Building equipment state detection system, method and equipment

Abstract

The invention discloses a building equipment state detection system, method and equipment, and a state detection device in the building equipment state detection system is configured as follows: performing information extraction on operation standard data of target equipment to obtain safety state standard data; the method comprises the following steps: controlling multiple types of sensors pre-installed at different positions in a building, and carrying out data acquisition on target equipment to obtain acquisition data of the multiple types of sensors; detecting the state of the target equipment according to the collected data of the multiple types of sensors and the safety state standard data; and when it is detected that the target device is in the abnormal state, performing abnormal risk early warning on the target device. According to the method, the state detection of the target equipment can be realized according to the safety state standard data and the data acquired by the sensor, the abnormal state of the target equipment can be quickly and accurately identified, the abnormal risk early warning can be carried out on the abnormal target equipment, the identification accuracy and identification efficiency of the abnormal state of the equipment are improved, and the safety of the equipment and the building is improved.

Description

Technical Field

[0001] This application relates to the field of condition monitoring, and more particularly to a building equipment condition monitoring system, method, and device. Background Technology

[0002] With the rapid development of information technologies such as the Internet of Things, cloud computing, and big data, the construction industry has been profoundly impacted. The construction industry is moving towards networking, intelligence, and humanization, and smart buildings, green buildings, and energy-saving buildings have become the general trend.

[0003] Traditional building management systems have relatively simple safety detection schemes for buildings and their equipment, which are prone to parameter misjudgment, low accuracy and efficiency in identifying abnormal equipment conditions, thus affecting the safety of equipment and buildings. Summary of the Invention

[0004] This application provides a building equipment status detection system, method, and device, which can improve the accuracy and efficiency of identifying abnormal equipment status and enhance the safety of equipment and buildings.

[0005] Firstly, a building equipment status monitoring system is provided, comprising:

[0006] Various types of sensors are installed in different locations within the building to monitor the status of different devices within the building;

[0007] The state detection device is connected to each sensor and is configured as follows:

[0008] Obtain safety status standard data for at least one target device in the building. The safety status standard data is obtained by extracting information from the operating specification data of the target device. The operating specification data is used to standardize the installation and operating status of the target device.

[0009] Control multiple types of sensors pre-installed in different locations in the building to collect data from the target device and obtain the collected data from multiple types of sensors;

[0010] The status of the target device is detected based on data collected from multiple sensors and safety status standard data.

[0011] When an abnormal state is detected in the target device, an abnormal risk warning is issued for the target device.

[0012] In this embodiment, safety status standard data of the target device is extracted based on the operating specification data of the target device. The status of the target device can be detected by combining the safety status standard data with the data collected by the sensors. Since the safety status standard data is obtained by extracting information from the operating specification data of the target device, the abnormal status of the target device can be quickly and accurately identified by intelligently comparing the collected data with the safety status standard data. After the identifier detects the abnormal status, an abnormal risk warning is issued to the target device, which improves the accuracy and efficiency of identifying abnormal status of the device. This allows for accurate and timely warning of abnormal device status, enabling relevant personnel to promptly discover and resolve abnormal situations and improve the safety of the device and the building.

[0013] In one embodiment, the standard wiring topology diagram and standard operating parameters are included in the safety status standard data; the data collected by multiple types of sensors includes at least the on-site wiring images of the target device collected by the image sensor and the electrical signal data of the target device during operation collected by the electrical signal sensor; the status detection device detects the status of the target device based on the data collected by multiple types of sensors and the safety status standard data, and is configured to: identify the wiring status of the target device based on the standard wiring topology diagram and the on-site wiring images to identify wiring faults in the target device; and identify abnormal operating states of the target device based on the standard operating parameters and the electrical signal data of the target device during operation. By collecting on-site wiring images of the target device by the image sensor and combining them with the standard wiring topology diagram, the device intelligently identifies whether there are abnormalities in the wiring structure. At the same time, by collecting electrical signal data and comparing it with the standard operating parameters, dynamic detection of the operating status of the target device is achieved, realizing high-precision, highly automated, and scalable intelligent building equipment status diagnosis.

[0014] In one embodiment, the electrical signal data of the target device includes voltage and current data. The status detection device identifies the wiring status of the target device based on a standard wiring topology diagram and on-site wiring images to identify wiring faults. It is configured to: acquire a pre-trained wiring fault identification model, which is a neural network model trained based on historical building inspection data and corresponding equipment operation specification data; and use the wiring fault identification model to identify wiring faults in the target device based on the voltage and current data, the standard wiring topology diagram, and the on-site wiring images, thereby obtaining the wiring fault status of the target device. Introducing a neural network wiring fault identification model trained on historical data enables intelligent identification of the wiring status of the target device, effectively identifying structural wiring faults such as reverse connection, missing connection, and poor contact, thus improving the accuracy and efficiency of equipment wiring anomaly identification.

[0015] In one embodiment, the status detection device, based on the voltage and current data of the target device, as well as the standard wiring topology diagram and the field wiring image, uses a wiring fault identification model to identify the wiring fault type of the target device and obtain the wiring fault status of the target device. The device is configured to: acquire the terminal block connection status and relay opening / closing signals of the target device, and acquire the circuit grounding information of the target device; extract voltage and current data from the target device to obtain voltage-related and current-related information; and input the standard wiring topology diagram, field wiring image, terminal block connection status and relay opening / closing signals, as well as the circuit grounding information, voltage-related signals, and current-related information into the wiring fault identification model to identify the wiring fault type of the target device and obtain the wiring fault status of the target device. This solution improves the diversity of identification data input, not only enhancing the accuracy and location capability of fault detection, but also identifying control failure faults and grounding anomalies that pose safety hazards, significantly improving the accuracy of wiring fault identification.

[0016] In one embodiment, the state detection device extracts voltage and current data from the target device to obtain voltage-related and current-related information. This is configured to: process the voltage data of the target device to obtain voltage time-domain waveform data showing voltage changes over time, and process the current data of the target device to obtain current time-domain waveform data showing current changes over time; extract information from the voltage time-domain waveform data to obtain the voltage amplitude and preset voltage index value of the target device, and extract information from the current time-domain waveform data to obtain the current amplitude and preset current index value of the target device; use the voltage time-domain waveform data, voltage amplitude, preset voltage index value, and the voltage polarity and phase difference of the target device as voltage-related information of the target device; and use the current time-domain waveform data, current amplitude, preset current index value, and the current polarity and phase difference of the target device as current-related information of the target device. In the wiring fault identification process, the introduction of processing and feature extraction of the voltage and current time-domain waveforms of the target device enhances the modeling capability of dynamic electrical behavior and improves the accuracy, identification range, and interpretability of wiring fault identification.

[0017] In one embodiment, the state detection device detects and identifies abnormal operating states of the target equipment based on standard operating parameters and electrical signal data during the operation of the target equipment. It is configured to: identify abnormal electrical signal states during the operation of the target equipment based on electrical signal calibration data in the standard operating parameters and electrical signal data during the operation of the target equipment; identify abnormal forward and reverse rotation states during the operation of the target equipment based on running direction calibration data in the standard operating parameters and electrical signal data during the operation of the target equipment; and identify range exceedances during the operation of the target equipment based on range calibration data in the standard operating parameters and electrical signal data during the operation of the target equipment. By combining real-time electrical signal data during the operation of the target equipment, intelligent identification of abnormal electrical signal states, abnormal forward and reverse rotation states, and range exceedances during equipment operation is achieved, enabling accurate identification of dynamic errors, directional control failures, or electrical limit violations during equipment operation.

[0018] In one embodiment, when the status detection device detects an abnormal state of the target device, it provides an abnormal risk warning for the target device. The device is configured to: determine the abnormal risk level of the target device based on the abnormal state of the target device; and provide an abnormal risk warning for the target device based on the abnormal risk level and the installation location of the target device through a pre-constructed 3D building model. This can intuitively display the location and risk level of the abnormal device, thereby improving the efficiency of fault location and response.

[0019] In one embodiment, the status detection device provides anomaly risk warnings for the target equipment based on its anomaly risk level and installation location using a pre-constructed 3D building model. It is configured to: generate equipment anomaly handling suggestions based on the target equipment's anomaly risk level and anomaly state type; and provide interface-based warnings regarding the target equipment's installation location and anomaly risk level using the 3D building model, while also displaying the equipment anomaly handling suggestions on the interface. This solution, combined with a 3D building model, achieves integrated and visualized early warning display of equipment spatial location, risk level, and handling suggestions. It enables a closed-loop process for fault location, risk assessment, and response guidance, improving fault response speed and operational standardization.

[0020] Secondly, a method for detecting the status of building equipment is provided, including:

[0021] Obtain safety status standard data for at least one target device in the building. The safety status standard data is obtained by extracting information from the operating specification data of the target device. The operating specification data is used to standardize the installation and operating status of the target device.

[0022] Control multiple types of sensors pre-installed in different locations in the building to collect data from the target device and obtain the collected data from multiple types of sensors;

[0023] The status of the target device is detected based on data collected from multiple sensors and safety status standard data.

[0024] When an abnormal state is detected in the target device, an abnormal risk warning is issued for the target device.

[0025] Thirdly, a state detection device is provided, comprising:

[0026] The acquisition module is configured to acquire safety status standard data of at least one target device in the building. The safety status standard data is obtained by extracting information from the operation specification data of the target device. The operation specification data is used to standardize the installation and operation status of the target device.

[0027] The control module is configured to control multiple types of sensors pre-installed in different locations in the building to collect data from the target device and obtain the collected data from the multiple types of sensors.

[0028] The detection module is configured to detect the status of the target device based on data collected by multiple types of sensors and safety status standard data;

[0029] The early warning module is configured to issue an early warning of abnormal risks to the target device when an abnormal state is detected.

[0030] Fourthly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the function of the status detection device in the above-mentioned building equipment status detection system, or implements the steps of the above-mentioned building equipment status detection method.

[0031] Fifthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a status detection device, enables the status detection device to perform the functions of a status detection device in the aforementioned building equipment status detection system, or to perform the steps of the aforementioned building equipment status detection method.

[0032] Sixthly, a computer program product is provided, comprising: a computer program that, when run by a status detection device, enables the status detection device to perform the functions of a status detection device in the aforementioned building equipment status detection system, or to perform the steps of the aforementioned building equipment status detection method. Attached Figure Description

[0033] Figure 1A schematic diagram of the structure of a building equipment status monitoring system provided in some embodiments of this application is shown;

[0034] Figure 2 A flowchart of a building equipment status detection method provided in some embodiments of this application is shown;

[0035] Figure 3 An example is shown Figure 2 A schematic diagram of the implementation process of step S30;

[0036] Figure 4 An example is shown Figure 3 A schematic diagram of the implementation process of step S31;

[0037] Figure 5 An example is shown Figure 4 A schematic diagram of the implementation process of step S312;

[0038] Figure 6 An example is shown Figure 2 A schematic diagram of the implementation process of step S40;

[0039] Figure 7 An example is shown Figure 6 A schematic diagram of the implementation process of step S42;

[0040] Figure 8 An example is shown in the display interface diagram of equipment anomaly handling suggestions and building 3D model;

[0041] Figure 9 An example is shown Figure 1 A schematic diagram of the structure for mid-state detection;

[0042] Figure 10 Exemplary schematic diagrams of the structure of electronic devices provided in some embodiments of this application are shown; Detailed Implementation

[0043] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in this text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0044] Hereinafter, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include one or more of that feature.

[0045] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of the invention include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0046] Specific details, such as particular system architectures and techniques, are set forth for illustrative purposes and not for limitation, to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted to avoid unnecessary detail that could obscure the description of this application.

[0047] It should be understood that the sequence number of each step in the following embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0048] To facilitate a further understanding of the technical solutions in some embodiments of this application, the technical solutions of the building equipment status detection system and the building equipment status detection method, and how these technical solutions solve the aforementioned technical problems, are described in detail below with reference to some specific embodiments and accompanying drawings. The embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0049] Traditional building management systems have relatively simple safety detection schemes for buildings and their equipment. They usually rely on manual inspections and simple rule engines or thresholds for equipment status diagnosis, which can easily lead to parameter misjudgments. The accuracy and efficiency of identifying abnormal equipment status are low, which affects the safety of equipment and buildings.

[0050] For example, during equipment commissioning before a building is put into use, and during equipment inspections during maintenance after the building is put into use, the wiring and parameters of the equipment are usually checked and adjusted by relevant personnel. This method relies on subjective judgment based on personal experience, which is prone to oversights or misinterpretations of parameters. For example, oversights such as incorrect wiring or incorrect recognition of sensor signal types can lead to equipment burnout and malfunctions, and is also inefficient and costly. In addition, because the interface standards of equipment produced by different manufacturers are not uniform, relevant personnel need to manually match the signal type (such as 4-20mA / 0-10V) and range according to different interface standards when checking the equipment, which is inefficient and prone to errors.

[0051] To address this, building management systems can collect sensor signals from different devices and use rule engines or simple thresholds to identify abnormal conditions, eliminating the need for manual inspection. However, relying on simple rule engines or thresholds for device status diagnosis results in low accuracy and cannot identify complex wiring errors or hidden faults (such as abnormal valve rotation) in real time, leading to prolonged operation of faulty equipment and jeopardizing both equipment and building safety.

[0052] In view of this, embodiments of this application provide a building equipment status detection system, method, and device, which can improve the accuracy and efficiency of identifying abnormal equipment status and enhance the safety of equipment and buildings. The status detection device acquires operational specification data of at least one target device in the building. This operational specification data is used to regulate the installation and operational status of the target device. Information is extracted from the operational specification data to obtain safety status standard data, which includes a standard wiring topology diagram. Multiple types of sensors pre-installed at different locations in the building are controlled to collect data from the target device, obtaining the collected data from the multiple types of sensors. Based on the collected data from the multiple types of sensors and the safety status standard data, the status of the target device is detected. When an abnormal status is detected in the target device, an abnormal risk warning is issued. The abnormal status of the target device includes at least a wiring fault determined based on the standard wiring topology diagram and the collected data from the multiple types of sensors. This solution enables status detection of target equipment based on safety status standard data and sensor-collected data. Since the safety status standard data is obtained by extracting information from the target equipment's operational specifications, intelligent comparison between the collected data and the safety status standard data can quickly and accurately identify abnormal states of the target equipment. For example, intelligent comparison between the standard wiring topology diagram and sensor-collected data can quickly and accurately identify faults such as wiring errors and loose connections. After identifying abnormal states of the target equipment, an abnormal risk warning is issued, improving the accuracy and efficiency of abnormal state identification. This allows for accurate and timely equipment anomaly warnings, enabling relevant personnel to promptly detect and resolve equipment abnormalities, thereby improving the safety of equipment and buildings.

[0053] The building equipment status detection method provided in this invention can be applied to, for example... Figure 1 The building equipment status monitoring system shown includes at least one target device installed in the building, multiple types of sensors located at different locations, and a status monitoring device connected to each sensor. The multiple types of sensors include sensors installed on the target device and sensors located outside the target device (such as image sensors). Each sensor communicates with the status monitoring device via cables or a network.

[0054] The status detection device is configured to: acquire standard safety status data of at least one target device in the building, including a standard wiring topology diagram, obtained by extracting information from the target device's operational specifications; control multiple sensors pre-installed at different locations in the building to collect data from the target device, obtaining sensor data; detect the status of the target device based on the sensor data and the standard safety status data; and issue an anomaly risk warning when an abnormal status is detected in the target device. The abnormal status of the target device includes at least wiring faults determined based on the standard wiring topology diagram and sensor data.

[0055] In this embodiment, the status detection device can extract the safety status standard data of the target equipment based on the installation specifications, electrical connection standards, operating parameters, and other operational specification data. By combining this safety status standard data with sensor-collected data, the device can achieve status detection of the target equipment. Specifically, by constructing a standard wiring topology diagram of the target equipment using the operational specification data, the device establishes the required wiring structure. Intelligent comparison between the standard wiring topology diagram and sensor-collected data allows for rapid and accurate identification of wiring errors, disconnected wiring, and other faults. Upon identifying abnormal states such as wiring faults, the device provides an abnormal risk warning for the target equipment, improving the accuracy and efficiency of abnormal status identification. This timely and accurate warning allows relevant personnel to promptly detect and resolve equipment anomalies, effectively improving building operation and maintenance efficiency and safety levels, and enhancing the safety of both equipment and the building.

[0056] The sensors can be of various types, including external sensors such as image sensors (e.g., cameras), and sensors mounted on the target device and related structures, such as voltage and current sensors on the device's circuitry, vibration sensors on the device's piping, and sensors on various valves, such as flow sensors on air valves and water valves. The status monitoring device can be various independently operable electronic devices, such as personal computers, laptops, smartphones, tablets, and other terminal devices, as well as various servers. Servers can be implemented using independent servers or server clusters composed of multiple servers.

[0057] In one embodiment, such as Figure 2 As shown, a method for detecting the status of building equipment is provided, which is applied to... Figure 1 Taking the status detection device in the building equipment status monitoring system as an example, the status detection device is configured to perform the following steps:

[0058] S10: Obtain the safety status standard data of at least one target device in the building. The safety status standard data is obtained by extracting information from the operating specification data of the target device.

[0059] The status monitoring device can acquire the operational specification data of the target equipment. This operational specification data is used to standardize the installation and operational status of the target equipment. This data may include installation diagrams of the target equipment, such as structural installation diagrams, wiring connection diagrams, and other CAD drawings, as well as the equipment manual, which includes various parameter data that standardize the normal operation of the target equipment.

[0060] The operational specifications data of the target device can be pre-stored in the database of the status detection device, and retrieved from the database when the status detection device receives a detection command from the target device. In other embodiments, the status detection device can also obtain the data from relevant Internet platforms based on information such as the target device's name, model, and manufacturer after receiving the detection command.

[0061] After acquiring the operating specification data of the target equipment, the status detection device can extract information from this data to obtain the target equipment's safety status standard data, which is data indicating whether the target equipment is installed correctly and is in a safe operating state. This safety status standard data includes the target equipment's standard wiring topology diagram, standard structural installation diagram, and standard operating parameters indicating the target equipment's safe operating state, such as standard parameters of electrical signals (e.g., voltage and current signals), the equipment's forward and reverse rotation status, and measurement range.

[0062] S20: Controls multiple types of sensors pre-installed in different locations in the building to collect data from the target device and obtain the collected data from the multiple types of sensors.

[0063] After acquiring standard safety status data of one or more target devices in a building, the status detection device controls multiple types of sensors pre-installed in different locations in the building to collect data from one or more target devices, thus obtaining the collected data from multiple types of sensors.

[0064] The sensors include various types, such as image sensors (e.g., cameras), voltage sensors, current sensors, vibration sensors, and sensors on various valves, such as flow sensors on air valves and water valves. Correspondingly, the data collected by these sensors includes equipment image information (including field wiring diagrams) collected by image sensors, electrical signal data (including current and voltage signals) of the target equipment collected by electrical signal sensors such as voltage and current sensors, vibration data of the target equipment collected by vibration sensors (used to monitor abnormal equipment operation, such as abnormal mechanical resonance), and flow data related to the target equipment collected by flow sensors, such as airflow and water flow.

[0065] S30: Detect the status of the target device based on data collected from multiple sensors and safety status standard data.

[0066] After acquiring data from multiple types of sensors and safety status standard data, the status detection device detects the status of the target device based on the data acquired by the multiple types of sensors and the safety status standard data, including the standard wiring topology diagram.

[0067] For example, safety status standard data can include a standard wiring topology diagram of the target device extracted from operational specification data. Based on data collected from multiple types of sensors and the standard wiring topology diagram, the wiring status of the target device (such as the association between device interfaces and cables) can be detected and identified to determine if there are wiring faults. By constructing a standard wiring topology diagram using operational specification data, the required wiring structure of the target device is formed. Intelligent comparison of the standard wiring topology diagram with sensor data can quickly and accurately identify faults such as wiring errors and disconnected wires.

[0068] S40: When an abnormal state is detected in the target device, an abnormal risk warning is issued for the target device.

[0069] After detecting the status of the target device, if an abnormal state is detected, such as a wiring fault, an abnormal risk warning will be issued for the target device with the abnormal state. For example, based on the abnormal state of the target device and basic device information (such as the device's installation location and device type), information can be sent to the user's terminal device to indicate the device abnormality and control relevant devices to issue audible, visual, and / or voice warnings.

[0070] In this embodiment, based on the target equipment's installation specifications, electrical connection standards, operating parameters, and other operational specification data, safety status standard data of the target equipment is extracted. By combining this safety status standard data with sensor-collected data, the status of the target equipment can be detected. Since the safety status standard data is obtained by extracting information from the target equipment's operational specification data, intelligent comparison between the collected data and the safety status standard data can quickly and accurately identify abnormal states of the target equipment. Furthermore, an abnormal risk warning is issued to the target equipment upon detecting an abnormal state, improving the accuracy and efficiency of abnormal state identification. This allows for accurate and timely equipment anomaly warnings, enabling relevant personnel to promptly detect and resolve equipment malfunctions, thereby enhancing the safety of equipment and buildings.

[0071] In one embodiment, the standard wiring topology and standard operating parameters are included in the safety status standard data. Data collected by various sensors includes at least field wiring images of the target device acquired by image sensors, and electrical signal data of the target device during operation, such as voltage and current data, acquired by electrical signal sensors. Figure 3 As shown, in step S30, the state detection device detects the state of the target device based on the data collected by multiple sensors and the safety state standard data, and is configured as follows:

[0072] S31: Based on the standard wiring topology diagram and the field wiring image, identify the wiring status of the target device in order to identify the wiring faults of the target device.

[0073] In this embodiment, the data collected by various sensors includes at least image sensors, vibration sensors, and electrical signal sensors such as voltage sensors, current sensors, and current transformers. Image sensors are used to collect actual images of the target equipment, such as field wiring images, equipment rotation status images, and environmental images. The safety status standard data includes the standard wiring topology diagram and standard operating parameters of the target equipment.

[0074] The status detection device can identify the wiring status of the target device based on the standard wiring topology diagram and the field wiring images collected by the image sensor, so as to identify the wiring faults of the target device, such as whether there are wiring misalignment (interface and line misalignment), wiring phase sequence / polarity error, poor wiring (such as loose contact), etc.

[0075] For example, a condition detection device can pre-train a preset neural network model based on historical building inspection data and corresponding equipment operation specification data (such as wiring relationship data of different devices) to obtain a wiring fault identification model. This preset neural network model can be an improved YOLOv8-seg model. Improvements to the improved YOLOv8-seg model include: optimizing the decoder branch structure to enhance the detection capability of small targets (such as thinner cable terminals); enhancing the spatial resolution of the mask head network to improve image segmentation accuracy; and introducing a coordinate attention network with a coordinate attention mechanism to improve the localization capability of complex wiring areas.

[0076] The condition detection device can preprocess field wiring images by cropping, noise reduction, and color enhancement to obtain preprocessed field wiring images, thereby improving the accuracy of subsequent model recognition. The device uses a trained wiring fault recognition model to perform image segmentation on the preprocessed field wiring images and standard wiring topology diagrams, resulting in multiple image segmentation results for the field wiring images. These segmentation results can include sub-images of each interface and cable, bounding box coordinates, and category confidence scores. Then, based on the connection relationships of interfaces and cables in the multiple image segmentation results and their matching degree with the corresponding connection relationships of interfaces and cables in the standard wiring topology diagram, the wiring fault status of the target device is determined.

[0077] Specifically, the coordinates of cable endpoints and interface center points can be extracted from each image segmentation result using methods such as geometric center and skeleton line analysis. Euclidean distances are then calculated between each cable endpoint and all interface center points to find the interface with the shortest distance as the connection between the interface and the cable. These identified interface-cable connections are used as actual connection pairs to form an actual wiring connection map. Similarly, the corresponding interface-cable connections extracted from the standard wiring topology are used as standard connection pairs to form a standard wiring connection map. Finally, the connection relationships of each interface and line are matched one-to-one against the actual wiring connection map and the standard wiring connection map. If a match fails, the interface that failed to match is marked as an abnormal interface, indicating an abnormal wiring configuration in the target device.

[0078] The installation locations, equipment requirements, and corresponding detection purposes for various types of sensors are shown in Table 1 below:

[0079] Table 1

[0080]

[0081] S32: Identify abnormal operating states of the target equipment based on standard operating parameters and electrical signal data of the target equipment during operation.

[0082] The status detection device can also identify abnormal operating states of the target equipment based on the standard operating parameters in the safety status standard data and the electrical signal data of the target equipment during operation, such as voltage data, current data, and data collected by current transformers, in order to determine whether the target equipment has at least one abnormal operating state, such as abnormal electrical signal, abnormal range, or abnormal forward and reverse rotation (abnormal steering).

[0083] This solution acquires on-site wiring images of target equipment using image sensors and combines them with standard wiring topology diagrams to intelligently identify any abnormalities in the equipment's wiring structure, overcoming the limitations of traditional threshold-based diagnostic methods. Simultaneously, by collecting electrical signal data and comparing it with standard operating parameters, it achieves dynamic detection of the target equipment's operational status. This constructs a multi-dimensional status detection system combining static structure recognition and dynamic behavior judgment, realizing high-precision, highly automated, and scalable intelligent building equipment status diagnosis, effectively improving building operation and maintenance efficiency and safety levels.

[0084] In one embodiment, the electrical signal data of the target device includes voltage data and current data of the target device. For example... Figure 4 As shown, in step S31, the status detection device identifies the wiring status of the target device based on the standard wiring topology diagram and the field wiring image, in order to identify the wiring fault condition of the target device, and is configured as follows:

[0085] S311: Obtain the pre-trained wiring fault identification model, which is a neural network model trained based on historical building inspection data and corresponding equipment operation specification data.

[0086] The condition detection device can pre-train a preset neural network model based on historical building inspection data and corresponding equipment operation specification data (such as wiring relationship data of different devices) to obtain a wiring fault identification model. This preset neural network model can be a neural network model optimized by using the chaotic adaptive whale optimization algorithm to optimize a hybrid classification model of convolutional neural network (CNN) and support vector machine (SVM).

[0087] S312: Based on the voltage and current data of the target device, as well as the standard wiring topology diagram and field wiring image, a wiring fault identification model is used to identify wiring faults in the target device and obtain the wiring fault status of the target device.

[0088] After acquiring the field wiring images of the target device, the status detection device can perform preprocessing on the field wiring images, such as cropping, noise reduction, color enhancement, and contrast enhancement, to obtain preprocessed field wiring images, thereby enhancing the accuracy of subsequent model recognition.

[0089] The condition detection device uses a trained wiring fault identification model. Based on the preprocessed field wiring image and standard wiring topology diagram, as well as the voltage and current data of the target equipment, it uses the optimized wiring fault identification model to identify the fault type and obtain the wiring fault status of the target equipment.

[0090] This solution utilizes field wiring diagrams, voltage and current signal data, and standard wiring topology. Figure 3 By combining these technologies and introducing a neural network wiring fault identification model trained on historical data, intelligent identification of the wiring status of target equipment is achieved. This solution features high accuracy, high automation, and strong generalization capabilities, effectively identifying structural wiring faults such as reverse connection, missing connection, and poor contact. It improves the accuracy and efficiency of equipment wiring anomaly identification, significantly enhancing the intelligence level and safety assurance capabilities of building equipment operation and maintenance.

[0091] In one embodiment, the condition detection device is based on voltage and current data of the target device, as well as a standard wiring topology diagram and a field wiring diagram. For example... Figure 5 As shown, in step S312, the status detection device uses a wiring fault identification model to identify the wiring fault type of the target device, obtains the wiring fault status of the target device, and is configured as follows:

[0092] S3121: Obtain the terminal block connection status and relay opening / closing signal of the target device, and obtain the circuit grounding information of the target device.

[0093] The terminal block connection status and relay opening / closing signals of the target device can be obtained through the target device's controller. The target device itself is equipped with sensors that collect various signals, which are then sent to the controller so that the controller can monitor the device's status. The target device's circuit grounding information includes grounding resistance, grounding voltage, and grounding current.

[0094] S3122: Extract voltage and current data from the target device to obtain voltage-related and current-related information of the target device.

[0095] The condition monitoring device can extract voltage and current data from the target device to obtain voltage-related and current-related information. The voltage-related information can include real-time acquired voltage values, voltage changes at different times, voltage polarity, and phase information. Similarly, the current-related information can include real-time acquired current values, current changes at different times, current polarity, and phase information.

[0096] S3123: Input the standard wiring topology diagram, field wiring image, terminal block connection status and relay opening and closing signals, as well as circuit grounding information, voltage-related signals and current-related information into the wiring fault identification model to identify the wiring fault type of the target device and obtain the wiring fault status of the target device.

[0097] The status detection device can directly input standard wiring topology diagrams, field wiring images, terminal block connection status and relay opening and closing signals, as well as circuit grounding information, voltage-related signals and current-related information into the wiring fault identification model. This allows the wiring fault identification model to process the input data, identify the wiring fault type of the target device, and obtain the wiring fault status of the target device.

[0098] Among them, an optimized CNN and SVM hybrid classification model was trained based on historical building inspection data and corresponding equipment operation specification data, resulting in a wiring fault identification model with high accuracy. The model was tested using actual equipment fault data and can accurately identify wiring fault types such as reversed current polarity, open or short circuit in the current loop, incorrect three-phase voltage phase sequence (cross-phase / reverse phase), short circuit or grounding in the voltage loop, missing or incorrect protective grounding, mixed terminal block polarity or loop connection, mixed or unisolated strong and weak current lines, confusion between neutral and ground wires, and inconsistent polarity in parallel branches (reverse polarity when multiple relays, capacitors, or batteries are connected in parallel). This improves the scope and accuracy of wiring fault identification, increasing identification efficiency by 40%.

[0099] This solution integrates the terminal block connection status, relay control signals, and circuit grounding information of the target equipment, based on on-site wiring images, electrical signal data, and wiring topology diagrams. It also incorporates key feature information extracted from voltage and current data and inputs it into the wiring fault identification model to identify wiring fault types. This improves the diversity of identification data input and achieves more refined, structured, and intelligent equipment wiring fault identification capabilities. It not only enhances the accuracy and location of fault detection but also identifies control failure faults and grounding anomalies that pose safety hazards, significantly improving the accuracy of wiring fault identification.

[0100] In one embodiment, step S3122, where the state detection device extracts voltage and current data from the target device to obtain voltage-related and current-related information of the target device, is configured as follows:

[0101] S31221: Process the voltage data of the target device to obtain voltage time-domain waveform data of voltage changing with time, and process the current data of the target device to obtain current time-domain waveform data of current changing with time.

[0102] Taking voltage data processing as an example, the specific processing procedure is as follows:

[0103] The state detection device acquires the real-time voltage signal collected by the voltage sensor, discretizes it at a certain sampling frequency through an analog-to-digital converter, and timestamps the collected signal to form a voltage time-domain waveform with time-voltage as the coordinate. The voltage time-domain waveform is filtered by sampling moving average, median filtering, wavelet denoising or low-pass filtering to remove high-frequency interference, improve waveform smoothness, and obtain voltage time-domain waveform data.

[0104] S31222: Extract information from the voltage time-domain waveform data to obtain the voltage amplitude and preset voltage index value of the target device, and extract information from the current time-domain waveform data to obtain the current amplitude and preset current index value of the target device.

[0105] Taking the processing of voltage time-domain waveform data as an example, the specific processing procedure is as follows:

[0106] The state detection device extracts the voltage amplitude from the voltage time-domain waveform data to obtain the voltage amplitude of the target device. It also uses peak detection, root mean square (RMS) and other methods to extract the values ​​of preset indicators from the voltage time-domain waveform data to obtain the preset voltage indicator values ​​of the target device.

[0107] The preset voltage index values ​​can include effective index values ​​such as the mean, variance, peak value, and root mean square (RMS) of the voltage. Correspondingly, the preset current index values ​​can include effective index values ​​such as the mean, variance, peak value, and RMS of the current.

[0108] S31222: Use voltage time-domain waveform data, voltage amplitude, preset voltage index value, and voltage polarity and voltage phase difference of the target device as voltage-related information of the target device.

[0109] After obtaining the voltage time-domain waveform data, voltage amplitude, and preset voltage index value of the target device, the status detection device uses the actual voltage value, voltage time-domain waveform data, voltage amplitude, and preset voltage index value of the target device, as well as the voltage polarity and voltage phase difference of the target device, as voltage-related information of the target device.

[0110] S31224: The current time-domain waveform data, current amplitude and preset current index value, as well as the current polarity and current phase difference of the target device, are used as the current-related information of the target device.

[0111] After obtaining the current time-domain waveform data, current amplitude, and preset current index value of the target device, the status detection device uses the actual current value, current time-domain waveform data, current amplitude, and preset current index value of the target device, as well as the voltage polarity and voltage phase difference of the target device, as current-related information of the target device.

[0112] This solution introduces the processing and feature extraction of the time-domain waveforms of the target device's voltage and current during the wiring fault identification process. It obtains multi-dimensional electrical information, including voltage / current amplitude, polarity, phase difference, and comparison with preset index values. This enhances the modeling capability of dynamic electrical behavior and enables the identification of various complex electrical wiring faults, including phase sequence errors, reverse polarity connection, and abnormal power quality. This improves the accuracy, identification range, and interpretability of wiring fault identification.

[0113] In one embodiment, in step S32, the status detection device detects and identifies abnormal operating states of the target equipment based on standard operating parameters in the safety status standard data and electrical signal data during the operation of the target equipment, and is configured as follows:

[0114] S321: Based on the electrical signal calibration data in the standard operating parameters and the electrical signal data of the target equipment during operation, identify abnormal electrical signal states during the operation of the target equipment.

[0115] Among them, the condition detection device can train the Long Short Memory (LSTM) model based on the electrical signal calibration data in the standard operating parameters of different equipment and the electrical signal fault data of different equipment to obtain an electrical signal anomaly recognition model.

[0116] After acquiring electrical signal data (including voltage and current signals) of the target device during operation, the state detection device first performs time-series acquisition of the electrical signal data through a preset sliding window (e.g., 1 second / 512 points), obtaining electrical signal data within multiple time windows. The electrical signal sequence within each time window is preprocessed, including DC component removal, filtering, and normalization. The electrical signal, after removing bias effects and high-frequency noise, is normalized to obtain a standard normal distribution, yielding the target electrical signal data for that time window. Fast Fourier Transform is then performed on the target electrical signal data within each time window to extract the frequency domain features of each signal. The frequency domain features from different time windows are then concatenated to obtain the frequency domain feature data of the electrical signal data. This frequency domain feature data is then input into a trained electrical signal anomaly recognition model to identify abnormal electrical signal states during the target device's operation.

[0117] Abnormal electrical signal states include various types such as noise interference (sudden changes, spikes), fluctuation and instability (frequency drift, amplitude jitter), distortion (waveform distortion, nonlinearity, impedance mismatch), missing (signal interruption, amplitude too low), and signal access errors (such as high-voltage signals being connected to low-voltage modules, and analog and digital signals being incorrectly connected).

[0118] S322: Based on the running direction calibration data in the standard operating parameters and the electrical signal data of the target equipment during actual operation, identify abnormal forward and reverse rotation states during the operation of the target equipment.

[0119] The standard operating parameters, including the forward and reverse direction calibration data, can include the forward current direction, phase sequence, and voltage-current phase relationship of the motor in the target equipment. The electrical signal data of the target equipment during actual operation can include current direction, three-phase sequence, and power factor.

[0120] Condition detection devices can use the phase difference between voltage and current to determine the load type and direction of equipment. For example, if the power factor is positive, the equipment rotates forward; if the power factor is negative, the equipment rotates in reverse or provides feedback. This allows the device to determine whether the equipment's operating direction under the specified operating condition matches the direction calibrated in the forward / reverse direction calibration data, thus identifying abnormal forward / reverse states. Condition detection devices can also match the actual flow direction of water or air valves collected by sensors with the calibrated direction of the water or air valves in the forward / reverse direction calibration data to determine whether the flow direction of the water or air valves is abnormal under the corresponding operating condition.

[0121] The condition detection device can also analyze the phase sequence of three-phase voltage or current (e.g., ABC vs ACB) and determine whether it is reversed / reversed using a phase sequence detection algorithm (utilizing the zero-crossing time difference). Furthermore, the condition detection device can use Hall effect sensors or bidirectional current detection to determine in real time whether the current flow direction is consistent with the calibrated operating direction.

[0122] S323: Based on the range calibration data in the standard operating parameters and the electrical signal data of the target equipment during operation, identify the range exceeding the limit during the operation of the target equipment.

[0123] The standard operating parameters, including the range calibration data, can include calibration parameters such as voltage range, maximum current, power range, and speed. The condition monitoring device can match the voltage, current, speed, and power of the target equipment during operation with the corresponding calibration parameters in the range calibration data to identify range exceedances during operation. Inconsistent ranges are identified as abnormal ranges, and the extent or percentage of range exceedances during operation is determined.

[0124] This solution introduces electrical signal calibration data, running direction calibration data, and range calibration data from standard operating parameters, and combines them with real-time electrical signal data of the target equipment during operation. This enables intelligent identification of abnormal electrical signal states, abnormal forward and reverse rotation states, and range exceedances during equipment operation. It can accurately identify dynamic errors, directional loss of control, or electrical limit violations during equipment operation, and has higher diagnostic depth, real-time performance, and safety assurance capabilities. This provides key support for predictive maintenance, fault warning, and energy efficiency optimization.

[0125] In one embodiment, such as Figure 6 As shown, in step S40, when the status detection device detects an abnormal state in the target device, it issues an abnormal risk warning to the target device, which is configured as follows:

[0126] S41: When an abnormal state is detected in the target device, the abnormal risk level of the target device shall be determined according to the nature of the abnormal state.

[0127] The abnormal states of the target equipment include wiring faults, abnormal electrical signals, exceeding the range limit, and abnormal forward / reverse states. Different types of abnormal states correspond to different risk levels, or each type of abnormal state may correspond to multiple risk levels.

[0128] Each abnormal state can be associated with one of five levels of increasing safety risk: extremely low risk, low risk, medium risk, high risk, and extremely high risk.

[0129] For example, wiring faults such as incorrect connection of spare terminals or failure to ground the shielding wire correspond to an extremely low risk level; wiring faults such as incorrect connection of control signal terminals (e.g., confusion between start and stop lines) correspond to a low risk level; wiring faults such as reversed connection of the power supply neutral and live wires correspond to a medium risk level; wiring faults such as phase-to-phase short circuit or incorrect grounding correspond to a high risk level; and wiring faults such as reversed power supply polarity (e.g., reversed positive and negative terminals of the equipment) correspond to an extremely high risk level.

[0130] For example, when the range of measurement is exceeded by the following abnormal conditions: range exceedance ≤10%, range exceedance 10%-30%, range exceedance 30%-50%, range exceedance 50%-100%, and range exceedance >100% (such as no-load operation), the corresponding abnormal risk levels are extremely low risk, low risk, medium risk, high risk, and extremely high risk, respectively.

[0131] For example, the abnormal forward and reverse states are: occasional short-term reverse (corrected during startup), frequent start-stop of equipment that allows reverse rotation (≤5 times / minute), excessive start-stop of equipment that allows reverse rotation (>5 times / minute), occasional reverse rotation of equipment that prohibits reverse rotation (not stuck), and continuous reverse rotation of equipment that prohibits reverse rotation (such as centrifugal pump reverse rotation). The corresponding abnormal risk levels are extremely low risk, low risk, medium risk, high risk, and extremely high risk, respectively.

[0132] S42: Based on the abnormal risk level of the target equipment and the installation location of the target equipment, an abnormal risk warning is given to the target equipment through a pre-constructed 3D building model. The 3D building model is a 3D model pre-constructed based on the structural topology of the building.

[0133] After determining the abnormal risk level of the target equipment, the condition detection device can provide an abnormal risk warning for the target equipment based on the abnormal risk level and the installation location of the target equipment, using a pre-constructed 3D building model. The 3D building model is a 3D model pre-constructed based on the structural topology of the building.

[0134] For example, based on the installation location of the target equipment (its corresponding location on the floor), an anomaly risk warning can be issued for the target equipment in a pre-constructed 3D building model. This could be achieved by using flashing nodes to indicate anomalies, and the warning method of the 3D building model can be determined based on the anomaly risk level. For instance, different colors could represent different anomaly risk levels, with darker colors indicating greater risk. For example, light green, light yellow, orange, pink, and red could be displayed in the 3D building model to represent extremely low risk, low risk, medium risk, high risk, and extremely high risk, respectively.

[0135] After identifying the abnormal state of the target equipment, this solution constructs an anomaly risk level assessment mechanism to quantify and classify different types and severity of anomalies. Combined with the specific installation location of the equipment within the building, this is mapped onto a pre-constructed 3D building structure model, creating a spatially visualized anomaly risk early warning effect. This mechanism not only intuitively displays the location and risk level of abnormal equipment, improving fault location and response efficiency, but also supports graded response and maintenance resource scheduling based on risk level. This achieves visualized, intelligent, differentiated, and proactive equipment management, comprehensively improving the operational efficiency and safety assurance capabilities of building facilities.

[0136] In one embodiment, such as Figure 7 As shown, in step S42, the status detection device, based on the abnormal risk level of the target equipment and its installation location, uses a pre-built 3D building model to provide an abnormal risk warning for the target equipment. This is configured as follows:

[0137] S421: Generate equipment anomaly handling suggestions based on the anomaly risk level and anomaly status type of the target equipment.

[0138] This involves pre-generating anomaly handling measures for different anomaly states and levels to form an anomaly handling measures database. After determining the anomaly risk level and anomaly state type of the target device, data matching is performed in the anomaly handling measures database to match mobile anomaly handling measures according to the device type, anomaly state type, and risk level of the target device, and outputting as an equipment anomaly handling suggestion for the target device.

[0139] Alternatively, a pre-trained natural language model (such as a large language model) can be used to generate processing suggestions based on the target device's device type, abnormal state type, and risk level, thus obtaining device abnormality handling suggestions for the target device.

[0140] The equipment anomaly handling recommendations include structured handling measures for the current anomaly of the target equipment, the anomaly handling recommendation level (e.g., emergency / recommended handling / observation), and the possible scope of impact of the anomaly (whether it affects adjacent systems, etc.).

[0141] S422: The interface provides early warnings on the installation location and abnormal risk level of target equipment through the 3D building model, and displays suggestions for handling equipment abnormalities.

[0142] For example, the 3D model of the building can be visualized on the operating interface of the status detection device, and the status information of the target equipment can be displayed at the corresponding location on the 3D model, including the installation location of the target equipment (coordinates / floor / room number), the abnormal status of the equipment, and the level of abnormal risk (e.g., displayed using different colors). Simultaneously, the interface can display suggestions for handling equipment anomalies, and can also display buttons for confirming handling or reporting anomaly handling results to mark changes in the status of the suggested handling. Furthermore, handling records can be generated based on user actions and uploaded to the system backend for easy traceability and statistics.

[0143] In this solution, an intelligent handling suggestion mechanism based on abnormal state type and risk level is generated, and combined with a 3D building model, the integrated visualization and early warning display of equipment spatial location, risk level and handling suggestions is realized. It can realize the closed-loop processing of the entire process of fault location, risk assessment and response guidance, reduce the dependence of operation and maintenance personnel on experience, and improve fault response speed and operational standardization.

[0144] In one embodiment, step S422, where the status detection device provides an interface warning based on the installation location and abnormal risk level of the target equipment using the building's 3D model, is configured as follows:

[0145] S4221: Determine the location of abnormal equipment in the building 3D model based on the installation location of the target equipment.

[0146] The status detection device can map the installation location of a target device in the physical building to the coordinate system of a 3D model, accurately locating the graphic position of the abnormal device in the model, that is, determining the location of the abnormal device in the 3D building model. For example, by binding the installation location information of the target device, such as the physical floor and room number, to the spatial node of the target device in the 3D building model, and marking the graphic ID, spatial path, and floor level of the target device, the spatial entity or coordinate point of the target device in the model can be determined, thus obtaining the location of the abnormal device in the 3D building model.

[0147] S4222: Determine the model early warning strategy based on the abnormal risk level of the target equipment, and update the building 3D model based on the location of the abnormal equipment and the model early warning strategy.

[0148] The status detection device can implement different early warning display strategies for the target equipment in the building's 3D model based on different risk levels. For example, it can use different colors, animations, and labels to provide risk warnings. Then, according to the early warning display strategy, it renders the graphical nodes of the abnormal equipment locations in the building's 3D model to update the model. For instance, it can modify the material color of the equipment nodes located at the abnormal equipment locations in the building's 3D model and add colors and flashing effects corresponding to different anomaly risk levels.

[0149] S4223: Display suggestions for handling equipment anomalies on the preset interface, and display the updated 3D model of the building to provide interface warnings on the installation location and anomaly risk level of the target equipment.

[0150] For example, suggestions for handling device malfunctions can be displayed in the first area of ​​the preset interface (the right or bottom area of ​​the interface), and an updated 3D building model can be displayed in the first area of ​​the preset interface (the left or top area of ​​the interface) to provide interface warnings about the installation location and malfunction risk level of the target device. Figure 8 As shown.

[0151] This solution achieves precise spatial positioning in a 3D model based on the equipment's installation location and introduces a multi-strategy model early warning mechanism driven by abnormal risk levels, dynamically adjusting the display method of early warnings in the 3D model. By displaying early warning information and handling suggestions in regional divisions within the interface, the clarity of interface interaction, the flexibility of response, and the user's perception efficiency are improved.

[0152] In one embodiment, in step S4223, the status detection device displays suggestions for handling equipment anomalies on a preset interface and displays an updated 3D building model to provide an interface warning regarding the installation location and anomaly risk level of the target equipment. The second area of ​​the preset interface is configured as follows:

[0153] S42231: Generate early warning information for the target device based on the abnormal status of the target device, the device abnormality handling suggestions, and the installation location of the target device in the building.

[0154] Among them, the status detection device can generate structured data based on the abnormal status of the target equipment, the equipment abnormality handling suggestions, and the installation location of the target equipment in the building, and obtain early warning information for the target equipment.

[0155] For example, the target equipment is a water pump; the abnormal condition of the target equipment is abnormal current, specifically: the current current threshold of 58.7A is greater than the calibrated threshold of 50A; the abnormal risk of the target equipment is relatively high, and the target equipment is installed in the main building's third-floor manhole room. Recommended handling for equipment abnormality: Immediately disconnect the power and check if the pump body is stuck or if there is a power supply abnormality; replace the motor if necessary. Based on this information, the next structured early warning message can be generated:

[0156] [Emergency Warning] (Warning Level);

[0157] Equipment: 3F water pump A1 (main building, third floor, pipe well machine room);

[0158] Anomaly type: Current anomaly (Current current: 58.7A, Calibration threshold: 50A);

[0159] Abnormal risk level: High risk;

[0160] Recommended action: Immediately disconnect the power and check if the pump body is stuck or if there is an abnormal power supply. Replace the motor if necessary.

[0161] Anomaly occurred at 16:30:25 on July 2, 2025.

[0162] S42232: Display the warning information of the target device in the first area of ​​the preset interface, and display the updated 3D model of the building in the second area of ​​the preset interface, so as to provide interface warnings on the installation location and abnormal risk level of the target device.

[0163] For example, the warning information of the target device can be displayed in the first area of ​​the preset interface of the status detection device in the form of text or card control, and the three-dimensional model of the building can be displayed in the second area of ​​the preset interface. The abnormal risk level (red = high, orange = medium, yellow = low) warning can be given in the three-dimensional model of the building by flashing different colors.

[0164] This solution generates structured equipment early warning information by integrating the abnormal status type, risk level, handling suggestions, and installation location information of the target equipment. Through a partitioned interface display strategy, the early warning information is displayed in the first area of ​​the interface, and the updated 3D building model is visualized in the second area, which effectively improves the readability, response efficiency, and spatial understanding of the abnormal information.

[0165] The above text combined Figures 2 to 8 The present application describes in detail the building equipment status detection method according to embodiments of this application. The following will be combined with... Figure 9 This document describes in detail the device embodiments of this application. It should be understood that the status detection device in the embodiments of this application can execute the various building equipment status detection methods described in the foregoing embodiments of this application. That is, the specific working processes of the various products described below can be referred to the corresponding processes in the foregoing method embodiments.

[0166] Figure 9 This is a schematic diagram of the state detection device provided in the embodiments of this application.

[0167] It should be understood that the condition detection device can perform... Figures 2 to 8 The building equipment status detection method shown; such as Figure 9 As shown, the state detection device includes:

[0168] The acquisition module 901 is configured to acquire safety status standard data of at least one target device in the building. The safety status standard data is obtained by extracting information from the operating specification data of the target device.

[0169] The control module 902 is configured to control multiple types of sensors pre-installed in different locations in the building to collect data from the target device and obtain the collected data from the multiple types of sensors;

[0170] The detection module 903 is configured to detect the status of the target device based on data collected by multiple types of sensors and safety status standard data;

[0171] The early warning module 904 is configured to issue an early warning of abnormal risks to the target device when an abnormal state is detected.

[0172] In one embodiment, the standard wiring topology diagram and standard operating parameters are included in the standard safety status data; the data collected by multiple sensors includes at least the field wiring image of the target device acquired by the image sensor, and the electrical signal data of the target device during operation acquired by the electrical signal sensor. The detection module 903 is configured to: identify the wiring status of the target device based on the standard wiring topology diagram and the field wiring image to identify wiring faults in the target device; and identify abnormal operating states of the target device based on the standard operating parameters and the electrical signal data of the target device during operation.

[0173] In one embodiment, the electrical signal data of the target device includes voltage data and current data of the target device; the detection module 903 is configured to: acquire a pre-trained wiring fault identification model, the wiring fault identification model being a neural network model trained based on historical building detection data and corresponding equipment operation specification data; and, based on the voltage and current data of the target device, as well as a standard wiring topology diagram and on-site wiring images, use the wiring fault identification model to identify wiring faults in the target device to obtain the wiring fault status of the target device.

[0174] In one embodiment, the detection module 903 is configured to: acquire the terminal block connection status and relay opening / closing signal of the target device, and acquire the circuit grounding information of the target device; extract the voltage and current data of the target device to obtain voltage-related information and current-related information of the target device; input the standard wiring topology diagram, field wiring image, terminal block connection status and relay opening / closing signal, as well as the circuit grounding information, voltage-related signals and current-related information into the wiring fault identification model to identify the wiring fault type of the target device and obtain the wiring fault status of the target device.

[0175] In one embodiment, the detection module 903 is configured to: process the voltage data of the target device to obtain voltage time-domain waveform data showing voltage changes over time, and process the current data of the target device to obtain current time-domain waveform data showing current changes over time; extract information from the voltage time-domain waveform data to obtain the voltage amplitude and preset voltage index value of the target device, and extract information from the current time-domain waveform data to obtain the current amplitude and preset current index value of the target device; use the voltage time-domain waveform data, voltage amplitude, preset voltage index value, and the voltage polarity and voltage phase difference of the target device as voltage-related information of the target device; and use the current time-domain waveform data, current amplitude, preset current index value, and the current polarity and current phase difference of the target device as current-related information of the target device.

[0176] In one embodiment, the detection module 903 is configured to: identify abnormal electrical signal states during the operation of the target device based on electrical signal calibration data in the standard operating parameters and electrical signal data of the target device during operation; identify abnormal forward and reverse rotation states during the operation of the target device based on running direction calibration data in the standard operating parameters and electrical signal data of the target device during operation; and identify range exceeding limits during the operation of the target device based on range calibration data in the standard operating parameters and electrical signal data of the target device during operation.

[0177] In one embodiment, the early warning module 904 is configured to: when an abnormal state is detected in the target device, determine the abnormal risk level of the target device based on the abnormal state of the target device; and provide an abnormal risk warning for the target device based on the abnormal risk level of the target device and the installation location of the target device through a pre-constructed three-dimensional building model.

[0178] In one embodiment, the early warning module 904 is configured to: generate equipment anomaly handling suggestions for the target equipment based on the anomaly risk level and anomaly state type of the target equipment; provide interface early warnings on the installation location and anomaly risk level of the target equipment through a 3D building model; and display the equipment anomaly handling suggestions on the interface.

[0179] In one embodiment, the early warning module 904 is configured to: determine the location of the abnormal device in the building 3D model based on the installation location of the target device; determine the model early warning strategy based on the abnormal risk level of the target device; update the building 3D model based on the location of the abnormal device and the model early warning strategy; display the device abnormality handling suggestions on a preset interface, and display the updated building 3D model to provide an interface early warning for the installation location and abnormal risk level of the target device.

[0180] Each unit module of the state detection device can execute the corresponding steps in the above method embodiment, so the details of each unit module will not be elaborated here. Please refer to the description of the corresponding steps above for details.

[0181] It should be noted that the aforementioned state detection device is embodied in the form of a functional unit. The term "unit" here can be implemented in software and / or hardware, without specific limitations.

[0182] For example, a "unit" can be a software program, hardware circuitry, or a combination of both that implements the above-described functions. Hardware circuitry may include application-specific integrated circuits (ASICs), electronic circuitry, a processor (e.g., a shared processor, a proprietary processor, or a group processor) and memory for executing one or more software or firmware programs, integrated logic circuitry, and / or other suitable components that support the described functions.

[0183] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0184] This application also provides an electronic device, such as... Figure 10 As shown, the electronic device 10 includes: at least one processor 101, a memory 102, and a computer program 103 stored in the memory 102 and executable on the at least one processor 101. When the processor 101 executes the computer program 103, it implements the steps in any of the above method embodiments, or when the processor 101 executes the computer program 103, it implements the functions of each module / unit in the above device embodiments.

[0185] For example, the computer program 103 may be divided into one or more modules / units, which are stored in the memory 102 and executed by the processor 101 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 103 in the electronic device 10.

[0186] Those skilled in the art will understand that Figure 10 The electronic device described is merely an example and does not constitute a limitation on the electronic device. It may include more or fewer components than shown, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0187] The processor mentioned above can be a central processing unit, or it can be other general-purpose processors, digital signal processors, application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0188] The memory can be an internal storage unit of the electronic device, such as a hard drive or RAM. The memory can also be an external storage device of the electronic device, such as a plug-in hard drive, smart memory card, security digital card, flash memory card, etc. Furthermore, the memory can include both internal and external storage units of the electronic device.

[0189] This application also provides a computer program product that, when executed by the controller 250, implements the display control method of any method embodiment in this application.

[0190] The computer program product can be stored in memory, for example, as a program. The program is eventually converted into an executable object file that can be executed by the controller 250 after processes such as preprocessing, compilation, assembly and linking.

[0191] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer, implements the display control method of any of the method embodiments of this application. The computer program may be a high-level language program or an executable object program.

[0192] The computer-readable storage medium is, for example, memory. Memory can be volatile or non-volatile, or it can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0193] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0194] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0195] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0196] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0197] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0198] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0199] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0200] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A building equipment status monitoring system, characterized in that, include: Multiple types of sensors are installed in different locations within the building to monitor the status of different devices within the building; A state detection device connected to each of the aforementioned sensors, the state detection device being configured to: Obtain safety status standard data for at least one target device in the building. The safety status standard data is obtained by extracting information from the operation specification data of the target device. The operation specification data is data used to regulate the installation and operation status of the target device. Control multiple types of sensors pre-installed at different locations in the building to collect data from the target device and obtain the collected data from the multiple types of sensors; The status of the target device is detected based on the data collected by the various sensors and the safety status standard data. When an abnormal state is detected in the target device, an abnormal risk warning is issued for the target device.

2. The building equipment status monitoring system as described in claim 1, characterized in that, The safety status standard data includes standard wiring topology diagrams and standard operating parameters. The data collected by the various types of sensors includes at least the field wiring images of the target device collected by the image sensor and the electrical signal data of the target device during operation collected by the electrical signal sensor. The status detection device detects the status of the target device based on the data collected by the multiple types of sensors and the safety status standard data, and is configured as follows: Based on the standard wiring topology diagram and the field wiring image, the wiring status of the target device is identified in order to identify the wiring faults of the target device. Based on the standard operating parameters and the electrical signal data of the target device during operation, the abnormal operating state of the target device is identified.

3. The building equipment status monitoring system as described in claim 2, characterized in that, The electrical signal data of the target device includes the voltage and current data of the target device; the status detection device identifies the wiring status of the target device based on the standard wiring topology diagram and the field wiring image, in order to identify wiring faults in the target device, and is configured as follows: A pre-trained wiring fault identification model is obtained, which is a neural network model trained based on historical building detection data and corresponding equipment operation specification data. Based on the voltage and current data of the target device, as well as the standard wiring topology diagram and the field wiring image, the wiring fault identification model is used to identify wiring faults in the target device, thereby obtaining the wiring fault status of the target device.

4. The building equipment status monitoring system as described in claim 3, characterized in that, The status detection device, based on the voltage and current data of the target device, the standard wiring topology diagram, and the field wiring image, uses the wiring fault identification model to identify the wiring fault type of the target device, thereby obtaining the wiring fault status of the target device, and is configured as follows: Acquire the terminal block connection status and relay opening / closing signals of the target device, and acquire the circuit grounding information of the target device; The voltage and current data of the target device are extracted to obtain voltage-related and current-related information of the target device; The standard wiring topology diagram, the field wiring image, the terminal block connection status and relay opening / closing signals, as well as the circuit grounding information, the voltage-related signals and current-related information, are input into the wiring fault identification model to identify the wiring fault type of the target device and obtain the wiring fault status of the target device.

5. The building equipment status monitoring system as described in claim 4, characterized in that, The state detection device extracts voltage and current data from the target device to obtain voltage-related and current-related information of the target device, and is configured as follows: The voltage data of the target device is processed to obtain voltage time-domain waveform data of voltage changing with time, and the current data of the target device is processed to obtain current time-domain waveform data of current changing with time. Information is extracted from the voltage time-domain waveform data to obtain the voltage amplitude and preset voltage index value of the target device, and information is extracted from the current time-domain waveform data to obtain the current amplitude and preset current index value of the target device. The voltage time-domain waveform data, the voltage amplitude and the preset voltage index value, as well as the voltage polarity and voltage phase difference of the target device, are used as the voltage-related information of the target device. The current time-domain waveform data, the current amplitude and the preset current index value, as well as the current polarity and current phase difference of the target device, are used as the current-related information of the target device.

6. The building equipment status monitoring system as described in any one of claims 1-5, characterized in that, When the status detection device detects an abnormal state in the target device, it issues an abnormal risk warning for the target device, and is configured to: When an abnormal state is detected in the target device, the abnormal risk level of the target device is determined according to the nature of the abnormal state. Based on the abnormal risk level of the target device and the installation location of the target device, an abnormal risk warning is issued for the target device through a pre-constructed 3D building model, which is a 3D model pre-constructed based on the structural topology of the building.

7. The building equipment status monitoring system as described in claim 6, characterized in that, The status detection device, based on the abnormal risk level of the target equipment and its installation location, uses a pre-constructed 3D building model to provide abnormal risk warnings for the target equipment. It is configured as follows: Based on the abnormal risk level and abnormal state type of the target device, generate equipment abnormality handling suggestions for the target device; The interface provides early warnings about the installation location of the target equipment and the level of abnormal risk through the 3D building model, and displays suggestions for handling the equipment abnormalities.

8. A method for detecting the status of building equipment, characterized in that, include: Obtain safety status standard data for at least one target device in a building, wherein the safety status standard data is obtained by extracting information from the operating specification data of the target device; The system controls multiple types of sensors pre-installed at different locations in the building to collect data from the target device, obtaining the collected data from the multiple types of sensors. The operational specification data is used to standardize the installation and operational status of the target device. The status of the target device is detected based on the data collected by the various sensors and the safety status standard data. When an abnormal state is detected in the target device, an abnormal risk warning is issued for the target device.

9. The building equipment status detection method as described in claim 8, characterized in that, The safety status standard data includes standard wiring topology diagrams and standard operating parameters. The data collected by the various types of sensors includes at least the field wiring images of the target device collected by the image sensor and the electrical signal data of the target device during operation collected by the electrical signal sensor. The step of detecting the status of the target device based on the data collected by the multiple types of sensors and the safety status standard data includes: Based on the standard wiring topology diagram and the field wiring image, the wiring status of the target device is identified in order to identify the wiring faults of the target device. Based on the standard operating parameters and the electrical signal data of the target device during operation, the abnormal operating state of the target device is identified.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the function of the status detection device in the building equipment status detection system as described in any one of claims 1 to 6, or implements the steps of the building equipment status detection method as described in any one of claims 8 to 9.

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