Logging multi-battery control method and system in complex environment

Abstract

The invention relates to the technical field of multi-battery control, in particular to a logging multi-battery control method and system in a complex environment, and the method comprises the steps: collecting various monitoring data of each single battery in a battery pack in a logging environment in real time; aiming at a single battery cell, through the distribution of various monitoring data before the current moment, obtaining each characteristic interval of the various monitoring data before the current moment; acquiring a state characteristic value and a change difference characteristic value of various monitoring data of each single battery at the current moment, and acquiring a power distribution adjustment coefficient of each single battery at the current moment; and in combination with various key state parameters of each single battery at the current moment, obtaining a comprehensive score of each single battery at the current moment, and distributing power to each single battery in the battery pack. The invention aims to improve the stability of multi-battery cooperative control power supply and the energy utilization efficiency by optimizing the power distribution of multiple batteries in a complex logging environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-battery control, and in particular to a well logging multi-battery control method and system under complex environment. BACKGROUND

[0002] In the well logging operation of oil, natural gas and other resources, complex environmental conditions such as high temperature, high pressure, strong vibration and electromagnetic interference put high requirements on the performance of the instrument power supply system. As a core support means, multi-battery control technology, with the help of redundant design and dynamic adjustment mechanism, ensures that the well logging instrument can obtain continuous and stable power supply in extreme environment, effectively avoids the risk of operation interruption or data loss caused by single battery failure; and through real-time monitoring of battery state and implementation of intelligent power distribution, the energy utilization efficiency of the battery pack is significantly improved, thereby prolonging the endurance time of well logging operation and supporting the smooth development of high-difficulty well logging tasks.

[0003] However, the multiple interference characteristics in the complex environment greatly reduce the stability and accuracy of the traditional single battery control method and the simple battery pack management strategy. Among them, temperature fluctuations will accelerate the chemical reaction rate inside the battery, causing the battery capacity to rapidly decay and the internal resistance to abnormally fluctuate; the strong vibration environment is easy to cause the battery pack wiring to loosen, causing the contact resistance to mutate, and then interfering with the power distribution accuracy. Therefore, in the process of multi-battery collaborative control facing complex well logging environment, the traditional power distribution method does not fully consider the differentiated influence of environmental changes on the state of different single batteries, resulting in a large deviation in actual power distribution, which seriously affects the stability of well logging power supply. SUMMARY

[0004] In view of the above, it is necessary to provide a well logging multi-battery control method and system under complex environment, which, compared with the traditional well logging multi-battery control method and system under complex environment, optimizes the power distribution of multi-battery under complex well logging environment, thereby improving the stability and energy utilization efficiency of multi-battery collaborative control power supply: In a first aspect, an embodiment of the present application provides a well logging multi-battery control method under complex environment, which comprises the following steps: Real-time acquisition of various monitoring data of each single battery in the battery pack under well logging environment; For a single single battery, obtain each feature interval of various monitoring data before the current time through the distribution of various monitoring data before the current time; obtain the state feature value of various monitoring data of the single single battery at the current time through the correlation between various monitoring data in its each feature interval and the rest of each monitoring data, combined with the dispersion of various monitoring data in its each feature interval; The state characteristic value of each kind of monitoring data of each single battery at the current time is obtained according to the difference between each kind of monitoring data of each single battery at the current time and each kind of monitoring data of each single battery at the current time, and the change difference characteristic value of each kind of monitoring data of each single battery at the current time is obtained, 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 according to the power distribution adjustment coefficient and the key state parameter of each single battery at the current time, and then the power of each single battery in the battery pack is distributed.

[0005] In one embodiment, the feature interval is obtained by: The mean of any kind of monitoring data before the current time is calculated, the time when the monitoring data greater than the mean is counted, and the acquisition time interval of the any kind of monitoring data before the current time is divided by using the counting result, and each sub-interval obtained by the division is used as each feature interval of the any kind of monitoring data.

[0006] In one embodiment, the state characteristic value is obtained by: The correlation characteristic value of each kind of monitoring data in each feature interval before the current time is obtained according to the correlation between each kind of monitoring data in each feature interval before the current time and each kind of monitoring data. The expression of the state characteristic value of each kind of monitoring data of each single battery at the current time is: In the formula, The state characteristic value of the xth kind of monitoring data of the tth single battery at the current time is represented by xth. The number of feature intervals of the xth kind of monitoring data of the tth single battery before the current time is represented by yth. The dispersion of the xth kind of monitoring data of the tth single battery in the yth feature interval before the current time is represented by yth. The correlation characteristic value of the xth kind of monitoring data of the tth single battery in the yth feature interval before the current time is represented by yth.

[0007] In one embodiment, the correlation characteristic value is obtained by: The correlation coefficient of each kind of monitoring data in any feature interval before the current time and each kind of monitoring data in time sequence is calculated, and the mean of the absolute value of the correlation coefficient of each kind of monitoring data in the any feature interval before the current time and all kinds of monitoring data is used as the correlation characteristic value of each kind of monitoring data in the any feature interval before the current time.

[0008] In one embodiment, the process of obtaining the change difference feature value is as follows: calculating the difference between the state feature value of the all monitoring data of the current moment of each single battery and the all monitoring data of each single battery; arranging the various monitoring data of each single battery before the current moment in time sequence to form the monitoring sequence of the various monitoring data of each single battery at the current moment, and calculating the measurement distance of the monitoring sequence of the various monitoring data between each single battery and each single battery at the current moment; The change difference feature value can be further obtained by the difference and the distance.

[0009] In one embodiment, the expression of the change difference feature value is as follows: ; In the formula, indicates the change difference feature value of the xth monitoring data of the tth single battery at the current moment; m indicates the number of single batteries in the battery pack; indicates the measurement distance of the xth monitoring data between the tth single battery and the vth single battery at the current moment; indicates the difference between the tth single battery and the vth single battery at the current moment.

[0010] In one embodiment, the power distribution adjustment coefficient is the average of the change difference feature values of all kinds of monitoring data of each single battery at the current moment.

[0011] In one embodiment, the calculation relationship of the comprehensive score is as follows: standardizing each key state parameter of each single battery at the current moment; The expression of the comprehensive score of each single battery at the current moment is as follows: ; In the formula, indicates the comprehensive score of the tth single battery at the current moment; indicates the normalized value of the power distribution adjustment coefficient of the tth single battery at the current moment; r indicates the number of key state parameter types; indicates the standardization result of the zth key state parameter of the tth single battery at the current moment; indicates the preset weight of the standardization result of the zth key state parameter of the tth single battery at the current moment, and the sum of the preset weights of the standardization results of all key state parameters of the tth single battery at the current moment is 1.

[0012] In one embodiment, 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.

[0013] In a second aspect, the embodiments of the present application also provide a logging multi-battery control system in a complex environment, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the logging multi-battery control method in a complex environment according to any one of the above embodiments when executing the computer program.

[0014] The present application has at least the following beneficial effects: The present application can analyze the correlation between various monitoring data in its stable time range and the rest of the monitoring data, and the dispersion of various monitoring data in its stable time range, can more accurately reflect the state change characteristics of the single battery; by calculating the change difference characteristic value, the state difference of different single batteries caused by the change of the environment state in the logging multi-battery monitoring control process is quantified, which is beneficial to provide a reliable basis for subsequent power distribution, and then by combining the change difference characteristic values of all kinds of monitoring data of each single battery, the power distribution adjustment coefficient is obtained, the possibility of each single battery affected by the environment to cause the power supply stability to decrease is evaluated, which can provide a more reliable basis for subsequent power distribution; and then combined with various key state parameters of each single battery, the power supply capacity of each single battery can be more accurately measured, so that the key interference influence characteristics in the complex logging environment can be fully integrated, and power can be dynamically allocated to each single battery, and power distribution is optimized, thereby improving the stability and energy utilization efficiency of multi-battery collaborative control power supply in a complex logging environment. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings according to these drawings without creative labor.

[0016] Figure 1 A step flow chart of a logging multi-battery control method in a complex environment provided by an embodiment of the present application; Figure 2 A schematic diagram of the comprehensive score acquisition process. DETAILED DESCRIPTION

[0017] In the description of the embodiments of the present application, the words "exemplary", "or", "for example" are used to mean serving as an example, instance, or illustration, and not to imply any preference or superiority. In the embodiments of the present application, any embodiment or design scheme described as "exemplary" or "for example" should not be interpreted as being more preferred or having superiority over other embodiments or design schemes. Rather, the use of "exemplary", "or", "for example" is intended to present the relevant concept in a specific manner.

[0018] 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 in the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application. It is to be understood that the use of "or" in the present application is meant to encompass both a and b as well as a or b.

[0019] In addition, it should be pointed out that the terms "first", "second" in the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence.

[0020] The specific scheme of the well logging multi-battery control method and system in a complex environment provided by the present application is described in detail below in combination with the drawings.

[0021] Please refer to Figure 1 which shows a step flow chart of a well logging multi-battery control method in a complex environment provided by an embodiment of the present application, which comprises the following steps: Step 1, real-time acquisition of various monitoring data of each single battery in the battery pack in the well logging environment.

[0022] For the power supply control of well logging in a complex environment, real-time monitoring of battery state parameters and environmental parameters needs to be realized through high-precision and anti-interference hardware devices. Specifically, a high-temperature-resistant and high-pressure-resistant distributed sensor array is used for data acquisition, which specifically includes a battery state parameter acquisition unit and an environmental parameter acquisition unit. The acquisition device of the battery state parameter acquisition unit includes a voltage sensor, a current sensor and a temperature sensor, which are respectively used to acquire the voltage data, current data and temperature data of each single battery in the battery pack in real time. The acquisition device of the environmental parameter acquisition unit includes a pressure sensor, a vibration sensor and an electromagnetic interference detector, which are respectively used to acquire the pressure data, vibration data and magnetic field intensity data of the environment in which each single battery in the battery pack is located in real time. The voltage data, current data and temperature data are collectively referred to as battery state parameters, and the pressure data, vibration data and magnetic field intensity data are collectively referred to as environmental parameters. The voltage data, current data, temperature data, pressure data, vibration data and magnetic field intensity data are collectively referred to as various monitoring data of the single battery.

[0023] In this embodiment, the collection frequency of various monitoring data is 10Hz, and the value of the collection frequency is preset by human, which can be set by the implementer according to the actual situation, and the application does not make special limitations.

[0024] Step 2, by comprehensively analyzing all kinds of monitoring data of all single batteries collected before the current time, the power distribution adjustment coefficient of each single battery at the current time is obtained.

[0025] Due to the complex downhole environment, environmental changes and data acquisition equipment state changes during logging process and other interference factors have influence on monitoring data collection, and reduce the data quality of the collected monitoring data, therefore, in the present application, for each kind of monitoring data collected, a Gaussian filter is used for filtering processing to obtain preprocessed monitoring data. Further, considering the state changes between single batteries in the logging battery pack in the complex environment, the state changes between single batteries have correlation and dynamics, and environmental interference will exacerbate the state difference through differentiation, and the monitoring parameters of a single battery show nonlinear, sudden and cumulative state change characteristics; for example, single batteries are usually cooperatively powered in parallel or series, so that the state change of single batteries will affect the accuracy of power distribution; and a single battery is affected by temperature, pressure, vibration and electromagnetic interference, resulting in unstable voltage and current change characteristics.

[0026] It should be noted that the Gaussian filter is only one embodiment of the present application, and as other embodiments, on the basis of realizing filtering processing of the collected various monitoring data, the implementer can use other existing feasible technologies, and the application does not make special limitations.

[0027] Based on the above analysis, the state change characteristics in the logging multi-battery control process are analyzed, and the specific analysis and processing process is as follows: Step 2.1, for a single battery, the feature intervals of various monitoring data before the current time are obtained through the distribution of various monitoring data before the current time; the state characteristic 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 kind of monitoring data, combined with the dispersion of various monitoring data in its feature interval.

[0028] The various monitoring data of each single battery before the current time are normalized respectively to avoid the influence of different dimensions.

[0029] In this embodiment, the Min-Max normalization method is used to normalize various monitoring data respectively, and the Min-Max normalization method is a known technology, which will not be described herein.

[0030] Taking the xth monitoring data of the tth single battery in the battery pack as an example, the average value of the xth monitoring data of the tth single battery before the current time is calculated, the time point of the monitoring data greater than the average value in the xth monitoring data of the tth single battery before the current time is counted, and is recorded as each statistical time point. Each statistical time point is used as each segmentation point, and the collection time interval of the xth monitoring data of the tth single battery before the current time is divided into each sub-interval by using each statistical time point. Each sub-interval obtained by the division is used as each feature interval of the xth monitoring data of the tth single battery at the current time.

[0031] According to the method for obtaining each feature interval of the xth monitoring data of the tth single battery at the current time, each feature interval of each kind of monitoring data of each single battery at the current time is obtained.

[0032] Further, the disorder features in each feature interval determined by each kind of monitoring data are analyzed, and then the state change of the single battery in the actual monitoring process is accurately analyzed. First, the correlation feature value of each kind of monitoring data in each feature interval thereof before the current time between each kind of monitoring data is obtained through the correlation between each kind of monitoring data in each feature interval thereof before the current time and each kind of monitoring data, and the correlation feature value is specifically: Still taking the xth monitoring data of the tth single battery in the battery pack as an example, the correlation coefficient of the xth monitoring data in the yth feature interval thereof before the current time and each kind of monitoring data in time sequence is calculated; the average value of the absolute value of the correlation coefficient of the xth monitoring data in the yth feature interval thereof before the current time and all kinds of monitoring data is used as the correlation feature value of the xth monitoring data in the yth feature interval thereof before the current time. The smaller the feature value is, the greater the difference between the xth monitoring data in the yth feature interval thereof and each kind of monitoring data in state change is.

[0033] In this embodiment, the correlation coefficient is a Pearson correlation coefficient, and the Pearson correlation coefficient is a known technology, which will not be described herein. As other embodiments, other existing technologies such as Spearman correlation coefficient and Kendall rank correlation coefficient can be used to measure the correlation between each kind of monitoring data and each kind of monitoring data in time sequence, and the present application does not make special limitation.

[0034] Then, the state feature value of each kind of monitoring data of each single battery at the current time is obtained through the correlation feature value and the dispersion of each kind of monitoring data of each single battery in each feature interval thereof before the current time, and the expression is: ; in the formula, represents the state feature value of the xth monitoring data of the tth single battery at the current time; a number of feature intervals of the xth monitoring data of the tth single battery before the current time; a dispersion of the xth monitoring data of the tth single battery before the current time in the yth feature interval thereof; a correlation feature value of the xth monitoring data of the tth single battery before the current time in the yth feature interval thereof.

[0035] In the embodiment, the dispersion is a coefficient of variation, and the coefficient of variation is a known technology. The present application will not be described again. As other embodiments, on the basis of measuring the unevenness of the distribution of the xth monitoring data in the yth feature interval thereof, implementers can use other existing technologies, such as variance and standard deviation. The present application does not make special limitations.

[0036] It should be noted that the greater the state feature value calculated is, the more accurate the state change characteristics of the single battery reflected by the monitoring data of the tth single battery in the power supply process are.

[0037] In step 2.2, by the difference between the state feature values of various monitoring data between each single battery and the remaining each single battery at the current time, and the difference between various monitoring data of each single battery and the remaining each single battery before the current time, the change difference feature value of various monitoring data of each single battery at the current time is obtained, and then the power distribution adjustment coefficient of each single battery at the current time is obtained.

[0038] Based on the above analysis, by comparing the parameter change response characteristics of each single battery state change in the logging multi-battery monitoring control process, the response characteristics of different monitoring parameters to the state change of the single battery state characteristics in the actual monitoring control process are accurately analyzed.

[0039] Further, considering that all single batteries are cooperatively powered during the logging process, if the response characteristics of the monitoring parameters of different single batteries to state changes are more different during the power supply process, the possibility of unstable power supply caused by environmental interference during the logging multi-battery cooperative power supply process is greater. In order to accurately reflect the difference in response characteristics of state changes between different single batteries in different monitoring parameters during the logging multi-battery control process, and then accurately analyze the state characteristics of the single battery during the logging multi-battery control process, the difference between the state characteristic value of all monitoring data of the tth single battery at the current time and the state characteristic value of all monitoring data of each single battery is calculated, specifically: the state characteristic values of all monitoring data of each single battery at the current time are combined to form the state characteristic sequence of each single battery at the current time, the elements at the same position in the state characteristic sequence of all single batteries are the state characteristic values of the same monitoring data, and the distance between the state characteristic sequence of the tth single battery and each single battery at the current time is taken as the difference. The greater the difference, the more significant the state change characteristics of the tth single battery in the response characteristics of different monitoring data between different single batteries during the comprehensive monitoring process.

[0040] In this embodiment, the distance of the state characteristic sequence is the Euclidean distance. As other embodiments, on the basis of being able to measure the difference between the elements at the same position in two state characteristic sequences, the implementer can use other existing technologies such as Manhattan distance, cosine distance, etc., which are not specially limited by the present application.

[0041] Further, the various monitoring data of each single battery before the current time are arranged in time sequence to form the monitoring sequence of the various monitoring data of each single battery at the current time, and the metric distance between the monitoring sequence of the various monitoring data of each single battery and each single battery at the current time is calculated. The greater the metric distance, the greater the difference in single battery stability caused by the state difference of the single battery during the logging multi-battery control process.

[0042] In this embodiment, the metric distance between the monitoring sequences is the DTW (Dynamic Time Warping) distance, and the calculation of the DTW distance is a known technology, which will not be described herein. As other embodiments, on the basis of being able to measure the difference between two monitoring sequences, the implementer can use other existing technologies such as Euclidean distance, etc., which are not specially limited by the present application.

[0043] Further, the change difference characteristic value of the various monitoring data of each single battery at the current time is obtained through the difference between the state characteristic values of the various monitoring data of each single battery and each single battery at the current time, and the difference between the various monitoring data of each single battery and each single battery before the current time, and the expression is: ; wherein, 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; represents the measurement distance of the xth monitoring data between the tth single battery and the vth single battery at the current time; represents the gap between the tth single battery and the vth single battery at the current time.

[0044] It should be noted that the greater the change difference characteristic value calculated, the greater the state difference of the single battery located at different positions due to the influence of the complex environment during the well logging multi-battery control process, and accordingly, the response change difference of the monitoring parameter reflecting the state change of the single battery is more significant.

[0045] Further, in the traditional well logging multi-battery control process, the change difference characteristic of the state change of the single battery caused by the position difference of the single battery in the complex well logging environment over time is ignored, which causes deviation in the adjustment of power distribution in the actual control process and affects the stability of multi-battery cooperative power supply in the actual well logging process; therefore, based on the above analysis, the power distribution adjustment coefficient of each single battery at the current time is obtained by combining the change difference characteristic value of all kinds of monitoring data of each single battery at the current time, and the expression is: ; wherein, represents the power distribution adjustment coefficient of the tth single battery at the current time; u represents the number of monitoring parameter types; represents the change difference characteristic value of the xth monitoring data of the tth single battery at the current time.

[0046] It should be noted that the greater the power distribution adjustment coefficient calculated, the greater the possibility that the tth single battery is affected by the environment to cause the power supply stability to decrease, so that the error of the power distribution controlled by the state of the single battery is possibly greater.

[0047] Step 3, obtaining various key state parameters of each single battery at the current time, combining the power distribution adjustment coefficient, obtaining the comprehensive score of each single battery at the current time, and then distributing power to each single battery in the battery pack.

[0048] Further, in the implementation of power distribution of the logging multi-battery, the state of charge (SOC), the state of health (SOH), the temperature and the internal resistance of the single battery are generally considered comprehensively, and in normal circumstances, a relatively high power value is allocated to the single battery with high state of charge, good state of health, appropriate temperature and low internal resistance; however, in the collaborative control process of the logging multi-battery, due to the complex environmental interference, the state changes of the single batteries at different positions have great differences, and therefore, only through the state of charge (SOC), the state of health (SOH), the temperature and the internal resistance of the single battery can result in a great deviation of the allocated power value, so that the single battery with reduced power supply stability still maintains a large power output, which affects the stability of the collaborative power supply of the logging multi-battery. The state of charge (SOC), the state of health (SOH), the temperature and the internal resistance are denoted as various key state parameters.

[0049] Therefore, based on the above analysis, in the implementation of power distribution optimization of the logging multi-battery, first, the various key state parameters of each single battery are standardized respectively, and the standardization processing method is as follows: the standardization of the state of charge (SOC) is realized by calculating the ratio of the state of charge (SOC) of each single battery at the current time to the full capacity; the standardization of the state of health (SOH) is realized by calculating the ratio of the capacity of each single battery at the current time to the initial capacity; the optimal temperature interval is set, if the temperature of the single battery is within the optimal temperature interval, the temperature standardization result is 1, if not, the temperature standardization result is 0, that is, a higher power can be allocated under the appropriate temperature, and if the set temperature range is exceeded, the allocated power value is relatively low; the standardization of the internal resistance is realized by calculating the ratio of the initial internal resistance of each single battery to the internal resistance at the current time. In this embodiment, the optimal temperature interval is , the optimal temperature interval is preset by human, and the implementer can set it according to the actual situation, which is not specially limited in this application.

[0050] Further, combined with the standardization processing results of the various key state parameters of each single battery at the current time, and the power distribution adjustment coefficient of each single battery at the current time, the comprehensive score of each single battery at the current time is obtained, and the expression is as follows: ; in the formula, represents the comprehensive score of the tthsingle battery at the current time; represents the normalized value of the power distribution adjustment coefficient of the tthsingle battery at the current time; r represents the number of key state parameter types; represents the standardization processing result of the zthkey state parameter of the tthsingle battery at the current time; a preset weight of a normalized processing result of the zth key state parameter of the tth single battery at the current time, and a sum of preset weights of normalized processing results of all key state parameters of the tth single battery at the current time is 1.

[0051] In this embodiment, the Sigmoid function is used to obtain the normalized value of the power distribution adjustment coefficient. The Sigmoid function is a known technology, and will not be described here.

[0052] In this embodiment, the preset weight of the SOC is 0.4, the preset weight of the SOH is 0.3, the preset weight of the temperature is 0.2, and the preset weight of the internal resistance is 0.1. Under the premise that the sum of the preset weights of the SOC, the SOH, the temperature and the internal resistance is 1, the implementer can allocate the weights of the SOC, the SOH, the temperature and the internal resistance according to the actual situation, and the present application does not make special restrictions.

[0053] It should be noted that: the greater the power distribution adjustment coefficient, the greater the difference in state change of the tth single battery in the complex logging environment, the more likely the power supply stability of the tth single battery is reduced, and the smaller the comprehensive score of the tth single battery. The smaller the comprehensive score, the more the proportion of the power distribution of the tth single battery should be reduced when the power distribution is based on the key state parameters of the single battery, so as to improve the stability of the multi-battery power supply in the complex logging environment. The flowchart of obtaining the comprehensive score is shown in Figure 2

[0054] Further, the power is allocated to each single battery in the battery pack through the comprehensive scores of all single batteries at the current time. Specifically, the proportion of the power allocated to each single battery in the power allocated 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.

[0055] Based on the same inventive concept as the above method, the present embodiment also provides a logging multi-battery control system in a complex environment, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor. The processor executes the computer program to implement the steps of any one of the above logging multi-battery control methods in a complex environment.

[0056] ​To sum up, by dividing the feature interval, the application can analyze the correlation between various monitoring data in the stable time range and the rest of the monitoring data, and the dispersion of various monitoring data in the stable time range, so as to more accurately reflect the state change characteristics of the single battery; by calculating the change difference characteristic value, the state difference of different single batteries caused by the change of the environmental state in the well logging multi-battery monitoring control process is quantified, which is beneficial to providing a reliable basis for subsequent power distribution, and then by combining the change difference characteristic values of all kinds of monitoring data of each single battery, the power distribution adjustment coefficient is obtained, the possibility of each single battery being affected by the environment to cause the power supply stability to decrease is evaluated, and a more reliable basis for subsequent power distribution is provided; and then by combining various key state parameters of each single battery, the power supply capacity of each single battery can be more accurately measured, so that the key interference influence characteristics in the complex logging environment can be fully integrated, power is dynamically allocated to each single battery, power distribution is optimized, and thus the stability and energy utilization efficiency of the multi-battery collaborative control power supply in the complex logging environment are improved.

[0057] The flowcharts and block diagrams in the drawings show the possible implementation architecture, function and operation of the system, method and computer program product according to the embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logic function. In some alternative implementations, the functions noted in the blocks can also occur in an order different from that noted in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. In the description corresponding to the flowcharts and block diagrams in the drawings, the operations or steps corresponding to different blocks can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. Each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0058] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the essential characteristics of the present application. Therefore, the above-described embodiments of the present application should be regarded as exemplary and non-limiting in any respect.

Claims

1. A multi-cell control method for well logging under complex environments, characterized in that, 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.

2. The method of claim 1, wherein the method is performed in a complex environment. The feature interval acquisition process is: The mean of any kind of monitoring data before the current time is calculated, the time when the monitoring data greater than the mean is counted, and the acquisition time interval of any kind of monitoring data before the current time is divided by using the statistical result, and each sub-interval obtained by dividing is used as the feature interval of any kind of monitoring data.

3. The method of claim 1, wherein the method further comprises: The state feature value acquisition process is: 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: wherein, represents the state feature value of the xth monitoring data of the tth single battery at the current time; represents the number of feature intervals of the xth monitoring data of the tth single battery before the current time; represents the dispersion of the xth monitoring data of the tth single battery within the yth feature interval thereof before the current time; represents the related feature value of the xth monitoring data of the tth single battery within the yth feature interval thereof before the current time.

4. The method of claim 3, wherein the method further comprises: The correlation feature value acquisition process is: The correlation coefficient of any kind of monitoring data in its 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 any kind of monitoring data in its feature interval before the current time and the rest of all kinds of monitoring data is used as the correlation feature value of any kind of monitoring data in its feature interval before the current time.

5. The method of claim 1, wherein the method is performed in a complex environment. The change difference feature value acquisition process is: 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.

6. The method of claim 5, wherein the method further comprises: The expression of the change difference feature value is: wherein, 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; represents the metric distance of the xth monitoring data between the tth single battery and the vth single battery at the current time; represents the gap between the tth single battery and the vth single battery at the current time.

7. The method of claim 1, wherein the method is used 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.

8. The method of claim 1, wherein the method is used in a complex environment. The calculation relationship of the comprehensive score is: 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, denotes the comprehensive score of the tth single battery at the current moment; denotes the normalized value of the power distribution adjustment coefficient of the tth single battery at the current moment; r denotes the number of critical state parameter categories; denotes the standardization processing result of the zth critical state parameter of the tth single battery at the current moment; denotes the preset weight of the standardization 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 standardization processing results of all the critical state parameters of the tth single battery at the current moment is 1.

9. 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.

10. 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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