Intelligent power and environment resource census method and device, electronic equipment and storage medium

Abstract

The invention provides an intelligent power and environment resource census method and device, electronic equipment and a storage medium, and relates to the technical field of communication network equipment management, and the method comprises the steps: recognizing an equipment image based on a preset large model, and obtaining the equipment information of target power and environment equipment; identifying the terminal panel image based on a preset large model to obtain terminal state information of each terminal of the target moving ring equipment; identifying the capacity identification image based on a preset large model to obtain terminal capacity information of each terminal; and associating the equipment information, the terminal state information and the terminal capacity information of the target moving ring equipment to generate resource census information of the target moving ring equipment. According to the method and the device provided by the invention, manual data acquisition is replaced by image acquisition and preset large model identification, so that the statistical efficiency of basic data of equipment resources is improved; the basic data of the equipment resources in the power and environment system are accurately obtained, and the management level of the equipment resources in the power and environment system is improved.

Description

Technical Field

[0001] This application relates to the field of communication network equipment management technology, and in particular to a method, apparatus, electronic device and storage medium for intelligent survey of environmental resources. Background Technology

[0002] In the current wave of digitalization, telecommunications operators face increasingly fierce market competition. Providing stable, efficient, and reliable communication network services is the cornerstone of maintaining a competitive edge. The environmental system, as a key system for power equipment (such as switching power supplies, uninterruptible power supplies, and batteries) and environmental systems (such as temperature, humidity, smoke, and water immersion monitoring) within the communication equipment room, directly affects the security of the entire communication network and is crucial for ensuring uninterrupted network operation and service continuity. Therefore, refined management and accurate control of environmental resources are prerequisites for efficient network operation and maintenance.

[0003] The relevant technologies rely on manual maintenance to acquire basic data on equipment resources in the environmental system and manage these resources. However, this manual process is cumbersome and slow, resulting in low efficiency in resource management. Manual data entry is prone to errors and inconsistent data is difficult to guarantee, leading to poor accuracy. Furthermore, cross-disciplinary collaborative management of equipment resources depends on manual intervention, resulting in high management costs and poor scalability.

[0004] Therefore, how to accurately obtain basic data on equipment resources in the environmental system and improve the management level of equipment resources in the environmental system has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for intelligent surveying of environmental resources, which addresses the technical problem of how to accurately obtain basic data on equipment resources in an environmental system and improve the management level of equipment resources in the environmental system.

[0006] This application provides a method for intelligent survey of environmental resources, including: Acquire the equipment image of the target dynamic environment equipment, and identify the equipment image based on a preset large model to obtain the equipment information of the target dynamic environment equipment; Acquire the terminal panel image of the target dynamic environmental device, and identify the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target dynamic environmental device; Acquire the capacity identification images of each terminal of the target dynamic environment device, and identify the capacity identification images based on the preset large model to obtain the terminal capacity information of each terminal; The equipment information, terminal status information, and terminal capacity information of the target dynamic environmental device are correlated to generate resource survey information of the target dynamic environmental device.

[0007] In some embodiments, the step of identifying the device image based on a preset large model to obtain the device information of the target environmental device includes: Based on the preset large model, the device panel layout and / or device label characters in the device image are identified to obtain the device information of the target environmental device; In the event of an error in the device image recognition, the device information of multiple candidate dynamic environment devices is obtained from a preset dynamic environment device information database. Based on the user selection results of the device information of each candidate dynamic environment device, the device information of the target dynamic environment device is determined.

[0008] In some embodiments, the step of identifying the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target environmental monitoring device includes: Based on the preset large model, target detection is performed on the terminal panel image to determine the image area and terminal position information corresponding to each terminal, and the image area corresponding to each terminal is identified to determine the terminal type and terminal status information of each terminal. If a pre-occupancy marker exists in the image area corresponding to any terminal, the terminal status information of that terminal is determined to be pre-occupancy. The terminal types include switch-type terminals and fuse-type terminals; the terminal position information includes terminal number, row position, and column position. When the switch of the switch-type terminal is switched to the first preset position, the terminal status information is occupied; when the switch of the switch-type terminal is switched to the second preset position, the terminal status information is idle. When the terminal of the fuse-type terminal has a ceramic fuse and is connected, the terminal status information is occupied; when the terminal of the fuse-type terminal has no ceramic fuse or is not connected, the terminal status information is idle.

[0009] In some embodiments, the step of identifying the capacity identification image based on the preset large model to obtain the terminal capacity information of each terminal includes: Based on the preset large model, the terminal label characters and capacity label characters in the capacity identification image are identified to determine the terminal name and terminal capacity information of each terminal.

[0010] In some embodiments, associating the device information, terminal status information, and terminal capacity information of the target environmental monitoring device to generate resource survey information of the target environmental monitoring device includes: Based on the terminal name of each terminal, the terminal capacity information of each terminal is associated with the terminal type and terminal status information of each terminal to generate the terminal survey information of the target environmental device. Obtain the data center information and upstream and downstream equipment information of the target environmental equipment; The equipment information, upstream and downstream equipment information, computer room information, and terminal survey information of the target environmental equipment are correlated to generate resource survey information of the target environmental equipment. The resource survey information of the target environmental equipment is entered into the resource management system.

[0011] In some embodiments, the method further includes: The cable label images of the wiring of each terminal of the target dynamic environmental device are obtained, and the cable label images are identified based on the preset large model to obtain the upstream and downstream equipment information of the target dynamic environmental device.

[0012] In some embodiments, the method further includes: Acquire an image set of multiple sample environmental devices; the image set includes at least one of sample device images, sample terminal panel images, and sample capacity identification images; Each sample image in the image set is labeled to obtain a label for each sample image; the label includes at least one of sample device information, sample terminal status information, and sample terminal capacity information; The preset large model is trained based on the image set of the multiple sample environmental devices.

[0013] This application provides an intelligent survey device for environmental resources, comprising: The equipment identification module is used to acquire the equipment image of the target environmental equipment and identify the equipment image based on a preset large model to obtain the equipment information of the target environmental equipment. The terminal identification module is used to acquire the terminal panel image of the target dynamic environmental equipment, and to identify the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target dynamic environmental equipment; The capacity identification module is used to acquire the capacity identification images of each terminal of the target dynamic environment equipment, and to identify the capacity identification images based on the preset large model to obtain the terminal capacity information of each terminal. The information association module is used to associate the equipment information, terminal status information and terminal capacity information of the target environmental device to generate resource survey information of the target environmental device.

[0014] This application provides an electronic device, 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 intelligent environmental resource survey method described above.

[0015] This application provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned intelligent survey method for environmental resources.

[0016] The intelligent survey method, device, electronic equipment, and storage medium for environmental resources provided in this application identify equipment images, terminal panel images, and terminal capacity identification images of target environmental equipment through a preset large model, obtaining equipment information, terminal status information, and terminal capacity information. After correlation, the resource survey information of the target environmental equipment is obtained. By replacing manual data collection with image acquisition and preset large model recognition, the statistical efficiency of basic data of equipment resources is improved. The accuracy and consistency of data collection are significantly improved, effectively avoiding input errors and data deviations caused by manual operation, realizing accurate acquisition of basic data of equipment resources in the environmental system, and improving the management level of equipment resources in the environmental system. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is one of the flowcharts of the intelligent survey method for environmental resources provided in this application.

[0020] Figure 2 This is a flowchart illustrating the terminal identification method provided in this application.

[0021] Figure 3 This is the second flowchart of the intelligent survey method for environmental resources provided in this application.

[0022] Figure 4 This is a schematic diagram of the intelligent environmental resource survey device provided in this application.

[0023] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps, units, or modules is not necessarily limited to those explicitly listed, but may include other steps, units, or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0026] ‌ Figure 1 This is one of the flowcharts illustrating the intelligent survey method for environmental resources provided in this application, such as... Figure 1 As shown, the method includes steps 110, 120, 130 and 140.

[0027] Step 110: Obtain the equipment image of the target dynamic and environmental equipment, and identify the equipment image based on the preset large model to obtain the equipment information of the target dynamic and environmental equipment.

[0028] Specifically, the executing entity of the intelligent environmental resource survey method provided in this application embodiment is an intelligent environmental resource survey device or system. This device can be implemented in software, such as an intelligent environmental resource survey program running on a computer; or it can be implemented in hardware, such as a processor, mobile terminal, computer, or server that executes the intelligent environmental resource survey method.

[0029] The method provided in this application can be applied to scenarios such as telecommunications operators and data centers where equipment resources of power and environment (hereinafter referred to as "dynamic environment") systems in computer rooms are inventoried, verified, and managed.

[0030] Target environmental monitoring equipment refers to physical devices related to power or environmental monitoring that require resource surveys. These devices are the infrastructure that ensures the stable operation of communication equipment rooms or data centers. For example, target environmental monitoring equipment may include, but is not limited to: power equipment such as switching power supplies, uninterruptible power supplies, battery banks, AC power distribution systems, and DC power distribution systems, as well as environmental monitoring equipment such as air conditioners, temperature and humidity sensors, smoke sensors, and water immersion sensors.

[0031] Equipment images refer to digital images captured by image acquisition devices (such as smartphone cameras, dedicated scanning equipment, etc.) that reflect the appearance characteristics of the target environmental equipment. For ease of identification, equipment images can be one or more pictures that clearly show the overall panel of the equipment, equipment nameplate, model label, or asset label.

[0032] A pre-trained large model refers to an artificial intelligence (AI) model that has been pre-trained and possesses powerful image recognition and information extraction capabilities. Here, "large model" is a broad concept; it can be a single, comprehensive model with multiple capabilities, or a model system composed of multiple cooperating sub-models (e.g., a model for object detection, a model for optical character recognition (OCR), etc.). The core capability of this model lies in accurately analyzing and extracting the required information from complex image backgrounds.

[0033] In one specific embodiment, the images and recognition results from the environmental monitoring device can be used to train a multimodal large language model (MLLM) from related technologies, which can then be used as the preset large model in this application embodiment. The multimodal large model can simultaneously process multiple modal data such as text, images, audio, and video.

[0034] Equipment information refers to attribute data that can uniquely identify and describe a target environmental equipment. This information is usually structured and may include, but is not limited to: equipment type, equipment name, equipment model, equipment manufacturer, asset number, etc.

[0035] In one specific embodiment, a user uses an application on a mobile terminal to take a picture of the target environmental monitoring equipment to be surveyed, thereby obtaining an image of the equipment. The application uploads the equipment image to a backend server, or uses an inference engine deployed locally on the terminal to call a preset large model for analysis. The preset large model processes the equipment image, using technologies such as image classification, object detection, and character recognition to identify brand text, model text, label characters, etc. on the equipment panel, thereby resolving the equipment information of the equipment.

[0036] Step 120: Obtain the terminal panel image of the target dynamic environmental equipment, and identify the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target dynamic environmental equipment.

[0037] Specifically, a terminal panel image refers to a digital image that clearly displays all or some of the terminals (also often referred to as ports or tag numbers) and their current status on a target environmental control device. For example, for switching power supply equipment, the terminal panel image should clearly show the layout and status of all air switches (circuit breakers) or fuses (tethers) on its panel.

[0038] Terminals are physical interfaces on equipment used for power output or signal connection. Common terminal types include circuit breaker terminals and fuse terminals. Terminal status information refers to data describing the current usage of each terminal. The most basic statuses can include occupied and idle. Occupied typically indicates that the terminal is supplying power to downstream equipment or is connected to a cable, while idle indicates that the terminal is not currently in use and can be used for future service activation. In some management scenarios, other statuses such as pre-occupied and faulty may also exist.

[0039] In one specific embodiment, the user takes a picture of the terminal panel of the target environmental monitoring device to obtain an image of the terminal panel. After receiving the image, the pre-defined large model first analyzes it to detect the position, arrangement, and quantity of all terminals on the panel. Next, the model performs a detailed analysis of the local image area containing each terminal to determine its status. For example, the model can identify whether the circuit breaker lever is in the closed (occupied) or open (idle) position, or determine whether a fuse holder has a fuse installed and a load cable connected. Finally, the model generates corresponding terminal status information for each identified terminal.

[0040] Step 130: Obtain the capacity identification images of each terminal of the target dynamic environment equipment, and identify the capacity identification images based on the preset large model to obtain the terminal capacity information of each terminal.

[0041] Specifically, a capacity label image is a digital image that clearly displays the terminal capacity information. In actual equipment, capacity information is usually printed or affixed to the surface of or near each terminal (such as an air switch) in the form of numbers and units (e.g., "32A", "63A", where A represents amperes). Therefore, a capacity label image is typically a close-up image of the terminal panel. Terminal capacity information describes the maximum rated current or power that a terminal can carry. This is the core basis for power planning and capacity management.

[0042] In one specific embodiment, the user can take one or more close-up images that clearly show the capacity markings of each terminal. A pre-defined large model processes these images to identify the numbers and letters associated with each terminal, thereby resolving the terminal capacity information of each terminal.

[0043] Step 140: Associate the equipment information, terminal status information and terminal capacity information of the target dynamic and environmental equipment to generate resource survey information of the target dynamic and environmental equipment.

[0044] Specifically, association refers to the process of logically matching and integrating different dimensions of information (equipment information, status of each terminal, capacity of each terminal) obtained separately in the preceding steps regarding the same target environmental device, to form a complete and unified data record.

[0045] Resource survey information is a structured data set that is ultimately generated by this survey task and comprehensively describes the target environmental equipment and its port resource status.

[0046] The intelligent environmental resource survey method provided in this application identifies the equipment image, terminal panel image, and terminal capacity identification image of the target environmental equipment using a preset large model, thereby obtaining equipment information, terminal status information, and terminal capacity information. This information is then correlated to obtain the resource survey information of the target environmental equipment. By replacing manual data collection with image acquisition and preset large model recognition, the statistical efficiency of basic equipment resource data is improved. The method significantly improves the accuracy and consistency of data collection, effectively avoiding input errors and data deviations caused by manual operation. This enables accurate acquisition of basic equipment resource data in the environmental system and improves the management level of equipment resources in the environmental system.

[0047] It should be noted that each implementation method of this application can be freely combined, rearranged, or executed individually, and does not need to rely on or depend on a fixed execution order.

[0048] In some embodiments, the method further includes: By scanning the room label, the room where the target environmental equipment is located can be identified, which prepares for subsequent information identification and entry.

[0049] In some embodiments, the device information of the target environmental equipment is obtained by recognizing the device image based on a preset large model, including: Based on a pre-set large model, the device panel layout and / or device label characters in the device image are identified to obtain the device information of the target environmental device; In the event of an error in the device image recognition, the device information of multiple candidate dynamic and environmental devices is obtained from a preset dynamic and environmental device information database. Based on the user selection results of the device information of each candidate dynamic and environmental device, the device information of the target dynamic and environmental device is determined.

[0050] Specifically, device panel layout refers to the macroscopic features such as the arrangement, shape, and relative position of components (e.g., displays, indicator lights, buttons, ventilation holes, etc.) on the front or key surfaces of a device. Different models and manufacturers of devices typically have unique panel layouts. A pre-defined large model can use an image classification or feature comparison network to compare the overall layout features of an input device image with layout templates of various known devices stored in a model library. When the template with the highest similarity is matched, the model can output the device information (e.g., device type, model number, etc.) corresponding to that template.

[0051] Equipment label characters refer to the text and numbers printed or engraved on equipment nameplates, asset labels, and model stickers. This is the most direct way to obtain accurate equipment information. In this method, the pre-built large model first locates the area where the label is located (i.e., the equipment label) in the equipment image using object detection technology. Then, it calls an optical character recognition engine to recognize and extract the characters within the label area. Finally, the extracted string is structured to form equipment information.

[0052] In one specific embodiment, the two identification methods described above can be combined. For example, panel layout identification can be used to quickly determine the approximate type or series of the device, narrowing the search scope, and then label character identification can be used to obtain precise model and manufacturer information. This combined approach ensures both the accuracy and efficiency of identification.

[0053] Errors in device image recognition include several situations: (1) Low confidence recognition: After the preset large model analysis, it is impossible to find a matching result with a high enough confidence score (e.g., higher than the preset threshold of 90%); (2) Multiple high confidence results: The model identifies multiple candidate devices with high similarity and cannot make a unique judgment; (3) Obvious error: The results output by the model are obviously inconsistent with the user's visual judgment; (4) Unrecognizable: Due to poor image quality or the device being too old / rare, the model cannot give any effective recognition results.

[0054] The pre-built environmental equipment information database is a backend database that stores massive amounts of structured information about known environmental equipment, including but not limited to equipment type, manufacturer, model, and standard appearance images. It serves as the data foundation for training and comparing pre-built large models.

[0055] When an error is detected in the device's image recognition, the system will not directly interrupt the process, but will instead initiate an interactive correction procedure: The system retrieves equipment information from a pre-defined environmental equipment information database that most closely resembles the preliminary identification results of a pre-defined large-scale model. For example, if the pre-defined large-scale model initially determines that the equipment model might be "Model A" or "Model B," the system will provide detailed information (including standard appearance drawings) for these two models as a candidate list. This candidate list will be displayed to the user on the application interface, prompting the user to make a selection. The user selects the correct equipment information from the candidate list based on the actual equipment conditions on site. This user selection will be adopted by the system and used as the final identified target environmental equipment information. If no correct option is found in the candidate list, the user can also manually enter the correct equipment information.

[0056] The intelligent environmental resource census method provided in this application significantly improves the robustness and accuracy of environmental equipment information identification by introducing two dimensions of recognition: equipment panel layout and equipment label characters, combined with a human-machine collaborative fault-tolerant correction mechanism. It can not only efficiently handle automatic identification under normal circumstances but also identify uncertain or failed abnormal scenarios using a large model, ensuring the smooth progress of the entire census process and the high reliability of the final data. Simultaneously, user correction behavior can be used as new labeled data for subsequent iterative optimization of the preset large model, forming a continuously learning and improving closed-loop system.

[0057] In some embodiments, the terminal status information of each terminal of the target environmental monitoring device is obtained by recognizing the terminal panel image based on a preset large model, including: Based on a pre-set large model, target detection is performed on the terminal panel image to determine the image area and terminal position information corresponding to each terminal. The image area corresponding to each terminal is then identified to determine the terminal type and terminal status information of each terminal. If a pre-occupancy marker exists in the image area corresponding to any terminal, the terminal status information of any terminal will be determined as pre-occupancy. The terminal types include switch-type terminals and fuse-type terminals; the terminal position information includes terminal number, row position, and column position. When the switch of the switch-type terminal is switched to the first preset position, the terminal status information is occupied; when the switch of the switch-type terminal is switched to the second preset position, the terminal status information is idle. When a fuse-type terminal has a ceramic fuse and is connected, the terminal status information is "occupied"; when a fuse-type terminal has no ceramic fuse or is not connected, the terminal status information is "idle".

[0058] Specifically, the pre-defined large model first analyzes the input terminal panel image to identify and locate the position of all terminals in the image.

[0059] For each detected terminal, the pre-defined large model will output its bounding box in the image. The area enclosed by this bounding box is the image region corresponding to that terminal. Subsequent refined analysis will only be performed on this region to eliminate background interference.

[0060] The pre-built large model not only locates the terminals but also understands their arrangement logic. By analyzing the coordinates of all terminal bounding boxes, the pre-built large model can calculate their relative positional relationships, thereby generating structured terminal position information. This information can include terminal serial number, row position, and column position.

[0061] Each terminal is assigned a unique serial number starting from 1 according to preset rules (e.g., from left to right, from top to bottom). This serial number is the terminal number.

[0062] For terminal blocks arranged in a matrix, a preset large model can determine the row and column number of each terminal, which can be used as the row and column position of the terminal. For example, the row position of a certain terminal is "row 1" and the column position is "column 3".

[0063] After determining the image region corresponding to each terminal, the preset large model will further identify each region.

[0064] The pre-defined large model analyzes the terminal image area to determine which predefined terminal type it belongs to. In this embodiment, the terminal types include at least switch-type terminals and fuse-type terminals.

[0065] Switch-type terminals typically refer to air switches (circuit breakers), characterized by a lever (switch) that can be moved up and down or left and right.

[0066] A fuse-type terminal typically refers to a terminal that uses ceramic or glass tube fuses (fuse wires) for circuit protection, characterized by having a socket for holding the fuse.

[0067] The pre-defined large model calls the corresponding status judgment logic based on the identified terminal type.

[0068] For switch-type terminals, their status is mainly determined by the orientation of the switch lever.

[0069] If the switch of a switch-type terminal is set to the first preset position, its terminal status information is "occupied". The first preset position here typically refers to the physically closed (ON) position. According to industry practice and equipment design, this is usually upward or leftward. For example, when the model detects that the switch lever is in the upward position, it determines that the terminal is occupied.

[0070] If the switch of a switch-type terminal is set to the second preset position, its terminal status information is "idle". The second preset position here typically refers to the physical OFF position, opposite to the first preset position. This is usually downwards or to the right. For example, when the model detects that the switch lever is in the downward position, it determines that the terminal is in an idle state.

[0071] For fuse-type terminals, their status needs to be determined by a combination of the fuse itself and the wiring conditions.

[0072] If a fuse-type terminal has a ceramic fuse and is connected, its terminal status information is "occupied." This means that the port not only has the necessary protective element (ceramic fuse) installed, but its load side is also connected to a cable, indicating that it is supplying power to downstream devices or is preparing to do so. The model needs to simultaneously detect the presence of a cylindrical ceramic fuse object within the socket and a visible connection (i.e., a cable) on the terminal's terminal post.

[0073] If a fuse-type terminal has no ceramic fuse or no wiring, its terminal status is "idle". This includes two situations: one is that the fuse socket is empty (no ceramic fuse); the other is that although there is a fuse in the socket, no cable is connected to its load end (no wiring). As long as either of these two situations is met, the port can be considered to be currently unused and determined to be in an idle state.

[0074] Pre-reservation markings are artificially created visual markers used to indicate terminals that have been planned but not yet officially used. They can take many forms, such as a sticker of a specific color, a handwritten label, a hangtag, or any other conspicuous marking placed near the terminal. Pre-defined large models need to be trained to recognize these pre-defined pre-reservation markings.

[0075] In practice, this step can have a higher priority than regular status checks. Specifically, when analyzing a terminal image area, the model first checks for a pre-emption flag. If a pre-emption flag is detected, the system directly determines the terminal's status as pre-emption, regardless of whether the terminal is physically closed or open. If no pre-emption flag is detected, the system then continues with the aforementioned regular check logic based on switch orientation or fuse / wiring status.

[0076] Based on the recognition results of the pre-set large model, the terminal status is directly updated in the model during the final manual confirmation. If the manual recognition result shows the terminal as occupied, but the model recognizes the terminal status as idle, an errata can be initiated.

[0077] The intelligent survey method for environmental resources provided in this application improves the accuracy and reliability of status identification by performing precise target detection, positioning, and type classification of terminals, and establishing clear judgment rules for the physical characteristics of different types of terminals (such as switch orientation, fuses, and wiring).

[0078] In some embodiments, the terminal capacity information of each terminal is obtained by recognizing the capacity identification image based on a preset large model, including: Based on a pre-defined large model, the terminal label characters and capacity label characters in the capacity identification image are identified to determine the terminal name and terminal capacity information of each terminal.

[0079] Specifically, terminal label characters refer to characters that directly identify the terminal's identity or serial number. On many devices, each terminal (such as an air switch) has its serial number printed or affixed next to it, such as "F1", "F2", "1", "2", etc. The default large model will first recognize these characters, which are key indexes for subsequent information association.

[0080] Capacity label characters are characters that directly indicate the rated current of the terminals. This typically consists of a number and a unit, such as "32A". The default large model will recognize these strings representing capacity.

[0081] The capacity label image can be a close-up of a panel containing multiple terminal label characters and their capacity label characters, or a clearer partial image of a single terminal. A pre-defined large model recognizes the capacity label image, and after identifying all terminal label characters and capacity label characters, it associates them based on their spatial proximity within the image.

[0082] Typically, a terminal's capacity label is printed either next to its terminal label or directly on the terminal body. The default large model uses this prior knowledge to pair the terminal label characters that are physically closest to the capacity label characters.

[0083] Through this process, the pre-defined large model generates a corresponding terminal name (determined by the terminal label characters) and terminal capacity information (determined by the capacity label characters) for each terminal in the image.

[0084] The intelligent survey method for environmental resources provided in this application utilizes the powerful image recognition capabilities of a pre-set large model to directly read the terminal label characters and capacity label characters on the device panel, and intelligently associates them based on their spatial position. This method avoids the high time cost and error risk caused by manually searching, reading and recording capacity information.

[0085] Figure 2 This is a flowchart illustrating the terminal identification method provided in this application, as shown below. Figure 2 As shown, the terminal identification method includes calling a preset large model to identify the terminal panel image and capacity label image. The main process includes terminal target detection, three-dimensional coordinate calculation, terminal type identification, terminal capacity identification, and identification result summarization.

[0086] To improve recognition accuracy, the system can perform preprocessing operations such as denoising, contrast enhancement, and image correction on the image before calling the preset large model.

[0087] In some embodiments, the device information, terminal status information, and terminal capacity information of the target environmental monitoring equipment are correlated to generate resource survey information of the target environmental monitoring equipment, including: Based on the terminal name of each terminal, the terminal capacity information of each terminal is associated with the terminal type and terminal status information of each terminal to generate terminal survey information of the target environmental equipment. Obtain information about the computer room and upstream and downstream equipment of the target environmental equipment; The equipment information, upstream and downstream equipment information, computer room information, and terminal survey information of the target environmental equipment are correlated to generate resource survey information of the target environmental equipment. Enter the resource survey information of the target dynamic and environmental equipment into the resource management system.

[0088] Specifically, the terminal name, capacity information, type, and status information of each terminal are associated to generate a complete descriptive record for each terminal. The collection of all these terminal records constitutes the terminal survey information for the target environmental equipment.

[0089] The data center information refers to the data center where the target environmental monitoring equipment is located. For example, by scanning the QR code or electronic tag at the entrance of the data center, the system can automatically obtain and record information such as the name of the data center, its geographical location, and its classification. This information provides a unified physical location context for all equipment surveyed within that data center.

[0090] Upstream and downstream equipment information includes both upstream and downstream equipment information. Upstream equipment information refers to the equipment that supplies power to the current target environmental monitoring device. For example, for a switching power supply, its upstream equipment might be a power supply cabinet. Downstream equipment information refers to the equipment that supplies power to the current target environmental monitoring device via its terminals. For example, a terminal on a switching power supply that is currently occupied might be downstream of a server, router, or base station device.

[0091] The equipment information, upstream and downstream equipment information, equipment room information, and terminal survey information of the target environmental equipment are correlated to generate resource survey information (also known as cross-disciplinary correlation information) for the target environmental equipment. The resource survey information can be presented in tabular form, as shown in Table 1.

[0092] Table 1 Resource Survey Information Field Chinese Name Content Description Terminal name Equipment Name + Serial Number Terminal serial number Arranged from left to right and from top to bottom Terminal status Occupied / Idle / Pre-occupied Terminal row position The row where the terminal is located Terminal row position Terminal column Terminal capacity Number + Current Unit Terminal equipment Related Devices Table Terminal equipment type Switching power supply Terminal equipment manufacturer A certain brand Terminal equipment model A certain model The equipment is located in the computer room Related data center table Downstream equipment name A base station Upstream equipment name A certain AC power distribution system The system automatically submits and writes the generated structured resource survey information to the backend resource management system according to predefined interfaces and data formats.

[0093] This data entry process is automated and requires no manual intervention. Once the data is entered into the database, it can be used by other platforms and applications such as network monitoring, operation and maintenance management, and resource planning. If the data discovered during the census does not match the existing data in the system, the system can also automatically trigger a data correction or change approval process.

[0094] The intelligent environmental resource survey method provided in this application can generate a highly structured, complete, and logically clear resource survey data by hierarchical association (first associating port information within the device, and then associating the device with external environmental information).

[0095] In some embodiments, the method further includes: The system acquires cable label images of the wiring at each terminal of the target environmental monitoring equipment, and identifies the cable label images based on a preset large model to obtain upstream and downstream equipment information of the target environmental monitoring equipment.

[0096] Specifically, wiring refers to the cable or wire connected to the terminal of the target dynamic and environmental equipment. In standardized computer room management, each important cable has a cable tag attached to both ends, with characters printed or written on the tag identifying the cable and its counterpart equipment information.

[0097] When a user finds that a terminal is occupied, they can use the camera of their mobile device to take a picture of the label on the cable connected to that terminal, thereby obtaining a clear image of the cable label.

[0098] The pre-set large model can recognize cable tag images and obtain information about the upstream and downstream equipment of the target environmental equipment.

[0099] After obtaining upstream and downstream equipment information through the above identification and parsing, the system will associate it with the terminals currently being surveyed. Thus, in the final information integration step, there is no longer a need for manual input or selection of upstream and downstream equipment; instead, the automatically identified results can be used directly.

[0100] The intelligent survey method for environmental resources provided in this application transforms the previously labor-intensive and time-consuming cable tracing work, which relied heavily on manual labor, into a simple photo-taking operation by automatically identifying cable tags, thereby greatly improving the efficiency and accuracy of obtaining information on upstream and downstream equipment.

[0101] In some embodiments, the method further includes: Acquire an image set of multiple sample environmental devices; the image set includes at least one of sample device images, sample terminal panel images, and sample capacity label images; Each sample image in the image set is labeled to obtain a label for each sample image; the label includes at least one of sample device information, sample terminal status information, and sample terminal capacity information. A pre-set large model is trained using image sets from multiple sample environmental devices.

[0102] Specifically, sample dynamic environment equipment refers to dynamic environment equipment used to collect training data with known and accurate information.

[0103] An image set is a large-scale collection of images specifically designed for model training. It should include at least one or more of the following types of images: (1) Sample equipment images: Images of the entire equipment or nameplate used to identify the equipment model and manufacturer. When collecting data, different manufacturers and different models of equipment should be covered, and the images should be taken under different lighting, angles, clarity and degree of damage to enhance the generalization ability of the model.

[0104] (2) Sample terminal panel image: A panel image used to identify the location, type and status of the terminal. It is necessary to include various types of terminals (switch type, fuse type, etc.) and ensure that all possible states (occupied, idle, pre-occupied, etc.) are covered during the acquisition.

[0105] (3) Sample capacity labeling image: A close-up image used to identify the terminal capacity. Capacity labels with different fonts, formats, and resolutions need to be covered.

[0106] (4) Sample cable label images: Cable label images used to identify upstream and downstream equipment information.

[0107] The process of adding labels to each sample image can be done manually or using semi-automatic tools. Accordingly, these labels include sample device information, sample terminal status information, sample terminal capacity information, and upstream and downstream device information.

[0108] A set of labeled images is input into a deep learning framework. Through algorithms such as backpropagation and gradient descent, the parameters (weights and biases) inside the model are continuously adjusted so that the model's predicted output of the input image is infinitely close to the true label, thus enabling the training of a pre-set large model.

[0109] This training process can be continuous. In practical applications, incorrect identification cases reported by users can be collected and added to the image set as new training samples for incremental training or fine-tuning of the model, thereby continuously improving the model's performance.

[0110] The intelligent survey method for environmental resources provided in this application provides a method that trains a pre-set large model, enabling the pre-set large model to evolve and continuously optimize itself.

[0111] Figure 3 This is the second flowchart of the intelligent survey method for environmental resources provided in this application, as shown below. Figure 3 As shown, the method may include steps such as computer room positioning, environmental equipment identification, terminal status identification, resource data matching, terminal capacity identification, and automatic information entry.

[0112] The above steps can all be designed as prompts for a pre-defined large model. After the prompts are input into the pre-defined large model, the model will guide the user to input images to complete the intelligent survey of environmental resources.

[0113] In one specific embodiment, the prompt words can be designed as follows: "You are a professional environmental resource survey assistant with rich professional knowledge of communications and rigorous logical reasoning ability. Your tasks are: (1) to accurately understand the user's analysis needs and plan a reasonable operation path based on the user's intention; (2) to arrange the order of calling survey tools in a reasonable manner to ensure that each step of the survey has clear input and output logic; (3) to follow the principle of "step-by-step execution and chain from point to point" for all reasoning, unfold layer by layer to ensure the operation is closed loop, and finally generate cross-professional associations of environmental resources." Given the above prompts, the default large model can output: "Step 1: I want to conduct a cross-disciplinary survey of environmental protection and control equipment (EPA / CVD). Please provide guidance and assistance. Step 2: Please take a photo of the computer room label. Once the computer room is confirmed, the EPA / CVD survey will officially begin. Step 3: Okay, the computer room has been confirmed. Please take a photo of the EPA / CVD equipment panel. I will identify the type and name of the EPA / CVD equipment based on the image. Step 4: The EPA / CVD equipment has been confirmed. Please scan the terminal panels of EPA / CVD circuit breakers and ceramic fuses. I will identify the terminal arrangement and status based on the image. Step 5: The EPA / CVD terminals and status have been confirmed. Please scan the EPA / CVD capacity label or panel. I will identify the EPA / CVD terminal capacity information based on the image. Step 6: All operations have been successfully completed. The system is analyzing cross-disciplinary relationships and outputting the intelligent EPA / CVD resource survey. Please wait." The apparatus provided in the embodiments of this application is described below. The apparatus described below can be referred to in correspondence with the method described above.

[0114] Figure 4This is a schematic diagram of the intelligent environmental resource survey device provided in this application, as shown below. Figure 4 As shown, the device includes: The equipment identification module 410 is used to acquire the equipment image of the target dynamic and environmental equipment, and to identify the equipment image based on a preset large model to obtain the equipment information of the target dynamic and environmental equipment. Terminal recognition module 420 is used to acquire terminal panel images of the target dynamic and environmental equipment, and recognize the terminal panel images based on a preset large model to obtain terminal status information of each terminal of the target dynamic and environmental equipment; The capacity identification module 430 is used to acquire the capacity identification images of each terminal of the target dynamic and environmental equipment, and to identify the capacity identification images based on a preset large model to obtain the terminal capacity information of each terminal. The information association module 440 is used to associate the equipment information, terminal status information and terminal capacity information of the target dynamic and environmental equipment to generate resource survey information of the target dynamic and environmental equipment.

[0115] The intelligent environmental resource survey device provided in this application identifies the equipment image, terminal panel image, and terminal capacity label image of the target environmental equipment using a preset large model, obtaining equipment information, terminal status information, and terminal capacity information. After association, the resource survey information of the target environmental equipment is obtained. By replacing manual data collection with image acquisition and preset large model recognition, the statistical efficiency of basic data of equipment resources is improved. The accuracy and consistency of data collection are significantly improved, effectively avoiding input errors and data deviations caused by manual operation, realizing accurate acquisition of basic data of equipment resources in the environmental system, and improving the management level of equipment resources in the environmental system.

[0116] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communications bus 540. The processor 510 can call logical commands stored in the memory 530 to execute the methods described in the above embodiments, for example: The system acquires equipment images of the target environmental monitoring equipment and identifies the equipment images based on a preset large model to obtain equipment information of the target environmental monitoring equipment; it acquires terminal panel images of the target environmental monitoring equipment and identifies the terminal panel images based on a preset large model to obtain terminal status information of each terminal of the target environmental monitoring equipment; it acquires capacity identification images of each terminal of the target environmental monitoring equipment and identifies the capacity identification images based on a preset large model to obtain terminal capacity information of each terminal; and it associates the equipment information, terminal status information, and terminal capacity information of the target environmental monitoring equipment to generate resource survey information of the target environmental monitoring equipment.

[0117] Furthermore, the logical commands in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several commands to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The processor in the electronic device provided in this application embodiment can call logical instructions in the memory to implement the above method. Its specific implementation method is the same as the aforementioned method implementation method and can achieve the same beneficial effect, which will not be repeated here.

[0119] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided in the above embodiments.

[0120] The specific implementation method is the same as the aforementioned method implementation method and can achieve the same beneficial effects, so it will not be repeated here.

[0121] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0122] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for intelligent surveying of environmental resources, characterized in that, include: Acquire the equipment image of the target dynamic environment equipment, and identify the equipment image based on a preset large model to obtain the equipment information of the target dynamic environment equipment; Acquire the terminal panel image of the target dynamic environmental device, and identify the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target dynamic environmental device; Acquire the capacity identification images of each terminal of the target dynamic environment device, and identify the capacity identification images based on the preset large model to obtain the terminal capacity information of each terminal; The equipment information, terminal status information, and terminal capacity information of the target environmental device are correlated to generate resource survey information of the target environmental device.

2. The intelligent survey method for environmental resources according to claim 1, characterized in that, The process of identifying the device image based on a preset large model to obtain the device information of the target environmental equipment includes: Based on the preset large model, the device panel layout and / or device label characters in the device image are identified to obtain the device information of the target environmental device; In the event of an error in the device image recognition, the device information of multiple candidate dynamic environment devices is obtained from a preset dynamic environment device information database. Based on the user selection results of the device information of each candidate dynamic environment device, the device information of the target dynamic environment device is determined.

3. The intelligent survey method for environmental resources according to claim 1, characterized in that, The step of identifying the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target environmental monitoring device includes: Based on the preset large model, target detection is performed on the terminal panel image to determine the image area and terminal position information corresponding to each terminal, and the image area corresponding to each terminal is identified to determine the terminal type and terminal status information of each terminal. If a pre-occupancy marker exists in the image area corresponding to any terminal, the terminal status information of that terminal is determined to be pre-occupancy. The terminal types include switch-type terminals and fuse-type terminals; the terminal position information includes terminal number, row position, and column position. When the switch of the switch-type terminal is switched to the first preset position, the terminal status information is occupied; when the switch of the switch-type terminal is switched to the second preset position, the terminal status information is idle. When the terminal of the fuse-type terminal has a ceramic fuse and is connected, the terminal status information is occupied; when the terminal of the fuse-type terminal has no ceramic fuse or is not connected, the terminal status information is idle.

4. The intelligent survey method for environmental resources according to claim 1, characterized in that, The step of identifying the capacity identification image based on the preset large model to obtain the terminal capacity information of each terminal includes: Based on the preset large model, the terminal label characters and capacity label characters in the capacity identification image are identified to determine the terminal name and terminal capacity information of each terminal.

5. The intelligent survey method for environmental resources according to any one of claims 1 to 4, characterized in that, The process of associating the equipment information, terminal status information, and terminal capacity information of the target environmental monitoring device to generate resource survey information for the target environmental monitoring device includes: Based on the terminal name of each terminal, the terminal capacity information of each terminal is associated with the terminal type and terminal status information of each terminal to generate the terminal survey information of the target environmental device. Obtain the data center information and upstream and downstream equipment information of the target environmental equipment; The equipment information, upstream and downstream equipment information, computer room information, and terminal survey information of the target environmental equipment are correlated to generate resource survey information of the target environmental equipment. The resource survey information of the target environmental equipment is entered into the resource management system.

6. The intelligent survey method for environmental resources according to claim 5, characterized in that, The method further includes: The cable label images of the wiring of each terminal of the target dynamic environmental device are obtained, and the cable label images are identified based on the preset large model to obtain the upstream and downstream equipment information of the target dynamic environmental device.

7. The intelligent survey method for environmental resources according to any one of claims 1 to 4, characterized in that, The method further includes: Acquire an image set of multiple sample environmental devices; the image set includes at least one of sample device images, sample terminal panel images, and sample capacity identification images; Each sample image in the image set is labeled to obtain a label for each sample image; the label includes at least one of sample device information, sample terminal status information, and sample terminal capacity information; The preset large model is trained based on the image set of the multiple sample environmental devices.

8. An intelligent survey device for environmental resources, characterized in that, include: The equipment identification module is used to acquire the equipment image of the target environmental equipment and identify the equipment image based on a preset large model to obtain the equipment information of the target environmental equipment. The terminal identification module is used to acquire the terminal panel image of the target dynamic environmental equipment, and to identify the terminal panel image based on the preset large model to obtain the terminal status information of each terminal of the target dynamic environmental equipment; The capacity identification module is used to acquire the capacity identification images of each terminal of the target dynamic environment equipment, and to identify the capacity identification images based on the preset large model to obtain the terminal capacity information of each terminal. The information association module is used to associate the equipment information, terminal status information and terminal capacity information of the target environmental device to generate resource survey information of the target environmental device.

9. 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 intelligent survey method for environmental resources as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the intelligent survey method for environmental resources as described in any one of claims 1 to 7.

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