A Multi-Battery Control Method and System for Well Logging in Complex Environments

Abstract

This invention relates to the field of multi-battery control technology, specifically to a method and system for controlling multiple batteries in well logging under complex environments. The method includes: real-time acquisition of various monitoring data from each individual battery in a battery pack under well logging conditions; for each individual battery, obtaining characteristic intervals of various monitoring data from previous times based on the distribution of these data; acquiring the state characteristic values ​​and change difference characteristic values ​​of various monitoring data for each individual battery at the current time, and obtaining the power allocation adjustment coefficient for each individual battery at the current time; combining various key state parameters of each individual battery at the current time to obtain a comprehensive score for each individual battery at the current time, and allocating power to each individual battery in the battery pack. This invention aims to improve the stability and energy utilization efficiency of multi-battery collaborative control power supply by optimizing the power allocation of multiple batteries under complex well logging environments.

Description

Technical Field

[0001] This invention relates to the field of multi-battery control technology, specifically to a multi-battery control method and system for well logging in complex environments. Background Technology

[0002] In well logging operations involving oil and natural gas resources, complex environmental conditions such as high temperature, high pressure, strong vibration, and electromagnetic interference place extremely high demands on the performance of the instrument's power supply system. Multi-battery control technology, as a core supporting mechanism, utilizes redundant design and dynamic adjustment mechanisms to ensure that the logging instrument can obtain a continuous and stable power supply in extreme environments, effectively avoiding the risk of operation interruption or data loss caused by single-battery failure. Furthermore, by monitoring battery status in real time and implementing intelligent power allocation, it significantly improves the energy utilization efficiency of the battery pack, thereby extending the endurance of well logging operations and supporting the successful execution of highly challenging well logging tasks.

[0003] However, the multiple disturbances in complex environments significantly reduce the stability and accuracy of traditional single-cell control methods and simplified battery pack management strategies. Specifically, temperature fluctuations accelerate the internal chemical reaction rate of the battery, leading to a sharp decline in battery capacity and abnormal fluctuations in internal resistance; strong vibration environments can easily cause loose battery pack wiring, triggering sudden changes in contact resistance and thus interfering with power distribution accuracy. Therefore, in the multi-cell collaborative control process for complex logging environments, traditional power distribution methods fail to fully consider the differentiated impact of environmental changes on the states of different individual batteries, resulting in significant deviations in actual power distribution and severely affecting the stability of logging power supply. Summary of the Invention

[0004] In view of the above, it is necessary to provide a multi-battery control method and system for well logging in complex environments. Compared with traditional multi-battery control methods and systems for well logging in complex environments, this method improves the stability and energy utilization efficiency of multi-battery collaborative control power supply by optimizing the power distribution of multiple batteries in complex well logging environments.

[0005] In a first aspect, embodiments of the present invention provide a multi-cell control method for well logging in complex environments, the method comprising the following steps:

[0006] Real-time acquisition of various monitoring data of each individual cell in the battery pack under well logging conditions;

[0007] For a single cell, the characteristic intervals of various monitoring data before the current time are obtained by the distribution of various monitoring data before the current time; by the correlation between various monitoring data before the current time and each other monitoring data in their respective characteristic intervals, and combined with the dispersion of various monitoring data in their respective characteristic intervals, the state characteristic values ​​of various monitoring data of the single cell at the current time are obtained.

[0008] By comparing the state characteristic values ​​of various monitoring data between each individual cell and each other individual cell at the current moment, and the differences of various monitoring data between each individual cell and each other individual cell before the current moment, the change difference characteristic values ​​of various monitoring data of each individual cell at the current moment are obtained, and then the power distribution adjustment coefficient of each individual cell at the current moment is obtained.

[0009] The system obtains various key state parameters of each individual battery cell at the current moment, combines them with the power allocation adjustment coefficient, obtains the comprehensive score of each individual battery cell at the current moment, and then allocates power to each individual battery cell in the battery pack.

[0010] In one embodiment, the process of obtaining the feature interval is as follows:

[0011] Calculate the mean of any monitoring data before the current time, count the times of the monitoring data that are greater than the mean before the current time, and use the statistical results to divide the collection time interval of any monitoring data before the current time into sub-intervals, and use each sub-interval as the characteristic interval of the monitoring data.

[0012] In one embodiment, the process of obtaining the state feature value is as follows:

[0013] By analyzing the correlation between various monitoring data prior to the current time and each other monitoring data within their respective characteristic intervals, the relevant characteristic values ​​of various monitoring data prior to the current time within their respective characteristic intervals can be obtained.

[0014] The expressions for the state characteristic values ​​of various monitoring data of each individual battery cell at the current moment are:

[0015] In the formula, This represents the state characteristic value of the x-th monitoring data for the t-th individual battery at the current time. This represents the number of characteristic intervals of the x-th type of monitoring data for the t-th individual cell before the current time. This represents the dispersion of the x-th monitoring data of the t-th individual battery before the current time within its y-th characteristic interval; This represents the relevant characteristic value of the x-th type of monitoring data of the t-th individual cell before the current time within its y-th characteristic interval.

[0016] In one embodiment, the process of obtaining the relevant feature values ​​is as follows:

[0017] Calculate the time-series correlation coefficient between each monitoring data point before the current time and each other monitoring data point within any characteristic interval; take the mean of the absolute values ​​of the correlation coefficients between each monitoring data point before the current time and all other monitoring data points within the same characteristic interval as the correlation characteristic value of each monitoring data point before the current time within the same characteristic interval.

[0018] In one embodiment, the process of obtaining the change difference feature value is as follows:

[0019] Calculate the difference in state characteristic values ​​between all monitoring data of each individual cell at the current moment and all monitoring data of the remaining individual cells.

[0020] Arrange the various monitoring data of each individual cell before the current time in chronological order to form the monitoring sequence of various monitoring data of each individual cell at the current time, and calculate the metric distance between the monitoring sequences of various monitoring data of each individual cell and each other individual cell at the current time.

[0021] The variation feature value can be further obtained through the gap and the distance.

[0022] In one embodiment, the expression for the variation difference feature value is:

[0023] In the formula, This represents the characteristic value of the change in the x-th monitoring data of the t-th individual cell at the current time; m represents the number of individual cells in the battery pack. The metric distance between the t-th and v-th individual cells at the current time, based on the x-th type of monitoring data; This represents the difference between the t-th and v-th individual cells at the current time.

[0024] In one embodiment, the power allocation adjustment coefficient is the average of the variation characteristics of all monitoring data of each individual battery cell at the current moment.

[0025] In one embodiment, the formula for calculating the comprehensive score is:

[0026] The key state parameters of each individual battery cell at the current moment are standardized.

[0027] The expression for the overall score of each individual cell at the current moment is:

[0028] In the formula, This represents the overall score of the t-th individual cell at the current moment; This represents the normalized value of the power distribution adjustment coefficient for the t-th individual cell at the current moment; r represents the number of key state parameter types. This represents the standardized result of the z-th key state parameter of the t-th individual cell at the current moment; The preset weight represents the standardized processing result of the z-th key state parameter of the t-th cell at the current time. The sum of the preset weights of the standardized processing results of all key state parameters of the t-th cell at the current time is 1.

[0029] In one embodiment, the method for allocating power to each individual cell in the battery pack is as follows: the proportion of power allocated to each individual cell at the current moment to the total power allocated to all individual cells is equal to the proportion of the overall score of each individual cell to the overall score of all individual cells.

[0030] Secondly, embodiments of the present invention also provide a multi-battery logging control system for complex environments, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the multi-battery logging control method for complex environments described above.

[0031] The present invention has at least the following beneficial effects:

[0032] This invention, by dividing characteristic intervals, can analyze the correlation between various monitoring data and other monitoring data within their stable time range, as well as the dispersion of various monitoring data within their stable time range, thus more accurately reflecting the state change characteristics of individual cells. By calculating the characteristic value of change difference, it quantifies the state differences of different individual cells caused by environmental state changes during the multi-cell monitoring and control process of well logging, which is beneficial to providing a reliable basis for subsequent power allocation. Furthermore, by combining the characteristic values ​​of change difference of all monitoring data of each individual cell, the power allocation adjustment coefficient can be obtained to assess the possibility of a decrease in power supply stability of each individual cell due to environmental influences, providing a more reliable basis for subsequent power allocation. In addition, by combining various key state parameters of each individual cell, the power supply capacity of each individual cell can be more accurately measured, enabling the full integration of key interference influence characteristics in complex well logging environments, dynamic power allocation to each individual cell, and optimization of power allocation, thereby improving the stability and energy utilization efficiency of multi-cell collaborative control power supply in complex well logging environments. Attached Figure Description

[0033] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A flowchart illustrating the steps of a multi-cell control method for well logging in a complex environment, as provided in one embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram illustrating the process of obtaining the overall score. Detailed Implementation

[0036] In the description of the embodiments in this application, the words "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary," "or," and "for example" is intended to present the relevant concepts in a specific manner.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. It should be understood that, unless otherwise stated, " / " in this application means "or".

[0038] It should also be noted that the terms "first" and "second" in this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0039] The following description, in conjunction with the accompanying drawings, details the specific scheme of the multi-battery control method and system for well logging in complex environments provided by this invention.

[0040] Please see Figure 1 The diagram illustrates a flowchart of a multi-cell control method for well logging in complex environments, according to an embodiment of the present invention. The method includes the following steps:

[0041] Step 1: Real-time acquisition of various monitoring data of each individual cell in the battery pack under the logging environment.

[0042] For power supply control in well logging under complex environments, high-precision, interference-resistant hardware is required to achieve real-time monitoring of battery status parameters and environmental parameters. Specifically, a high-temperature and high-pressure resistant distributed sensor array is used for data acquisition. This array includes a battery status parameter acquisition unit and an environmental parameter acquisition unit. The battery status parameter acquisition unit includes voltage, current, and temperature sensors, used to collect real-time voltage, current, and temperature data for each individual battery in the battery pack. The environmental parameter acquisition unit includes pressure, vibration, and electromagnetic interference detectors, used to collect real-time pressure, vibration, and magnetic field strength data for the environment in which each individual battery is located. Voltage, current, and temperature data are collectively referred to as battery status parameters, while pressure, vibration, and magnetic field strength data are collectively referred to as environmental parameters. These data are then recorded as various monitoring data for each individual battery.

[0043] In this embodiment, the acquisition frequency of all monitoring data is 10Hz. The acquisition frequency value is preset by the user and the implementer can set it according to the actual situation. This application does not impose any special restrictions.

[0044] Step 2: By comprehensively analyzing all types of monitoring data of all individual cells collected before the current moment, the power distribution adjustment coefficient of each individual cell at the current moment is obtained.

[0045] Due to the complex downhole environment, interference factors such as environmental changes and changes in the status of data acquisition equipment can affect the acquisition of monitoring data during the logging process, reducing the quality of the acquired monitoring data. Therefore, this invention uses a Gaussian filter to filter each type of acquired monitoring data to obtain preprocessed monitoring data. Furthermore, considering the correlation and dynamism of state changes between individual cells in the logging battery bank under complex environments, environmental interference can exacerbate state differences through differential effects, while the monitoring parameters of individual cells exhibit nonlinear, sudden, and cumulative state change characteristics. For example, individual cells are often connected in parallel or series for coordinated power supply, so changes in the state of individual cells can affect the accuracy of power distribution; and the voltage and current of individual cells can exhibit unstable changes due to the influence of temperature, pressure, vibration, and electromagnetic interference.

[0046] It should be noted that the Gaussian filter is only one embodiment of the present invention. As other implementation methods, implementers may adopt other existing feasible technologies based on the ability to filter various monitoring data collected. This application does not impose any special restrictions.

[0047] Based on the above analysis, the state change characteristics during the multi-cell control process of well logging are analyzed. The specific analysis and processing procedure is as follows:

[0048] Step 2.1: For a single cell, obtain the characteristic intervals of various monitoring data before the current time by analyzing the distribution of various monitoring data before the current time; obtain the state characteristic values ​​of various monitoring data of the single cell at the current time by analyzing the correlation between various monitoring data before the current time and each other monitoring data within their respective characteristic intervals, combined with the dispersion of various monitoring data within their respective characteristic intervals.

[0049] The monitoring data of each individual cell prior to the current moment are normalized to avoid the influence of different dimensions.

[0050] In this embodiment, the Min-Max normalization method is used to normalize various monitoring data respectively. The Min-Max normalization method is a well-known technology and will not be described in detail in this application.

[0051] Taking the x-th monitoring data of the t-th individual cell in the battery pack as an example, calculate the average value of the x-th monitoring data of the t-th individual cell before the current time, count the times of the monitoring data of the x-th individual cell before the current time that are greater than the average value, and record them as each statistical time. Use each statistical time as a segmentation point, and use each statistical time to divide the collection time interval of the x-th monitoring data of the t-th individual cell before the current time into each sub-interval. Use each sub-interval as the characteristic interval of the x-th monitoring data of the t-th individual cell at the current time.

[0052] According to the method for obtaining the characteristic intervals of the x-th type of monitoring data of the t-th individual cell at the current time, obtain the characteristic intervals of various monitoring data of each individual cell at the current time.

[0053] Furthermore, the disorder characteristics within each characteristic interval determined by various monitoring data are analyzed, thereby enabling precise analysis of the state changes of individual cells during the actual monitoring process. First, by analyzing the correlation between various monitoring data prior to the current time within each characteristic interval and each other monitoring data type, the relevant characteristic values ​​of various monitoring data prior to the current time within each characteristic interval are obtained, specifically:

[0054] Taking the x-th monitoring data of the t-th individual cell in the battery pack as an example, calculate the temporal correlation coefficient between the x-th monitoring data and each of the other monitoring data within its y-th characteristic interval before the current time. Take the average of the absolute values ​​of these correlation coefficients between the x-th monitoring data and all other monitoring data within its y-th characteristic interval before the current time as the correlation characteristic value of the x-th monitoring data within its y-th characteristic interval before the current time. The smaller the characteristic value, the greater the difference in state change between the x-th monitoring data and the other monitoring data within its y-th characteristic interval.

[0055] In this embodiment, the correlation coefficient is the Pearson correlation coefficient, which is a well-known technology and will not be described in detail here. As other implementation methods, in order to measure the correlation between various monitoring data and each other monitoring data in time series, implementers may use other existing technologies, such as Spearman correlation coefficient, Kendall rank correlation coefficient, etc., and this application does not impose any special restrictions.

[0056] Then, by using the correlation characteristic values ​​and dispersion of various monitoring data of each individual cell in its respective characteristic intervals before the current time, the state characteristic values ​​of various monitoring data of each individual cell at the current time are obtained, and the expression is:

[0057] In the formula, This represents the state characteristic value of the x-th monitoring data for the t-th individual battery at the current time. This represents the number of characteristic intervals of the x-th type of monitoring data for the t-th individual cell before the current time. This represents the dispersion of the x-th monitoring data of the t-th individual battery before the current time within its y-th characteristic interval; This represents the relevant characteristic value of the x-th type of monitoring data of the t-th individual cell before the current time within its y-th characteristic interval.

[0058] In this embodiment, the dispersion is the coefficient of variation, which is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to measure the uneven distribution of the x-th monitoring data in its y-th characteristic interval, the implementer may use other existing techniques, such as variance, standard deviation, etc. This application does not impose any special restrictions.

[0059] It should be noted that the larger the calculated state characteristic value, the more accurate the state change characteristics of the t-th individual cell reflected by the monitoring data during the power supply process.

[0060] Step 2.2: By comparing the state characteristic values ​​of various monitoring data between each individual cell and the other individual cells at the current time, and the differences in various monitoring data between each individual cell and the other individual cells before the current time, the change difference characteristic values ​​of various monitoring data of each individual cell at the current time are obtained, and then the power distribution adjustment coefficient of each individual cell at the current time is obtained.

[0061] Based on the above analysis, by comparing the parameter change response characteristics of each individual cell under the state changes during the multi-cell monitoring and control process of well logging, the response characteristics of different monitoring parameters to the state changes of individual cell state characteristics during the actual monitoring and control process can be accurately analyzed.

[0062] Furthermore, considering that all individual cells provide power collaboratively during the logging process, the greater the difference in the response characteristics of the monitoring parameters of different individual cells to state changes during power supply, the greater the possibility of power supply instability due to environmental interference during the multi-cell collaborative power supply process. To accurately reflect the differences in the response characteristics of state changes among different individual cells to different monitoring parameters during the multi-cell control process, and to accurately analyze the state characteristics of individual cells during the multi-cell control process, the difference in state characteristic values ​​between all monitoring data of the t-th individual cell at the current time and all monitoring data of the remaining individual cells is calculated. Specifically, the state characteristic values ​​of all monitoring data of each individual cell at the current time are used to form a state characteristic sequence of each individual cell at the current time. Elements at the same position in the state characteristic sequence of all individual cells represent the state characteristic value of the same monitoring data. The distance between the state characteristic sequences of the t-th individual cell and the remaining individual cells at the current time is taken as the difference. The larger the difference, the more significant the response characteristics of different monitoring data of different individual cells to state changes during the comprehensive monitoring process, and the more significant the state change characteristics of the t-th individual cell.

[0063] In this embodiment, the distance between the state feature sequences is Euclidean distance. As other implementation methods, based on the ability to measure the degree of difference between elements at the same position in two state feature sequences, the implementer may use other existing technologies, such as Manhattan distance, cosine distance, etc. This application does not impose any special restrictions.

[0064] Furthermore, the various monitoring data of each individual cell before the current moment are arranged in chronological order to form the monitoring sequence of various monitoring data of each individual cell at the current moment. The metric distance between the monitoring sequences of various monitoring data of each individual cell and each other individual cell at the current moment is calculated. The larger the metric distance, the greater the difference in stability of the individual cells due to the difference in the state of the individual cells during the well logging multi-cell control process.

[0065] In this embodiment, the distance between monitoring sequences is the DTW (Dynamic Time Warping) distance. The calculation of DTW distance is a well-known technique and will not be described in detail here. As other implementation methods, based on the ability to measure the degree of difference between two monitoring sequences, implementers may use other existing techniques, such as Euclidean distance, etc. This application does not impose any special restrictions.

[0066] Furthermore, by considering the differences in state characteristic values ​​of various monitoring data between each individual cell and all other individual cells at the current moment, and the differences in various monitoring data between each individual cell and all other individual cells before the current moment, the change difference characteristic values ​​of various monitoring data of each individual cell at the current moment are obtained, expressed as:

[0067] In the formula, This represents the characteristic value of the change in the x-th monitoring data of the t-th individual cell at the current time; m represents the number of individual cells in the battery pack. The metric distance between the t-th and v-th individual cells at the current time, based on the x-th type of monitoring data; This represents the difference between the t-th and v-th individual cells at the current time.

[0068] It should be noted that the larger the calculated variation characteristic value, the greater the state difference of individual cells located in different positions due to the influence of complex environment during the multi-cell control process of well logging. Correspondingly, the more significant the response change difference of the monitoring parameters reflecting the state change of individual cells.

[0069] Furthermore, considering that traditional multi-cell control in well logging neglects the differences in state changes over time caused by the positional differences of individual cells in complex logging environments, this leads to deviations in power allocation adjustments during actual control, affecting the stability of multi-cell coordinated power supply in actual logging. Therefore, based on the above analysis, and combining the characteristic values ​​of the changes in all types of monitoring data for each individual cell at the current moment, the power allocation adjustment coefficient for each individual cell at the current moment is obtained, expressed as:

[0070] In the formula, This represents the power distribution adjustment coefficient of the t-th individual cell at the current time; u represents the number of monitoring parameter types. This represents the characteristic value of the change difference in the x-th type of monitoring data for the t-th individual cell at the current time.

[0071] It should be noted that the larger the calculated power distribution adjustment coefficient, the greater the possibility that the power supply stability of the t-th cell will decrease due to environmental influences, which is caused by the difference in parameter response changes caused by the state changes of different individual cells in complex logging environments. This makes the error of power distribution control through individual cell state control potentially larger.

[0072] Step 3: Obtain the key state parameters of each individual battery cell at the current moment, and combine them with the power allocation adjustment coefficient to obtain the comprehensive score of each individual battery cell at the current moment, and then allocate power to each individual battery cell in the battery pack.

[0073] Furthermore, when implementing power allocation for multiple logging cells, it is typically necessary to comprehensively consider the state of charge (SOC), state of health (SOH), temperature, and internal resistance of individual cells. Under normal circumstances, cells with high charge, good health, suitable temperature, and low internal resistance are allocated relatively higher power values. However, during the collaborative control of multiple logging cells, due to complex environmental interference, the state changes of individual cells at different locations vary significantly. Therefore, relying solely on the SOC, SOH, temperature, and internal resistance of individual cells may still result in significant deviations in the allocated power values, allowing cells with reduced power supply stability to maintain a high power output, thus affecting the stability of collaborative power supply across multiple logging cells. Here, SOC, SOH, temperature, and internal resistance are denoted as various key state parameters.

[0074] Therefore, based on the above analysis, when optimizing the power allocation of multiple cells in well logging, the key state parameters of each individual cell are first standardized. The standardization method is as follows: State of Charge (SOC) is standardized by calculating the ratio of the current state of charge (SOC) to the full charge of each individual cell; State of Health (SOH) is standardized by calculating the ratio of the current capacity to the initial capacity of each individual cell; an optimal temperature range is set. If the temperature of an individual cell is within the optimal temperature range, the temperature standardization result is 1; if it is not within the optimal temperature range, the temperature standardization result is 0. That is, higher power can be allocated at suitable temperatures, while the allocated power value is relatively lower if the temperature exceeds the set temperature range; Internal resistance is standardized by calculating the ratio of the initial internal resistance to the current internal resistance of each individual cell. In this embodiment, the optimal temperature range is... The optimal temperature range is preset by the user, and the implementer can set it according to the actual situation. This application does not impose any special restrictions.

[0075] Furthermore, by combining the standardized results of various key state parameters of each individual cell at the current moment with the power distribution adjustment coefficient of each individual cell at the current moment, the comprehensive score of each individual cell at the current moment is obtained, expressed as:

[0076] In the formula, This represents the overall score of the t-th individual cell at the current moment; This represents the normalized value of the power distribution adjustment coefficient for the t-th individual cell at the current moment; r represents the number of key state parameter types. This represents the standardized result of the z-th key state parameter of the t-th individual cell at the current moment; The preset weight represents the standardized processing result of the z-th key state parameter of the t-th cell at the current time. The sum of the preset weights of the standardized processing results of all key state parameters of the t-th cell at the current time is 1.

[0077] In this embodiment, the Sigmoid function is used to obtain the normalized value of the power allocation adjustment coefficient. The Sigmoid function is a well-known technology and will not be described in detail in this application.

[0078] In this embodiment, the preset weight of SOC is 0.4, the preset weight of SOH is 0.3, the preset weight of temperature is 0.2, and the preset weight of internal resistance is 0.1. Under the premise that the sum of the preset weights of SOC, SOH, temperature and internal resistance is 1, the implementer can allocate the weights of SOC, SOH, temperature and internal resistance according to the actual situation. This application does not impose any special restrictions.

[0079] It should be noted that a larger power allocation adjustment coefficient indicates a greater difference in the state changes of the t-th individual cell under complex logging conditions. This suggests a higher likelihood of decreased power supply stability for the t-th individual cell, resulting in a lower overall score. A lower overall score indicates that when allocating power based on the key state parameters of individual cells, the proportion of power allocated to the t-th individual cell should be reduced to improve the stability of multi-cell power supply under complex logging conditions. A schematic diagram of the overall score acquisition process is shown below. Figure 2 As shown.

[0080] Furthermore, based on the comprehensive score of all individual cells at the current moment, power is allocated to each individual cell in the battery pack. Specifically, the proportion of power allocated to each individual cell at the current moment in the total power allocated to all individual cells is equal to the proportion of the comprehensive score of each individual cell in the total comprehensive score of all individual cells.

[0081] Based on the same inventive concept as the above method, this application embodiment also provides a well logging multi-battery control system in a complex environment, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described well logging multi-battery control methods in a complex environment.

[0082] In summary, this invention, by dividing characteristic intervals, can analyze the correlation between various monitoring data and other types of monitoring data within their stable time range, as well as the dispersion of various monitoring data within their stable time range, thus more accurately reflecting the state change characteristics of individual cells. By calculating the characteristic value of change difference, it quantifies the state differences of different individual cells caused by environmental state changes during the multi-cell monitoring and control process of well logging, which is beneficial for providing a reliable basis for subsequent power allocation. Furthermore, by combining the characteristic values ​​of change difference of all types of monitoring data of each individual cell, the power allocation adjustment coefficient can be obtained, and the possibility of a decrease in power supply stability of each individual cell due to environmental influences can be assessed, providing a more reliable basis for subsequent power allocation. In addition, by combining various key state parameters of each individual cell, the power supply capacity of each individual cell can be more accurately measured, enabling the full integration of key interference influence characteristics in complex well logging environments, dynamic power allocation to each individual cell, and optimization of power allocation, thereby improving the stability and energy utilization efficiency of multi-cell collaborative control power supply in complex well logging environments.

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0084] It will be apparent to those skilled in the art that this application is not limited to the details of the exemplary embodiments described above, and that this application can be implemented in other specific forms without departing from its essential characteristics. Therefore, the embodiments described above should be considered exemplary and non-limiting in all respects.

Claims

1. A method for logging multi-battery control in complex environments, the method comprising: The method comprises the following steps: Real-time acquisition of various monitoring data of each single battery in the battery pack under the logging environment; For a single battery, each feature interval of various monitoring data before the current time is obtained through the distribution of various monitoring data before the current time; the state feature value of various monitoring data of the single battery at the current time is obtained through the correlation between various monitoring data in its feature interval and the rest of each monitoring data, combined with the dispersion of various monitoring data in its feature interval; The change difference feature value of various monitoring data of each single battery at the current time is obtained through the difference of the state feature value of various monitoring data between each single battery and the rest of each single battery at the current time, and the difference of various monitoring data between each single battery and the rest of each single battery before the current time, and then the power distribution adjustment coefficient of each single battery at the current time is obtained; The comprehensive score of each single battery at the current time is obtained by combining the power distribution adjustment coefficient with various key state parameters of each single battery at the current time, and then the power of each single battery in the battery pack is distributed. The feature interval acquisition process is as follows: The mean of any kind of monitoring data before the current time is calculated, the time when the monitoring data greater than the mean in the any kind of monitoring data before the current time is counted, and the counting result is used to divide the acquisition time interval of the any kind of monitoring data before the current time, and each sub-interval obtained by the division is taken as each feature interval of the any kind of monitoring data; The state feature value acquisition process is as follows: The correlation feature value of various monitoring data in its feature interval before the current time is obtained through the correlation between various monitoring data in its feature interval and the rest of each monitoring data before the current time; The expression of the state feature value of various monitoring data of each single battery at the current time is as follows: wherein k t,x denotes the state feature value of the xth monitoring data of the tth single battery at the current time; n x denotes the number of feature intervals of the xth monitoring data of the tth single battery before the current time; l tx,y denotes the dispersion of the xth monitoring data of the tth single battery within the yth feature interval thereof before the current time; p tx,y denotes the relevant feature value of the xth monitoring data of the tth single battery within the yth feature interval thereof before the current time.

2. The method of claim 1, wherein the method is performed in a complex environment. The correlation feature value acquisition process is as follows: The correlation coefficient of various monitoring data in any feature interval before the current time and the rest of each monitoring data in time sequence is calculated; the mean of the absolute value of the correlation coefficient of various monitoring data in the any feature interval before the current time and the rest of all kinds of monitoring data is taken as the correlation feature value of various monitoring data in the any feature interval before the current time.

3. The method of claim 1, wherein the method is performed in a complex environment. The change difference feature value acquisition process is as follows: The gap of the state feature value between all monitoring data of each single battery and all monitoring data of the rest of each single battery at the current time is calculated; The monitoring sequence of various monitoring data of each single battery at the current time is composed by arranging various monitoring data of each single battery before the current time in time sequence, and the metric distance of the monitoring sequence of various monitoring data between each single battery and the rest of each single battery at the current time is calculated; The change difference feature value can be further obtained by the gap and the distance.

4. The method of claim 3, wherein the method further comprises: The expression of the change difference feature value is as follows: wherein h t,x represents the change difference characteristic value of the xth monitoring data of the tth single battery at the current time; m represents the number of single batteries in the battery pack; d t,vx represents the metric distance of the xth monitoring data between the tth single battery and the vth single battery at the current time; w t,v represents the difference between the tth single battery and the vth single battery at the current time.

5. The method of claim 1, wherein the method is performed in a complex environment. The power distribution adjustment coefficient is the mean of the change difference feature value of all kinds of monitoring data of each single battery at the current time.

6. The method of claim 1, wherein the method is performed in a complex environment. The calculation relationship of the comprehensive score is as follows: The various key state parameters of each single battery at the current time are standardized respectively; The expression of the comprehensive score of each single battery at the current time is: wherein δ t denotes the overall score of the tth single battery at the current time point; s′ t a normalized value representing a power distribution adjustment coefficient of the tth single battery at the current moment; r represents the number of critical state parameter categories; f t,z a normalized processing result of the zth critical state parameter of the tth single battery at the current moment; σ t,z a preset weight of the normalized processing result of the zth critical state parameter of the tth single battery at the current moment, and the sum of the preset weights of the normalized processing results of all the critical state parameters of the tth single battery at the current moment is 1.

7. The method of claim 1, wherein the method is used in a complex environment. The method for distributing power to each single battery in the battery pack is that the proportion of the power distributed to each single battery in the power distributed to all single batteries at the current time is equal to the proportion of the comprehensive score of each single battery in the comprehensive scores of all single batteries.

8. A logging multi-cell control system in a complex environment, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor implements the steps of the well logging multi-battery control method in a complex environment when executing the computer program.

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