Equipment detection early warning system and device

Abstract

The invention belongs to the technical field of equipment detection and early warning, and discloses an equipment detection and early warning system and device.The equipment detection and early warning system comprises the steps that historical data of oil storage equipment are collected; processing the historical data, and analyzing the processed historical data to obtain a risk value of the oil storage equipment; constructing an early warning evaluation model of the oil storage equipment in combination with the historical data and the neural network model, and obtaining an early warning evaluation value of the oil storage equipment by using the early warning evaluation model; calculating a comprehensive hidden danger value of the oil storage equipment based on the risk value and the early warning evaluation value, and judging whether the oil storage equipment has potential safety hazards or not according to the comprehensive hidden danger value; and generating an early warning prompt according to a potential safety hazard judgment result, feeding back the generated early warning prompt to related personnel, and instructing the related personnel to maintain the oil storage equipment. According to the invention, through the risk value and the early warning evaluation value, potential faults or problems can be effectively predicted, early warning is realized, and accidents are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection and early warning, and in particular to an equipment detection and early warning system and device. Background Art

[0002] Oil storage equipment, as an indispensable key infrastructure in industrial production, transportation, and civil construction, is becoming increasingly important as global energy demand continues to grow. As an important device for storing oil, the quality and reliability of oil storage equipment are directly related to the production operation and wealth accumulation of enterprises. Therefore, it is crucial to ensure the long-term, stable and safe operation of such important facilities.

[0003] Since oil storage equipment stores a large amount of flammable, explosive and volatile oil products, once an accident occurs, it may cause serious consequences such as fire and explosion, posing a serious threat to personnel, environment and property. Therefore, regular inspection and early warning of oil storage equipment, timely discovery and identification of potential safety hazards, and formulation of corresponding preventive measures are key links to ensure the safe operation of oil storage equipment.

[0004] However, existing technologies often rely on a single early warning value when evaluating the safety status of oil storage equipment. Although this method is simple and easy to use, it ignores many complex factors that may affect the safety of oil storage equipment, such as the operating status of the equipment, environmental conditions, maintenance records and other risk values. Therefore, this evaluation method may lead to incomplete and inaccurate results and cannot provide strong support for the safety management of oil storage equipment. Summary of the invention

[0005] The embodiments of the present invention provide an equipment detection and early warning system and device to solve the problem in the prior art that possible risks cannot be foreseen in advance and corresponding measures cannot be taken to prevent them, resulting in failure to respond in time when risks occur.

[0006] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0007] According to a first aspect of an embodiment of the present invention, a device detection and early warning system is provided.

[0008] In one embodiment, the equipment detection warning system includes:

[0009] A data collection unit, used to collect historical data of oil storage equipment;

[0010] A data processing and analysis unit is used to process historical data and analyze the processed historical data to obtain the risk value of the oil storage equipment;

[0011] An early warning evaluation value acquisition unit is used to combine historical data with a neural network model to construct an early warning evaluation model for oil storage equipment, and to obtain an early warning evaluation value for the oil storage equipment using the early warning evaluation model;

[0012] A safety hazard judgment unit is used to calculate the comprehensive hazard value of the oil storage equipment based on the risk value and the early warning assessment value, and judge whether the oil storage equipment has safety hazards according to the comprehensive hazard value;

[0013] The early warning and response unit is used to generate early warning prompts based on the safety hazard judgment results, and feed back the generated early warning prompts to relevant personnel, instructing relevant personnel to repair the oil storage equipment.

[0014] Furthermore, the data processing and analysis unit includes:

[0015] A data preprocessing module is used to preprocess the historical data collected from the oil storage equipment;

[0016] A feature extraction module is used to extract feature data from the preprocessed historical data;

[0017] A data analysis module is used to analyze the extracted feature data and obtain the weights corresponding to the feature data;

[0018] The risk value calculation module is used to calculate the risk value by combining the weights of the acquired feature data.

[0019] Furthermore, the formula for calculating the risk value is:

[0020]

[0021] In the formula, T represents the risk value of oil storage equipment;

[0022] Mmax represents the maximum value in the characteristic data;

[0023] Ma represents the ath feature value in the feature data;

[0024] Na represents the weight corresponding to the ath feature value in the feature data;

[0025] K represents the integration coefficient for calculating risk value.

[0026] Furthermore, the early warning evaluation value acquisition unit includes:

[0027] Data screening and grouping module, used to screen historical data and divide the screened historical data into several indicator features;

[0028] The early warning assessment model building module is used to analyze several indicator characteristics and integrate the analysis results with the neural network model to build an early warning assessment model;

[0029] The early warning assessment value acquisition module inputs historical data into the early warning assessment model and outputs the early warning assessment value.

[0030] Furthermore, the historical data is filtered and divided into several indicator features including:

[0031] Use median filtering algorithm to clean historical data, and normalize the cleaned historical data;

[0032] Extracting index features related to oil storage equipment evaluation from normalized historical data;

[0033] The similarity between each indicator feature is calculated using Euclidean distance, and a similarity matrix is ​​established based on the similarity;

[0034] Based on the similarity matrix, the region discovery algorithm is used to identify the regional structure of historical data, and the indicator features are divided into different regions according to the identification results.

[0035] Furthermore, the indicator characteristics include: structural indicator characteristics and functional indicator characteristics.

[0036] Furthermore, several indicator characteristics are analyzed, and the analysis results are integrated with the neural network model to build an early warning evaluation model including:

[0037] Use the principal component analysis algorithm to extract several indicator variables from several indicator features and calculate the weight of each indicator variable;

[0038] Based on the weight of each indicator variable, the feature information is reconstructed using programming software to obtain a feature matrix set containing weight information, and the feature matrix set is divided into a training set and a test set;

[0039] The feature matrix is ​​converted into pixel points to form an index feature matrix, and the values ​​in the index feature matrix are converted into the brightness values ​​of the corresponding grayscale image to obtain a grayscale image data set;

[0040] Based on the neural network model, an initial early warning evaluation model is set up, and the grayscale image data set in the training set is input into the initial early warning evaluation model to obtain the initial early warning evaluation result;

[0041] Compare the initial warning assessment results with the actual situation, optimize the parameters of the initial warning assessment model, and obtain the optimized initial warning assessment model;

[0042] The optimized initial early warning assessment model is tested using the grayscale image dataset in the test set to evaluate the performance of the initial early warning assessment model and obtain the early warning assessment model.

[0043] Furthermore, the potential safety hazard judgment unit includes:

[0044] A comprehensive hidden danger value calculation module is used to calculate the comprehensive hidden danger value of the oil storage equipment according to the acquired risk value and early warning assessment value of the oil storage equipment;

[0045] The safety hazard judgment module is used to compare the calculated comprehensive hazard value with the preset comprehensive hazard threshold to determine whether there is a safety hazard in the oil storage equipment.

[0046] Furthermore, the calculated comprehensive hidden danger value is compared with the preset comprehensive hidden danger threshold to determine whether there are safety hazards in the oil storage equipment, including:

[0047] If the comprehensive hidden danger value is less than the preset comprehensive hidden danger threshold, it is judged that there is no safety hazard in the oil storage equipment;

[0048] If the comprehensive hidden danger value is greater than or equal to the preset comprehensive hidden danger threshold, it is judged that there is a safety hazard in the oil storage equipment.

[0049] According to a second aspect of an embodiment of the present invention, there is provided a device detection and early warning device, the device detection and early warning device comprising: a processor and a memory for storing instructions executable by the processor;

[0050] The processor is configured as follows:

[0051] Collect historical data of oil storage equipment;

[0052] Process the historical data and analyze the processed historical data to obtain the risk value of the oil storage equipment;

[0053] Combining historical data with the neural network model, an early warning assessment model for oil storage equipment is constructed, and the early warning assessment value of the oil storage equipment is obtained using the early warning assessment model;

[0054] Based on the risk value and the early warning assessment value, the comprehensive hidden danger value of the oil storage equipment is calculated, and it is judged whether there are safety hazards in the oil storage equipment according to the comprehensive hidden danger value;

[0055] An early warning is generated based on the safety hazard judgment results, and the generated early warning is fed back to relevant personnel, instructing them to repair the oil storage equipment.

[0056] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0057] 1. The present invention processes historical data in detail through a data processing and analysis unit, and more accurately calculates the risk value of oil storage equipment, so as to effectively predict potential failures or problems, achieve early warning, and reduce the occurrence of accidents. At the same time, the historical data and the neural network model are combined to continuously optimize and train the early warning evaluation model, which can continuously improve the accuracy and efficiency of its early warning with the passage of time and the accumulation of data, thereby enhancing the safety of the equipment, reducing the risk of safety accidents caused by equipment failures, and protecting the safety of workers and the environment from potential pollution.

[0058] 2. The present invention calculates the comprehensive hazard value of the oil storage equipment by combining the risk value and the early warning assessment value through the safety hazard judgment unit, so as to more comprehensively understand the overall safety status of the equipment and quickly feedback to relevant personnel. This real-time response capability greatly shortens the time from problem discovery to action, and improves the efficiency of handling potential safety problems. At the same time, through timely maintenance and processing instructions, the early warning system can not only discover problems, but also prevent the development and deterioration of problems, which helps to keep the equipment in the best working condition, extend the service life of the equipment, and improve the reliability and efficiency of the entire equipment system.

[0059] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0061] Figure 1 is a principle block diagram of a device detection and early warning system according to an exemplary embodiment;

[0062] Figure 2 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0063] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0064] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0065] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0066] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0067] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0068] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0069] The device or equipment detection and early warning system of the present application and each module in the device can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0070] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0071] Figure 1 An embodiment of the equipment detection and early warning system of the present invention is shown.

[0072] In this optional embodiment, the equipment detection and early warning system includes:

[0073] The data collection unit 101 is used to collect historical data of the oil storage equipment.

[0074] It should be noted that collection tools (such as sensors, data loggers, etc.) are installed at appropriate locations on the oil storage equipment to ensure that the equipment operation data can be accurately obtained.

[0075] The data processing and analysis unit 103 is used to process the historical data and analyze the processed historical data to obtain the risk value of the oil storage equipment.

[0076] In this optional embodiment, the data processing and analysis unit 103 includes:

[0077] The data preprocessing module is used to preprocess the historical data collected from the oil storage equipment.

[0078] The feature extraction module is used to extract feature data from the preprocessed historical data.

[0079] The data analysis module is used to analyze the extracted feature data and obtain the weights corresponding to the feature data.

[0080] It should be noted that for each feature data, a separate classifier (such as SVM) is constructed, the performance of each classifier is evaluated, the number of correct classifications is counted, the number of correct classifications is normalized, and the weight of each feature is obtained.

[0081] The risk value calculation module is used to calculate the risk value by combining the weights of the acquired feature data.

[0082] In this optional embodiment, the calculation formula of the risk value is:

[0083]

[0084] In the formula, T represents the risk value of oil storage equipment;

[0085] Mmax represents the maximum value in the characteristic data;

[0086] Ma represents the ath feature value in the feature data;

[0087] Na represents the weight corresponding to the ath feature value in the feature data;

[0088] K represents the integration coefficient for calculating risk value.

[0089] The early warning evaluation value acquisition unit 105 is used to combine historical data with the neural network model to build an early warning evaluation model for the oil storage equipment, and use the early warning evaluation model to obtain the early warning evaluation value of the oil storage equipment.

[0090] In this optional embodiment, the early warning evaluation value acquisition unit 105 includes:

[0091] The data screening and grouping module is used to screen historical data and divide the screened historical data into several indicator features.

[0092] In this optional embodiment, historical data is filtered and divided into several indicator features including:

[0093] Use median filtering algorithm to clean historical data, and normalize the cleaned historical data;

[0094] The index features related to oil storage equipment evaluation are extracted from the normalized historical data.

[0095] In this optional embodiment, the indicator characteristics include: structural indicator characteristics and functional indicator characteristics.

[0096] It should be noted that the structural indicator characteristics include the physical parameters of the oil storage equipment (such as tank capacity, shape, material, etc.), the diameter and length of the connecting pipes, the number of valves, etc.; the functional indicator characteristics include the oil flow change rate, oil temperature fluctuation range, oil level height stability, oil quality qualification rate, oil storage equipment failure rate, etc.

[0097] The Euclidean distance is used to calculate the similarity between each indicator feature, and a similarity matrix is ​​established based on the similarity.

[0098] It should be noted that the calculation formula of Euclidean distance is:

[0099]

[0100] In the formula, PQ represents the similarity between two indicator features;

[0101] x represents the characteristic value of any indicator;

[0102] y represents the characteristic value of any indicator.

[0103] Based on the similarity matrix, the region discovery algorithm is used to identify the regional structure of historical data, and the indicator features are divided into different regions according to the identification results.

[0104] It should be noted that the region discovery algorithm is the community discovery algorithm, such as the Louvain algorithm, the Girvan-Newman algorithm, the label propagation algorithm, etc.; the similarity matrix is ​​used as input, and the selected Louvain algorithm is applied to convert the similarity values ​​in the similarity matrix into the weights of the edges in the network, and the community structure is identified by optimizing indicators such as modularity; the community structure in the historical data is determined based on the output results of the Louvain algorithm; each community can be regarded as a region, and the indicator features contained in it have high similarity; each indicator feature is assigned to the corresponding region according to the community to which it belongs, and the indicator features in each region have similar characteristic patterns or behaviors; the accuracy of the community discovery results can be verified through visualization, modularity evaluation and other methods; the algorithm parameters are adjusted according to the verification results to obtain better regional division effects.

[0105] The early warning assessment model building module is used to analyze several indicator characteristics and integrate the analysis results with the neural network model to build an early warning assessment model.

[0106] In this optional embodiment, analyzing several indicator features and integrating the analysis results with the neural network model to construct an early warning assessment model includes:

[0107] The principal component analysis algorithm is used to extract several indicator variables from several indicator features, and the weight of each indicator variable is calculated.

[0108] Based on the weight of each indicator variable, the feature information is reconstructed using programming software to obtain a feature matrix set containing weight information, and the feature matrix set is divided into a training set and a test set.

[0109] The feature matrix is ​​converted into pixel points to form an indicator feature matrix, and the values ​​in the indicator feature matrix are converted into the brightness values ​​of the corresponding grayscale image to obtain a grayscale image data set.

[0110] Based on the neural network model, an initial early warning assessment model is set up, and the grayscale image data set in the training set is input into the initial early warning assessment model to obtain the initial early warning assessment results.

[0111] Compare the initial warning assessment results with the actual situation, optimize the parameters of the initial warning assessment model, and obtain the optimized initial warning assessment model.

[0112] The optimized initial early warning assessment model is tested using the grayscale image dataset in the test set to evaluate the performance of the initial early warning assessment model and obtain the early warning assessment model.

[0113] The early warning assessment value acquisition module inputs historical data into the early warning assessment model and outputs the early warning assessment value.

[0114] The potential safety hazard judging unit 107 is used to calculate the comprehensive potential safety hazard value of the oil storage equipment based on the risk value and the early warning assessment value, and judge whether there is a potential safety hazard in the oil storage equipment according to the comprehensive potential safety hazard value.

[0115] In this optional embodiment, the potential safety hazard determination unit 107 includes:

[0116] The comprehensive hidden danger value calculation module is used to calculate the comprehensive hidden danger value of the oil storage equipment according to the acquired risk value and early warning assessment value of the oil storage equipment.

[0117] It should be noted that the calculation formula for the comprehensive hidden danger value is: L=T*Q, where L represents the comprehensive hidden danger value of the oil storage equipment, T represents the risk value of the oil storage equipment; and Q represents the early warning assessment value of the oil storage equipment.

[0118] The safety hazard judgment module is used to compare the calculated comprehensive hazard value with the preset comprehensive hazard threshold to determine whether there is a safety hazard in the oil storage equipment.

[0119] In this optional embodiment, comparing the calculated comprehensive hidden danger value with a preset comprehensive hidden danger threshold value to determine whether the oil storage equipment has a safety hazard includes:

[0120] If the comprehensive hidden danger value is less than the preset comprehensive hidden danger threshold, it is judged that there is no safety hazard in the oil storage equipment;

[0121] If the comprehensive hidden danger value is greater than or equal to the preset comprehensive hidden danger threshold, it is judged that there is a safety hazard in the oil storage equipment.

[0122] The early warning and response unit 109 is used to generate early warning prompts according to the potential safety hazard judgment results, and feed back the generated early warning prompts to relevant personnel, instructing the relevant personnel to perform maintenance on the oil storage equipment.

[0123] It should be noted that, based on the results of the safety hazard judgment, corresponding early warning information should be generated. The early warning should clearly point out the potential safety issues, possible consequences and recommended maintenance measures; the early warning should be promptly fed back to relevant personnel through SMS, e-mail, special applications and other communication methods; the efficiency and accuracy of the early warning communication should be ensured; clear maintenance instructions should be included in the early warning to guide relevant personnel to carry out necessary maintenance on the oil storage equipment; the instructions should include the specific steps of the maintenance, required materials, tools and safety requirements; track and record the progress and results of the maintenance to ensure that the maintenance measures are effectively implemented and the safety hazards are closed in time. After the maintenance is completed, the maintenance effect should be evaluated to confirm whether the safety hazards have been completely resolved.

[0124] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0125] Those skilled in the art will understand that Figure 2 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0126] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0127] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0128] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. An equipment detection and early warning system, characterized in that: The equipment detection and early warning system includes: A data collection unit, used to collect historical data of oil storage equipment; A data processing and analysis unit is used to process historical data and analyze the processed historical data to obtain the risk value of the oil storage equipment; An early warning evaluation value acquisition unit is used to combine historical data with a neural network model to construct an early warning evaluation model for oil storage equipment, and to obtain an early warning evaluation value for the oil storage equipment using the early warning evaluation model; A safety hazard judgment unit is used to calculate the comprehensive hazard value of the oil storage equipment based on the risk value and the early warning assessment value, and judge whether the oil storage equipment has safety hazards according to the comprehensive hazard value; The early warning and response unit is used to generate early warning prompts based on the safety hazard judgment results, and feed back the generated early warning prompts to relevant personnel, instructing relevant personnel to repair the oil storage equipment.

2. The equipment detection and early warning system according to claim 1, characterized in that: The data processing and analysis unit comprises: A data preprocessing module is used to preprocess the historical data collected from the oil storage equipment; A feature extraction module is used to extract feature data from the preprocessed historical data; A data analysis module is used to analyze the extracted feature data and obtain the weights corresponding to the feature data; The risk value calculation module is used to calculate the risk value by combining the weights of the acquired feature data.

3. The equipment detection and early warning system according to claim 2, characterized in that: The calculation formula of the risk value is: In the formula, T represents the risk value of oil storage equipment; Mmax represents the maximum value in the characteristic data; Ma represents the ath feature value in the feature data; Na represents the weight corresponding to the ath feature value in the feature data; K represents the integration coefficient for calculating risk value.

4. The equipment detection and early warning system according to claim 1, characterized in that: The early warning evaluation value acquisition unit includes: Data screening and grouping module, used to screen historical data and divide the screened historical data into several indicator features; The early warning assessment model building module is used to analyze several indicator characteristics and integrate the analysis results with the neural network model to build an early warning assessment model; The early warning assessment value acquisition module inputs historical data into the early warning assessment model and outputs the early warning assessment value.

5. The equipment detection and early warning system according to claim 4, characterized in that: The filtering of historical data and dividing the filtered historical data into several indicator features include: Use median filtering algorithm to clean historical data, and normalize the cleaned historical data; Extracting index features related to oil storage equipment evaluation from normalized historical data; The similarity between each indicator feature is calculated using Euclidean distance, and a similarity matrix is ​​established based on the similarity; Based on the similarity matrix, the region discovery algorithm is used to identify the regional structure of historical data, and the indicator features are divided into different regions according to the identification results.

6. The equipment detection and early warning system according to claim 5, characterized in that: The indicator characteristics include: structural indicator characteristics and functional indicator characteristics.

7. The equipment detection and early warning system according to claim 4, characterized in that: The analysis of several indicator characteristics and integration of the analysis results with the neural network model to construct an early warning assessment model includes: Use the principal component analysis algorithm to extract several indicator variables from several indicator features and calculate the weight of each indicator variable; Based on the weight of each indicator variable, the feature information is reconstructed using programming software to obtain a feature matrix set containing weight information, and the feature matrix set is divided into a training set and a test set; The feature matrix is ​​converted into pixel points to form an index feature matrix, and the values ​​in the index feature matrix are converted into the brightness values ​​of the corresponding grayscale image to obtain a grayscale image data set; Based on the neural network model, an initial early warning evaluation model is set up, and the grayscale image data set in the training set is input into the initial early warning evaluation model to obtain the initial early warning evaluation result; Compare the initial warning assessment results with the actual situation, optimize the parameters of the initial warning assessment model, and obtain the optimized initial warning assessment model; The optimized initial early warning assessment model is tested using the grayscale image dataset in the test set to evaluate the performance of the initial early warning assessment model and obtain the early warning assessment model.

8. The equipment detection and early warning system according to claim 1, characterized in that: The potential safety hazard judgment unit comprises: A comprehensive hidden danger value calculation module is used to calculate the comprehensive hidden danger value of the oil storage equipment according to the acquired risk value and early warning assessment value of the oil storage equipment; The safety hazard judgment module is used to compare the calculated comprehensive hazard value with the preset comprehensive hazard threshold to determine whether there is a safety hazard in the oil storage equipment.

9. The equipment detection and early warning system according to claim 8, characterized in that: The step of comparing the calculated comprehensive hidden danger value with the preset comprehensive hidden danger threshold value to determine whether the oil storage equipment has a safety hidden danger includes: If the comprehensive hidden danger value is less than the preset comprehensive hidden danger threshold, it is judged that there is no safety hazard in the oil storage equipment; If the comprehensive hidden danger value is greater than or equal to the preset comprehensive hidden danger threshold, it is judged that there is a safety hazard in the oil storage equipment.

10. An equipment detection and early warning device, characterized in that: The equipment detection and early warning device comprises: a processor and a memory for storing instructions executable by the processor; Wherein, the processor is configured to: Collect historical data of oil storage equipment; Process the historical data and analyze the processed historical data to obtain the risk value of the oil storage equipment; Combining historical data with the neural network model, an early warning assessment model for oil storage equipment is constructed, and the early warning assessment value of the oil storage equipment is obtained using the early warning assessment model; Based on the risk value and the early warning assessment value, the comprehensive hidden danger value of the oil storage equipment is calculated, and it is judged whether there are safety hazards in the oil storage equipment according to the comprehensive hidden danger value; An early warning is generated based on the safety hazard judgment results, and the generated early warning is fed back to relevant personnel, instructing them to repair the oil storage equipment.

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