Distribution box configuration power supply analysis model construction method and system based on multiple working conditions

Abstract

The invention relates to the technical field of configuration power supplies, in particular to a distribution box configuration power supply analysis model construction method and system based on multiple working conditions, and the method comprises the steps: dividing configuration power supply working condition data, obtaining a multi-working-condition data set, sequentially extracting a target data set from the multi-working-condition data set, and executing the following operations, obtaining an identification working condition label and an identification data set based on the target data set, sequentially extracting identification data, and executing the following operations: obtaining rated data and use data contained in the identification data, obtaining an aging model based on the use data, obtaining a use model based on the aging model, summarizing the use model, and obtaining a use model set, and based on rated data in the use model set, recursive operation is performed on the use model set, a recursive use model sequence is obtained, a multi-working-condition query table is obtained, and construction of the distribution box configuration power supply analysis model based on the multiple working conditions is completed according to the multi-working-condition query table. According to the invention, the performance analysis and model construction of the multi-working-condition configuration power supply can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power supply configuration, and particularly to a method, a system, an electronic device and a computer-readable storage medium for constructing an analysis model of a distribution box configuration power supply based on multiple working conditions. Background Art

[0002] With the development of technology, electric energy has become one of the important energy sources for daily use. As the core equipment for electric energy distribution, the power supply configuration of the distribution box is directly related to the safety, stability and energy efficiency of the power system.

[0003] However, in practical applications, the configuration of the distribution box often needs to be dynamically adjusted according to different working conditions, such as different working conditions, environmental conditions, etc., to meet diverse electricity consumption requirements. When it is necessary to configure the power supply of the distribution box for a certain working condition, it is often configured by asking experienced experimental personnel.

[0004] Although the above method can realize the access and analysis of the power supply configuration of the distribution box under different working conditions according to the target working condition, so as to select a suitable power supply configuration, the configuration methods of the power supply configuration under different working conditions are different, so the parameters of the configured power supply are different, and there is no table that can be easily accessed without experience. The configuration process depends on the experience level of technical personnel, and it is easy to cause unreasonable configuration or potential safety hazards due to human factors. Summary of the Invention

[0005] The present invention provides a method for constructing an analysis model of a distribution box configuration power supply based on multiple working conditions, and a computer can analyze the performance of the power supply configuration under multiple working conditions and construct a model.

[0006] To achieve the above object, a method for constructing an analysis model of a distribution box configuration power supply based on multiple working conditions provided by the present invention includes:

[0007] Obtain the power supply configuration working condition data, and divide the power supply configuration working condition data by using a plurality of preset standard working condition division thresholds to obtain a multi-working condition data set, where the multi-working condition data set includes: a low-temperature data set, a high-temperature data set, a low-temperature ultra-high power data set and a high-temperature ultra-high power data set;

[0008] Sequentially extract target data sets from the multi-working condition data set, and perform the following operations on the extracted target data sets:

[0009] Identify the working condition corresponding to the target data set, and perform an identification operation on the target data set by using the working condition corresponding to the target data set to obtain an identified working condition label and an identified data set. Sequentially extract identified data from the identified data set, and perform the following operations on all the identified data:

[0010] Obtain the rated data and usage data included in the identification data, where the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge condition, and the usage data includes: capacity time series set, temperature time series set, and resistance time series set;

[0011] Construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model;

[0012] Associate the identification condition label, rated data with the aging model to obtain the usage model corresponding to the identification data;

[0013] Summarize the usage models to obtain the usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence;

[0014] Construct a condition lookup table based on the recursive usage model sequence, summarize the condition lookup table to obtain a multi-condition query table, and complete the construction of the power distribution box configuration power analysis model based on multiple conditions according to the multi-condition query table.

[0015] Optionally, the dividing the configured power supply condition data by a preset plurality of standard condition division thresholds to obtain a multi-condition data set includes:

[0016] Extract the low-temperature standard condition division threshold, high-temperature standard condition division interval, ultra-high voltage standard condition division threshold, and low-voltage standard condition division threshold from the plurality of standard condition division thresholds;

[0017] Extract unit data from the configured power supply condition data in sequence, and identify the rated condition temperature corresponding to the unit data;

[0018] If the rated condition temperature is less than the low-temperature standard condition division threshold, confirm the unit data as low-temperature data. If the rated condition temperature is within the high-temperature standard condition division interval, confirm the unit data as high-temperature data. Otherwise, skip the unit data;

[0019] Summarize the low-temperature data and the high-temperature data respectively to obtain a low-temperature data set and a high-temperature data set;

[0020] Perform a re-division operation on the low-temperature data set and the high-temperature data set based on the ultra-high voltage standard condition division threshold and the low-voltage standard condition division threshold to obtain a low-temperature low-power data set, a low-temperature ultra-high power data set, a high-temperature low-power data set, and a high-temperature ultra-high power data set. Summarize the low-temperature data set, the high-temperature data set, the low-temperature low-power data set, the low-temperature ultra-high power data set, the high-temperature low-power data set, and the high-temperature ultra-high power data set to obtain a multi-condition data set.

[0021] Optionally, performing a re - division operation on the low - temperature data set and the high - temperature data set based on the ultra - high - voltage standard condition division threshold and the low - voltage standard condition division threshold to obtain a low - temperature low - power data set, a low - temperature ultra - high - power data set, a high - temperature low - power data set, and a high - temperature ultra - high - power data set, includes:

[0022] Sequentially extract low - temperature data from the low - temperature data set, identify the low - temperature power capacity corresponding to the low - temperature data. If the low - temperature power capacity is greater than or equal to the ultra - high - voltage standard condition division threshold, then confirm the low - temperature data as low - temperature ultra - high - power data. If the low - temperature power capacity is less than or equal to the low - voltage standard condition division threshold, then confirm the low - temperature data as low - temperature low - power data. Otherwise, skip the extracted low - temperature data;

[0023] Sequentially extract high - temperature data from the high - temperature data set, identify the high - temperature power capacity corresponding to the high - temperature data. If the high - temperature power capacity is greater than or equal to the ultra - high - voltage standard condition division threshold, then confirm the high - temperature data as high - temperature ultra - high - power data. If the high - temperature power capacity is less than or equal to the low - voltage standard condition division threshold, then confirm the high - temperature data as high - temperature low - power data. Otherwise, skip the extracted high - temperature data;

[0024] Respectively summarize the low - temperature low - power data, the high - temperature low - power data, the low - temperature ultra - high - power data, and the high - temperature ultra - high - power data to obtain a low - temperature low - power data set, a high - temperature low - power data set, a low - temperature ultra - high - power data set, and a high - temperature ultra - high - power data set.

[0025] Optionally, constructing a capacity attenuation model and a resistance growth model based on the usage data, includes:

[0026] Extract a capacity time series set from the usage data and identify the capacity detection frequency corresponding to the capacity time series set,

[0027] Extract a temperature time series set and a resistance time series set from the usage data, respectively identify the temperature detection frequency and the resistance detection frequency corresponding to the temperature time series set and the resistance time series set, and integrate the temperature detection frequency and the resistance detection frequency based on the capacity detection frequency to obtain a common frequency;

[0028] Use the temperature time series set and the common frequency to construct a temperature time series trend graph, and construct a usage thermal model based on the temperature time series trend graph;

[0029] Use the resistance time series set and the common frequency to construct a resistance time series trend graph, and construct a resistance growth model based on the resistance time series trend graph and the usage thermal model;

[0030] Construct a capacity attenuation model based on the capacity detection frequency.

[0031] Optionally, constructing a capacity attenuation model based on the capacity detection frequency, includes:

[0032] Construct a capacity time series coordinate system based on the capacity detection frequency, convert the capacity time series set into a capacity coordinate set based on the capacity time series coordinate system, and map the capacity coordinate set to the capacity time series coordinate system to obtain an initial capacity time series diagram;

[0033] Perform data cleaning operations on the initial capacity time series diagram using a pre-constructed data cleaning method to obtain an optimized capacity time series diagram, perform curve fitting operations on the optimized capacity time series diagram to obtain a capacity time series trend diagram, extract a set of characteristic parameters from the capacity time series trend diagram, and construct a capacity decay model based on the set of characteristic parameters.

[0034] Optionally, the capacity decay model is as follows:

[0035]

[0036] Among them, C B represents the time-series power supply capacity, represents the initial power supply capacity, represents the power supply capacity decay rate, and are both preset constant parameters in the capacity decay model, represents the apparent activation energy during the capacity decay process, represents the power supply current multiple, R represents the ideal gas constant, and R = 8.314 J / (mol·K), represents the power supply operating temperature with the minimum capacity decay, represents the temperature compensation constant in the capacity decay model, represents the ampere-hour flux during the power supply charge and discharge process, T B represents the temperature time-series parameter corresponding to the thermal model.

[0037] Optionally, the resistance growth model is as follows:

[0038]

[0039] Among them, R B represents the time-series power supply resistance, represents the initial power supply resistance, and the measurement temperature of the initial power supply resistance and the time-series power supply resistance is the same, represents the resistance growth rate, and are both preset constant parameters in the resistance growth model, represents the apparent activation energy during the resistance growth process, represents the power supply operating temperature with the minimum resistance growth, represents the temperature compensation constant in the resistance growth model.

[0040] Optionally, the step of associating the identification working condition label, the rated data with the aging model to obtain the usage model corresponding to the identification data includes:

[0041] Extract the usage temperature range according to the usage thermal model;

[0042] Obtain a hash function, import the usage temperature range, the identification working condition label and the rated data into the hash function to obtain an associated hash value, compress the usage temperature range and the rated data to obtain compressed data, and perform an identification operation on the compressed data and the aging model using the associated hash value to obtain the usage model corresponding to the identification data, where the associated hash value is expressed as:

[0043] M s = SM3(M‖KDF(H,n),m)

[0044] M = C1‖C2‖C3‖C4‖F w ‖T5

[0045] where, M s represents the associated hash value, SM3(*) represents the first hash function, KDF(*) represents the second hash function, M represents the rated parameter, C1 represents the rated voltage, C2 represents the rated current, C3 represents the rated power, C4 represents the rated capacity, F w represents the discharge condition, H represents the identification working condition label, n represents the upper limit of the length of the second hash value using the second hash function, m represents the upper limit of the length of the first hash value using the first hash function, and T5 represents the usage temperature range.

[0046] Optionally, the step of performing a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence includes:

[0047] Successively extract the data to be sorted from the rated data, and perform the following operations on the extracted data to be sorted:

[0048] Perform a duplicate removal operation on the data to be sorted to obtain a set of parameters to be sorted, and perform a sorting operation on the set of parameters to be sorted in ascending order of numerical value to obtain a unit parameter sequence;

[0049] Summarize the unit parameter sequence, and perform a sorting operation on the unit parameter sequence using a preset parameter sorting method to obtain a working condition lookup table data sequence, and map the usage model set to the working condition lookup table data sequence to obtain a recursive usage model sequence.

[0050] To achieve the above object, the present invention also provides a power distribution box configuration power analysis model construction system based on multiple working conditions, including:

[0051] A multi - operating - condition dataset acquisition module, which is used to acquire configured power supply operating - condition data, and divide the configured power supply operating - condition data by using a plurality of preset standard operating - condition division thresholds to obtain a multi - operating - condition dataset. Among them, the multi - operating - condition dataset includes: a low - temperature dataset, a high - temperature dataset, a low - temperature ultra - high - power dataset, and a high - temperature ultra - high - power dataset;

[0052] An aging model acquisition module, which is used to sequentially extract target datasets from the multi - operating - condition dataset, and perform the following operations on the extracted target datasets: identify the operating conditions corresponding to the target datasets, and perform identification operations on the target datasets by using the operating conditions corresponding to the target datasets to obtain labeled operating - condition tags and labeled datasets. Sequentially extract labeled data from the labeled datasets, and perform the following operations on all the labeled data: obtain the rated data and usage data included in the labeled data. Among them, the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge operating conditions. The usage data includes: capacity time - series set, temperature time - series set, and resistance time - series set. Construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the labeled data according to the capacity attenuation model and the resistance growth model;

[0053] A recursively - used model sequence acquisition module, which is used to associate the labeled operating - condition tags, rated data with the aging model to obtain a usage model corresponding to the labeled data, summarize the usage models to obtain a usage model set corresponding to the target dataset, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursively - used model sequence;

[0054] A multi - operating - condition query table generation module, which is used to construct an operating - condition lookup table based on the recursively - used model sequence, summarize the operating - condition lookup tables to obtain a multi - operating - condition query table, and complete the construction of a configured power supply analysis model for a distribution box based on multiple operating conditions according to the multi - operating - condition query table.

[0055] To solve the above problems, the present invention also provides an electronic device, and the electronic device includes:

[0056] A memory that stores at least one instruction;

[0057] A processor that executes the instructions stored in the memory to implement the above - mentioned method for constructing a configured power supply analysis model for a distribution box based on multiple operating conditions.

[0058] To solve the above problems, the present invention also provides a computer - readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the above - mentioned method for constructing a configured power supply analysis model for a distribution box based on multiple operating conditions.

[0059] To solve the problems described in the background art, the present invention first obtains the configured power supply operating condition data, divides the configured power supply operating condition data using a plurality of preset standard operating condition division thresholds to obtain a multi-operating condition data set, eliminates the data of the configured power supply that does not correspond to the embodiments of the present invention from the configured power supply operating condition data, and divides the actual operating conditions corresponding to the embodiments of the present invention and the unit power supplies contained in the distribution box, so as to make the division and screening of the configured power supply operating condition data meaningful. And the present invention constructs a model and analyzes the performance with the configured power supply as the smallest unit, which can effectively ensure the safety of the configured power supply of the distribution box during use. Sequentially extract target data sets from the multi-operating condition data set, and perform the following operations on the extracted target data sets: identify the operating conditions corresponding to the target data sets, and use the operating conditions corresponding to the target data sets to perform identification operations on the target data sets to obtain identification operating condition labels and identification data sets. Sequentially extract identification data from the identification data sets, and perform the following operations on all the identification data: obtain the rated data and usage data included in the identification data. Among them, the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge operating conditions. The usage data includes: capacity time series set, temperature time series set, and resistance time series set. Construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model. The present invention unifies the capacity detection frequency, temperature detection frequency, and resistance detection frequency into a common frequency, which is convenient for the construction of the aging model. Through the aging model, it is possible to explore the performance of the configured power supply in terms of capacity attenuation and resistance increase during use, and it is also possible to confirm under what ambient temperature the configured power supply still does not show abnormalities and maintains normal and safe operation according to the usage thermal model. Associate the identification operating condition labels, rated data with the aging model to obtain a usage model corresponding to the identification data, summarize the usage models to obtain a usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence. Construct a working condition lookup table based on the recursive usage model sequence, summarize the working condition lookup tables to obtain a multi-operating condition lookup table, and complete the construction of the analysis model for the configured power supply of the distribution box based on multiple operating conditions according to the multi-operating condition lookup table. The present invention stores the recursive usage model sequence in the working condition lookup table, integrates low-temperature operating conditions, high-temperature operating conditions, low-temperature high-power capacity operating conditions, low-temperature low-power capacity operating conditions, high-temperature low-power capacity operating conditions, and high-temperature high-power capacity operating conditions, making it convenient to query the performance data of the configured power supply under different operating conditions. Therefore, the present invention can analyze the performance of the configured power supply under multiple operating conditions and construct a model. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 FIG. is a schematic flowchart of a method for constructing an analysis model for a configured power supply of a distribution box based on multiple operating conditions provided by an embodiment of the present invention;

[0061] Figure 2 It is a functional module diagram of a system for constructing a power supply analysis model for a distribution box based on multiple working conditions provided by an embodiment of the present invention;

[0062] Figure 3 It is a schematic structural diagram of an electronic device for implementing the method for constructing a power supply analysis model for a distribution box based on multiple working conditions provided by an embodiment of the present invention.

[0063] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0064] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0065] An embodiment of the present application provides a method for constructing a power supply analysis model for a distribution box based on multiple working conditions. The execution subject of the method for constructing a power supply analysis model for a distribution box based on multiple working conditions includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for constructing a power supply analysis model for a distribution box based on multiple working conditions can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0066] Referring to Figure 1 As shown, it is a flow chart of a method for constructing a power supply analysis model for a distribution box based on multiple working conditions provided by an embodiment of the present invention. In this embodiment, the method for constructing a power supply analysis model for a distribution box based on multiple working conditions includes:

[0067] S1. Obtain the configured power supply working condition data, and use a preset plurality of standard working condition division thresholds to divide the configured power supply working condition data to obtain a multi-working condition data set, where the multi-working condition data set includes: a low-temperature data set, a high-temperature data set, a low-temperature ultra-high power data set, and a high-temperature ultra-high power data set.

[0068] It can be understood that the configured power supply working condition data is the data obtained by real-time monitoring of the configured power supply under multiple different working condition scenarios. Optionally, there are various ways to obtain the configured power supply working condition data, including but not limited to: the data obtained from the performance test of the configured power supply before it is actually put into use, the data collected from the actual use scenario. For example, for a certain working condition where lighting is required, the configured power supply after configuring each power supply in the distribution box is monitored to obtain the data. The embodiment of the present invention does not limit this. The standard working condition division threshold is a preset value for dividing the working condition situation.

[0069] Further, the step of dividing the configured power supply condition data by a plurality of preset standard condition division thresholds to obtain a multi-condition data set includes:

[0070] Extracting a low-temperature standard condition division threshold, a high-temperature standard condition division range, an ultra-high voltage standard condition division threshold, and a low voltage standard condition division threshold from the plurality of standard condition division thresholds;

[0071] Sequentially extracting unit data from the configured power supply condition data and identifying the rated condition temperature corresponding to the unit data;

[0072] If the rated condition temperature is less than the low-temperature standard condition division threshold, the unit data is confirmed as low-temperature data. If the rated condition temperature is within the high-temperature standard condition division range, the unit data is confirmed as high-temperature data. Otherwise, the unit data is skipped;

[0073] Respectively summarizing the low-temperature data and the high-temperature data to obtain a low-temperature data set and a high-temperature data set;

[0074] Performing a re-division operation on the low-temperature data set and the high-temperature data set based on the ultra-high voltage standard condition division threshold and the low voltage standard condition division threshold to obtain a low-temperature low-power data set, a low-temperature ultra-high power data set, a high-temperature low-power data set, and a high-temperature ultra-high power data set. Summarizing the low-temperature data set, the high-temperature data set, the low-temperature low-power data set, the low-temperature ultra-high power data set, the high-temperature low-power data set, and the high-temperature ultra-high power data set to obtain a multi-condition data set.

[0075] It should be noted that in the embodiment of the present invention, the low-temperature standard condition division threshold is: the temperature is 40 °C, the high-temperature standard condition division range is: the temperature is greater than or equal to 100 °C and less than or equal to 200 °C, the ultra-high voltage standard condition division threshold is: the power capacity is 2 MW, and the low voltage standard condition division threshold is: the power capacity is 0.5 MW.

[0076] It is understandable that the configuration power supply includes multiple unit power supplies. After the multiple unit power supplies are fixedly configured, the configuration power supply formed together is used in actual scenarios. For example, the multiple unit power supplies are 3 220V batteries, and the 3 220V batteries are connected in series, and the power supply obtained is the configuration power supply. Therefore, the parameters divided by the multiple standard working condition division thresholds in the embodiment of the present invention are not the full set of real numbers. This is mainly based on the actual use working conditions corresponding to the embodiment of the present invention and the unit power supply settings contained in the distribution box. For example, in real life, there is a configuration power supply that can be normally illuminated and safe in an environment greater than or equal to 200°C, but it is not used as the configuration power supply corresponding to the embodiment of the present invention. The reason may be that there is no unit power supply that can be normally illuminated and safe in an environment greater than or equal to 200°C in the distribution box, and then there is no configuration power supply that can be normally illuminated and safe in an environment greater than or equal to 200°C. Therefore, it is necessary to remove the data that is not the configuration power supply corresponding to the embodiment of the present invention from the configuration power supply working condition data, and divide the actual processing configuration power supply corresponding to the embodiment of the present invention, so that the division and screening of the configuration power supply working condition data have practical significance.

[0077] It should also be noted that, although the configured power supply includes multiple unit power supplies, in the embodiment of the present invention, the configured power supply is used as the smallest unit for model construction and performance analysis. This is because in many cases the unit power supply may be able to work under a certain high voltage, but the configured power supply composed of different unit power supplies may not be able to work normally under the certain high voltage. Therefore, using the configured power supply as the smallest unit for model construction and performance analysis can effectively ensure the safety of the configured power supply of the distribution box during use.

[0078] For example, the rated voltage of the first unit power supply is 10kV, and the rated voltage of the second unit power supply is 9.5kV. However, the configured power supply after the first unit power supply and the second unit power supply are connected in parallel meets the rated voltage limits of both power supplies, that is, it cannot exceed 9.5kV. When the application scenario of the configured power supply after the first unit power supply and the second unit power supply are connected in parallel is 10kv, safety hazards will arise.

[0079] It is understandable that the low temperature standard working condition division threshold is a temperature division value preset by humans, and the rated working condition temperature is the rated temperature that the configured power supply corresponding to the unit data can correspond to, and the meaning of the rated temperature is: the highest temperature at which the configured power supply can be used normally and safely. When the rated working condition temperature corresponding to the unit data is less than the low temperature standard working condition division threshold, it means that the working temperature at which the configured power supply can be used normally and safely is low, and thus the unit data is classified as low temperature data. The low temperature data set is a collection of low temperature data.

[0080] Furthermore, the division range of the high-temperature standard working conditions is a manually preset temperature division range. When the rated working condition temperature corresponding to the unit data is within the division range of the high-temperature standard working conditions, it indicates that the working temperature at which the configured power supply can be used normally and safely is relatively high. Therefore, the unit data is classified as high-temperature data. The high-temperature data set is a set of high-temperature data. The multi-working-condition data set is a set composed of the low-temperature data set, the high-temperature data set, the low-temperature low-power data set, the low-temperature ultra-high-power data set, the high-temperature low-power data set, and the high-temperature ultra-high-power data set.

[0081] Furthermore, performing a re-division operation on the low-temperature data set and the high-temperature data set based on the ultra-high-pressure standard working condition division threshold and the low-pressure standard working condition division threshold to obtain the low-temperature low-power data set, the low-temperature ultra-high-power data set, the high-temperature low-power data set, and the high-temperature ultra-high-power data set includes:

[0082] Sequentially extract low-temperature data from the low-temperature data set, identify the low-temperature power capacity corresponding to the low-temperature data. If the low-temperature power capacity is greater than or equal to the ultra-high-pressure standard working condition division threshold, then confirm the low-temperature data as low-temperature ultra-high-power data. If the low-temperature power capacity is less than or equal to the low-pressure standard working condition division threshold, then confirm the low-temperature data as low-temperature low-power data. Otherwise, skip the extracted low-temperature data;

[0083] Sequentially extract high-temperature data from the high-temperature data set, identify the high-temperature power capacity corresponding to the high-temperature data. If the high-temperature power capacity is greater than or equal to the ultra-high-pressure standard working condition division threshold, then confirm the high-temperature data as high-temperature ultra-high-power data. If the high-temperature power capacity is less than or equal to the low-pressure standard working condition division threshold, then confirm the high-temperature data as high-temperature low-power data. Otherwise, skip the extracted high-temperature data;

[0084] Respectively summarize the low-temperature low-power data, the high-temperature low-power data, the low-temperature ultra-high-power data, and the high-temperature ultra-high-power data to obtain the low-temperature low-power data set, the high-temperature low-power data set, the low-temperature ultra-high-power data set, and the high-temperature ultra-high-power data set.

[0085] It can be understood that the meaning of the re-division is: the operation of dividing the high-temperature data set and the low-temperature data set by using the low-pressure standard working condition division threshold and the ultra-high-pressure standard working condition division threshold. The low-temperature power capacity is the power capacity corresponding to the low-temperature data in the low-temperature data set. The high-temperature power capacity is the power capacity corresponding to the high-temperature data in the high-temperature data set. The low-temperature ultra-high-power data is the low-temperature data whose low-temperature power capacity is greater than or equal to the ultra-high-pressure standard working condition division threshold. The same applies to the low-temperature low-power data, the high-temperature ultra-high-power data, and the high-temperature low-power data, which will not be elaborated here again.

[0086] It is understandable that the low-temperature ultra-high-power data set is a set composed of low-temperature ultra-high-power data, the high-temperature ultra-high-power data set is a set composed of high-temperature ultra-high-power data, and the low-temperature low-power data set and the high-temperature low-power data set are similar by analogy, which will not be elaborated here.

[0087] S2. Sequentially extract the target data sets from the multi-condition data sets, and perform the following operations on the extracted target data sets: identify the conditions corresponding to the target data sets, and use the conditions corresponding to the target data sets to perform labeling operations on the target data sets to obtain labeled condition tags and labeled data sets.

[0088] It is understandable that the target data sets are low-temperature data sets, high-temperature data sets, low-temperature ultra-high-power data sets or high-temperature ultra-high-power data sets extracted from the multi-condition data sets. For example, if the extracted data set is a low-temperature data set, the target data set extracted when sequentially extracting the target data sets is the low-temperature data set. The condition is the condition corresponding to the target data set. For example, if the target data set is low-temperature data, the condition is the low-temperature condition; if the target data set is high-temperature data, the condition is the high-temperature condition; if the target data set is a low-temperature ultra-high-power data set, the condition is the low-temperature high-power capacity condition; if the target data set is a high-temperature ultra-high-power data set, the condition is the high-temperature high-power capacity condition; if the target data set is a low-temperature low-power data set, the condition is the low-temperature low-power capacity condition, and so on, which will not be elaborated here.

[0089] It should be noted that in the embodiments of the present invention, using temperature and power capacity as the division of conditions can make the corresponding analysis model applicable to most application scenarios. For example, the conditions corresponding to devices such as household lighting and household refrigerators belong to the low-temperature low-power capacity conditions in terms of temperature and power capacity. Therefore, the same type of configured power supply can be used in actual application scenarios.

[0090] It should be noted that the operation of performing the labeling operation on the target data set using the condition corresponding to the target data set is: after converting the condition corresponding to the target data set into a unique identifier, use the unique identifier to set a label for the target data set. Therefore, the labeled condition tag is the unique identifier and is the target data set after setting the label.

[0091] S3. Sequentially extract the labeled data from the labeled data sets, and perform the following operations on all the labeled data: obtain the rated data and usage data included in the labeled data, where the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge condition, and the usage data includes: capacity time series set, temperature time series set, and resistance time series set.

[0092] It is understandable that since there is no unique way to obtain the configured power supply operating condition data, there is also no unique method to obtain the rated data. However, according to the way of obtaining the configured power supply operating condition data, a method capable of obtaining the rated data can be corresponding to it. For example: If the configured power supply operating condition data is the data obtained from the performance test of the configured power supply before it is put into actual use in a certain factory, the rated data can be obtained through experiments. If the configured power supply operating condition data is the data collected from the actual use scenario, for example: the data obtained from the lighting monitoring of a certain building, the rated data can be obtained through the device identifiers of all lighting devices corresponding to a certain building. The embodiments of the present invention do not make any limitations in this regard.

[0093] Further, the power supply target is the object powered by the configured power supply, such as: factory lighting, civil lighting, commercial air conditioning, factory production, hospital emergency power supply, etc. The discharge condition is the discharge mode of the configured power supply, such as: pulsed discharge, continuous discharge, cyclic discharge, constant current discharge, constant power discharge, etc. Different discharge conditions have different effects on the performance of the configured power supply, so it is necessary to confirm the discharge condition.

[0094] It should be noted that one usage data corresponds to one configured power supply, and the rated data is the rated parameter of the configured power supply corresponding to the usage data.

[0095] S4. Construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model.

[0096] It is understandable that the usage data is the capacity, temperature and resistance that change with the usage time when the configured power supply is in use. It is understandable that when obtaining the usage data, the necessary parameters for the usage data also need to be obtained, such as current, voltage, etc. However, obtaining the usage data can be achieved through existing technologies, so the detailed obtaining process will not be elaborated here. The aging model is the total model composed of a thermal model, a capacity attenuation model and a resistance growth model.

[0097] Further, the constructing the capacity attenuation model and the resistance growth model based on the usage data includes:

[0098] Extract a capacity time series set from the usage data, and identify the capacity detection frequency corresponding to the capacity time series set.

[0099] Extract a temperature time series set and a resistance time series set from the usage data, respectively identify the temperature detection frequency and the resistance detection frequency corresponding to the temperature time series set and the resistance time series set, and integrate the temperature detection frequency and the resistance detection frequency based on the capacity detection frequency to obtain a common frequency.

[0100] Use the temperature time series set and the common frequency to construct a temperature time series trend graph, and construct a thermal model for use based on the temperature time series trend graph.

[0101] Construct a resistance timing trend graph using a resistance timing set and a common frequency, and build a resistance growth model based on the resistance timing trend graph and using a thermal model;

[0102] Build a capacity decay model based on the capacity detection frequency.

[0103] It can be understood that the capacity timing set is a set composed of data of the actual capacity of the configured power supply changing with time. For example, let the capacity detection frequency be once per second, and let the initial detection time be 0 seconds. Therefore, the actual capacity of the configured power supply is detected at 0 seconds, the actual capacity of the configured power supply is detected at 1 second,..., and the actual capacity of the configured power supply is detected at n seconds, and so on. The set composed of all the obtained actual capacities and the times corresponding to the actual capacities is the capacity timing set, and the actual capacity and time are in one-to-one correspondence. Similarly, the acquisition methods of the temperature timing set, the resistance timing set, the temperature detection frequency, and the resistance detection frequency are the same and can achieve the same effect. Therefore, it will not be elaborated here.

[0104] Furthermore, it can be understood that in the actual detection process, the capacity detection frequency, the temperature detection frequency, and the resistance detection frequency corresponding to the actual capacity, the actual temperature, and the actual resistance may be different, which is determined by the parameters to be detected. Therefore, it is necessary to integrate the capacity detection frequency, the temperature detection frequency, and the resistance detection frequency so that the capacity detection frequency, the temperature detection frequency, and the resistance detection frequency are unified into one detection frequency, and this unified detection frequency is the common frequency.

[0105] Exemplarily, if the capacity detection frequency, the temperature detection frequency, and the resistance detection frequency are once per second, once every two seconds, and once every thirty seconds respectively, then once per second can be used as the common frequency. Therefore, there is actually a missing value set in the temperature timing set. For example, the actual temperature is detected at the 0th second and the 2nd second, so two actual temperatures are obtained. However, the actual capacity is detected at the 0th second, the 1st second, and the 2nd second, and there are three actual parameters. Therefore, at the 1st second, the corresponding actual temperature cannot be extracted from the temperature timing set. Therefore, during the process of constructing the temperature timing trend graph using the temperature timing set and the common frequency, a data interpolation operation is also performed on the temperature timing set, and performing the data interpolation operation is an existing technology. For example, taking the average value of the 0th second and the 2nd second as the actual temperature at the 1st second, or using the Lagrange interpolation method, or using the linear interpolation method and other existing technologies. Therefore, there are multiple existing technologies that can achieve the data interpolation operation, and it will not be elaborated here.

[0106] It can be understood that the temperature timing trend graph is a curve of the ambient temperature where the configured power supply is located changing with time, and the thermal model used is a model constructed by correlating the real-time temperature with the surface temperature and the core temperature of the configured power supply. The thermal model used is as follows:

[0107]

[0108] Among them, T n represents the core temperature model of the configured power supply, T w the surface temperature model of the configured power supply, T wi represents the surface temperature of the configured power supply at the i-th second, T ni represents the core temperature of the configured power supply at the i-th second, P z represents the heat generation power per unit of the configured power supply, R n represents the thermal resistance of heat conduction between the core of the configured power supply and its surface, R u represents the convective heat transfer thermal resistance of the surface of the configured power supply, N n represents the core heat capacity of the configured power supply, T Ei represents the ambient temperature at the i-th second, N w represents the surface heat capacity of the configured power supply.

[0109] It can be understood that the temperature time series trend graph represents the curve corresponding to the ambient temperature, and the thermal model used is the temperature corresponding to the configured power supply. Therefore, it is possible to confirm under what ambient temperature the configured power supply still does not show abnormalities and maintains normal and safe operation according to the thermal model used. At this time, the available temperature range of the configured power supply can be obtained based on the highest temperature and the lowest temperature of the configured power supply in the thermal model used, and the maximum ambient temperature and the minimum ambient temperature are extracted from the temperature time series trend graph to obtain the working ambient temperature range.

[0110] Furthermore, the capacity decay model constructed based on the capacity detection frequency includes:

[0111] Construct a capacity time series coordinate system based on the capacity detection frequency, convert the capacity time series set into a capacity coordinate set based on the capacity time series coordinate system, and map the capacity coordinate set to the capacity time series coordinate system to obtain an initial capacity time series graph;

[0112] Perform data cleaning operations on the initial capacity time series graph using a pre-constructed data cleaning method to obtain an optimized capacity time series graph, perform curve fitting operations on the optimized capacity time series graph to obtain a capacity time series trend graph, extract a set of characteristic parameters from the capacity time series trend graph, and construct a capacity decay model based on the set of characteristic parameters.

[0113] It should be noted that in the capacity time series coordinate system, the horizontal axis is time and the vertical axis is the real-time capacity. Since the capacity time series coordinate system in the embodiments of the present invention is constructed based on the capacity detection frequency, the interval corresponding to time on the horizontal axis corresponds to the capacity detection frequency. For example: if the capacity detection frequency is once per second, the interval corresponding to time on the horizontal axis is one second.

[0114] It can be understood that the operation of converting the capacity time series set into the capacity coordinate set is: extracting the capacity time series data from the capacity time series set in sequence, wherein the capacity time series data includes: the actual capacity and the time corresponding to the actual capacity, taking the time corresponding to the actual capacity as the horizontal coordinate, and taking the real-time capacity as the vertical coordinate, the coordinates formed are the capacity coordinates. Each capacity time series data in the capacity time series set is converted into the capacity coordinates, and the obtained set is the capacity coordinate set.

[0115] It should be noted that the mapping of the capacity coordinate set to the capacity time series coordinate system is: extracting the capacity coordinates in the capacity coordinate set in sequence, and plotting the capacity coordinates in the capacity time series coordinate system. The data cleaning method is: removing outliers in the capacity coordinate set, and interpolating the capacity coordinate set after removing the outliers to obtain a filled capacity coordinate set. There are many existing technologies that can be used to implement the data cleaning method. For example, the above operation can be implemented using Python's Pandas library, so the embodiment of the present invention will not be repeated here.

[0116] Furthermore, the curve fitting operation is obtained as an operation of drawing curves for all coordinates in the optimized capacity timing diagram using a MATLAB fitting algorithm. This technology is prior art and will not be described in detail here.

[0117] Furthermore, the capacity attenuation model is as follows:

[0118]

[0119] Among them, C B Indicates the timing power supply capacity, Indicates the initial power capacity, Indicates the power capacity attenuation rate, and All of them are constant parameters preset in the capacity decay model. represents the apparent activation energy during capacity decay, represents the power supply current multiplier, R represents the ideal gas constant, and R = 8.314 J / (mol·K), Indicates the operating temperature of the power supply with the smallest capacity attenuation. represents the temperature compensation constant in the capacity decay model, Indicates the ampere-hour flux during the charging and discharging process of the power supply, T B Indicates the temperature timing parameters corresponding to the thermal model.

[0120] It is understandable that the time-sequential power capacity is a capacity parameter used to represent the configured power capacity at a certain moment in the capacity attenuation model. The initial power capacity is the capacity of the configured power before use. The power capacity attenuation rate is the reduction ratio of the available capacity of the configured power relative to the initial power capacity after being used for a period of time. The operating temperature of the power supply with the smallest capacity attenuation is the operating temperature of the configured power supply when the capacity has not yet attenuated. Since the resistance growth model, the capacity attenuation model, and the thermal model are constructed using a common frequency in the embodiments of the present invention, the operating temperature of the power supply with the smallest capacity attenuation can be looked up from the thermal model. The ampere-hour throughput represents the electrical energy that the configured power supply can output. The temperature time-sequential parameter represents half of the sum of the core temperature model and the surface temperature model in the thermal model at the corresponding moment.

[0121] It is understandable that the characteristic parameter set includes: the initial power capacity, the power capacity attenuation rate, the constant parameters preset in the capacity attenuation model, and the ideal gas constant.

[0122] Further, the resistance growth model is as follows:

[0123]

[0124] wherein, R B represents the time-sequential power supply resistance, represents the initial power supply resistance, and the measurement temperature of the initial power supply resistance and the time-sequential power supply resistance is the same, represents the resistance growth rate, and are both constant parameters preset in the resistance growth model, represents the apparent activation energy during the resistance growth process, represents the operating temperature of the power supply with the smallest resistance growth, represents the temperature compensation constant in the resistance growth model.

[0125] It is understandable that the parameters in the resistance growth model correspond to the parameters in the capacity attenuation model, and the acquisition methods are the same, so they will not be elaborated here.

[0126] It should be noted that the construction methods of the temperature time-sequential trend chart and the resistance time-sequential trend chart are the same as those of the capacity time-sequential trend chart, so they will not be elaborated one by one here.

[0127] S5. Associate the identification operating condition label, the rated data with the aging model to obtain the usage model corresponding to the identification data.

[0128] Further, the step of associating the identification operating condition label, the rated data with the aging model to obtain the usage model corresponding to the identification data includes:

[0129] Extract the usage temperature range according to the thermal model;

[0130] Obtain a hash function, import the use temperature range, identification operating condition label, and rated data into the hash function to obtain an associated hash value, compress the use temperature range and rated data to obtain compressed data, and perform an identification operation on the compressed data and the aging model using the associated hash value to obtain a use model corresponding to the identified data, where the associated hash value is expressed as:

[0131] M s = SM3(M‖KDF(H,n),m)

[0132] M = C1‖C2‖C3‖C4‖F w ‖T5

[0133] where M s represents the associated hash value, SM3(*) represents the first hash function, KDF(*) represents the second hash function, M represents the rated parameters, C1 represents the rated voltage, C2 represents the rated current, C3 represents the rated power, C4 represents the rated capacity, F w represents the discharge operating condition, H represents the identification operating condition label, n represents the upper limit of the length of the second hash value using the second hash function, m represents the upper limit of the length of the first hash value using the first hash function, and T5 represents the use temperature range.

[0134] It can be understood that in the embodiments of the present invention, the use temperature range is: the available temperature range of the configured power supply is obtained according to the highest temperature and the lowest temperature of the configured power supply in the use thermal model. The hash function includes a first hash function and a second hash function. Both the first hash function and the second hash function are commercial hash functions and are prior arts, so they will not be elaborated here.

[0135] It should be noted that the operation of compressing the use temperature range, rated data, and identification operating condition label to obtain compressed data is: after compressing each data in the rated data, connect them with a connection operator, and the obtained data is the compressed data. The compression operation is a prior art and will not be elaborated here. The upper limit of the length of the second hash value and the upper limit of the length of the first hash value are both used to limit the length of the calculated hash value to avoid the increased computational energy consumption caused by too long hash values.

[0136] S6. Aggregate the use models to obtain a set of use models corresponding to the target data set, and perform a recursive operation on the set of use models based on the rated data in the set of use models to obtain a recursive use model sequence.

[0137] Furthermore, the performing a recursive operation on the set of use models based on the rated data in the set of use models to obtain a recursive use model sequence includes:

[0138] Successively extract the data to be sorted from the rated data, and perform the following operations on the extracted data to be sorted:

[0139] Perform a duplicate removal operation on the data to be sorted to obtain a set of parameters to be sorted. Perform a sorting operation on the set of parameters to be sorted in ascending order of numerical values to obtain a unit parameter sequence;

[0140] Summarize the unit parameter sequence and perform a sorting operation on the unit parameter sequence using a preset parameter sorting method to obtain a working condition lookup table data sequence. Map the usage model set to the working condition lookup table data sequence to obtain a recursively used model sequence.

[0141] It can be understood that the data to be sorted is the data extracted from the rated data. For example, if the rated voltage is extracted from the rated data, the data to be sorted is the set composed of the rated voltage. The duplicate removal operation is to remove the duplicate data in the data to be sorted. For example, if the extracted data to be sorted is: a, a, a, a, b, c, then after removing the duplicates from the data to be sorted, the obtained set of parameters to be sorted is: a, b, c, and the order of the parameters to be sorted in ascending order of numerical values is: in the order from a to z. For example, if the set of parameters to be sorted is: b, a, c, then sort the set of parameters to be sorted in the order from a to z, and the obtained unit parameter sequence is: a, b, c.

[0142] It can be understood that since multiple unit parameter sequences are extracted based on the rated data, but the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge condition, and these data have no order of numerical magnitude, a parameter sorting method is artificially set to sort these parameters.

[0143] Exemplarily, assume that the order of the parameters in the parameter sorting method is: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge condition. Then first extract the unit parameter sequence corresponding to the rated voltage, and then extract the unit parameter sequence corresponding to the rated current, and so on. Arrange all the unit parameter sequences in order to obtain the working condition lookup table data sequence.

[0144] Exemplarily, for the convenience of explanation, the number of rated data parameters is simplified. Assume that the parameters corresponding to the existing usage model set are: [1, a], [4, b], [2, f], [2, a], and the first parameter is the rated voltage, and the unit parameter sequence corresponding to the rated voltage is: 1, 2, 4, and the second parameter is the rated current, and the corresponding unit parameter sequence is: a, b, f. Then the working condition lookup table data sequence is: 1 - a, 1 - b, 1 - f, 2 - a, 2 - b, 2 - f, 4 - a, 4 - b, 4 - f. Map [1, a], [4, b], [2, f], [2, a] to the working condition lookup table data sequence, then it is:

[0145] 1-a: [1, a], 1-b: null, 1-f: null, 2-a: [2, a], 2-b: null, 2-f: [2, f], 4-a: null, 4-b: [4, b], 4-f: null.

[0146] Among them, null indicates no data available.

[0147] S7. Based on recursively using the model sequence to construct a working condition lookup table, summarize the working condition lookup table to obtain a multi-working condition query table, and complete the construction of a power supply analysis model for a distribution box based on multiple working conditions according to the multi-working condition query table.

[0148] It can be understood that the working condition lookup table is a table under one working condition constructed using the data sequence of the working condition lookup table, and the model sequence for recursive use is stored in the working condition lookup table. The multi-working condition query table is a query table jointly composed of low-temperature working conditions, high-temperature working conditions, low-temperature high-power capacity working conditions, low-temperature low-power capacity working conditions, high-temperature low-power capacity working conditions, and high-temperature high-power capacity working conditions, that is, the table composed of all working conditions in the embodiments of the present invention. The multi-working condition query table can query the performance data of the configured power supply under different working conditions. When it is necessary to construct the configured power supply for a wiring box for a certain working condition, it can be queried from the multi-working condition query table. Therefore, the multi-working condition query table not only includes the usage model of each configured power supply, but also should include various parameters (such as power supply model, rated parameters of the power supply, etc.) and configuration methods (such as series-parallel relationships between multiple unit power supplies, etc.) of the unit power supply corresponding to the configured power supply.

[0149] To solve the problems described in the background art, the present invention first obtains the configured power supply operating condition data, divides the configured power supply operating condition data using a plurality of preset standard operating condition division thresholds to obtain a multi-operating condition data set, eliminates the data of the configured power supply that does not correspond to the embodiments of the present invention from the configured power supply operating condition data, and divides the actual operating conditions corresponding to the embodiments of the present invention and the unit power supplies contained in the distribution box, so as to make the division and screening of the configured power supply operating condition data meaningful. And the present invention constructs a model and performs performance analysis with the configured power supply as the smallest unit, which can effectively ensure the safety of the configured power supply of the distribution box during use. Sequentially extract target data sets from the multi-operating condition data set, and perform the following operations on the extracted target data sets: identify the operating conditions corresponding to the target data sets, and use the operating conditions corresponding to the target data sets to perform identification operations on the target data sets to obtain identification operating condition labels and identification data sets. Sequentially extract identification data from the identification data sets, and perform the following operations on all the identification data: obtain the rated data and usage data included in the identification data, where the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge operating conditions, and the usage data includes: capacity time series set, temperature time series set, and resistance time series set. Construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model. The present invention unifies the capacity detection frequency, temperature detection frequency, and resistance detection frequency into a common frequency, which is convenient for the construction of the aging model. Through the aging model, it is possible to explore the performance of the configured power supply in terms of capacity attenuation and resistance increase during use, and it is also possible to confirm under what ambient temperature the configured power supply still does not show abnormalities and maintains normal and safe operation according to the usage thermal model. Associate the identification operating condition labels, rated data with the aging model to obtain the usage model corresponding to the identification data, summarize the usage models to obtain the usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence. Construct a working condition lookup table based on the recursive usage model sequence, summarize the working condition lookup tables to obtain a multi-operating condition lookup table, and complete the construction of the distribution box configured power supply analysis model based on multi-operating conditions according to the multi-operating condition lookup table. The present invention stores the recursive usage model sequence in the working condition lookup table, integrates low-temperature operating conditions, high-temperature operating conditions, low-temperature high-power capacity operating conditions, low-temperature low-power capacity operating conditions, high-temperature low-power capacity operating conditions, and high-temperature high-power capacity operating conditions, making it convenient to query the performance data of the configured power supply under different operating conditions. Therefore, the present invention can realize the analysis of the performance of the configured power supply under multiple operating conditions and the construction of the model.

[0150] As Figure 2 shown, it is a functional module diagram of a system for constructing an analysis model of a distribution box configured power supply based on multiple operating conditions provided by an embodiment of the present invention.

[0151] The power supply analysis model construction system 100 for distribution boxes based on multiple working conditions according to the present invention can be installed in an electronic device. According to the functions achieved, the power supply analysis model construction system 100 for distribution boxes based on multiple working conditions can include a multi-working-condition data set acquisition module 101, an aging model acquisition module 102, a recursive usage model sequence acquisition module 103, and a multi-working-condition query table generation module 104. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0152] The multi-working-condition data set acquisition module 101 is configured to acquire power supply working condition data of a distribution box, and divide the power supply working condition data by using a plurality of preset standard working condition division thresholds to obtain a multi-working-condition data set, where the multi-working-condition data set includes: a low-temperature data set, a high-temperature data set, a low-temperature ultra-high power data set, and a high-temperature ultra-high power data set;

[0153] The aging model acquisition module 102 is configured to sequentially extract target data sets from the multi-working-condition data set, and perform the following operations on the extracted target data sets: identify the working conditions corresponding to the target data sets, and perform identification operations on the target data sets by using the working conditions corresponding to the target data sets to obtain identification working condition labels and identification data sets, sequentially extract identification data from the identification data sets, and perform the following operations on all the identification data: obtain the rated data and usage data included in the identification data, where the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge working condition, and the usage data includes: capacity time series set, temperature time series set, and resistance time series set, construct a capacity decay model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity decay model and the resistance growth model;

[0154] The recursive usage model sequence acquisition module 103 is configured to associate the identification working condition labels, rated data, and aging models to obtain usage models corresponding to the identification data, summarize the usage models to obtain a usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence;

[0155] The multi-working-condition query table generation module 104 is configured to construct a working condition lookup table based on the recursive usage model sequence, summarize the working condition lookup table to obtain a multi-working-condition query table, and complete the construction of the power supply analysis model for the distribution box based on multiple working conditions according to the multi-working-condition query table.

[0156] Specifically, each module in the power supply analysis model construction system 100 for distribution boxes based on multiple working conditions in the embodiments of the present invention is used in the same manner as the above Figure 1The technical means are the same as those described in the method for constructing a power supply analysis model for a distribution box based on multiple working conditions, and can produce the same technical effects, which will not be elaborated here.

[0157] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing the method for constructing a power supply analysis model for a distribution box based on multiple working conditions provided by an embodiment of the present invention.

[0158] The electronic device 1 may include a processor 10, a memory 11, and a bus 12, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a program for the method for constructing a power supply analysis model for a distribution box based on multiple working conditions.

[0159] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as the mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 1. Further, the memory 11 further includes the internal storage unit of the electronic device 1 and also includes an external storage device. The memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as the code of the program for the method for constructing a power supply analysis model for a distribution box based on multiple working conditions, but also be used to temporarily store data that has been output or will be output.

[0160] In some embodiments, the processor 10 may be composed of integrated circuits. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more Central Processing Units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as the program for the method for constructing a power supply analysis model for a distribution box based on multiple working conditions, etc.), and calling data stored in the memory 11, to perform various functions of the electronic device 1 and process data.

[0161] The bus 12 can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to implement connection communication between the memory 11 and at least one processor 10, etc.

[0162] Figure 3 Only an electronic device with components is shown. Those skilled in the art can understand that Figure 3 The shown structure does not constitute a limitation on the electronic device 1, and it may include fewer or more components than shown, or combine certain components, or have a different component arrangement.

[0163] For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management system, so as to implement functions such as charge management, discharge management, and power consumption management through the power management system. The power source may also include any components such as one or more DC or AC power sources, a recharge system, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device 1 may also include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0164] Further, the electronic device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0165] Optionally, the electronic device 1 may further include a user interface. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the electronic device 1 and to display a visual user interface.

[0166] The program of the method for constructing a power distribution box configuration power analysis model based on multiple working conditions stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0167] Obtain the configuration power working condition data, and use a plurality of preset standard working condition division thresholds to divide the configuration power working condition data to obtain a multi-working condition data set. Among them, the multi-working condition data set includes: a low-temperature data set, a high-temperature data set, a low-temperature ultra-high power data set, and a high-temperature ultra-high power data set;

[0168] Sequentially extract target data sets from the multi-working condition data set, and perform the following operations on the extracted target data sets:

[0169] Identify the working condition corresponding to the target data set, and use the working condition corresponding to the target data set to perform a labeling operation on the target data set to obtain a labeled working condition label and a labeled data set. Sequentially extract labeled data from the labeled data set, and perform the following operations on all the labeled data:

[0170] Obtain the rated data and usage data included in the labeled data. Among them, the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge working condition. The usage data includes: capacity time series set, temperature time series set, and resistance time series set;

[0171] Construct a capacity decay model and a resistance growth model based on the usage data, and construct an aging model corresponding to the labeled data according to the capacity decay model and the resistance growth model;

[0172] Associate the labeled working condition label, rated data with the aging model to obtain a usage model corresponding to the labeled data;

[0173] Summarize the usage models to obtain a usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence;

[0174] Construct a working condition lookup table based on the recursive usage model sequence, summarize the working condition lookup table to obtain a multi-working condition query table, and complete the construction of the power distribution box configuration power analysis model based on multiple working conditions according to the multi-working condition query table.

[0175] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 the description of the relevant steps in the corresponding embodiment, which will not be elaborated here.

[0176] Furthermore, if the modules / units integrated in the electronic device 1 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0177] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor of an electronic device, it can implement:

[0178] Obtain configured power operating condition data, and use a plurality of preset standard operating condition division thresholds to divide the configured power operating condition data to obtain a multi-operating condition data set. Among them, the multi-operating condition data set includes: a low-temperature data set, a high-temperature data set, a low-temperature ultra-high power data set, and a high-temperature ultra-high power data set;

[0179] Successively extract target data sets from the multi-operating condition data set, and perform the following operations on the extracted target data sets:

[0180] Identify the operating condition corresponding to the target data set, and use the operating condition corresponding to the target data set to perform an identification operation on the target data set to obtain an identified operating condition label and an identified data set. Successively extract identified data from the identified data set, and perform the following operations on all the identified data:

[0181] Obtain the rated data and usage data included in the identified data. Among them, the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target, and discharge operating condition. The usage data includes: a capacity time series set, a temperature time series set, and a resistance time series set;

[0182] Construct a capacity decay model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identified data according to the capacity decay model and the resistance growth model;

[0183] Associate the identified operating condition label, rated data with the aging model to obtain a usage model corresponding to the identified data;

[0184] Summarize the usage models to obtain a usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence;

[0185] Construct an operating condition lookup table based on the recursive usage model sequence, summarize the operating condition lookup table to obtain a multi-operating condition query table, and complete the construction of a power distribution box configured power analysis model based on the multi-operating condition according to the multi-operating condition query table.

[0186] In several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and there may be other partitioning methods in actual implementation.

[0187] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, the functional modules in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0189] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0190] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for constructing a power supply analysis model for a distribution box configuration based on multiple working conditions, characterized in that: The method comprises: Acquire configuration power supply condition data, and divide the configuration power supply condition data using a plurality of preset standard condition division thresholds to obtain a multi-condition data set, wherein the multi-condition data set includes: a low temperature data set, a high temperature data set, a low temperature ultra-high power data set, and a high temperature ultra-high power data set; Extract target data sets from the multi-condition data sets in sequence, and perform the following operations on the extracted target data sets: Identify the working condition corresponding to the target data set, and use the working condition corresponding to the target data set to perform an identification operation on the target data set to obtain an identification working condition label and an identification data set, extract identification data from the identification data set in sequence, and perform the following operations on the identification data: Obtain the rated data and usage data contained in the identification data, wherein the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target and discharge condition, and the usage data includes: capacity timing set, temperature timing set and resistance timing set; Build a capacity attenuation model and a resistance growth model based on the usage data, and build an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model; Associating the identification condition label, the rated data and the aging model to obtain a usage model corresponding to the identification data; Summarize the usage models to obtain a usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence; Based on the recursive use of the model sequence, a working condition lookup table is constructed, the working condition lookup table is summarized to obtain a multi-working condition lookup table, and the construction of a distribution box configuration power supply analysis model based on multiple working conditions is completed according to the multi-working condition lookup table.

2. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions according to claim 1, characterized in that: The method of dividing the configured power supply operating condition data by using a plurality of preset standard operating condition division thresholds to obtain a multi-operating condition data set includes: Extracting a low-temperature standard operating condition division threshold, a high-temperature standard operating condition division interval, an ultra-high-pressure standard operating condition division threshold, and a low-pressure standard operating condition division threshold from a plurality of standard operating condition division thresholds; Extract unit data from the configured power supply operating condition data in sequence, and identify the rated operating temperature corresponding to the unit data; If the rated operating temperature is less than the low-temperature standard operating condition division threshold, the unit data is confirmed as low-temperature data; if the rated operating temperature is within the high-temperature standard operating condition division interval, the unit data is confirmed as high-temperature data; otherwise, the unit data is skipped; Summarize low temperature data and high temperature data respectively to obtain low temperature data set and high temperature data set; Based on the ultra-high voltage standard operating condition division threshold and the low voltage standard operating condition division threshold, the low temperature data set and the high temperature data set are re-divided to obtain a low temperature low pressure power data set, a low temperature ultra-high power data set, a high temperature low pressure power data set and a high temperature ultra-high power data set. The low temperature data set, the high temperature data set, the low temperature low pressure power data set, the low temperature ultra-high power data set, the high temperature low pressure power data set and the high temperature ultra-high power data set are summarized to obtain a multi-operating condition data set.

3. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions as claimed in claim 2, characterized in that: The re-dividing operation is performed on the low temperature data set and the high temperature data set based on the ultra-high voltage standard working condition division threshold and the low voltage standard working condition division threshold to obtain a low temperature low pressure power data set, a low temperature ultra-high power data set, a high temperature low pressure power data set and a high temperature ultra-high power data set, including: Extract low temperature data from the low temperature data set in sequence, identify the low temperature power capacity corresponding to the low temperature data, if the low temperature power capacity is greater than or equal to the ultra-high voltage standard working condition division threshold, then confirm the low temperature data as low temperature ultra-high power data, if the low temperature power capacity is less than or equal to the low voltage standard working condition division threshold, then confirm the low temperature data as low temperature low pressure power data, otherwise, skip the extracted low temperature data; Extract high temperature data from the high temperature data set in sequence, identify the high temperature power capacity corresponding to the high temperature data, if the high temperature power capacity is greater than or equal to the ultra-high voltage standard working condition division threshold, then confirm the high temperature data as high temperature ultra-high power data, if the high temperature power capacity is less than or equal to the low voltage standard working condition division threshold, then confirm the high temperature data as high temperature low pressure power data, otherwise, skip the extracted high temperature data; The low-temperature low-pressure power data, high-temperature low-pressure power data, low-temperature ultra-high power data and high-temperature ultra-high power data are summarized respectively to obtain the low-temperature low-pressure power data set, the high-temperature low-pressure power data set, the low-temperature ultra-high power data set and the high-temperature ultra-high power data set.

4. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions as claimed in claim 3, characterized in that: The method of constructing a capacity attenuation model and a resistance growth model based on usage data includes: Extract the capacity time series set from the usage data and identify the capacity detection frequency corresponding to the capacity time series set. Extracting a temperature timing set and a resistance timing set from the usage data, identifying the temperature detection frequency and the resistance detection frequency corresponding to the temperature timing set and the resistance timing set respectively, integrating the temperature detection frequency and the resistance detection frequency based on the capacity detection frequency to obtain a common frequency; A temperature time series trend graph is constructed using a temperature time series set and a common frequency, and a thermal model is constructed based on the temperature time series trend graph; A resistance timing trend graph is constructed using the resistance timing set and the common frequency, and a resistance growth model is constructed based on the resistance timing trend graph and using a thermal model; A capacity decay model is constructed based on the capacity detection frequency.

5. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions as claimed in claim 4, characterized in that: The method of constructing a capacity attenuation model based on the capacity detection frequency includes: A capacity time series coordinate system is constructed based on the capacity detection frequency, a capacity time series set is converted into a capacity coordinate set based on the capacity time series coordinate system, and the capacity coordinate set is mapped to the capacity time series coordinate system to obtain an initial capacity time series diagram; A pre-built data cleaning method is used to perform data cleaning operations on the initial capacity time series diagram to obtain an optimized capacity time series diagram. A curve fitting operation is performed on the optimized capacity time series diagram to obtain a capacity time series trend diagram. A feature parameter set is extracted from the capacity time series trend diagram, and a capacity attenuation model is constructed based on the feature parameter set.

6. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions as claimed in claim 5, characterized in that: The capacity decay model is as follows: Among them, C B Indicates the timing power supply capacity, Indicates the initial power capacity, Indicates the power capacity attenuation rate, and All of them are constant parameters preset in the capacity decay model. represents the apparent activation energy during capacity decay, represents the power supply current multiplier, R represents the ideal gas constant, and R = 8.314 J / (mol·K), Indicates the operating temperature of the power supply with the smallest capacity attenuation. represents the temperature compensation constant in the capacity decay model, Indicates the ampere-hour flux during the charging and discharging process of the power supply, T B Indicates the temperature timing parameters corresponding to the thermal model.

7. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions according to claim 6, characterized in that: The resistance growth model is as follows: Among them, R B represents the timing power supply resistance, represents the initial power supply resistance, and the initial power supply resistance is the same as the measurement temperature of the sequential power supply resistance. represents the resistance growth rate, and All of them are constant parameters preset in the resistance growth model. represents the apparent activation energy during the resistance growth process, Indicates the power supply operating temperature at which resistance increase is minimal, Represents the temperature compensation constant in the resistance growth model.

8. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions according to claim 7, characterized in that: The step of associating the identification condition label, the rated data and the aging model to obtain a usage model corresponding to the identification data includes: Extract the use temperature range based on the use thermal model; Obtain a hash function, import the use temperature range, identification condition label and rated data into the hash function, obtain an associated hash value, compress the use temperature range and rated data to obtain compressed data, perform an identification operation on the compressed data and the aging model using the associated hash value, and obtain a use model corresponding to the identification data, wherein the associated hash value is expressed as: M s =SM3(M‖KDF(H,n),m) M=C1‖C2‖C3‖C4‖F w ‖T5 Among them, M s represents the associated hash value, SM3(*) represents the first hash function, KDF(*) represents the second hash function, M represents the rated parameter, C1 represents the rated voltage, C2 represents the rated current, C3 represents the rated power, C4 represents the rated capacity, F w represents the discharge condition, H represents the identification condition label, n represents the upper limit of the second hash value length using the second hash function, m represents the upper limit of the first hash value length using the first hash function, and T5 represents the operating temperature range.

9. The method for constructing a power distribution box configuration power supply analysis model based on multiple working conditions as claimed in claim 8, characterized in that: The method of performing a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence includes: Extract the data to be sorted from the rated data in sequence, and perform the following operations on the extracted data to be sorted: Perform a deduplication operation on the data to be sorted to obtain a parameter set to be sorted, and perform a sorting operation on the parameter set to be sorted in ascending order of values ​​to obtain a unit parameter sequence; The unit parameter sequence is summarized, and a sorting operation is performed on the unit parameter sequence using a preset parameter sorting method to obtain a working condition lookup table data sequence, and the usage model set is mapped to the working condition lookup table data sequence to obtain a recursive usage model sequence.

10. A system for constructing a power distribution box configuration power supply analysis model based on multiple working conditions, characterized in that: The system comprises: A multi-operating condition data set acquisition module is used to acquire the configured power supply operating condition data, and divide the configured power supply operating condition data using a plurality of preset standard operating condition division thresholds to obtain a multi-operating condition data set, wherein the multi-operating condition data set includes: a low temperature data set, a high temperature data set, a low temperature ultra-high power data set, and a high temperature ultra-high power data set; An aging model acquisition module is used to sequentially extract a target data set from a multi-condition data set, and perform the following operations on the extracted target data set: identify the condition corresponding to the target data set, and perform an identification operation on the target data set using the condition corresponding to the target data set to obtain an identification condition label and an identification data set, sequentially extract identification data from the identification data set, and perform the following operations on the identification data: obtain rated data and usage data contained in the identification data, wherein the rated data includes: rated voltage, rated current, rated power, rated capacity, power supply target and discharge condition, and the usage data includes: capacity time series set, temperature time series set and resistance time series set, construct a capacity attenuation model and a resistance growth model based on the usage data, and construct an aging model corresponding to the identification data according to the capacity attenuation model and the resistance growth model; A recursive usage model sequence acquisition module is used to associate the identification condition label, the rated data and the aging model to obtain the usage model corresponding to the identification data, summarize the usage models to obtain the usage model set corresponding to the target data set, and perform a recursive operation on the usage model set based on the rated data in the usage model set to obtain a recursive usage model sequence; The multi-condition query table generation module is used to construct a condition query table based on a recursive use model sequence, summarize the condition query table, obtain a multi-condition query table, and complete the construction of a distribution box configuration power supply analysis model based on multiple conditions according to the multi-condition query table.

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