A method and system for patrolling oil and gas field ground safety production

Abstract

The application provides a kind of oil and gas field ground safety production patrol method and system, method includes: the digital modeling of oil and gas field ground equipment, creates three-dimensional digital twin model;According to the geographical data of oil and gas field ground is patrolled, and sets initial patrol frequency and initial patrol stay time;When the geographical data of oil and gas field ground is detected by patrol and appears first abnormal data, the initial patrol frequency is adjusted according to first abnormal data;When the geographical data of oil and gas field ground is detected by patrol and appears second abnormal data, compare with first abnormal data, obtain abnormal data change rate according to comparison result, adjust initial patrol stay time according to abnormal data change rate, obtain adjusted patrol stay time;Risk prediction is carried out through three-dimensional digital twin model combined with adjusted patrol frequency and patrol stay time, obtains risk prediction value and is handled according to risk prediction value.

Description

Technical Field

[0001] This invention relates to the field of equipment inspection technology, and in particular to an inspection method and system for safe production on the surface of oil and gas fields. Background Technology

[0002] Oil and gas field surface production refers to the processing and handling of crude oil, natural gas, or other combustible gases produced during oil and gas field extraction to meet the requirements of commercial production. It includes various processes and operations, such as collecting and processing underground oil and gas, separating impurities from the oil and gas, adjusting the composition and properties of the oil and gas, and storing and transporting oil and gas products. Oil and gas field surface production requires the use of various equipment and tools, such as drilling equipment, separators, compressors, and storage tanks. Through various technological steps and control measures, crude oil and natural gas are processed into products that meet market demands, while ensuring the safety and environmental protection of the production process. Oil and gas field surface production is a crucial link in the entire oil and gas extraction process. It not only plays a decisive role in the development and utilization of resources but also directly relates to the stability of energy supply and economic development.

[0003] By adopting various measures and management methods, the safety of workplaces, equipment, facilities, and personnel is ensured, potential safety risks and accidents are prevented and controlled, and the continuity and stability of oil and gas field production operations are guaranteed. This encompasses many aspects, including safety assessment and maintenance of equipment and facilities, safety training and operating procedures for personnel, on-site safety inspections and monitoring, accident prevention, and emergency response. Through effective safety management and the application of technical means, safe production on the oil and gas field surface can minimize the possibility of accidents, protect the lives and property of personnel, and ensure the safe and efficient operation of oil and gas production.

[0004] Safety inspections of equipment are crucial in oil and gas field surface production, determining the stability of operations. However, traditional equipment inspections often rely on manual labor, which is inefficient, especially for large oil and gas field equipment clusters, requiring significant time and manpower. Manual inspections struggle to monitor and detect equipment anomalies in real time, making it difficult to identify and address hidden safety hazards promptly, potentially leading to accidents and losses. Furthermore, traditional methods cannot accurately predict equipment risk conditions, lack scientific risk assessment tools, and hinder proactive management measures, increasing safety risks. Therefore, it is necessary to design a new inspection method and system for safe oil and gas field surface production to address these challenges. Summary of the Invention

[0005] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] This invention proposes a patrol method for safe production on the surface of oil and gas fields, the improvement of which includes:

[0007] (1) Digital modeling of oil and gas field surface equipment to create a three-dimensional digital twin model;

[0008] (2) Conduct patrols based on the geographical data of the oil and gas field surface, and set the initial patrol frequency and initial patrol dwell time;

[0009] (3) When the patrol detects the first abnormal data in the geographical data of the oil and gas field, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after the patrol frequency is adjusted.

[0010] (4) When the inspection detects a second abnormal data in the geographic data of the oil and gas field, it is compared with the first abnormal data. The rate of change of the abnormal data is obtained based on the comparison result. The initial inspection dwell time is adjusted according to the rate of change of the abnormal data, and the adjusted inspection dwell time is obtained.

[0011] (5) Risk prediction is performed by combining the three-dimensional digital twin model with the adjusted patrol frequency and patrol dwell time to obtain the risk prediction value, and the risk prediction value is processed accordingly.

[0012] Preferably, in step (2), a drone is used to inspect the ground equipment of the oil and gas field, and the geographical data of the oil and gas field includes the area of ​​the oil and gas field △M and the average spacing between the equipment △H.

[0013] Furthermore, step (2) includes

[0014] (2-1) Set the first preset oil and gas field area M1, the second preset oil and gas field area M2, the third preset oil and gas field area M3 and the fourth preset oil and gas field area M4, and M1 < M2 < M3 < M4.

[0015] (2-2) Set the first preset patrol frequency P1, the second preset patrol frequency P2, the third preset patrol frequency P3 and the fourth preset patrol frequency P4, and P1 < P2 < P3 < P4.

[0016] (2-3) Set the first preset dwell time S1, the second preset dwell time S2, the third preset dwell time S3 and the fourth preset dwell time S4, and S1 < S2 < S3 < S4;

[0017] (2-4) Based on the relationship between the area ΔM of the oil and gas field and each preset area, set the initial patrol frequency and initial dwell time;

[0018] (2-5) When M1≤△M<M2, the initial patrol frequency is set to the first preset patrol frequency P1, and the initial patrol dwell time is set to the first preset dwell time S1.

[0019] (2-6) When M2≤△M<M3, the initial patrol frequency is set to the second preset patrol frequency P2, and the initial patrol dwell time is set to the second preset dwell time S2.

[0020] (2-7) When M3≤△M<M4, the initial patrol frequency is set to the third preset patrol frequency P3, and the initial patrol dwell time is set to the third preset dwell time S3.

[0021] (2-8) When M4≤△M, the initial patrol frequency is set to the fourth preset patrol frequency P4, and the initial patrol dwell time is set to the fourth preset dwell time S4.

[0022] Furthermore, after setting the i-th preset patrol frequency Pi and the i-th preset patrol dwell time Si as the initial patrol frequency and initial patrol dwell time, i = 1, 2, 3, 4, the initial patrol frequency and initial patrol dwell time are set according to the geographical data of the oil and gas field surface, including:

[0023] (2-9) Set the first preset spacing H1, the second preset spacing H2, the third preset spacing H3 and the fourth preset spacing H4, and H1 < H2 < H3 < H4;

[0024] (2-10) Set the first preset correction coefficient A1, the second preset correction coefficient A2, the third preset correction coefficient A3 and the fourth preset correction coefficient A4, and A1 < A2 < A3 < A4;

[0025] (2-11) Based on the relationship between the average equipment spacing △H and each preset spacing, select correction coefficients to correct the initial patrol frequency and initial patrol dwell time respectively, and use the corrected patrol frequency and patrol dwell time as the initial patrol frequency and initial patrol dwell time;

[0026] (2-12) When H1 ≤ ΔH < H2, select the fourth preset correction coefficient A4 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A4 and inspection stay time Si*A4;

[0027] (2-13) When H2 ≤ ΔH < H3, select the third preset correction coefficient A3 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A3 and inspection stay time Si*A3;

[0028] (2-14) When H3 ≤ ΔH < H4, select the second preset correction coefficient A2 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A2 and inspection stay time Si*A2;

[0029] ((2-15) When H4 ≤ ΔH, select the first preset correction coefficient A1 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A1 and inspection stay time Si*A1.

[0030] Preferably, the step (3) includes:

[0031] (3-1) Obtain the environmental data ΔQ, and set the first preset environmental data Q1, the second preset environmental data Q2, the third preset environmental data Q3, and the fourth preset environmental data Q4, and Q1 < Q2 < Q3 < Q4;

[0032] (3-2) Set the first preset correction coefficient B1, the second preset correction coefficient B2, the third preset correction coefficient B3, and the fourth preset correction coefficient B4, and B1 < B2 < B3 < B4;

[0033] (3-3) According to the magnitude relationship between the environmental data ΔQ and each preset environmental data, select a correction coefficient to correct the first abnormal data, and obtain the corrected first abnormal data;

[0034] (3-4) When Q1 ≤ ΔQ < Q2, select the first preset correction coefficient B1 to correct the first abnormal data ΔJ, and obtain the corrected first abnormal data ΔJ*B1;

[0035] (3-5) When Q2 ≤ ΔQ < Q, select the second preset correction coefficient B2 to correct the first abnormal data ΔJ, and obtain the corrected first abnormal data ΔJ*B2;

[0036] (3-6) When Q3≤△Q<Q4, the third preset correction coefficient B3 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B3 is obtained.

[0037] (3-7) When Q4≤△Q, the fourth preset correction coefficient B4 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B4 is obtained.

[0038] Furthermore, after selecting the i-th preset correction coefficient Bi to correct the first abnormal data △J, and obtaining the corrected first abnormal data △J*Bi, where i = 1, 2, 3, 4, when the patrol detects the first abnormal data in the geographical data of the oil and gas field surface, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after adjusting the patrol frequency, including...

[0039] (3-8) Set the first preset data J1, the second preset data J2, the third preset data J3 and the fourth preset data J4, and J1 < J2 < J3 < J4;

[0040] (3-9) Set the first preset adjustment coefficient C1, the second preset adjustment coefficient C2, the third preset adjustment coefficient C3 and the fourth preset adjustment coefficient C4, and C1 < C2 < C3 < C4;

[0041] (3-10) Based on the relationship between the corrected first abnormal data △J* / Bi and each preset data, select an adjustment coefficient to adjust the initial patrol frequency;

[0042] (3-11) When J1≤△J*Bi<J2, select the first preset adjustment coefficient C1 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C1.

[0043] (3-12) When J2≤△J*Bi<J3, select the second preset adjustment coefficient C2 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C2;

[0044] (3-13) When J3≤△J*Bi<J4, select the third preset adjustment coefficient C3 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C3;

[0045] (3-14) When J4≤△J*Bi, the fourth preset adjustment coefficient C4 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C4 is obtained.

[0046] Preferably, step (4) includes: obtaining second abnormal data ΔE, wherein the rate of change V of the abnormal data is calculated by the following formula:

[0047] V = (△E - △J*Bi) / △J*Bi.

[0048] Furthermore, adjusting the initial patrol dwell time based on the rate of change of the abnormal data to obtain the adjusted patrol dwell time includes:

[0049] (4-1) Set a first preset rate of change V1, a second preset rate of change V2, a third preset rate of change V3 and a fourth preset rate of change V4, and V1 < V2 < V3 < V4;

[0050] (4-2) Set the first preset time adjustment coefficient T1, the second preset time adjustment coefficient T2, the third preset time adjustment coefficient T3 and the fourth preset time adjustment coefficient T4, and T1 < T2 < T3 < T4;

[0051] (4-3) Based on the relationship between the abnormal data change rate V and each preset change rate, select a preset time adjustment coefficient to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time.

[0052] (4-4) When V1≤V<V2, select the first preset time adjustment coefficient T1 to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time Si*Ai*T1;

[0053] (4-5) When V2≤V<V3, select the second preset time adjustment coefficient T2 to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time Si*Ai*T2;

[0054] (4-6) When V3≤V<V4, the third preset time adjustment coefficient T3 is selected to adjust the initial patrol dwell time Si*Ai, and the adjusted patrol dwell time Si*Ai*T3 is obtained.

[0055] (4-7) When V4≤V, the fourth preset time adjustment coefficient T4 is selected to adjust the initial patrol dwell time Si*Ai, and the adjusted patrol dwell time Si*Ai*T4 is obtained.

[0056] Preferably, step (5) includes

[0057] Risk prediction is performed using the aforementioned three-dimensional digital twin model, combined with the adjusted patrol frequency and patrol dwell time, to obtain a risk prediction value. The risk prediction value is calculated using the following formula:

[0058] R=w1*(k1*x+k2*y)+w2*(a*exp(-b*x)+c*exp(-d*y))

[0059] Where k1 and k2 are weighting coefficients; x represents the drone patrol frequency; y represents the patrol dwell time; a, b, c, and d are constant parameters used to adjust the degree of influence of drone patrol frequency and patrol dwell time on equipment anomaly frequency;

[0060] Obtaining risk prediction values ​​and processing them accordingly includes:

[0061] Set a risk prediction threshold R0;

[0062] When 0 ≤ R < 0.3R0, a blue warning is issued, notifying staff to pay attention to the abnormal equipment;

[0063] When 0.3R0≤R<0.6R0, a yellow warning is issued, and special attention should be paid to checking when abnormal equipment is shut down for maintenance;

[0064] When 0.6R0≤R, a red warning is issued, and the abnormal equipment is immediately stopped and repaired.

[0065] The present invention also provides an inspection system for safe production on the surface of oil and gas fields, the improvement of which is that the system includes

[0066] Data Acquisition Module: Used for digital modeling of oil and gas field surface equipment, creating three-dimensional digital twin models, and for inspecting the geographical data of the oil and gas field surface, and setting the initial inspection frequency and initial inspection dwell time;

[0067] Adjustment module: When the patrol detects the first abnormal data in the geographic data of the oil and gas field surface, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after the patrol frequency is adjusted;

[0068] Comparison module: When the inspection detects a second abnormal data in the geographic data of the oil and gas field surface, it compares it with the first abnormal data, obtains the rate of change of the abnormal data based on the comparison result, adjusts the initial inspection dwell time based on the rate of change of the abnormal data, and obtains the adjusted inspection dwell time.

[0069] Management module: Risk prediction is performed by combining the three-dimensional digital twin model with the adjusted patrol frequency and patrol dwell time to obtain the risk prediction value, and then the risk prediction value is processed.

[0070] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0071] This invention proposes a method and system for inspecting the surface safety production of oil and gas fields, which solves the problems of low inspection efficiency, poor inspection effect, and high safety production risk caused by heavy reliance on manual labor in the current surface equipment inspection technology of oil and gas fields.

[0072] This invention utilizes digital modeling of oil and gas field surface equipment to create three-dimensional digital twin models, enabling real-time monitoring and simulation of equipment status, thus overcoming the shortcomings of traditional inspection methods. By employing unmanned aerial vehicles (UAVs) for inspections and combining this with geographical data of the oil and gas field surface to set initial inspection frequencies and dwell times, the efficiency and accuracy of inspections are improved. When the first abnormal data of oil and gas field surface equipment is detected during inspection, the initial inspection frequency is adjusted based on this abnormal data, allowing for timely adjustments to the inspection strategy and enhanced monitoring and handling of abnormal equipment. Acquiring second abnormal data from the equipment and comparing it with the first abnormal data, the rate of change of the abnormal data is obtained based on the comparison results, further adjusting the initial dwell time of the inspection, thereby improving the accuracy and targeting of the inspection. By obtaining risk prediction values, resource allocation can be optimized, production efficiency improved, the probability of safety accidents reduced, and the safe production of oil and gas field surface equipment maximized.

[0073] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0074] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0075] Figure 1 This is a schematic diagram illustrating a patrol method for ensuring safe production on the surface of an oil and gas field, according to an exemplary embodiment.

[0076] Figure 2 This is a schematic diagram of an inspection system for surface safety production in an oil and gas field, according to an exemplary embodiment. Detailed Implementation

[0077] The following description and accompanying drawings fully illustrate specific embodiments of the invention to enable those skilled in the art to practice them. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of embodiments of the invention encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, each embodiment may be referred to individually or collectively with the term "invention," which is merely for convenience and, if more than one invention is disclosed, is not intended to automatically limit the scope of application to any single invention or inventive concept. Relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, without requiring or implying any actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed. The various embodiments in this document are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the structures, products, etc., disclosed in the embodiments, since they correspond to the disclosed parts, the descriptions are relatively simple; relevant details can be found in the method section.

[0078] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0079] This invention combines technologies such as digital modeling, drone patrols, and risk prediction. Through real-time monitoring, anomaly detection, and risk assessment, it improves the efficiency and accuracy of safe production on the surface of oil and gas fields, prevents and detects potential safety hazards early, and maximizes the safety and stability of production, providing important support and protection for surface production in oil and gas fields.

[0080] like Figure 1 As shown, the present invention provides a patrol method for safe production on the surface of an oil and gas field, comprising:

[0081] Step S1: Digitally model the oil and gas field surface equipment and create a three-dimensional digital twin model.

[0082] Digital modeling of oil and gas field surface equipment, creating 3D digital twin models, enables real-time monitoring and simulation of equipment status, improving the overall understanding and predictive capabilities of the equipment. This provides accurate and reliable basic data for subsequent inspections and risk prediction.

[0083] Step S2: Use drones to patrol the surface of the oil and gas field based on the geographical data, and set the initial patrol frequency and initial patrol dwell time.

[0084] Unmanned aerial vehicles (UAVs) are used to inspect surface equipment in oil and gas fields. Based on the geographical data of the oil and gas field (including the field area ΔM and the average equipment spacing ΔH), the initial inspection frequency and initial inspection dwell time are set, including:

[0085] Set the area of ​​the first preset oil and gas field M1, the area of ​​the second preset oil and gas field M2, the area of ​​the third preset oil and gas field M3 and the area of ​​the fourth preset oil and gas field M4, and M1 < M2 < M3 < M4.

[0086] Set a first preset patrol frequency P1, a second preset patrol frequency P2, a third preset patrol frequency P3 and a fourth preset patrol frequency P4, and P1 < P2 < P3 < P4.

[0087] Set a first preset dwell time S1, a second preset dwell time S2, a third preset dwell time S3 and a fourth preset dwell time S4, and S1 < S2 < S3 < S4;

[0088] Based on the relationship between the area ΔM of the oil and gas field and the size of each preset area, the initial patrol frequency and initial dwell time are set.

[0089] When M1≤△M<M2, the initial patrol frequency is set to the first preset patrol frequency P1, and the initial patrol dwell time is set to the first preset dwell time S1.

[0090] When M2≤△M<M3, the initial patrol frequency is set to the second preset patrol frequency P2, and the initial patrol dwell time is set to the second preset dwell time S2.

[0091] When M3≤△M<M4, the initial patrol frequency is set to the third preset patrol frequency P3, and the initial patrol dwell time is set to the third preset dwell time S3.

[0092] When M4≤△M, the initial patrol frequency is set to the fourth preset patrol frequency P4, and the initial patrol dwell time is set to the fourth preset dwell time S4.

[0093] This invention sets the initial patrol frequency and initial patrol dwell time based on the geographical data of the oil and gas field surface, and uses drones to patrol the ground equipment of the oil and gas field to achieve a more accurate and efficient patrol process. In the specific implementation steps, the patrol strategy is matched with the scale and characteristics of the oil and gas field according to the preset oil and gas field area, patrol frequency, and dwell time, thus realizing a targeted and flexible patrol plan.

[0094] The present invention sets an initial inspection frequency and an initial inspection stay time according to the size relationship of the surface area of the oil and gas field, so as to achieve the purpose of adjusting the inspection strategy according to the different scales of the oil and gas field. It can improve the inspection coverage rate and detection accuracy of the surface equipment of the oil and gas field, and reduce the risks of missed inspection and false inspection. According to the preset area and the selection of inspection frequency and stay time, the inspection strategy is flexibly adjusted according to the actual situation, so that the inspection resources can be used more reasonably and efficiently.

[0095] In the above technical solution, after setting the i-th preset inspection frequency Pi and the i-th preset inspection stay time Si as the initial inspection frequency and the initial inspection stay time, i = 1, 2, 3, 4;

[0096] Set a first preset spacing H1, a second preset spacing H2, a third preset spacing H3 and a fourth preset spacing H4, and H1 < H2 < H3 < H4;

[0097] Set a first preset correction coefficient A1, a second preset correction coefficient A2, a third preset correction coefficient A3 and a fourth preset correction coefficient A4, and A1 < A2 < A3 < A4;

[0098] According to the size relationship between the average spacing △H of the equipment and each preset spacing, select the correction coefficient to correct the initial inspection frequency and the initial inspection stay time respectively, and use the corrected inspection frequency and inspection stay time as the initial inspection frequency and the initial inspection stay time;

[0099] When H1 ≤ △H < H2, select the fourth preset correction coefficient A4 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A4 and inspection stay time Si*A4;

[0100] When H2 ≤ △H < H3, select the third preset correction coefficient A3 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A3 and inspection stay time Si*A3;

[0101] When H3 ≤ △H < H4, select the second preset correction coefficient A2 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A2 and inspection stay time Si*A2;

[0102] When H4 ≤ △H, select the first preset correction coefficient A1 to correct the initial inspection frequency Pi and the initial inspection stay time Si respectively, and obtain the corrected inspection frequency Pi*A1 and inspection stay time Si*A1.

[0103] This invention optimizes the inspection strategy by selecting a correction coefficient based on the relationship between the average equipment spacing and a preset spacing, thereby adjusting the initial inspection frequency and initial inspection dwell time. Specifically, the initial inspection frequency and initial inspection dwell time are adjusted according to the preset spacing and the selected correction coefficient to better suit the actual situation and equipment layout requirements. The selection of the correction coefficient, based on the relationship between equipment spacing, reflects the characteristics of the equipment layout. By correcting the inspection frequency and dwell time, the inspection strategy can be flexibly adjusted according to the density and changes in the equipment layout, further improving the targeting and efficiency of inspections. Therefore, this invention effectively improves the surface production inspection process in oil and gas fields, enhancing safety and production efficiency.

[0104] Step S3: When the patrol detects the first abnormal data in the geographic data of the oil and gas field surface, adjust the initial patrol frequency according to the first abnormal data, and continue patrolling after adjusting the patrol frequency.

[0105] Acquire environmental data △Q, set the first preset environmental data Q1, the second preset environmental data Q2, the third preset environmental data Q3 and the fourth preset environmental data Q4, and Q1 < Q2 < Q3 < Q4;

[0106] Set a first preset correction coefficient B1, a second preset correction coefficient B2, a third preset correction coefficient B3 and a fourth preset correction coefficient B4, and B1 < B2 < B3 < B4;

[0107] Based on the relationship between the environmental data △Q and each preset environmental data, a correction coefficient is selected to correct the first abnormal data, and the corrected first abnormal data is obtained.

[0108] When Q1≤△Q<Q2, select the first preset correction coefficient B1 to correct the first abnormal data △J, and obtain the corrected first abnormal data △J*B1.

[0109] When Q2≤△Q<Q3, the second preset correction coefficient B2 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B2 is obtained.

[0110] When Q3≤△Q<Q4, the third preset correction coefficient B3 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B3 is obtained.

[0111] When Q4≤△Q, the fourth preset correction coefficient B4 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B4 is obtained.

[0112] The first abnormal data includes equipment temperature, vibration frequency, or pressure. Environmental data includes ambient temperature, ambient pressure, and ambient vibration frequency. The environmental data is used to correct the acquired first abnormal data to improve the accuracy of the abnormal data and reduce interference caused by the environment.

[0113] This invention, by considering the influence of environmental data such as ambient temperature, pressure, and vibration frequency, more accurately identifies equipment anomalies, avoiding misjudging environmental factors as equipment malfunctions. Based on the relationship between the environmental data and preset environmental data, an appropriate correction coefficient is selected to correct the first anomaly data, further improving the reliability and accuracy of inspections. The corrected first anomaly data more accurately reflects the equipment's condition, helping staff to promptly identify and address potential problems, thereby reducing the risk of malfunctions and accidents during production. By accurately identifying equipment anomalies and taking timely and appropriate maintenance measures, the reliability and safety of equipment are improved, reducing production interruptions and losses.

[0114] In the above technical solution, the first abnormal data △J is corrected by selecting the i-th preset correction coefficient Bi, and after obtaining the corrected first abnormal data △J*Bi, i = 1, 2, 3, 4;

[0115] Set the first preset data J1, the second preset data J2, the third preset data J3 and the fourth preset data J4, and J1 < J2 < J3 < J4;

[0116] Set a first preset adjustment coefficient C1, a second preset adjustment coefficient C2, a third preset adjustment coefficient C3 and a fourth preset adjustment coefficient C4, and C1 < C2 < C3 < C4;

[0117] Based on the relationship between the corrected first abnormal data △J* / Bi and each preset data, an adjustment coefficient is selected to adjust the initial patrol frequency;

[0118] When J1≤△J*Bi<J2, the first preset adjustment coefficient C1 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C1 is obtained.

[0119] When J2≤△J*Bi<J3, the second preset adjustment coefficient C2 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C2 is obtained.

[0120] When J3≤△J*Bi<J4, the third preset adjustment coefficient C3 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C3 is obtained.

[0121] When J4≤△J*Bi, the fourth preset adjustment coefficient C4 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C4 is obtained.

[0122] This invention compares the corrected abnormal data with the preset data based on the selection of preset data and adjustment coefficients. Based on the comparison result, an appropriate adjustment coefficient is selected to adjust the initial patrol frequency. When the corrected abnormal data falls within a certain preset data range, the corresponding adjustment coefficient is selected to adjust the initial patrol frequency, resulting in the adjusted patrol frequency.

[0123] This invention adjusts the initial inspection frequency based on equipment malfunctions to better meet inspection needs, promptly identify and resolve equipment problems, thereby improving production efficiency, reducing production risks, and ensuring the safe and stable operation of oil and gas field surface equipment.

[0124] Step S4: When the patrol detects a second anomaly in the geographic data of the oil and gas field surface, it is compared with the first anomaly. Based on the comparison result, the rate of change of the anomaly data is obtained. The initial patrol dwell time is adjusted according to the rate of change of the anomaly data to obtain the adjusted patrol dwell time.

[0125] The second abnormal data ΔE is obtained, and the rate of change V of the abnormal data is calculated using the following formula:

[0126] V = (△E - △J*Bi) / △J*Bi;

[0127] In this process, if abnormal data is first discovered during an equipment inspection, it is recorded as the first abnormal data. When the inspection frequency is adjusted and a second inspection is conducted, the abnormal data obtained from the same equipment is recorded as the second abnormal data. If an abnormality is detected the first time but not the second time, no data is recorded. If multiple inspections still find no abnormalities, the adjusted inspection frequency is reset to the initial frequency. If the first data is abnormal and the second is also abnormal, the inspection continues according to the established procedure.

[0128] In the above technical solution, abnormal conditions of the equipment are analyzed and the changing trend of abnormal data is determined. By comparing the difference between the first and second abnormal data, the rate of change of the abnormal data is calculated. This rate is used to assess the severity and development trend of the equipment problem. Through the calculation and comparison of the abnormal data change rate, the health status of the equipment and the abnormal development trend can be assessed in a timely manner.

[0129] If the abnormal data from the equipment remains stable or disappears during continuous inspections, the normal inspection frequency should be gradually restored to avoid unnecessary inspection costs and resource waste. However, if the abnormal data persists or its rate of change intensifies, further measures should be taken to strengthen equipment monitoring and maintenance to prevent potential failures and accidents. By analyzing the rate of change of abnormal data, changes in equipment status can be identified in a timely manner, and the inspection frequency can be adjusted accordingly, thereby improving inspection efficiency, reducing resource waste, and providing important information for equipment maintenance and management.

[0130] In the above technical solution, a first preset rate of change V1, a second preset rate of change V2, a third preset rate of change V3 and a fourth preset rate of change V4 are set, and V1 < V2 < V3 < V4.

[0131] Set a first preset time adjustment coefficient T1, a second preset time adjustment coefficient T2, a third preset time adjustment coefficient T3 and a fourth preset time adjustment coefficient T4, and T1 < T2 < T3 < T4;

[0132] Based on the relationship between the abnormal data change rate V and each preset change rate, a preset time adjustment coefficient is selected to adjust the initial patrol dwell time Si*Ai, and the adjusted patrol dwell time is obtained.

[0133] When V1≤V<V2, the initial patrol dwell time Si*Ai is adjusted by the first preset time adjustment coefficient T1, and the adjusted patrol dwell time Si*Ai*T1 is obtained.

[0134] When V2≤V<V3, the initial patrol dwell time Si*Ai is adjusted by the second preset time adjustment coefficient T2, and the adjusted patrol dwell time Si*Ai*T2 is obtained.

[0135] When V3≤V<V4, the initial patrol dwell time Si*Ai is adjusted by the third preset time adjustment coefficient T3, and the adjusted patrol dwell time Si*Ai*T3 is obtained.

[0136] When V4≤V, the initial patrol dwell time Si*Ai is adjusted by selecting the fourth preset time adjustment coefficient T4, and the adjusted patrol dwell time Si*Ai*T4 is obtained.

[0137] This invention adjusts the initial inspection dwell time based on the rate of change of abnormal data, thus matching the inspection dwell time with changes in equipment status. When the abnormal data changes rapidly, the adjusted inspection dwell time can be longer to more thoroughly monitor the equipment's condition; conversely, when the abnormal data changes slowly, the adjusted inspection dwell time can be shorter, reducing unnecessary inspection time. This method of adjusting inspection dwell time makes inspections more flexible and efficient, dynamically adjusting the inspection strategy according to the actual situation of the equipment and changes in abnormal data. By matching the rate of change of abnormal data, the accuracy and timeliness of inspections are improved, enabling better detection and resolution of equipment anomalies, thereby enhancing equipment stability and safety. Simultaneously, it avoids excessively long inspection dwell times when the equipment has no anomalies or when anomalies are changing slowly, saving resources and costs.

[0138] Step S5: Risk prediction is performed using the three-dimensional digital twin model combined with the adjusted patrol frequency and patrol dwell time to obtain a risk prediction value, and then processed based on the risk prediction value.

[0139] The risk prediction value is calculated using the following formula:

[0140] R=w1*(k1*x+k2*y)+w2*(a*exp(-b*x)+c*exp(-d*y))

[0141] Where k1 and k2 are weighting coefficients; x represents the drone patrol frequency; y represents the patrol dwell time; and a, b, c, and d are constant parameters used to adjust the degree of influence of drone patrol frequency and patrol dwell time on equipment anomaly frequency.

[0142] Assuming the risk level R ranges from [0,1], representing the degree of risk, where 0 indicates no risk and 1 indicates high risk. The probability of a safety accident is specifically represented as follows: the probability of a safety accident is positively correlated with the drone patrol frequency x and patrol dwell time y, expressed as a linear function: P = k1*x + k2*y, where k1 and k2 are weighting coefficients, representing the degree of influence of drone patrol frequency and patrol dwell time on the probability of a safety accident. The frequency of equipment anomalies is specifically represented as follows: the frequency of equipment anomalies is inversely proportional to the drone patrol frequency x and patrol dwell time y, expressed as an exponential function: E = a*exp(-b*x) + c*exp(-d*y), where a, b, c, and d are constants, representing the degree of influence of drone patrol frequency and patrol dwell time on the frequency of equipment anomalies. The specific representation of equipment anomaly frequency: Equipment anomaly frequency is inversely proportional to the drone patrol frequency x and patrol dwell time y, and is represented by an exponential function: E=a*exp(-b*x)+c*exp(-d*y), where a, b, c, and d are constants, representing the degree of influence of drone patrol frequency and patrol dwell time on equipment anomaly frequency.

[0143] This invention utilizes a three-dimensional digital twin model combined with adjusted patrol frequency and patrol dwell time for risk prediction, enabling accurate assessment of the risk level of oil and gas field surface equipment. Based on the risk prediction values, corresponding management measures are taken, including strengthening equipment maintenance, optimizing resource allocation, and adjusting patrol strategies, thereby reducing the probability of safety accidents and ensuring safe production in oil and gas fields.

[0144] This invention increases the frequency of equipment status monitoring and inspection by employing higher drone patrol frequencies and longer patrol stay times, thereby increasing the chances of detecting anomalies. When drone patrol frequencies and stay times increase, it is more likely to promptly identify potential equipment problems and risk factors, thus reducing the probability of safety accidents. Higher drone patrol frequencies and longer stay times allow for more frequent monitoring and inspection of equipment, enabling timely detection of anomalies and reducing the frequency of abnormal events. Increased drone patrol frequencies and stay times provide more opportunities to capture equipment anomalies, reducing the frequency of equipment anomalies. Therefore, in the risk prediction formula, the probability of safety accidents is set to be positively correlated with drone patrol parameters, while the frequency of equipment anomalies is set to be inversely proportional to drone patrol parameters. This setting better reflects the positive role of drone patrols in the safe production of oil and gas fields.

[0145] In conjunction with the above embodiments, the specific formula for calculating the risk prediction value is as follows:

[0146] R=w1*[k1*(Pi*Ai*Ci)+k2*(Si*Ai*Ti)]+w2*[a*exp(-b*Pi*Ai*Ci)+c*exp(-d*Si*Ai*Ti)].

[0147] Combining adjusted patrol frequency and dwell time for risk prediction better reflects the positive role of UAV patrols in ensuring safe production on the oil and gas field surface. By considering patrol frequency and dwell time, the degree of risk is quantitatively assessed, providing a basis for decision-making. Through digital twin models and parameter adjustments, the accuracy and reliability of risk prediction are improved, helping decision-makers better understand and manage risks, take appropriate measures to reduce potential safety risks, and ensure safe production on the oil and gas field surface.

[0148] The above technical solution involves obtaining a risk prediction value and taking management measures based on that value, including setting a risk prediction threshold R0. When 0 ≤ R < 0.3R0, a blue warning is issued, notifying staff to pay attention to the abnormal equipment; when 0.3R0 ≤ R < 0.6R0, a yellow warning is issued, requiring close inspection during equipment shutdown and maintenance; when 0.6R0 ≤ R, a red warning is issued, immediately stopping the abnormal equipment and carrying out maintenance.

[0149] According to the management measures given in the embodiments of the present invention, a risk prediction threshold R0 is set; when the risk prediction value R falls within different ranges, corresponding early warnings and operational guidance are taken; when 0≤R<0.3R0, a blue warning is issued, notifying staff to pay attention to the abnormal equipment; this indicates that the risk prediction value is in a low range, but there is still a certain degree of risk. By issuing a blue warning, staff are promptly informed that they need to pay attention to the abnormal equipment so as to further monitor and take necessary measures; when 0.3R0≤R<0.6R0, a yellow warning is issued, and the abnormal equipment should be inspected during shutdown maintenance. This indicates that the risk prediction value is in a medium range and there is a certain risk; by issuing a yellow warning, it is emphasized that a key inspection is needed during the shutdown maintenance of the abnormal equipment to ensure that the problem is handled and repaired in a timely manner; when 0.6R0≤R, a red warning is issued, and the abnormal equipment is immediately stopped and maintenance is carried out; this indicates that the risk prediction value is in a high-risk range and there is a serious risk. By issuing red alerts, the operation of malfunctioning equipment is immediately halted, and necessary inspections and maintenance are carried out to avoid potential accidents and losses. These management measures, based on risk prediction value classification and early warning, help staff understand the current level of risk and take appropriate actions according to different warning levels. Through early warnings and operational guidance, potential risks and problems are identified in a timely manner, and necessary measures are taken to reduce risks and prevent accidents. Therefore, this invention, through risk prediction value classification, early warning, and operational guidance, helps staff take timely and appropriate measures to address risks, minimizing potential safety risks and the likelihood of accidents, and ensuring safe production on the oil and gas field surface.

[0150] like Figure 2 As shown, based on another objective, the present invention also provides an inspection system for safe production on the surface of oil and gas fields, comprising:

[0151] Data Acquisition Module: Digitally models the surface equipment of the oil and gas field, creating a three-dimensional digital twin model; conducts inspections based on the geographical data of the oil and gas field surface, and sets the initial inspection frequency and initial inspection dwell time.

[0152] Adjustment module: When the patrol detects the first abnormal data in the geographical data of the oil and gas field surface, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after the patrol frequency is adjusted.

[0153] Comparison module: When the inspection detects a second abnormal data in the geographic data of the oil and gas field surface, it compares it with the first abnormal data, obtains the rate of change of the abnormal data based on the comparison result, adjusts the initial inspection dwell time based on the rate of change of the abnormal data, and obtains the adjusted inspection dwell time.

[0154] Management module: Risk prediction is performed by combining the three-dimensional digital twin model with the adjusted patrol frequency and patrol dwell time to obtain the risk prediction value, and then the risk prediction value is processed.

[0155] It should be understood that the foregoing description of specific exemplary embodiments of the invention has been presented for purposes of illustration and description. It is not intended to exclude or limit the invention to the precise forms disclosed, and it is obvious that many modifications and variations are possible in light of the above teachings. Exemplary embodiments were chosen and described to explain certain principles of the invention and their practical application, so that others skilled in the art can make or utilize various exemplary embodiments of the invention, and their various alternatives and modifications. The purpose is that the scope of the invention will be defined by the appended claims and their equivalents.

[0156] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A patrol method for safe production on the surface of an oil and gas field, characterized in that, include: (1) Digital modeling of oil and gas field surface equipment to create a three-dimensional digital twin model; (2) Conduct patrols based on the geographical data of the oil and gas field surface, and set the initial patrol frequency and initial patrol dwell time; (3) When the patrol detects the first abnormal data in the geographical data of the oil and gas field, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after the patrol frequency is adjusted. (4) When the inspection detects a second abnormal data in the geographic data of the oil and gas field, it is compared with the first abnormal data. The rate of change of the abnormal data is obtained based on the comparison result. The initial inspection dwell time is adjusted according to the rate of change of the abnormal data, and the adjusted inspection dwell time is obtained. (5) Risk prediction is performed using the three-dimensional digital twin model combined with the adjusted patrol frequency and patrol dwell time to obtain a risk prediction value. The risk prediction value is then processed and calculated using the following formula: R=w1*(k1*x+k2*y)+w2*(a*exp(-b*x)+c*exp(-d*y)); Where k1 and k2 are weighting coefficients; x represents the drone patrol frequency; y represents the patrol dwell time; and a, b, c, and d are constant parameters used to adjust the degree of influence of drone patrol frequency and patrol dwell time on equipment anomaly frequency.

2. The patrol method for safe production on the surface of an oil and gas field according to claim 1, characterized in that, In step (2), drones are used to inspect the ground equipment of the oil and gas field. The geographical data of the oil and gas field includes the area of ​​the oil and gas field △M and the average spacing between the equipment △H.

3. The patrol method for safe production on the surface of an oil and gas field according to claim 2, characterized in that, Step (2) includes (2-1) Set the first preset oil and gas field area M1, the second preset oil and gas field area M2, the third preset oil and gas field area M3 and the fourth preset oil and gas field area M4, and M1 < M2 < M3 < M4. (2-2) Set the first preset patrol frequency P1, the second preset patrol frequency P2, the third preset patrol frequency P3 and the fourth preset patrol frequency P4, and P1 < P2 < P3 < P4. (2-3) Set the first preset dwell time S1, the second preset dwell time S2, the third preset dwell time S3 and the fourth preset dwell time S4, and S1 < S2 < S3 < S4; (2-4) Based on the relationship between the area ΔM of the oil and gas field and each preset area, set the initial patrol frequency and initial dwell time; (2-5) When M1≤△M<M2, the initial patrol frequency is set to the first preset patrol frequency P1, and the initial patrol dwell time is set to the first preset dwell time S1. (2-6) When M2≤△M<M3, the initial patrol frequency is set to the second preset patrol frequency P2, and the initial patrol dwell time is set to the second preset dwell time S2. (2-7) When M3≤△M<M4, the initial patrol frequency is set to the third preset patrol frequency P3, and the initial patrol dwell time is set to the third preset dwell time S3. (2-8) When M4≤△M, the initial patrol frequency is set to the fourth preset patrol frequency P4, and the initial patrol dwell time is set to the fourth preset dwell time S4.

4. The patrol method for safe production on the surface of an oil and gas field according to claim 3, characterized in that, After setting the i-th preset patrol frequency Pi and the i-th preset patrol dwell time Si as the initial patrol frequency and initial patrol dwell time, i = 1, 2, 3, 4, the initial patrol frequency and initial patrol dwell time are set according to the geographical data of the oil and gas field surface, including: (2-9) Set the first preset spacing H1, the second preset spacing H2, the third preset spacing H3 and the fourth preset spacing H4, and H1 < H2 < H3 < H4; (2-10) Set the first preset correction coefficient A1, the second preset correction coefficient A2, the third preset correction coefficient A3 and the fourth preset correction coefficient A4, and A1 < A2 < A3 < A4; (2-11) Based on the relationship between the average equipment spacing △H and each preset spacing, select correction coefficients to correct the initial patrol frequency and initial patrol dwell time respectively, and use the corrected patrol frequency and patrol dwell time as the initial patrol frequency and initial patrol dwell time; (2-12) When H1≤△H<H2, the fourth preset correction coefficient A4 is selected to correct the initial patrol frequency Pi and the initial patrol dwell time Si respectively, and the corrected patrol frequency Pi*A4 and patrol dwell time Si*A4 are obtained. (2-13) When H2≤△H<H3, the third preset correction coefficient A3 is selected to correct the initial patrol frequency Pi and the initial patrol dwell time Si respectively, and the corrected patrol frequency Pi*A3 and patrol dwell time Si*A3 are obtained. (2-14) When H3≤△H<H4, select the second preset correction coefficient A2 to correct the initial patrol frequency Pi and the initial patrol dwell time Si respectively, and obtain the corrected patrol frequency Pi*A2 and patrol dwell time Si*A2; (2-15) When H4≤△H, the first preset correction coefficient A1 is selected to correct the initial patrol frequency Pi and the initial patrol dwell time Si respectively, and the corrected patrol frequency Pi*A1 and patrol dwell time Si*A1 are obtained.

5. The patrol method for safe production on the surface of an oil and gas field according to claim 1, characterized in that, Step (3) includes: (3-1) Obtain environmental data △Q, set the first preset environmental data Q1, the second preset environmental data Q2, the third preset environmental data Q3 and the fourth preset environmental data Q4, and Q1 < Q2 < Q3 < Q4; (3-2) Set the first preset correction coefficient B1, the second preset correction coefficient B2, the third preset correction coefficient B3 and the fourth preset correction coefficient B4, and B1 < B2 < B3 < B4; (3-3) Based on the relationship between the environmental data △Q and each preset environmental data, a correction coefficient is selected to correct the first abnormal data, and the corrected first abnormal data is obtained. (3-4) When Q1≤△Q<Q2, select the first preset correction coefficient B1 to correct the first abnormal data △J, and obtain the corrected first abnormal data △J*B1; (3-5) When Q2≤△Q<Q3, select the second preset correction coefficient B2 to correct the first abnormal data △J, and obtain the corrected first abnormal data △J*B2; (3-6) When Q3≤△Q<Q4, the third preset correction coefficient B3 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B3 is obtained. (3-7) When Q4≤△Q, the fourth preset correction coefficient B4 is selected to correct the first abnormal data △J, and the corrected first abnormal data △J*B4 is obtained.

6. The patrol method for safe production on the surface of an oil and gas field according to claim 5, characterized in that, After selecting the i-th preset correction coefficient Bi to correct the first abnormal data △J, and obtaining the corrected first abnormal data △J*Bi, where i = 1, 2, 3, 4, when the patrol detects the first abnormal data in the geographical data of the oil and gas field surface, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after adjusting the patrol frequency, including... (3-8) Set the first preset data J1, the second preset data J2, the third preset data J3 and the fourth preset data J4, and J1 < J2 < J3 < J4; (3-9) Set the first preset adjustment coefficient C1, the second preset adjustment coefficient C2, the third preset adjustment coefficient C3 and the fourth preset adjustment coefficient C4, and C1 < C2 < C3 < C4; (3-10) Based on the relationship between the corrected first abnormal data △J*Bi and each preset data, select an adjustment coefficient to adjust the initial patrol frequency; (3-11) When J1≤△J*Bi<J2, select the first preset adjustment coefficient C1 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C1; (3-12) When J2≤△J*Bi<J3, select the second preset adjustment coefficient C2 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C2; (3-13) When J3≤△J*Bi<J4, select the third preset adjustment coefficient C3 to adjust the initial patrol frequency Pi*Ai, and obtain the adjusted patrol frequency Pi*Ai*C3; (3-14) When J4≤△J*Bi, the fourth preset adjustment coefficient C4 is selected to adjust the initial patrol frequency Pi*Ai, and the adjusted patrol frequency Pi*Ai*C4 is obtained.

7. The patrol method for safe production on the surface of an oil and gas field according to claim 1, characterized in that, Step (4) includes: obtaining the second abnormal data △E, wherein the rate of change V of the abnormal data is calculated by the following formula: V=(△E-△J*Bi) / △J*Bi.

8. The patrol method for safe production on the surface of an oil and gas field according to claim 7, characterized in that, The step of adjusting the initial patrol dwell time based on the rate of change of the abnormal data to obtain the adjusted patrol dwell time includes: (4-1) Set a first preset rate of change V1, a second preset rate of change V2, a third preset rate of change V3 and a fourth preset rate of change V4, and V1 < V2 < V3 < V4; (4-2) Set the first preset time adjustment coefficient T1, the second preset time adjustment coefficient T2, the third preset time adjustment coefficient T3 and the fourth preset time adjustment coefficient T4, and T1 < T2 < T3 < T4; (4-3) Based on the relationship between the abnormal data change rate V and each preset change rate, select a preset time adjustment coefficient to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time; (4-4) When V1≤V<V2, select the first preset time adjustment coefficient T1 to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time Si*Ai*T1; (4-5) When V2≤V<V3, select the second preset time adjustment coefficient T2 to adjust the initial patrol dwell time Si*Ai, and obtain the adjusted patrol dwell time Si*Ai*T2; (4-6) When V3≤V<V4, the third preset time adjustment coefficient T3 is selected to adjust the initial patrol dwell time Si*Ai, and the adjusted patrol dwell time Si*Ai*T3 is obtained; (4-7) When V4≤V, the fourth preset time adjustment coefficient T4 is selected to adjust the initial patrol dwell time Si*Ai, and the adjusted patrol dwell time Si*Ai*T4 is obtained.

9. The patrol method for safe production on the surface of an oil and gas field according to claim 1, characterized in that, Obtaining risk prediction values ​​and processing them accordingly includes: Set a risk prediction threshold R0; When 0 ≤ R < 0.3R0, a blue warning is issued, notifying staff to pay attention to the abnormal equipment; When 0.3R0≤R<0.6R0, a yellow warning is issued, and special attention should be paid to checking when abnormal equipment is shut down for maintenance; When 0.6R0≤R, a red warning is issued, and the abnormal equipment is immediately stopped and repaired.

10. A patrol system for safe production on the surface of an oil and gas field, characterized in that, The system includes Data Acquisition Module: Used for digital modeling of oil and gas field surface equipment, creating three-dimensional digital twin models, and for inspecting the geographical data of the oil and gas field surface, and setting the initial inspection frequency and initial inspection dwell time; Adjustment module: When the patrol detects the first abnormal data in the geographic data of the oil and gas field surface, the initial patrol frequency is adjusted according to the first abnormal data, and the patrol continues after the patrol frequency is adjusted; Comparison module: When the inspection detects a second abnormal data in the geographic data of the oil and gas field surface, it compares it with the first abnormal data, obtains the rate of change of the abnormal data based on the comparison result, adjusts the initial inspection dwell time based on the rate of change of the abnormal data, and obtains the adjusted inspection dwell time. Management Module: Risk prediction is performed using the 3D digital twin model combined with adjusted patrol frequency and patrol dwell time to obtain a risk prediction value. This risk prediction value is then processed using the following formula: R=w1*(k1*x+k2*y)+w2*(a*exp(-b*x)+c*exp(-d*y)); Where k1 and k2 are weighting coefficients; x represents the drone patrol frequency; y represents the patrol dwell time; and a, b, c, and d are constant parameters used to adjust the degree of influence of drone patrol frequency and patrol dwell time on equipment anomaly frequency.

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