Domestic Oil and Gas Field Electric Energy Metering and Control Method and System Based on HarmonyOS

Abstract

The present invention discloses a domestic oil and gas field electric energy metering and control method and system based on the HarmonyOS. The electric energy metering and control system built based on the HarmonyOS realizes the electric energy metering and distribution of the oil and gas field. First, the minimum expected operation duration of the oil well production unit under the current situation is obtained through a pre-trained backpropagation neural network, and further the corresponding electric energy influence degree parameter of the oil well production unit and the expected power consumption required under the expected operation condition are determined, which can improve the accuracy of electric energy metering in the oil and gas field. Then, the correlation degree of all oil well production units is divided to obtain multiple oil well operation groups, and further the weight parameters corresponding to the oil well operation groups are determined by means of weight assignment. The weight parameter characterizes the electric energy demand load situation of the oil well operation group. Finally, according to the weight parameters corresponding to the oil well operation groups and the expected power consumption of each oil well production unit, the precise electric energy distribution for the oil well operation groups is realized, which is beneficial to improving the overall production efficiency of the oil and gas field.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield production management, and in particular to a method and system for oil and gas field electric energy metering control based on the domestic HarmonyOS system, and a computer-readable storage medium. Background Art

[0002] Since the production operations in oil and gas fields are relatively complex and the process cycle is not short, multiple operating systems often need to cooperate with each other to effectively complete the production operations. At present, the cooperation operation method has been mostly adopted for oil and gas field production on the market. Although the energy consumption of each operating system can be monitored in real time during the production process, considering the actual influence of factors such as the difference in the oil and gas field exploitation environment, the possible insufficient energy consumption utilization rate of the operating system itself, or the inability to grasp the operating time of the operating system, the monitored energy consumption of the operating system may not enable the operating system to operate in an expected good working state, resulting in the inability to guarantee the overall production efficiency of the oil and gas field. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to some extent. For this purpose, the present invention provides a method and system for oil and gas field electric energy metering control based on the domestic HarmonyOS system, which can improve the accuracy of oil and gas field electric energy metering and guarantee the overall production efficiency of the oil and gas field.

[0004] In a first aspect, an embodiment of the present invention provides a method for oil and gas field electric energy metering control based on the domestic HarmonyOS system, which is applied to an electric energy metering control system built based on the HarmonyOS system. The oil and gas field is configured with multiple oil well production units, and the electric energy metering control system is respectively connected to each of the oil well production units; the method includes the following steps:

[0005] Step S1: Traverse the operation conditions of the oil well production units in a preset historical period to obtain multiple groups of historical operation data. Each group of the historical operation data includes the historical power consumption, historical operation power, and historical effective operation duration of one of the oil well production units in the preset historical period; input each group of the historical operation data as training data into a backpropagation neural network built based on the HarmonyOS system for training to obtain the pre-trained backpropagation neural network;

[0006] Step S2: For each of the oil well production units, obtain the current power consumption and the current operating power of the oil well production unit, and use the current power consumption and the current operating power as input data and input them into the pre-trained backpropagation neural network, so as to obtain the minimum expected operating duration of the oil well production unit; determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating duration, the current power consumption and the current operating power, and perform an expected correction on the current power consumption according to the power influence degree parameter to obtain the expected power consumption;

[0007] Step S3: According to the matching relationship between each of the power influence degree parameters and a plurality of pre-configured power influence characteristic intervals, perform an association degree division on all the oil well production units, so as to obtain a plurality of oil well operation groups, where each of the oil well operation groups includes several of the oil well production units;

[0008] Step S4: For each of the oil well operation groups, perform a weight assignment on the oil well operation group according to the power influence degree parameters and the current operating power of all the oil well production units in the oil well operation group, so as to obtain the weight parameter corresponding to the oil well operation group, and the weight parameter characterizes the power demand load situation of the oil well operation group;

[0009] Step S5: Allocate electric energy to each of the oil well operation groups respectively according to the weight parameters corresponding to each of the oil well operation groups and the expected power consumption of each of the oil well production units.

[0010] Optionally, in an embodiment of the present invention, the step in step S2 of determining the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating duration, the current power consumption and the current operating power includes the following steps:

[0011] Step S21: Perform a normalization update process on the current power consumption and the current operating power respectively, so as to obtain the updated current power consumption and the current operating power; determine the expected start time and the expected end time corresponding to the minimum expected operating duration according to the minimum expected operating duration;

[0012] Step S22: Calculate the sum of the current power consumption of all the oil well production units to obtain the first total power consumption; determine the power influence degree parameter corresponding to the oil well production unit according to the updated current power consumption, the current operating power, the expected start time, the expected end time and the first total power consumption.

[0013] Optionally, in an embodiment of the present invention, the step in step S22 of determining the power influence degree parameter corresponding to the oil well production unit according to the updated current power consumption, the current operating power, the expected start time of operation, the expected end time of operation, and the first total power consumption includes the following steps:

[0014] Step S221: Substitute the updated current power consumption, the current operating power, the expected start time of operation, the expected end time of operation, and the first total power consumption into the power characteristic calculation formula for calculation, so as to obtain the power influence degree parameter corresponding to the oil well production unit, where the power characteristic calculation formula is as follows:

[0015] ;

[0016] is the power influence degree parameter corresponding to the oil well production unit, is the minimum expected operation duration, is the expected start time of operation, is the expected end time of operation, is the energy consumption coefficient of the oil well production unit, , is the current power consumption of the oil well production unit, is the first total power consumption, is the current operating power of the oil well production unit.

[0017] Optionally, in an embodiment of the present invention, the step in step S2 of performing an expected correction on the current power consumption according to the power influence degree parameter to obtain an expected power consumption includes the following steps:

[0018] Step S23: Determine whether the power influence degree parameter corresponding to the oil well production unit is less than or equal to a preset power influence balance coefficient. If so, use the current power consumption as the expected power consumption; otherwise, calculate the expected power consumption through the power consumption correction formula, where the power consumption correction formula is as follows:

[0019] ;

[0020] is the expected power consumption of the oil well production unit, is the current power consumption of the oil well production unit, is the power influence degree parameter corresponding to the oil well production unit, is the preset power influence balance coefficient, is the power factor of the oil well production unit, 。

[0021] Optionally, in an embodiment of the present invention, the steps in step S4, according to the power influence degree parameters and the current operating power of all the oil well production units in the oil well operation group, assign weights to the oil well operation group, so as to obtain the weight parameters corresponding to the oil well operation group, including the following steps:

[0022] Step S41: Calculate the average operating power of the oil well operation group according to the current operating power of all the oil well production units in the oil well operation group;

[0023] Step S42: For each oil well production unit in the oil well operation group, determine the weight component corresponding to the oil well production unit according to the relative relationship between the current operating power of the oil well production unit and the average operating power;

[0024] Step S43: Obtain the weight parameters corresponding to the oil well operation group according to the weight components corresponding to all the oil well production units.

[0025] Optionally, in an embodiment of the present invention, step S5 includes the following steps:

[0026] Step S51: For each oil well operation group, obtain the sum of the expected power consumption of all the oil well production units in the oil well operation group to obtain the first total power consumption;

[0027] Step S52: Calculate the quotient of the first total power consumption and the weight parameters corresponding to the oil well operation group to obtain the standard allocated power;

[0028] Step S53: Allocate electric energy not less than the standard allocated power to the oil well operation group.

[0029] Optionally, in an embodiment of the present invention, the steps in step S1, input each group of the historical operation data as training data into a backpropagation neural network constructed based on the HarmonyOS for training, so as to obtain the pre-trained backpropagation neural network, including the following steps:

[0030] Step S11: Input any group of the historical operation data as training data into a backpropagation neural network constructed based on the HarmonyOS, use the historical power consumption and the historical operating power as input data, and use the historical effective operation duration as the preset output data to perform one training on the backpropagation neural network;

[0031] Step S12: Execute step S11 multiple times until the number of training times of the backpropagation neural network reaches the number of groups of the historical operation data.

[0032] In a second aspect, an embodiment of the present invention provides an oil and gas field power metering control system based on the domesticated HarmonyOS, including:

[0033] At least one processor;

[0034] At least one memory for storing at least one program;

[0035] When at least one of the programs is executed by at least one of the processors, the oil and gas field power metering control method based on the domesticated HarmonyOS described in the first aspect is implemented.

[0036] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, in which a program executable by a processor is stored, and when the program executable by the processor is executed by the processor, it is used to implement the oil and gas field power metering control method based on the domesticated HarmonyOS described in the first aspect.

[0037] The oil and gas field power metering control method proposed by the present invention realizes oil and gas field power metering and distribution through a power metering control system based on the HarmonyOS, and has greater flexibility and scalability. Specifically, by traversing the operation conditions of the oil well production units in a preset historical period to train a backpropagation neural network, and then obtaining the minimum expected operation duration of the oil well production units in the current situation based on the pre-trained backpropagation neural network. This minimum expected operation duration reflects the expected operation conditions of the oil well production units. Based on this, the power influence degree parameters corresponding to the oil well production units and the expected power consumption required in the expected operation situation can be further determined, which can improve the accuracy of oil and gas field power metering. Then, by performing correlation division on all oil well production units, multiple oil well operation groups are obtained. Since the expected operation conditions of the oil well production units in each oil well operation group match, the corresponding weight parameters are determined by assigning weights to the oil well operation groups. This weight parameter characterizes the power demand load situation of the oil well operation group. Finally, based on the weight parameters corresponding to the oil well operation groups and the expected power consumption of each oil well production unit, precise power distribution for the oil well operation groups can be realized to ensure that the oil and gas field is in a good operating state, which is beneficial to improving the overall production efficiency of the oil and gas field. Description of the Drawings

[0038] Figure 1 is a flowchart of an oil and gas field power metering control method based on the domesticated HarmonyOS provided by an embodiment of the present invention;

[0039] Figure 2 is Figure 1Partial flowchart of the step "Input each group of historical operation data as training data into a backpropagation neural network built based on the HarmonyOS for training, so as to obtain a pre-trained backpropagation neural network" in step S1;

[0040] Figure 3 Yes Figure 1 Partial flowchart of the step "Determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operation duration, the current power consumption, and the current operation power" in step S2;

[0041] Figure 4 Yes Figure 1 Partial flowchart of the step "Perform an expected correction on the current power consumption according to the power influence degree parameter to obtain the expected power consumption" in step S2;

[0042] Figure 5 Yes Figure 1 Partial flowchart of the step "Assign weights to the oil well operation group according to the power influence degree parameters and the current operation power of all oil well production units in the oil well operation group, so as to obtain the weight parameter corresponding to the oil well operation group" in step S4;

[0043] Figure 6 Yes Figure 1 Flowchart of step S5;

[0044] Figure 7 Schematic structural diagram of an oil and gas field power metering control system based on the HarmonyOS localization provided by an embodiment of the present invention. Detailed implementation manners

[0045] Such as Figure 1As shown in the figure, an embodiment of the present invention provides a method for oil and gas field power metering and control based on the domesticated HarmonyOS system. This method can be, but is not limited to, applied to a power metering and control system built based on the HarmonyOS system. The oil and gas field is equipped with multiple oil well production units, and the power metering and control system is respectively connected to each oil well production unit. Specifically, it can include, but is not limited to, steps S1 to S5; among them, the power metering and control system in this embodiment can be, but is not limited to, built based on the domesticated open-source HarmonyOS system. For example, both the hardware architecture and software architecture associated with the power metering and control system can be built based on the microkernel of the open-source HarmonyOS system, thus realizing the construction of the associated architecture based on the domesticated HarmonyOS system. It can be understood that through the efficient computing environment and stable data logic framework provided by the open-source HarmonyOS system, the power metering and distribution of the oil and gas field are realized, thereby improving the accuracy of oil and gas field power metering and ensuring the overall production efficiency of the oil and gas field. Since the HarmonyOS system is a well-known distributed operating system in the art, to avoid redundancy, the domesticated HarmonyOS system is not elaborated here. The specific architecture and parameters of the power metering and control system built based on the HarmonyOS system can be set according to needs, and there is no limitation here, as long as it can be, but is not limited to, applied to the oil and gas field power metering and control method provided in this embodiment.

[0046] It should be noted that in different application scenarios, the number, type, etc. of the oil well production units configured in the oil and gas field may be different, and specific settings need to be made according to the actual scenario. For example, the oil well production unit can be, but is not limited to, an oil production unit (such as a pumping unit and its related supporting systems), a water injection unit, an oil transportation unit, and a pollution treatment unit, etc. However, regardless of the scenario, it can be applied to the oil and gas field power metering and control method provided in this embodiment. The only difference lies in the associated parameters involved in each embodiment, which are different in different application scenarios, but this does not uniquely limit.

[0047] Step S1: Traverse the operation conditions of the oil well production units within a preset historical period to obtain multiple groups of historical operation data. Among them, each group of historical operation data includes the historical power consumption, historical operation power, and historical effective operation duration of one of the oil well production units within the preset historical period; input each group of historical operation data as training data into the backpropagation neural network built based on the HarmonyOS system for training to obtain a pre-trained backpropagation neural network;

[0048] It should be noted that the preset historical period can be selected according to the actual situation. Generally, it is recommended to select a historical period relatively close to the current scenario for research. In this way, the error of the historical operation data obtained may be relatively small, but this is not the only limitation. The operation situation of the oil well production unit in the preset historical period can be determined by referring to the recorded work logs, historical monitoring data, etc. Among them, the historical power consumption can, but is not limited to, represent the total power consumption of the oil well production unit in the preset historical period. The historical operating power can, but is not limited to, represent the average operating power of the oil well production unit in the preset historical period. The historical effective operating duration can, but is not limited to, represent the actual operating duration of the oil well production unit in the preset historical period. That is to say, for example, if the time occupied by the oil well production unit stopping or pausing work due to specific working conditions occurs in the preset historical period, it is not included in the historical effective operating duration. In other words, the historical effective operating duration reflects the actual operating situation of the oil well production unit in the preset historical period;

[0049] Step S2: For each oil well production unit, obtain the current power consumption and current operating power of the oil well production unit, and use the current power consumption and current operating power as input data and input them into the pre-trained backpropagation neural network to obtain the minimum expected operating duration of the oil well production unit; determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating duration, current power consumption and current operating power, and perform expected correction on the current power consumption according to the power influence degree parameter to obtain the expected power consumption;

[0050] Step S3: According to the matching relationship between each power influence degree parameter and multiple pre-configured power influence characteristic intervals, perform correlation division on all oil well production units to obtain multiple oil well operation groups, where each oil well operation group includes several oil well production units;

[0051] Step S4: For each oil well operation group, assign weights to the oil well operation group according to the power influence degree parameters and current operating powers of all oil well production units in the oil well operation group to obtain the weight parameter corresponding to the oil well operation group, and the weight parameter represents the power demand load situation of the oil well operation group;

[0052] Step S5: Allocate electric energy to each oil well operation group according to the weight parameters corresponding to each oil well operation group and the expected power consumption of each oil well production unit.

[0053] In this step, by traversing the operation of the oil well production unit within a preset historical period, a backpropagation neural network is trained. Then, based on the pre-trained backpropagation neural network, the minimum expected operation duration of the oil well production unit under the current situation is obtained. This minimum expected operation duration reflects the expected operation of the oil well production unit. Based on this, the corresponding power influence degree parameter of the oil well production unit and the expected power consumption required under the expected operation situation can be further determined, which can improve the accuracy of oil and gas field power metering. Then, by dividing the association degree of all oil well production units, multiple oil well operation groups are obtained. Since the expected operation situations of the oil well production units in each oil well operation group match, the corresponding weight parameters are determined by assigning weights to the oil well operation groups. This weight parameter characterizes the power demand load situation of the oil well operation group. Finally, based on the weight parameters corresponding to the oil well operation groups and the expected power consumption of each oil well production unit, precise power distribution for the oil well operation groups can be achieved to ensure that the oil and gas field is in a good operating state, which is beneficial to improving the overall production efficiency of the oil and gas field.

[0054] In one embodiment, the multiple power influence characteristic intervals in step S3 can be configured accordingly according to the actual application scenario. For example, by setting multiple power influence threshold parameters for distinction. Specifically, if a first power influence threshold parameter and a second power influence threshold parameter are set, and the first power influence threshold parameter is less than the second power influence threshold parameter, then each power influence degree parameter is compared with the first power influence threshold parameter and the second power influence threshold parameter one by one. All power influence degree parameters less than the first power influence threshold parameter are classified into one category, all power influence degree parameters greater than the second power influence threshold parameter are classified into another category, and all power influence degree parameters greater than or equal to the first power influence threshold parameter and less than or equal to the second power influence threshold parameter are classified into another category. In this way, 3 different power influence characteristic intervals can be determined. The oil well production units corresponding to all power influence degree parameters within each power influence characteristic interval form an oil well operation group. Finally, multiple oil well operation groups can be obtained, and all oil well production units in each oil well operation group can also be determined accordingly.

[0055] As Figure 2 shown in an embodiment of the present invention, for the steps in step S1, each group of historical operation data is used as training data and input into a backpropagation neural network constructed based on the HarmonyOS for training, so as to obtain a pre-trained backpropagation neural network. Specifically, it may but is not limited to include the following steps:

[0056] Step S11: Input any set of historical operation data as training data into the backpropagation neural network built based on the HarmonyOS. Use the historical power consumption and historical operation power as input data, and use the historical effective operation duration as the preset output data to perform one training on the backpropagation neural network.

[0057] Step S12: Execute Step S11 multiple times until the number of training times of the backpropagation neural network reaches the number of sets of historical operation data.

[0058] In this step, by inputting any set of historical operation data as training data into the backpropagation neural network built based on the HarmonyOS, using the historical power consumption and historical operation power as input data, and using the historical effective operation duration as the preset output data to perform one training on the backpropagation neural network, the training effect of the historical effective operation duration as the output data can be strengthened, so as to improve the input recognition performance of the backpropagation neural network. Especially, by performing single training on the backpropagation neural network separately with multiple sets of historical operation data multiple times, the training effect of the historical operation data can be further strengthened, which is beneficial to optimizing the training accuracy of the backpropagation neural network, thereby improving the robustness of the backpropagation neural network. It should be noted that the backpropagation neural network is a well-known network model in the art, and for the sake of avoiding redundancy, it will not be elaborated here.

[0059] As Figure 3 shown, in an embodiment of the present invention, the steps in Step S2 for determining the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operation duration, the current power consumption, and the current operation power include the following steps:

[0060] Step S21: Perform normalization update processing on the current power consumption and the current operation power respectively to obtain the updated current power consumption and the current operation power; determine the expected operation start time and the expected operation end time corresponding to the minimum expected operation duration according to the minimum expected operation duration.

[0061] Step S22: Calculate the sum of the current power consumption of all oil well production units to obtain the first total power consumption; determine the power influence degree parameter corresponding to the oil well production unit according to the updated current power consumption, the current operation power, the expected operation start time, the expected operation end time, and the first total power consumption.

[0062] In this step, through the normalization update process of the current power consumption and the current operating power, the standardized and unified current power consumption and the current operating power are obtained, so as to facilitate effective and accurate calculation in the follow-up. And the corresponding expected start time and expected end time of operation are determined according to the minimum expected operation duration, and the specific operation period of the minimum expected operation duration can be known. On this basis, considering the sum of the current power consumption of all oil well production units, the first total power consumption is obtained. Thus, according to the updated current power consumption, the current operating power, the expected start time of operation, the expected end time of operation and the first total power consumption, the power influence degree parameter corresponding to the oil well production unit is effectively and reliably determined.

[0063] In one embodiment, the step in step S22 of determining the power influence degree parameter corresponding to the oil well production unit according to the updated current power consumption, the current operating power, the expected start time of operation, the expected end time of operation and the first total power consumption includes the following steps:

[0064] Step S221: Substitute the updated current power consumption, the current operating power, the expected start time of operation, the expected end time of operation and the first total power consumption into the power characteristic calculation formula for calculation, so as to obtain the power influence degree parameter corresponding to the oil well production unit. Among them, the power characteristic calculation formula is as follows:

[0065] ;

[0066] is the power influence degree parameter corresponding to the oil well production unit, is the minimum expected operation duration, is the expected start time of operation, is the expected end time of operation, is the energy consumption coefficient of the oil well production unit, , is the current power consumption of the oil well production unit, is the first total power consumption, is the current operating power of the oil well production unit, The magnitude of represents the level of the energy conversion efficiency of the oil well production unit, and can be determined by the inherent performance of the oil well production unit. For example, while traversing the operation conditions of the oil well production unit in the preset historical period, the energy consumption coefficient of the oil well production unit in this period can be determined according to its historical operation conditions, that is, obtain .

[0067] In one embodiment, the normalization update process may but is not limited to: performing data cleaning on the current power consumption and the current operating power, and converting the current power consumption and the current operating power into their respective corresponding normalized values according to a preset normalization ratio, so as to obtain the updated current power consumption and the current operating power; the expected start time and the expected end time corresponding to the minimum expected operating duration may but is not limited to be segmented, that is, there may be corresponding pause times and restart times between the expected start time and the expected end time. For example, if the expected start time is 09:00 and the expected end time is 18:00 and it is continuous as a whole, then is 09:00, is 18:00; if the expected start time is 09:00, correspondingly, it ends at 12:00, and then restarts at 14:00 and finally ends at 18:00, then during the first stage of operation, is 09:00, is 12:00, similarly, during the second stage of operation, is 14:00, is 18:00. The calculations are performed separately for the two stages of operation and then summarized. It can be seen that even if the operation processes in different stages are distinguished, it can still be applicable to the electric energy characteristic calculation formula provided in the above embodiment.

[0068] As Figure 4 shown, in one embodiment of the present invention, the steps in step S2, according to the electric energy influence degree parameter, perform an expected correction on the current power consumption to obtain the expected power consumption, which may but is not limited to include the following steps:

[0069] Step S23: Determine whether the electric energy influence degree parameter corresponding to the oil well production unit is less than or equal to a preset electric energy influence balance coefficient. If so, use the current power consumption as the expected power consumption, otherwise calculate the expected power consumption through a power consumption correction formula, where the power consumption correction formula is as follows:

[0070] ;

[0071] is the expected power consumption of the oil well production unit, is the current power consumption of the oil well production unit, is the electric energy influence degree parameter corresponding to the oil well production unit, is the preset electric energy influence balance coefficient, is the power factor of the oil well production unit, , is the ratio of the active power to the apparent power in the oil well production unit, and numerically equals the cosine value of the phase difference between the voltage and the current of the oil well production unit.

[0072] In this step, the preset power influence balance coefficient is not the only limitation and needs to be determined accordingly according to the actual application scenario. If it is judged that the power influence degree parameter corresponding to the oil well production unit is less than or equal to the preset power influence balance coefficient, it means that the power influence degree parameter corresponding to the oil well production unit is within the normal threshold range, and there is no need to provide additional power for it. Therefore, the current power consumption is used as the expected power consumption. On the contrary, if it is judged that the power influence degree parameter corresponding to the oil well production unit is greater than the preset power influence balance coefficient, it means that the power influence degree parameter corresponding to the oil well production unit is within a range exceeding the threshold. This part of the additional power influence degree parameter needs to be compensated separately. Therefore, by calculating the difference between the preset power influence balance coefficient and the power influence degree parameter, and combining the current power consumption and power factor of the oil well production unit, the power consumption to be compensated is calculated, and then added to the current power consumption of the oil well production unit to obtain the expected power consumption. The expected power consumption calculated in this way can reflect the actual power consumption demand of the oil well production unit and more accurately estimate the effective power consumption of the oil well production unit.

[0073] As Figure 5 shown, in an embodiment of the present invention, the steps in step S4, according to the power influence degree parameters and current operating powers of all oil well production units in the oil well operation group, assign weights to the oil well operation group to obtain the weight parameters corresponding to the oil well operation group, which may but are not limited to including the following steps:

[0074] Step S41: Calculate the average operating power of the oil well operation group according to the current operating powers of all oil well production units in the oil well operation group;

[0075] Step S42: For each oil well production unit in the oil well operation group, determine the weight component corresponding to the oil well production unit according to the relative relationship between the current operating power of the oil well production unit and the average operating power;

[0076] Step S43: Obtain the weight parameter corresponding to the oil well operation group according to the weight components corresponding to all oil well production units.

[0077] In this step, by judging the relative relationship between the current operating power of the oil well production unit and the average operating power, the weight component corresponding to the oil well production unit is determined, so that the weight parameter corresponding to the oil well operation group can be calculated by summarizing the weight components corresponding to all oil well production units. Specifically, the method of determining the weight component corresponding to the oil well production unit according to the relative relationship between the current operating power of the oil well production unit and the average operating power can be various. For example, the following weight configuration formula is used to assign corresponding weight components to each oil well production unit:

[0078] ;

[0079] Among them, is the weight component corresponding to each oil well production unit, is the power influence degree parameter corresponding to the oil well production unit, is the th power influence degree parameter corresponding to the oil well production unit, is the preset weight allocation parameter, is the current operating power of the oil well production unit, is the average operating power of the oil well operation group, is the total number of all oil well production units in the oil well operation group, .

[0080] In one embodiment, after determining the weight components corresponding to all oil well production units, calculate the sum of the weight components corresponding to all oil well production units, and the weight parameter corresponding to the oil well operation group can be obtained.

[0081] As Figure 6 shown, in one embodiment of the present invention, step S5 may but is not limited to include the following steps:

[0082] Step S51: For each oil well operation group, obtain the sum of the expected power consumption of all oil well production units in the oil well operation group to obtain the first total power consumption;

[0083] Step S52: Calculate the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group to obtain the standard allocated power;

[0084] Step S53: Allocate electric energy not less than the standard allocated power to the oil well operation group.

[0085] In this step, by obtaining the sum of the expected power consumption of all oil well production units in the oil well operation group to obtain the total power consumption required for the operation of the oil well operation group. Since the weight parameter characterizes the power demand load situation of the oil well operation group, considering the influence of the weight parameter, the actual power consumption required for the operation of the oil well operation group needs to be able to match the weight parameter. Therefore, by calculating the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group, the standard allocated power required for the actual operation of the oil well operation group at least can be obtained, and then allocate electric energy not less than the standard allocated power to the oil well operation group to ensure that the oil well operation group can maintain a normal operation state, which is beneficial to improving the overall production efficiency of the oil and gas field.

[0086] Figure 7 is a schematic structural diagram of an oil and gas field electric energy metering control system 1000 based on the domestic HarmonyOS. As Figure 7As shown, the domestic oil and gas field power metering control system 1000 based on HarmonyOS includes a memory 1100 and a processor 1200. The number of the memory 1100 and the processor 1200 can be one or more. Figure 7 In Figure 7 , one memory 1100 and one processor 1200 are taken as an example; the memory 1100 and the processor 1200 in the device can be connected through a bus or other means. Figure 7 In Figure 7 , taking the connection through a bus as an example.

[0087] The memory 1100, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the domestic oil and gas field power metering control method provided in any embodiment of the present invention. The processor 1200 realizes the above-mentioned domestic oil and gas field power metering control method by running the software programs, instructions, and modules stored in the memory 1100.

[0088] The memory 1100 may mainly include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function. In addition, the memory 1100 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the memory 1100 may further include a memory remotely set relative to the processor 1200, and these remote memories can be connected to the device through a network. Examples of the above-mentioned network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0089] An embodiment of the present invention also provides a computer-readable storage medium storing computer-executable instructions for executing the domestic oil and gas field power metering control method provided in any embodiment of the present invention.

[0090] An embodiment of the present invention also provides a computer program product including a computer program or computer instructions. The computer program or computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the domestic oil and gas field power metering control method provided in any embodiment of the present invention.

[0091] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0092] In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be executed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and that can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0093] The terms "component", "module", "system", etc. as used in this specification are used to refer to a computer-related entity, hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, an execution thread, a program, or a computer. By way of illustration, both an application running on a computing device and the computing device can be components. One or more components may reside within a process or execution thread, and a component may be located on one computer or distributed between 2 or more computers. In addition, these components may execute from various computer-readable media having various data structures stored thereon. A component may communicate, for example, by signals according to one or more data packets (e.g., data from two components interacting with each other from a local system, a distributed system, or another component across a network, such as via the Internet interacting with other systems).

Claims

1. A method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system, characterized in that: Applied to an electric energy metering control system built based on the Hongmeng system, the oil and gas field is equipped with multiple oil well production units, and the electric energy metering control system is respectively connected to each of the oil well production units; the method comprises the following steps: Step S1, traversing the operation status of the oil well production unit in a preset historical period, thereby obtaining multiple groups of historical operation data, wherein each group of the historical operation data includes the historical power consumption, historical operation power and historical effective operation time of one of the oil well production units in the preset historical period; inputting each group of the historical operation data as training data into a back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network; Step S2: for each of the oil well production units, obtain the current power consumption and the current operating power of the oil well production unit, use the current power consumption and the current operating power as input data, and input them into the pre-trained back propagation neural network, so as to obtain the minimum expected operating time of the oil well production unit; determine the power influence degree parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, and make an expected correction to the current power consumption according to the power influence degree parameter to obtain the expected power consumption; Step S3, according to the matching relationship between each of the electric energy impact parameters and the pre-configured multiple electric energy impact characteristic intervals, all the oil well production units are divided according to the correlation degree, so as to obtain multiple oil well operation groups, wherein each of the oil well operation groups includes a plurality of the oil well production units; Step S4: for each of the oil well operation groups, weighting the oil well operation group is performed according to the power influence parameters and the current operating power of all the oil well production units in the oil well operation group, thereby obtaining a weight parameter corresponding to the oil well operation group, wherein the weight parameter represents the power demand load of the oil well operation group; Step S5, allocating electric energy to each of the oil well operation groups according to the weight parameters corresponding to each of the oil well operation groups and the expected power consumption of each of the oil well production units; The step in step S2, determining the electric energy impact parameter corresponding to the oil well production unit according to the minimum expected operating time, the current power consumption and the current operating power, includes the following steps: Step S21, respectively normalizing and updating the current power consumption and the current operating power to obtain updated current power consumption and the current operating power; determining the expected operation start time and the expected operation end time corresponding to the minimum expected operation time according to the minimum expected operation time; Step S22, calculating the sum of the current power consumption of all the oil well production units to obtain a first total power consumption; determining the power impact parameter corresponding to the oil well production unit according to the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption; The step in step S22, determining the electric energy impact parameter corresponding to the oil well production unit according to the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption, comprises the following steps: Step S221: Substitute the updated current power consumption, the current operating power, the expected operation start time, the expected operation end time and the first total power consumption into the electric energy characteristic calculation formula for calculation, so as to obtain the electric energy influence degree parameter corresponding to the oil well production unit, wherein the electric energy characteristic calculation formula is as follows: M is the electric energy impact parameter corresponding to the oil well production unit, t is the minimum expected operating time, T1 is the expected operation start time, T2 is the expected operation end time, ɑ is the energy consumption coefficient of the oil well production unit, 0<α<1, S0 is the current power consumption of the oil well production unit, S is the first total power consumption, and p is the current operating power of the oil well production unit.

2. According to the method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system according to claim 1, it is characterized in that: The step in step S2, performing expected correction on the current power consumption according to the power impact parameter to obtain expected power consumption, includes the following steps: Step S23, determine whether the power impact parameter corresponding to the oil well production unit is less than or equal to the preset power impact balance coefficient. If so, take the current power consumption as the expected power consumption. Otherwise, calculate the expected power consumption by the power consumption correction formula, wherein the power consumption correction formula is as follows: S1 is the expected power consumption of the oil well production unit, S0 is the current power consumption of the oil well production unit, M is the power impact parameter corresponding to the oil well production unit, M0 is the preset power impact balance coefficient, P f is the power factor of the oil well production unit, 0 <P f <1.

3. According to the method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system according to claim 1, it is characterized in that: The step in step S4, weighting the oil well operation group according to the electric energy influence parameter and the current operating power of all the oil well production units in the oil well operation group, thereby obtaining the weight parameter corresponding to the oil well operation group, includes the following steps: Step S41, calculating the average operating power of the oil well operation group according to the current operating powers of all the oil well production units in the oil well operation group; Step S42: for each of the oil well production units in the oil well operation group, determine a weight component corresponding to the oil well production unit according to a relative relationship between the current operating power and the average operating power of the oil well production unit; Step S43: Obtain the weight parameter corresponding to the oil well operation group according to the weight components corresponding to all the oil well production units.

4. According to the method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system according to claim 1, it is characterized in that: The step S5 comprises the following steps: Step S51: for each of the oil well operation groups, obtain the sum of the expected power consumption of all the oil well production units in the oil well operation group to obtain a first total power consumption; Step S52, calculating the quotient of the first total power consumption and the weight parameter corresponding to the oil well operation group to obtain a standard allocated power; Step S53: Allocate electric energy not less than the standard allocated electric energy to the oil well operation group.

5. According to the method for controlling electric energy metering in oil and gas fields based on the localization of Hongmeng system according to claim 1, it is characterized in that: The step in step S1, inputting each group of the historical operation data as training data into the back propagation neural network constructed based on the Hongmeng system for training, thereby obtaining the pre-trained back propagation neural network, includes the following steps: Step S11, inputting any group of the historical operation data as training data into a back propagation neural network built based on the Hongmeng system, taking the historical power consumption and the historical operation power as input data, and taking the historical effective operation time as preset output data, training the back propagation neural network once; Step S12, executing step S11 multiple times until the number of training times of the back propagation neural network reaches the number of groups of the historical operation data.

6. An oil and gas field electric energy metering control system based on the localization of Hongmeng system, characterized in that: include: at least one processor; at least one memory for storing at least one program; When at least one of the programs is executed by at least one of the processors, the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in any one of claims 1 to 5 is implemented.

7. A computer-readable storage medium, characterized in that: A processor-executable program is stored therein, and when the processor-executable program is executed by the processor, it is used to implement the oil and gas field electricity metering control method based on the localization of the Hongmeng system as described in any one of claims 1 to 5.

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