Power supply pipe gallery management system and method based on Internet of Things and other devices

Abstract

The invention provides a power supply pipe gallery management system and method based on the Internet of Things and other devices, and relates to the technical field of the Internet of Things. Comprising an Internet of Things equipment module, a central processing unit and an equipment monitoring management module, the Internet of Things equipment module comprises a pipe gallery sensing module and a communication module, and is placed in each equipment, needing to be monitored, of the power supply pipe gallery; the pipe gallery sensing module is used for collecting original monitoring data of the Internet of Things equipment module; the communication module is used for transmitting the original monitoring data to the central processing unit and encrypting the data; when the central processing unit analyzes an abnormal value, alarm information is formed and transmitted to the equipment monitoring management module. According to the system, real-time monitoring and data analysis are realized, manual inspection is reduced, the fault processing time is shortened, potential safety hazards can be found and processed in time, and the continuity and stability of power supply are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a power supply corridor management system, method and other devices based on the Internet of Things. Background Art

[0002] Power supply corridors, as the lifeline of underground power transmission in modern cities, bear the important responsibility of ensuring the continuity and stability of urban power supply. They are an indispensable part of urban infrastructure and are directly related to the convenience of residents' daily lives and the normal operation of the social economy.

[0003] In the traditional mode, the status monitoring of equipment in the power supply corridor mainly relies on manual inspections. This method is not only inefficient, but also prone to missing key information, making it difficult to discover and deal with equipment hazards in a timely manner. Secondly, the accuracy of data analysis needs to be improved. Due to limited data collection methods and backward analysis tools, traditional methods are unable to handle massive amounts of monitoring data, and it is difficult to extract valuable information from them to guide operation and maintenance decisions. Finally, the timeliness of fault response is not strong. Once equipment failure or abnormal conditions occur in the power supply corridor, the traditional repair and response mechanism often takes a long time, and it is impossible to quickly locate the problem and take measures, which affects the reliability and continuity of power supply. Therefore, how to efficiently realize the risk operation and maintenance management of urban power supply corridors is an urgent problem to be solved. Summary of the invention

[0004] The purpose of this application is to provide a power supply corridor management system, method and other devices based on the Internet of Things, so as to manage and operate the power supply corridor through intelligent means.

[0005] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, an embodiment of the present application provides a power supply corridor management system based on the Internet of Things, including: an Internet of Things device module, a central processing unit, and an equipment monitoring management module;

[0007] The IoT device module includes a pipe gallery sensing module and a communication module, and is placed in each device that needs to be monitored in the power supply pipe gallery;

[0008] The pipe gallery sensing module is used to collect the original monitoring data of the Internet of Things device module;

[0009] The communication module is used to transmit the original monitoring data to the central processing unit and encrypt the data;

[0010] The central processing unit also includes a decryption module, a data processing and analysis module, and a fault alarm module; the data processing and analysis module is used to process the original monitoring data; the data analysis module is provided with equipment status, operating parameters and environmental indicators. When the data analysis module analyzes an abnormal value, the fault alarm module generates an alarm message and transmits it to the equipment monitoring and management module. The equipment monitoring and management module is used to manage various types of equipment.

[0011] Furthermore, the tunnel sensing module includes any one of passive temperature measurement tags, humidity sensors, surveillance cameras, air quality sensors, stress sensors, photosensors, light intensity sensors, brightness sensors, magnetic field intensity sensors, flow rate sensors, pressure sensors, water quality sensors, current transformers, voltage transformers, and rainfall sensors, or any of them are used simultaneously.

[0012] Furthermore, the communication module is arranged in the Internet of Things device module, and the communication module is used for communication between the Internet of Things device modules or communication with the central processing unit.

[0013] Furthermore, the communication module encrypts the collected original monitoring data to satisfy the formula:

[0014] C=E(K,P)

[0015] Among them, C represents the encrypted original data, E represents the encryption function, K represents the preset key, and P represents the original monitoring data.

[0016] Furthermore, the data processing and analysis module includes a decision tree model module, which is used to identify and predict the processed original monitoring data. The fault alarm module performs anomaly detection and fault prediction based on the results output by the decision tree model module, generates early warning information, and the fault alarm module transmits the early warning information to the equipment monitoring and management module.

[0017] Further, the decision tree model module implements the following steps when processing the identification and prediction of the original monitoring data;

[0018] Step 1, decrypting the encrypted original monitoring data through the decryption module to obtain the decrypted original data;

[0019] Step 2: First, the decrypted original monitoring data is cleaned and normalized; secondly, the cleaned data is subjected to feature extraction; thirdly, a data set is constructed and divided into a training set and a validation set; finally, all features and partition points of the constructed data set are traversed, the Gini coefficient after partition is calculated, and the partition with the smallest Gini coefficient is selected as the optimal partition. The equipment is labeled, the normal state is marked as 0, the abnormal state is marked as 1, and the threshold is preset;

[0020] Step 3: Use the CART recursive tree algorithm to train the training set;

[0021] Step 4: construct a decision tree model by recursively dividing the data feature space until the training set reaches the preset threshold;

[0022] Step 5: Evaluate the decision tree model by cross-validation. If the validation result does not meet expectations, further adjust the parameters and repeat steps 1 to 4 until the validation result meets expectations.

[0023] In a second aspect, an embodiment of the present application further provides an IoT power supply corridor management method, wherein the cable core temperature determination device comprises:

[0024] The Internet of Things power supply corridor management method comprises:

[0025] Step 1: Deploy a tunnel sensing module at the tunnel site to collect raw data of the power supply system in real time;

[0026] Step 2: The original data is transmitted to any one of the above-mentioned IoT power supply corridor management systems for analysis and prediction. When an abnormal value is analyzed, an alarm message is generated by the fault alarm module and transmitted to the equipment monitoring management module;

[0027] Step 3: Implement daily operation and management of the power supply corridor through the Internet of Things power supply corridor management system.

[0028] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0029] A memory for storing one or more programs; a processor; when the one or more programs are executed by the processor, the above-mentioned Internet of Things power supply corridor management system is implemented.

[0030] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned Internet of Things power supply corridor management system is implemented.

[0031] Compared with the prior art, this application has the following beneficial effects:

[0032] 1. The system realizes real-time monitoring and data analysis, reduces manual inspections, and shortens fault handling time;

[0033] 2. The system can promptly detect and handle potential safety hazards, such as equipment overheating, abnormal humidity, etc., effectively avoiding safety accidents caused by equipment failure or environmental factors. At the same time, the system's rapid response mechanism can quickly locate faults and take measures to ensure the continuity and stability of power supply;

[0034] 3. Automated operation and maintenance reduces the need for manual inspection and maintenance, and reduces labor and time costs.

[0035] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 Schematic diagram of the Internet of Things-based power supply corridor management system provided in an embodiment of the present application.

[0038] Figure 2 A schematic diagram of a module of an electronic device provided in an embodiment of the present application.

[0039] Figure 3 A schematic diagram of raw data prediction for a power supply corridor provided in an embodiment of the present application.

[0040] Figure 4 A schematic diagram of a power supply corridor management method provided in an embodiment of the present application.

[0041] In the figure: 1-power supply cable; 2-IoT device module; 3-central processing unit; 4-manhole cover; 5-equipment monitoring and management module; 100-electronic device; 101-processor; 102-memory; 103-communication interface. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0044] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0045] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0046] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0047] As described in the background technology, in order to efficiently realize the risk operation and maintenance management of urban power supply corridors, a power supply corridor management system based on the Internet of Things is needed. Generally speaking, the status monitoring of equipment in the power corridor mainly relies on manual inspections. This method is not only inefficient, but also easy to miss key information, resulting in equipment hidden dangers that are difficult to discover and deal with in a timely manner. Secondly, the accuracy of data analysis needs to be improved. Due to the limited means of data collection and the backwardness of analysis tools, traditional methods are unable to cope with massive monitoring data, and it is difficult to extract valuable information from them to guide operation and maintenance decisions. Finally, the timeliness of fault response is not strong. Once an equipment failure or abnormal situation occurs in the power supply corridor, the traditional repair and response mechanism is often time-consuming and unable to quickly locate the problem and take measures, thus affecting the reliability and continuity of power supply.

[0048] In view of this, in order to solve the above problems, an embodiment of the present application provides a power supply corridor management system based on the Internet of Things, which manages and operates the power supply corridor through means based on the Internet of Things and intelligent means.

[0049] The following is an exemplary description of the Internet of Things-based power supply corridor management system provided by this application.

[0050] It should be noted that the Internet of Things-based power supply corridor management system provided in this application can be applied to electronic equipment. Figure 2A schematic structural block diagram of an electronic device 100 provided in an embodiment of the present application is shown, and the electronic device 100 includes a memory 102, a processor 101, and a communication bus 103. The memory 102, the processor 101, and the communication bus 103 are electrically connected to each other directly or indirectly to achieve data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses or signal lines.

[0051] The memory 102 can be used to store software programs and modules, such as program instructions or modules corresponding to the power supply corridor management system based on the Internet of Things provided in the embodiment of the present application. The processor 101 executes various functional applications and data processing by executing the software programs and modules stored in the memory 102, thereby executing the power supply corridor management system based on the Internet of Things provided in the embodiment of the present application. The communication bus 103 can be used to communicate signaling or data with other node devices.

[0052] Among them, the memory 102 can be, but is not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only memory (Programmable Read-Only Memory, PROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable programmable read-only memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.

[0053] The processor 101 may be an integrated circuit chip with signal processing capability. The processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0054] Understandably, Figure 2 The structure shown is for illustration only. The electronic device 100 may also include Figure 1More or fewer components as shown, or with Figure 1 Different configurations are shown. Figure 1 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0055] The following uses the electronic device 100 as an illustrative execution entity to exemplarily illustrate the Internet of Things-based power supply corridor management system provided in the embodiment of the present application.

[0056] See also Figure 1 and Figure 3 The Internet of Things-based power supply corridor management system provided in this application includes:

[0057] Internet of Things device module, central processing unit, equipment monitoring and management module;

[0058] The IoT device module includes a corridor sensing module and a communication module, and is placed in each device that needs to be monitored in the power corridor;

[0059] The tunnel perception module is used to collect the original monitoring data of the IoT device module;

[0060] A communication module, used for transmitting the raw monitoring data to a central processing unit and encrypting the data;

[0061] The central processing unit also includes a decryption module, a data processing and analysis module, and a fault alarm module; the data processing and analysis module is used to process the original monitoring data; the data analysis module is provided with equipment status, operating parameters and environmental indicators. When the data analysis module analyzes an abnormal value, the fault alarm module generates an alarm message and transmits it to the equipment monitoring and management module. The equipment monitoring and management module is used to manage various types of equipment.

[0062] It is understandable that the Internet of Things device module is installed on the key equipment in the power supply corridor, and the communication between the equipment monitoring and management module and the central processing unit is debugged to ensure that all devices can establish a connection with the central processing unit through the wireless network. Install and configure the central processing unit and the equipment monitoring and management module to complete the system initialization. After that, the corridor perception module begins to collect the status information and environmental parameters of the equipment in the power supply corridor in real time, and encrypts the collected data through the communication module and transmits it to the central processing unit for processing. Subsequently, the central processing unit decrypts and processes the received data. Among them, the decryption module in the central processing unit is responsible for decrypting the encrypted information, and the data analysis module is set with equipment status, operating parameters and environmental indicators. When the data analysis module analyzes abnormal values, the team goes to the site for processing. The operation and maintenance personnel judge the cause of the fault according to the alarm information and the on-site situation, and take corresponding measures to repair it. After the repair is completed, the system records the fault handling process and results to provide a reference for subsequent operation and maintenance work.

[0063] The IoT-based power supply corridor management system provided in this example can provide real-time monitoring and fault warning, significantly improving operation and maintenance efficiency and safety, and reducing operation and maintenance costs and time.

[0064] Furthermore, the tunnel sensing module includes any one of passive temperature measurement tags, humidity sensors, surveillance cameras, air quality sensors, stress sensors, photosensors, light intensity sensors, brightness sensors, magnetic field intensity sensors, flow rate sensors, pressure sensors, water quality sensors, current transformers, voltage transformers, and rainfall sensors, or any of them used simultaneously.

[0065] As can be understood, passive temperature tags can measure the surface temperature of objects in a non-contact manner without the need for external power supply, monitor temperature changes of cables, equipment, etc., detect overheating in time, and prevent accidents such as fire. Humidity sensors monitor the humidity level in the power supply corridor to ensure that the ambient humidity is within the appropriate range, prevent the equipment from being damp or corroded, and ensure the normal operation of the equipment. Monitoring cameras provide real-time video images inside the corridor, which facilitates remote monitoring of the corridor status and timely detection of abnormal conditions such as intrusion and equipment damage. Air quality sensors detect the content of harmful substances in the air in the corridor, such as carbon dioxide and sulfide, to ensure that the air quality meets the standards and protect the health of operation and maintenance personnel. Stress sensors monitor stress changes in structures such as cables and pipelines to prevent damage or breakage caused by stress concentration and ensure the safety of power transmission. Photosensitive sensors, light intensity sensors, and brightness sensors work together to monitor the lighting conditions in the corridor and provide necessary lighting information for operation and maintenance personnel. They can also be used to detect abnormal lighting in the corridor to prevent light pollution or equipment malfunctions. Magnetic field strength sensors are used to detect the magnetic field strength in the pipe gallery. They are of great significance for monitoring the electromagnetic radiation of power equipment and help protect personnel and equipment from electromagnetic interference. Flow rate sensor: In systems involving fluid transmission in the pipe gallery, flow rate sensors are used to monitor the flow rate of the fluid, ensure the stability of the flow rate, and prevent equipment damage or accidents caused by abnormal flow rate. Pressure sensors monitor the pressure changes of pressure vessels in the pipe gallery, such as pipelines, storage tanks, etc., to prevent leakage or explosion accidents caused by excessive or low pressure. Water quality sensors In systems involving water cooling or water transmission, water quality sensors are used to detect the quality of water to ensure that the water quality meets the use standards and prevent equipment from being damaged due to water quality problems. Current transformers, voltage transformers: These sensors are used to monitor the current and voltage of power equipment, ensure the stability and safety of power transmission, and prevent equipment failure or damage caused by abnormal current or voltage. Rainfall sensors are set above or around the pipe gallery to monitor rainfall, provide data support for flood control work, and ensure the safe operation of the pipe gallery under extreme weather conditions.

[0066] Through their unique functions and effects, the above sensors jointly realize comprehensive and real-time monitoring of the equipment status and environmental parameters in the power supply corridor, providing comprehensive original data for the intelligent management of the power supply corridor.

[0067] Furthermore, a communication module is arranged in the IoT device module, and the communication module is used for communication between IoT device modules or communication with a central processing unit.

[0068] It is understandable that integrating the communication module into the IoT device module also facilitates the subsequent expansion and upgrade of the system. With the development of IoT technology and the changing needs of power supply corridor management, the communication module can be replaced or upgraded to meet new communication protocols, increase data transmission rates and other needs. At the same time, setting the communication module in the IoT device module can ensure that the communication connection between various devices is stable and reliable in the complex underground corridor environment. This helps to reduce problems such as packet loss and delay during data transmission and improve the overall reliability of the system. Through the communication between IoT device modules, different devices can coordinate and share information with each other, thereby realizing more complex monitoring and management tasks. In addition, the communication module is responsible for transmitting the collected raw data to the central processing unit to facilitate remote monitoring, management, data analysis, risk warning and other operations.

[0069] Further communication modules include 4G network, RFID, NFC, Bluetooth and other wireless communication methods, among which RFID, NFC, Bluetooth and other wireless communication methods are used for data interaction between IoT device modules, and 4G network is used for data communication with the central processing unit.

[0070] It is understandable that by adopting short-range wireless communication methods such as RFID, NFC, and Bluetooth, short-range data interaction can be easily achieved between IoT device modules. This method is suitable for power supply corridor environments with dense equipment and inconvenient wiring, which can reduce wiring costs and improve the flexibility and scalability of the system. RFID (radio frequency identification) and NFC (near field communication) technologies have the characteristics of contactless identification and fast data transmission. They can be used for rapid identification and configuration of IoT device modules, such as device identity authentication, information reading and writing, etc., thereby improving the efficiency and accuracy of device management. Bluetooth technology is also suitable for short-range, low-power wireless communication scenarios. In the power supply corridor, Bluetooth can be used for real-time data exchange between IoT device modules, such as sensor data collection and transmission, to ensure that data is transmitted quickly and stably between devices. Considering that power supply corridors are often located underground and have a wide coverage area, a reliable and high-speed remote communication method is required. 4G network (or higher version of wireless communication technology) has the characteristics of wide coverage and high bandwidth, which can meet the remote transmission requirements of large amounts of real-time data between IoT device modules and central processing units, ensuring the integrity and real-time nature of the data. At the same time, the combined use of multiple wireless communication methods in the communication module not only improves the flexibility and scalability of the system, but also ensures the efficient and stable transmission of data between IoT device modules and between IoT device modules and the central processing unit, thereby improving the intelligent management level of the power supply corridor.

[0071] Furthermore, the communication module encrypts the collected original monitoring data to satisfy the formula:

[0072] C=E(K,P)

[0073] Among them, C represents the encrypted original data, E represents the encryption function, K represents the preset key, and P represents the original monitoring data.

[0074] It can be understood that the communication module uses the principle of symmetric encryption to encrypt the collected original monitoring data. Through encryption processing, it can be ensured that the original monitoring data will not be intercepted or tampered by unauthorized third parties during transmission, thereby ensuring the security and integrity of the data.

[0075] Furthermore, the data processing and analysis module includes a decision tree model module, which is used to identify and predict the processed original monitoring data. The fault alarm module performs anomaly detection and fault prediction based on the results output by the decision tree model module, generates early warning information, and the fault alarm module transmits the early warning information to the equipment monitoring and management module.

[0076] Further, the decision tree model module implements the following steps when processing the identification and prediction of the original monitoring data;

[0077] Step 1: decrypt the encrypted original monitoring data through the decryption module.

[0078] Obtaining the decrypted original data;

[0079] Step 2: First, the decrypted original monitoring data is cleaned and normalized; second, the cleaned data is feature extracted; third, a data set is constructed and divided into a training set and a validation set; finally, the constructed data set is labeled, with the normal state marked as 0 and the abnormal state marked as 1, and a threshold is preset;

[0080] Step 3: Use the CART regression tree algorithm to train the training set;

[0081] Step 4: construct a decision tree model by recursively dividing the data feature space until the training set reaches the preset threshold;

[0082] Step 5: Evaluate the decision tree model by cross-validation. If the validation result does not meet expectations, further adjust the parameters and repeat steps 1 to 4 until the validation result meets expectations.

[0083] As you can understand, the decision tree algorithm makes decisions by building a tree structure. Each internal node represents a judgment condition on an attribute (or feature), each branch represents the output of a judgment condition, and each leaf node represents a decision result (a category for classification problems and a predicted value for regression problems). The CART algorithm is a type of decision tree algorithm, which is particularly suitable for classification and regression tasks.

[0084] In the CART algorithm, for classification problems, the Gini Impurity or Information Gain is usually used to select the optimal partitioning features and partitioning points. For regression problems, the Mean Squared Error (MSE) is used as the partitioning criterion.

[0085] Among them, the Gini Impurity is used for classification and the Mean Squared Error (MSE) is used for regression.

[0086] Understandable, such as Figure 3 As shown, the specific implementation steps for building a decision tree model module are as follows:

[0087] Step 1: decrypt the encrypted data using a preset key and an inverse function of the encryption function;

[0088] Step 2: After data decryption, clean the data, including noise removal, missing value processing, outlier detection and processing, etc. After cleaning the data, scale the data to the same scale so that features of different dimensions can be compared fairly, that is, normalize the data; after normalization, extract features useful for classification or regression from the original data; then, construct a data set and divide the data into a training set and a validation set (sometimes also a test set); finally, traverse all features and partition points of the constructed data set, calculate the Gini coefficient after partition, select the partition with the smallest Gini coefficient as the optimal partition, label the equipment, mark the normal state as 0, and the abnormal state as 1, and preset the threshold;

[0089] Step 3: Use the CART recursive tree algorithm to train the training set. By recursively dividing the data feature space, a decision tree model is constructed until the training set reaches a preset threshold (such as the number of samples in a node no longer meets a certain condition).

[0090] Step 4: construct a decision tree model by recursively partitioning the data feature space until the training set reaches the preset threshold. That is, a decision tree model is constructed based on the result of recursive partitioning, each internal node represents a partitioning condition and a partitioning result, and each leaf node represents a category.

[0091] Step 5: Evaluate the decision tree model by cross-validation. If the validation result does not meet expectations, further adjust the parameters and repeat steps 1 to 4 until the validation result meets expectations. Cross-validation is used to evaluate the performance of the model on different data subsets to reduce the risk of overfitting.

[0092] It can be understood that the collected data includes temperature (T), humidity (H), current (I) and voltage (V), as well as a label: device status (normal = 0, abnormal = 1). After the central processing unit receives these raw data, it decrypts the encrypted raw data, and then cleans the raw data to remove missing values ​​and outliers, and normalizes the temperature, humidity, current and voltage data; then performs feature extraction, directly using temperature, humidity, current and voltage as features; then constructs a data set, and divides the data into a training set and a validation set; and uses the CART algorithm for training. First, the algorithm may select temperature as the optimal partitioning feature and divide the data set into two parts: a subset with a temperature above a certain threshold and a subset with a temperature below or equal to the threshold; for each subset, continue to select the optimal partitioning feature and partitioning point for partitioning until the stop condition is met;

[0093] Assume that the final decision tree model is as follows: Root node: temperature > 30°C Left child node (temperature > 30°C): humidity > 70% Left child node (humidity > 70%): device status = 1 (abnormal);

[0094] Right child node (humidity ≤ 70%): current > 10A Left child node (current > 10A): device status = 1 (abnormal);

[0095] Right child node (current ≤ 10A): device status = 0 (normal);

[0096] Right child node (temperature ≤ 30°C). Assume that no further partitioning is required at this time because the device states of all remaining samples are the same or the subset size is smaller than the preset threshold.

[0097] Therefore, the right child node (temperature ≤ 30° C.) directly becomes a leaf node with the label device status = 0 (normal).

[0098] Based on the above division results, a complete decision tree model is constructed. This model can now be used to predict new data, that is, according to the input temperature, humidity, current and voltage values, the state of the device (normal or abnormal) is determined by traversing the decision tree.

[0099] Finally, the validation set is used to evaluate the performance of the model. For each sample in the validation set, its feature value is input into the decision tree model, and the device state predicted by the model is compared with the actual device state.

[0100] Further, such as Figure 4 As shown, the embodiment of the present application provides an IoT power supply corridor management method, comprising the following steps:

[0101] Step 1: Deploy a tunnel sensing module at the tunnel site to collect raw data of the power supply system in real time;

[0102] Step 2: transmitting the original data to the Internet of Things power supply corridor management system according to any one of claims 1 to 7 for analysis and prediction; when an abnormal value is analyzed, an alarm message is generated by a fault alarm module and transmitted to the equipment monitoring management module;

[0103] Step 3: Implement daily operation and management of the power supply corridor through the Internet of Things power supply corridor management system.

[0104] Furthermore, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the Internet of Things power supply corridor management system described in the above embodiment is implemented.

[0105] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment or a part of a code, and the module, program segment or a part of a code includes one or more executable instructions for implementing a specified logical function.

[0106] It should also be noted that in some alternative implementations, the functions marked in the blocks may also occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the opposite order, depending on the functions involved.

[0107] It should also be noted that each box in the block diagram and / or flowchart, and combinations of boxes in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0108] In addition, the functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0109] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk. Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, so the scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A power supply corridor management system based on the Internet of Things, characterized in that: include: Internet of Things device module, central processing unit, equipment monitoring and management module; The IoT device module includes a pipe gallery sensing module and a communication module, and is placed in each device that needs to be monitored in the power supply pipe gallery; The pipe gallery sensing module is used to collect the original monitoring data of the Internet of Things device module; The communication module is used to transmit the original monitoring data to the central processing unit and encrypt the data; The central processing unit also includes a decryption module, a data processing and analysis module, and a fault alarm module; the data processing and analysis module is used to process the original monitoring data; the data analysis module is provided with equipment status, operating parameters and environmental indicators. When the data analysis module analyzes an abnormal value, the fault alarm module generates an alarm message and transmits it to the equipment monitoring and management module. The equipment monitoring and management module is used to manage various types of equipment.

2. The Internet of Things power supply corridor management system according to claim 1, characterized in that: The corridor sensing module includes any one of passive temperature measurement tags, humidity sensors, surveillance cameras, air quality sensors, stress sensors, photosensors, light intensity sensors, brightness sensors, magnetic field intensity sensors, flow rate sensors, pressure sensors, water quality sensors, current transformers, voltage transformers, and rainfall sensors, or any of them used simultaneously.

3. The Internet of Things power supply corridor management system according to claim 1, characterized in that: The communication module is arranged in the IoT device module, and the communication module is used for communication between the IoT device modules or with the central processing unit.

4. The Internet of Things power supply corridor management system according to claim 3, characterized in that: The communication module includes 4G network, RFID, NFC, Bluetooth and other wireless communication methods, among which the RFID, NFC, Bluetooth and other wireless communication methods are used for data interaction between the Internet of Things device modules, and the 4G network is used for data communication with the central processing unit.

5. The Internet of Things power supply corridor management system according to claim 1, characterized in that: The communication module encrypts the collected original monitoring data to satisfy the formula: C=E(K,P) Among them, C represents the encrypted original data, E represents the encryption function, K represents the preset key, and P represents the original monitoring data.

6. The Internet of Things power supply corridor management system according to claim 1, characterized in that: The data processing and analysis module includes a decision tree model module, which is used to identify and predict the processed original monitoring data. The fault alarm module performs anomaly detection and fault prediction based on the results output by the decision tree model module, generates early warning information, and the fault alarm module transmits the early warning information to the equipment monitoring and management module.

7. The Internet of Things power supply corridor management system according to claim 6, characterized in that: The decision tree model module implements the following steps when processing the identification and prediction of the original monitoring data; Step 1, decrypting the encrypted original monitoring data through the decryption module to obtain the decrypted original data; Step 2: First, the decrypted original monitoring data is cleaned and normalized; secondly, the cleaned data is subjected to feature extraction; thirdly, a data set is constructed and divided into a training set and a validation set; finally, all features and partition points of the constructed data set are traversed, the Gini coefficient after partition is calculated, and the partition with the smallest Gini coefficient is selected as the optimal partition. The equipment is labeled, the normal state is marked as 0, the abnormal state is marked as 1, and the threshold is preset; Step 3: Use the CART recursive tree algorithm to train the training set; Step 4: construct a decision tree model by recursively dividing the data feature space until the training set reaches the preset threshold; Step 5: Evaluate the decision tree model by cross-validation. If the validation result does not meet expectations, further adjust the parameters and repeat steps 1 to 4 until the validation result meets expectations.

8. A method for managing an Internet of Things power supply corridor, characterized in that: The Internet of Things power supply corridor management method comprises: Step 1: Deploy a tunnel sensing module at the tunnel site to collect raw data of the power supply system in real time; Step 2: transmitting the original data to the Internet of Things power supply corridor management system according to any one of claims 1 to 7 for analysis and prediction; when an abnormal value is analyzed, an alarm message is generated by a fault alarm module and transmitted to the equipment monitoring management module; Step 3: Implement daily operation and management of the power supply corridor through the Internet of Things power supply corridor management system.

9. An electronic device, characterized in that: include: A memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the Internet of Things power supply corridor management system as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the Internet of Things power supply corridor management system as described in any one of claims 1 to 7 is implemented.

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