Method, system and electronic equipment for identifying nameplate labels of electrical systems

Abstract

The present invention provides a method, system, device, and storage medium for identifying nameplates and labels on electrical systems. The method includes mapping the electrical appliance nameplate into a digital model; the digital model includes a nameplate component node and attribute feature edges; the nameplate component node includes an identification subnode and a description subnode; the attribute feature edges include: an identification attribute edge of the identification subnode, a description attribute edge of the description subnode, and association attribute edges between nameplate component nodes; selecting a start node and a target node in the digital model, extracting a set consisting of multiple feasible identification sequences from a minimum identification sequence; performing multiple attribute simulations, generating attribute parameters of the attribute feature edges in each feasible identification sequence according to attribute requirements, and obtaining simulated attributes of each feasible identification sequence; and using the optimal feasible identification sequence as the recognition result of the electrical appliance nameplate based on the simulated attributes. The present invention can effectively improve the accuracy and efficiency of electrical appliance nameplate identification.

Description

Technical Field

[0001] The present invention relates to the field of automatic identification technology, and more specifically, to a method and system for identifying nameplate labels of an electrical system, and electronic equipment. Background Art

[0002] In the field of automatic recognition of electrical system nameplates, existing technologies primarily utilize image recognition and optical character recognition (OCR). These technologies capture images of the nameplates and then use image processing algorithms to extract textual information. While these methods have improved recognition efficiency to a certain extent, they still face numerous challenges in practical application. For example, they require high image quality and are sensitive to factors such as lighting conditions and background interference, resulting in unstable recognition accuracy. Furthermore, OCR technology is prone to errors when processing complex fonts and blurred handwriting.

[0003] During the implementation of the present invention, the inventors discovered at least the following problems or deficiencies in the prior art: The prior art often lacks sufficient flexibility and accuracy when processing the diverse information on nameplates, such as model, specifications, parameters, and functional descriptions. Furthermore, the prior art relies heavily on the nameplate's layout and format. Changes to the nameplate design require retraining the model, which undoubtedly increases the complexity and cost of applying the technology. These issues limit the application of automatic identification technology in a wider range of scenarios, particularly in dynamic industrial environments. Summary of the Invention

[0004] The present invention provides a method, system, medium and computing equipment for identifying nameplate labels of an electrical system.

[0005] In a first aspect of the present invention, a method for identifying nameplate labels of an electrical system is provided, comprising:

[0006] Mapping the appliance label nameplate into a digital model; the digital model includes a nameplate component node and an attribute feature edge; the nameplate component node includes an identification subnode and a description subnode;

[0007] The attribute feature edges include: identification attribute edges for identifying sub-nodes, description attribute edges for describing sub-nodes, and association attribute edges between nameplate component nodes;

[0008] Selecting a starting node and a target node in the digital model to determine a minimum recognition sequence, and extracting a set of multiple feasible recognition sequences from the minimum recognition sequence based on recognition constraints;

[0009] Perform multiple attribute simulations. In each simulation, generate attribute parameters of attribute feature edges in each feasible recognition sequence according to attribute requirements, and obtain simulation attributes of each feasible recognition sequence.

[0010] Based on the simulated attributes, an optimal feasible recognition sequence of the electrical appliance label nameplate is identified, and the optimal feasible recognition sequence is used as a recognition result of the electrical appliance label nameplate.

[0011] Furthermore, the identification sub-node is used to describe the model and specification identification information of the appliance label;

[0012] The description sub-node is used to describe the parameters and function description information of the appliance label.

[0013] Furthermore, the identification attribute edge is used to describe the attributes of the identification sub-node processing model and specification identification information;

[0014] The description attribute edge is used to describe the attributes of the description sub-node processing parameters and function description information;

[0015] The association attribute edge is used to describe the association relationship and attributes between nameplate component nodes.

[0016] Furthermore, the identifying of the optimal feasible identification sequence of the appliance label nameplate includes:

[0017] For each feasible identification sequence, multiple attribute simulations are performed to obtain simulation attributes and determine the attribute expectation and attribute variance of the feasible identification sequence;

[0018] Determining an attribute evaluation index of a feasible recognition sequence based on the attribute expectation and attribute variance of the recognition sequence;

[0019] The recognition sequence corresponding to the highest value of the attribute evaluation index is taken as the optimal feasible recognition sequence.

[0020] Furthermore, the attribute evaluation index of each feasible recognition sequence is calculated.

[0021] Furthermore, the identification constraints include identification information integrity constraints, description information accuracy constraints and association attribute consistency constraints.

[0022] Furthermore, extracting a set consisting of multiple feasible recognition sequences from the minimum recognition sequence includes:

[0023] Acquire real-time data and performance parameters of the appliance nameplate when it is mapped to a digital model, wherein the real-time data includes real-time identification information, real-time description information, and real-time associated attributes of each nameplate component node, as well as real-time associated attributes of each edge;

[0024] The performance parameters include node performance indicators of each nameplate component node;

[0025] For each recognition sequence in the minimum recognition sequence, determine whether the recognition constraint is satisfied;

[0026] Extracting the recognition sequence that meets the recognition constraints as a feasible recognition sequence;

[0027] All the extracted feasible recognition sequences are summarized to form a set.

[0028] Furthermore, determining whether the identification constraint is satisfied includes:

[0029] If the real-time identification information and real-time description information of each nameplate component node in the current identification sequence are complete, the identification information integrity constraint is satisfied;

[0030] If the real-time association attribute of each edge in the current recognition sequence meets the preset association rule, the total association attribute of the appliance label meets the total association rule, and the sum of the association attributes of the current recognition sequence does not exceed the association attribute threshold, then the association attribute consistency constraint is satisfied;

[0031] If the performance index of each nameplate component node in the current identification sequence meets the performance requirements, and the sum of the performance indexes of all nameplate component nodes meets the overall performance requirements, then the performance index constraint is satisfied;

[0032] Recognition sequences that simultaneously meet the above constraints are extracted as feasible recognition sequences.

[0033] In a second aspect of the present invention, a system for identifying nameplates and labels of an electrical system is provided, comprising:

[0034] A digital model generation module is used to map the appliance label nameplate into a digital model; the digital model includes a nameplate component node and attribute feature edges; the nameplate component node includes an identification subnode and a description subnode; the attribute feature edges include an identification attribute edge of the identification subnode, a description attribute edge of the description subnode, and association attribute edges between nameplate component nodes;

[0035] A feasible recognition sequence set determination module is used to select a starting node and a target node in the digital model to determine a minimum recognition sequence, and extract a set from the minimum recognition sequence based on recognition constraints;

[0036] The attribute simulation module performs multiple attribute simulations. During each simulation, the attribute parameters of the attribute feature edges in each feasible recognition sequence are generated according to the attribute requirements, and the simulation attributes of each feasible recognition sequence are obtained.

[0037] The optimal feasible recognition sequence determination module identifies the optimal feasible recognition sequence of the electrical appliance label nameplate based on the simulation attributes, and uses the sequence as the recognition result of the electrical appliance label nameplate.

[0038] In a third aspect of the present invention, an electronic device is provided, comprising: at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute any one of the methods described in the first aspect.

[0039] The above-described embodiments of the present invention have at least the following beneficial effects: The method for identifying nameplates and labels on electrical systems of the present invention constructs a digital model to accurately map the nameplate's component nodes and their attribute feature edges, thereby achieving efficient recognition of electrical system nameplates. This method utilizes attribute simulation technology to simulate feasible recognition sequences multiple times, obtain simulated attributes, and calculate attribute evaluation indicators for each sequence based on these attributes. This method effectively evaluates and selects the optimal recognition sequence, ensuring the accuracy and reliability of the recognition results. Furthermore, the method incorporates the calculation of attribute expectations and attribute variances, further optimizing the recognition process and improving recognition efficiency. Furthermore, the method of the present invention comprehensively considers recognition constraints, such as identification information integrity, description information accuracy, and associated attribute consistency, ensuring a rigorous and systematic recognition process. This method not only reduces human interference but also improves the real-time and dynamic adaptability of recognition through analysis of real-time data and performance parameters. Ultimately, the implementation of this invention can enhance the automatic recognition capabilities of electrical system nameplates, providing strong technical support for industrial automation and intelligentization, with broad application prospects and significant socioeconomic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0041] Figure 1 A flow chart of a method for identifying nameplate labels of an electrical system provided by one embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the structure of a nameplate label recognition system for an electrical system according to an embodiment of the present invention;

[0043] Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0045] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0046] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0047] Reference below Figure 1 , Figure 1 This is a flow chart of a method for identifying nameplate labels of an electrical system provided by an embodiment of the present invention. Figure 1 As shown, a method 100 for identifying a nameplate label of an electrical system includes:

[0048] Step 101: Map the appliance label nameplate into a digital model; the digital model includes a nameplate component node and attribute feature edges; the nameplate component node includes an identification subnode and a description subnode;

[0049] Step 102: The attribute feature edges include: identification attribute edges for identifying child nodes, description attribute edges for describing child nodes, and association attribute edges between nameplate component nodes.

[0050] Step 103: selecting a starting node and a target node in the digital model to determine a minimum recognition sequence, and extracting a set consisting of multiple feasible recognition sequences from the minimum recognition sequence based on the recognition constraints;

[0051] Step 104: perform multiple attribute simulations. In each simulation, attribute parameters of attribute feature edges in each feasible recognition sequence are generated according to attribute requirements, and simulated attributes of each feasible recognition sequence are obtained.

[0052] Step 105 : identifying an optimal feasible recognition sequence of the appliance label nameplate based on the simulated attributes, and using the optimal feasible recognition sequence as a recognition result of the appliance label nameplate.

[0053] Specifically, implementing the electrical system nameplate identification method of the present invention first requires mapping the appliance nameplate into a digital model. This digital model consists of nameplate component nodes and attribute feature edges. The nameplate component nodes are further subdivided into identification subnodes and description subnodes. The identification subnode stores identification information such as the appliance model and specifications, while the description subnode records detailed information such as parameter and function descriptions.

[0054] Furthermore, attribute feature edges are used to connect and describe the relationships between different nodes in the model. Specifically, identification attribute edges describe how the identification sub-node handles model and specification information, while description attribute edges describe how the description sub-node handles parameters and functional descriptions. Furthermore, association attribute edges are used to express the relationships and attributes between nameplate component nodes.

[0055] After the digital model is built, a starting node and a target node need to be selected to determine a minimum recognition sequence. This sequence is the sequence of nodes that must be passed during the recognition process and will serve as the basis for the recognition process.

[0056] Preferably, based on the set recognition constraints, multiple feasible recognition sequences are extracted from this minimum recognition sequence, which will serve as the basis for subsequent attribute simulation and evaluation.

[0057] Furthermore, multiple attribute simulations are performed on these feasible recognition sequences. In each simulation, attribute parameters of attribute feature edges in each sequence need to be generated according to attribute requirements, so as to obtain the simulated attributes of each sequence.

[0058] Specifically, based on these simulated attributes, the optimal feasible recognition sequence for appliance nameplates can be evaluated and identified. This sequence serves as the final recognition result, representing the best recognition path under the current conditions, ensuring recognition accuracy and efficiency.

[0059] In some embodiments, the identification sub-node is used to describe the model and specification identification information of the appliance label;

[0060] The description sub-node is used to describe the parameters and function description information of the appliance label.

[0061] It should be noted that in the construction and application of the identification sub-node, the function of the identification sub-node is to describe key identification information such as the model and specifications of the appliance nameplate. The key identification information provides the basic classification and characteristics of the device when identifying the appliance.

[0062] In some embodiments, the description subnode is used to record and describe the parameters and functional description information on the appliance nameplate. The functional description information provides users with detailed information about the performance and purpose of the device, and is an important reference for the use and maintenance of the appliance.

[0063] Based on the identification subnode and description subnode, it is necessary to further define identification attribute edges and description attribute edges. Identification attribute edges specifically describe how to process and identify the model and specification information in the identification subnode. In some embodiments, algorithms or rules can be used to parse and utilize this identification information to accurately identify the appliance model and specifications.

[0064] Furthermore, the description attribute edge will be used to describe how the parameter and function description information in the description child node is processed. This includes parsing, classifying, and storing this information so that it can be quickly retrieved and used when needed.

[0065] In some embodiments, the identification attribute edge is used to describe the attributes of the identification sub-node processing model and specification identification information;

[0066] The description attribute edge is used to describe the attributes of the description sub-node processing parameters and function description information;

[0067] The association attribute edge is used to describe the association relationship and attributes between nameplate component nodes.

[0068] In some embodiments, the specific functions of the identification attribute edge and the description attribute edge must be clearly defined. The identification attribute edge is used to describe and process the model and specification information in the identification subnode. When mapping the nameplate in the digital model, specific attribute edges are defined for this identification information. These attribute edges guide the system in how to recognize and process the model and specification identification on the nameplate.

[0069] Preferably, the description attribute edge needs to pass through the parameter and function description information in the description child node. These attribute edges will define how the system parses and understands the parameter details and function description on the nameplate, ensuring that this information can be accurately captured and used in the subsequent recognition process.

[0070] Furthermore, the associative attribute edges in the digital model not only describe the associations between nameplate component nodes, but also encompass the attributes of those nodes. In this way, associative attribute edges help the system understand the connections between different nameplate information, providing a more comprehensive and in-depth understanding during the recognition process.

[0071] Specifically, we can define corresponding rules and algorithms for these attribute edges to ensure that they accurately reflect the characteristics and relationships of the nameplate information. These rules and algorithms will be based on the specific requirements and standards of electrical system nameplates to achieve optimal recognition results.

[0072] In some embodiments, identifying the optimal feasible identification sequence of the appliance label nameplate includes:

[0073] For each feasible identification sequence, multiple attribute simulations are performed to obtain simulation attributes and determine the attribute expectation and attribute variance of the feasible identification sequence;

[0074] Determining an attribute evaluation index of a feasible recognition sequence based on the attribute expectation and attribute variance of the recognition sequence;

[0075] The recognition sequence corresponding to the highest value of the attribute evaluation index is taken as the optimal feasible recognition sequence.

[0076] It should be noted that multiple attribute simulations must first be performed on each feasible recognition sequence. This step is a key step in the recognition process, and simulations can be used to predict and evaluate the attribute performance of different recognition sequences.

[0077] When performing attribute simulation, we can generate corresponding attribute parameters for the attribute feature edges in each feasible recognition sequence based on the attribute requirements. These parameters will guide the simulation process, helping us obtain the simulated attributes of each sequence and thus evaluate the sequence's potential performance.

[0078] Preferably, we use these simulation properties to determine the expected and variance properties of each feasible recognition sequence. The expected property reflects the average performance of the sequence in simulation, while the variance measures the volatility of the sequence's performance. These two metrics provide important insights into the stability and reliability of the recognition sequence.

[0079] Based on the attribute expectation and attribute variance, we can calculate the attribute evaluation index for each recognition sequence. This evaluation index will comprehensively consider the expected performance and volatility of the sequence, providing a quantitative basis for selecting the optimal recognition sequence.

[0080] Furthermore, based on the calculated attribute evaluation index, we select the recognition sequence corresponding to the highest attribute evaluation index value as the optimal feasible recognition sequence. This sequence represents the best recognition path under the current conditions and will be used as the recognition result for the appliance nameplate, ensuring the accuracy and efficiency of the recognition process.

[0081] In some embodiments, the attribute evaluation index of each feasible recognition sequence is calculated according to formula (1):

[0082] (1)

[0083] Where, It is an attribute evaluation index of a feasible recognition sequence, used to quantitatively evaluate the overall attribute performance of the recognition sequence. is the attribute expectation, which represents the average attribute value exhibited by the identification sequence in multiple simulations. is the attribute variance, which is used to measure the fluctuation or dispersion of the attribute performance of the recognition sequence. The expected average value of all identified sequence properties serves as the expected reference point. is the attribute expectation weight, which reflects the importance of attribute expectation in the evaluation index. It is the penalty coefficient for attribute fluctuation, which is used to penalize the volatility of attribute performance. is a coefficient related to the tightness of attribute distribution, which is used to evaluate the deviation between the expectation of the recognition sequence and the overall average expectation.

[0084] 、 、 is a non-negative real number, .

[0085] Specifically, and It is calculated by the attribute value of the recognition sequence obtained through multiple simulations. Specifically, it is obtained through the following steps:

[0086] Simulate each recognition sequence multiple times and record the attribute value of each simulation. Calculate the average value of all simulation results as Calculate the sum of the squares of the differences between all simulation results and the mean, and then divide it by the number of simulations minus one to get .

[0087] It is obtained by statistical analysis of the attribute expectations of all recognition sequences. The average value of the attribute expectations of all recognition sequences is calculated. . 、 、 are parameters pre-set according to the specific requirements and importance of the recognition task. They are non-negative real numbers and satisfy the following normalization conditions: The above parameters can be adjusted according to different application scenarios and recognition targets to achieve the best recognition effect.

[0088] In some embodiments, the identification constraints include identification information integrity constraints, description information accuracy constraints, and association attribute consistency constraints.

[0089] It’s important to note that first, we need to define identification constraints. These constraints are key factors in ensuring the effectiveness and accuracy of the electrical system nameplate identification method. Identification constraints mainly include identification information integrity constraints, description information accuracy constraints, and association attribute consistency constraints.

[0090] Preferably, the identification information integrity constraint ensures that key information on the appliance nameplate, such as model and specifications, must be intact during the identification process. This means that at each node in the identification sequence, the relevant identification information should be accurately captured and used in subsequent identification steps.

[0091] Furthermore, the description accuracy constraint focuses on the accuracy of information such as parameter and function descriptions on appliance labels and nameplates. This constraint requires the system to accurately parse and utilize this description information during the recognition process to ensure the correctness of the recognition results.

[0092] Specifically, the association attribute consistency constraint involves the association relationships between nameplate component nodes and the consistency of their attributes. The system needs to ensure that during the recognition process, the associations between different information on the nameplate are correctly processed and conform to the preset association rules.

[0093] During the recognition process, we obtain real-time data and performance parameters when mapping appliance nameplates to digital models. This data includes real-time identification information, real-time description information, and real-time associated attributes for each nameplate component node, as well as real-time associated attributes for each edge. Performance parameters cover the node performance indicators of each nameplate component node.

[0094] Next, for each identification sequence in the minimum identification sequence, we determine whether it satisfies the identification constraints. If the real-time identification information and real-time description information in the sequence are complete and meet the performance requirements, and the sum of the edge's real-time association attributes and node performance indicators also meets the overall requirements, then the identification sequence satisfies all identification constraints.

[0095] Finally, we extract all recognition sequences that meet the recognition constraints to form a set of feasible recognition sequences. This set will serve as the basis for subsequent attribute simulation and optimal recognition sequence selection, thus ensuring the efficiency and accuracy of the entire recognition process.

[0096] In some embodiments, extracting a set of multiple feasible recognition sequences from the minimum recognition sequence includes:

[0097] Acquire real-time data and performance parameters of the appliance nameplate when it is mapped to a digital model, wherein the real-time data includes real-time identification information, real-time description information, and real-time associated attributes of each nameplate component node, as well as real-time associated attributes of each edge;

[0098] The performance parameters include node performance indicators of each nameplate component node;

[0099] For each recognition sequence in the minimum recognition sequence, determine whether the recognition constraint is satisfied;

[0100] Extracting the recognition sequence that meets the recognition constraints as a feasible recognition sequence;

[0101] All the extracted feasible recognition sequences are summarized to form a set.

[0102] It should be noted that we first need to extract multiple feasible recognition sequences from the minimum recognition sequence. This step is the key part of the recognition method because it involves screening out sequences that meet specific conditions from all possible recognition paths.

[0103] Preferably, we first obtain real-time data and performance parameters when mapping the appliance nameplate to a digital model. This data includes real-time identification information, real-time description information, and real-time associated attributes for each nameplate component node, as well as real-time associated attributes for each edge. Performance parameters include node performance indicators for each nameplate component node, which are important for evaluating the feasibility of the recognition sequence.

[0104] We then evaluate each recognition sequence in the minimum recognition sequence to determine whether it satisfies the previously defined recognition constraints. These constraints include the completeness of identification information, the accuracy of description information, and the consistency of associated attributes, ensuring the accuracy and reliability of the recognition sequence.

[0105] For recognition sequences that meet the recognition constraints, we mark them as feasible recognition sequences and extract them from the set. This step ensures that the recognition sequences we obtain not only meet the technical requirements but also work effectively in practical applications.

[0106] Specifically, we aggregate all extracted feasible recognition sequences into a set. This set, containing all qualified recognition sequences, serves as the basis for subsequent attribute simulation and optimal recognition sequence selection. This approach ensures that only those recognition sequences that meet all technical requirements and constraints are considered and used during the recognition process.

[0107] In some embodiments, determining whether the identification constraint is satisfied includes:

[0108] If the real-time identification information and real-time description information of each nameplate component node in the current identification sequence are complete, the identification information integrity constraint is satisfied;

[0109] If the real-time association attribute of each edge in the current recognition sequence meets the preset association rule, the total association attribute of the appliance label meets the total association rule, and the sum of the association attributes of the current recognition sequence does not exceed the association attribute threshold, then the association attribute consistency constraint is satisfied;

[0110] If the performance index of each nameplate component node in the current identification sequence meets the performance requirements, and the sum of the performance indexes of all nameplate component nodes meets the overall performance requirements, then the performance index constraint is satisfied;

[0111] Recognition sequences that simultaneously meet the above constraints are extracted as feasible recognition sequences.

[0112] It should be noted that we first need to make a detailed judgment on whether the identification constraints are satisfied. This step is a key step in ensuring the feasibility of the identification sequence and involves a comprehensive evaluation of the real-time information and performance indicators of the nameplate component nodes.

[0113] Preferably, we first check the integrity of the real-time identification information and description information for each nameplate component node in the current recognition sequence. If this information is intact, the recognition sequence is considered to satisfy the identification information integrity constraint. Satisfaction of this constraint is the basis for the accuracy of the recognition process, ensuring that the key information on the nameplate is accurately captured.

[0114] We then evaluate whether the real-time association attributes of each edge conform to the pre-defined association rules. Furthermore, we must ensure that the total association attributes of the appliance nameplate conform to the total association rule and that the sum of the association attributes of the current recognition sequence does not exceed the association attribute threshold. If these conditions are met, the recognition sequence is considered to satisfy the association attribute consistency constraint. Satisfaction of this constraint ensures that the logical relationships and consistency between nameplate information are correctly processed.

[0115] Specifically, we also need to evaluate whether the performance indicators of each nameplate component node in the current identification sequence meet the performance requirements. If the sum of the performance indicators of all nodes also meets the overall performance requirements, then the identification sequence is considered to meet the performance indicator constraints. Satisfaction of this constraint ensures that the identification sequence performs as expected and can operate stably in real-world applications. Only when an identification sequence satisfies all of these constraints will it be considered a feasible identification sequence.

[0116] The above-mentioned embodiments of the present invention have the following beneficial effects: The electrical system nameplate label recognition method of the present invention can realize the efficient and accurate recognition of electrical appliance labels and nameplates through a series of innovative technical means. First, by mapping the nameplate into a digital model, the present method can improve the systematicity and structuredness of information processing. Second, by selecting the starting node and the target node to determine the minimum recognition sequence, and extracting the feasible recognition sequence based on the recognition constraints, the present method can optimize the recognition process and reduce invalid or inefficient recognition paths. Third, by performing multiple attribute simulations and evaluating the optimal recognition sequence based on the simulated attributes, the present method can enhance the accuracy and reliability of the recognition results. In addition, by introducing the calculation of attribute evaluation indicators, the present method further improves the recognition efficiency and can ensure adaptability and flexibility under changing conditions. Finally, by comprehensively considering the recognition constraints, including the integrity of the identification information, the accuracy of the description information and the consistency of the associated attributes, the present method can ensure the rigor and systematicity of the recognition process, reduce the interference of human factors, and improve the level of automation.

[0117] like Figure 2As shown, in some embodiments, an electrical system nameplate label recognition system 200 includes:

[0118] A digital model generation module is used to map the appliance label nameplate into a digital model; the digital model includes a nameplate component node and attribute feature edges; the nameplate component node includes an identification subnode and a description subnode; the attribute feature edges include an identification attribute edge of the identification subnode, a description attribute edge of the description subnode, and association attribute edges between nameplate component nodes;

[0119] A feasible recognition sequence set determination module is used to select a starting node and a target node in the digital model to determine a minimum recognition sequence, and extract a set from the minimum recognition sequence based on recognition constraints;

[0120] The attribute simulation module performs multiple attribute simulations. During each simulation, the attribute parameters of the attribute feature edges in each feasible recognition sequence are generated according to the attribute requirements, and the simulation attributes of each feasible recognition sequence are obtained.

[0121] The optimal feasible recognition sequence determination module identifies the optimal feasible recognition sequence of the electrical appliance label nameplate based on the simulation attributes, and uses the sequence as the recognition result of the electrical appliance label nameplate.

[0122] It is understandable that the modules recorded in the electrical system nameplate label recognition system 200 are similar to those in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the electrical system nameplate label recognition method are also applicable to the electrical system nameplate label recognition system 200 and the modules included therein, and will not be repeated here.

[0123] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device structure 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0124] like Figure 3As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0125] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as needed.

[0126] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, server, mobile phone, or tablet.

[0127] The above descriptions merely illustrate some preferred embodiments of the present invention and the underlying technical principles. Those skilled in the art should understand that the scope of the invention encompassed by the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned inventive concept. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for identifying nameplate labels of an electrical system, characterized in that: include: Mapping the appliance label nameplate into a digital model; the digital model includes nameplate component nodes and attribute feature edges; The nameplate component node includes an identification subnode and a description subnode; The attribute feature edges include: identification attribute edges for identifying sub-nodes, description attribute edges for describing sub-nodes, and association attribute edges between nameplate component nodes; Selecting a starting node and a target node in the digital model to determine a minimum recognition sequence, and extracting a set of multiple feasible recognition sequences from the minimum recognition sequence based on recognition constraints; Perform multiple attribute simulations. In each simulation, generate attribute parameters of attribute feature edges in each feasible recognition sequence according to attribute requirements, and obtain simulation attributes of each feasible recognition sequence. Based on the simulated attributes, the optimal feasible recognition sequence of the appliance label nameplate is identified, and the optimal feasible recognition sequence is used as the recognition result of the appliance label nameplate. The identification sub-node is used to describe the model and specification identification information of the appliance nameplate; the description sub-node is used to describe the parameters and function description information of the appliance nameplate; the identification attribute edge is used to describe the attributes of the identification sub-node processing the model and specification identification information; the description attribute edge is used to describe the attributes of the description sub-node processing the parameters and function description information; the association attribute edge is used to describe the association relationship and attributes between the nameplate component nodes. The optimal feasible recognition sequence for identifying the nameplate of the electrical appliance includes: For each feasible identification sequence, multiple attribute simulations are performed to obtain simulation attributes and determine the attribute expectation and attribute variance of the feasible identification sequence; Determining an attribute evaluation index of a feasible recognition sequence based on the attribute expectation and attribute variance of the recognition sequence; The recognition sequence corresponding to the highest value of the attribute evaluation index is taken as the optimal feasible recognition sequence.

2. The method for identifying nameplates and labels of electrical systems according to claim 1, characterized in that: Calculate the attribute evaluation index of each feasible recognition sequence.

3. The method for identifying nameplates and labels of an electrical system according to claim 1, characterized in that: The identification constraints include identification information integrity constraints, description information accuracy constraints and association attribute consistency constraints.

4. The method for identifying nameplates and labels of an electrical system according to claim 3, characterized in that: The step of extracting a set of multiple feasible recognition sequences from the minimum recognition sequence includes: Acquire real-time data and performance parameters of the appliance nameplate when it is mapped to a digital model, wherein the real-time data includes real-time identification information, real-time description information, and real-time associated attributes of each nameplate component node, as well as real-time associated attributes of each edge; The performance parameters include node performance indicators of each nameplate component node; For each recognition sequence in the minimum recognition sequence, determine whether the recognition constraint is satisfied; Extracting the recognition sequence that meets the recognition constraints as a feasible recognition sequence; All the extracted feasible recognition sequences are summarized to form a set.

5. The method for identifying nameplates and labels of an electrical system according to claim 3, characterized in that: Judging whether the identification constraints are satisfied includes: If the real-time identification information and real-time description information of each nameplate component node in the current identification sequence are complete, the identification information integrity constraint is satisfied; If the real-time association attribute of each edge in the current recognition sequence meets the preset association rule, the total association attribute of the appliance label meets the total association rule, and the sum of the association attributes of the current recognition sequence does not exceed the association attribute threshold, then the association attribute consistency constraint is satisfied; If the performance index of each nameplate component node in the current identification sequence meets the performance requirements, and the sum of the performance indexes of all nameplate component nodes meets the overall performance requirements, then the performance index constraint is satisfied; Recognition sequences that simultaneously meet the above constraints are extracted as feasible recognition sequences.

6. An electrical system nameplate label recognition system, characterized in that: include: A digital model generation module is used to map the appliance label nameplate into a digital model; the digital model includes a nameplate component node and attribute feature edges; the nameplate component node includes an identification subnode and a description subnode; the attribute feature edges include an identification attribute edge of the identification subnode, a description attribute edge of the description subnode, and association attribute edges between nameplate component nodes; A feasible recognition sequence set determination module is used to select a starting node and a target node in the digital model to determine a minimum recognition sequence, and extract a set from the minimum recognition sequence based on recognition constraints; The attribute simulation module performs multiple attribute simulations. During each simulation, the attribute parameters of the attribute feature edges in each feasible recognition sequence are generated according to the attribute requirements, and the simulation attributes of each feasible recognition sequence are obtained. The module for determining the optimal feasible identification sequence identifies the optimal feasible identification sequence of the appliance nameplate based on the simulation attributes and uses the sequence as the identification result of the appliance nameplate. The identification sub-node is used to describe the model and specification identification information of the appliance nameplate; the description sub-node is used to describe the parameters and function description information of the appliance nameplate; the identification attribute edge is used to describe the attributes of the identification sub-node processing the model and specification identification information; the description attribute edge is used to describe the attributes of the description sub-node processing the parameters and function description information; the association attribute edge is used to describe the association relationship and attributes between the nameplate component nodes. The optimal feasible recognition sequence for identifying the nameplate of the electrical appliance includes: For each feasible identification sequence, multiple attribute simulations are performed to obtain simulation attributes and determine the attribute expectation and attribute variance of the feasible identification sequence; Determining an attribute evaluation index of a feasible recognition sequence based on the attribute expectation and attribute variance of the recognition sequence; The recognition sequence corresponding to the highest value of the attribute evaluation index is taken as the optimal feasible recognition sequence.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

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