Information processing method and apparatus, and electronic device and storage medium

Abstract

Embodiments of the present application disclose an information processing method and device, electronic equipment and a storage medium. The running state of a controlled object is identified. If the running state is a load state, a second data set is obtained based on a first data set, a second type of parameter and a speed prediction model. The first data set is a subset of the second data set. The second data set contains a plurality of data pairs. Each data pair includes a value of the first type of parameter and a movement speed corresponding to the value of the first type of parameter based on the speed prediction model. The speed prediction model is used to predict the movement speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter. The value of the first type of parameter corresponding to the maximum movement speed in the second data set is determined as the target value of the first type of parameter. The first type of parameter is an operable parameter in a work process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control, more particularly, to an information processing method and device, electronic equipment and storage medium. BACKGROUND

[0002] The drilling process is a very complex process, and the process control of drilling directly affects the drilling performance and efficiency, and also affects the service life of the drill bit. Drilling speed optimization is an important means to improve the efficiency of the drilling process. In actual drilling engineering, drilling speed optimization is often achieved by replacing the drill bit or impactor and other drilling tools. However, this method has high cost and high price, and it takes time to replace the drilling tools and it is difficult to make timely adjustments according to the actual situation. SUMMARY

[0003] The purpose of the present application is to provide an information processing method and device, electronic equipment and storage medium, including the following technical solutions:

[0004] An information processing method, the method comprising:

[0005] Identifying the running state of a controlled object;

[0006] If the running state is a load state, obtaining a second data set based on a first data set, a second type of parameter, and a speed prediction model; the first data set is a subset of the second data set; the second data set contains a plurality of data pairs, each data pair including a value of a first type of parameter and a corresponding movement speed of the value of the first type of parameter obtained based on the speed prediction model; the speed prediction model is used to predict the movement speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter;

[0007] Determining the value of the first type of parameter corresponding to the maximum movement speed in the second data set as the target value of the first type of parameter;

[0008] The first type of parameter is an operable parameter in the operation process.

[0009] The above method, optionally, obtaining a second data set based on a first data set, a second type of parameter, and a speed prediction model, comprises:

[0010] Updating the first data set based on the following process until the update end condition is met:

[0011] Constructing a proxy model of the speed prediction model based on the latest first data set;

[0012] Sampling within the value range of the first type of parameter according to the proxy model, and the obtained first sampling value is different from each value of the first type of parameter in the latest first data set.

[0013] determining a second sampling value based on the first sampling value;

[0014] processing the second sampling value and a value of the second type of parameter by the speed prediction model to obtain a motion speed corresponding to the second sampling value;

[0015] adding the second sampling value and the corresponding motion speed to the first data set;

[0016] The second data set is the latest first data set obtained at the end of the updating.

[0017] Optionally, the second sampling value is the same as or different from the first sampling value.

[0018] Optionally, the method further includes:

[0019] if the condition is met, obtaining a result of adjusting the first sampling value by using a target adjustment mode as the second sampling value;

[0020] if the condition is not met, determining the first sampling value as the second sampling value.

[0021] Optionally, the condition includes:

[0022] a confidence degree of the first sampling value determined based on the proxy model is less than or equal to a confidence degree of a result of adjusting the first sampling value by using the target adjustment mode.

[0023] Optionally, the proxy model is a Gaussian model; and the adjusting the first sampling value by using the target adjustment mode includes:

[0024] adjusting the first sampling value by using a Cauchy distribution function.

[0025] Optionally, the sampling according to the proxy model within the value range of the first type of parameter includes:

[0026] determining a target range within the value range of the first type of parameter according to a current value of the first type of parameter; and the target range includes the current value of the first type of parameter;

[0027] sampling within the target range according to the proxy model.

[0028] An information processing device, the device comprising:

[0029] a recognition module configured to recognize a running state of a controlled object;

[0030] obtaining a second data set based on a first data set, a second type of parameter, and a speed prediction model if the running state is a load state; the first data set is a subset of the second data set; the second data set includes a plurality of data pairs, each data pair including a value of the first type of parameter and a movement speed corresponding to the value of the first type of parameter based on the speed prediction model; the speed prediction model is used to predict the movement speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter;

[0031] determining a target value of the first type of parameter as a value of the first type of parameter corresponding to a maximum movement speed in the second data set;

[0032] The first type of parameter is an operable parameter in a work process.

[0033] An electronic device includes:

[0034] a memory configured to store a program;

[0035] a processor configured to invoke and execute the program in the memory, and realize each step of the information processing method according to any one of the above by executing the program.

[0036] A readable storage medium having a computer program stored thereon, the computer program being executed by a processor to realize each step of the information processing method according to any one of the above.

[0037] According to the above scheme, the information processing method, device, electronic device, and storage medium provided by the present application identify the running state of a controlled object, obtain a second data set based on a first data set, a second type of parameter, and a speed prediction model if the running state is a load state; the first data set is a subset of the second data set; the second data set includes a plurality of data pairs, each data pair including a value of the first type of parameter and a movement speed corresponding to the value of the first type of parameter based on the speed prediction model; the speed prediction model is used to predict the movement speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter; a target value of the first type of parameter is determined as a value of the first type of parameter corresponding to a maximum movement speed in the second data set; and the first type of parameter is an operable parameter in a work process. The present application finds an optimal value of the first type of parameter that can produce an optimal movement speed based on an existing first data set, a second type of parameter, and a speed prediction model, realizes the purpose of optimizing the movement speed of the controlled object by optimizing the operable parameter, and reduces the optimization cost and improves the optimization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required by the embodiments will be briefly introduced as follows. Obviously, the drawings described below only constitute some of the embodiments of the present application, and all other drawings obtained by those of ordinary skill in the art without creative effort based on the embodiments of the present application shall fall within the scope of protection of the present application.

[0039] Figure 1 An implementation flowchart of the information processing method provided by the embodiments of the present application;

[0040] Figure 2 An implementation flowchart of the data processing method provided by the embodiments of the present application;

[0041] Figure 3 An implementation flowchart of sampling in the value range of the first type of parameter according to the proxy model provided by the embodiments of the present application;

[0042] Figure 4 An implementation flowchart of optimizing the drilling speed of a drill bit provided by the embodiments of the present application;

[0043] Figure 5 A structural schematic diagram of the information processing device provided by the embodiments of the present application;

[0044] Figure 6 A structural schematic diagram of the electronic device provided by the embodiments of the present application.

[0045] The terms "first", "second", "third", "fourth" and the like (if any) in the description, claims and above-mentioned drawings are used to distinguish similar parts, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated herein. DETAILED DESCRIPTION

[0046] The technical solutions of the embodiments of the present application will be described clearly and completely in the embodiments of the present application in combination with the drawings. Obviously, the described embodiments only constitute some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort shall fall within the scope of protection of the present application.

[0047] The information processing method provided by the embodiments of the present application is used in an electronic device, which can be a device in a work site (such as a drilling site, etc.), or a device capable of communicating with the device in the work site.

[0048] As Figure 1As shown, an implementation flowchart of the information processing method provided by the embodiment of the present application can include the following steps:

[0049] Step S101: Identify the running state of the controlled object.

[0050] According to different work sites, the controlled object is different. For example, in a drilling site, the controlled object can be a drill bit or a hammer and other drilling tools.

[0051] Taking the drilling tool as an example, the running state of the controlled object can include but is not limited to the following: load state, sliding state. When the drilling tool is in the load state, it means that the drilling tool is in the footage state. When the drilling tool is in the sliding state, it means that the drilling tool is in the non-footage state.

[0052] Optionally, the running state of the controlled object can be determined by some related parameters of the controlled object. As an example, the running state of the drilling tool can be determined by monitoring the drilling depth and / or position of the drilling tool. As an example, if the drilling depth of the drilling tool does not change within a preset time, it can be determined that the drilling tool is in the sliding state, otherwise it is determined that the drilling tool is in the load state. As an example, if the drilling position of the drilling tool does not change in the target direction (i.e. the drilling direction) within a preset time, it can be determined that the drilling tool is in the sliding state, otherwise it is determined that the drilling tool is in the load state.

[0053] Step S102: If the running state of the controlled object is the load state, obtain the second data set based on the first data set, the second type of parameters, and the speed prediction model.

[0054] Wherein, the first data set is a subset of the second data set; the second data set contains a plurality of data pairs, each data pair includes the value of the first type of parameters and the corresponding movement speed of the value of the first type of parameters (which can be obtained based on the speed prediction model or can be an empirical value); the speed prediction model is used to predict the movement speed of the controlled object according to the value of the first type of parameters and the value of the second type of parameters. The first type of parameters is the operable parameter in the work process.

[0055] Taking the drilling process as an example, the first type of parameters can include but is not limited to: weight on bits (WOB), rotation speed (RPM_SURF, i.e. the rotation speed of the drill pipe), mud speed (FLOWIN, i.e. the speed of injecting mud into the bottom of the well) and the like. Correspondingly, the first data set contains at least one data pair, each data pair contains the value of the above-mentioned first type of parameters, and the movement speed (i.e. the drilling speed) corresponding to the value of the first type of parameters.

[0056] The second data set is obtained by adding new data pairs in the first data set based on the first data set and the second type of parameters.

[0057] In the operation process, in addition to the parameters (i.e., the first type of parameters) that can be operated by the user, there are also parameters (i.e., the second type of parameters) that cannot be operated by the user, at least part of these parameters will change with the change of the user-operable parameters, such as torque, standpipe pressure and other parameters in the drilling process, and some parameters do not change with the change of the user-operable parameters, such as the rock layer where the drilling tool is located.

[0058] In the embodiments of the present application, the speed prediction model is pre-trained, which is used to predict the movement speed of the controlled object according to the values of the first type of parameters and the values of the second type of parameters. Taking the drilling process as an example, the speed prediction model is used to predict the drilling speed (referred to as drilling speed) of the drilling tool according to the values of the first type of parameters and the values of the second type of parameters.

[0059] Further, if the running state of the controlled object is the sliding state, the second data set is prohibited to be obtained based on the first data set, the second type of parameters and the speed prediction model. That is, if the controlled object is in the non-footage state, the second data set is obtained based on the first data set, the second type of parameters and the speed prediction model after the controlled object is in the footage state.

[0060] Step S103: The value of the first type of parameters corresponding to the maximum movement speed in the second data set is determined as the target value of the first type of parameters.

[0061] The target value of the first type of parameters can be sent to the field operator, and the field operator determines the value of the operable parameter as the target value, so that the movement speed of the controlled object reaches the maximum movement speed in the second data set.

[0062] In the drilling process, the target value of the first type of parameters can be sent to the field operator, and the field operator determines the value of the operable parameter as the target value, so that the movement speed of the controlled object reaches the maximum movement speed in the second data set.

[0063] The information processing method provided by the embodiments of the present application finds the optimal value of the first type of parameters that can produce the optimal movement speed based on the existing first data set, the second type of parameters and the speed prediction model, realizes the purpose of optimizing the movement speed of the controlled object by optimizing the operable parameters, and reduces the optimization cost and improves the optimization efficiency.

[0064] Based on the present application, the information processing method of the present application can be performed multiple times during the drilling process, such as periodically performing the above information processing method to periodically optimize the movement speed of the controlled object. Based on this, the initial first data set can be determined by the field operator based on experience according to the field operation environment (i.e. the values of the first type of parameters and the corresponding movement speed are determined according to experience), or the field operator can determine the possible values of the first type of parameters based on experience according to the field operation environment, and then use the speed prediction model to obtain the movement speed of the controlled object corresponding to the values of the first type of parameters (i.e. the values of the first type of parameters are determined according to experience, and the movement speed corresponding to the values of the first type of parameters is obtained through the speed prediction model). Every time an optimization period is reached, the second data set determined in the last optimization period is determined as the new first data set to optimize the movement speed of the controlled object based on the new first data set.

[0065] In an optional embodiment, the above process of obtaining the second data set based on the first data set, the second type of parameters and the speed prediction model can be implemented in the following way:

[0066] Based on Figure 2 The data processing method shown in the figure updates the first data set until the update end condition is met. Figure 2 An implementation flowchart of the data processing method provided by the embodiments of the present application can include:

[0067] Step S201: Construct a proxy model of the speed prediction model based on the latest first data set.

[0068] As an example, the proxy model of the speed prediction model can include but is not limited to: Gaussian model.

[0069] Step S202: Sample within the value range of the first type of parameters according to the proxy model, and the obtained first sampling value is different from each value of the first type of parameters in the latest first data set.

[0070] The value range of the first type of parameters is the range of the effective values of the first parameters determined according to objective conditions (such as mechanical equipment (such as drilling tools or drilling tool related equipment, etc.) and operation environment (such as the rock formation it is in, etc.) etc. (which belong to strong constraint conditions).

[0071] When sampling within the value range of the first type of parameters, it is necessary to ensure that the value obtained by sampling is not in the first data set.

[0072] Step S203: Determine the second sampling value based on the first sampling value.

[0073] In the present application, the second sampling value can or can not be the first sampling value. That is, the second sampling value can be the same as or different from the first sampling value.

[0074] By determining the second sampling value based on the first sampling value, the optimization process can avoid falling into a local optimum as much as possible, i.e., increase the probability of jumping out of a local optimal solution.

[0075] Step S204: processing the second sampling value and the value of the second type of parameter through the speed prediction model to obtain the motion speed corresponding to the second sampling value.

[0076] In the case where at least part of the second type of parameters is associated with the first type of parameters, the value of at least part of the second type of parameters based on the second sampling value can be obtained, and then the speed prediction model is input based on the second sampling value, the value of the at least part of the second type of parameters, and the value of the second type of parameters not associated with the second sampling value, to obtain the motion speed output by the speed prediction model, which is the motion speed corresponding to the second sampling value.

[0077] Step S205: adding the second sampling value and its corresponding motion speed to the first data set.

[0078] The second sampling value and its corresponding motion speed are added to the first data set to obtain an updated first data set.

[0079] Step S206: determining whether the update end condition is met. If the update end condition is met, the update is ended. If the update end condition is not met, the step S201 is executed.

[0080] The update end condition can include but is not limited to that the number of iterations reaches a target number.

[0081] The second data set is the latest first data set obtained at the end of the update.

[0082] In an optional embodiment, the above-mentioned implementation of determining the second sampling value based on the first sampling value can include:

[0083] If the condition is met, the result of adjusting the first sampling value by using the target adjustment method is obtained as the second sampling value.

[0084] If the condition is met, it means that the first sampling value makes the optimization process fall into a local optimum with a relatively large probability. At this time, the first sampling value needs to be disturbed, i.e., the first sampling value is adjusted by using the target adjustment method, and the adjustment result is obtained as the second sampling value, so that the optimization process can avoid falling into a local optimum as much as possible, i.e., increase the probability of jumping out of a local optimal solution.

[0085] If the condition is not satisfied, the first sampling value is determined as the second sampling value.

[0086] If the condition is not satisfied, it means that the first sampling value has a small probability of making the optimization process fall into a local optimum, and the first sampling value can not be disturbed.

[0087] In an optional embodiment, the above-mentioned condition can include:

[0088] The confidence of the first sampling value determined based on the surrogate model (for ease of description and differentiation, denoted as the first confidence) is less than or equal to the confidence of the result (for ease of description and differentiation, denoted as the second confidence) obtained by adjusting the first sampling value based on the target adjustment mode.

[0089] In this application, the first sampling value is first adjusted based on the target adjustment mode, and then the confidence of the first sampling value determined based on the surrogate model and the confidence of the adjusted sampling value obtained by adjusting the first sampling value based on the target adjustment mode are compared. Based on the comparison result, it is determined whether the condition is satisfied.

[0090] If the first confidence is less than or equal to the second confidence, it means that the condition is satisfied, and the adjusted sampling value is used to replace the first sampling value to execute step S204 and the subsequent process; if the first confidence is greater than the second confidence, it means that the condition is not satisfied, and the first sampling value is directly used to execute step S204 and the subsequent process.

[0091] As an example, the surrogate model can be a Gaussian model; accordingly, adjusting the first sampling value based on the target adjustment mode can include adjusting the first sampling value using a Cauchy distribution function. Optionally, the process of adjusting the first sampling value using the Cauchy distribution function can be represented by the formula:

[0092] X_2=X_1+X_1*Cauchy(γ,x0)

[0093] Wherein, X_2 represents the adjusted sampling value, X_1 represents the first sampling value, and Cauchy(γ,x0) is the Cauchy distribution function.

[0094] As an example, the Cauchy distribution function can be a standard Cauchy distribution function, or a non-standard Cauchy distribution function. For example, the Cauchy distribution function can be Cauchy(1,0), or Cauchy(0,1), etc.

[0095] The confidence level of the first sampling value determined based on the Gaussian model can be represented by a confidence level or a confidence interval of the Gaussian model; similarly, the confidence level of the adjusted sampling value obtained by adjusting the first sampling value based on the Cauchy distribution function can be represented by a confidence level or a confidence interval of the Cauchy distribution function.

[0096] As an example, in the case of representing the confidence level by the confidence level, the confidence level of the first confidence interval of the Gaussian model (denoted as the first confidence level for ease of description and differentiation) and the confidence level of the above-mentioned first confidence interval of the Cauchy distribution function (denoted as the second confidence level for ease of description and differentiation) can be obtained. If the first confidence level is less than or equal to the second confidence level, the first confidence of the first sampling value determined based on the Gaussian model is less than or equal to the confidence of the adjusted sampling value obtained by adjusting the first sampling value based on the Cauchy distribution function; if the first confidence level is greater than the second confidence level, the first confidence of the first sampling value determined based on the Gaussian model is greater than the confidence of the adjusted sampling value obtained by adjusting the first sampling value based on the Cauchy distribution function.

[0097] As an example, in the case of representing the confidence level by the confidence interval, the confidence interval of the first confidence level of the Gaussian model (denoted as the first confidence interval for ease of description and differentiation) and the confidence interval of the above-mentioned first confidence level of the Cauchy distribution function (denoted as the second confidence interval for ease of description and differentiation) can be obtained. If the width of the first confidence interval is greater than or equal to the width of the second confidence interval, the first confidence of the first sampling value determined based on the Gaussian model is less than or equal to the confidence of the adjusted sampling value obtained by adjusting the first sampling value based on the Cauchy distribution function; if the width of the first confidence interval is less than the width of the second confidence interval, the first confidence of the first sampling value determined based on the Gaussian model is greater than the confidence of the adjusted sampling value obtained by adjusting the first sampling value based on the Cauchy distribution function.

[0098] The present application uses Gaussian process in determining the second data set, adds prior experience, i.e. considers the parameter information used in previous iterations (i.e. the first data set), and then iteratively updates the new prior, which can eliminate many invalid parameter combinations, requires fewer iteration times, and quickly finds the appropriate value combination of the first type of parameter. When the optimization objective is a non-convex function, the optimization effect also has strong robustness. To avoid falling into local optimal value, a certain randomness is introduced by introducing Cauchy distribution function, and a trade-off is made between random exploration (i.e. the adjusted sampling value after introducing Cauchy distribution) and posterior distribution (i.e. the first sampling value based on Gaussian model), so the convergence is good.

[0099] In addition, the introduction of the Cauchy distribution function increases the diversity of the parameter combination, improves the global search ability of the algorithm, and increases the search space. The Cauchy distribution function has a smaller peak at the origin and a larger value at the two ends, and can generate a larger disturbance near the parameter combination, so that the range of the Cauchy distribution function is larger.

[0100] In an optional embodiment, an implementation flowchart of sampling according to the proxy model in the value range of the first type of parameters is as shown in Figure 3

[0101] Step S301: determining a target range in the value range of the first type of parameters according to the value of the current first type of parameter; the target range includes the value of the current first type of parameter.

[0102] The value of the current first type of parameter refers to the value of the first type of parameter currently used in the real-time operation process.

[0103] In this application, the target range is smaller than the value range of the first type of parameter, that is, the target range is a sub-range of the value range of the first type of parameter, and the target range includes the value of the current first type of parameter. That is, sampling is performed near the value of the current first type of parameter.

[0104] Step S302: sampling according to the proxy model in the target range.

[0105] In this application, the sampling method can include but is not limited to any one of the following: inverse transform method, rejection sampling, importance sampling, Markov Monte Carlo sampling method, etc.

[0106] The following takes drilling operation as an example to explain the process of optimizing the drilling speed of the drill bit. As shown in Figure 4 An implementation flowchart of optimizing the drilling speed of the drill bit provided by the embodiment of the application can include:

[0107] Step S401: identifying the running state of the drill bit. If the running state of the drill bit is a load state, go to step S402; otherwise, continue to identify the running state of the drill bit.

[0108] Step S402: constructing a proxy Gaussian model of the drilling speed prediction model based on the latest first data set.

[0109] The first data set includes at least one data pair, and each data pair includes the value of the first type of parameter and the corresponding drilling speed. The first type of parameter includes: weight on bit, rotary speed and mud speed.

[0110] Step S403: sampling according to the proxy Gaussian model in the value range of the first type of parameter, and the obtained first sampling value is different from each value of the first type of parameter in the latest first data set. ​

[0111] Step S404: adjusting the first sampling value by using the Cauchy distribution function to obtain an adjusted sampling value.

[0112] Step S405: determining a second sampling value according to the confidence of the first sampling value and the confidence of the adjusted sampling value.

[0113] If the confidence of the first sampling value is less than or equal to the confidence of the adjusted sampling value, the adjusted sampling value is determined as the second sampling value; otherwise, the first sampling value is determined as the second sampling value.

[0114] Step S406: processing the second sampling value and the value of the second type parameter by using the drilling speed prediction model to obtain a drilling speed corresponding to the second sampling value.

[0115] Step S407: adding the second sampling value and the corresponding drilling speed into the first data set.

[0116] Step S408: determining whether an update end condition is met, if yes, proceeding to step S409, otherwise returning to execute step S402.

[0117] The second data set is the latest first data set obtained at the end of the update.

[0118] Step S409: determining the value of the first type parameter corresponding to the maximum drilling speed in the second data set as a target value of the first type parameter.

[0119] Step S410: sending the target value of the first type parameter to a target device. The target device belongs to a driller at the drilling site, and is used to enable the driller to obtain the target value of the first type parameter.

[0120] The driller at the drilling site sets the value of the first type parameter in the drilling control system to the target value, so that the drilling control system controls the drilling speed of the drill bit to reach the maximum drilling speed in the second data set.

[0121] Real-time optimization control of the drilling speed in the drilling operation is achieved.

[0122] Corresponding to the method embodiment, the embodiment of the present application further provides an information processing device, as shown in Figure 5 Fig. 1 is a structural schematic diagram of an information processing device provided by the embodiment of the present application, which can include:

[0123] an identification module 501, an obtaining module 502 and a determination module 503; wherein,

[0124] The identification module 501 is configured to identify the running state of the controlled object.

[0125] The obtaining module 502 is configured to obtain a second data set based on a first data set, a second type parameter, and a speed prediction model if the running state is a load state; the first data set is a subset of the second data set; the second data set includes a plurality of data pairs, each data pair including a value of the first type parameter and a movement speed corresponding to the value of the first type parameter based on the speed prediction model; and the speed prediction model is configured to predict the movement speed of the controlled object according to the value of the first type parameter and the value of the second type parameter.

[0126] The determining module 503 is configured to determine a value of the first type parameter corresponding to a maximum movement speed in the second data set as a target value of the first type parameter.

[0127] The first type parameter is an operable parameter in a work process.

[0128] The information processing device provided by the embodiments of the present application finds an optimal value of the first type parameter that can generate an optimal movement speed based on an existing first data set, a second type parameter, and a speed prediction model, and thus optimizes the movement speed of the controlled object by optimizing the operable parameter, thereby reducing the optimization cost and improving the optimization efficiency.

[0129] In an optional embodiment, when the obtaining module 502 obtains the second data set based on the first data set, the second type parameter, and the speed prediction model, the obtaining module 502 is configured to:

[0130] The first data set is updated based on the following process until an update end condition is met:

[0131] An agent model of the speed prediction model is constructed based on the latest first data set;

[0132] The first type parameter is sampled in a value range according to the agent model, and a first sampling value obtained by the sampling is different from each value of the first type parameter in the latest first data set;

[0133] A second sampling value is determined based on the first sampling value;

[0134] The second sampling value and the value of the second type parameter are processed by the speed prediction model to obtain a movement speed corresponding to the second sampling value;

[0135] The second sampling value and the corresponding movement speed are added to the first data set;

[0136] The second data set is the latest first data set obtained at the end of the update.

[0137] In an optional embodiment, the second sampling value is the same as or different from the first sampling value.

[0138] In an optional embodiment, when the obtaining module 502 determines the second sampling value based on the first sampling value, the method comprises:

[0139] if the condition is met, obtaining a result of adjusting the first sampling value by using the target adjustment mode as the second sampling value;

[0140] if the condition is not met, determining the first sampling value as the second sampling value.

[0141] In an optional embodiment, the condition is met, comprising:

[0142] based on the confidence of the first sampling value determined by the proxy model, the confidence of the result of adjusting the first sampling value by using the target adjustment mode is less than or equal to the confidence of the result of adjusting the first sampling value by using the target adjustment mode.

[0143] In an optional embodiment, the proxy model is a Gaussian model; when the obtaining module 502 adjusts the first sampling value by using the target adjustment mode, the method comprises:

[0144] adjusting the first sampling value by using a Cauchy distribution function.

[0145] In an optional embodiment, when the obtaining module 502 samples according to the proxy model in the value range of the first type of parameters, the method comprises:

[0146] determining a target range according to the current value of the first type of parameters in the value range of the first type of parameters; the target range includes the current value of the first type of parameters;

[0147] sampling according to the proxy model in the target range.

[0148] Corresponding to the method embodiments, the present application also provides an electronic device, a structural schematic diagram of which is shown in Figure 6 The electronic device can include at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4.

[0149] In the embodiments of the present application, the number of processors 1, communication interfaces 2, memories 3 and communication buses 4 is at least one, and the processors 1, communication interfaces 2 and memories 3 complete communication with each other through the communication bus 4.

[0150] The processor 1 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), one or more integrated circuits configured to implement one or more embodiments of the application, etc.

[0151] The memory 3 can include a high-speed RAM memory and can also include a non-volatile memory, such as at least one disk memory.

[0152] The memory 3 stores a program, and the processor 1 can invoke the program stored in the memory 3, and the program is configured to:

[0153] identify a running state of a controlled object;

[0154] if the running state is a load state, obtain a second data set based on a first data set, a second type of parameter, and a speed prediction model; the first data set is a subset of the second data set; the second data set includes a plurality of data pairs, each data pair including a value of the first type of parameter and a corresponding motion speed of the first type of parameter based on the speed prediction model; the speed prediction model is configured to predict a motion speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter;

[0155] determine a target value of the first type of parameter corresponding to a maximum motion speed in the second data set as the target value of the first type of parameter;

[0156] The first type of parameter is an operable parameter in a work process.

[0157] Optionally, the detailed functions and extended functions of the program can refer to the description above.

[0158] The embodiments of the application also provide a storage medium which can store a program suitable for a processor to execute, and the program is configured to:

[0159] identify a running state of a controlled object;

[0160] if the running state is a load state, obtain a second data set based on a first data set, a second type of parameter, and a speed prediction model; the first data set is a subset of the second data set; the second data set includes a plurality of data pairs, each data pair including a value of the first type of parameter and a corresponding motion speed of the first type of parameter based on the speed prediction model; the speed prediction model is configured to predict a motion speed of the controlled object according to the value of the first type of parameter and the value of the second type of parameter;

[0161] The value of the first type parameter corresponding to the maximum motion speed in the second data set is determined as a target value of the first type parameter.

[0162] The first type parameter is an operable parameter in a work process.

[0163] Optionally, the refinement function and the expansion function of the program can refer to the description above.

[0164] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0165] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0166] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0167] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit.

[0168] It should be understood that the features in the embodiments of the present application, each embodiment, feature can be combined with each other, and can realize the solution to the above technical problems.

[0169] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0170] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information processing method, comprising: identifying an operating state of a drill bit; if the operating state is a load state, constructing a proxy Gaussian model of a penetration rate prediction model based on a latest first data set, the first data set including at least one data pair, each data pair including a value of a first type of parameter and a corresponding penetration rate, the first type of parameter including: weight on bit, rotary speed, and mud speed; sampling within a value range of the first type of parameter according to the proxy Gaussian model, the obtained first sampling value being different from each value of the first type of parameter in the latest first data set; adjusting the first sampling value using a Cauchy distribution function to obtain an adjusted sampling value; determining a second sampling value according to a confidence degree of the first sampling value and a confidence degree of the adjusted sampling value; if the confidence degree of the first sampling value is less than or equal to the confidence degree of the adjusted sampling value, determining the adjusted sampling value as the second sampling value; otherwise, determining the first sampling value as the second sampling value; processing the second sampling value and a value of a second type of parameter through the penetration rate prediction model to obtain a penetration rate corresponding to the second sampling value; adding the second sampling value and the corresponding penetration rate to the first data set; determining whether an update end condition is met, and if yes, determining a value of the first type of parameter corresponding to a maximum penetration rate in the second data set as a target value of the first type of parameter; otherwise, returning to the step of constructing the proxy Gaussian model of the penetration rate prediction model based on the latest first data set; sending the target value of the first type of parameter to a target device; the target device belonging to a driller on a drilling site, and being configured to enable the driller to obtain the target value of the first type of parameter.

2. The method of claim 1, wherein the second sampling value is the same as or different from the first sampling value.

3. The method of claim 1, wherein the sampling within the value range of the first type of parameter according to the proxy Gaussian model comprises: determining a target range within the value range of the first type of parameter according to a current value of the first type of parameter; the target range including the current value of the first type of parameter; sampling within the target range according to the proxy Gaussian model.

4. An information processing apparatus, comprising: an identification module configured to identify an operating state of a drill bit; The obtaining module is configured to, if the running state is a load state, construct a proxy Gaussian model of a drilling speed prediction model based on a latest first data set, the first data set including at least one data pair, each data pair including a value of a first type parameter and a corresponding drilling speed, the first type parameter including: weight on bit, rotary speed, and mud speed; sampling within a value range of the first type parameter according to the proxy Gaussian model, the obtained first sampling value being different from each value of the first type parameter in the latest first data set; adjusting the first sampling value by using a Cauchy distribution function to obtain an adjusted sampling value; determining a second sampling value according to a confidence degree of the first sampling value and a confidence degree of the adjusted sampling value; if the confidence degree of the first sampling value is less than or equal to the confidence degree of the adjusted sampling value, determining the adjusted sampling value as the second sampling value; otherwise, determining the first sampling value as the second sampling value; processing the second sampling value and a value of a second type parameter by the drilling speed prediction model to obtain a drilling speed corresponding to the second sampling value; and adding the second sampling value and the corresponding drilling speed to the first data set; The determining module is configured to determine whether an update end condition is met, and if yes, determine the value of the first type parameter corresponding to the maximum drilling speed in the second data set as a target value of the first type parameter; otherwise, return to the step of constructing the proxy Gaussian model of the drilling speed prediction model based on the latest first data set performed by the obtaining module; and send the target value of the first type parameter to a target device; the target device belongs to a driller in a drilling operation site, and is configured to enable the driller to obtain the target value of the first type parameter. 5.An electronic device, comprising: a memory configured to store a program; a processor configured to invoke and execute the program in the memory, and implement each step of the information processing method according to any one of claims 1-3 by executing the program. 6.A readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement each step of the information processing method according to any one of claims 1-3.

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