Risk management system and device based on oil depot

Abstract

The invention belongs to the technical field of oil depot risk management, and discloses a risk management system and device based on an oil depot, and the risk management system comprises the steps: constructing a management and control platform based on an industrial Internet technology, and obtaining oil depot production data collected by monitoring equipment in an operation range through the management and control platform; combining the oil depot production data with a partial least square method technology to construct a risk judgment model, and obtaining an abnormal data value in the working process of the oil depot by using the risk judgment model; judging an exceeded value of the abnormal data value by utilizing a risk standard threshold value, and analyzing a risk trend in the working process of the oil depot based on the exceeded value; and feeding back the risk trend to the management and control platform, and adjusting the working process of the oil depot by using the management and control platform. According to the method, the abnormal data value in the working process of the oil depot can be accurately identified, quantitative assessment of risks is realized, future risk changes can be predicted, and prospective guidance is provided for risk management.

Description

Technical Field

[0001] The present invention relates to the technical field of oil depot risk management, and particularly relates to a risk management system and device based on an oil depot. Background Art

[0002] An oil depot is a facility and place for storing crude oil, refined oil, liquefied petroleum gas, natural gas, and other petroleum products. It is a link coordinating crude oil production, crude oil processing, refined oil supply, and transportation, and is a base for petroleum storage and supply. An oil depot can generally be divided into: an oil storage area, a loading and unloading area, and an auxiliary facility area, etc. The oil storage area is the core area of the oil depot, containing multiple oil storage tanks for storing a large amount of oil products.

[0003] Since an oil depot is a place for storing a large amount of petroleum products, these products have characteristics such as flammability, explosiveness, and volatility. Once an accident occurs, it may lead to serious consequences such as fires and explosions, posing a huge threat to personnel safety, environmental safety, and property safety. Therefore, risk management of the oil depot, identifying potential safety hazards, and formulating corresponding preventive measures are necessary contents in the process of oil depot work.

[0004] However, the existing technology cannot predict the risks existing in the oil depot in advance during oil depot risk management, lacks foresight, and cannot foresee possible risks in advance and take corresponding measures for prevention, resulting in the inability to respond in a timely manner when the risk occurs. Summary of the Invention

[0005] Embodiments of the present invention provide a risk management system and device based on an oil depot to solve the problem in the existing technology that it is impossible to foresee possible risks in advance and take corresponding measures for prevention, resulting in the inability to respond in a timely manner when the risk occurs.

[0006] To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a preface to the subsequent detailed description.

[0007] According to the first aspect of the embodiments of the present invention, a risk management system based on an oil depot is provided.

[0008] In one embodiment, the risk management system based on an oil depot includes:

[0009] A control platform building unit, configured to build a control platform based on industrial Internet technology and use the control platform to obtain the oil depot production data collected by monitoring devices within the operation range;

[0010] An oil depot risk assessment unit, which is used to combine oil depot production data with partial least squares method technology to build a risk judgment model, and use the risk judgment model to obtain abnormal data values during the operation process of the oil depot;

[0011] An oil depot risk analysis unit, which is used to judge the exceeded value of the abnormal data value by using the risk standard threshold, and analyze the risk trend during the operation process of the oil depot based on the exceeded value;

[0012] An oil depot adjustment analysis unit, which is used to feedback the risk trend to the control platform, and use the control platform to adjust the operation process of the oil depot.

[0013] Preferably, the control platform construction unit includes:

[0014] A platform management requirement determination module, which is used to define the management requirements of the management platform according to the operation process of the oil depot;

[0015] A management platform operation and maintenance construction module, which is used to build the underlying architecture by using industrial Internet of Things technology and management requirements, and generate a management platform in combination with terminal hardware configuration information;

[0016] An oil depot production data acquisition module, which is used to acquire oil depot production data collected by monitoring devices within the storage operation range by using the management platform.

[0017] Preferably, building the underlying architecture by using industrial Internet of Things technology and management requirements, and generating a management platform in combination with terminal hardware configuration information includes:

[0018] Determine the management main interface of the management platform based on management requirements, define the port application layer in the management main interface, and build the headquarters data center in the management main interface by using industrial Internet of Things technology to determine the underlying architecture;

[0019] Integrate the underlying architecture with oil depot terminal hardware devices to obtain an edge perception layer, and generate a management platform through the edge perception layer, the underlying architecture and the port application layer;

[0020] Carry out security reinforcement and function analysis and statistics on the management platform through the call interface, and deploy the management platform on an encrypted authentication storage disk after determining the security of the management platform based on the analysis results.

[0021] Preferably, the underlying architecture includes a service layer, a data layer and a resource layer; the oil depot terminal hardware devices include an oil depot data acquisition monitor, an oil depot security device, an oil depot fire protection device and an environmental monitor.

[0022] Preferably, the oil depot risk assessment unit includes:

[0023] A feature index screening module, which is used to select test samples from oil depot production data, divide them into a training set and a test set according to a preset ratio, and screen risk feature indexes based on the test samples;

[0024] A risk judgment model construction module, which is used to establish a partial least squares discriminant analysis method to fit the training set, and calculate the model classification threshold by using the receiver operating characteristic (ROC) curve to construct a risk judgment model;

[0025] An abnormal data value calculation module, which is used to input the oil depot production data into the risk judgment model in sequence to output the abnormal data values during the operation of the oil depot.

[0026] Preferably, test samples are selected from the oil depot production data and divided into a training set and a test set according to a preset ratio, and risk characteristic indicators are screened based on the test samples, including:

[0027] The oil depot production data is pre-classified according to the data type to obtain category data, and test samples for risk judgment are selected from the category data. The test samples are divided into a training set, a validation set and a test set according to a preset ratio;

[0028] The risk factor characteristic indicators are screened by using the training set and the validation set respectively, and two groups of initial least squares discriminant analysis models are established to decompose the test samples;

[0029] The variable projection importance of each index in the two groups of initial least squares discriminant analysis models is judged, and the indexes with importance results greater than the threshold are screened based on the judgment results;

[0030] The indexes with importance greater than the threshold are sorted in reverse order, and an index merging operation is performed. The merging result is used as the risk characteristic indicator.

[0031] Preferably, establishing a partial least squares discriminant analysis method to fit the training set, and calculating the model classification threshold by using the receiver operating characteristic (ROC) curve to construct a risk judgment model includes:

[0032] Using the partial least squares discriminant analysis method to fit the training set for initial model construction, obtaining model parameters, and predicting the validation set according to the obtained initial model to obtain an initial receiver operating characteristic (ROC) curve;

[0033] Using the initial model to predict the test set to obtain predicted risk prediction scores, and selecting the prediction score threshold within a preset range as the classification threshold based on the score results;

[0034] The risk prediction sensitivity and specificity of the classification threshold are judged respectively, and the receiver operating characteristic (ROC) curve is drawn based on the judgment results. The receiver operating characteristic (ROC) curve is compared with the initial receiver operating characteristic (ROC) curve for coincidence;

[0035] Based on the comparison results, the classification threshold with sensitivity and specificity equal to the standard value is selected as the threshold result, and the threshold result is combined with the initial model to obtain a risk judgment model.

[0036] Preferably, the expression of the risk judgment model is:

[0037]

[0038] In the formula, R represents the risk judgment result, represents the risk characteristic index, β represents the weight of each risk characteristic index in risk prediction, σ represents the optimal classification threshold determined by the sensitivity curve analysis, and sinn represents the sign function.

[0039] Preferably, the oil depot risk analysis unit includes:

[0040] A threshold key generation module, configured to obtain a risk standard threshold and an abnormal data value by using a control platform, and perform an encryption operation on the risk standard threshold and the abnormal data value to generate two groups of ciphertext data;

[0041] An excess value calculation module, configured to calculate the excess value of the two groups of ciphertext data by using a control platform;

[0042] A risk trend analysis module, configured to analyze the change trend of abnormal points based on the excess value, and judge the probability trend of risk occurrence in the oil depot working process according to the change trend.

[0043] According to the second aspect of the embodiments of the present invention, a risk management device based on an oil depot is provided. The risk management device includes a processor and a memory for storing processor-executable instructions;

[0044] Wherein, the processor is configured to:

[0045] Build a control platform based on industrial Internet technology, and obtain oil depot production data collected by monitoring devices within the operation range by using the control platform;

[0046] Combine the oil depot production data with the partial least squares method technology to build a risk judgment model, and obtain the abnormal data value in the oil depot working process by using the risk judgment model;

[0047] Judge the excess value of the abnormal data value by using the risk standard threshold, and analyze the risk trend during the oil depot working process based on the excess value;

[0048] Feed back the risk trend to the control platform, and use the control platform to adjust the working process of the oil depot.

[0049] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0050] 1. First, the present invention constructs a control platform through industrial Internet technology to be able to obtain in real time the oil depot production data collected by monitoring devices within the operation range, ensuring real-time monitoring of the operation status of the oil depot, providing a timely and accurate information basis for later risk management and decision-making. At the same time, combining the oil depot production data with partial least squares method technology to construct a risk judgment model, it can accurately identify abnormal data values during the operation process of the oil depot, and use the risk standard threshold to judge the exceeded values of the abnormal data values, realizing the quantitative assessment of risks, helping to predict future risk changes, and providing forward-looking guidance for risk management.

[0051] 2. The present invention combines the oil depot production data with partial least squares method technology to construct a risk judgment model, which can accurately identify abnormal data values during the operation process of the oil depot, providing strong support for the safety management of the oil depot, realizing real-time processing of the oil depot production data, timely discovering and responding to abnormal situations, ensuring the safe operation of the oil depot, and providing strong support for the safety management of the oil depot.

[0052] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0054] Figure 1 is a schematic block diagram of a risk management system based on an oil depot shown according to an exemplary embodiment;

[0055] Figure 2 is a schematic structural diagram of a computer device shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] The following description and the accompanying drawings fully disclose specific embodiments herein, enabling those skilled in the art to 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 herein includes the entire scope of the claims and all available equivalents of the claims. In this document, terms such as "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 "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a structure, device or equipment comprising a series of elements not only includes those elements but also other elements not expressly listed, or elements inherent to such structure, device or equipment. Without further limitation, an element defined by the statement "comprising one..." does not exclude the existence of additional identical elements in the structure, device or equipment comprising the element. The embodiments herein are described in a progressive manner, with each embodiment highlighting the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other.

[0057] In this document, terms such as "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. They are only for the convenience of describing this document and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation on the present invention. In the description herein, unless otherwise specified and limited, the terms "mounted", "connected", "coupled" shall be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the communication inside two elements. It can be directly connected or indirectly connected through an intermediate medium. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0058] In this document, unless otherwise stated, the term "plurality" means two or more.

[0059] In this document, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B.

[0060] In this document, the term "and / or" is a description of the associative relationship of an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B these three relationships.

[0061] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless there is a clear indication in this document, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0062] Each module in the device of the present application or the risk management system and device based on the oil depot can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor in the computer device in the form of hardware or independent of it, or stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0063] Without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0064] Figure 1 An embodiment of the risk management system based on the oil depot of the present invention is shown.

[0065] In this alternative embodiment, the risk management system based on the oil depot includes:

[0066] The control platform construction unit 101 is used to construct a control platform based on industrial Internet technology and obtain the oil depot production data collected by the monitoring devices within the operation range by using the control platform;

[0067] The oil depot risk evaluation unit 103 is used to combine the oil depot production data with the partial least squares method technology to construct a risk judgment model, and use the risk judgment model to obtain the abnormal data values during the operation of the oil depot;

[0068] The oil depot risk analysis unit 105 is used to judge the exceeded value of the abnormal data value by using the risk standard threshold, and analyze the risk trend during the operation of the oil depot based on the exceeded value;

[0069] The oil depot adjustment analysis unit 107 is used to feedback the risk trend to the control platform and use the control platform to adjust the operation process of the oil depot.

[0070] In this alternative embodiment, the control platform construction unit 101 includes:

[0071] The platform management requirement determination module is used to define the management requirements of the management platform according to the operation process of the oil depot;

[0072] The management platform operation and maintenance construction module is used to build the underlying architecture by using industrial Internet of Things technology and management requirements, and generate the management platform in combination with the terminal hardware configuration information.

[0073] The oil depot production data acquisition module is used to obtain the oil depot production data collected by the monitoring devices within the storage operation range by using the management platform.

[0074] In this optional embodiment, building the underlying architecture by using industrial Internet of Things technology and management requirements, and generating the management platform in combination with the terminal hardware configuration information includes: determining the management main interface of the management platform based on management requirements, defining the port application layer within the management main interface, and building the headquarters data center within the management main interface by applying industrial Internet of Things technology to determine the underlying architecture; integrating the underlying architecture with the oil depot terminal hardware devices to obtain the edge perception layer, and generating the management platform through the edge perception layer, the underlying architecture and the port application layer; performing security reinforcement and function analysis and statistics on the management platform through the call interface, and deploying the management platform on the encrypted authentication storage disk after determining the security of the management platform based on the analysis results.

[0075] In this optional embodiment, the underlying architecture includes a service layer, a data layer and a resource layer; the oil depot terminal hardware devices include an oil depot data acquisition monitor, an oil depot security device, an oil depot fire protection device and an environmental monitor.

[0076] It should be explained that the edge perception layer, as the data support platform of the management platform, mainly includes process automation instruments, combustible gas monitoring devices, video monitoring devices, personnel access management devices, vehicle identification devices, environmental monitoring devices, etc. The devices of the perception layer are installed on the oil depot site, and the devices communicate with production systems such as the oil depot automatic control system (SCADA system) and the security system to collect data.

[0077] The existing automatic control system, security system, fire protection system and environmental monitoring system of the oil depot are deployed in the oil depot production duty center. The oil depot conducts production operations through these systems, and the systems integrate the device status, operation data, oil product inventory data, security monitoring data, etc. of the perception layer. The systems and devices of this layer access the production data to the aviation oil company's safety production monitoring platform and liquid level measurement system through the production private network.

[0078] The data layer designs a data exchange system and a data warehouse system to realize services such as data collection, storage, governance, and analysis, and is deployed in the data center computer room. The data warehouse is the platform data center, providing data support for various applications and providing operations such as query, storage, statistics, and analysis.

[0079] The service layer is located in the data center, providing AI analysis services, identification services, early warning analysis services, electronic map services, directory services, and deploying a middle platform system to realize functions such as low-code rapid development, visualization system, AI training analysis, big data mining and analysis, interface standards, report development, etc., and building the basic capabilities platform of the intelligent control platform for security risks, providing efficient development services and capability services for developing application functions.

[0080] The resource layer is the hardware resource center for the platform to run, mainly including devices such as storage resources, computing resources, network resources, and security protection, providing hardware device facilities with functions such as operation, storage, publishing, security protection, and communication network for the platform to run, and the devices are installed in the data center computer room of China National Aviation Fuel Co., Ltd.

[0081] The application layer enables the oil depot to access the platform through the production private network, and uses a visual operation interface to realize the application requirements of oil depot management functions such as major hazard source early warning monitoring, dual prevention mechanism management, special operation management, and all-element management of work safety.

[0082] In this alternative embodiment, the oil depot risk assessment unit 103 includes:

[0083] A feature index screening module, configured to select test samples from the oil depot production data, divide the training set and the test set according to a preset ratio, and screen risk feature indexes based on the test samples;

[0084] A risk judgment model construction module, configured to establish a partial least squares discriminant analysis method to fit the training set, and calculate the model classification threshold using the receiver operating characteristic curve to construct a risk judgment model;

[0085] An abnormal data value calculation module, configured to sequentially input the oil depot production data into the risk judgment model to output the abnormal data values during the operation of the oil depot.

[0086] In this alternative embodiment, the operation of selecting test samples from the oil depot production data, dividing the training set and the test set according to a preset ratio, and screening risk feature indexes based on the test samples includes: classifying the oil depot production data according to data types in advance to obtain category data, selecting test samples for risk judgment from the category data, and dividing the test samples into a training set, a validation set, and a test set according to a preset ratio; respectively using the training set and the validation set for screening risk factor feature indexes, establishing two groups of initial partial least squares discriminant analysis models to decompose the test samples; judging the variable projection importance of each index in the two groups of initial partial least squares discriminant analysis models, and screening the indexes with importance results greater than the threshold based on the judgment results; sorting the screened indexes with importance greater than the threshold in reverse order, and performing an index merging operation, and taking the merging result as the risk feature index.

[0087] In this alternative embodiment, the screening of risk factor characteristic indicators using the training set and the validation set respectively, and the decomposition of the test samples by establishing two groups of initial least squares discriminant analysis models includes: using the training set for feature selection to identify the variables most relevant to the risk, which can be completed by statistical methods such as correlation analysis, principal component analysis (PCA) or other feature selection techniques; repeating the above process, this time using the validation set to screen features; using the features selected from the training set to establish the first group of partial least squares discriminant analysis models, and similarly using the features selected from the validation set to establish the second group of partial least squares discriminant analysis models; using the cross-validation technique for the two groups of models to evaluate their prediction accuracy and stability; applying the two trained PLS-DA models to the test samples to analyze their performance and risk prediction ability.

[0088] In this alternative embodiment, the judgment of the variable projection importance of each index in the two groups of initial least squares discriminant analysis models, and the screening of the indexes with importance results greater than the threshold based on the judgment results includes: calculating the projection importance (VIP) of each variable in the partial least squares discriminant analysis model; calculating and analyzing the VIP values of the two groups of models respectively, and comparing the VIP values of the same variables in the two groups of models to evaluate which variables show high importance in both models; setting a VIP threshold according to experience or statistical methods, usually this threshold is set to 1.0 or higher, indicating that the variable has a significant influence in the model; screening out the variables with VIP values greater than the set threshold from each model, integrating the results screened out by the two groups of models, and determining the final variable set for constructing or optimizing the least squares discriminant analysis model.

[0089] Specifically, the calculation formula of the projection importance (VIP) is:

[0090]

[0091] In the formula, VIP j represents the projection importance of the j-th variable, w jk represents the weight of the j-th variable on the k-th component, represents the explanatory power of the k-th component to the model, q represents the total number of variables, and A represents the number of components.

[0092] In this alternative embodiment, establishing a partial least squares discriminant analysis method to fit a training set and calculating a model classification threshold using a sensitivity curve to construct a risk judgment model includes: using the partial least squares discriminant analysis method to fit a training set for initial model construction to obtain model parameters, and predicting a validation set based on the obtained initial model to obtain an initial sensitivity curve; predicting a test set using the initial model to obtain predicted risk prediction scores, and selecting a prediction score threshold within a preset interval range as the classification threshold based on the score results; respectively judging the risk prediction sensitivity and specificity of the classification threshold, plotting a sensitivity curve based on the judgment results, and overlapping and comparing the sensitivity curve with the initial sensitivity curve; selecting a classification threshold with sensitivity and specificity equal to the standard value as the threshold result based on the comparison results, and combining the threshold result with the initial model to obtain a risk judgment model.

[0093] In this alternative embodiment, the expression of the risk judgment model is:

[0094]

[0095] In the formula, R represents the risk judgment result, represents risk characteristic indicators, β represents the weight of each risk characteristic indicator in risk prediction, σ represents the optimal classification threshold determined by sensitivity curve analysis, and sinn represents the sign function.

[0096] In this alternative embodiment, the oil depot risk analysis unit 105 includes:

[0097] A threshold key generation module, configured to use a management and control platform to obtain a risk standard threshold and abnormal data values, and perform an encryption operation on the risk standard threshold and abnormal data values to generate two sets of ciphertext data;

[0098] An excess value calculation module, configured to use a management and control platform to perform an excess value calculation on the two sets of ciphertext data;

[0099] A risk trend analysis module, configured to analyze the change trend of abnormal points based on the excess value, and judge the probability trend of risk occurrence in the oil depot operation process according to the change trend.

[0100] In this alternative embodiment, using a management and control platform to obtain a risk standard threshold and abnormal data values, and performing an encryption operation on the risk standard threshold and abnormal data values to generate two sets of ciphertext data includes:

[0101] Obtain the risk standard threshold and abnormal data values through the control platform, select symmetric encryption (such as AES) or asymmetric encryption (such as RSA) to generate keys for the two groups of data respectively, ensure the secure storage and transmission of the keys, use the first group of keys to encrypt the risk standard threshold, and use the second group of keys to encrypt the abnormal data values; verify the correctness of the encryption process to ensure the integrity and confidentiality of the data after encryption, and securely store the two groups of ciphertext data generated in the control platform or other secure storage locations.

[0102] In one embodiment, a risk management device based on an oil depot is provided. The risk management device includes a processor and a memory for storing instructions executable by the processor.

[0103] Wherein, the processor is configured to:

[0104] Build a control platform based on industrial Internet technology, and use the control platform to obtain the oil depot production data collected by monitoring devices within the operation scope.

[0105] Combine the oil depot production data with partial least squares method technology to build a risk judgment model, and use the risk judgment model to obtain the abnormal data values during the operation process of the oil depot.

[0106] Use the risk standard threshold to judge the exceeding value of the abnormal data value, and analyze the risk trend during the operation process of the oil depot based on the exceeding value.

[0107] Feed back the risk trend to the control platform, and use the control platform to adjust the operation process of the oil depot.

[0108] In one embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 2 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 external terminals through a network connection. When the computer program is executed by the processor, it realizes the steps in the above method embodiments.

[0109] Those skilled in the art can understand, Figure 2The structure shown is only a block diagram of some structures 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 some components, or have a different component arrangement.

[0110] In addition, the present invention also 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 embodiments are implemented.

[0111] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. 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 above method embodiments. Among them, any reference to a memory, storage, database or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. 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. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0112] The present invention is not limited to the structure already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A risk management system based on an oil depot, characterized in that: The risk management system includes: A control platform building unit is used to build a control platform based on industrial Internet technology, and use the control platform to obtain oil depot production data collected by monitoring equipment within the operating range; The oil depot risk assessment unit is used to combine the oil depot production data with the partial least squares method to build a risk judgment model, and use the risk judgment model to obtain abnormal data values ​​during the oil depot operation process; The oil depot risk analysis unit is used to determine the excess value of abnormal data values ​​using the risk standard threshold, and analyze the risk trend of the oil depot work process based on the excess value; The oil depot adjustment analysis unit is used to feed back risk trends to the management and control platform, and use the management and control platform to adjust the work progress of the oil depot.

2. The oil depot-based risk management system according to claim 1, characterized in that: The control platform building unit includes: The platform management requirement determination module is used to define the management requirements of the management platform according to the oil depot work process; The management platform operation and maintenance construction module is used to build the underlying architecture using industrial Internet of Things technology and management requirements, and generate a management platform in combination with terminal hardware configuration information; The oil depot production data acquisition module is used to use the management platform to obtain the oil depot production data collected by the monitoring equipment within the storage operation range.

3. The oil depot-based risk management system according to claim 2, characterized in that: The use of industrial Internet of Things technology and management requirements to build the underlying architecture and generate a management platform in combination with terminal hardware configuration information includes: Determine the main management interface of the management platform based on management needs, define the port application layer in the main management interface, and use industrial Internet of Things technology to build the headquarters data center in the main management interface to determine the underlying architecture; Integrate the underlying architecture with the oil depot terminal hardware equipment to obtain the edge perception layer, and generate a management platform through the edge perception layer, the underlying architecture and the port application layer; The management platform is security-hardened and function-analyzed by calling interfaces. The security of the management platform is determined based on the analysis results and then deployed on an encrypted authentication storage disk.

4. The oil depot-based risk management system according to claim 3 is characterized in that: The underlying architecture includes a service layer, a data layer and a resource layer; The oil depot terminal hardware equipment includes an oil depot data acquisition monitor, an oil depot security device, an oil depot fire fighting device and an environmental monitor.

5. The oil depot-based risk management system according to claim 1, characterized in that: The oil depot risk assessment unit includes: The feature index screening module is used to select test samples from the oil depot production data, divide the training set and the test set according to a preset ratio, and screen the risk feature indicators based on the test samples; The risk judgment model building module is used to establish a partial least squares discriminant analysis method to fit the training set, and use the sensitivity curve to calculate the model classification threshold to build a risk judgment model; The abnormal data value obtaining module is used to input the oil depot production data into the risk judgment model in sequence and output the abnormal data values ​​during the oil depot operation.

6. The oil depot-based risk management system according to claim 5, characterized in that: The selecting of test samples from the oil depot production data to divide the training set and the test set according to a preset ratio, and screening of risk characteristic indicators based on the test samples include: Classify the oil depot production data in advance according to the data type to obtain category data, select test samples for risk judgment from the category data, and divide the test samples into training set, verification set and test set according to the preset ratio; The risk factor characteristic indicators were screened using the training set and the validation set, and two groups of initial least squares discriminant analysis models were established to decompose the test samples; Determine the variable projection importance of each indicator in the two initial least squares discriminant analysis models, and screen indicators whose importance results are greater than the threshold based on the judgment results; The screened indicators whose importance is greater than the threshold are arranged in reverse order, and the indicator merging operation is performed, and the merging result is used as the risk characteristic indicator.

7. The oil depot-based risk management system according to claim 5, characterized in that: The method of establishing a partial least squares discriminant analysis method to fit the training set and using the sensitivity curve to calculate the model classification threshold to construct a risk judgment model includes: The partial least squares discriminant analysis method is used to fit the training set to construct the initial model, obtain the model parameters, and predict the validation set based on the obtained initial model to obtain the initial sensitivity curve; The initial model is used to predict the test set to obtain a predicted risk prediction score, and based on the score result, a prediction score threshold within a preset range is selected as a classification threshold; The risk prediction sensitivity and specificity of the classification threshold are determined respectively, and the sensitivity curve is drawn based on the determination results, and the sensitivity curve is overlapped and compared with the initial sensitivity curve; Based on the comparison results, the classification threshold with sensitivity and specificity equal to the standard value is selected as the threshold result, and the threshold result is combined with the initial model to obtain the risk judgment model.

8. The oil depot-based risk management system according to claim 7, characterized in that: The expression of the risk judgment model is: In the formula, R represents the risk judgment result, represents the risk characteristic index, β represents the weight of each risk characteristic index in risk prediction, σ represents the optimal classification threshold determined by sensitivity curve analysis, and sinn represents the sign function.

9. The oil depot-based risk management system according to claim 1, characterized in that: The oil depot risk analysis unit comprises: The threshold key generation module is used to obtain the risk standard threshold and the abnormal data value by using the management and control platform, and perform encryption operations on the risk standard threshold and the abnormal data value to generate two sets of ciphertext data; An excess value calculation module is used to calculate the excess values ​​of two sets of ciphertext data using the management and control platform; The risk trend analysis module is used to analyze the changing trend of abnormal points based on the exceeding values, and to determine the probability trend of risk occurrence in the oil depot operation process according to the changing trend.

10. A risk management device based on an oil depot, characterized in that: The risk management device includes: a processor and a memory for storing instructions executable by the processor; Wherein, the processor is configured to: Build a management and control platform based on industrial Internet technology, and use the management and control platform to obtain oil depot production data collected by monitoring equipment within the operating range; The oil depot production data is combined with the partial least squares method to build a risk judgment model, and the risk judgment model is used to obtain abnormal data values ​​during the oil depot operation process; Use risk standard thresholds to determine the excess value of abnormal data values, and analyze the risk trend of the oil depot work process based on the excess value; Feedback risk trends to the management and control platform, and use the management and control platform to adjust the work progress of the oil depot.

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