status, characterized in that: The power transmission status monitoring method comprises: Acquire a first characteristic parameter of a power transmission device; wherein the first characteristic parameter is used to characterize data associated with a power transmission state of the power transmission device, wherein the power transmission state includes a normal state and an abnormal state; Performing an item set search on the first feature parameter to obtain a plurality of first frequent item sets; wherein the first frequent item sets are item sets in the first feature parameter whose support is greater than or equal to a preset support threshold; Acquire a feature association rule according to the plurality of first frequent item sets; wherein the feature association rule includes at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold; Acquire a second frequent item set; wherein the second frequent item set is a data set of the power transmission device in an abnormal state; According to the second frequent item set, a plurality of first feature subsets are obtained; wherein the first feature subset is a non-empty subset in the second frequent item set; Acquire multiple first feature subsets and acquire multiple second feature subsets; wherein the second feature frequent subset is a first feature subset whose support is greater than or equal to a preset support threshold; respectively calculating the confidence of the association rule between each of the second feature subsets and the abnormal cause; According to the plurality of the second feature subsets, a plurality of third feature subsets are obtained to construct the abnormal cause association rule; wherein each of the third feature subsets is a second feature subset whose confidence is greater than or equal to a confidence threshold; According to the abnormal cause association rule and the feature association rule, abnormal situation information related to the power transmission device is acquired; wherein the abnormal situation information is used to characterize the power transmission state of the power transmission device.

2. The method according to claim 1, characterized in that: The step of obtaining a first characteristic parameter of the power transmission equipment comprises: Acquire the data to be processed of the power transmission device; wherein the data to be processed includes at least one of the temperature, wind speed, wind direction and wind deflection angle of the power transmission device; Dividing the data to be processed into multiple groups of feature data sets; Data fusion processing is performed according to the plurality of sets of feature data sets to obtain the first feature parameter.

3. The method according to claim 2, characterized in that The step of performing data fusion processing according to the plurality of sets of feature data sets to obtain the first feature parameter comprises: Obtaining each group of feature data sets for calculation, and obtaining feature fusion data of each group of feature data sets; Data fusion processing is performed based on the feature fusion data of the plurality of sets of the feature data sets to obtain the first feature parameter.

4. The method according to claim 1, characterized in that The step of acquiring feature association rules according to the plurality of first frequent item sets comprises: Obtaining the confidence of the first frequent itemset; At least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold is obtained as the feature association rule.

5. The method according to any one of claims 1 to 4, characterized in that: The steps of obtaining the second frequent itemset include: Acquire a second characteristic parameter of the power transmission device; wherein the second characteristic parameter is data associated with an abnormal state of the power transmission device; According to the second characteristic parameter, the second frequent item set is obtained; wherein the second frequent item set is an item set in the second characteristic parameter whose support is greater than or equal to a preset support threshold.

6. A power transmission status monitoring system, characterized in that: The system comprises: The calculation and analysis module is used to obtain a first characteristic parameter of a power transmission device, wherein the first characteristic parameter is used to characterize data associated with a power transmission state of the power transmission device, and the power transmission state includes a normal state and an abnormal state; perform an item set search on the first characteristic parameter to obtain a plurality of first frequent item sets, wherein the first frequent item set is an item set in the first characteristic parameter whose support is greater than or equal to a preset support threshold; obtain a feature association rule based on the plurality of first frequent item sets, wherein the feature association rule includes at least one first frequent item set whose confidence is greater than or equal to a preset confidence threshold; obtain a second frequent item set, wherein the second frequent item set is a first frequent item set of the power transmission device; A data set of a device in an abnormal state; obtaining multiple first feature subsets based on the second frequent item set; wherein the first feature subset is a non-empty subset in the second frequent item set; obtaining multiple first feature subsets, obtaining multiple second feature subsets; wherein the second feature frequent subset is a first feature subset whose support is greater than or equal to a preset support threshold; respectively calculating the confidence of the association rule between each of the second feature subsets and the abnormal cause; obtaining multiple third feature subsets based on the multiple second feature subsets to construct the abnormal cause association rule; wherein each of the third feature subsets is a second feature subset whose confidence is greater than or equal to the confidence threshold; and, The state monitoring module is used to obtain abnormal situation information related to the power transmission equipment according to the abnormal cause association rule and the feature association rule, wherein the abnormal situation information is used to characterize the power transmission state of the power transmission equipment.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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