An intelligent monitoring method for power distribution cabinet

Abstract

The application provides a kind of intelligent monitoring method for switchgear. A kind of intelligent monitoring method for switchgear, comprising: S1, receiving maintenance information;S2, authenticating the equipment to be maintained, in response to the authentication of the equipment to be maintained, jump to step S3;S3, obtain equipment association information, calculate the first variable ratio according to the equipment association information, generate the first configuration information;S4, send the first configuration information to the switchgear system;S5, receive the second configuration information issued by the switchgear system, and perform configuration proofreading verification. The application authenticates the current transformer, calculates the variable ratio of the current transformer according to the basic information of the current transformer and the multifunctional ammeter, and automatically configures the current transformer according to the variable ratio, avoiding errors in the process of configuring the variable ratio of the current transformer by manual, improving the installation and configuration efficiency of the current transformer, and eliminating measurement errors caused by manual configuration errors.

Description

Technical Field

[0001] This invention relates to the field of power distribution cabinet technology, and in particular to an intelligent monitoring method for power distribution cabinets. Background Technology

[0002] During the installation of current transformers in power cabinets, engineers must manually identify the current transformer ratios and manually configure them on the power cabinet's multifunction meter. Due to the numerous manual operations, incorrect current transformer ratios may be set, making troubleshooting difficult. Summary of the Invention

[0003] This application provides an intelligent monitoring method for power distribution cabinets to solve the above-mentioned technical problems.

[0004] The technical solution of this invention is: an intelligent monitoring method for power distribution cabinets, applied to power distribution cabinet systems and intelligent detection terminals, comprising the following steps:

[0005] S1: Receive maintenance information sent by the power distribution cabinet system;

[0006] The maintenance information includes the first device ID, the first device information, and the maintenance request time;

[0007] S2: Based on the maintenance information, authenticate the device to be maintained. In response to the successful authentication of the device to be maintained, proceed to step S3.

[0008] S3: Obtain device association information, calculate the first transformation ratio value based on the device association information, and generate the first configuration information based on the first transformation ratio value;

[0009] S4: Send the first configuration information to the power distribution cabinet system;

[0010] S5: Receive the second configuration information sent by the power distribution cabinet system, and perform configuration verification based on the first and second configuration information.

[0011] Furthermore, the equipment to be maintained is certified, including:

[0012] Match the first device ID with the maintenance device database, and check if the first device ID exists in the maintenance device database;

[0013] If it does not exist, it means that the identity authentication failed and an identity authentication failure message will be returned;

[0014] If it exists, it means that the identity authentication is successful, and the maintenance request time is authenticated;

[0015] Determine whether the maintenance request time is within the preset maintenance time range;

[0016] If not, it means that the time authentication failed and a time authentication failure message will be returned;

[0017] If so, it means that the time-based authentication has passed, and feedback will be provided regarding the authentication success of the equipment to be maintained.

[0018] Furthermore, step S3 includes:

[0019] The device association information includes the second device ID, the second device information, the third device ID, and the third device information;

[0020] Authenticate the device to be installed based on the third device ID;

[0021] In response to the successful certification of the equipment to be installed, a first ratio value is calculated based on the first equipment information, the second equipment information, and the third equipment information;

[0022] Based on the first transformation ratio value, first configuration information is generated, which includes the first transformation ratio value and the third device ID.

[0023] Further, step S4 includes:

[0024] After receiving the first configuration information, the power distribution cabinet system automatically configures the equipment to be installed according to the first transformation ratio value in the first configuration information;

[0025] The power distribution cabinet system obtains the second transformation ratio value of the equipment to be installed, generates second configuration information based on the second transformation ratio value, and sends the second configuration information to the intelligent detection terminal.

[0026] Furthermore, step S5 includes:

[0027] The second configuration information includes the second ratio value and the third device ID;

[0028] Compare the first ratio value in the first configuration information with the second ratio value in the second configuration information to determine whether the first ratio value and the second ratio value are equal;

[0029] If yes, it means the automatic configuration was successful; otherwise, it means the automatic configuration failed, and an automatic configuration failure message will be displayed.

[0030] Furthermore, it also includes:

[0031] Obtain users' electricity consumption data, calculate risk values ​​based on the electricity consumption data, and perform limit judgment on the calculated risk values. Determine whether the risk value is greater than or equal to the preset risk threshold. If it is, mark the corresponding electricity consumption data as abnormal data; otherwise, mark it as normal data.

[0032] Abnormal data is input into a preset model, which estimates the probability of a fire. The probability of a fire is then judged to determine if it exceeds a preset probability threshold. If so, the power transmission is reduced to a safe level, and risk information is fed back.

[0033] Furthermore, for the preset model, it includes:

[0034] The default model is a neural network model. The construction and training of the default model includes:

[0035] Build a neural network model;

[0036] Obtain the electricity consumption dataset, classify and label the data in the electricity consumption dataset to obtain risk data and normal data, create the labeled electricity consumption dataset as the training dataset, and divide the training dataset into training set and test set according to the preset ratio;

[0037] The neural network model is trained using a training set and tested using a test set to obtain a well-trained neural network model.

[0038] Furthermore, it also includes:

[0039] Temperature information is acquired periodically at a preset frequency. After each acquisition of temperature information, it is determined whether the maximum temperature in the temperature information is greater than a preset temperature threshold.

[0040] If so, it indicates a risk and a warning message will be sent; otherwise, a message indicating that the temperature is normal will be sent.

[0041] The present invention has the following advantages:

[0042] 1. This invention certifies the current transformer and calculates the transformation ratio of the current transformer based on the basic information of the current transformer and the multi-function ammeter. It then automatically configures the current transformer based on the transformation ratio, avoiding errors that occur when manually configuring the transformation ratio of the current transformer. This improves the efficiency of current transformer installation and configuration and eliminates measurement errors caused by manual configuration mistakes.

[0043] 2. This invention reduces the probability of fire by using a preset model to estimate the probability of fire and periodically obtains the temperature of the distribution cabinet, thereby achieving intelligent monitoring of the distribution cabinet. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating an intelligent monitoring method for a power distribution cabinet provided in an embodiment of the present invention. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, some embodiments of this application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to facilitate a better understanding of the application. However, the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0047] Example 1

[0048] See Figure 1 Embodiment 1 of the present invention provides an intelligent monitoring method for power distribution cabinets, which is applied to power distribution cabinet systems and intelligent detection terminals, and includes the following steps:

[0049] S1: Receive maintenance information sent by the power distribution cabinet system;

[0050] The maintenance information includes at least the first device ID, the first device information, and the maintenance request time. The first device ID represents the unique identifier of the power distribution cabinet. The first device information is the basic information of the power distribution cabinet, such as the specifications and operating parameters of the power distribution cabinet. The maintenance request time is the time when the maintenance request is issued.

[0051] S2: Based on the maintenance information, authenticate the device to be maintained. In response to the successful authentication of the device to be maintained, proceed to step S3.

[0052] The authentication of equipment to be maintained includes both identity authentication and time authentication.

[0053] S3: Obtain device association information, calculate the first transformation ratio value based on the device association information, and generate the first configuration information based on the first transformation ratio value;

[0054] The device association information includes a second device ID, second device information, a third device ID, and third device information. The second device ID represents the unique identifier of the multi-function ammeter, and the second device information specifically includes the basic information of the multi-function ammeter, such as its range and operating frequency. The third device ID represents the unique identifier of the current transformer, and the third device information specifically includes the basic information of the current transformer, such as its transformation ratio adjustment range.

[0055] S4: Send the first configuration information to the power distribution cabinet system;

[0056] The distribution cabinet system receives the first configuration information, generates the second configuration information based on the first configuration information, and sends the second configuration information to the intelligent detection terminal.

[0057] S5: Receive the second configuration information sent by the power distribution cabinet system, and perform configuration verification based on the first and second configuration information.

[0058] The present invention provides a method for intelligent monitoring of distribution cabinets, which authenticates current transformers through device IDs, calculates the transformation ratio of current transformers based on the basic information of current transformers and multi-function ammeters, and automatically configures current transformers based on transformation ratios. This avoids errors that occur when configuring the transformation ratio of current transformers manually, improves the efficiency of current transformer installation and configuration, and eliminates measurement errors caused by manual configuration mistakes.

[0059] In a further implementation, the equipment to be maintained is certified, including:

[0060] Match the first device ID with the maintenance device database, and check if the first device ID exists in the maintenance device database;

[0061] If it does not exist, it means that the identity authentication failed and an identity authentication failure message will be returned;

[0062] If it exists, it means that the identity authentication is successful, and the maintenance request time is authenticated;

[0063] It should be added that the maintenance equipment database records the equipment IDs and corresponding basic information of the power distribution cabinets that need maintenance. The identity of the power distribution cabinet is authenticated by matching the first equipment ID in the maintenance equipment database.

[0064] Authentication of maintenance request time includes:

[0065] Determine whether the maintenance request time is within the preset maintenance time range;

[0066] If not, it means that the time authentication failed and a time authentication failure message will be returned;

[0067] If so, it means that the time-based authentication has passed, and feedback will be provided regarding the authentication success of the equipment to be maintained.

[0068] It should be added that the preset maintenance time range is a pre-set maintenance time range, which can be reasonably set according to the actual situation.

[0069] In a further embodiment, step S3 includes:

[0070] Authenticate the device to be installed based on the third device ID;

[0071] In response to the successful certification of the equipment to be installed, a first ratio value is calculated based on the first equipment information, the second equipment information, and the third equipment information;

[0072] Based on the first transformation ratio value, first configuration information is generated, which includes the first transformation ratio value and the third device ID.

[0073] It should be added that the equipment to be installed is a current transformer. The equipment to be installed is authenticated according to the current transformer database. The first equipment information, the second equipment information, and the third equipment information include the basic information of the distribution cabinet, the multi-function ammeter, and the current transformer, respectively. The first transformation ratio value used for automatic configuration of the current transformer can be calculated based on the basic information of the distribution cabinet, the multi-function ammeter, and the current transformer. The first transformation ratio value is then associated with the third equipment ID to generate the first configuration information.

[0074] In a further embodiment, step S4 includes:

[0075] After receiving the first configuration information, the power distribution cabinet system automatically configures the equipment to be installed according to the first transformation ratio value in the first configuration information;

[0076] The power distribution cabinet system obtains the second transformation ratio value of the equipment to be installed, generates second configuration information based on the second transformation ratio value, and sends the second configuration information to the intelligent detection terminal.

[0077] It should be added that the distribution cabinet system automatically adjusts the transformation ratio of the current transformer according to the first transformation ratio value in the first configuration information to realize the automatic configuration of the current transformer, and generates the second configuration information by associating the second transformation ratio value with the third device ID.

[0078] In a further embodiment, step S5 includes:

[0079] The second configuration information includes the second ratio value and the third device ID;

[0080] Compare the first ratio value in the first configuration information with the second ratio value in the second configuration information to determine whether the first ratio value and the second ratio value are equal;

[0081] If yes, it means the automatic configuration was successful; otherwise, it means the automatic configuration failed, and an automatic configuration failure message will be displayed.

[0082] It should be added that before comparing the first ratio value in the first configuration information and the second ratio value in the second configuration information, it is first determined whether the device ID in the first configuration information is the same as the device ID in the second configuration information. If the device ID in the first configuration information is the same as the device ID in the second configuration information, then the first ratio value in the first configuration information and the second ratio value in the second configuration information are compared.

[0083] In a further embodiment, it also includes:

[0084] Obtain users' electricity consumption data, calculate risk values ​​based on the electricity consumption data, and perform limit judgment on the calculated risk values. Determine whether the risk value is greater than or equal to the preset risk threshold. If it is, mark the corresponding electricity consumption data as abnormal data; otherwise, mark it as normal data.

[0085] Abnormal data is input into a preset model, which estimates the probability of a fire. The probability of a fire is then judged to determine if it exceeds a preset probability threshold. If so, the power transmission is reduced to a safe level, and risk information is fed back.

[0086] It should be added that the preset risk threshold is a pre-set risk reference value. If the risk value is greater than or equal to the preset risk threshold, it means that there is a certain probability of a fire. The preset model is a neural network model, which is used to further analyze abnormal data and improve the accuracy of the prediction.

[0087] Specifically, the construction and training of the pre-defined model includes:

[0088] Construct a preset model, which is a neural network model;

[0089] Obtain an electricity consumption dataset, classify and label the data in the electricity consumption dataset to obtain risk data and normal data, create a training dataset from the labeled electricity consumption dataset, and divide the training dataset into a training set and a test set according to a preset ratio; in this embodiment, the preset ratio is 9:1.

[0090] The neural network model is trained using the training set. Once the convergence condition is met, the training of the neural network is stopped, and the neural network model after training is stopped is tested using the test set to obtain the trained neural network model.

[0091] In a further embodiment, it also includes:

[0092] Temperature information is acquired periodically at a preset frequency. After each acquisition of temperature information, it is determined whether the maximum temperature in the temperature information is greater than a preset temperature threshold.

[0093] If so, it indicates a risk and a warning message will be sent; otherwise, a message indicating that the temperature is normal will be sent.

[0094] It should be added that by periodically acquiring the temperature information of the distribution cabinet, intelligent monitoring of the distribution cabinet can be achieved, reducing the probability of fire.

[0095] It should be understood that those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims. Parts not described in detail in this specification are prior art known to those skilled in the art.

Claims

1. A method for intelligent monitoring of power distribution cabinets, characterized in that, Applied to power distribution cabinet systems and intelligent detection terminals, the process includes the following steps: S1: Receive maintenance information sent by the power distribution cabinet system; The maintenance information includes the first device ID, the first device information, and the maintenance request time; S2: Based on the maintenance information, authenticate the device to be maintained. If the device to be maintained passes the authentication, proceed to step S3. S3: Obtain device association information, calculate the first transformation ratio value based on the device association information, and generate the first configuration information based on the first transformation ratio value; S4: Send the first configuration information to the power distribution cabinet system; S5: Receive the second configuration information sent by the power distribution cabinet system, and perform configuration verification based on the first and second configuration information; Certification of equipment to be maintained includes: Match the first device ID with the maintenance device database, and check if the first device ID exists in the maintenance device database; If it does not exist, it means that the identity authentication failed and an identity authentication failure message will be returned; If it exists, it means that the identity authentication is successful, and the maintenance request time is authenticated; Determine whether the maintenance request time is within the preset maintenance time range; If not, it means that the time authentication failed and a time authentication failure message will be returned; If so, it means that the time certification has passed, and feedback will be provided on the certification of the equipment to be maintained; For step S3, the following are included: The device association information includes the second device ID, the second device information, the third device ID, and the third device information; Authenticate the third device based on the third device ID; In response to the successful authentication of the third device, a first ratio value is calculated based on the information of the first device, the second device, and the third device. Based on the first transformation ratio value, first configuration information is generated, which includes the first transformation ratio value and the third device ID; For step S4, the following are included: After receiving the first configuration information, the power distribution cabinet system automatically configures the third device according to the first transformation ratio value in the first configuration information; The power distribution cabinet system obtains the second transformation ratio value of the third device, generates second configuration information based on the second transformation ratio value, and sends the second configuration information to the intelligent detection terminal. For step S5, the following are included: The second configuration information includes the second ratio value and the third device ID; Compare the first ratio value in the first configuration information with the second ratio value in the second configuration information to determine whether the first ratio value and the second ratio value are equal; If yes, it means the automatic configuration was successful; otherwise, it means the automatic configuration failed and a failure message will be displayed. The first device ID represents the unique identifier of the power distribution cabinet, and the first device information specifically refers to the basic information of the power distribution cabinet. The second device ID represents the unique identifier of the multi-function ammeter; The third device ID represents the unique identifier of the current transformer; The third device is a current transformer. The third device is certified according to the current transformer database. The first device information, the second device information and the third device information include the basic information of the distribution cabinet, the multi-function ammeter and the current transformer respectively. The first transformation ratio value for automatic configuration of the current transformer can be calculated based on the basic information of the distribution cabinet, the multi-function ammeter and the current transformer. Also includes: Obtain users' electricity consumption data, calculate risk values ​​based on the electricity consumption data, and perform limit judgment on the calculated risk values. Determine whether the risk value is greater than or equal to the preset risk threshold. If it is, mark the corresponding electricity consumption data as abnormal data; otherwise, mark it as normal data. Abnormal data is input into a preset model, which estimates the probability of a fire. The probability of a fire is then judged to determine if it exceeds the preset probability threshold. If so, the power transmission is reduced to a safe level and risk information is fed back. For the preset model, including: The default model is a neural network model. The construction and training of the default model includes: Construct a neural network model; Obtain the electricity consumption dataset, classify and label the data in the electricity consumption dataset to obtain risk data and normal data, create the labeled electricity consumption dataset as the training dataset, and divide the training dataset into training set and test set according to the preset ratio; The neural network model is trained using a training set and tested using a test set to obtain a trained neural network model. Also includes: Temperature information is acquired periodically at a preset frequency. After each acquisition of temperature information, it is determined whether the maximum temperature in the temperature information is greater than a preset temperature threshold. If so, it indicates a risk and a warning message will be sent; otherwise, a message indicating that the temperature is normal will be sent.

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