Station yard state evaluation and early warning method and system based on algorithm fusion technology

Abstract

The invention discloses a station yard state evaluation and early warning method and system based on an algorithm fusion technology, and relates to the technical field of station yard monitoring and early warning, and the method comprises the steps: carrying out the noise filtering and abnormal value detection of large-scale real-time station yard data collected from station yard equipment according to a frequency range and a noise type; performing rapid feature extraction and dimension reduction processing on the large-scale real-time station yard data subjected to noise filtering and abnormal value detection by using a quantum support vector machine to generate a low-dimensional feature vector; the method comprises the following steps: constructing an information weighted graph by utilizing physical connection, functional dependence and historical fault records among station equipment, representing each equipment as a node in the graph, and representing a data flow relationship, an energy transmission relationship and a physical connection relationship among the station equipment as edges; performing multi-round iterative training on the graph neural network by using the information weighted graph to obtain an equipment relation graph neural network model; and generating a final equipment state prediction vector in combination with the equipment relation graph neural network model and the low-dimensional feature vector.

Description

Technical Field

[0001] The present invention relates to the technical field of station field monitoring and early warning, and particularly to a method and system for evaluating and warning the state of a station field based on algorithm fusion technology. Background Art

[0002] In the existing station field state monitoring technology, traditional methods mostly rely on single algorithms or static models, which have significant limitations. First of all, traditional data preprocessing is easily affected by the mixing of noise frequency bands, resulting in the residue of high-frequency interference or the misdeletion of low-frequency effective signals. Feature extraction mostly relies on linear dimensionality reduction methods (such as PCA) or classical support vector machines, which are difficult to capture high-dimensional non-linear feature associations, and the computational efficiency decreases significantly with the rapid increase in the amount of data, unable to meet the real-time requirements.

[0003] Traditional equipment relationship modeling usually only considers the physical connection topology, ignoring the dynamic coupling of data flow and energy transmission and the historical fault propagation path, resulting in the deviation of equipment state evaluation from the real risk. For example, the fault correlation between a base station and a cooling system is often simplified to independent analysis, and the chain effect of abnormal heat load on the performance of communication equipment cannot be quantified, resulting in an increase in the false alarm rate of early warning. The above defects seriously restrict the accurate state evaluation and early warning timeliness of complex station fields. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for evaluating and warning the state of a station field based on algorithm fusion technology to solve the problems that traditional data preprocessing is easily affected by the mixing of noise frequency bands and traditional equipment relationship modeling leads to the deviation of equipment state evaluation from the real risk.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, the present invention provides a method for evaluating and warning the state of a station field based on algorithm fusion technology, which includes filtering noise and detecting outliers for large-scale real-time station field data collected from station field equipment according to the frequency range and noise type; Using a quantum support vector machine to perform fast feature extraction and dimensionality reduction processing on the large-scale real-time station field data after noise filtering and outlier detection to generate a low-dimensional feature vector; Constructing an information weighted graph by using the physical connection, functional dependence, and historical fault records between station field equipment, where each equipment is represented as a node, and the data flow relationship, energy transmission relationship, and physical connection relationship between station field equipment are represented as edges; Using the information weighted graph to perform multiple rounds of iterative training on a graph neural network to obtain an equipment relationship graph neural network model; Combine the device relationship graph neural network model with the low-dimensional feature vector to generate the final device state prediction vector and perform state evaluation; Judge the state warning requirements based on the evaluation results.

[0007] As a preferred solution of the station field state evaluation and warning method based on the algorithm fusion technology of the present invention, wherein: the station field equipment includes a base station antenna, a transmitter, a receiver, a router, a switch, and a cooling device; The noise filtering is median filtering; the outlier detection is the interquartile range method.

[0008] As a preferred solution of the station field state evaluation and warning method based on the algorithm fusion technology of the present invention, wherein: the rapid feature extraction and dimensionality reduction processing are carried out, and the specific steps are as follows, Input the real-time station field data processed by median filtering and the interquartile range method into the quantum support vector machine, select the quantum kernel function based on quantum phase estimation, perform non-linear mapping on the real-time station field data, and obtain the high-dimensional features after mapping; Use the quantum computing simulator to perform parallel computing on the high-dimensional features after mapping, and accelerate the generation of feature vectors through the superposition state of quantum bits; Based on the feature weight distribution of the quantum kernel function, adopt a sparsification strategy to screen key features, and compress the feature dimension to the target dimension through principal component analysis to generate a low-dimensional feature vector.

[0009] As a preferred solution of the station field state evaluation and warning method based on the algorithm fusion technology of the present invention, wherein: the construction of the information weighted graph is carried out, and the specific steps are as follows, Utilize the physical connection, functional dependence, and historical fault records between station field devices to construct a preliminary relationship graph of station field devices, and obtain preliminary device nodes and connection relationships; Adopt a weighting method to weight the relationships between nodes, and obtain weighted edges according to various factors such as data flow, energy transmission, and physical connection between devices; Utilize historical fault records and fault propagation path analysis to adjust the weights of the edges, and further refine the weighted information weighted graph; Convert the device relationship graph into an information weighted graph through the relationships between nodes and edges in the information weighted graph.

[0010] As a preferred solution of the station field state evaluation and warning method based on the algorithm fusion technology of the present invention, wherein: the multi-round iterative training of the graph neural network using the information weighted graph is carried out, and the specific steps are as follows, Utilize the nodes and edges in the information weighted graph to initialize the graph neural network model, and obtain a preliminary device relationship graph neural network structure; Adopt the message passing mechanism of the graph neural network to perform information transmission and node update in the preliminary device relationship graph neural network structure, and obtain the preliminary trained device relationship network; Use multiple rounds of iterative training, through backpropagation and gradient update, continuously optimize the weights between nodes and the structure of the graph, and obtain the device relationship graph neural network model; Evaluate the graph neural network after multiple rounds of training. By comparing the prediction results with the actual fault data, further adjust the device relationship graph neural network model to obtain the trained device relationship graph neural network model.

[0011] As a preferred scheme of the station field state evaluation and early warning method based on algorithm fusion technology of the present invention, wherein: combine the device relationship graph neural network model with the low-dimensional feature vector to generate the final device state prediction vector and perform state evaluation. The specific steps are as follows. Use the device relationship graph neural network model to combine the state information of each node in the graph with the low-dimensional feature vector to obtain the comprehensive state information of the device; Adopt the weighted average method to combine the low-dimensional feature vector with the comprehensive state information of the device to generate the device state prediction vector of the device; Use the generated device state prediction vector to evaluate the device health state and evaluate whether the device is in a normal working state.

[0012] As a preferred scheme of the station field state evaluation and early warning method based on algorithm fusion technology of the present invention, wherein: judge the state early warning requirement according to the evaluation result. The specific steps are as follows. Based on the state evaluation result, classify the devices, determine whether there are potential fault hazards, and evaluate its influence range and risk level; Finally, according to the evaluation result, judge whether it is necessary to trigger a fault early warning and take corresponding emergency measures.

[0013] In the second aspect, the present invention provides a station field state evaluation and early warning system based on algorithm fusion technology, including a data processing module for filtering noise and detecting outliers for the large-scale real-time station field data collected from the station field devices according to the frequency range and noise type; A quantum dimensionality reduction module for using a quantum support vector machine to perform fast feature extraction and dimensionality reduction processing on the large-scale real-time station field data after noise filtering and outlier detection to generate a low-dimensional feature vector; A relationship modeling module for constructing an information weighted graph by using the physical connection, functional dependence and historical fault records between the station field devices. Each device is represented as a node, and the data flow relationship, energy transmission relationship and physical connection relationship between the station field devices are represented as edges; A graph network training module, which is used to perform multiple rounds of iterative training on a graph neural network using an information-weighted graph to obtain a device relationship graph neural network model; A state evaluation module, which is used to combine the device relationship graph neural network model and the low-dimensional feature vector to generate a final device state prediction vector and perform state evaluation; A warning decision-making module, which is used to judge the state warning requirement according to the evaluation result.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the computer program is executed by the processor, any step of the station field state evaluation and warning method based on the algorithm fusion technology as described in the first aspect of the present invention is implemented.

[0015] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by the processor, any step of the station field state evaluation and warning method based on the algorithm fusion technology as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by integrating a variety of advanced algorithm technologies, the accuracy and real-time performance of the station field equipment state evaluation and warning are significantly improved. For the large-scale real-time station field data collected from the station field equipment, the median filtering and interquartile range methods are used for noise filtering and outlier detection, effectively purifying the data, reducing interference, and improving the data quality for subsequent analysis. Using the quantum support vector machine, through the quantum phase estimation kernel function and the quantum bit superposition state calculation, fast feature extraction and dimensionality reduction processing are realized, generating a low-dimensional feature vector, greatly improving the data processing efficiency, and providing guarantee for real-time monitoring. An information-weighted graph is constructed based on the physical connection, functional dependence, and historical fault records between devices, and combined with multiple rounds of iterative training of the graph neural network, fully mining the relationship characteristics between devices, making the state evaluation more comprehensive and accurate. By comprehensively generating a state prediction vector from the low-dimensional feature vector and the device relationship graph neural network model, and making a warning judgment according to the evaluation result, potential faults can be found in time, the risk level can be evaluated, and emergency measures can be triggered. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0018] Figure 1 It is a flowchart of the station field state evaluation and warning method based on the algorithm fusion technology in Embodiment 1.

[0019] Figure 2 It is a module diagram of the station field status evaluation and early warning system based on algorithm fusion technology in Embodiment 1.

[0020] Figure 3 It is a schematic diagram of quantum feature acceleration in Embodiment 1.

[0021] Figure 4 It is a dynamic weight adjustment mechanism diagram in Embodiment 1. Specific Embodiments

[0022] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0023] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0025] Embodiment 1, referring to Figures 1 to 4 , is the first embodiment of the present invention. This embodiment provides a method for evaluating and warning the station field status based on algorithm fusion technology, including the following steps: S1. Filter the noise and detect outliers in the large-scale real-time station field data collected from the station field equipment according to the frequency range and noise type. Specifically, Use median filtering technology to process the large-scale real-time station field data collected from the station field equipment (including base station antennas, transmitters, receivers, routers, switches, and cooling equipment). Select an appropriate window size according to the equipment data frequency range and noise type. For example, for the high-frequency data of the base station antenna, the window size is set to 5, that is, with each data point as the center, combine the 2 data points before and after (a total of 5 points) to calculate the median value to smooth the high-frequency noise; for the low-frequency data of the cooling equipment, the window size is also 5.

[0026] The interquartile range (IQR) method is used to detect outliers. The specific steps are as follows: Calculate the 25th percentile (Q1) and 75th percentile (Q3) of the station data. IQR = Q3 - Q1. The outlier threshold is set to 1.5 times IQR. Data points below Q1 - 1.5IQR or above Q3 + 1.5IQR are considered outliers and removed.

[0027] Furthermore, considering the multi-source heterogeneous characteristics of the station equipment data, a median filtering algorithm with an adaptive window size is designed. The window size is dynamically adjusted according to the equipment type and data acquisition frequency. For example, for the high-frequency data of the base station antenna, the window size is reduced to 3 to retain more details; for the low-frequency data of the cooling equipment, the window is increased to 7 to enhance the noise suppression effect. By adaptively adjusting, a balance is achieved between noise suppression and data fidelity.

[0028] It should be noted that the selection of the median filtering and IQR methods is based on the temporal characteristics and noise distribution characteristics of the station equipment data. Median filtering can effectively remove high-frequency noise without introducing too much smoothing distortion, and the IQR method is suitable for detecting outliers in non-normally distributed data, providing a high-quality data basis for subsequent feature extraction.

[0029] S2. Use a quantum support vector machine to perform fast feature extraction and dimensionality reduction on the large-scale real-time station data after noise filtering and outlier detection, generating low-dimensional feature vectors. Specifically, input the real-time station data processed by the median filtering and IQR methods into a quantum support vector machine (QSVM). Select a quantum kernel function based on quantum phase estimation to perform a non-linear mapping on the processed real-time station data, generating a high-dimensional feature space. The number of qubits is 4, the feature mapping uses ZZFeatureMap(), and the mapping depth is 2. Use a quantum computing simulator (such as Qiskit) to perform parallel computing on the mapped high-dimensional features, and accelerate the generation of feature vectors through the superposition state of qubits. Based on the feature weight distribution of the quantum kernel function, adopt a sparsification strategy to screen key features, and then compress the feature dimension to the target dimension, such as reducing it to 10 dimensions, to generate low-dimensional feature vectors.

[0030] Furthermore, a quantum-accelerated feature selection and dimensionality reduction method is proposed. Utilize the parallelism of quantum computing to quickly calculate the correlation between low-dimensional feature vectors, and accelerate the eigenvalue decomposition process of PCA through the superposition and interference characteristics of quantum states, significantly improving the computational efficiency when processing large-scale and high-dimensional station equipment data.

[0031] It should be noted that QSVM combines the advantages of quantum computing and can efficiently process high-dimensional data, especially suitable for scenarios where the data volume of station equipment is large and the dimension is high. Through rapid feature extraction and dimensionality reduction, not only the computational complexity is reduced, but also the key information is retained, laying a foundation for subsequent training.

[0032] S3. Construct an information-weighted graph using the physical connections, functional dependencies, and historical fault records between station equipment. Each equipment is represented as a node, and the data flow relationship, energy transmission relationship, and physical connection relationship between station equipment are represented as edges. Specifically, based on the physical connections (such as cables, optical fibers, etc.), functional dependencies (such as data streams, control signals, etc.), and historical fault records between station equipment, construct preliminary equipment nodes and connection relationships. Each equipment is used as a node, the physical connection is an undirected edge, and the functional dependency is a directed edge. Use a weighting method to calculate the initial weight of the edge, based on the data flow intensity, energy transmission volume, and physical connection reliability between equipment. Data flow weight = data transmission rate / maximum rate. Energy transmission weight = energy transmission volume / maximum energy. Physical connection weight = connection reliability coefficient (range 0 - 1).

[0033] Furthermore, a dynamic weighted graph construction method based on multi-source information is proposed. Combining physical connections, functional dependencies, and historical fault data, the weight of the graph is updated in real time, enabling the graph structure to dynamically reflect the true relationship and potential risks between equipment.

[0034] It should be noted that the information-weighted graph is not only a representation of the static connection of equipment, but also integrates dynamic data streams and historical fault information. The comprehensive design enables the information-weighted graph to accurately reflect the equipment operation status and potential fault propagation paths, providing a reliable basis for subsequent analysis.

[0035] S4. Use the information-weighted graph to perform multiple rounds of iterative training on the graph neural network to obtain a device relationship graph neural network model. Specifically, use the information-weighted graph to initialize the graph neural network (GNN), and adopt the graph convolutional network (GCN) structure. The number of layers is 3, the hidden layer dimension is 64, and the activation function is ReLU. The learning rate is 0.01, the number of training rounds is 100, and the loss function is mean squared error (MSE). Use actual fault data to verify the model performance and adjust the hyperparameters to improve the accuracy.

[0036] Furthermore, dynamically adjust the learning rate according to the change of loss during the training process. For example, when the loss decreases slowly, reduce the learning rate to avoid overfitting or underfitting, thereby improving the model convergence speed and prediction accuracy.

[0037] It should be noted that through multiple rounds of iterative training, the complex relationships between devices can be effectively captured. The learned model not only reflects static connections but also reveals potential associations between dynamic states, providing strong support for subsequent state prediction.

[0038] S5. Combine the device relationship graph neural network model with the low-dimensional feature vector to generate the final device state prediction vector and perform state evaluation. Specifically, combine the node representation output by the device relationship graph neural network model with the low-dimensional feature vector, and use the feature concatenation technique to generate a comprehensive feature vector. Use the random forest method to combine the comprehensive feature vector to generate the device state prediction vector. The number of trees is 100, and the maximum depth is 10. Evaluate the device state according to the prediction vector, and set the evaluation threshold: a predicted value < 0.3 is normal, 0.3 - 0.7 is a warning, and > 0.7 is a failure.

[0039] Furthermore, by combining the graph structure information of the GNN and the classification ability of the random forest, the robustness and accuracy of the prediction are improved, and the limitations of a single model are reduced.

[0040] It should be noted that by fusing the GNN and the low-dimensional feature vector, the relationships between devices and individual characteristics are comprehensively considered, making the state evaluation more comprehensive and reliable, and providing a solid foundation for early warning judgment.

[0041] S6. Judge the state early warning requirements based on the evaluation results. Specifically, according to the evaluation results of S5, the devices are divided into three categories: normal, potential failure, and high-risk failure. Potential failure corresponds to a predicted value in the range of 0.3 - 0.7, and high-risk failure corresponds to a predicted value > 0.7. Use information-weighted graph analysis to analyze the influence range and risk level of potential failure devices, identify the fault propagation path, and evaluate the number and criticality of affected devices. Decide whether to trigger an early warning according to the risk level. The risk level is divided into low, medium, and high, and medium and high risks trigger an early warning.

[0042] Furthermore, by simulating the fault propagation process, calculate the risk index of each device to provide quantitative support for early warning decision-making.

[0043] It should be noted that the early warning requirement judgment combines state evaluation and risk analysis, can timely detect potential failures and evaluate the influence range, thus providing a decision-making basis for emergency measures and ensuring the stable operation of the station equipment.

[0044] This embodiment also provides a station state evaluation and early warning system based on algorithm fusion technology, including: A data processing module for filtering noise and detecting outliers in the large-scale real-time station data collected from the station equipment according to the frequency range and noise type. A quantum dimensionality reduction module, which is used to perform fast feature extraction and dimensionality reduction processing on large-scale real-time station data that has undergone noise filtering and outlier detection using a quantum support vector machine, and generate low-dimensional feature vectors; A relationship modeling module, which is used to construct an information-weighted graph by using the physical connections, functional dependencies, and historical fault records between station devices. Each device is represented as a node, and the data flow relationship, energy transmission relationship, and physical connection relationship between station devices are represented as edges; A graph network training module, which is used to perform multiple rounds of iterative training on a graph neural network using the information-weighted graph to obtain a device relationship graph neural network model; A state evaluation module, which is used to combine the device relationship graph neural network model and the low-dimensional feature vectors to generate a final device state prediction vector and perform state evaluation; An early warning decision-making module, which is used to judge the early warning requirements of the state based on the evaluation results.

[0045] This embodiment also provides a computer device, which is applicable to the situation of the station state evaluation and early warning method based on the algorithm fusion technology, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the station state evaluation and early warning method based on the algorithm fusion technology proposed in the above embodiment.

[0046] This computer device can be a terminal. This computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computer device is used to provide computing and control capabilities. The memory of this computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the computer device housing, or an external keyboard, touchpad, or mouse, etc.

[0047] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for evaluating and warning the station field state based on the algorithm fusion technology as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM for short), electrically erasable programmable read-only memory (EEPROM for short), erasable programmable read-only memory (EPROM for short), programmable read-only memory (PROM for short), read-only memory (ROM for short), magnetic memory, flash memory, magnetic disk or optical disk.

[0048] In summary, the present invention: By integrating a variety of advanced algorithm technologies, it significantly improves the accuracy and real-time performance of the evaluation and warning of the station field equipment status. For the large-scale real-time station field data collected from the station field equipment, the median filtering and interquartile range methods are used for noise filtering and outlier detection, effectively purifying the data, reducing interference, and improving the data quality for subsequent analysis. Using the quantum support vector machine, through the quantum phase estimation kernel function and the calculation of the quantum bit superposition state, fast feature extraction and dimensionality reduction processing are realized, generating a low-dimensional feature vector, greatly improving the data processing efficiency and providing guarantee for real-time monitoring. An information weighted graph is constructed based on the physical connection, functional dependence and historical fault records between devices, and combined with multiple rounds of iterative training of the graph neural network, the relationship characteristics between devices are fully mined, making the status evaluation more comprehensive and accurate. By comprehensively generating a status prediction vector from the low-dimensional feature vector and the device relationship graph neural network model, and making a warning judgment based on the evaluation result, potential faults can be detected in time, the risk level can be evaluated and emergency measures can be triggered.

[0049] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A station status assessment and early warning method based on algorithm fusion technology, characterized by: include, Perform noise filtering and outlier detection on large-scale real-time field data collected from field equipment according to frequency range and noise type; Use quantum support vector machine to extract features and reduce dimension of large-scale real-time station data after noise filtering and outlier detection to generate low-dimensional feature vectors; The information weighted graph is constructed by using the physical connection, functional dependency and historical fault records between the field equipment. Each device is represented as a node, and the data flow relationship, energy transmission relationship and physical connection relationship between the field equipment are represented as edges. Use the information weighted graph to perform multiple rounds of iterative training on the graph neural network to obtain the device relationship graph neural network model; Combine the equipment relationship graph neural network model with the low-dimensional feature vector to generate the final equipment status prediction vector and perform status evaluation; Determine the status warning needs based on the assessment results.

2. The station status assessment and early warning method based on algorithm fusion technology according to claim 1 is characterized by: The site equipment includes base station antennas, transmitters, receivers, routers, switches and cooling equipment; The noise filtering is median filtering; the outlier detection is the interquartile range method.

3. The station status assessment and early warning method based on algorithm fusion technology according to claim 2 is characterized by: The feature extraction and dimensionality reduction processing are performed in the following specific steps: The real-time station data processed by median filtering and interquartile range method are input into the quantum support vector machine, and the quantum kernel function based on quantum phase estimation is selected to perform nonlinear mapping on the real-time station data to obtain the high-dimensional features after mapping. Use quantum computing simulators to perform parallel calculations on the mapped high-dimensional features, and accelerate the generation of feature vectors through quantum bit superposition states; Based on the feature weight distribution of quantum kernel function, a sparse strategy is adopted to screen key features, and the feature dimension is compressed to the target dimension through principal component analysis to generate a low-dimensional feature vector.

4. The station status assessment and early warning method based on algorithm fusion technology as claimed in claim 3 is characterized by: The specific steps of constructing the information weighted graph are as follows: Using the physical connections, functional dependencies and historical fault records between station equipment, a preliminary relationship diagram of station equipment is constructed to obtain preliminary equipment nodes and connection relationships; The relationship between nodes is weighted by using a weighted method, and weighted edges are obtained based on multiple factors such as data flow, energy transmission, and physical connection between devices; Use historical fault records and fault propagation path analysis to adjust edge weights; The preliminary relationship graph is converted into an information weighted graph through the relationship between nodes and edges in the preliminary relationship graph.

5. The station status assessment and early warning method based on algorithm fusion technology according to claim 4 is characterized in that: The information weighted graph is used to perform multiple rounds of iterative training on the graph neural network. The specific steps are as follows: Using the nodes and edges in the information weighted graph, the graph neural network model is initialized to obtain a preliminary device relationship graph neural network structure; Adopting the message passing mechanism of graph neural network, information transmission and node update are performed in the preliminary device relationship graph neural network structure to obtain a preliminarily trained device relationship network. Through multiple rounds of iterative training, back propagation and gradient update, the weights between nodes and the structure of the information weighted graph are continuously optimized to obtain a device relationship graph neural network model; The graph neural network after multiple rounds of training is evaluated, and the equipment relationship graph neural network model is further adjusted by comparing the prediction results with the actual fault data to obtain a trained equipment relationship graph neural network model.

6. The station status assessment and early warning method based on algorithm fusion technology according to claim 5 is characterized by: Combine the equipment relationship graph neural network model with the low-dimensional feature vector to generate the final equipment state prediction vector and perform state evaluation. The specific steps are as follows: Using the equipment relationship graph neural network model, the state information of each node in the information weighted graph is combined with the low-dimensional feature vector to obtain the comprehensive state information of the equipment; The weighted average method is used to combine the low-dimensional feature vector with the comprehensive status information of the device to generate the device status prediction vector of the device; The generated equipment status prediction vector is used to evaluate the equipment health status and whether the equipment is in normal working condition.

7. The station status assessment and early warning method based on algorithm fusion technology according to claim 6 is characterized by: According to the evaluation results, the status warning demand is judged. The specific steps are as follows: Based on the status assessment results, classify the equipment to determine whether there are potential failure hazards, and assess the impact scope and risk level; Based on the evaluation results, determine whether it is necessary to trigger a fault warning and take corresponding emergency measures.

8. A station status assessment and early warning system based on algorithm fusion technology, based on the station status assessment and early warning method based on algorithm fusion technology according to any one of claims 1 to 7, characterized in that: include, The data processing module is used to filter noise and detect outliers on large-scale real-time station data collected from station equipment according to frequency range and noise type; The quantum dimension reduction module is used to use quantum support vector machines to quickly extract features and reduce the dimension of large-scale real-time station data that has been noise filtered and detected as outliers, and generate low-dimensional feature vectors; The relationship modeling module is used to construct an information weighted graph using the physical connections, functional dependencies, and historical fault records between field equipment. Each device is represented as a node, and the data flow relationship, energy transmission relationship, and physical connection relationship between field equipment are represented as edges. The graph network training module is used to perform multiple rounds of iterative training on the graph neural network using the information weighted graph to obtain a device relationship graph neural network model; The state assessment module is used to combine the equipment relationship graph neural network model with the low-dimensional feature vector to generate the final equipment state prediction vector and perform state assessment; The early warning decision module is used to judge the status early warning demand based on the evaluation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the station status assessment and early warning method based on algorithm fusion technology described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the station status assessment and early warning method based on algorithm fusion technology described in any one of claims 1 to 7 are implemented.

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