Power parameter alarm device, method and electronic equipment based on box-type transformer

Abstract

The application discloses a kind of power parameter alarm device based on box transformer, method and electronic equipment, and the application belongs to the technical field of Internet of Things.The device comprises: power parameter acquisition module, for obtaining the power parameter data transmitted by box transformer;Power parameter analysis module, for obtaining the power parameter of box transformer using the decryption key corresponding to the preset key;And determine the amplitude and duration of the parameter item in power parameter exceeding the set threshold;Abnormal level determination module, for obtaining the identification result of the abnormal level of current parameter item using big data model;Alarm module, for generating alarm information according to the identification result, and sending to monitoring terminal.The technical scheme can solve the problem of false alarm or unclear power parameter in the prior art, and through monitoring of parameter item and multi-dimensional fault identification mechanism, the accuracy of fault identification can be improved, and it is specific to parameter item, which is beneficial to improve the work efficiency of maintenance personnel.

Description

Technical Field

[0001] This application belongs to the field of power equipment technology, specifically relating to a power parameter alarm device, method, and electronic equipment based on a box-type transformer. Background Technology

[0002] With the development of urban construction and the improvement of living facilities in my country, box-type transformers are widely used in residential communities, urban power grid renovation, and industrial factories. When a box-type transformer fails, it may evolve into a series of concurrent failures, which may result in large-scale failures of banking facilities, security systems, manufacturing plants, food refrigeration, communication networks, and traffic control systems. If these problems and failures are not resolved quickly and in a timely manner, power outages will occur, ultimately affecting people's normal production and life.

[0003] Current box-type transformer alarms utilize wireless trunking communication technology and microcomputer detection technology, and are manufactured using imported integrated circuits. The alarm can be used with a long-range wireless alarm network host to form a long-range wireless network alarm system for box-type transformers. The long-range wireless alarm network host is installed in the main control room. When the power transformer and power transmission lines experience theft, phase loss, or power outages, the host will sound an alarm. For example, the screen will display the alarm date, time, alarm extension number, faulty transformer, and other possible faulty circuits, and will also display the actual location of the alarmed transformer and possible faulty lines to the control room via an electronic map.

[0004] However, current technology cannot determine the specific electrical parameters causing a fault within the transformer, relying solely on on-site inspection by maintenance personnel, which is prone to false alarms. Furthermore, the wireless alarm range between the main unit and the alarm device is 20 kilometers, increasing the time required for maintenance personnel to handle faults and reducing their efficiency. Therefore, how to detect the specific electrical parameters of a fault in real time and resolve false alarms is a problem that needs to be solved in this field. Summary of the Invention

[0005] The purpose of this application is to provide a power parameter alarm device, method, and electronic device based on a box-type transformer. The aim is to solve the problems of false alarms or unclear power parameters in the prior art. By monitoring the parameter items and using a multi-dimensional fault identification mechanism, the accuracy of fault identification can be improved and specified down to the parameter items, which is conducive to improving the work efficiency of maintenance personnel.

[0006] In a first aspect, embodiments of this application provide a power parameter alarm device based on a box-type transformer, the device comprising:

[0007] The power parameter acquisition module is used to acquire power parameter data transmitted by the box-type transformer; wherein, the power parameter data is obtained by encryption using a preset key;

[0008] The power parameter parsing module is used to decrypt the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and to determine whether there are any parameter items in the power parameters that exceed a set threshold, as well as the magnitude and duration of exceeding the set threshold.

[0009] An anomaly level determination module is used to identify the magnitude and duration of the exceedance of the set threshold using a pre-determined big data model, and obtain the anomaly level identification result of the current parameter item.

[0010] An alarm module is used to generate alarm information based on the identification results and send the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

[0011] Furthermore, the device also includes a big data model building module, which is used for:

[0012] Acquire historical power parameters, determine the magnitude and duration of historical parameter items exceeding the set threshold, and obtain the alarm information processing results of historical parameter items;

[0013] Based on the alarm information processing results, the magnitude and duration of the exceedance of the set threshold are weighted and classified into abnormal levels.

[0014] A big data model is constructed based on the magnitude and duration of the historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results.

[0015] Furthermore, the device also includes:

[0016] The monitoring and reporting module is used to obtain the processing results of the alarm information through the monitoring terminal;

[0017] The big data model update module is used to update the parameters of the big data model based on the obtained processing results every preset period.

[0018] Furthermore, the device also includes:

[0019] The attribute data acquisition module is used to acquire the attribute data of the box-type transformer;

[0020] Accordingly, the anomaly level determination module includes:

[0021] The attribute matching unit is used to determine the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item.

[0022] The identification result determination unit is used to determine the amplitude weight and duration weight according to the algorithm rules, so as to determine the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

[0023] Furthermore, the power parameter parsing module is also used for:

[0024] If the decryption of the power parameter data fails, a decryption error message is generated and sent to the monitoring terminal.

[0025] Secondly, embodiments of this application provide a power parameter alarm method based on a box-type transformer, the method comprising:

[0026] The power parameter data transmitted by the box-type transformer is obtained through the power parameter acquisition module; wherein, the power parameter data is obtained by encryption using a preset key;

[0027] The power parameter parsing module decrypts the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and determines whether any parameter in the power parameters exceeds a set threshold, as well as the magnitude and duration of exceeding the set threshold.

[0028] The anomaly level determination module uses a pre-determined big data model to identify the magnitude and duration of the exceedance of the set threshold, and obtains the anomaly level identification result of the current parameter item; the alarm module generates alarm information based on the identification result and sends the alarm information to the monitoring terminal.

[0029] The alarm module generates alarm information based on the identification results and sends the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

[0030] Furthermore, before acquiring the power parameter data transmitted by the box-type transformer and encrypted using a preset key through the power parameter acquisition module, the method further includes:

[0031] Acquire historical power parameters, determine the magnitude and duration of historical parameter items exceeding the set threshold, and obtain the alarm information processing results of historical parameter items;

[0032] Based on the alarm information processing results, the magnitude and duration of the exceedance of the set threshold are weighted and classified into abnormal levels.

[0033] A big data model is constructed based on the magnitude and duration of the historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results.

[0034] Furthermore, after generating alarm information based on the identification results through the alarm module and sending the alarm information to the monitoring terminal, the method further includes:

[0035] The processing results of the alarm information are obtained on the monitoring terminal through the monitoring and reporting module.

[0036] At each preset period, the parameters of the big data model are updated by the big data model update module based on the obtained processing results.

[0037] Furthermore, before using a big data model through the anomaly level determination module to identify the magnitude and duration exceeding a set threshold and obtain the anomaly level identification result for the current parameter item, the method further includes:

[0038] The attribute data of the box-type transformer is obtained through the attribute data acquisition module;

[0039] Correspondingly, the anomaly level determination module uses a big data model to identify the magnitude and duration exceeding the set threshold, obtaining the anomaly level identification result for the current parameter item, including:

[0040] The attribute matching unit determines the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item.

[0041] The identification result determination unit determines the amplitude weight and duration weight according to the algorithm rules, and determines the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

[0042] Furthermore, after decrypting the power parameter data using the decryption key corresponding to the preset key, the method further includes:

[0043] If the decryption of the power parameter data fails, a decryption error message is generated and sent to the monitoring terminal.

[0044] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0045] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0046] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0047] In this embodiment, a power parameter acquisition module is used to acquire power parameter data transmitted by the box-type transformer; wherein the power parameter data is encrypted using a preset key; a power parameter parsing module is used to decrypt the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and to determine whether any parameter in the power parameters exceeds a set threshold, and the magnitude and duration of exceeding the set threshold; an anomaly level determination module is used to identify the magnitude and duration of exceeding the set threshold using a pre-determined big data model to obtain the identification result of the anomaly level of the current parameter; an alarm module is used to generate alarm information based on the identification result and send the alarm information to the monitoring terminal; wherein the alarm information includes the name of the current parameter, the magnitude of exceeding the set threshold, the duration, and the anomaly level. Through the above-mentioned power parameter alarm device based on the box-type transformer, monitoring personnel can view alarm information in real time, discover problematic power parameters, determine the priority of maintenance based on the anomaly level of the power parameters, and inform maintenance personnel of the specific problem and maintenance location. Furthermore, the big data model can be continuously updated after the problem is resolved to improve the efficiency of problem-solving. Meanwhile, monitoring personnel can determine whether there are false alarms based on alarm information online, saving maintenance personnel time. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 1 of this application;

[0049] Figure 2 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 2 of this application;

[0050] Figure 3 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 3 of this application;

[0051] Figure 4 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 4 of this application;

[0052] Figure 5 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 5 of this application.

[0053] Figure 6This is a flowchart illustrating the power parameter alarm method based on a box-type transformer provided in Embodiment Six of this application;

[0054] Figure 7 This is a schematic diagram of the structure of the electronic device provided in Embodiment 7 of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, specific embodiments of this application will be described in further detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely for explaining this application and not for limiting it. It should also be noted that, for ease of description, only the parts relevant to this application are shown in the drawings, not all of them. Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0056] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0057] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0058] The power parameter alarm device, method, and electronic equipment based on a box-type transformer provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0059] Example 1

[0060] Figure 1This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 1 of this application. Figure 1 As shown, it specifically includes the following:

[0061] The power parameter acquisition module 101 is used to acquire power parameter data transmitted by the box-type transformer; wherein, the power parameter data is obtained by encryption using a preset key;

[0062] The power parameter parsing module 102 is used to decrypt the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box transformer; and to determine whether there are any parameter items in the power parameters that exceed a set threshold, as well as the magnitude and duration of exceeding the set threshold.

[0063] Anomaly level determination module 103 is used to identify the magnitude and duration of the exceedance of the set threshold using a pre-determined big data model, and obtain the identification result of the anomaly level of the current parameter item.

[0064] The alarm module 104 is used to generate alarm information based on the identification result and send the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

[0065] Firstly, this solution can be implemented in scenarios where sensors monitor various electrical parameters of a box-type transformer and trigger alarms upon detecting anomalies. Specifically, anomaly identification can be performed using intelligent terminal devices, such as desktop computers, laptops, mobile phones, and tablets, when monitoring personnel receive and process alarm information. Due to the different alarm types, monitoring personnel can prioritize maintenance based on the anomaly level and determine in real time whether false alarms have occurred.

[0066] Based on the above usage scenarios, it is understood that the subject of this application can be the smart terminal, or it can be a device for communication between monitoring personnel and maintenance personnel; no further limitations are made here.

[0067] In this embodiment, the box-type transformer is a enclosure in the power system used to transform, concentrate, and distribute electrical energy voltage and current, aiming to ensure power quality and equipment safety. Power parameters are the foundation for building big data models and generating alarm information; they may include rated capacity, rated voltage, rated current, capacity ratio, voltage ratio, impedance voltage (%), short-circuit loss, no-load loss, no-load current (%), and connection group, etc. A preset key is a parameter input into algorithms that convert plaintext to ciphertext or vice versa. It can be either a symmetric key or an asymmetric key and is a prerequisite for acquiring and parsing power parameters. The preset key used in this scheme can be an asymmetric key; encryption using the preset key ensures the secure transmission of power parameters.

[0068] The acquisition process can involve obtaining the power parameter data transmitted by the box-type transformer through electronic devices, specifically through chips, infrared detectors, and three-phase detection. Encryption using a preset key involves altering the original information data with a special algorithm, ensuring that even if an unauthorized user obtains the encrypted information, they cannot understand its content because they do not know the decryption method. The encryption methods used in this solution can include substitution ciphers and transposition ciphers. Substitution ciphers use a set of ciphertext letters to replace a set of plaintext letters to hide the plaintext, while maintaining the original position of the plaintext letters. Transposition ciphers do not change the plaintext letters; they simply rearrange their order. The power parameter acquisition module 101 can then acquire the power parameter data transmitted by the box-type transformer.

[0069] A threshold value, also called a critical value, refers to the lowest or highest value at which an effect can occur. It can be the maximum or minimum value of an abnormal power parameter. In this scheme, the threshold value can be set based on the normal operating conditions of the box-type transformer. For example, the current parameter normally fluctuates within ±10% of 5 amps; therefore, 4.5 amps and 5.5 amps can be used as threshold values. The amplitude is the range by which the power parameter exceeds or falls below the critical value. It can be an integer, decimal, or percentage, such as exceeding the threshold by 1 amp, falling below 3.5 amps, or rising above 6.5 amps. The duration is the time the power parameter exceeds or falls below the critical value, which can be 5 seconds, 10 seconds, 20 seconds, or longer. This duration can be obtained through continuous monitoring of the power parameter.

[0070] Decryption involves processing encrypted information to make it readable and viewable. This module can obtain the power parameters of the box-type transformer and determine whether any of the power parameters exceed a set threshold, as well as the magnitude and duration of the exceedance.

[0071] The big data model includes the magnitude and duration of historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results. The anomaly level is a manually assigned level when a parameter item becomes abnormal, and can be represented by letters, numbers, or words. For example, the urgency level can be classified as a, b, and c from mild to severe.

[0072] Identifying the magnitude and duration exceeding the set threshold can be achieved through electronic devices receiving abnormal parameter data and determining the level of abnormality. Alternatively, it can involve intelligent detectors that preprocess the collected data using a specific algorithm and pre-identify the power parameter status based on certain criteria, thus working in conjunction with an alarm controller to complete the power parameter detection and alarm function. The anomaly level determination module 103 can obtain the identification result of the current parameter's anomaly level.

[0073] In this solution, alarm information can be sent to personnel using light, sound, mechanical, electrical, and odor signals to indicate malfunctions, accidents, or other hazards. Monitoring personnel will use alarm information to determine and handle abnormal power parameters. The monitoring terminal is the display part of the monitoring system and its standard output. Only with a monitor can we view the images sent from the front end. Terminal equipment mainly includes sequential video / audio switchers, video image processors, matrix switching controllers, and professional video recorders. Monitoring personnel must receive and process abnormal information at the monitoring terminal.

[0074] The alarm system can be transmitted via mobile communication networks, combining the alarm system with a mobile communication transmission platform. Alarms can be sent via GSM Chinese SMS and voice messages to a GSM receiving host. Upon receiving the alarm signal, the receiving host transmits the data to a microcomputer. The microcomputer, through its alarm receiving software, analyzes and determines the alarm host and potentially faulty lines, triggering an audible alarm. The screen displays the current parameter names, the magnitude of exceeding the set threshold, the duration, and the anomaly level, and shows the actual location of the alarm transformer and the faulty line on an electronic map. This module provides access to alarm information.

[0075] In this application example, a power parameter acquisition module is used to acquire power parameter data transmitted by a box-type transformer; wherein, the power parameter data is obtained by encryption using a preset key; a power parameter parsing module is used to decrypt the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and to determine whether any parameter in the power parameters exceeds a set threshold, and the magnitude and duration of exceeding the set threshold; an anomaly level determination module is used to identify the magnitude and duration of exceeding the set threshold using a pre-determined big data model to obtain the identification result of the anomaly level of the current parameter; an alarm module is used to generate alarm information based on the identification result and send the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter, the magnitude of exceeding the set threshold, the duration, and the anomaly level. The technical solution provided by this embodiment allows monitoring personnel to view alarm information in real time, discover problematic power parameters, determine the priority of maintenance based on the anomaly level of the power parameters, and inform maintenance personnel of the specific problem and maintenance location, and can continuously update the big data model after the problem is solved to improve the efficiency of problem solving. Meanwhile, monitoring personnel can determine whether there are false alarms based on alarm information online, saving maintenance personnel time.

[0076] Example 2

[0077] Figure 2 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 2 of this application. Figure 2 As shown, it specifically includes the following:

[0078] The device further includes a big data model building module 105, which is used for:

[0079] Historical power parameters are acquired, and the magnitude and duration of the historical parameter items exceeding the set threshold are determined. Alarm information processing results for the historical parameter items are also acquired. Based on the alarm information processing results, weight determination results and anomaly level classification results are obtained for the magnitude and duration of the parameters exceeding the set threshold. Based on the magnitude and duration of the historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results, a big data model is constructed.

[0080] In this embodiment, a big data module can be constructed using historical events. Specifically, historical parameter items can be parameter items from power data that have previously triggered alarm events. The alarm information processing result can be a set of processing result data obtained by monitoring personnel through the terminal after judging the problem based on the alarm information, including the name of the parameter item that previously showed an anomaly, the magnitude of exceeding the set threshold, the duration, and the anomaly level, and then informing the maintenance personnel. The maintenance personnel then summarize the results after resolving the problem.

[0081] In this scheme, the weight determination result can be the assignment of importance by the monitoring personnel to the magnitude and duration of exceeding the set threshold. The weight can be letters, numbers, or words. For example, the total weight level of the magnitude and duration of the set threshold for each power parameter in the box-type transformer is 10. The weight level of each item can be divided into 1-10 from light to heavy. If the magnitude weight level of the current parameter set threshold is assigned as 1, then the duration weight level is assigned as 9. The calculation method is to multiply the value exceeding or falling below the set threshold by the magnitude weight level of the set threshold, and add the result of multiplying the duration by the duration weight level. If the magnitude of the current parameter exceeds 4 amperes and the duration is 5 seconds, the weight determination result is 4×1+5×9=49. The abnormality level can be divided according to the size of the value. The larger the value, the higher the abnormality level.

[0082] Building a big data model involves using big data modeling tools to model based on the magnitude and duration of historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results. These tools can be regression analysis models, random forests, time series models, neural networks, and SVMs, etc. This solution can use a random forest model for modeling, using the magnitude and duration of historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results as data, to create a decision tree.

[0083] The technical solution provided in this embodiment constructs a big data model by setting up a big data model building module. Specifically, it builds a big data model based on the magnitude and duration of historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results. This big data model allows for the rapid and accurate determination of fault levels, informing maintenance personnel to perform repairs. Maintenance personnel can also use this model to determine the priority of repairs, achieving the effect of prioritizing the resolution of critical problems.

[0084] Example 3

[0085] Figure 3 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 3 of this application. Figure 3 As shown, it specifically includes the following:

[0086] The monitoring and reporting module 106 is used to obtain the processing result of the alarm information through the monitoring terminal;

[0087] The big data model update module 107 is used to update the parameters of the big data model based on the obtained processing results every time a preset period is reached.

[0088] In this embodiment, updating the parameters of the big data model can be achieved by continuously adjusting the weight levels based on the processing results after an anomaly is resolved, thereby constantly adjusting the big data model and finding optimized solutions for the problem. For example, after maintenance personnel resolve a fault caused by a problem with the current parameters, they find that the weight of the magnitude of the current parameter exceeding the set threshold should be adjusted from 1 to 3, and the weight of the duration should be adjusted from 9 to 7.

[0089] The technical solution provided in this embodiment, by setting up a monitoring and reporting module and a big data model construction module, specifically obtains the processing results of the alarm information through the monitoring terminal, and updates the parameters of the big data model based on the obtained processing results at each preset period. The big data model can be continuously optimized according to the processing results of solving problems in one stage, thereby improving the accuracy of solving abnormal problems.

[0090] Example 4

[0091] Figure 4 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 4 of this application. Figure 4 As shown, it specifically includes the following:

[0092] The attribute data acquisition module 108 is used to acquire the attribute data of the box-type transformer;

[0093] Accordingly, the anomaly level determination module 103 includes:

[0094] The attribute matching unit 1031 is used to determine the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item;

[0095] The identification result determination unit 1032 is used to determine the amplitude weight and duration weight according to the algorithm rules, so as to determine the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

[0096] In this embodiment, attribute data is divided into qualitative and quantitative types. The former includes name, type, and characteristics, such as current land use status, rock type, administrative division, and certain soil properties; the latter includes quantity and grade, such as area, length, and land grade. In this scheme, the attribute data of the box-type transformer can be its size attributes, power attributes, and external facility attributes. Algorithm rules are the sequential representation of the various steps in the operation of a thing, using rules and operations. They can be classification, regression analysis, clustering, or association rules. In this scheme, regression analysis can be used as the corresponding algorithm rule. The dependency relationship between the attribute data of the box-type transformer and the name of the current parameter item is discovered by expressing the mapping relationship between the attribute data of the box-type transformer and the name of the current parameter item through a function.

[0097] The matching is based on the attribute data of the box-type transformer and the name of the current parameter item to determine the algorithm rules, in order to prepare for determining the anomaly level identification result.

[0098] Understandably, this technical solution can employ different algorithm rules to determine the anomaly level for box-type transformers with different attribute data. The advantage of this approach is that different evaluation indicators can be used to evaluate different types of box-type transformers, thereby improving the accuracy of the anomaly level determination results.

[0099] The technical solution provided in this embodiment acquires attribute data and determines an anomaly level by setting up an attribute data acquisition module and an anomaly level determination module. Specifically, it acquires the attribute data of the box-type transformer, determines the matching algorithm rules based on the attribute data and the name of the current parameter item, and determines the amplitude weight and duration weight based on the algorithm rules. The anomaly level of the parameter item is then determined based on these weights. This allows for matching appropriate algorithm rules to different box-type transformers, solving the problem of incompatibility when using the same algorithm rules for different box-type transformers, and the need to set separate algorithm rules for incompatible transformers. This improves the efficiency of algorithm rule design for staff.

[0100] Example 5

[0101] Figure 5 This is a schematic diagram of the power parameter alarm device based on a box-type transformer provided in Embodiment 5 of this application. This solution makes further improvements to the above embodiments, such as... Figure 5 As shown, the specific improvement is as follows: the power parameter analysis module 102 is also used for:

[0102] If the decryption of the power parameter data fails, a decryption error message is generated and sent to the monitoring terminal.

[0103] In this embodiment, the abnormal decryption information is the information sent to the monitoring personnel after the power parameter data decryption fails, and it can be the reason for the decryption failure.

[0104] The technical solution provided in this embodiment generates decryption error information and sends it to the monitoring terminal when power parameter data decryption fails. This solves the problem of not knowing the reason for the failure and being unable to perform subsequent tasks such as generating alarm information after power parameter data decryption fails, thereby improving the efficiency of monitoring personnel in finding solutions after discovering problems.

[0105] Example 6

[0106] Figure 6 This is a flowchart illustrating the power parameter alarm method based on a box-type transformer provided in Embodiment Six of this application. Figure 6 As shown, the method includes:

[0107] S601, the power parameter data transmitted by the box-type transformer is obtained through the power parameter acquisition module; wherein, the power parameter data is obtained by encryption using a preset key;

[0108] S602, the power parameter parsing module decrypts the power parameter data using the decryption key corresponding to the preset key to obtain the power parameters of the box transformer; and determines whether there are any parameter items in the power parameters that exceed the set threshold, as well as the magnitude and duration of exceeding the set threshold;

[0109] S603, the anomaly level determination module uses a pre-determined big data model to identify the magnitude and duration of the exceedance of the set threshold, and obtains the identification result of the anomaly level of the current parameter item; the alarm module generates alarm information based on the identification result and sends the alarm information to the monitoring terminal;

[0110] S604, the alarm module generates alarm information based on the identification result and sends the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

[0111] Furthermore, before acquiring the power parameter data transmitted by the box-type transformer and encrypted using a preset key through the power parameter acquisition module, the method further includes:

[0112] Acquire historical power parameters, determine the magnitude and duration of historical parameter items exceeding the set threshold, and obtain the alarm information processing results of historical parameter items;

[0113] Based on the alarm information processing results, the magnitude and duration of the exceedance of the set threshold are weighted and classified into abnormal levels.

[0114] A big data model is constructed based on the magnitude and duration of the historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results.

[0115] Furthermore, after generating alarm information based on the identification results through the alarm module and sending the alarm information to the monitoring terminal, the method further includes:

[0116] The processing results of the alarm information are obtained on the monitoring terminal through the monitoring and reporting module.

[0117] At each preset period, the parameters of the big data model are updated by the big data model update module based on the obtained processing results.

[0118] Furthermore, before using a big data model through the anomaly level determination module to identify the magnitude and duration exceeding a set threshold and obtain the anomaly level identification result for the current parameter item, the method further includes:

[0119] The attribute data of the box-type transformer is obtained through the attribute data acquisition module;

[0120] Correspondingly, the anomaly level determination module uses a big data model to identify the magnitude and duration exceeding the set threshold, obtaining the anomaly level identification result for the current parameter item, including:

[0121] The attribute matching unit determines the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item.

[0122] The identification result determination unit determines the amplitude weight and duration weight according to the algorithm rules, and determines the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

[0123] Furthermore, after decrypting the power parameter data using the decryption key corresponding to the preset key, the method further includes:

[0124] If the decryption of the power parameter data fails, a decryption error message is generated and sent to the monitoring terminal.

[0125] In this embodiment, the power parameter acquisition module acquires the power parameter data transmitted by the box-type transformer, which is encrypted using a preset key. The power parameter parsing module decrypts the power parameter data using a decryption key to obtain the power parameters of the box-type transformer and determines whether any parameter exceeds a set threshold, as well as the magnitude and duration of the exceedance. The anomaly level determination module uses a big data model to identify the magnitude and duration of the exceedance, obtaining the anomaly level of the current parameter. The alarm module generates alarm information based on the identification results and sends the alarm information to the monitoring terminal. This allows monitoring personnel to view alarm information in real time, identify problematic power parameters, prioritize repairs based on the anomaly level, and inform maintenance personnel of the specific problem and repair location. Furthermore, the big data model can be continuously updated after the problem is resolved to improve problem-solving efficiency. Simultaneously, monitoring personnel can determine whether there are false alarms online based on the alarm information, saving maintenance personnel time.

[0126] The power parameter alarm method based on a box-type transformer provided in this embodiment corresponds to the device provided in the above embodiments and has a corresponding execution process and beneficial effects, which will not be described in detail here.

[0127] Example 7

[0128] Figure 7 This is a schematic diagram of the electronic device provided in Embodiment 7 of this application. Figure 7 As shown, this application embodiment also provides an electronic device 700, including a processor 701, a memory 702, and a program or instructions stored in the memory 702 and executable on the processor 701. When the program or instructions are executed by the processor 701, they implement the various processes of the above-described embodiment of the power parameter alarm method based on a box-type transformer and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0129] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0130] Example 8

[0131] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described power parameter alarm method embodiment based on a box-type transformer and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0132] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0133] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0134] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0135] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

[0136] The above description is merely a preferred embodiment and the technical principles employed in this application. This application is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions that can be made by those skilled in the art will not depart from the scope of protection of this application. Therefore, although this application has been described in detail through the above embodiments, this application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of this application, the scope of which is determined by the scope of the claims.

Claims

1. A power parameter alarm device based on a box-type transformer, characterized in that, The device includes: The big data model building module is used to acquire historical power parameters, determine the magnitude and duration of historical parameter items exceeding a set threshold, and acquire alarm information processing results for historical parameter items; based on the alarm information processing results, determine the weight and anomaly level classification results for the magnitude and duration of exceeding the set threshold; and construct a big data model based on the magnitude and duration of historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results. The power parameter acquisition module is used to acquire power parameter data transmitted by the box-type transformer; wherein, the power parameter data is obtained by encryption using a preset key; The power parameter parsing module is used to decrypt the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and to determine whether there are any parameter items in the power parameters that exceed a set threshold, as well as the magnitude and duration of exceeding the set threshold. An anomaly level determination module is used to identify the magnitude and duration of the exceedance of the set threshold using a pre-determined big data model, and obtain the anomaly level identification result of the current parameter item. An alarm module is used to generate alarm information based on the identification results and send the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

2. The power parameter alarm device based on a box-type transformer according to claim 1, characterized in that, The device further includes: The monitoring and reporting module is used to obtain the processing results of the alarm information through the monitoring terminal; The big data model update module is used to update the parameters of the big data model based on the obtained processing results every preset period.

3. The power parameter alarm device based on a box-type transformer according to claim 1, characterized in that, The device further includes: The attribute data acquisition module is used to acquire the attribute data of the box-type transformer; Accordingly, the anomaly level determination module includes: The attribute matching unit is used to determine the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item. The identification result determination unit is used to determine the amplitude weight and duration weight according to the algorithm rules, so as to determine the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

4. The power parameter alarm device based on a box-type transformer according to claim 1, characterized in that, The power parameter analysis module is also used for: If the decryption of the power parameter data fails, a decryption error message is generated and sent to the monitoring terminal.

5. A power parameter alarm method based on a box-type transformer, characterized in that, The method includes: Acquire historical power parameters, determine the magnitude and duration of historical parameter items exceeding the set threshold, and obtain the alarm information processing results of historical parameter items; Based on the alarm information processing results, the magnitude and duration of the exceedance of the set threshold are weighted and classified into abnormal levels. A big data model is constructed based on the magnitude and duration of the historical parameter items exceeding the set threshold, as well as the weight determination results and anomaly level classification results. The power parameter data transmitted by the box-type transformer is obtained through the power parameter acquisition module; wherein, the power parameter data is obtained by encryption using a preset key; The power parameter parsing module decrypts the power parameter data using a decryption key corresponding to the preset key to obtain the power parameters of the box-type transformer; and determines whether any parameter in the power parameters exceeds a set threshold, as well as the magnitude and duration of exceeding the set threshold. The anomaly level determination module uses a pre-determined big data model to identify the magnitude and duration of the exceedance of the set threshold, and obtains the anomaly level identification result of the current parameter item; the alarm module generates alarm information based on the identification result and sends the alarm information to the monitoring terminal. The alarm module generates alarm information based on the identification results and sends the alarm information to the monitoring terminal; wherein, the alarm information includes the name of the current parameter item, the magnitude of exceeding the set threshold, the duration, and the anomaly level.

6. The power parameter alarm method based on a box-type transformer according to claim 5, characterized in that, After generating alarm information based on the identification results through the alarm module and sending the alarm information to the monitoring terminal, the method further includes: The processing results of the alarm information are obtained on the monitoring terminal through the monitoring and reporting module. At each preset period, the parameters of the big data model are updated by the big data model update module based on the obtained processing results.

7. The power parameter alarm method based on a box-type transformer according to claim 5, characterized in that, Before using a big data model through the anomaly level determination module to identify the magnitude and duration exceeding a set threshold and obtain the anomaly level identification result for the current parameter item, the method further includes: The attribute data of the box-type transformer is obtained through the attribute data acquisition module; Correspondingly, the anomaly level determination module uses a big data model to identify the magnitude and duration exceeding the set threshold, obtaining the anomaly level identification result for the current parameter item, including: The attribute matching unit determines the matching algorithm rules based on the attribute data of the box-type transformer and the name of the current parameter item. The identification result determination unit determines the amplitude weight and duration weight according to the algorithm rules, and determines the identification result of the abnormality level of the parameter item based on the amplitude weight and the duration weight.

8. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and running on the processor, wherein when the program or instructions are executed by the processor, they implement the steps of the power parameter alarm method based on a box-type transformer as described in any one of claims 5-7.

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