Abnormal temperature monitoring method and device for power equipment, electronic equipment and storage medium

Abstract

The invention provides an abnormal temperature monitoring method and device for power equipment, electronic equipment and a storage medium, and belongs to the technical field of power supply, and the method comprises the steps: determining a temperature state sequence of a plurality of temperature measurement points based on the temperature sequence data of the plurality of temperature measurement points of the power equipment and a corresponding temperature threshold; determining a transaction data set based on the temperature state sequences of the plurality of temperature measurement points, and mining a first frequent item set in the transaction data set based on an Apriori algorithm; each first frequent item set is a set of a plurality of first-class temperature measurement points; the temperature state of the first type of temperature measurement points is a first state, and the first state is used for indicating that the corresponding temperature measurement point is in an abnormal state; determining a target temperature measurement point based on a time sequence of a plurality of first type temperature measurement points in the first frequent item set; and displaying the target temperature measurement point. According to the abnormal temperature monitoring method and device for the power equipment, the electronic equipment and the storage medium provided by the invention, the troubleshooting efficiency of the power equipment can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of power supply, and more specifically, relates to a method and device for monitoring abnormal temperature of power equipment, an electronic device, and a storage medium. Background Art

[0002] When an electrical device fails, it has a strong thermal effect. By measuring the temperature information of nodes, potential faults can be detected in a timely manner. The existing temperature measurement methods are mainly manual, and infrared thermometers and infrared imagers are used for temperature measurement operations regularly. The degree of automation is low and it depends on personnel experience. Even for users who have installed an on-line temperature monitoring system, the background software can only monitor and display the temperatures of each temperature measurement point in real time. When the temperatures of multiple temperature measurement points are all abnormal, separate fault troubleshooting is required, which is time-consuming and laborious. Summary of the Invention

[0003] The purpose of the present disclosure is to provide a method and device for monitoring abnormal temperature of power equipment, an electronic device, and a storage medium, so as to improve the efficiency of power equipment fault troubleshooting.

[0004] In the first aspect of the embodiments of the present disclosure, a method for monitoring abnormal temperature of power equipment is provided, including: Determining a temperature status sequence of multiple temperature measurement points based on the temperature sequence data of multiple temperature measurement points of the power equipment and the corresponding temperature thresholds; the temperature status sequence is a time sequence of the temperature status of the temperature measurement points; Determining a transaction data set based on the temperature status sequences of multiple temperature measurement points, and mining a first frequent item set in the transaction data set by using the Apriori algorithm; each first frequent item set is a set of multiple first-type temperature measurement points; the first-type temperature measurement points are temperature measurement points with a temperature status of a first status, and the first status is used to indicate that the corresponding temperature measurement point is in an abnormal status; Determining a target temperature measurement point based on the time order of multiple first-type temperature measurement points in the first frequent item set; Displaying the target temperature measurement point.

[0005] In the second aspect of the embodiments of the present disclosure, a device for monitoring abnormal temperature of power equipment is provided, including: A first processing module, configured to determine a temperature status sequence of multiple temperature measurement points based on the temperature sequence data of multiple temperature measurement points of the power equipment and the corresponding temperature thresholds; the temperature status sequence is a time sequence of the temperature status of the temperature measurement points; A data mining module, configured to determine a transaction dataset based on temperature status sequences of multiple temperature measurement points, and mine first frequent item sets in the transaction dataset based on the Apriori algorithm; each of the first frequent item sets is a set of multiple first-class temperature measurement points; the first-class temperature measurement points are temperature measurement points with a first status, and the first status is used to indicate that the corresponding temperature measurement point is in an abnormal status; A data screening module, configured to determine target temperature measurement points based on the time sequence of multiple first-class temperature measurement points in the first frequent item sets; An information display module, configured to display the target temperature measurement points.

[0006] In a third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the above-mentioned abnormal temperature monitoring method for power equipment are implemented.

[0007] In a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned abnormal temperature monitoring method for power equipment are implemented.

[0008] The beneficial effects of the abnormal temperature monitoring method, device, electronic device, and storage medium for power equipment provided by the embodiments of the present disclosure are as follows: By analyzing the temperature status sequences of multiple temperature measurement points, the embodiments of the present disclosure can determine combinations of temperature measurement points with simultaneous temperature anomalies, that is, the first frequent item sets. On this basis, the temperature measurement point that first appears abnormal is determined based on the time sequence, that is, the target temperature measurement point. The target temperature measurement point is the source or key part of the abnormal temperature of the power equipment. Therefore, by displaying the target temperature measurement point to the staff, the staff can first perform fault troubleshooting on the target temperature measurement point, thereby improving the efficiency of fault troubleshooting for power equipment. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0010] Figure 1 It is a schematic flowchart of an abnormal temperature monitoring method for power equipment provided by an embodiment of the present disclosure; <s Figure 2 It is a structural block diagram of an abnormal temperature monitoring system for power equipment provided by an embodiment of the present disclosure; Figure 3 A structural block diagram of an abnormal temperature monitoring device for electric power equipment according to an embodiment of the present disclosure; Figure 4 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0011] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0012] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0013] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for monitoring abnormal temperature of power equipment provided by one embodiment of the present disclosure, the method comprising: S101: Determine a temperature state sequence of multiple temperature measurement points based on temperature sequence data of multiple temperature measurement points of an electric power device and corresponding temperature thresholds; the temperature state sequence is a time series of temperature states of the temperature measurement points.

[0014] In this embodiment, considering that different types of power equipment have their own distribution of hot spots (i.e., locations where over-temperature failures are prone to occur), the hot spots of different power equipment can be determined in advance through experiments and simulation analysis, and temperature sensors can be arranged at the hot spots to achieve temperature collection at multiple temperature measurement points.

[0015] For example, for the collector line, the location prone to overheating is the cable terminal head, so online temperature monitoring can be performed on all cable terminal heads on the collector line. For the box-type transformer, the location prone to overheating is the incoming cable head, so online temperature monitoring can be performed on the three incoming cable heads on the high-voltage side of the box-type transformer.

[0016] Specifically, the temperature sensor may be an optical fiber temperature sensor, which can overcome interference from high voltage and strong electric fields and ensure the accuracy of temperature monitoring.

[0017] like Figure 2 As shown, the temperature signal collected by the temperature sensor is connected to the temperature measurement terminal, and the temperature measurement terminal sends the temperature data to the monitoring management center via wireless transmission. The monitoring management center performs online monitoring, analysis, evaluation and diagnosis on the temperature data.

[0018] The temperature sequence data of multiple temperature measurement points refers to the temperature data of multiple temperature measurement points at each moment within a set time range. At any moment, by comparing the temperature data of each temperature measurement point with the corresponding temperature threshold, the temperature state of each temperature measurement point at that moment can be obtained. For example, at a certain moment, if the temperature of temperature measurement point A exceeds its corresponding temperature threshold, then the temperature state of temperature measurement point A is marked as an abnormal state, otherwise it is a normal state. Sorting the temperature states of multiple temperature measurement points corresponding to multiple moments in chronological order can obtain the temperature state sequence of multiple temperature measurement points.

[0019] S102: Determine the transaction dataset based on the temperature state sequence of multiple temperature measurement points, and mine the first frequent item sets in the transaction dataset based on the Apriori algorithm; each first frequent item set is a set of multiple first-class temperature measurement points; the first-class temperature measurement points are the temperature measurement points with the temperature state being the first state, and the first state is used to indicate that the corresponding temperature measurement point is in an abnormal state.

[0020] In this embodiment, the temperature state data of multiple temperature measurement points at any moment is used as a transaction, and the temperature state data of multiple temperature measurement points at multiple moments constitutes a transaction dataset. For example, a certain power device is provided with three temperature measurement points, namely temperature measurement point A, temperature measurement point B, and temperature measurement point C. The set time range is 50 minutes. Within the set time range, the temperature states of each temperature measurement point are collected at fixed time intervals (such as every 10 minutes), and the obtained transaction dataset is as follows: Transaction 1 (the first 10 minutes): [Temperature measurement point A is normal, temperature measurement point B is abnormal, temperature measurement point C is normal]; Transaction 2 (the second 10 minutes): [Temperature measurement point A is abnormal, temperature measurement point B is normal, temperature measurement point C is abnormal]; Transaction 3 (the third 10 minutes): [Temperature measurement point A is normal, temperature measurement point B is normal, temperature measurement point C is normal]; Transaction 4 (the fourth 10 minutes): [Temperature measurement point A is abnormal, temperature measurement point B is abnormal, temperature measurement point C is normal]; Transaction 5 (the fifth 10 minutes): [Temperature measurement point A is normal, temperature measurement point B is normal, temperature measurement point C is abnormal].

[0021] On this basis, the Apriori algorithm can be used to mine the first frequent item sets in the transaction dataset. The Apriori algorithm is a classic association rule mining algorithm, which discovers potential associations in data by finding frequently occurring item sets. In this embodiment, the first frequent item sets are sets of multiple temperature measurement points that simultaneously exhibit temperature anomalies, and these sets reflect which temperature measurement points on the power device have a high correlation of temperature anomalies with each other.

[0022] S103: Determine the target temperature measurement point based on the time sequence of multiple first-type temperature measurement points in the first frequent item set.

[0023] In this embodiment, if among the multiple temperature measurement points in the first frequent item set, the temperature of temperature measurement point A abnormally rises earlier than that of temperature measurement point B, then temperature measurement point A is very likely to be the source causing the abnormal temperature of temperature measurement point B. Accordingly, the temperature measurement point with the earliest abnormal occurrence time among the multiple first-type temperature measurement points in the first frequent item set is used as the target temperature measurement point, and this target temperature measurement point is the key part of the abnormal temperature of the power equipment.

[0024] S104: Display the target temperature measurement point.

[0025] In this embodiment, the target temperature measurement point can be displayed at the monitoring and management center so that the staff can intuitively obtain the information of the key part. The display methods can include: highlighting the position of the target temperature measurement point on the monitoring interface of the power equipment, or informing the staff through alarm prompts, etc., so that the staff can timely check and maintain the part where the target temperature measurement point is located.

[0026] It can be concluded from the above that in this embodiment, by analyzing the temperature status sequences of multiple temperature measurement points, the combination of temperature measurement points with simultaneous abnormal temperatures, that is, the first frequent item set, can be determined. On this basis, the temperature measurement point with the earliest abnormal occurrence is determined based on the time sequence, that is, the target temperature measurement point. The target temperature measurement point is the source or key part of the abnormal temperature of the power equipment. Therefore, by displaying the target temperature measurement point to the staff, the staff can first conduct a fault investigation on the target temperature measurement point, thereby improving the efficiency of fault investigation of the power equipment.

[0027] In an embodiment of the present disclosure, mining the first frequent item set in the transaction dataset based on the Apriori algorithm includes: Performing frequent item set screening operations multiple times until the stop condition is met; In the first frequent item set screening operation, screen the frequent 1-item sets with the state of the single temperature measurement point being the first state from the transaction dataset according to the first support threshold; In the kth frequent item set screening operation, connect every two frequent k - 1-item sets to obtain multiple candidate k-item sets, and screen the frequent k-item sets from the candidate k-item sets based on the second support threshold; where k is a natural number greater than 1; The stop condition is: no new frequent item set can be generated; Determine the frequent k-item set obtained in the last frequent item set screening operation as the first frequent item set.

[0028] In this embodiment, in the first frequent itemset screening operation, the first support of a single temperature measurement point with an abnormal temperature state can be counted. Specifically, for any temperature measurement point A, the first quantity of transactions in which the temperature measurement point A is in an abnormal state can be counted, and the ratio of the first quantity to the total number of transactions in the transaction dataset is used as the first support corresponding to the temperature measurement point A.

[0029] On this basis, single temperature measurement points with corresponding first supports greater than the first support threshold are screened as frequent 1-itemsets. By screening frequent 1-itemsets, temperature measurement points that rarely show abnormalities can be filtered out, focusing on single temperature measurement points that are in an abnormal state multiple times, laying a foundation for subsequent mining of the associations between multiple temperature measurement points.

[0030] In the second frequent itemset screening operation, every two frequent 1-itemsets are joined to obtain multiple candidate 2-itemsets. For any candidate 2-itemset, the same calculation method as the first support can be used to count the second quantity of transactions containing the candidate 2-itemset, and the ratio of the second quantity to the total number of transactions in the transaction dataset is used as the second support of the candidate 2-itemset.

[0031] On the basis of obtaining the second supports corresponding to each candidate 2-itemset, candidate 2-itemsets with corresponding second supports greater than the second support threshold are screened as frequent 2-itemsets.

[0032] The above process is executed multiple times to screen higher-order frequent itemsets until no new frequent itemsets can be generated. The frequent k-itemsets obtained in the last frequent itemset screening operation are determined as the first frequent itemsets. Among them, both the first support threshold and the second support threshold are preset constants, and those skilled in the art can flexibly design the specific values of the first support threshold and the second support threshold according to actual needs. For example, the first support threshold can be 0.3, and the second support threshold can be 0.1.

[0033] From the above, it can be concluded that by analyzing which temperature measurement points have abnormal states occurring simultaneously in this embodiment, it can help the staff understand the potential associations between different parts of the power equipment, so as to conduct equipment inspections and maintenance more pertinently, improving the efficiency and accuracy of fault troubleshooting.

[0034] In an embodiment of the present disclosure, the power equipment abnormal temperature monitoring method further includes: Determining a second type of temperature measurement points based on the historical temperature records of multiple temperature measurement points; the second type of temperature measurement points are those with a proportion of corresponding abnormal temperature records greater than the first threshold; Determining a third support threshold based on the proportion of the second type of temperature measurement points; Determining a fourth support threshold based on the importance level of the power equipment; Determine the first support threshold based on the third support threshold and the fourth support threshold.

[0035] In this embodiment, considering that if the temperature at a certain temperature measurement point frequently shows an abnormal state in the historical data, it indicates that the probability of abnormality at this temperature measurement point is relatively high. To capture abnormalities in a timely manner, the second support threshold needs to be set relatively small. Therefore, in this embodiment, first, calculate the proportion of abnormal temperature records at each temperature measurement point based on the historical temperature records of each temperature measurement point (that is, the ratio between the number of abnormal temperature records and the number of all historical temperature records), screen the second type of temperature measurement points (temperature measurement points that frequently show abnormal states in the historical data) based on the proportion of abnormal temperature records, and then determine the second support threshold based on the negative correlation between the proportion of the second type of temperature measurement points and the second support threshold. That is: if the proportion of the second type of temperature measurement points is relatively large, the second support can be set relatively small; conversely, if the proportion of the second type of temperature measurement points is relatively small, the second support can be set relatively large.

[0036] At the same time, considering that the consequences of failures of key power equipment are more serious than those of other power equipment, the corresponding first support threshold needs to be set relatively small to more sensitively monitor abnormal states.

[0037] Therefore, in this embodiment, the corresponding first support threshold can be set based on the proportion of the second type of temperature measurement points and the importance level of the power equipment.

[0038] Specifically, the following first formula can be used to determine the third support threshold:

[0039] Among them, represents the third support threshold, represents a preset first proportional parameter, represents the proportion of the second type of temperature measurement points.

[0040] At the same time, the following second formula can be used to determine the fourth support threshold:

[0041] Among them, represents the fourth support threshold, represents a preset second proportional parameter, represents the importance level of the power equipment, which can be represented by 1 - 5. The greater the importance level, the more important the power equipment.

[0042] Based on obtaining the third support threshold and the fourth support threshold, the third support threshold and the fourth support threshold can be weighted and summed to obtain the first support threshold.

[0043] From the above, it can be concluded that this embodiment comprehensively considers the historical temperature records and importance levels of the power equipment to determine the first support threshold, which can more accurately capture the abnormal temperature patterns of the power equipment, avoid abnormal omissions or false alarms caused by unreasonable fixed threshold settings, and improve the accuracy and reliability of abnormal monitoring.

[0044] In one embodiment of the present disclosure, the method for monitoring abnormal temperature of electric power equipment further includes: Calculate the first distance between each temperature measurement point and the center point in all frequent k-1 item sets; the center point is the center point of multiple temperature measurement points in all frequent k-1 item sets; Calculate the relative standard deviation of the first distance corresponding to each temperature measurement point; The second support threshold corresponding to the frequent k-itemset is determined based on the standard deviation.

[0045] In this embodiment, during the k-th frequent item set screening operation, each two frequent k-1 item sets may be concatenated to obtain multiple candidate k-item sets. The center point of the multiple temperature measurement points in the frequent k-1 item set may be obtained by calculating the average of the spatial coordinates of the multiple temperature measurement points in the frequent k-1 item set. The first distance between each temperature measurement point and the center point is then calculated to obtain multiple first distances, and the relative standard deviation of the multiple first distances is calculated.

[0046] In this embodiment, the relative standard deviation of multiple first distances can reflect the degree of dispersion of the temperature measurement point distribution. If the relative standard deviation is large, it indicates that the abnormal temperature measurement points are relatively dispersed in space, and abnormalities may occur simultaneously at temperature measurement points in multiple different regions. To more comprehensively mine these possible abnormal combinations, the second support threshold can be appropriately lowered to discover more potential frequent k-itemsets and capture more complex abnormal patterns. Conversely, if the relative standard deviation is small, it indicates that the abnormal temperature measurement points are relatively concentrated. In this case, the second support threshold can be appropriately increased to reduce unnecessary mining results and improve mining efficiency and accuracy. Therefore, the third support threshold can be determined based on the negative correlation between the relative standard deviation and the second support threshold.

[0047] It can be concluded from the above that this embodiment dynamically adjusts the second support threshold according to the spatial distribution characteristics of each temperature measurement point, so that the mining process is more consistent with the actual distribution of abnormal temperatures of power equipment.

[0048] In one embodiment of the present disclosure, before performing the frequent item set screening operation multiple times, the method for monitoring abnormal temperature of power equipment further includes: Encode the temperature status of multiple temperature measurement points; Sort the transactions in the transaction dataset based on the order in which they are encoded.

[0049] In this embodiment, the temperature states of multiple temperature measurement points can be assigned to encode the temperature states of multiple temperature measurement points. For example, a certain power equipment has five temperature measurement points A, B, C, D, and E. The temperature states of the five temperature measurement points are encoded as follows: 1 indicates that the temperature of equipment A is abnormal, 2 indicates that the temperature of equipment B is abnormal, 3 indicates that the temperature of equipment C is abnormal, 4 indicates that the temperature of equipment D is abnormal, and 5 indicates that the temperature of equipment E is abnormal; 6 indicates that the temperature of equipment A is normal, 7 indicates that the temperature of equipment B is normal, 8 indicates that the temperature of equipment C is normal, 9 indicates that the temperature of equipment D is normal, and 10 indicates that the temperature of equipment E is normal.

[0050] The corresponding transaction dataset can be expressed as: Transaction 1: [1, 7, 8, 9, 10]; Transaction 2: [6, 2, 3, 9, 10]; Transaction 3: [1, 7, 8, 9, 10]; Transaction 4: [6, 2, 8, 4, 10]; Transaction 5: [1, 7, 3, 9, 10].

[0051] Sorting the above transaction dataset according to the size of the encoding, we can get: Transaction 1: [1, 7, 8, 9, 10]; Transaction 2: [2, 3, 6, 9, 10]; Transaction 3: [1, 7, 8, 9, 10]; Transaction 4: [2, 6, 4, 8, 10]; Transaction 5: [1, 7, 3, 9, 10].

[0052] On this basis, filtering the frequent 1-itemsets of temperature anomalies, where: Item 1 (temperature anomaly of equipment A) appears 3 times (appears in Transaction 1, Transaction 3, and Transaction 5); Item 2 (temperature anomaly of equipment B) appears 2 times (appears in Transaction 2 and Transaction 4); Item 3 (temperature anomaly of equipment C) appears 2 times (appears in Transaction 2 and Transaction 5); Item 4 (temperature anomaly of equipment D) appears 1 time (appears in Transaction 4); Item 5 (temperature anomaly of equipment E) appears 0 times (does not appear in all transactions); Assuming that the second support threshold is 0.4, the frequent 1-itemsets of temperature anomalies are: {1}, {2}, {3}.

[0053] Further, based on the obtained temperature anomaly 1-itemsets {1}, {2}, {3}, a join operation is performed to generate candidate 2-itemsets: {1, 2}, {1, 3}, {2, 3}.

[0054] For the candidate 2-itemset {1, 2}, in transactions 1, 2, 3, 4, 5, there is no case where 1 and 2 appear simultaneously, that is, the number of occurrences is 0; For the candidate 2-itemset {1, 3}, in transaction 5, 1 and 3 appear simultaneously, and the number of occurrences is 1; For the candidate 2-itemset {2, 3}, in transaction 2, 2 and 3 appear simultaneously, and the number of occurrences is 1; Assuming the second support is 0.2, the frequent 2-itemsets that meet the conditions are: {1, 3}, {2, 3}.

[0055] In the above process, by sorting the transactions, the computational amount can be reduced when screening the frequent 2-itemsets. For example, when determining whether there is a candidate 2-itemset in a transaction, the sorted transaction can be checked sequentially from left to right. If it is found that the previous item is less than the smaller item in the candidate 2-itemset, the subsequent items of the transaction can be directly skipped, because there cannot be a satisfying combination later, reducing unnecessary comparison operations. If the transaction is not sorted, more complex combinatorial comparisons of the items in the transaction are required, greatly increasing the computational amount and the time cost of the algorithm. Therefore, the sorting operation in this embodiment effectively improves the execution efficiency of the algorithm.

[0056] In an embodiment of the present disclosure, the abnormal temperature monitoring method for power equipment further includes: Determining a first adjustment parameter based on the ambient temperature and ambient humidity; Adjusting the first reference values corresponding to multiple temperature measurement points based on the first adjustment parameter to obtain the temperature thresholds corresponding to multiple temperature measurement points.

[0057] In this embodiment, the temperature threshold can be determined according to the design parameters of the power equipment, the safe operating range, etc. Considering that the ambient temperature and ambient humidity will have a greater impact on the operating temperature of the power equipment, in a high-temperature and high-humidity environment, it is difficult for the power equipment to dissipate heat, and the temperature threshold should be appropriately reduced to ensure the safe operation of the equipment.

[0058] Therefore, in this embodiment, the reference values of the temperature thresholds of multiple temperature measurement points, that is, the first reference values, can be first determined according to the design parameters of the power equipment and the safe operating range. Then, the first adjustment parameter is determined based on the ambient temperature and ambient humidity, and the first reference values corresponding to multiple temperature measurement points are adjusted based on the first adjustment parameter to obtain the temperature thresholds corresponding to multiple temperature measurement points.

[0059] Specifically, the first adjustment parameter and the temperature threshold can be calculated through the following third formula, and the third formula is specifically:

[0060] Wherein, represents the first adjustment parameter, represents a preset third proportional parameter, represents a preset fourth proportional parameter, represents the ambient temperature, represents the reference value of the ambient temperature, represents the ambient humidity, represents the reference value of the ambient humidity; represents the temperature threshold, represents the first reference value, that is, the temperature threshold under the conditions that the ambient temperature is and the ambient humidity is .

[0061] It can be seen from the above that this embodiment takes into account the influence of the ambient temperature and the ambient humidity on the temperature of the power equipment, and dynamically adjusts the temperature threshold through the first adjustment parameter, which can more accurately judge whether the power equipment is in an abnormal temperature state, reduce false judgments and missed judgments caused by environmental factor interference, and improve the accuracy and reliability of abnormal temperature monitoring.

[0062] In an embodiment of the present disclosure, there are multiple first frequent item sets, and the power equipment abnormal temperature monitoring method further includes: Displaying the multiple first frequent item sets in different forms.

[0063] In this embodiment, the different forms of display can be to display each first frequent item set with different shapes or colors. The different shapes or colors facilitate users to distinguish each first frequent item set, thereby improving the efficiency of users to identify different frequent item sets and helping to quickly locate and analyze specific frequent item sets.

[0064] Corresponding to the power equipment abnormal temperature monitoring method in the above embodiment, Figure 3 This is a structural block diagram of a power equipment abnormal temperature monitoring device provided in an embodiment of the present disclosure. For the sake of convenience of description, only the parts related to the embodiments of the present disclosure are shown. Referring to Figure 3 The power equipment abnormal temperature monitoring device 20 includes: a first processing module 21, a data mining module 22, a data screening module 23, and an information display module 24. Among them, the first processing module 21 is configured to determine a temperature state sequence of multiple temperature measurement points based on the temperature sequence data of multiple temperature measurement points of the power equipment and the corresponding temperature threshold; the temperature state sequence is a time sequence of the temperature states of the temperature measurement points; A data mining module 22 is configured to determine a transaction data set based on temperature status sequences of multiple temperature measurement points, and mine a first frequent item set in the transaction data set based on the Apriori algorithm; each first frequent item set is a set of multiple first-type temperature measurement points; the first-type temperature measurement points are temperature measurement points with a temperature status being a first status, and the first status is used to indicate that the corresponding temperature measurement point is in an abnormal status. A data screening module 23 is configured to determine a target temperature measurement point based on the time sequence of multiple first-type temperature measurement points in the first frequent item set. An information display module 24 is configured to display the target temperature measurement point.

[0065] In an embodiment of the present disclosure, the data mining module 22 is specifically configured to: Execute a frequent item set screening operation multiple times until a stop condition is met; In the first frequent item set screening operation, screen frequent 1-item sets with the status of a single temperature measurement point being the first status from the transaction data set according to a first support threshold; In the k-th frequent item set screening operation, perform a joining operation on every two frequent k-1 item sets to obtain multiple candidate k-item sets, and screen frequent k-item sets from the candidate k-item sets based on a second support threshold; The stop condition is: no new frequent item set can be generated; Determine the frequent k-item set obtained in the last frequent item set screening operation as the first frequent item set.

[0066] In an embodiment of the present disclosure, the data mining module 22 is specifically further configured to: Determine second-type temperature measurement points based on historical temperature records of multiple temperature measurement points; the second-type temperature measurement points are temperature measurement points with a proportion of corresponding abnormal temperature records being greater than a first threshold; Determine a third support threshold based on the proportion of the second-type temperature measurement points; Determine a fourth support threshold based on the importance level of the power equipment; Determine the first support threshold based on the third support threshold and the fourth support threshold.

[0067] In an embodiment of the present disclosure, the data mining module 22 is specifically further configured to: Calculate a first distance between each temperature measurement point in all frequent k-1 item sets and a center point; the center point is the center point of multiple temperature measurement points in all frequent k-1 item sets; Calculate a relative standard deviation of the first distances corresponding to each temperature measurement point; Determine a second support threshold corresponding to the frequent k-item set based on the standard deviation.

[0068] In an embodiment of the present disclosure, before executing the frequent item set screening operation multiple times, the data mining module 22 is specifically further configured to: Encode the temperature states of multiple temperature measurement points; Sort the transactions in the transaction dataset based on the encoded order.

[0069] In an embodiment of the present disclosure, the first processing module 21 is specifically configured to: [[ID=,8]]Determine a first adjustment parameter based on the ambient temperature and ambient humidity; Adjust the first reference values corresponding to multiple temperature measurement points based on the first adjustment parameter to obtain temperature thresholds corresponding to multiple temperature measurement points.

[0070] In an embodiment of the present disclosure, the information display module 24 is specifically configured to: Display multiple first frequent item sets in different forms.

[0071] See Figure 4 , Figure 4 is a schematic block diagram of an electronic device provided in an embodiment of the present disclosure. As Figure 4 shown, the electronic device 300 in this embodiment may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The above-mentioned processors 301, input devices 302, output devices 303, and memories 304 communicate with each other through a communication bus 305. The memory 304 is used to store a computer program, and the computer program includes program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. Among them, the processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 3 the functions of the first processing module 21, data mining module 22, data screening 23, and data display 24 shown.

[0072] It should be understood that in the embodiments of the present disclosure, the so-called processor 301 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0073] The input device 302 may include a touchpad, a fingerprint acquisition sensor (for acquiring the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 303 may include a display (such as an LCD), a speaker, etc.

[0074] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A part of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0075] In a specific implementation, the processor 301, the input device 302, and the output device 303 described in the embodiments of the present disclosure may implement the implementation manners described in the first embodiment and the second embodiment of the abnormal temperature monitoring method for power equipment provided by the embodiments of the present disclosure, and may also implement the implementation manner of the electronic device described in the embodiments of the present disclosure, which will not be elaborated herein.

[0076] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, all or part of the processes in the above-mentioned embodiment methods are implemented. It can also be completed by instructing relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0077] A computer-readable storage medium may be an internal storage unit of the electronic device in any of the foregoing embodiments, such as the hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0078] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this disclosure.

[0079] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described electronic devices and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other may be an indirect coupling or communication connection through some interfaces or units, or may also be an electrical, mechanical, or other form of connection.

[0081] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of this disclosure.

[0082] In addition, in each of the embodiments of the present disclosure, each functional unit may be integrated into one processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0083] The above is only the specific implementation manner of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present disclosure can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims

1. A method for monitoring abnormal temperature of power equipment, characterized in that, Including: Determining a temperature status sequence of multiple temperature measurement points based on temperature sequence data of multiple temperature measurement points of an electrical device and corresponding temperature thresholds; The temperature status sequence is a time sequence of the temperature status of the temperature measurement points; Determining a transaction dataset based on the temperature status sequences of multiple temperature measurement points, and mining a first frequent item set in the transaction dataset based on the Apriori algorithm; each of the first frequent item sets is a set of multiple first-class temperature measurement points; The first-class temperature measurement points are temperature measurement points with a temperature status of a first status, and the first status is used to indicate that the corresponding temperature measurement point is in an abnormal status; Determining a target temperature measurement point based on the time order of multiple first-class temperature measurement points in the first frequent item set; Displaying the target temperature measurement point.

2. The abnormal temperature monitoring method for power equipment according to claim 1, characterized in that, The mining of the first frequent item set in the transaction dataset based on the Apriori algorithm includes: Performing frequent item set screening operations multiple times until a stop condition is met; In the first frequent item set screening operation, screening frequent 1-item sets with the status of a single temperature measurement point being the first status from the transaction dataset according to a first support threshold; In the k-th frequent item set screening operation, connecting every two frequent k-1 item sets to obtain multiple candidate k-item sets, and screening frequent k-item sets from the candidate k-item sets based on a second support threshold; The stop condition is: no new frequent item set can be generated; Determining the frequent k-item set obtained in the last frequent item set screening operation as the first frequent item set.

3. The abnormal temperature monitoring method for power equipment according to claim 2, wherein Also including: Determining second-class temperature measurement points based on historical temperature records of multiple temperature measurement points; The second-class temperature measurement points are temperature measurement points with a proportion of corresponding abnormal temperature records greater than a first threshold; Determining a third support threshold based on the proportion of second-class temperature measurement points; Determining a fourth support threshold based on the importance level of the electrical device; Determining the first support threshold based on the third support threshold and the fourth support threshold.

4. The abnormal temperature monitoring method for power equipment according to claim 2, wherein Also including: Calculating a first distance between each temperature measurement point in all frequent k-1 item sets and a center point; The center point is the center point of multiple temperature measurement points in all frequent k-1 item sets; Calculating the relative standard deviation of the first distances corresponding to each temperature measurement point; Determining a second support threshold corresponding to the frequent k-item set based on the standard deviation.

5. The abnormal temperature monitoring method for power equipment according to claim 2, wherein Before performing the frequent item set screening operations multiple times, it also includes: Encoding the temperature status of the multiple temperature measurement points; Sorting the transactions in the transaction dataset based on the order of the encoding.

6. The abnormal temperature monitoring method for power equipment according to claim 1, characterized in that Also including: Determining a first adjustment parameter based on the ambient temperature and ambient humidity; Adjusting a first reference value corresponding to multiple temperature measurement points based on the first adjustment parameter to obtain temperature thresholds corresponding to multiple temperature measurement points.

7. The abnormal temperature monitoring method for power equipment according to claim 1, characterized in that, There are multiple first frequent item sets, and the electrical device abnormal temperature monitoring method also includes: Displaying the multiple first frequent item sets in different forms.

8. An abnormal temperature monitoring device for power equipment, characterized in that, Including: A first processing module for determining a temperature status sequence of multiple temperature measurement points based on temperature sequence data of multiple temperature measurement points of an electrical device and corresponding temperature thresholds; The temperature status sequence is a time sequence of the temperature status of the temperature measurement points; A data mining module, configured to determine a transaction dataset based on temperature status sequences of multiple temperature measurement points, and mine first frequent item sets in the transaction dataset based on the Apriori algorithm; each of the first frequent item sets is a set of multiple first-class temperature measurement points; The first-class temperature measurement points are temperature measurement points with a first status, and the first status is used to indicate that the corresponding temperature measurement point is in an abnormal state; A data screening module, configured to determine target temperature measurement points based on the time sequence of multiple first-class temperature measurement points in the first frequent item sets; An information display module, configured to display the target temperature measurement points.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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