An AI-based safety production regulation method and system

Abstract

The present invention provides an AI-based safety production regulation method and system. The method includes collecting in real time the pressure data of the wellhead and pipeline through a pressure sensor, and adjusting the pressure of the wellhead and pipeline according to the deviation between the pressure data of the wellhead and pipeline and the production requirement indicators; when the deviation between the pressure data of the wellhead and pipeline and the production requirement indicators still cannot meet the preset deviation requirements, a safety warning is issued; collecting in real time the vibration operation parameters of each production device through a sensor group, and performing safety regulation on the operation of the device according to the vibration operation parameters of each production device; when the vibration operation of each production device still cannot meet its corresponding vibration operation standard, a safety warning is issued for the production device whose vibration operation still cannot meet its corresponding vibration operation standard. The system includes modules corresponding to the method steps.

Description

Technical Field

[0001] The present invention provides a safety production regulation method and system based on AI, belonging to the technical field of safety production monitoring. Background Art

[0002] Oil extraction is a complex and high-risk industry. Its production process involves numerous links and equipment, and any mistake in any link may lead to safety accidents. Traditional safety production regulation methods for oil extraction mainly rely on manual monitoring and simple sensor monitoring. These methods have many limitations and cannot meet the high requirements for safety production in the current oil extraction industry. Existing methods for regulating the pressure of wellheads and pipelines mainly rely on data collected by pressure sensors, but often can only achieve simple pressure regulation and cannot perform precise regulation according to production requirement indicators. When the deviation between the pressure data and the production requirement indicators exceeds the preset range, existing technologies often can only give simple alarms and lack intelligent early warning and regulation mechanisms. At the same time, existing methods for monitoring the vibration operation parameters of production equipment mainly rely on data collected by vibration sensors, but often can only achieve data collection and storage and cannot perform real-time analysis and early warning on the operation status of the equipment. When the vibration operation parameters of the equipment exceed the standard range, existing technologies often can only perform after-treatment and cannot give safety early warnings and regulations in advance.

[0003] In view of the limitations of traditional safety production regulation methods and existing technologies, the present invention provides a safety production regulation method for oil extraction based on AI. By introducing artificial intelligence technology, this method realizes the real-time collection, intelligent analysis, and precise regulation of key data such as the pressure of wellheads and pipelines and the vibration operation parameters of production equipment. Summary of the Invention

[0004] The present invention provides a safety production regulation method and system based on AI to solve the technical problems existing in the above-mentioned existing technologies. The technical solutions adopted are as follows:

[0005] A safety production regulation method based on AI, the safety production regulation method based on AI includes:

[0006] Real-time collection of the pressure data of wellheads and pipelines through pressure sensors, and adjustment of the pressure of wellheads and pipelines according to the deviation between the pressure data of wellheads and pipelines and the production requirement indicators;

[0007] After the pressure of wellheads and pipelines is regulated, when the deviation between the pressure data of wellheads and pipelines and the production requirement indicators still cannot meet the preset deviation requirements, safety early warning is carried out;

[0008] Vibration operation parameters of each production device are collected in real time through a sensor group, and the operation of the device is safely regulated according to the vibration operation parameters of each production device;

[0009] After the safety regulation of each production device is completed, when the vibration operation of each production device still cannot meet its corresponding vibration operation standard, a safety warning is given to the production device whose vibration operation still cannot meet its corresponding vibration operation standard.

[0010] Furthermore, pressure data of the wellhead and pipeline are collected in real time through pressure sensors, and the pressure of the wellhead and pipeline is adjusted according to the deviation between the pressure data of the wellhead and pipeline and the production requirement index, including:

[0011] A first pressure sensor is installed at each of multiple pressure monitoring positions of the wellhead;

[0012] A second pressure sensor is installed at each of multiple pressure detection node positions of the pipeline;

[0013] The pressure data of the wellhead is collected in real time by using the first pressure sensor, and the pressure of the wellhead is regulated according to the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index;

[0014] The pressure data of the pipeline is collected in real time by using the second pressure sensor, and the pressure of the pipeline is regulated according to the deviation between the pressure data of the pipeline and its corresponding pipeline pressure production requirement index.

[0015] Furthermore, the pressure data of the wellhead is collected in real time by using the first pressure sensor, and the pressure of the wellhead is regulated according to the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index, including:

[0016] Extract the pressure data of the wellhead collected in real time by each of the first pressure sensors;

[0017] Compare the deviation between the pressure data of each wellhead and its corresponding wellhead pressure production requirement index with a first pressure deviation threshold and a second pressure deviation threshold;

[0018] When the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index exceeds the preset first pressure deviation threshold but does not exceed the second pressure deviation threshold, the historical pressure data collected by the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold is retrieved;

[0019] Obtain a pressure deviation evaluation coefficient by using the historical pressure data collected by the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold;

[0020] Compare the pressure deviation evaluation coefficient with a preset pressure deviation evaluation threshold value;

[0021] When the pressure deviation evaluation coefficient exceeds the preset pressure deviation evaluation threshold value, adjust the pressure of the wellhead by increasing or reducing the pressure according to the deviation between the pressure data of the wellhead exceeding the preset first pressure deviation threshold value and its corresponding wellhead pressure production requirement index, so as to reduce the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index;

[0022] When the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index exceeds the preset second pressure deviation threshold value, adjust the pressure of the wellhead by increasing or reducing the pressure according to the deviation between the pressure data of the wellhead exceeding the preset second pressure deviation threshold value and its corresponding wellhead pressure production requirement index, so as to reduce the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement index.

[0023] Further, obtaining the pressure deviation evaluation coefficient by using the historical pressure data collected by the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold value includes:

[0024] When the number of first pressure sensors corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold value is one, use the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold value as the target pressure sensor, and retrieve the historical pressure data collected by the target pressure sensor;

[0025] Obtain the pressure deviation evaluation coefficient by using the first evaluation coefficient model in combination with the historical pressure data collected by the target pressure sensor; wherein, the pressure deviation evaluation coefficient is obtained through the following formula:

[0026]

[0027] Wherein, S 01 represents the pressure deviation evaluation coefficient obtained by the first evaluation coefficient model; a, b, and c respectively represent the preset first adjustment parameter, second adjustment parameter, and third adjustment parameter, and the value ranges of the first adjustment parameter, second adjustment parameter, and third adjustment parameter are 0.58 - 1.13, 0.89 - 1.22, and 1.14 - 1.39; P b represents the pressure standard deviation corresponding to the historical pressure data collected by the target pressure sensor; n represents the number of pressure data of the historical pressure data; P i represents the pressure data value corresponding to the i-th historical pressure data; P i-1represents the pressure data value corresponding to the (i - 1)-th historical pressure data; P be represents the preset reference value of the force standard deviation; P fmaxi represents the maximum value of the pressure change amplitude at the adjacent two pressure data acquisition moments corresponding to the i-th historical pressure data; P fmini represents the minimum value of the pressure change amplitude at the adjacent two pressure data acquisition moments corresponding to the i-th historical pressure data;

[0028] When there are multiple first pressure sensors corresponding to the pressure data of the wellhead that exceed the preset first pressure deviation threshold, the first pressure sensors corresponding to the pressure data of the wellhead that exceed the preset first pressure deviation threshold are used as target pressure sensors, and the historical pressure data collected by multiple said target pressure sensors is retrieved;

[0029] Using the second evaluation coefficient model and combining the historical pressure data collected by multiple said target pressure sensors to obtain a pressure deviation evaluation coefficient; wherein, the pressure deviation evaluation coefficient is obtained through the following formula:

[0030]

[0031] wherein, S 02 represents the pressure deviation evaluation coefficient obtained by the second evaluation coefficient model; m represents the number of target pressure sensors; P bi represents the standard deviation of the pressure data corresponding to the historical pressure data of the i-th target pressure sensor; G i represents the average value of the correlation coefficients between the i-th target pressure sensor and other target pressure sensors; w i represents the weight value corresponding to the i-th target pressure sensor; P fmax01i represents the maximum value of the pressure change amplitude corresponding to the i-th target pressure sensor; P fmax02i represents the maximum value of the pressure change amplitude of the target pressure sensor corresponding to the maximum correlation coefficient with the i-th target pressure sensor.

[0032] Furthermore, using the second pressure sensor to collect the pressure data of the pipeline in real time, and regulating the pressure of the pipeline according to the deviation between the pressure data of the pipeline and the corresponding pipeline pressure production requirement index, including:

[0033] Retrieving in real time the pressure data of the pipeline collected by the second pressure sensor;

[0034] Comparing the pressure data of each pipeline with its corresponding pressure risk threshold and pressure warning threshold;

[0035] When the pressure data of the pipeline exceeds its corresponding pressure warning threshold, directly adjust the pipeline valve corresponding to the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure warning threshold to reduce the pressure value corresponding to the pressure detection node position of the pipeline;

[0036] When the pressure data of the pipeline exceeds its corresponding pressure risk threshold, screen out the target pressure detection node positions from the pressure detection node positions of all pipelines, obtain the pressure risk evaluation coefficient using the target pressure data corresponding to the target pressure detection node positions, and perform pressure regulation determination for the pressure detection node positions through the pressure risk evaluation coefficient.

[0037] Further, when the pressure data of the pipeline exceeds its corresponding pressure risk threshold, screen out the target pressure detection node positions from the pressure detection node positions of all pipelines, obtain the pressure risk evaluation coefficient using the target pressure data corresponding to the target pressure detection node positions, and perform pressure regulation determination for the pressure detection node positions through the pressure risk evaluation coefficient, including:

[0038] When the pressure data of the pipeline exceeds its corresponding pressure risk threshold, retrieve the correlation coefficient between the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure risk threshold and the pressure detection node positions of other pipelines;

[0039] Retrieve the pressure detection node positions of other pipelines whose correlation coefficient with the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure risk threshold exceeds the preset correlation coefficient threshold as the target pressure detection node positions, and use the pressure data corresponding to the target pressure detection node positions as the target pressure data;

[0040] Obtain the pressure risk evaluation coefficient using the pressure data of the pipeline corresponding to the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure risk threshold and its corresponding target pressure data;

[0041] Among them, the pressure risk evaluation coefficient is obtained through the following formula:

[0042]

[0043] Among them, K represents the pressure risk evaluation coefficient; k represents the number of target pressure detection node positions; P g represents the pressure data of the pipeline corresponding to the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure risk threshold; P gi represents the target pressure data corresponding to the i-th target pressure detection node position; F iThe correlation coefficient between the pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds its corresponding pressure risk threshold and the position of the i-th target pressure detection node; δ represents a preset time decay coefficient; t i represents the total duration of pressure data acquisition corresponding to the position of the i-th target pressure detection node; λ 01 and λ 02 respectively represent the first risk factor and the second risk factor, and the first risk factor is obtained through the following formula:

[0044]

[0045] where λ 01 represents the first risk factor; F i represents the correlation coefficient between the pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds its corresponding pressure risk threshold and the position of the i-th target pressure detection node; F y represents a preset correlation coefficient threshold; F max represents the maximum value of the correlation coefficients corresponding to the positions of n target pressure detection nodes and the pressure detection node positions of the pipelines indicating that the pressure data of the pipelines exceed their corresponding pressure risk thresholds;

[0046] And the second risk factor is obtained through the following formula:

[0047]

[0048] where λ 02 represents the second risk factor; P gb represents the standard deviation of the pressure data corresponding to the pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds its corresponding pressure risk threshold; P gbi represents the standard deviation of the pressure data corresponding to the position of the i-th target pressure detection node; F i represents the correlation coefficient between the pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds its corresponding pressure risk threshold and the position of the i-th target pressure detection node;

[0049] Compare the pressure risk evaluation coefficient with a preset pressure risk evaluation threshold;

[0050] When the pressure risk evaluation coefficient exceeds the preset pressure risk evaluation threshold, the pipeline valve corresponding to the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure warning threshold is adjusted to reduce the pressure value corresponding to the pressure detection node position of the pipeline.

[0051] Further, vibration operation parameters of each production device are collected in real time by a sensor group, and safety regulation of the device operation is performed according to the vibration operation parameters of each production device, including:

[0052] Vibration operation parameters of each production device are collected in real time by a sensor group, where the sensor group includes an acceleration sensor, a velocity sensor, and a displacement sensor, and the vibration operation parameters include vibration acceleration, vibration velocity, and vibration displacement generated by vibration;

[0053] The vibration acceleration, vibration velocity, and vibration displacement generated by vibration of each production device are respectively compared with their corresponding acceleration threshold, velocity threshold, and vibration displacement threshold;

[0054] When any one of the vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold, it is determined whether safety regulation of the production device is required;

[0055] When it is determined that safety regulation of the production device is required, deceleration regulation of the production device is performed.

[0056] Further, when any one of the vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold, it is determined whether safety regulation of the production device is required, including:

[0057] When any one of the vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold, the corresponding moment when any one of the vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold is used as the data retrieval moment;

[0058] From the data retrieval moment, vibration operation parameters of the production device are retrieved in real time; where the vibration operation parameters include vibration acceleration, vibration velocity, and vibration displacement generated by vibration;

[0059] Vibration operation evaluation coefficients are obtained by using the vibration operation parameters of the production device;

[0060] Among them, the vibration operation evaluation coefficient is obtained by the following formula:

[0061]

[0062] Among them, U represents the vibration operation evaluation coefficient; A, V, and D respectively represent the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; RMS(A, V, D) represents the sum of the square root values of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; MAD(A, V, D) represents the sum of the mean absolute deviation values of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; STD(A, V, D) represents the sum of the standard deviations of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; Var(A, V, D) represents the sum of the variances of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; K b represents a preset basic adjustment coefficient, and the value range of the basic adjustment coefficient is 1.32 - 1.47; σ represents a preset scaling coefficient, and the value range of the scaling coefficient is 0.18 - 0.53; ε represents a preset minimum constant used to prevent the denominator from being zero;

[0063] Compare the vibration operation evaluation coefficient with a preset vibration operation evaluation coefficient threshold;

[0064] When the vibration operation evaluation coefficient exceeds the preset vibration operation evaluation coefficient, it is determined that safety regulation of the production equipment is required.

[0065] Furthermore, when it is determined that safety regulation of the production equipment is required, deceleration regulation of the production equipment is performed, including:

[0066] When it is determined that safety regulation of the production equipment is required, retrieve the vibration operation evaluation coefficient;

[0067] Use the vibration operation evaluation coefficient to obtain the deceleration regulation ratio of the production equipment, and perform deceleration regulation on the production equipment according to the deceleration regulation ratio of the production equipment;

[0068] Among them, the deceleration regulation ratio of the production equipment is obtained through the following formula:

[0069]

[0070] Among them, B represents the deceleration regulation ratio of the production equipment; r represents a preset ratio adjustment coefficient, and the value range of the ratio adjustment coefficient is 0.27 - 0.34; U represents the vibration operation evaluation coefficient.

[0071] An AI-based safety production regulation system, the AI-based safety production regulation system includes: The AI-based safety production regulation system includes:

[0072] A first adjustment module for real-time collecting the pressure data of the wellhead and pipeline through a pressure sensor, and adjusting the pressure of the wellhead and pipeline according to the deviation between the pressure data of the wellhead and pipeline and the production requirement index;

[0073] The first safety warning module is used to give a safety warning when, after the pressure of the wellhead and the pipeline is regulated, the deviation between the pressure data of the wellhead and the pipeline and the production requirement index still cannot meet the preset deviation requirement.

[0074] The second adjustment module is used to collect the vibration operation parameters of each production device in real time through a sensor group, and perform safety regulation on the device operation according to the vibration operation parameters of each production device.

[0075] The second safety warning module is used to give a safety warning to the production device whose vibration operation still cannot meet the corresponding vibration operation standard when, after the safety regulation of each production device is completed, the vibration operation of each production device still cannot meet the corresponding vibration operation standard.

[0076] Advantages of the present invention:

[0077] A safety production regulation method and system based on AI proposed by the present invention can, by collecting and analyzing the operation data of the wellhead, pipeline and production devices in real time, enable the AI system to timely discover and handle potential safety hazards, thereby greatly reducing the probability of safety accidents. The AI system can accurately regulate the pressure of the wellhead and the pipeline according to the production requirement index to ensure the stability and continuity of the production process. At the same time, by regulating the vibration operation parameters of the production devices in real time, it is possible to avoid the shutdown or damage of the devices caused by abnormal vibration, thereby improving the utilization rate and production efficiency of the devices. Through the intelligent safety production regulation method, enterprises can reduce the losses and downtime caused by safety accidents and equipment failures. At the same time, the application of the AI system can also help enterprises optimize resource allocation and reduce maintenance costs. The AI system can provide key production data and analysis results in real time, providing a scientific basis for the decision-making of enterprises. This helps enterprises quickly respond to market changes and changes in production requirements, and thus formulate more reasonable production plans and strategies. Description of the drawings

[0078] Figure 1 is a flowchart of the method described in the present invention;

[0079] Figure 2 is a system block diagram of the system described in the present invention. Detailed implementation manners

[0080] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0081] An embodiment of the present invention proposes a safety production regulation method based on AI, as Figure 1As shown, the AI - based safety production regulation method includes:

[0082] S1. Real - time collect the pressure data of the wellhead and pipeline through pressure sensors, and adjust the pressure of the wellhead and pipeline according to the deviation between the pressure data of the wellhead and pipeline and the production requirement indicators.

[0083] S2. After the pressure of the wellhead and pipeline is regulated, when the deviation between the pressure data of the wellhead and pipeline and the production requirement indicators still cannot meet the preset deviation requirements, a safety warning is issued.

[0084] S3. Real - time collect the vibration operation parameters of each production device through a sensor group, and conduct safety regulation on the device operation according to the vibration operation parameters of each production device.

[0085] S4. After the safety regulation of each production device is completed, when the vibration operation of each production device still cannot meet its corresponding vibration operation standard, a safety warning is issued for the production device whose vibration operation still cannot meet its corresponding vibration operation standard.

[0086] The working principle of the above - mentioned technical solution is as follows: Pressure sensors are deployed at the wellhead and pipeline to collect the pressure data of these key positions in real - time. The collected pressure data is input into the AI system. The system calculates the deviation between the pressure data and the production requirement indicators according to the preset production requirement indicators. The AI system adjusts the pressure of the wellhead and pipeline in real - time by controlling regulating devices such as valves and pumps according to the deviation to keep the pressure within a safe range. After the pressure regulation is completed, the AI system will check the deviation between the pressure data of the wellhead and pipeline and the production requirement indicators again. If the deviation still cannot meet the preset deviation requirements (i.e., exceeds the safe range), the AI system will trigger the safety warning mechanism, send an alarm to the operator, and may automatically take further emergency measures. The sensor group is deployed on each production device to collect the vibration operation parameters of the device (such as amplitude, frequency, etc.) in real - time. The AI system analyzes the vibration operation parameters of each device according to the preset vibration operation standard and judges whether the device is in normal operation. If it is found that the vibration operation parameters of the device exceed the standard range, the AI system will regulate the device in real - time by controlling the operation parameters of the device (such as rotation speed, load, etc.) to keep the device in a safe operation state. After the device operation regulation is completed, the AI system will check again whether the vibration operation parameters of each device meet the corresponding vibration operation standard. If there are still devices whose vibration operation parameters cannot meet the standard (i.e., the device is still in an abnormal state), the AI system will trigger the safety warning mechanism, send an alarm to the operator, and may recommend shutdown for maintenance or take other necessary measures.

[0087] The effects of the above technical solution are as follows: By collecting and analyzing the operation data of the wellhead, pipelines, and production equipment in real time, the AI system can promptly detect and handle potential safety hazards, thereby greatly reducing the probability of safety accidents. The AI system can precisely regulate the pressures of the wellhead and pipelines according to the production requirement indicators to ensure the stability and continuity of the production process. Meanwhile, by regulating the vibration operation parameters of the production equipment in real time, it is possible to avoid shutdowns or damages of the equipment caused by abnormal vibrations, thereby improving the utilization rate and production efficiency of the equipment. Through the intelligent safety production regulation method, enterprises can reduce the losses and downtime caused by safety accidents and equipment failures. At the same time, the application of the AI system can also help enterprises optimize resource allocation and reduce maintenance costs. The AI system can provide key production data and analysis results in real time, providing a scientific basis for the decision-making of enterprises. This helps enterprises quickly respond to market changes and changes in production demands, thereby formulating more reasonable production plans and strategies.

[0088] In summary, by introducing artificial intelligence technology, this technical solution realizes the real-time collection, intelligent analysis, and precise regulation of key parameters in the oil extraction process, thereby improving the safety production level, optimizing the production efficiency, reducing the operation cost, and enhancing the decision-making efficiency.

[0089] In an embodiment of the present invention, the pressure data of the wellhead and pipelines are collected in real time through pressure sensors, and the pressures of the wellhead and pipelines are adjusted according to the deviation between the pressure data of the wellhead and pipelines and the production requirement indicators, including:

[0090] S101: Install a first pressure sensor at each of multiple pressure monitoring positions of the wellhead;

[0091] S102: Install a second pressure sensor at each of multiple pressure detection node positions of the pipeline;

[0092] S103: Use the first pressure sensor to collect the pressure data of the wellhead in real time, and regulate the pressure of the wellhead according to the deviation between the pressure data of the wellhead and its corresponding wellhead pressure production requirement indicator;

[0093] S104: Use the second pressure sensor to collect the pressure data of the pipeline in real time, and regulate the pressure of the pipeline according to the deviation between the pressure data of the pipeline and its corresponding pipeline pressure production requirement indicator.

[0094] The working principle of the above technical solution is as follows: At multiple key positions of the wellhead (such as the wellhead entrance, exit, key connection points, etc.), a first pressure sensor is installed at each position to collect the pressure data of the wellhead in real time. At multiple important nodes of the pipeline (such as the pipeline starting point, ending point, branch point, turning point, etc.), a second pressure sensor is installed at each position to collect the pressure data of the pipeline in real time. These sensors are configured to be able to continuously and stably transmit the pressure data to the central control system or the data processing unit. The first pressure sensor and the second pressure sensor respectively collect the pressure data of the wellhead and the pipeline in real time, and transmit these data to the central control system or the data processing unit in the form of electrical signals or digital signals. The central control system or the data processing unit receives and stores these pressure data for subsequent analysis and processing. The central control system or the data processing unit performs real-time analysis on the received pressure data according to the preset production requirement indicators of the wellhead and the pipeline pressure. By comparing the deviation amount between the actual pressure data and the production requirement indicators, the central control system or the data processing unit can judge whether the pressure of the wellhead and the pipeline is within a safe and reasonable range. If it is found that the pressure data exceeds the preset range, the central control system or the data processing unit will automatically trigger the regulation mechanism, and adjust the operating states of devices such as valves and pumps to regulate the pressure of the wellhead and the pipeline in real time to keep the pressure within a safe range.

[0095] The effects of the above technical solution are as follows: By collecting and analyzing the pressure data of the wellhead and the pipeline in real time, this technical solution can timely detect and handle potential pressure abnormalities, thereby effectively preventing safety accidents caused by too high or too low pressure. By precisely regulating the pressure of the wellhead and the pipeline, this technical solution can ensure the stability and continuity of the production process, thereby improving production efficiency. At the same time, reducing the shutdown or production reduction caused by pressure abnormalities also helps to improve the overall production efficiency. This technical solution can reduce the equipment damage and maintenance costs caused by pressure abnormalities through an intelligent pressure regulation mechanism. At the same time, by optimizing the pressure control in the production process, it can also reduce energy consumption and operating costs. This technical solution realizes the real-time collection, analysis and regulation of the pressure of the wellhead and the pipeline, and improves the automation level of the production process. This helps to reduce the risks of manual intervention and misoperation, and improve the reliability and stability of the production process.

[0096] In summary, this technical solution realizes the intelligent management and optimization of the production process by collecting the pressure data of the wellhead and the pipeline in real time and precisely regulating according to the deviation amount between these data and the production requirement indicators. This not only improves the safety production level and production efficiency, but also reduces the operating costs and enhances the automation level.

[0097] In an embodiment of the present invention, the pressure data of the wellhead is collected in real time by using the first pressure sensor, and the pressure of the wellhead is regulated according to the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index, including:

[0098] S1031. Extract the pressure data of the wellhead collected in real time by each of the first pressure sensors;

[0099] S1032. Compare the deviation between the pressure data of each wellhead and the corresponding wellhead pressure production requirement index with a first pressure deviation threshold and a second pressure deviation threshold;

[0100] S1033. When the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index exceeds the preset first pressure deviation threshold but does not exceed the second pressure deviation threshold, the historical pressure data collected by the first pressure sensor corresponding to the pressure data of the wellhead that exceeds the preset first pressure deviation threshold is retrieved;

[0101] S1034. Obtain a pressure deviation evaluation coefficient by using the historical pressure data collected by the first pressure sensor corresponding to the pressure data of the wellhead that exceeds the preset first pressure deviation threshold;

[0102] S1035. Compare the pressure deviation evaluation coefficient with a preset pressure deviation evaluation threshold;

[0103] S1036. When the pressure deviation evaluation coefficient exceeds the preset pressure deviation evaluation threshold, the pressure of the wellhead is regulated by increasing or reducing the pressure according to the deviation between the pressure data of the wellhead that exceeds the preset first pressure deviation threshold and the corresponding wellhead pressure production requirement index, so as to reduce the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index;

[0104] S1037. When the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index exceeds the preset second pressure deviation threshold, the pressure of the wellhead is regulated by increasing or reducing the pressure according to the deviation between the pressure data of the wellhead that exceeds the preset second pressure deviation threshold and the corresponding wellhead pressure production requirement index, so as to reduce the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index.

[0105] The working principle of the above technical solution is as follows: Each first pressure sensor collects the pressure data of the wellhead where it is located in real time and sends this data to the central control system or the data processing unit. The central control system or the data processing unit extracts the pressure data sent by each first pressure sensor for subsequent analysis and regulation. For the pressure data of each wellhead, the central control system or the data processing unit calculates the deviation amount between it and the corresponding wellhead pressure production requirement index. The calculated deviation amount is compared with the preset first pressure deviation threshold and second pressure deviation threshold to determine whether the deviation is within the acceptable range. When the deviation amount exceeds the first pressure deviation threshold but does not exceed the second pressure deviation threshold, the central control system or the data processing unit will retrieve the historical pressure data collected by the first pressure sensor corresponding to the wellhead. Using these historical data, a pressure deviation evaluation coefficient is calculated to evaluate whether the current deviation amount is an abnormal or trend change. The calculated pressure deviation evaluation coefficient is compared with the preset pressure deviation evaluation threshold. If the evaluation coefficient exceeds the threshold, it indicates that the current deviation amount may be an abnormal or trend change that needs attention, and control measures need to be taken. According to the direction and magnitude of the deviation amount, the pressure of the wellhead is regulated by increasing or reducing the pressure to narrow the deviation amount and make it close to or reach the production requirement index. When the deviation amount exceeds the second pressure deviation threshold, it indicates that the current pressure condition has seriously deviated from the production requirements, and emergency control measures need to be taken immediately. According to the direction and magnitude of the deviation amount, the pressure of the wellhead is also regulated by increasing or reducing the pressure to quickly restore the pressure to a safe and reasonable range.

[0106] The effects of the above technical solution are as follows: By collecting and analyzing the pressure data of the wellhead in real time, potential pressure abnormalities can be discovered and processed in a timely manner, effectively preventing safety accidents caused by too high or too low pressure. According to different ranges of the deviation amount, different control measures are taken, which not only ensures the timeliness of pressure regulation but also avoids unnecessary energy consumption and equipment loss caused by over-regulation. This technical solution realizes the intelligent monitoring and regulation of the wellhead pressure, reduces manual intervention, and improves the automation and intelligent level of the production process. By introducing historical data analysis and pressure deviation evaluation coefficient, a scientific basis is provided for the regulation decision-making, making the regulation measures more accurate and effective. By precisely regulating the wellhead pressure, the equipment damage and maintenance costs caused by pressure abnormalities are reduced, and at the same time, the energy consumption is reduced, which helps to reduce the overall operation cost.

[0107] In summary, through real-time collection, analysis, and regulation of the wellhead pressure data, this technical solution realizes the intelligent management and optimization of the production process, improves the safety production level, optimizes the pressure regulation, enhances the automation and intelligent level, strengthens the scientific nature of decision-making, and helps to reduce the operation cost.

[0108] In an embodiment of the present invention, a pressure deviation evaluation coefficient is obtained by using historical pressure data collected by a first pressure sensor corresponding to the pressure data of the wellhead exceeding a preset first pressure deviation threshold, including:

[0109] Step 1: When the number of first pressure sensors corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold is one, use the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold as the target pressure sensor, and retrieve the historical pressure data collected by the target pressure sensor;

[0110] Step 2: Use a first evaluation coefficient model to obtain a pressure deviation evaluation coefficient in combination with the historical pressure data collected by the target pressure sensor; wherein, the pressure deviation evaluation coefficient is obtained by the following formula:

[0111]

[0112] wherein, S 01 represents the pressure deviation evaluation coefficient obtained by the first evaluation coefficient model; a, b, and c respectively represent preset first adjustment parameter, second adjustment parameter, and third adjustment parameter, and the value ranges of the first adjustment parameter, second adjustment parameter, and third adjustment parameter are 0.58 - 1.13, 0.89 - 1.22, and 1.14 - 1.39; P b represents the pressure standard deviation corresponding to the historical pressure data collected by the target pressure sensor; n represents the number of pressure data of the historical pressure data; P i represents the pressure data value corresponding to the i-th historical pressure data; P i-1 represents the pressure data value corresponding to the (i - 1)-th historical pressure data; P be represents a preset reference value of the force standard deviation; P fmaxi represents the maximum value of the pressure change amplitude at the pressure data acquisition moments of two adjacent pressure data corresponding to the i-th historical pressure data; P fmini represents the minimum value of the pressure change amplitude at the pressure data acquisition moments of two adjacent pressure data corresponding to the i-th historical pressure data;

[0113] Step 3: When the number of first pressure sensors corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold is multiple, use the first pressure sensors corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold as the target pressure sensors, and retrieve the historical pressure data collected by the multiple target pressure sensors;

[0114] Step 4: Use the second evaluation coefficient model to obtain the pressure deviation evaluation coefficient by combining the historical pressure data collected by multiple said target pressure sensors; wherein, the pressure deviation evaluation coefficient is obtained by the following formula:

[0115]

[0116] wherein, S 02 represents the pressure deviation evaluation coefficient obtained by the second evaluation coefficient model; m represents the number of target pressure sensors; P bi represents the standard deviation of the pressure data corresponding to the historical pressure data of the i-th target pressure sensor; G i represents the average value of the correlation coefficients between the i-th target pressure sensor and other target pressure sensors; w i represents the weight value corresponding to the i-th target pressure sensor; P fmax01i represents the maximum value of the pressure change amplitude corresponding to the i-th target pressure sensor; P fmax02i represents the maximum value of the pressure change amplitude of the target pressure sensor corresponding to the maximum value of the correlation coefficient with the i-th target pressure sensor.

[0117] The working principle of the above technical solution is as follows: When the pressure data of only one first pressure sensor exceeds the preset first pressure deviation threshold, this sensor is determined as the target pressure sensor, and the historical pressure data it collected is retrieved. When the pressure data of multiple first pressure sensors exceed the preset first pressure deviation threshold simultaneously, these sensors are all regarded as target pressure sensors, and the historical pressure data they collected is retrieved respectively.

[0118] For a single target pressure sensor, use the first evaluation coefficient model to calculate the pressure deviation evaluation coefficient by combining its historical pressure data. This model considers factors such as the standard deviation of the historical pressure data, the number of pressure data, and the pressure change amplitude between the acquisition times of two adjacent pressure data, and performs weighted calculation through preset adjustment parameters. For multiple target pressure sensors, use the second evaluation coefficient model to calculate the pressure deviation evaluation coefficient. This model not only considers the standard deviation of the historical pressure data of each target pressure sensor, but also considers the average value of the correlation coefficients between the sensors and the corresponding weight values, and also considers the maximum value of the pressure change amplitude of each sensor. These factors jointly determine the final pressure deviation evaluation coefficient.

[0119] The effects of the above technical solution are as follows: By introducing historical pressure data and multiple evaluation factors (such as standard deviation, pressure change range, correlation coefficient, etc.), this technical solution can more comprehensively evaluate the severity and potential risks of the current pressure deviation, thereby improving the accuracy of evaluation. The pressure deviation amount evaluation coefficient, as an important basis for decision-making, can guide subsequent pressure regulation measures. By accurately calculating this coefficient, a more scientific regulation strategy can be formulated to avoid unnecessary energy consumption and equipment wear caused by excessive or insufficient regulation. This technical solution combines advanced mathematical models and algorithms to achieve intelligent monitoring and evaluation of wellhead pressure. This helps to improve the intelligent level of the entire production system, reduce manual intervention, and improve production efficiency and safety. By accurately evaluating the pressure deviation and taking corresponding regulation measures, equipment damage and maintenance costs caused by abnormal pressure can be effectively avoided. At the same time, the intelligent monitoring and evaluation system can also reduce energy consumption and operating costs, improving the economic benefits of the enterprise.

[0120] At the same time, by introducing the first evaluation coefficient model and the second evaluation coefficient model, and combining multiple statistical characteristics of historical pressure data (such as pressure standard deviation, pressure change range, etc.), the pressure deviation amount evaluation coefficient can be calculated more accurately, thereby more accurately reflecting the abnormal situation of wellhead pressure data. When multiple pressure sensors detect pressure deviations exceeding the preset threshold, this solution can comprehensively consider the data of these sensors, and improve the accuracy and comprehensiveness of the evaluation coefficient by calculating the average value of the correlation coefficient and the weight value. This solution can handle the situation where a single or multiple pressure sensors detect pressure deviations, and flexibly respond to different monitoring scenarios through different evaluation coefficient models. The first adjustment parameter, the second adjustment parameter, and the third adjustment parameter in the formula have clear value ranges and can be adjusted according to actual situations to adapt to different monitoring requirements and pressure data characteristics. Using historical pressure data for evaluation can eliminate the influence of accidental factors on the data and improve the reliability and stability of the evaluation results. By comprehensively considering multiple statistical characteristics such as pressure standard deviation and pressure change range, the abnormal situation of pressure data can be more comprehensively evaluated, reducing the possibility of false alarms and missed alarms. At the same time, this solution realizes the automatic processing of the pressure deviation amount evaluation coefficient through model calculation and formula derivation, reducing manual intervention and calculation workload. The design of the formula and model is relatively simple and clear, easy to understand and implement, and convenient to promote and use in practical applications.

[0121] In summary, the technical solution exhibits remarkable technical effects in terms of accuracy, flexibility, reliability, and operational convenience. These effects together enhance the accuracy and efficiency of the abnormal evaluation of wellhead pressure data, providing strong technical support for safety monitoring in the oil extraction and production processes. At the same time, the technical solution calculates the pressure deviation evaluation coefficient by introducing historical pressure data and multiple evaluation factors, achieving precise evaluation and intelligent monitoring of the wellhead pressure. This not only improves the accuracy of evaluation and the scientific nature of decision-making but also enhances the intelligent level of the system and reduces the operating costs.

[0122] In an embodiment of the present invention, the pressure data of the pipeline is collected in real time by using the second pressure sensor, and the pressure of the pipeline is regulated according to the deviation between the pressure data of the pipeline and the corresponding pipeline pressure production requirement index, including:

[0123] S1041. Retrieve in real time the pressure data of the pipeline collected by the second pressure sensor;

[0124] S1042. Compare the pressure data of each pipeline with its corresponding pressure risk threshold and pressure warning threshold;

[0125] S1043. When the pressure data of the pipeline exceeds its corresponding pressure warning threshold, directly adjust the pipeline valve corresponding to the pressure detection node position of the pipeline where the pressure data exceeds its corresponding pressure warning threshold to reduce the pressure value corresponding to the pressure detection node position of the pipeline;

[0126] S1044. When the pressure data of the pipeline exceeds its corresponding pressure risk threshold, screen out the target pressure detection node position from the pressure detection node positions of all pipelines, obtain the pressure risk evaluation coefficient by using the target pressure data corresponding to the target pressure detection node position, and make a pressure regulation determination for the pressure detection node position through the pressure risk evaluation coefficient.

[0127] The working principle of the above technical solution is as follows: The second pressure sensor installed at the key nodes of the pipeline collects the pressure data of the pipeline in real time. The collected pressure data is compared with its corresponding pressure risk threshold and pressure warning threshold. These two thresholds are set according to factors such as the production requirements, safety standards, and historical data of the pipeline. When the pressure data of the pipeline exceeds the pressure warning threshold, the system will automatically trigger the warning mechanism and directly adjust the pipeline valve corresponding to the pressure detection node position of the pipeline where the pressure data exceeds the warning threshold to reduce the pressure value at this position and prevent the pressure from continuing to rise. When the pressure data of the pipeline exceeds the pressure risk threshold, it indicates that the pipeline may face serious pressure risks. At this time, the system will screen out the target pressure detection node positions from all the pressure detection node positions of the pipeline and calculate the pressure risk evaluation coefficient using the pressure data corresponding to these positions. Through this coefficient, the pressure regulation determination of the pressure detection node positions is carried out to determine whether further regulation measures need to be taken.

[0128] The effects of the above technical solution are as follows: By monitoring and regulating the pressure of the pipeline in real time, it can effectively avoid potential safety hazards such as water supply interruption, water quality problems, and pipe bursts caused by too high or too low pressure, and improve the operation safety of the pipeline. This technical solution can automatically adjust the valve opening according to the actual pressure situation of the pipeline to achieve the optimal control of the pipeline pressure. This can not only reduce energy consumption and operating costs, but also improve the stability and reliability of water supply. This technical solution combines sensor technology, data processing technology, and automatic control technology to achieve the intelligent management of the pipeline. Through the visual monitoring interface and data analysis tools, the management personnel can understand the pressure status of the pipeline in real time and conduct remote regulation, improving the management efficiency and response speed. By setting the pressure warning threshold and pressure risk threshold, this technical solution can issue warnings and take regulation measures in a timely manner when the pipeline pressure is abnormal, effectively preventing the occurrence of pressure problems. At the same time, it can also quickly respond when pressure problems occur, reducing losses and impacts.

[0129] In summary, through the real-time monitoring and regulation of the pressure data of the pipeline, this technical solution realizes the intelligent management and optimized operation of the pipeline, and improves the safety and stability of water supply.

[0130] In an embodiment of the present invention, when the pressure data of the pipeline exceeds its corresponding pressure risk threshold, the target pressure detection node positions are screened out from all the pressure detection node positions of the pipeline, and the pressure risk evaluation coefficient is obtained by using the target pressure data corresponding to the target pressure detection node positions, and the pressure regulation determination of the pressure detection node positions is carried out through the pressure risk evaluation coefficient, including:

[0131] Step 1: When the pressure data of the pipeline exceeds its corresponding pressure risk threshold, retrieve the correlation coefficient between the pressure detection node positions of the pipeline whose pressure data exceeds its corresponding pressure risk threshold and the pressure detection node positions of other pipelines;

[0132] Step 2: Retrieve the pressure detection node positions of other pipelines whose correlation coefficient between the pressure detection node positions of the pipeline whose pressure data exceeds its corresponding pressure risk threshold and the pressure detection node positions of other pipelines exceeds the preset correlation coefficient threshold as the target pressure detection node positions, and use the pressure data corresponding to the target pressure detection node positions as the target pressure data;

[0133] Step 3: Obtain a pressure risk evaluation coefficient by using the pressure data of the pipeline corresponding to the pressure detection node position where the pressure data of the pipeline exceeds its corresponding pressure risk threshold and its corresponding target pressure data;

[0134] Among them, the pressure risk evaluation coefficient is obtained through the following formula:

[0135]

[0136] Among them, K represents the pressure risk evaluation coefficient; k represents the number of target pressure detection node positions; P g represents the pressure data of the pipeline corresponding to the pressure detection node position where the pressure data of the pipeline exceeds its corresponding pressure risk threshold; P gi represents the target pressure data corresponding to the i-th target pressure detection node position; F i represents the correlation coefficient between the pressure detection node position where the pressure data of the pipeline exceeds its corresponding pressure risk threshold and the i-th target pressure detection node position; δ represents the preset time decay coefficient; t i represents the total duration of pressure data acquisition corresponding to the i-th target pressure detection node position; λ 01 and λ 02 respectively represent the first risk factor and the second risk factor, and the first risk factor is obtained through the following formula:

[0137]

[0138] Among them, λ 01 represents the first risk factor; F i represents the correlation coefficient between the pressure detection node position where the pressure data of the pipeline exceeds its corresponding pressure risk threshold and the i-th target pressure detection node position; F y represents the preset correlation coefficient threshold; F maxRepresents the maximum value of the correlation coefficient between the pipeline pressure detection node positions corresponding to the pressure data of n target pressure detection node positions that exceed their corresponding pressure risk thresholds;

[0139] And, the second risk factor is obtained through the following formula:

[0140]

[0141] Where, λ 02 Represents the second risk factor; P gb Represents the standard deviation of the pressure data corresponding to the pipeline pressure detection node positions where the pipeline pressure data exceeds its corresponding pressure risk threshold; P gbi Represents the standard deviation of the pressure data corresponding to the i-th target pressure detection node position; F i Represents the correlation coefficient between the pipeline pressure detection node positions where the pipeline pressure data exceeds its corresponding pressure risk threshold and the i-th target pressure detection node position;

[0142] Step 4: Compare the pressure risk evaluation coefficient with a preset pressure risk evaluation threshold;

[0143] Step 5: When the pressure risk evaluation coefficient exceeds the preset pressure risk evaluation threshold, adjust the pipeline valves corresponding to the pipeline pressure detection node positions where the pressure data exceeds its corresponding pressure warning threshold, for reducing the pressure value corresponding to the pipeline pressure detection node positions.

[0144] The working principle of the above technical solution is as follows: When the pressure data of a certain pipeline exceeds its corresponding pressure risk threshold, first retrieve the correlation coefficients between the pipeline pressure detection node positions of this pipeline and those of other pipelines. Then, screen out the pipeline pressure detection node positions of other pipelines that exceed the preset correlation coefficient threshold from these correlation coefficients as the target pressure detection node positions. Take the pressure data corresponding to these target pressure detection node positions as the target pressure data.

[0145] Using the pipeline pressure data that exceeds the pressure risk threshold and its corresponding target pressure data, combined with the correlation coefficient, time decay coefficient, total pressure data acquisition duration, and the first risk factor and the second risk factor, calculate the pressure risk evaluation coefficient. The first risk factor considers the relationship between the correlation coefficient and the preset threshold and the maximum correlation coefficient, reflecting the correlation intensity between the target pressure detection node positions and the pipeline pressure detection node positions that exceed the risk threshold. The second risk factor considers the relationship between the standard deviation of the pipeline pressure data that exceeds the risk threshold and the standard deviation of the target pressure data, as well as their correlation coefficient, reflecting the volatility and correlation of the pressure data.

[0146] Compare the calculated pressure risk assessment coefficient with the preset pressure risk assessment threshold. If the pressure risk assessment coefficient exceeds the preset threshold, it is determined that pressure regulation measures need to be taken. The specific regulation measure is to adjust the pipeline valve corresponding to the pipeline pressure detection node where the pressure data exceeds its corresponding pressure warning threshold to reduce the pipeline pressure.

[0147] The effects of the above technical solution are as follows: By comprehensively considering multiple factors (such as correlation coefficient, time decay coefficient, standard deviation of pressure data, etc.), the calculated pressure risk assessment coefficient can more accurately reflect the pressure risk status of the pipeline. Making pressure regulation decisions based on this coefficient can more precisely determine the pipelines and their locations that need to take regulation measures, avoiding excessive or insufficient regulation. Timely detecting and handling pipelines that exceed the pressure risk threshold can effectively prevent safety accidents such as pipeline rupture and leakage caused by excessive pressure. By adjusting the pipeline valve to reduce the pressure, it can ensure that the pipeline operates within a safe range and guarantee the smooth progress of production activities such as water supply. Reasonable pressure regulation can reduce energy consumption and pipeline wear, and extend the service life of the pipeline. At the same time, by optimizing the pressure distribution, it can improve the efficiency and quality of production activities such as water supply. This technical solution combines sensor technology, data processing technology, and automatic control technology to achieve intelligent monitoring and management of the pipeline. Through the visual monitoring interface and data analysis tools, managers can understand the pressure status of the pipeline in real time and conduct remote regulation, improving management efficiency and response speed.

[0148] At the same time, by retrieving the data of other pipeline pressure detection nodes related to the abnormal pressure data and calculating the correlation coefficient between them, it is possible to more comprehensively evaluate the pressure abnormality and improve the accuracy of risk assessment. Introducing the pressure risk assessment coefficient K and comprehensively considering multiple factors such as the target pressure data, correlation coefficient, time decay coefficient, and risk factors makes the risk assessment more comprehensive and accurate. The calculation of the first risk factor λ01 and the second risk factor λ02 takes into account the correlation coefficient and its maximum value, the standard deviation of pressure data, etc., and can be dynamically adjusted according to the actual situation, improving the flexibility and accuracy of risk assessment.

[0149] When the pipeline pressure data exceeds the risk threshold, it can quickly screen out the location of the target pressure detection node, calculate the pressure risk evaluation coefficient, and achieve real-time response and rapid regulation. According to the comparison result of the pressure risk evaluation coefficient and the preset threshold, the valve of the corresponding pipeline is intelligently adjusted to reduce the pressure value and achieve intelligent pressure regulation. By comprehensively considering multiple factors, the accuracy of risk assessment is improved, and improper pressure regulation caused by false alarms or missed alarms is reduced. This solution can handle different pipelines, different pressure detection node locations, and different pressure data characteristics, and has strong adaptability and versatility. By introducing the time decay coefficient δ, the influence of time factors on pressure data is considered, making the risk assessment more stable and reliable. The structure of this solution is clear, easy to maintain and expand, and can be functionally upgraded and optimized according to actual needs. Through intelligent pressure regulation, equipment damage and safety accidents caused by abnormal pressure are reduced, and the maintenance cost is lowered. Timely pressure regulation can ensure the normal operation of the pipeline, improve production efficiency, and provide strong support for the sustainable development of the enterprise.

[0150] In summary, this technical solution shows remarkable technical effects in aspects such as accurate risk assessment, efficient pressure regulation, enhanced system robustness, economy, and practicality. These effects together improve the intelligent level of pipeline pressure monitoring and regulation, providing strong guarantee for the safe production and efficient operation of the enterprise. At the same time, this technical solution realizes precise regulation and intelligent management of pipeline pressure by screening the location of the target pressure detection node, calculating the pressure risk evaluation coefficient, and making pressure regulation decisions, improving the safety and operation efficiency of the pipeline.

[0151] In an embodiment of the present invention, the vibration operation parameters of each production device are collected in real time through a sensor group, and the safe regulation of the device operation is performed according to the vibration operation parameters of each production device, including:

[0152] S301. The vibration operation parameters of each production device are collected in real time through a sensor group, where the sensor group includes an acceleration sensor, a velocity sensor, and a displacement sensor, and the vibration operation parameters include vibration acceleration, vibration velocity, and vibration displacement generated by vibration;

[0153] S302. The vibration acceleration, vibration velocity, and vibration displacement generated by vibration of each production device are respectively compared with their corresponding acceleration threshold, velocity threshold, and vibration displacement threshold;

[0154] S303. When any one of the vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold, it is determined whether it is necessary to perform safe regulation on the production device;

[0155] S304. When it is determined that safety regulation of the production equipment is required, deceleration regulation is performed on the production equipment.

[0156] The working principle of the above technical solution is as follows: The vibration operation parameters of each production equipment are collected in real time through a sensor group (including an acceleration sensor, a speed sensor, and a displacement sensor). These parameters specifically include vibration acceleration, vibration speed, and vibration displacement generated by vibration. The collected vibration acceleration, vibration speed, and vibration displacement are compared with their corresponding acceleration thresholds, speed thresholds, and vibration displacement thresholds. These thresholds are set based on factors such as the performance of the production equipment, safety standards, and historical data, and are used to determine whether the production equipment is in a normal operating state. When any one of the vibration acceleration, vibration speed, and vibration displacement exceeds its corresponding threshold, the system will automatically trigger the safety regulation determination mechanism. The system will decide whether safety regulation of the production equipment is required according to the preset determination logic (such as the duration of the parameter exceeding the threshold, the degree of exceeding the threshold, etc.). If it is determined that safety regulation of the production equipment is required, the system will execute the deceleration regulation measure. The deceleration regulation can be achieved by adjusting the operating speed or power of the production equipment to reduce the vibration level of the production equipment and ensure its safe operation.

[0157] The effects of the above technical solution are as follows: By real-time monitoring the vibration operation parameters of the production equipment and taking timely deceleration regulation measures when the parameters exceed the thresholds, it can effectively avoid equipment damage, production interruption, and even safety accidents caused by excessive equipment vibration. Reasonable deceleration regulation can ensure that the production equipment operates at a safe vibration level, while avoiding unnecessary downtime and maintenance costs. This helps to maintain the continuity and stability of the production line and improve production efficiency. Vibration acceleration, vibration speed, and vibration displacement are important indicators reflecting the operating state of the production equipment. By real-time monitoring these parameters, potential fault signs of the equipment can be detected in a timely manner, and measures can be taken for prevention and maintenance, thereby extending the service life of the equipment. This technical solution combines sensor technology, data processing technology, and automatic control technology to achieve intelligent monitoring and management of production equipment. Through a visual monitoring interface and data analysis tools, managers can understand the vibration status of production equipment in real time and perform remote regulation, improving management efficiency and response speed.

[0158] In summary, through real-time monitoring of the vibration operation parameters of the production equipment and performing safety regulation, this technical solution realizes precise monitoring and management of the equipment operating state, and improves the safety, production efficiency, and intelligent management level of the equipment.

[0159] In an embodiment of the present invention, when any one of the vibration acceleration, vibration speed, and vibration displacement generated by vibration exceeds its corresponding threshold, it is determined whether safety regulation of the production equipment is required, including:

[0160] S3031. When any one of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration exceeds its corresponding threshold value, use the corresponding moment when any one of the vibration acceleration, vibration velocity, and vibration displacement generated by the vibration exceeds its corresponding threshold value as the data retrieval moment;

[0161] S3032. Starting from the data retrieval moment, retrieve the vibration operation parameters of the production equipment in real time; wherein, the vibration operation parameters include vibration acceleration, vibration velocity, and vibration displacement generated by the vibration;

[0162] S3033. Obtain a vibration operation evaluation coefficient using the vibration operation parameters of the production equipment;

[0163] Among them, the vibration operation evaluation coefficient is obtained through the following formula:

[0164]

[0165] Among them, U represents the vibration operation evaluation coefficient; A, V, and D respectively represent vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; RMS(A, V, D) represents the sum of the square root values of vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; MAD(A, V, D) represents the sum of the mean absolute deviation values of vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; STD(A, V, D) represents the sum of the standard deviations of vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; Var(A, V, D) represents the sum of the variances of vibration acceleration, vibration velocity, and vibration displacement generated by the vibration; K b represents a preset basic adjustment coefficient, and the value range of the basic adjustment coefficient is 1.32 - 1.47; σ represents a preset scaling coefficient, and the value range of the scaling coefficient is 0.18 - 0.53; ε represents a preset minimum constant used to prevent the denominator from being zero;

[0166] S3034. Compare the vibration operation evaluation coefficient with a preset vibration operation evaluation coefficient threshold;

[0167] S3035. When the vibration operation evaluation coefficient exceeds the preset vibration operation evaluation coefficient, it is determined that the production equipment needs to be safely regulated.

[0168] The working principle of the above technical solution is as follows: When any one of the parameters of vibration acceleration, vibration velocity, and vibration displacement generated by vibration exceeds its corresponding threshold value, that moment is taken as the data retrieval moment. This means that the system has detected a possible abnormal vibration situation. From the data retrieval moment, the system starts to retrieve the vibration operation parameters of the production equipment in real time, including vibration acceleration, vibration velocity, and vibration displacement generated by vibration. The real-time nature of these data is crucial for subsequent analysis and determination. Using the vibration operation parameters retrieved in real time, the system calculates the vibration operation evaluation coefficient. This coefficient is a comprehensive index used to evaluate the vibration operation state of the production equipment. During the calculation process, the system considers the root mean square value (RMS) and mean absolute deviation value (MAD) of vibration acceleration, vibration velocity, and vibration displacement, and performs weighting and scaling through a preset basic adjustment coefficient (Kb), scaling coefficient (σ), and minimum constant (ε). The system compares the calculated vibration operation evaluation coefficient with the preset vibration operation evaluation coefficient threshold. This threshold is set based on factors such as the performance of the production equipment, safety standards, and historical data, and is used to determine whether the production equipment is in a state that requires safety regulation. If the vibration operation evaluation coefficient exceeds the preset vibration operation evaluation coefficient threshold, the system determines that safety regulation of the production equipment is required. This may include deceleration, shutdown, alarm, or other safety measures to ensure the safe operation of the production equipment.

[0169] The effect of the above technical solution is as follows: By monitoring the vibration operation parameters of the production equipment in real time and calculating the vibration operation evaluation coefficient, the system can timely detect potential vibration abnormalities, thereby taking necessary safety measures to avoid equipment damage, production interruption, and even safety accidents. The vibration operation evaluation coefficient provides a quantitative index for evaluating the vibration operation state of the production equipment. This helps managers formulate more reasonable maintenance plans, reduce unnecessary downtime and maintenance costs.

[0170] This technical solution combines sensor technology, data processing technology, and automatic control technology to achieve intelligent monitoring and management of production equipment. Through a visual monitoring interface and data analysis tools, managers can understand the vibration status of production equipment in real time and perform remote control. Timely detection and regulation of vibration abnormalities help maintain the continuity and stability of the production line, thereby improving production efficiency. In addition, by optimizing the maintenance strategy, the production interruption time caused by equipment failures can also be reduced. Vibration acceleration, vibration velocity, and vibration displacement are important indicators reflecting the operation state of production equipment. By monitoring these parameters in real time and calculating the vibration operation evaluation coefficient, potential fault signs of the equipment can be detected in a timely manner, and measures can be taken for prevention and maintenance, thereby extending the service life of the equipment.

[0171] This solution can immediately determine the data retrieval moment when any one of the vibration acceleration, vibration velocity, or vibration displacement exceeds the threshold, and retrieve the vibration operation parameters of the production equipment in real time. This real-time monitoring method can quickly capture the abnormal state of the production equipment, providing the possibility for timely taking control measures. By calculating the vibration operation evaluation coefficient and comparing it with the preset threshold, once it exceeds the threshold, it is determined that the production equipment needs to be safely regulated. This rapid response mechanism can effectively reduce the occurrence of equipment failures and ensure production safety. The vibration operation evaluation coefficient comprehensively considers the three parameters of vibration acceleration, vibration velocity, and vibration displacement. Through the calculation of the square root value and the mean absolute deviation value, it can more comprehensively reflect the vibration state of the production equipment. This comprehensive evaluation method improves the accuracy and reliability of the evaluation. According to the calculation result of the vibration operation evaluation coefficient, it can be flexibly determined whether the production equipment needs to be safely regulated. This data-driven regulation method is more scientific and reasonable, and can avoid unnecessary downtime and maintenance costs. Parameters such as the basic adjustment coefficient, scaling coefficient, and minimum constant in the formula can be adjusted and optimized according to the actual situation to meet the requirements of different production equipment and different working environments. This parameter optimization method improves the flexibility and applicability of the solution. By introducing the minimum constant ε, the situation of the denominator being zero is prevented, ensuring the stability and reliability of the formula. At the same time, the calculation of the square root value and the mean absolute deviation value also has a certain anti-interference ability, which can reduce the influence of noise and outliers on the evaluation result. This solution does not require large-scale transformation or upgrading of the production equipment. Only by adding vibration sensors and data acquisition modules to the existing equipment can real-time monitoring and evaluation be achieved. This implementation method is simple and convenient, and is easy to promote and apply. Since the solution mainly relies on data acquisition and calculation, the maintenance cost is relatively low. At the same time, by regularly calibrating the sensors and updating the algorithms, the accuracy and stability of the solution can be maintained.

[0172] In summary, this technical solution shows significant technical effects in aspects such as real-time monitoring and response, precise evaluation and regulation, parameter optimization and stability, and easy implementation and maintenance. These effects together improve the operation safety and reliability of the production equipment, providing strong guarantee for the production safety and efficient operation of the enterprise. At the same time, this technical solution realizes the precise monitoring and management of the equipment operation state by real-time monitoring the vibration operation parameters of the production equipment and calculating the vibration operation evaluation coefficient, improving the safety, production efficiency, and intelligent management level of the equipment.

[0173] In an embodiment of the present invention, when it is determined that the production equipment needs to be safely regulated, the production equipment is decelerated and regulated, including:

[0174] S3041. When it is determined that the production equipment needs to be safely regulated, retrieve the vibration operation evaluation coefficient;

[0175] S3042. Obtain the deceleration control ratio of the production equipment using the vibration operation evaluation coefficient, and perform deceleration control on the production equipment according to the deceleration control ratio of the production equipment;

[0176] Among them, the deceleration control ratio of the production equipment is obtained through the following formula:

[0177]

[0178] Among them, B represents the deceleration control ratio of the production equipment; r represents a preset ratio adjustment coefficient, and the value range of the ratio adjustment coefficient is 0.27 - 0.34; U represents the vibration operation evaluation coefficient.

[0179] The working principle of the above technical solution is as follows: When the system determines that safety control of the production equipment is required, it will first retrieve the vibration operation evaluation coefficient U calculated previously. This coefficient reflects the current vibration operation state of the production equipment and is an important basis for determining the deceleration control ratio. Using the vibration operation evaluation coefficient U and the preset ratio adjustment coefficient r, the deceleration control ratio B of the production equipment is calculated through a formula. The ratio adjustment coefficient r is a value set based on experience or experimental data and is used to adjust the sensitivity of the deceleration control ratio. According to the calculated deceleration control ratio B, the system will perform deceleration control on the production equipment. This is usually achieved by adjusting the operating speed or power of the production equipment to reduce its vibration level and ensure safe operation.

[0180] The effect of the above technical solution is as follows: Through deceleration control based on the vibration operation evaluation coefficient, this technical solution can more accurately determine whether the production equipment needs to decelerate, thereby avoiding equipment damage, production interruption, or safety accidents caused by excessive vibration. The deceleration control is carried out on the premise of ensuring safety, so it will not overly affect production efficiency. At the same time, by decelerating in a timely manner, it is possible to avoid long-term downtime caused by equipment failures, thereby maintaining the continuity and stability of the production line. This technical solution combines sensor technology, data processing technology, and automatic control technology to achieve intelligent monitoring and management of production equipment. Through automatic calculation and control, manual intervention is reduced, and management efficiency and response speed are improved. By real-time monitoring of vibration operation parameters and calculating the vibration operation evaluation coefficient, the system can timely detect potential vibration abnormalities of the equipment and take measures for prevention and maintenance. Deceleration control is one of the preventive measures and helps to extend the service life of the equipment. Long-term vibration abnormalities may lead to a decline in equipment performance and reliability. Through timely deceleration control, the wear and fatigue caused by vibration of the equipment can be reduced, thereby improving its overall reliability and stability.

[0181] In summary, through the deceleration regulation based on the vibration operation evaluation coefficient, this technical solution realizes the precise monitoring and management of the vibration state of production equipment, improves the safety, production efficiency and intelligent management level of the equipment, and also helps to prevent failures and improve the reliability of the equipment.

[0182] An AI-based safety production regulation system proposed in an embodiment of the present invention is as Figure 2 shown. The AI-based safety production regulation system includes: The AI-based safety production regulation system includes:

[0183] A first adjustment module, configured to collect the pressure data of the wellhead and the pipeline in real time through a pressure sensor, and adjust the pressure of the wellhead and the pipeline according to the deviation between the pressure data of the wellhead and the pipeline and the production requirement indicators;

[0184] A first safety warning module, configured to issue a safety warning when the deviation between the pressure data of the wellhead and the pipeline still cannot meet the preset deviation requirement after the pressure of the wellhead and the pipeline is regulated;

[0185] A second adjustment module, configured to collect the vibration operation parameters of each production equipment in real time through a sensor group, and perform safety regulation on the equipment operation according to the vibration operation parameters of each production equipment;

[0186] A second safety warning module, configured to issue a safety warning for the production equipment whose vibration operation still cannot meet the corresponding vibration operation standard when the vibration operation of each production equipment still cannot meet the corresponding vibration operation standard after the safety regulation of each production equipment is completed.

[0187] The working principle of the above technical solution is as follows: Pressure sensors are deployed at the wellhead and pipelines to collect pressure data at these key locations in real time. The collected pressure data is input into the AI system, which calculates the deviation between the pressure data and the production requirement indicators according to the preset production requirement indicators. Based on the deviation, the AI system adjusts the pressure of the wellhead and pipelines in real time by controlling regulating devices such as valves and pumps to keep the pressure within a safe range. After completing the pressure regulation, the AI system will check again the deviation between the pressure data of the wellhead and pipelines and the production requirement indicators. If the deviation still cannot meet the preset deviation requirements (i.e., exceeds the safe range), the AI system will trigger the safety warning mechanism, send an alarm to the operator, and may automatically take further emergency measures. Sensor groups are deployed on each production device to collect vibration operation parameters of the device in real time (such as amplitude, frequency, etc.). The AI system analyzes the vibration operation parameters of each device according to the preset vibration operation standards and determines whether the device is in normal operation. If it is found that the vibration operation parameters of the device exceed the standard range, the AI system will adjust the operation parameters of the device in real time (such as rotational speed, load, etc.) to keep the device in a safe operating state. After completing the device operation regulation, the AI system will check again whether the vibration operation parameters of each device meet the corresponding vibration operation standards. If there are still devices whose vibration operation parameters do not meet the standards (i.e., the devices are still in abnormal states), the AI system will trigger the safety warning mechanism, send an alarm to the operator, and may recommend shutdown for maintenance or take other necessary measures.

[0188] The effects of the above technical solution are as follows: By collecting and analyzing the operation data of the wellhead, pipelines, and production devices in real time, the AI system can timely detect and handle potential safety hazards, thus greatly reducing the occurrence probability of safety accidents. The AI system can accurately regulate the pressure of the wellhead and pipelines according to the production requirement indicators to ensure the stability and continuity of the production process. At the same time, by regulating the vibration operation parameters of the production devices in real time, it is possible to avoid shutdown or damage of the devices caused by abnormal vibration, thereby improving the utilization rate and production efficiency of the devices. Through the intelligent safety production regulation method, enterprises can reduce the losses and downtime caused by safety accidents and equipment failures. At the same time, the application of the AI system can also help enterprises optimize resource allocation and reduce maintenance costs. The AI system can provide key production data and analysis results in real time, providing a scientific basis for the decision-making of enterprises. This helps enterprises quickly respond to market changes and changes in production requirements, and thus formulate more reasonable production plans and strategies.

[0189] In summary, through the introduction of artificial intelligence technology, this technical solution realizes the real-time collection, intelligent analysis, and precise regulation of key parameters during oil extraction, thereby improving the safety production level, optimizing production efficiency, reducing operating costs, and enhancing decision-making efficiency.

[0190] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A safety production control method based on AI, characterized in that: The AI-based safe production control method includes: S1: The pressure data of the wellhead and pipeline are collected in real time through the pressure sensor, and the pressure of the wellhead and pipeline is adjusted according to the deviation between the pressure data of the wellhead and pipeline and the production requirement index; S2: After the pressure of the wellhead and pipeline is regulated, if the deviation between the pressure data of the wellhead and pipeline and the production requirement index still cannot meet the preset deviation requirement, a safety warning is issued; S3: collecting vibration operation parameters of each production equipment in real time through the sensor group, and safely regulating the operation of the equipment according to the vibration operation parameters of each production equipment; S4: After each production equipment completes safety regulation, if the vibration operation of each production equipment still cannot meet its corresponding vibration operation standard, a safety warning is issued to the production equipment whose vibration operation still cannot meet its corresponding vibration operation standard; wherein S3 includes: The vibration operation parameters of each production equipment are collected in real time by a sensor group, wherein the sensor group includes an acceleration sensor, a velocity sensor and a displacement sensor, and the vibration operation parameters include vibration acceleration, vibration velocity and vibration displacement generated by vibration; The vibration acceleration, vibration velocity and vibration displacement generated by the vibration of each production equipment are compared with the corresponding acceleration threshold, velocity threshold and vibration displacement threshold respectively; When any one of the vibration acceleration, vibration velocity and vibration displacement generated by vibration exceeds its corresponding threshold value, the corresponding time when any one of the vibration acceleration, vibration velocity and vibration displacement generated by vibration exceeds its corresponding threshold value is used as the data retrieval time; From the moment of data retrieval, the vibration operation parameters of the production equipment are retrieved in real time; the vibration operation evaluation coefficient is obtained using the vibration operation parameters of the production equipment; wherein the vibration operation evaluation coefficient is obtained by the following formula: in, U Indicates the vibration operation evaluation coefficient; A , V and D They represent vibration acceleration, vibration velocity and vibration displacement caused by vibration respectively; RMS ( A , V , D ) represents the sum of the square root values ​​of vibration acceleration, vibration velocity and vibration displacement caused by vibration; MAD ( A , V , D ) represents the sum of the mean absolute deviation values ​​of vibration acceleration, vibration velocity and vibration displacement generated by vibration; STD ( A , V , D ) represents the sum of the standard deviations of vibration acceleration, vibration velocity and vibration displacement generated by vibration; Var ( A , V , D ) represents the sum of the variances of vibration acceleration, vibration velocity and vibration displacement generated by vibration; K b represents a preset basic adjustment coefficient, and the value range of the basic adjustment coefficient is 1.32-1.47; σ represents a preset scaling factor, and the value range of the scaling factor is 0.18-0.53; ε Indicates the preset minimum constant, used to prevent the denominator from being zero; comparing the vibration operation evaluation coefficient with a preset vibration operation evaluation coefficient threshold; When the vibration operation evaluation coefficient exceeds a preset vibration operation evaluation coefficient, it is determined that safety regulation of the production equipment is required.

2. The AI-based production safety control method according to claim 1 is characterized in that: The pressure sensor collects the pressure data of the wellhead and pipeline in real time, and adjusts the pressure of the wellhead and pipeline according to the deviation between the pressure data of the wellhead and pipeline and the production requirement index, including: A first pressure sensor is installed at each of the multiple pressure monitoring positions at the wellhead; A second pressure sensor is installed at each of the multiple pressure detection node positions of the pipeline; Using the first pressure sensor to collect wellhead pressure data in real time, and regulating the wellhead pressure according to the deviation between the wellhead pressure data and the corresponding wellhead pressure production requirement index; The second pressure sensor is used to collect pipeline pressure data in real time, and the pipeline pressure is regulated according to the deviation between the pipeline pressure data and the corresponding pipeline pressure production requirement index.

3. The AI-based production safety control method according to claim 2 is characterized in that: The first pressure sensor is used to collect the pressure data of the wellhead in real time, and the pressure of the wellhead is regulated according to the deviation between the pressure data of the wellhead and the corresponding wellhead pressure production requirement index, including: Extracting the wellhead pressure data collected in real time by each of the first pressure sensors; Compare the deviation between the pressure data of each wellhead and its corresponding wellhead pressure production requirement index with a first pressure deviation threshold and a second pressure deviation threshold; When the deviation between the wellhead pressure data and the corresponding wellhead pressure production requirement index exceeds a preset first pressure deviation threshold, but does not exceed a second pressure deviation threshold, the historical pressure data collected by the first pressure sensor corresponding to the wellhead pressure data exceeding the preset first pressure deviation threshold is retrieved; Obtaining a pressure deviation evaluation coefficient using historical pressure data collected by a first pressure sensor corresponding to the wellhead pressure data exceeding a preset first pressure deviation threshold; comparing the pressure deviation evaluation coefficient with a preset pressure deviation evaluation threshold; When the pressure deviation evaluation coefficient exceeds the preset pressure deviation evaluation threshold, the pressure at the wellhead is regulated by increasing or reducing pressure according to the deviation between the pressure data at the wellhead exceeding the preset first pressure deviation threshold and the corresponding wellhead pressure production requirement index, so as to reduce the deviation between the pressure data at the wellhead and the corresponding wellhead pressure production requirement index; When the deviation between the wellhead pressure data and its corresponding wellhead pressure production requirement index exceeds a preset second pressure deviation threshold, the wellhead pressure is regulated by increasing or reducing the pressure according to the deviation between the wellhead pressure data exceeding the preset second pressure deviation threshold and its corresponding wellhead pressure production requirement index, so as to reduce the deviation between the wellhead pressure data and its corresponding wellhead pressure production requirement index.

4. The AI-based production safety control method according to claim 3 is characterized in that: Obtaining a pressure deviation evaluation coefficient using historical pressure data collected by a first pressure sensor corresponding to the wellhead pressure data exceeding a preset first pressure deviation threshold value comprises: When the number of first pressure sensors corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold is one, the first pressure sensor corresponding to the pressure data of the wellhead exceeding the preset first pressure deviation threshold is used as a target pressure sensor, and the historical pressure data collected by the target pressure sensor is retrieved; The pressure deviation evaluation coefficient is obtained by using the first evaluation coefficient model in combination with the historical pressure data collected by the target pressure sensor; wherein the pressure deviation evaluation coefficient is obtained by the following formula: in, S 01 represents the pressure deviation evaluation coefficient obtained by the first evaluation coefficient model; a , b and c Respectively represent a preset first adjustment parameter, a second adjustment parameter and a third adjustment parameter, and the value ranges of the first adjustment parameter, the second adjustment parameter and the third adjustment parameter are 0.58-1.13, 0.89-1.22 and 1.14-1.39; P b Indicates the pressure standard deviation corresponding to the historical pressure data collected by the target pressure sensor; n Indicates the number of pressure data of historical pressure data; P i Indicates i The pressure data value corresponding to each historical pressure data; P i-1 Indicates i -1 pressure data value corresponding to the historical pressure data; P be Indicates the preset force standard deviation reference value; P fmaxi Indicates i The maximum value of the pressure change amplitude between two adjacent pressure data collection moments corresponding to the historical pressure data; P fmini Indicates i The minimum value of the pressure change amplitude between two adjacent pressure data collection moments corresponding to the historical pressure data; When there are multiple first pressure sensors corresponding to the pressure data at the wellhead exceeding the preset first pressure deviation threshold, the first pressure sensor corresponding to the pressure data at the wellhead exceeding the preset first pressure deviation threshold is used as a target pressure sensor, and historical pressure data collected by the multiple target pressure sensors are retrieved; The pressure deviation evaluation coefficient is obtained by using the second evaluation coefficient model in combination with the historical pressure data collected by the plurality of target pressure sensors; wherein the pressure deviation evaluation coefficient is obtained by the following formula: in, S 02 represents the pressure deviation evaluation coefficient obtained by the second evaluation coefficient model; m Indicates the number of target pressure sensors; P bi Indicates i The standard deviation of the pressure data corresponding to the historical pressure data of the target pressure sensor; G i Indicates i The average value of the correlation coefficients between the target pressure sensor and other target pressure sensors; w i Indicates i The weight value corresponding to each target pressure sensor; P fmax01i Indicates i The maximum pressure change amplitude corresponding to each target pressure sensor; P fmax02i Indicates i The maximum value of the pressure change amplitude of the target pressure sensor corresponds to the maximum value of the correlation coefficient of the target pressure sensor.

5. The AI-based production safety control method according to claim 2 is characterized in that: The second pressure sensor is used to collect the pressure data of the pipeline in real time, and the pressure of the pipeline is regulated according to the deviation between the pressure data of the pipeline and the corresponding pipeline pressure production requirement index, including: Retrieving pipeline pressure data collected by the second pressure sensor in real time; Compare the pressure data of each pipeline with its corresponding pressure risk threshold and pressure warning threshold; When the pressure data of the pipeline exceeds the corresponding pressure warning threshold, the pipeline valve corresponding to the pressure detection node position of the pipeline where the pressure data exceeds the corresponding pressure warning threshold is directly adjusted to reduce the pressure value corresponding to the pressure detection node position of the pipeline; When the pressure data of the pipeline exceeds its corresponding pressure risk threshold, the target pressure detection node position is screened out from the pressure detection node positions of all pipelines, and the target pressure data corresponding to the target pressure detection node position is used to obtain the pressure risk assessment coefficient, and the pressure control judgment of the pressure detection node position is performed using the pressure risk assessment coefficient.

6. The AI-based production safety control method according to claim 5 is characterized in that: When the pressure data of the pipeline exceeds the corresponding pressure risk threshold, a target pressure detection node position is selected from the pressure detection node positions of all pipelines, and a pressure risk evaluation coefficient is obtained using the target pressure data corresponding to the target pressure detection node position, and the pressure control determination of the pressure detection node position is performed using the pressure risk evaluation coefficient, including: When the pressure data of the pipeline exceeds the corresponding pressure risk threshold, the correlation coefficient between the pressure detection node position of the pipeline whose pressure data exceeds the corresponding pressure risk threshold and the pressure detection node positions of other pipelines is retrieved; Retrieving the pressure detection node positions of the pipelines whose pressure data exceeds the corresponding pressure risk threshold and the pressure detection node positions of other pipelines whose correlation coefficients exceed the preset correlation coefficient threshold as the target pressure detection node positions, and taking the pressure data corresponding to the target pressure detection node positions as the target pressure data; Obtaining a pressure risk assessment coefficient using the pressure data of the pipeline corresponding to the pressure detection node position of the pipeline where the pressure data of the pipeline exceeds the corresponding pressure risk threshold and its corresponding target pressure data; The pressure risk assessment coefficient is obtained by the following formula: in, K It represents the pressure risk assessment coefficient; k Indicates the number of target pressure detection node locations; P g Indicates the pressure data of the pipeline corresponding to the pressure detection node position of the pipeline where the pressure data of the pipeline exceeds the corresponding pressure risk threshold; P gi Indicates i Target pressure data corresponding to the target pressure detection node position; F i The pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds the corresponding pressure risk threshold is i The correlation coefficient between the locations of target pressure detection nodes; δ Indicates the preset time decay coefficient; t i Indicates i The total pressure data collection time corresponding to each target pressure detection node position; λ 01 and λ 02 represent the first risk factor and the second risk factor respectively, and the first risk factor is obtained by the following formula: in, λ 01 represents the first risk factor; F i The pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds the corresponding pressure risk threshold is i The correlation coefficient between the locations of target pressure detection nodes; F y Indicates the preset correlation coefficient threshold; F max express n The maximum value of the correlation coefficient between the target pressure detection node position and the pressure detection node position of the pipeline whose pressure data exceeds the corresponding pressure risk threshold; Furthermore, the second risk factor is obtained by the following formula: in, λ 02 represents the second risk factor; P gb Indicates the standard deviation of the pressure data corresponding to the pressure detection node position of the pipeline where the pressure data of the pipeline exceeds the corresponding pressure risk threshold; P gbi Indicates i The standard deviation of the pressure data corresponding to the target pressure detection node position; F i The pressure detection node position of the pipeline indicating that the pressure data of the pipeline exceeds the corresponding pressure risk threshold is i The correlation coefficient between the locations of target pressure detection nodes; Comparing the stress risk assessment coefficient with a preset stress risk assessment threshold; When the pressure risk assessment coefficient exceeds the preset pressure risk assessment threshold, the pipeline valve corresponding to the pressure detection node position of the pipeline whose pressure data exceeds its corresponding pressure warning threshold is adjusted to reduce the pressure value corresponding to the pressure detection node position of the pipeline.

7. The AI-based production safety control method according to claim 1 is characterized in that: When it is determined that the production equipment needs to be safely regulated, the production equipment is decelerated and regulated, including: When it is determined that production equipment needs to be safely regulated, the vibration operation evaluation coefficient is retrieved; The vibration operation evaluation coefficient is used to obtain a deceleration control ratio of the production equipment, and the production equipment is decelerated and controlled according to the deceleration control ratio of the production equipment; The deceleration control ratio of the production equipment is obtained by the following formula: in, B Indicates the deceleration control ratio of production equipment; r Indicates the preset proportional adjustment coefficient, and the value range of the proportional adjustment coefficient is 0.27-0.34; U Indicates the vibration operation evaluation coefficient.

8. A safe production control system based on AI, characterized in that: The AI-based safe production control system includes: The AI-based safe production control system includes: The first adjustment module is used to collect the pressure data of the wellhead and the pipeline in real time through the pressure sensor, and adjust the pressure of the wellhead and the pipeline according to the deviation between the pressure data of the wellhead and the pipeline and the production requirement index; The first safety warning module is used to issue a safety warning when the deviation between the pressure data of the wellhead and the pipeline and the production requirement index still cannot meet the preset deviation requirement after the pressure of the wellhead and the pipeline is regulated; A second adjustment module is used to collect the vibration operation parameters of each production equipment in real time through a sensor group, and to safely regulate the operation of the equipment according to the vibration operation parameters of each production equipment; The second safety warning module is used for issuing a safety warning to the production equipment whose vibration operation still cannot meet its corresponding vibration operation standard when the vibration operation of each production equipment still cannot meet its corresponding vibration operation standard after the safety regulation of each production equipment is completed; Wherein, the second adjustment module includes: The vibration operation parameters of each production equipment are collected in real time by a sensor group, wherein the sensor group includes an acceleration sensor, a velocity sensor and a displacement sensor, and the vibration operation parameters include vibration acceleration, vibration velocity and vibration displacement generated by vibration; The vibration acceleration, vibration velocity and vibration displacement generated by the vibration of each production equipment are compared with the corresponding acceleration threshold, velocity threshold and vibration displacement threshold respectively; When any one of the vibration acceleration, vibration velocity and vibration displacement generated by vibration exceeds its corresponding threshold value, the corresponding time when any one of the vibration acceleration, vibration velocity and vibration displacement generated by vibration exceeds its corresponding threshold value is used as the data retrieval time; From the moment of data retrieval, the vibration operation parameters of the production equipment are retrieved in real time; the vibration operation evaluation coefficient is obtained using the vibration operation parameters of the production equipment; wherein the vibration operation evaluation coefficient is obtained by the following formula: in, U Indicates the vibration operation evaluation coefficient; A , V and D They represent vibration acceleration, vibration velocity and vibration displacement caused by vibration respectively; RMS ( A , V , D ) represents the sum of the square root values ​​of vibration acceleration, vibration velocity and vibration displacement caused by vibration; MAD ( A , V , D ) represents the sum of the mean absolute deviation values ​​of vibration acceleration, vibration velocity and vibration displacement generated by vibration; STD ( A , V , D ) represents the sum of the standard deviations of vibration acceleration, vibration velocity and vibration displacement generated by vibration; Var ( A , V , D ) represents the sum of the variances of vibration acceleration, vibration velocity and vibration displacement generated by vibration; K b represents a preset basic adjustment coefficient, and the value range of the basic adjustment coefficient is 1.32-1.47; σ represents a preset scaling factor, and the value range of the scaling factor is 0.18-0.53; ε Indicates the preset minimum constant, used to prevent the denominator from being zero; comparing the vibration operation evaluation coefficient with a preset vibration operation evaluation coefficient threshold; When the vibration operation evaluation coefficient exceeds a preset vibration operation evaluation coefficient, it is determined that safety regulation of the production equipment is required.

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