Intelligent equipment management and control system

Abstract

The invention discloses an intelligent equipment management and control system, and relates to the field of equipment management. In the intelligent equipment management and control system, temperature, humidity and smoke data of a field working environment are detected through a temperature, humidity and smoke sensor and are used as environment information; collecting image information of the working area through a camera; asset information is input through an asset management device; positioning information of the equipment is collected through GPS positioning equipment; detecting power information of the equipment through power detection equipment; the embedded controller obtains environment information, image information, asset information, positioning information and power information collected by the underlying sensing device through the serial port switch, and transmits the information to the monitoring computer through the network middleware, and the monitoring computer displays, stores and manages the information. The informatization level and the management efficiency of equipment management can be remarkably improved, and the management cost of enterprise equipment is saved.

Description

Technical Field

[0001] The present application relates to the technical field of equipment management, and in particular to an intelligent equipment management and control system. Background Art

[0002] Traditional equipment management methods usually use Excel spreadsheets for data management. On the one hand, its implementation is relatively simple, prone to omissions and non-standard, difficult to query, and cannot support simultaneous collaborative processing by multiple people; on the other hand, the data it saves is relatively simple and the information relevance is relatively poor; on the other hand, there are still problems such as scattered equipment, relatively scattered equipment information and equipment data, easy to lose, lack of unified management, and waste of resources due to inadequate utilization, which is not suitable for equipment management. Summary of the invention

[0003] In view of the problems existing in the above-mentioned prior art, the purpose of this application is to provide an intelligent equipment management and control system to improve the level of equipment management informationization and management efficiency, and save the management cost of enterprise equipment.

[0004] To achieve the above objectives, this application provides the following solutions.

[0005] The present application provides a device intelligent management and control system, including: a monitoring computer, a network middleware, an embedded controller, a serial port switch and an underlying sensing device; the underlying sensing device includes a temperature, humidity and smoke sensor, a camera, an asset management device, a GPS positioning device and a power detection device; the monitoring computer is connected to the embedded controller through the network middleware; the embedded controller is connected to the underlying sensing device through the serial port switch;

[0006] The temperature, humidity and smoke sensor is used to detect the temperature, humidity and smoke data of the on-site working environment as environmental information; the camera is used to collect image information of the working area; the asset management device is used to enter asset information; the GPS positioning device is used to collect the positioning information of the device; the power detection device is used to detect the power information of the device; the embedded controller obtains the environmental information, image information, asset information, positioning information and power information collected by the underlying sensing device through the serial port switch, and transmits it to the monitoring computer through the network middleware, and the monitoring computer displays, stores and manages the information.

[0007] Optionally, the monitoring computer is used to set temperature, humidity and smoke warning thresholds, and determine whether to issue an alarm based on real-time temperature, humidity and smoke data.

[0008] Optionally, the monitoring computer is also used to use time as the independent variable and temperature, humidity and smoke data as dependent variables, adopt a linear regression algorithm to fit the linear equation of the temperature, humidity and smoke data changing over time, and predict the future trend of the temperature, humidity and smoke data changes based on the linear equation.

[0009] Optionally, the monitoring computer is also used to call a pattern recognition algorithm to perform device identification based on image information collected by the camera.

[0010] Optionally, the monitoring computer is also used to identify abnormal events in the working area based on image information collected by the camera using intelligent video analysis technology and deep learning algorithms.

[0011] Optionally, the monitoring computer is also used to perform asset addition, deletion, modification, query and update management based on the entered asset information.

[0012] Optionally, the monitoring computer is also used to locate the coordinate position of each device based on the positioning information collected by the GPS positioning device and using a positioning algorithm based on arrival time difference.

[0013] Optionally, the monitoring computer is also used to detect the operating status of the device according to the power data change based on the power information detected by the power detection device.

[0014] Optionally, the monitoring computer is also used to realize on / off control of lights in the working area by controlling the I / O ports.

[0015] Optionally, the monitoring computer uses Mysql to store the collected information, uses a third-party xlnt to generate and export statistical reports, and uses Qt C++ as a development language to realize data visualization.

[0016] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0017] The present application provides an intelligent equipment management and control system, which detects ambient temperature, humidity, and smoke concentration data through temperature, humidity, and smoke sensors, collects image information of the working area through a camera, and uploads it to an embedded controller, which is then forwarded to a monitoring computer through a network middleware for display or early warning alarm; locates the position of each device through positioning information collected by a GPS positioning device; the asset management device records equipment or equipment data by scanning the asset code; the power detection device can detect the operating status of the equipment by detecting the power information of the equipment; the embedded controller is responsible for connecting to the underlying sensing device, performing data interaction and command transmission, and also plays the function of forwarding data to the monitoring computer; the monitoring computer is responsible for providing a user interaction interface, displaying, storing, and parsing the underlying data forwarded by the embedded controller, thereby improving the level of informationization and management efficiency of equipment management and saving the management cost of enterprise equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the overall structure of an equipment intelligent management and control system;

[0020] Figure 2 This is a schematic diagram of the three-layer architecture of the equipment intelligent management and control system hardware;

[0021] Figure 3 A schematic diagram of the underlying sensing device and its specific operation flow;

[0022] Figure 4 This is a schematic diagram of the technical performance parameters of the temperature and humidity smoke sensor;

[0023] Figure 5 This is a schematic diagram of the environmental information prediction process;

[0024] Figure 6 This is a schematic diagram of the principle of Mysql query method;

[0025] Figure 7 A schematic diagram of the process of generating separate statistical reports for each underlying sensing device;

[0026] Figure 8 Schematic diagram of the statistical generation process of the overall report. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] In response to the defects in the prior art, this application proposes an intelligent equipment management and control system to improve the level of equipment management informationization and management efficiency, and save the management costs of enterprise equipment.

[0029] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] In an exemplary embodiment, the present application provides a device intelligent management and control system, such as Figure 1 As shown, the equipment intelligent management and control system includes: a monitoring computer, a network middleware, an embedded controller, a serial port switch and an underlying sensing device. Specifically, the underlying sensing device includes a temperature, humidity and smoke sensor, a camera, an asset management device, a GPS positioning device and a power detection device. The monitoring computer is connected to the embedded controller through the network middleware. The embedded controller is connected to the underlying sensing device through the serial port switch. The temperature, humidity and smoke sensor is used to detect the temperature, humidity and smoke data of the on-site working environment as environmental information; the camera is used to collect image information of the working area; the asset management device is used to enter asset information; the GPS positioning device is used to collect the positioning information of the device; the power detection device is used to detect the power information of the device; the embedded controller obtains the environmental information, image information, asset information, positioning information and power information collected by the underlying sensing device through the serial port switch, and transmits it to the monitoring computer through the network middleware, and the monitoring computer displays, stores and manages the information.

[0031] In some embodiments, the embedded controller may use a Raspberry Pi development board, and each underlying sensing device is connected and communicated with a serial port switch through an RS232 communication protocol, and the serial port switch and the Raspberry Pi development board are connected and communicated through an RJ45 interface. The software part in the Raspberry Pi development board mainly includes an operating system, a driver package, a data integration software package, and a communication and control instruction set. The hardware and software work together to perform system integration, realize the control of multiple sensing means, realize intelligent perception and control of the work site, detect the environmental information of the work area, and ensure that the work area environment is suitable and safe.

[0032] like Figure 2As shown, the hardware of the equipment intelligent management and control system is divided into three parts: the upper computer layer (user interface layer), the lower computer layer (embedded system layer) and the bottom sensor layer. The upper computer layer is the user control end, which includes a monitoring computer (upper computer) and control software. The access, storage and human-computer interaction of the perception data are realized through the control software deployed on the monitoring computer. The lower computer layer includes an embedded controller (lower computer) and a serial port switch, which is responsible for connecting the underlying perception device through a serial port or a network port, and realizing data interaction with the data acquisition end. The underlying perception device aggregates the detection data to the lower computer through the serial port switch, and the lower computer uploads the data to the upper computer user interface through the network middleware (network switch). The upper computer manages the underlying data, displays the underlying data in the user interface and stores it in the database, realizing device control, data storage and human-computer interaction.

[0033] The control software of the system is deployed on the host computer. According to the main functions, it can be divided into environmental information detection and early warning prediction function, image information management and equipment entry and exit perception function, asset information management function, GPS positioning function, equipment operation status control function, light switch control function, data storage management function, statistical report generation and export function, data visualization display function, etc. The underlying sensing device and its specific operation and use process are as follows: Figure 3 shown.

[0034] 1) Environmental information detection and early warning prediction function

[0035] 1.1) Environmental information detection and early warning

[0036] like Figure 3 As shown in the figure, the environmental information of the on-site working environment is collected in real time by the temperature, humidity and smoke sensor. The temperature, humidity and smoke concentration are actually detected on-site by the temperature, humidity and smoke sensor and transmitted back to the host computer application interface in real time for display. In actual applications, the temperature, humidity and smoke sensor actively detects the data of the surrounding working environment and reports it to the host computer, which passively receives the sensor data and displays and processes it. For example, according to the actual situation on site, the user can set different temperature, humidity and smoke concentration thresholds, and alarm when the temperature, humidity and smoke concentration detection values ​​exceed the threshold, which plays a warning alarm function and provides accurate and timely response measures for equipment environment monitoring.

[0037] This application takes the acquisition of typical environmental data such as temperature, humidity, and smoke concentration as an example. After repeated verification of the requirements, the SM1972C temperature, humidity, and smoke sensor is selected. Its technical performance parameters are as follows: Figure 4As shown. The system collects the temperature, humidity and smoke concentration data of the working environment in real time through the temperature, humidity and smoke sensor and sends it to the host computer. The host computer continuously receives and refreshes it in real time and displays it on the information processing platform. At each refresh, it makes a warning (alarm) judgment. If the warning threshold (customizable) is reached, an alarm is issued. When the temperature, humidity and smoke detection are abnormal, the abnormal temperature, humidity and smoke concentration data will be reported to the host computer to assist the staff to make reasonable handling according to the actual abnormal situation.

[0038] 1.2) Environmental information prediction

[0039] The environmental information prediction process is as follows Figure 5 As shown, this application uses time as the independent variable and temperature and humidity or smoke concentration data as the dependent variable. There is a linear relationship between the independent variable and the dependent variable, so the linear regression algorithm is selected as the main method for data prediction. The linear regression algorithm is a prediction method based on the least squares method. It describes the relationship between the independent variable and the dependent variable by fitting a straight line. The linear equation can be expressed in the form of y=ax+b, where a is the slope and b is the intercept.

[0040] The principle of the linear regression algorithm is to find the best a and b values ​​through historical data so that the sum of squared errors between the predicted value and the actual value is minimized. Specifically, the linear regression algorithm needs to solve the following two problems: 1.2.1) Determine the best a and b values: Calculate the best a and b values ​​by minimizing the sum of squared errors between the actual value and the predicted value. This process can be achieved through methods such as gradient descent and least squares method. 1.2.2) Predict future trends: Based on the fitted straight line equation, future trends and results can be predicted. This process can be calculated by substituting the value of the independent variable into the equation.

[0041] There are many fitting methods for linear regression algorithms, and the least squares method is selected in the system. The least squares method is a mathematical optimization technique that estimates unknown parameters by minimizing the sum of squared errors. In linear regression, the least squares method is used to fit a straight line so that the sum of squared errors between the actual value and the predicted value is minimized. The calculation formula of the least squares method is:

[0042] a=(n*Σ(x i *y i )-Σ(x i )*Σ(y i )) / (n*Σ(x i ^2)-(Σ(y i ))^2) (1)

[0043] b=(Σ(y i )-a*Σ(x i )) / n (2)

[0044] Where n is the number of samples, x i and i are the actual values ​​of the independent variable and the dependent variable, respectively.

[0045] After determining the values ​​of a and b, the temperature, humidity or smoke concentration forecast data at subsequent time points can be calculated, thereby realizing the data prediction function in the system.

[0046] 2) Image information management and equipment entry and exit sensing function

[0047] 2.1) Image information management

[0048] like Figure 3 As shown in the figure, the image information of the working environment is collected by the camera. The user can customize the exposure value and exposure of the camera device, and can design the switches of the functions such as turning on the device, starting to collect (images), stopping to collect (images), starting to record, ending to record, saving recordings, video (recording) selection, and replaying recordings (videos) on the host computer interface.

[0049] The camera transmits the collected image information back to the host computer application interface in real time through the serial port switch. It can also record the video through the save function and then play back the corresponding video through the playback function. The camera can observe the working area of ​​the equipment through real-time video images. If there is any abnormal information, the real-time image can be saved. By recording video images, you can choose to play back or watch the recorded video to reasonably plan or optimize the working area of ​​the equipment. The image information management function based on the system camera is an intuitive expression of observing and recording the working area environment, safety, and abnormal events, which is used to provide a basis for optimizing and improving the working area. Based on the image information collected by the camera, abnormal events in the working area can be identified using intelligent video analysis technology and deep learning algorithms.

[0050] 2.2) Equipment entry and exit perception

[0051] The device entry and exit perception function is achieved by the host computer calling the pattern recognition algorithm to identify the device while the camera continuously detects image information. When the device enters and exits, the host computer makes a device entry and exit judgment based on the pattern recognition results. Image pattern recognition algorithm is a science used to extract useful information from images and build abstract models to interpret image data. The system introduces the method of transfer learning to optimize model training. Transfer learning can use the knowledge learned on one task to help solve another task. In device identification, the neural network model trained on other devices can be used as a pre-trained model, and then fine-tuned on the target device to speed up training and improve recognition accuracy.

[0052] The pattern recognition algorithm in the host computer of this application includes the following steps:

[0053] 2.2.1) Data Collection: First, you need to collect relevant data about the equipment, which may include the model, type, manufacturer, usage time, operating status, etc. The data can come from various sensors, log files, equipment maintenance records, etc.

[0054] 2.2.2) Data preprocessing: The collected raw data needs to be preprocessed to remove noise, fill missing values, standardize data, etc. The purpose of this step is to improve the accuracy and efficiency of subsequent steps.

[0055] 2.2.3) Feature extraction: Extract representative features from the preprocessed data based on the device characteristics that need to be identified. These features can include physical characteristics of the device (such as size, weight, shape, etc.), operating characteristics (such as energy consumption, operating speed, etc.), etc.

[0056] 2.2.4) Model building: Based on the extracted features, a neural network algorithm based on the attention mechanism is selected to build the model.

[0057] 2.2.5) Model training: Use data with known labels to train the model to learn the relationship between device features and labels. Here, the pre-learning method is introduced.

[0058] 2.2.6) Model evaluation: Use unlabeled data to evaluate the model to understand the performance of the model. Common evaluation indicators include accuracy, recall, F1 score, etc.

[0059] 2.2.7) Model optimization: Based on the evaluation results, the model is optimized to improve its performance. This may include adjusting model parameters, selecting different features or algorithms, etc.

[0060] 2.2.8) Model application: Apply the optimized model to actual scenarios to identify unknown devices.

[0061] The steps of the transfer learning part are as follows: pre-train the model on the dataset; transfer the first four convolutional layers of the pre-trained model and fix the network structure and parameters of this part; combine the transferred part with one new convolutional layer and three new fully connected layers into a new network; retrain the new network using the aerial target dataset, fine-tune the non-fixed parameters, and change the model output from 1000 categories to 4 categories.

[0062] Since the development environment uses QT (C++), this application uses the OpenCV open source computer vision algorithm library with good C++ support for pattern recognition algorithm processing. OpenCV is an open source library that is widely used in the fields of computer vision and image processing, providing a wealth of image analysis and pattern recognition algorithms.

[0063] 3) Asset information management function

[0064] In some embodiments, the asset management device of the present application is an RFID tag scanner. Figure 3 As shown in the figure, the asset management device enters the asset into the database by scanning the asset barcode, QR code or electronic tag, which facilitates management and data processing. After the asset information is entered into the database, the asset can be added, deleted, modified, queried, updated, etc. according to the QR code information, which facilitates customers to manage and control the asset information in an all-round way.

[0065] In some embodiments, the asset management device of the present application uses the FY-3226 fixed two-dimensional scanning module, and transmits the scanned asset electronic tag information to the embedded controller micro-terminal processor through the USB interface, and the embedded controller transmits it to the monitoring computer through the network middleware router. The monitoring computer writes the collected asset information into the database, that is, records the assets entering the work area. FY-3226 is a fixed two-dimensional scanning module suitable for general industrial environments. It is equipped with a 1280×800 megapixel C-MOS sensor with better scanning recognition, and the scanning results are sent via USB.

[0066] The asset information management function of the system passively obtains data by scanning the QR code or electronic tag in the device attribute identification, uploads the data to the host computer, and saves the data to the database. After the asset information is entered into the database, it can be queried, modified, deleted, and managed according to the QR code or electronic tag information, making it convenient for customers to manage and control the asset information in an all-round way.

[0067] On this basis, the function of sensing the entry and exit of equipment can also be realized with the assistance of cameras. For example, when equipment enters and exits, on the one hand, the QR code or electronic tag in the equipment attribute identification is scanned by the asset management device for entry, and on the other hand, the upper computer actively requests the camera to save the video, and first determines whether the link with the lower computer is normal. If it is normal, it notifies the camera to save the video, and if it is not normal, it displays an error; the lower computer forwards the intention of the upper computer to the underlying camera, and if the link is abnormal, it displays an error; the camera receives the upper computer instruction, and then transmits the data back to the upper computer for storage, and identifies the device through the pattern recognition algorithm.

[0068] 4) GPS positioning function

[0069] GPS positioning equipment realizes remote control of equipment through LORA wireless networking. LoRa technology can be used for remote control of equipment to realize remote monitoring and control of equipment. In the remote control system of equipment, LoRa gateway is usually installed at the work site, responsible for receiving data from various sensors and actuators, and transmitting data to the remote control center (host computer) through the Internet, so as to realize remote monitoring and control of equipment by remotely sending instructions to LoRa terminal devices. LoRa technology has the advantages of low power consumption, long distance, high reliability, etc., and is suitable for remote control of long-term online equipment. At the same time, LoRa technology also supports direct communication between multiple nodes, which can avoid the impact of intermediate node failure on the system. In addition, LoRa technology supports large-scale deployment and can easily expand the scale and function of the system. From the perspective of data transmission, remote control of LoRa devices needs to ensure the integrity and reliability of data to avoid data loss or tampering. This can be achieved by adopting reliable communication protocols and technologies. The system uses the LoRaWAN protocol for data transmission, which supports functions such as data encryption and integrity verification.

[0070] In some embodiments, the GPS positioning device may use a GPS / BD satellite positioning monitoring system. The GPS positioning device locates each device position through LORA wireless networking. On this basis, the system uses a positioning algorithm based on time difference of arrival (TDOA), which calculates the coordinate position of the terminal device by measuring the signal propagation time difference between the terminal device and multiple gateway devices. This algorithm does not require the terminal device to have complex processing capabilities, so it can reduce the power consumption and cost of the terminal device.

[0071] TDOA positioning is a passive technology that uses the difference in signal arrival times at multiple spatially separated receivers to locate and track transmitting objects. e and the propagation speed c in the medium, the arrival time (TOA) r1 and r2 of the signal at two receivers located within the distance can be calculated by the following formula:

[0072]

[0073] Since the true signal transmission time is usually unknown, the difference between the two, t1 and t2, can be used to obtain some information about the device's location. Specifically, given the location of each receiver, the time difference measurement corresponds to the difference in range from the two receivers to the unknown object.

[0074] Specifically, the process of the TDOA positioning algorithm is as follows: 4.1) The GPS positioning device sends the data obtained by measuring the same signal by different receivers at the same time to the host computer; 4.2) The host computer calculates the time difference between the radio signal reaching the two receivers; 4.3) The time difference between the two receivers is converted into a distance difference, and a hyperbola can be obtained; 4.4) The time difference measured by three or more receivers can be used to obtain two or more hyperbolas. The location of the transmitting source (device) can be realized by intersecting two or more hyperbolas, and its accurate coordinate position can be calculated, which facilitates the location management of multiple devices. The coordinate position of the device can be displayed through the host computer interface.

[0075] On this basis, the early warning function of device location changes can also be realized. The implementation process of the early warning function can be described as: the position change alarm threshold is set when the system is initialized, the positioning information is collected by the GPS positioning device, and the lower computer sends it to the upper computer for judgment. If the device is moved without permission and exceeds the position change alarm threshold, the upper computer will send an alarm message.

[0076] 5) Equipment operation status control function

[0077] In some embodiments, the power detection device may use a transient power tester. The power loss data of the power supply of the device is detected by the transient power tester as power information, and the host computer detects the working and stopping status of the device based on this to determine whether the device is running. The transient power tester is a device used to test the instantaneous power consumption of electronic equipment, and has the advantages of high precision, high speed, and high resolution. During the test, the transient power tester can monitor the operating status of the device in real time, record the instantaneous power changes, and provide strong support for equipment optimization and troubleshooting. The user only needs to set the corresponding test parameters on the host computer, such as test time, sampling frequency, etc., and the transient power tester will collect the power information of the device in real time, record the instantaneous power changes, and then send the power information to the host computer for display. Figure 3 As shown, the operating status of the device is determined, and the usage time of the device is recorded and saved in the database for subsequent analysis and evaluation, so as to manage the operating status of the device.

[0078] 6) Light switch control function

[0079] The lower computer is responsible for connecting to the underlying sensing device, mainly for data interaction and command transmission on the I / O, serial port, CAN port and other interfaces, and also plays the role of forwarding data to the upper computer. On this basis, the system can control the switch of the lights in the working area by controlling the I / O port.

[0080] 7) Data storage management function

[0081] The storage of system collected information is implemented using MySQL, which can meet the system's functional requirements for device information collection and storage. Due to its small size and fast speed, MySQL is the preferred persistent relational database for many small and medium-sized enterprises. MySQL provides a complete data backup and data recovery solution to meet data security needs. MySQL query methods are as follows Figure 6 As shown in the figure, the principle of MySQL query method is mainly divided into the following steps: ① Connector: This step is mainly responsible for establishing connection with the client, obtaining permissions, managing connections, etc. In order to optimize performance, long connections are generally used. ② Query cache: Before actually executing the SQL statement, MySQL will first check the query cache. If the corresponding query result exists, it will directly return the result. This method is more suitable for some static data, but it is best not to use it for data with high real-time requirements. ③ Parser: When the SQL statement is passed to the parser, it will be verified and parsed by the parser. It decomposes the SQL statement into a data structure and passes this structure to the subsequent steps. The subsequent transmission and processing of the SQL statement is based on this structure. ④ Optimizer: After the parser parses, the optimizer will optimize the SQL statement, such as adjusting the index to improve execution efficiency. ⑤ Executor: The last step is the executor, which will call the API interface provided by the storage engine to execute the SQL statement according to the optimized execution plan and return the query result to the client.

[0082] On this basis, data backup and data recovery functions can also be realized. Users can back up the database at any time as needed, and can also select manually backed up database files to restore system data as needed.

[0083] 8) Statistical report generation and export function

[0084] The generation and export of statistical reports of the system host computer uses the third-party xlnt. xlnt is an open source C++ library that can be used to create, read and write Excel files. It supports xlsx and xls formats and provides a rich API to operate Excel files. Using the xlnt library, you can easily create Excel files, including cell formats, fonts, colors, etc.

[0085] There are two modes for generating and exporting statistical reports. One mode is to generate statistical reports with customizable formats for each underlying sensing device. The output report format is the header and content automatically generated according to the format of the data set obtained by the query. The process design is as follows: Figure 7Another mode is the statistical generation of the overall report. The output report format is a fixed format. The fixed format in the report content is customized by the template edited by Excel in advance. The summary calculation function of the software calculates and exports it to the statistical template. The process design is as follows Figure 8 shown.

[0086] 9) Data visualization function

[0087] Data visualization is the process of presenting data through visual elements such as graphics and tables. It can help people better understand and analyze data and discover patterns and trends in the data. In the upper computer software of the system, detailed data and reports are provided for data processing and presentation. Data visualization is also used to describe data in the report and trend prediction functions. Specifically, the system uses Qt (C++) as the development language. Qt is a cross-platform application framework that can be used to develop GUI applications. In Qt, some libraries (such as QtCharts, Qt3D, and QwtPlot3D) are selected for data visualization. Among them, QtCharts is an open source chart library of Qt, which is used to create various types of charts in the system, such as line charts, bar charts, pie charts, etc. It provides a simple and easy-to-use API that can easily create and customize charts. Qt3D is a 3D graphics engine for Qt, which is used to provide site modeling, lighting, materials, textures and other functions in the system. QwtPlot3D is a 3D drawing library based on Qt and Qwt, which is used to create various types of 3D charts and graphics and scene descriptions in the system. These libraries are integrated with Qt to make data visualization in the system easier and more efficient.

[0088] When the user opens the host computer software, the software will perform a series of startup tests. To ensure that the software starts normally, the user should start the hardware device first before starting the software.

[0089] The equipment intelligent management and control system of this application uses the underlying sensing equipment to conduct scientific detection and comprehensive utilization of each link of the equipment environment. It has a certain environmental early warning function. Through the analysis of environmental data, it can provide good optimization, improvement methods and safe monitoring functions for the working area. And the system has flexible data storage and management functions, supports a variety of data formats and structures, and has fast query and retrieval capabilities. The system also has good scalability and customization capabilities, and supports functional expansion and customization. Provide a friendly user interface and operating experience, so that users can easily use the system for data collection and management. Ensure the stability and security of the system, ensure the safe storage and transmission of data, and prevent illegal access and data leakage.

[0090] The equipment intelligent management and control system of this application can timely discover and solve potential problems and avoid production interruptions caused by equipment failures by monitoring the operating status of the equipment in real time. The intelligent equipment management system monitors the temperature, humidity, smoke concentration, image, location, power and other information of the equipment through sensors and Internet of Things technology. Once an abnormality is found, it will immediately alarm and take measures to ensure that the equipment is always in the best operating state, which can significantly improve the efficiency of equipment management. Secondly, the equipment intelligent management and control system can avoid unnecessary downtime and maintenance costs by optimizing equipment maintenance and management, which helps to reduce operating costs. In addition, the equipment intelligent management and control system can timely discover and deal with safety hazards and prevent accidents by monitoring the operating status of the equipment in real time, which can improve the safety of the equipment. For example, for equipment working in a dangerous environment, the system can monitor its temperature, humidity and smoke concentration data, and immediately alarm and automatically shut down once an abnormality occurs to prevent the accident from expanding. The equipment intelligent management and control system can also optimize resource utilization. Through data analysis and optimization algorithms, it can also help enterprises find problems and bottlenecks in resource utilization and put forward improvement suggestions. For example, by analyzing the power and energy consumption data of the equipment, it is possible to find out the links of energy waste, optimize energy use strategies, and reduce production costs. Finally, the equipment intelligent management and control system provides powerful data analysis support functions. The system can collect and analyze a large amount of equipment-related information data to help enterprises make scientific decisions. For example, through big data processing technology, the system can conduct in-depth analysis of massive equipment data, form statistical reports, and mine valuable information to provide strong support for decision-making.

[0091] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0092] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An intelligent equipment management and control system, characterized in that: include: A monitoring computer, a network middleware, an embedded controller, a serial port switch and an underlying sensing device; the underlying sensing device includes a temperature, humidity and smoke sensor, a camera, an asset management device, a GPS positioning device and a power detection device; the monitoring computer is connected to the embedded controller through the network middleware; the embedded controller is connected to the underlying sensing device through the serial port switch; The temperature, humidity and smoke sensor is used to detect the temperature, humidity and smoke data of the on-site working environment as environmental information; the camera is used to collect image information of the working area; the asset management device is used to input asset information; The GPS positioning device is used to collect the positioning information of the device; the power detection device is used to detect the power information of the device; the embedded controller obtains the environmental information, image information, asset information, positioning information and power information collected by the underlying sensing device through the serial port switch, and transmits it to the monitoring computer through the network middleware, and the monitoring computer displays, stores and manages the information.

2. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is used to set temperature, humidity and smoke warning thresholds, and determine whether to issue an alarm based on real-time temperature, humidity and smoke data.

3. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to use time as an independent variable and temperature, humidity and smoke data as dependent variables, adopt a linear regression algorithm to fit the linear equation of the temperature, humidity and smoke data changing over time, and predict the future trend of the temperature, humidity and smoke data changes based on the linear equation.

4. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to call a pattern recognition algorithm to perform device recognition based on image information collected by the camera.

5. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to identify abnormal events in the working area based on image information collected by the camera using intelligent video analysis technology and deep learning algorithms.

6. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to perform asset addition, deletion, modification, query and update management based on the entered asset information.

7. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to locate the coordinate position of each device based on the positioning information collected by the GPS positioning device and using a positioning algorithm based on arrival time difference.

8. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to detect the operating status of the device according to the power data change based on the power information detected by the power detection device.

9. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer is also used to realize the on / off control of the lights in the working area by controlling the I / O ports.

10. The intelligent equipment management and control system according to claim 1, characterized in that: The monitoring computer uses Mysql to store the collected information, uses the third-party xlnt to generate and export statistical reports, and adopts Qt C++ as the development language to realize data visualization display.

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