A dynamic monitoring optimization method for a real-time transmission system of offshore stimulation operation

Abstract

The application provides a dynamic monitoring optimization method for a real-time transmission system of offshore stimulation operation, which comprises the following steps: collecting original signals of at least one monitoring device in an offshore stimulation operation site, sampling and analyzing analog signals and digital signals respectively, and generating time-synchronized and format-unified standardized data sets through photoelectric isolation and noise suppression processing; based on the standardized data sets, combining with the working condition characteristics of the offshore operation environment, constructing a link availability scoring model, predicting a link available time period, and outputting an available time period table; according to the available time period table and current process stage information, calculating the comprehensive priority of each monitoring data point to generate a priority scheduling matrix; and according to the priority scheduling matrix, dynamically adjusting the transmission power, redundant transmission times and packeting strategy in each predicted available time period to complete data transmission execution.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of data optimization, and particularly relates to a dynamic monitoring optimization method for a real-time transmission system for offshore stimulation operation. BACKGROUND

[0002] In the process of offshore oil and gas field stimulation operation, a large number of field monitoring sensors and instruments are often laid out for collecting key process parameters such as pressure, temperature and flow. These sensors mostly output analog quantities (such as 4-20 mA current signals) or digital communications (such as RS485) and are transmitted to the data acquisition and control system through wired mode. In the traditional mode, these wired signals need to be wired across the deck or even across the hoisting equipment to reach the control end, which not only has great difficulty in construction in the complex and changeable operation environment on the sea, but is also easily limited by factors such as space, wiring path, structural obstacles and personnel operating conditions. In addition, in the offshore operation environment, there are often strong corrosion, high humidity, strong wind and waves, and vibration and displacement caused by the work of large equipment, which can cause the service life of the wiring material to be shortened, the interface contact to be poor, and the maintenance frequency to be increased, thereby increasing the overall operation and maintenance cost. Although in recent years, wireless transmission schemes based on LoRa, WiFi or cellular networks have appeared, in the environment of offshore operation, which has complex electromagnetic interference, frequent physical shielding, and communication link stability significantly affected by tides and weather changes, these wireless methods still have obvious deficiencies in transmission real-time, data integrity and power consumption control. Specifically, the link quality fluctuates greatly with time and position, which easily causes data delay or packet loss; the transmission priority of different types of data is difficult to distinguish, which causes the high-value abnormal monitoring data to be delayed or lost; under the limited power supply conditions, the long-time operation of the node equipment is difficult to balance the high-reliable transmission and low-energy consumption requirements. These problems are more prominent in the scenes of multi-platform collaborative operation and temporary operation node layout. SUMMARY

[0003] The purpose of the present application is to design a dynamic monitoring optimization method for a real-time transmission system for offshore stimulation operation, which can consider multi-source signal access, transmission link stability, data priority management and energy efficiency optimization in the special environment of offshore stimulation operation.

[0004] In order to achieve the above purpose, the present application provides a dynamic monitoring optimization method for a real-time transmission system for offshore stimulation operation, which comprises:

[0005] Collecting the original signals of at least one monitoring device in the offshore stimulation operation site, sampling and analyzing the analog signals and digital signals respectively, and generating a standardized data set with time synchronization and unified format through photoelectric isolation and noise suppression processing;

[0006] Based on the standardized dataset, combined with the working condition characteristics of the offshore operation environment, a link availability scoring model is constructed to predict the link availability period and output an availability period table; wherein the working condition characteristics include tidal influence, cross-deck lifting, equipment noise and vibration, and environmental disturbance;

[0007] According to the availability period table and the current process stage information, the comprehensive priority of each monitoring data point is calculated to generate a priority scheduling matrix;

[0008] According to the priority scheduling matrix, the transmission power, redundant transmission times and packeting strategy are dynamically adjusted in each predicted availability period to complete data transmission execution.

[0009] Further, the standardized dataset includes device identifiers, measurement values and timestamps, wherein the timestamps are uniformly generated by a GNSS time module to ensure cross-platform time synchronization.

[0010] Further, the noise suppression process uses a sliding window mean method, and the window width can be adjusted according to the sensor response speed.

[0011] Further, the construction of the link availability scoring model specifically includes: calculating a record continuity index based on the timestamps in the standardized dataset, and calculating a noise intensity index based on the measurement values; introducing a working condition disturbance term, which quantifies the potential impact of sudden working conditions on the link by analyzing the synchronous fluctuation rate of the measurement values of at least one key monitoring point in the recent time window; also introducing a stability regularization term, which is the moving average of the link availability score in the recent time window, used to smooth short-term fluctuations; finally, the comprehensive link availability score is calculated by integrating the record continuity index, noise intensity index, working condition disturbance term and stability regularization term.

[0012] Further, the calculation of the comprehensive priority specifically includes: assigning a stage weight to all monitoring data points based on their current process stage; introducing an urgency correction term, which is activated to increase the priority of the monitoring data point when the change rate of its measurement value in the recent time period exceeds its safety threshold; introducing a working condition disturbance suppression term, which is calculated by combining the global working condition disturbance index with the preset disturbance weight of the physical area where the monitoring point is located, used to reduce the priority of non-critical data when the environment is unstable; and introducing a stage switching smoothing term, which is used to partially retain the priority value of the previous stage within a preset stabilization period after the switching of the operation process stage, to prevent the scheduling scheme from dramatic fluctuations.

[0013] Further, the preset disturbance weight of the physical area where the monitoring point is located is configured by a region mask, which is preset according to the actual physical location of the monitoring equipment on the offshore platform.

[0014] Further, the preemption protection logic is introduced when generating the priority scheduling matrix, specifically, when the priority of a monitoring point is located in the front of a certain proportion of the global and the urgency correction term is positive, the transmission resource of the monitoring point is locked.

[0015] Further, the adjustment of the transmission power and the number of redundant transmissions is based on the mapping calculation of the time period difficulty coefficient and the relative priority of the task.

[0016] Further, the time period difficulty coefficient is converted into a numerical coefficient by a preset lookup table method from the time period level in the available time period table.

[0017] Further, the packet strategy includes: cutting the data into at least one packet according to the resource proportion, and inserting the redundant packet into the same available time period in the form of first packet plus interval uniform distribution for transmission, so as to avoid congestion.

[0018] The beneficial technical effects of the present application are at least in the following points:

[0019] In view of the above problems, the present application provides a dynamic monitoring optimization method and system for a real-time transmission system for offshore stimulation operation, which realizes the rapid access and format unification of analog and digital signals through a unified multifunctional signal acquisition and conversion architecture; introduces a dynamic link state evaluation and transmission strategy scheduling mechanism before wireless transmission, combines the operation process and the change of the field environment, reasonably arranges the data sending time and the content priority, and ensures that the key monitoring data is transmitted out with high reliability under the most favorable link conditions; at the same time, through flexible power consumption management and energy utilization strategy, the continuous operation time of the equipment is prolonged under the condition of limited power supply. The core of the present application is to organically combine signal access, link adaptability scheduling, data priority control and energy optimization, form a closed-loop dynamic optimization system for the special needs of offshore stimulation operation, so as to effectively overcome the shortcomings of poor stability, insufficient real-time performance and high maintenance cost of the existing wireless transmission scheme in this field. BRIEF DESCRIPTION OF DRAWINGS

[0020] The present application is further described by using the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by those skilled in the art without creative labor on the premise of not paying creative labor.

[0021] Figure 1 A flowchart of a dynamic monitoring optimization method for a real-time transmission system for offshore stimulation operation of the present application. DETAILED DESCRIPTION

[0022] Embodiments of the present application are described below in detail with reference to the accompanying drawings, wherein the same or similar components are denoted by the same or similar reference numerals throughout. The embodiments described below by reference to the accompanying drawings are exemplary and are for the purpose of explanation only, and are not to be understood as limiting the present application.

[0023] In one or more embodiments, as shown in Figure 1 A dynamic monitoring optimization method for a real-time transmission system for offshore stimulation operations is disclosed, the method comprising the following:

[0024] S1: Collecting raw signals of at least one monitoring device at the offshore stimulation operation site, sampling and analyzing analog signals and digital signals respectively, and generating time-synchronized, format-unified standardized data sets through photoelectric isolation and noise suppression processing;

[0025] Specifically, the goal of this step is to collect raw signals from different types of core monitoring devices at the offshore stimulation operation site and uniformly convert them into standardized data sets that are consistent in format, time-synchronized, and can directly participate in subsequent link prediction and scheduling calculations. Due to the variety of monitoring devices on offshore platforms and workboats, and the influence of humidity, vibration, temperature changes, and strong electromagnetic interference, signal acquisition must consider anti-interference, electrical isolation, and time reference consistency. On this basis, this step designs a multi-channel data acquisition module that can be directly deployed in the offshore operation environment, and through explicit data structures and noise suppression methods, ensures that the collected signals are stable and reliable, providing high-quality data input for subsequent link prediction and priority scheduling.

[0026] Step one input signals are directly from the core sensors and instruments at the offshore stimulation operation site. Taking a common operation configuration as an example, the 4-20 mA current signal output by the pressure sensor is converted to voltage by a high-precision sampling resistor inside the module, and then sampled by a 16-bit resolution analog-to-digital conversion chip; the digital output of the temperature sensor or flow meter is read through the RS485 transceiver and protocol analysis program inside the module to obtain the original byte stream, and converted into recognizable numerical values. In addition, for some auxiliary process monitoring instruments (such as chemical agent dosage), if the output is a 0-10 V voltage signal, a corresponding voltage divider and buffer circuit is configured inside the acquisition module to avoid exceeding the range of the analog-to-digital converter. These different sources of signals are uniformly passed through an optoelectronic isolation module before entering the data processing link, with an isolation voltage of not less than 2.5 kV to prevent cross-platform electrical interference from affecting the data acquisition circuit.

[0027] The acquisition module is divided into two channels according to signal types: analog channel and digital channel. In the analog channel, the current signal is first converted into a voltage signal by a sampling resistor. The resistance value of the sampling resistor is selected to balance between accuracy and voltage drop. For example, the typical value is 100 Ω ± 0.1%, so that the output voltage is 0.4-2.0 V in the 4-20 mA range, which ensures the input accuracy of the analog-to-digital converter and avoids excessive burden on the sensor circuit. The analog-to-digital converter is set to a sampling frequency of 100 Hz, and the original value obtained by timing sampling is stored in the cache area after internal linearization correction. The digital channel communicates with the instrument through the RS485 bus and reads the register data using the Modbus RTU protocol. For example, read the 16-bit value at register address 0x0002 of the pressure sensor, and convert it into the actual physical quantity according to the coefficient in the device manual.

[0028] All collected values are packaged into a unified standardized record structure, which can be represented as follows:

[0029]

[0030] wherein, represents the time at which the standardized record is collected; is a unique identification code assigned during installation and debugging of the device, which is configured manually and stored in the non-volatile memory of the module; is the measurement value of the device at time, the analog quantity is output by the analog-to-digital converter, and the digital quantity is obtained by protocol analysis; is the time stamp generated by the GNSS timing module built-in the module, and is uniformly assigned to all channels to ensure time synchronization between platforms and nodes. Each variable has been normalized before integration to ensure consistent physical dimensions.

[0031] To suppress the sharp peak noise caused by short-term electromagnetic interference and mechanical vibration commonly seen in offshore operations, this step performs sliding window mean value smoothing on the original sampling data before generating . For example, in the case of a sampling frequency of 10 Hz, the window width is set to 5 sampling points, so each output value is the mean value of the current point and the two points before and after it. This processing method can significantly reduce random noise while maintaining a response delay of less than 0.5 s, thereby balancing signal smoothness and dynamic feature retention. In order to adapt to the varying characteristics of different signals, the window width parameter can be adjusted by engineers according to the response speed of specific sensors during system configuration. For example, for fast-changing flow signals, the window width can be reduced to improve followability.

[0032] ​S2: Based on the standardized data set, combined with the working condition characteristics of the offshore operation environment, a link availability scoring model is constructed to predict the link availability period and output the available period table; wherein the working condition characteristics include tidal influence, cross-deck lifting, equipment noise and vibration, environmental disturbance;

[0033] Specifically, this step takes the standardized data set output in step one , using these time-synchronized, format-unified, and interference-suppressed core monitoring data, without relying on other external data, combined with the special working condition characteristics of offshore production increase operations (tidal influence, cross-deck lifting, equipment noise and vibration, environmental disturbance, etc.), a short-term available period prediction model for wireless link is constructed. The core role of this step is to identify the time window with high link quality in a short period of time in the future, so that the subsequent priority scheduling can prioritize the transmission of critical data in these windows, thereby reducing the packet loss rate and reducing energy consumption. Unlike general availability prediction, this introduces "working condition disturbance terms" and "stability regularization terms" designed specifically for offshore scenarios, ensuring that the prediction results not only reflect the current link state, but also take into account the suppression needs of operation rhythm changes and environmental disturbances.

[0034] First, for each time slice, construct the basic component of the link availability index. Time continuity index Based on Calculate the sample completeness of anchor points in a fixed observation window; noise intensity index Based on Robust dispersion calculation of residual high-frequency components. The "anchor points" here are selected from a number of monitoring points with the highest relevance to subsequent scheduling, such as wellhead pressure, main flow rate, and key temperature points, which Are specified once in system configuration.

[0035] For the particularity of offshore operations, a working condition disturbance term Is introduced to quantify possible link quality mutations in the short term. The construction of Originates from the synchronized fluctuation pattern between different measuring points, for example, when multiple anchor points experience synchronous and severe changes in a short period of time, it is likely that the overall environmental disturbance is caused by lifting, switching procedures, or equipment start-stop. In this case, even if the current And Looks good, it will increase the risk of link instability. Can be calculated by the maximum correlation fluctuation rate of the anchor point set in the last Second, normalize it to make its value range consistent with 、 .

[0036] To integrate the above factors, the link availability score formula constructed in this step is:

[0037]

[0038] Where: is the integrated link availability score at time is the record continuity index, derived from the sample completeness of is the noise intensity indicator, derived from the residual dispersion of , normalized; is the working condition disturbance term, derived from the synchronization fluctuation rate between anchor points, normalized; is the working condition disturbance penalty coefficient, set by engineering experience, for example, 0.2-0.5; is the stability regularization term, used to smooth the short-term fluctuations of , which can be defined as the average of in the last seconds; is the regularization term weight, to prevent the prediction from being too sensitive, with a typical value of 0.1-0.3.

[0039] This formula introduces an explicit penalty mechanism for sudden working conditions at sea through , making the prediction results more consistent with field experience; uses as a stability regularization term to avoid large fluctuations in the prediction results due to short-term accidental changes, thereby maintaining consistency in scheduling; retains the conservatism of so that the prediction will not ignore the degradation of another indicator due to the excellence of a single indicator.

[0040] After obtaining the sequence, according to the set high availability threshold and medium availability threshold, the future prediction timeline is divided into several time slices, and the availability level and recommended transmission window length of each slice are marked. The future prediction value is obtained through the short-term stability assumption, i.e., assuming maintains the current trend within the window length, combined with the latest trend, the prediction window length is shortened or lengthened in advance.

[0041] S3: According to the availability period table and the current process stage information, calculate the comprehensive priority for each monitoring data point to generate a priority scheduling matrix;

[0042] Specifically, this step takes over the link availability score sequence and the availability period table of step two, while combining the standardized data set ​​​It also incorporates process stage information from offshore production enhancement operations to generate a priority scheduling matrix. Its function is to integrate link status, data value, and process urgency into a directly executable scheduling scheme under the constraints of limited bandwidth and limited link availability time. This prioritizes high-value, high-urgency data for transmission resources, thereby improving the real-time performance and reliability of the entire system under harsh maritime conditions. The innovation of this step lies in the addition of a "condition disturbance suppression term" and a "phase switching smoothing term" specifically for maritime scenarios to the comprehensive priority calculation. This ensures that the scheduling can respond to emergencies without causing unnecessary scheduling oscillations due to frequent phase switching.

[0043] Specifically, will according to Group them and associate them with their current process stage. and the corresponding stage weights For example, during the fracturing stage, the weight of the wellhead pressure measuring point is set to 1.0, the weight of the casing pressure measuring point is 0.8, and the weight of some flow measuring points is 0.6. These weights are set during system initialization.

[0044] Furthermore, for each future time slice in the available time slot table Extract the corresponding This is then multiplied by the process stage weight for each measuring point to obtain the basic priority score. To accommodate sudden changes in working conditions during offshore operations, this step introduces an urgency correction term. When measuring point in the past Rate of change over time (through) exist (Calculation of the slope of the linear fit in the interval) exceeds the safety threshold of this stage. hour, It takes a positive value, otherwise it is 0, which is used to temporarily increase its priority.

[0045] In offshore operations, phase transitions (such as from acidizing to fracturing) can cause significant fluctuations in the scheduling matrix, affecting the stable utilization of the pipeline. Therefore, a phase transition smoothing term is added to the formula. During the first stable period after a phase switch (e.g., 30 seconds), retain a portion of the priority score from the previous phase to prevent resource waste caused by sudden drops or increases.

[0046] The overall priority formula is as follows:

[0047] ;

[0048] in: For measuring points In time slice a comprehensive priority score; a process phase weight; a link availability score; a urgency modifier, positive when the rate of change exceeds a threshold; a process disturbance suppression term, the global process disturbance index output from step two a combined measurement point is calculated as wherein is the disturbance weight of the physical area the measurement point belongs to (preset in system configuration), which is significantly greater than 0 to reduce the priority to avoid sending non-critical data when the area environment is unstable; is a phase switching smoothing term, which partially retains the priority of the previous phase during the stable period after phase switching; is a weight coefficient, configured on site to balance the influence of link state, urgency, disturbance suppression, and switching smoothing.

[0049] To avoid high-priority tasks being occupied by low-priority tasks, this step introduces preemption protection logic when generating When is in the global front and , lock the time slice resources of the task and prohibit it from being replaced by other tasks. This mechanism is particularly suitable for situations such as pressure surges and flow drops that need to be reported immediately in offshore operations.

[0050] Finally, according to the in each time slice from high to low, the link resources of the time slice in the available period table are allocated in turn, and the available level of the time slice is written into as the period level field, which is convenient for subsequent direct mapping to the difficulty coefficient , forming a complete priority scheduling matrix .

[0051] S4: According to the priority scheduling matrix, dynamically adjust the transmission power, redundancy sending times, and packet organization strategy in each predicted available period to complete data transmission execution.

[0052] Specifically, this step converts the priority scheduling matrix and the comprehensive priority score matrix into a wireless transmission plan that can be directly executed in the microprocessor of the on-site node, and generates low-power and landable transmission parameters and packet organization methods in each time slice. This step proposes a power-redundancy double-mapping driven by the time slice difficulty coefficient, which is derived from The time period level field in the code is directly converted, which avoids repeated calculations and can also reflect the reinforcement of difficult time periods at the execution layer.

[0053] Before each time slice begins, the microprocessor starts from... Retrieve the film's task list and time slot rating, and then... Queuing from highest to lowest difficulty. To facilitate unified mapping at the execution layer, the time period level (high / medium / low) is converted into a difficulty coefficient via table lookup. The lookup table entries are embedded in the firmware during deployment and correspond one-to-one with the level codes in step three. Subsequently, based on the resource ratio of the task in this slice (already allocated),... The system allocates available air interface time (as given in the code) and generates two key parameters for each task: transmit power and redundant packet count. The firmware then calculates the spreading rate, coding rate, and packet length according to rules. The specific execution is as follows:

[0054] First, determine the power level. To provide stronger link protection for high-priority tasks during challenging periods while limiting overall energy consumption, this step employs a linearly bounded mapping. The relative priority of the task is amplified and multiplied by the period's difficulty coefficient, then mapped to the adjustable transmit power range.

[0055]

[0056] in, For measuring points In time slice The transmission power; This is the module's lowest power level (firmware constant). This represents the available power adjustment range (firmware constant). For the reason The difficulty coefficient obtained by mapping time period levels; This is the highest priority within that time slice, used for normalization to ensure consistent dimensions. For example, if the highest priority in this slice is 90, and a task's priority is 72... , Then the power increment is The final power output will be the closest available power setting within the legal range.

[0057] Next, determine the number of redundant packets. To balance reliability and air interface usage, the redundancy number increases monotonically with "difficulty × importance" and is rounded down within the resource ratio range:

[0058]

[0059] in, For measuring points In time slice Redundant transmission count; Redundancy scaling factor (firmware constant, based on the time slice) The upper limit of resource proportions is pre-set to avoid exceeding the limit. lower or When the size is small, the formula naturally gives or Redundancy is introduced; when both are high, even higher redundancy is provided, thus reinforcing "difficult and important" tasks. The above mapping has been normalized before entering the rounding stage.

[0060] get and Then, the firmware selects a spreading and coding rate combination that matches the power level based on the preset power-spreading-coding rate correspondence table, and then processes the task data according to... The resource allocation is divided into several packets; the size of each packet does not exceed the uniform segment of the air interface budget for that segment. Redundant packets are inserted into the same segment according to the strategy of "first packet + evenly distributed intervals" to avoid concentrated congestion. If there is still spare air interface within the same segment, the highest priority is given. The task adds a redundant copy; if there is insufficient time within the slice, only the highest-time copy is retained. The main package and postpones low-priority tasks to the next slice, by The order field ensures that the sequence remains unchanged. In the actual code, the send queue is arranged in a triplet of "(task ID, packet sequence number, redundancy)" and sent to the RF driver one by one according to the firmware timer trigger. For example: when... A certain task and hour, Round to The main packet and two redundant packets are evenly distributed and sent within the slice at a time interval of 1:2.

[0061] This invention also provides a dynamic monitoring and optimization device for a real-time transmission system for offshore production enhancement operations, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiment of a dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations, for example... Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0062] For example, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more modules can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation.

[0063] The dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation can be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation can include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation can also include an input / output device, a network access device, a bus, etc.

[0064] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or can also be any conventional processor, etc. The processor is the control center of the dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation, and connects all parts of the dynamic monitoring optimization equipment for the real-time transmission system for offshore stimulation operation through various interfaces and lines.

[0065] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the dynamic monitoring optimization equipment for the real-time transmission system of offshore stimulation operation by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required by a function, etc.; and the data storage area can store data created according to the running of the air conditioner controller, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash storage device, or other volatile solid-state storage devices.

[0066] The modules of the dynamic monitoring optimization equipment for the real-time transmission system of offshore stimulation operation can be stored in a computer readable storage medium if they are realized in the form of software function units and sold or used as independent products. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0067] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium, and the program can include the processes of the above-mentioned various method embodiments when executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0068] The above is the preferred embodiment of the present application, it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, can also make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations, characterized in that, The method includes: The system collects raw signals from at least one monitoring device at the offshore production enhancement operation site, samples and analyzes analog and digital signals separately, and generates a standardized dataset with time synchronization and uniform format through opto-isolation and noise suppression processing. Based on the standardized dataset and the operating conditions of the offshore working environment, a link availability scoring model is constructed to predict the available time periods of the link and output an available time period table. The operating conditions include tidal influences, cross-deck hoisting, equipment noise and vibration, and environmental disturbances. The link availability scoring model is constructed as follows: a record continuity index is calculated based on the timestamps in the standardized dataset; a noise intensity index is calculated based on the measured values; an operating condition disturbance term is introduced, which quantifies the potential impact of sudden operating conditions on the link by analyzing the synchronous fluctuation rate of measured values ​​at at least one key monitoring point within the most recent time window; a stability regularization term is also introduced, which is the moving average of the link availability score within the most recent time window, used to smooth short-term fluctuations; finally, a comprehensive link availability score is calculated by integrating the record continuity index, noise intensity index, operating condition disturbance term, and stability regularization term. Based on the available time period table and the current process stage information, calculate the comprehensive priority for each monitoring data point and generate a priority scheduling matrix. Based on the priority scheduling matrix, the transmit power, redundant transmission count, and packet splitting strategy are dynamically adjusted within each predicted available time period to complete the data transmission execution.

2. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 1, characterized in that, The standardized dataset includes device identifiers, measurement values, and timestamps, with the timestamps being uniformly generated by the GNSS timing module to ensure cross-platform time synchronization.

3. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 1, characterized in that, The noise suppression process employs a sliding window mean method, and the window width can be adjusted according to the sensor response speed.

4. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 1, characterized in that, The calculation of comprehensive priority specifically includes: assigning a stage weight to all monitoring data points based on their current process stage; introducing an urgency correction term, which is activated to increase the priority of a monitoring data point when its measured value changes at a rate exceeding its safety threshold in a recent time period; introducing a working condition disturbance suppression term, which is calculated by combining a global working condition disturbance index with a preset disturbance weight of the physical area where the monitoring point is located, and is used to reduce the priority of non-critical data when the environment is unstable; and introducing a stage switching smoothing term, which is used to partially retain the priority score of each monitoring data point in the previous stage within a preset stable period after a switch in the work process stage, in order to prevent drastic fluctuations in the scheduling scheme.

5. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 4, characterized in that, The preset disturbance weights of the physical area where the monitoring point is located are configured through a region mask, which is preset in groups according to the actual physical location of the monitoring equipment on the offshore platform.

6. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 4, characterized in that, When generating the priority scheduling matrix, a preemption protection logic is also introduced. Specifically, when the priority of a certain monitoring point is in the top proportion of the global priority and its urgency correction term is positive, the transmission resources of that monitoring point are locked and it is prohibited from being replaced by other tasks.

7. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 1, characterized in that, The adjustment of the transmission power and redundant transmission count is based on a mapping calculation of the time period difficulty coefficient and the relative priority of the task.

8. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 7, characterized in that, The difficulty coefficient of the time period is converted into a numerical coefficient from the time period level in the available time period table through a preset lookup table method.

9. The dynamic monitoring and optimization method for a real-time transmission system for offshore production enhancement operations according to claim 1, characterized in that, The packet splitting strategy includes: dividing the data into at least one packet according to the resource ratio, and sending redundant packets in the same available time period by evenly distributing them with the first packet plus intervals, in order to avoid concentrated congestion.

Images