Energy storage resource evaluation system

Abstract

The present application discloses an energy storage resource evaluation system. The system includes: a collection module for collecting log information of a preset type of energy storage system, and performing format standardization processing on the log information to obtain target log information; a storage module for storing target log information, wherein the target log information includes at least: operating status data of a preset type of energy storage system; a quantitative analysis module for receiving target log information from the storage module, and determining the potential utilization level of resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable. The present application solves the technical problem that a large amount of manpower and material resources are wasted due to the fact that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of battery energy storage systems, and cannot accurately measure the utilization potential of the battery throughout its life cycle.

Description

Technical Field

[0001] The present application relates to the field of battery detection, and in particular, to an evaluation system for energy storage resources. Background Art

[0002] Battery energy storage is an important branch of the development of energy storage technology today. Battery energy storage application technology is developing in the direction of distributed, mobile, heterogeneous, intelligent, and information-based. The quantitative evaluation of the utilization potential of battery energy storage systems plays an important role in the multi-scenario safety production work of mobile / heterogeneous battery energy storage systems. The traditional battery energy storage system utilization potential evaluation currently mostly adopts the method of battery pack integrated monitoring and regular manual inspections. Although a lot of manpower and material resources are invested, they are all sampling surveys of battery operating time and cannot accurately measure / quantify the utilization potential of the battery throughout its life cycle.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiment of the present application provides an energy storage resource evaluation system to at least solve the technical problem that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of battery energy storage systems, which wastes a lot of manpower and material resources and cannot accurately measure the utilization potential of the battery over its entire life cycle.

[0005] According to one aspect of an embodiment of the present application, a system for evaluating energy storage resources is provided, including: a collection module, used to collect log information of a preset type of energy storage system, and perform format standardization processing on the log information to obtain target log information; a storage module, used to store the target log information, wherein the target log information includes at least: operating status data of the preset type of energy storage system; a quantitative analysis module, used to receive the target log information from the storage module, and determine the potential utilization level of resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable.

[0006] Optionally, the quantitative analysis module includes: a knowledge base for providing standard state data of the energy storage system and potential utilization levels corresponding to the standard state data.

[0007] Optionally, the quantitative analysis module also includes: a machine learning module, which is used to classify the operating status data based on a clustering algorithm and update the knowledge base based on the classification results.

[0008] Optionally, the quantitative analysis module also includes: a real-time streaming computing module, which is used to compare the operating status data with the standard status data stored in the knowledge base in offline and / or online situations, and determine the potential utilization level of resources in the energy storage system based on the comparison results.

[0009] Optionally, the collection module collects log information of preset types of energy storage systems by integrating Flume into Kafka.

[0010] Optionally, the system further includes: a viewing module, configured to receive the utilization level of potential resources in a preset type of energy storage system from the quantitative analysis module, and to display the utilization level.

[0011] According to another aspect of an embodiment of the present application, there is also provided a method for evaluating energy storage resources. The evaluation method is implemented based on an evaluation system for any type of energy storage resources, and includes: collecting log information of a preset type of energy storage system, and performing format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type of energy storage system and / or a heterogeneous type of energy storage system; extracting operating status data of the preset type of energy storage system from the target log information; comparing the operating status data with the status data stored in a knowledge base, and determining a potential utilization level of resources in the energy storage system based on the comparison result, wherein the potential utilization level is used to indicate the probability of the resource being usable.

[0012] Optionally, after determining the potential utilization level of resources in the energy storage system according to the comparison result, the method further includes: sending the potential utilization level to a viewing module for display.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any energy storage resource evaluation method.

[0014] According to another aspect of an embodiment of the present application, a processor is further provided, and the processor is used to run a program, wherein the program executes any energy storage resource evaluation method when running.

[0015] In an embodiment of the present application, a method for quantitatively analyzing target log information is adopted, wherein a collection module is used to collect log information of a preset type of energy storage system, and the log information is formatted and standardized to obtain target log information; a storage module is used to store the target log information, wherein the target log information at least includes: operating status data of the preset type of energy storage system; a quantitative analysis module is used to receive the target log information from the storage module, and determine the potential utilization level of resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable, thereby achieving the technical effect of automatically and accurately determining the potential utilization level of resources in the energy storage system, thereby solving the technical problem that a large amount of manpower and material resources are wasted due to the fact that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of the battery energy storage system, and the utilization potential of the battery over its entire life cycle cannot be accurately measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a structural schematic diagram of an optional energy storage resource evaluation system according to an embodiment of the present application;

[0018] Figure 2 It is a flow chart of an optional method for evaluating the utilization potential of a mobile / heterogeneous battery energy storage system based on a Spark big data platform according to an embodiment of the present application;

[0019] Figure 3 It is an optional architecture diagram of the utilization potential evaluation of mobile / heterogeneous battery energy storage system based on the Spark big data platform according to an embodiment of the present application;

[0020] Figure 4 It is a flowchart of an optional method for evaluating energy storage resources according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] In order to facilitate those skilled in the art to better understand the relevant embodiments of the present application, the technical terms or some terms that may be involved in the embodiments of the present application are explained as follows:

[0024] SOC: State of Charge The state of charge of the battery;

[0025] SOE: State of Energy battery energy state;

[0026] SOP: State of Power The power state of the battery;

[0027] SOH: State of Health The health status of the battery;

[0028] OCV: Open Circuit Voltage;

[0029] RDD: Resilient Distributed Dataset;

[0030] API: Application Programming Interface;

[0031] HDFS:Hadoop Distributed File Systems distributed file system;

[0032] SVM: Support Vector Machine;

[0033] KNN: K-nearst Neighbors nearest neighbor method;

[0034] K-means: K-means clustering K-means algorithm.

[0035] According to an embodiment of the present application, an embodiment of an energy storage resource evaluation system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] Figure 1 An energy storage resource evaluation system according to an embodiment of the present application includes:

[0037] The collection module 40 is used to collect log information of a preset type of energy storage system and perform format standardization processing on the log information to obtain target log information;

[0038] The storage module 42 is used to store target log information, wherein the target log information at least includes: operating status data of a preset type of energy storage system;

[0039] The quantitative analysis module 44 is used to receive the target log information from the storage module, and determine the potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resources being available.

[0040] In the energy storage resource evaluation system, the acquisition module 40 is used to collect log information of a preset type of energy storage system and perform format standardization processing on the log information to obtain target log information; the storage module 42 is used to store the target log information, wherein the target log information at least includes: operating status data of the preset type of energy storage system; the quantitative analysis module 44 is used to receive the target log information from the storage module, and determine the potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable, thereby achieving the technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system, thereby solving the technical problem that a large amount of manpower and material resources are wasted due to the fact that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of the battery energy storage system, and the utilization potential of the battery over its entire life cycle cannot be accurately measured.

[0041] In some embodiments of the present application, the quantitative analysis module includes: a knowledge base for providing standard status data of the energy storage system and potential utilization levels corresponding to the standard status data.

[0042] In some embodiments of the present application, the quantitative analysis module also includes: a machine learning module, which is used to classify the operating status data based on a clustering algorithm and update the knowledge base based on the classification results.

[0043] In some optional embodiments of the present application, the quantitative analysis module also includes: a real-time streaming computing module, which is used to compare the operating status data with the standard status data stored in the knowledge base in offline and / or online situations, and determine the potential utilization level of resources in the energy storage system based on the comparison results.

[0044] It should be noted that the collection module collects log information of preset types of energy storage systems by integrating Flume into Kafka.

[0045] In some embodiments of the present application, the system further includes: a view module, configured to receive the utilization level of potential resources in a preset type of energy storage system from the quantitative analysis module, and to display the utilization level.

[0046] Figure 2 This is a flow chart of a method for evaluating the utilization potential of a mobile / heterogeneous battery energy storage system based on the Spark big data platform. Figure 2 As shown:

[0047] 1. Step 1: Collect logs in real time and standardize the log format. The collected logs are sent to HDFS for storage and to the Spark big data platform for transaction and actions operations, which are stored as RDD.

[0048] 2. Step 2: Use Spark Streaming and Spark MLlib to analyze the collected data. Use the historical data and online real-time data of the Spark big data platform to perform data mining and machine learning on energy storage resources. Perform real-time analysis on the logs in HDFS and combine them with the knowledge base to generate new knowledge items that are not included in the knowledge base and expand the knowledge base. Spark MLlib provides a variety of machine learning algorithms to aggregate and classify energy storage resources, generate multiple clusters, and compare / update the knowledge base.

[0049] 3. Step 3: Poll and determine the potential utilization level. By comparing the real-time collected log information with the information stored in the knowledge base, the potential utilization level of the energy storage resources can be determined.

[0050] 4. Step 4: Send the result of the third step to the view module for display.

[0051] An optional embodiment of this application proposes a method for quantitatively evaluating the utilization potential of mobile / heterogeneous battery energy storage systems based on the Spark big data platform. The technical task is achieved in the following way: design a battery energy storage data collection, processing, analysis, and early warning big data platform based on Spark Core and Spark SQL, including a collection module, a data analysis module, and a view module. The architecture diagram is as follows: Figure 3The specific implementation is as follows:

[0052] 1. The acquisition module uses the Catalyst query compiler to convert SQL / Data Frame / Dataset data into HDFS / RDD after a series of transformations. The device logs of various external energy storage resources are pre-processed and transmitted to the data storage module in real time. The pre-processing includes log filtering, merging and format standardization, and unified log specifications.

[0053] 2. The collection module integrates Flume into Kafka (Kafka is a distributed messaging system developed and open sourced by Linkedln) to realize log collection, log preprocessing and real-time transmission of logs. It can collect syslog logs, monitoring folder logs and TCP / UDP interface logs, etc., and can be well connected with Spark Streaming to realize real-time transmission of preprocessed log information to the data storage module.

[0054] 3. The data storage module filters and maps the collected battery energy storage system operating status data (such as OCV, output current, output voltage, battery capacity, etc.), environmental data (such as temperature, humidity, load power, etc.), and battery information (battery material, production date, number of cycles, etc.) into valid original data and returns it as an unmodifiable RDD.

[0055] 4. Quantitative analysis module, including Spark Streaming and Spark MLlib modules: The Spark Streaming module includes alarm generation, offline analysis, online analysis, and updates the knowledge base in real time based on the analysis results; the Spark MLlib module includes artificial intelligence-based classification, regression, and clustering algorithms for battery reorganization analysis of battery energy storage, and returns the analysis results and updates the knowledge base.

[0056] The knowledge base is the core database for quantitative evaluation of battery system utilization potential. It stores multi-scenario benchmarking data of heterogeneous battery energy storage systems, including battery evaluation of single influencing factors and quantitative analysis methods associated with multiple scenarios. Its functions include but are not limited to:

[0057] 1. Based on part of the content of a single log as analysis data, determine the value of at least one of the characteristics of the battery energy storage system in the quantification of utilization potential, such as SOC, SOE, SOP, SOH, capacity and battery internal resistance;

[0058] 2. According to the frequency of occurrence of special log content in a unit time, the battery energy storage system utilization potential is determined and recalibrated and modified, and the correct battery utilization potential is quantified and updated continuously;

[0059] 3. Determine the safe operation boundary based on the operation of the same type of battery energy storage resources distributed in different devices, and update the safe operation boundary in the knowledge base;

[0060] 4. Quantitative characteristic analysis of the utilization potential of battery energy storage systems under multiple influencing factors (temperature, load, operating conditions) with multiple correlations.

[0061] Compared with the prior art, the mobile / heterogeneous battery energy storage system utilization potential quantitative evaluation system based on the Spark big data platform of this application has the following outstanding beneficial effects:

[0062] 1. Spark, with its high-performance streaming computing and processing speed, rich ecology of multiple data sources (MySQL, Hbase, Kafka) and multiple API interfaces (Parquet, ORC, CSV, Json), and the ability to fully combine big data and AI, can fully tap the value of mobile / heterogeneous battery energy storage system data and quantify the utilization potential of battery energy storage systems in real time online.

[0063] 2. The distributed architecture of the big data platform is easy to expand and shrink, and can cope with changes in the scale of distributed energy storage to achieve effective resource utilization. It also solves the problem that existing technologies are difficult to handle massive logs.

[0064] 3. The proposed quantitative evaluation method for energy storage resource utilization potential can accurately remove false positives and provide detailed quantitative evaluation results to guide the optimal configuration and joint optimization operation of distributed / mobile / heterogeneous energy storage systems.

[0065] 4. Using big data technology for data mining and machine learning can effectively utilize massive historical log information, and the knowledge base can be expanded independently by combining the existing knowledge base with association analysis.

[0066] 5. Integrating Flume into Kafka can realize log collection of various energy storage devices and transmit them to the Spark big data platform HDFS / RDD / MySQL storage through Kafka, which can display and query data.

[0067] 6. The battery energy storage system utilization potential mentioned in this application refers to the battery's SOC, SOE, SOP, SOH, capacity and battery internal resistance. This application provides a quantitative evaluation method for the battery energy storage system utilization potential with real-time analysis and multi-factor correlation.

[0068] Figure 4 According to an embodiment of the present application, a method for evaluating energy storage resources is provided. The method for evaluating energy storage resources is implemented based on any energy storage resource evaluation system, such as Figure 4 As shown, the method includes:

[0069] Step S102, collecting log information of a preset type of energy storage system, and performing format standardization processing on the log information to obtain target log information; wherein the preset types include: a mobile type of energy storage system and / or a heterogeneous type of energy storage system;

[0070] Step S104, extracting the operating status data of the preset type of energy storage system from the target log information;

[0071] Step S106, comparing the operating status data with the status data stored in the knowledge base, and determining the potential utilization level of the resources in the energy storage system according to the comparison result, wherein the potential utilization level is used to indicate the probability of the resources being available.

[0072] In the energy storage resource evaluation method, first, log information of a preset type of energy storage system can be collected, and the format of the log information can be standardized to obtain target log information; wherein the preset types include: mobile type energy storage system and / or heterogeneous type energy storage system; then, operating status data of the preset type of energy storage system is extracted from the target log information; finally, the operating status data is compared with the status data stored in the knowledge base, and the potential utilization level of the resources in the energy storage system is determined according to the comparison result, wherein the potential utilization level is used to indicate the probability of the resource being usable, thereby achieving the technical effect of automatically and accurately determining the potential utilization level of the resources in the energy storage system, and further solving the technical problem that a large amount of manpower and material resources are wasted due to the fact that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of the battery energy storage system, and the utilization potential of the battery over its entire life cycle cannot be accurately measured.

[0073] Optionally, after determining the potential utilization level of resources in the energy storage system according to the comparison result, the method further includes: sending the potential utilization level to a viewing module for display.

[0074] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, the non-volatile storage medium including a stored program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute any energy storage resource evaluation method.

[0075] Specifically, the above storage medium is used to store program instructions for executing the following functions to achieve the following functions:

[0076] Collect log information of preset type energy storage systems, and perform format standardization on the log information to obtain target log information; wherein the preset types include: mobile type energy storage systems and / or heterogeneous type energy storage systems; extract operating status data of preset type energy storage systems from the target log information; compare the operating status data with the status data stored in the knowledge base, and determine the potential utilization level of resources in the energy storage system according to the comparison result, wherein the potential utilization level is used to indicate the probability of resource availability.

[0077] According to another aspect of an embodiment of the present application, a processor is further provided, and the processor is used to run a program, wherein the program executes any energy storage resource evaluation method when running.

[0078] Specifically, the processor is used to call the program instructions in the memory to implement the following functions:

[0079] Collect log information of preset type energy storage systems, and perform format standardization on the log information to obtain target log information; wherein the preset types include: mobile type energy storage systems and / or heterogeneous type energy storage systems; extract operating status data of preset type energy storage systems from the target log information; compare the operating status data with the status data stored in the knowledge base, and determine the potential utilization level of resources in the energy storage system according to the comparison result, wherein the potential utilization level is used to indicate the probability of resource availability.

[0080] In an embodiment of the present application, a method for quantitatively analyzing target log information is adopted, wherein a collection module is used to collect log information of a preset type of energy storage system, and the log information is formatted and standardized to obtain target log information; a storage module is used to store the target log information, wherein the target log information at least includes: operating status data of the preset type of energy storage system; a quantitative analysis module is used to receive the target log information from the storage module, and determine the potential utilization level of resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable, thereby achieving the technical effect of automatically and accurately determining the potential utilization level of resources in the energy storage system, thereby solving the technical problem that a large amount of manpower and material resources are wasted due to the fact that most relevant technicians use battery pack integrated monitoring and regular manual inspection to evaluate the utilization potential of the battery energy storage system, and the utilization potential of the battery over its entire life cycle cannot be accurately measured.

[0081] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0082] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0083] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0085] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0087] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An energy storage resource evaluation system, characterized in that: include: A collection module, used to collect log information of a preset type of energy storage system, and perform format standardization processing on the log information to obtain target log information; A storage module, used to store the target log information, wherein the target log information at least includes: operating status data of the preset type of energy storage system; A quantitative analysis module, used to receive the target log information from the storage module, and determine the potential utilization level of the resources in the preset type of energy storage system according to the target log information, wherein the potential utilization level is used to indicate the probability of the resource being usable; The quantitative analysis module includes: a knowledge base for providing standard state data of the energy storage system and potential utilization levels corresponding to the standard state data; The knowledge base stores multi-scenario benchmarking data of heterogeneous battery energy storage systems, including battery evaluation of a single influencing factor and quantitative analysis methods associated with multiple scenarios. Its functions include but are not limited to: According to the frequency of occurrence of special log content within a unit time, the battery energy storage system utilization potential is determined and recalibrated and modified, and the correct battery utilization potential is continuously quantified and updated; The safe operation boundary is determined based on the operation of the same battery energy storage resources distributed in different devices, and the safe operation boundary is updated in the knowledge base.

2. The system according to claim 1, characterized in that The quantitative analysis module also includes: a machine learning module, which is used to classify the operating status data based on a clustering algorithm and update the knowledge base based on the classification result.

3. The system according to claim 1, characterized in that The quantitative analysis module also includes: a real-time streaming computing module, which is used to compare the operating status data with the standard status data stored in the knowledge base in offline and / or online situations, and determine the potential utilization level of resources in the energy storage system based on the comparison results.

4. The system according to claim 1, characterized in that The collection module collects log information of preset types of energy storage systems by integrating Flume into Kafka.

5. The system according to claim 1, characterized in that The system further comprises: The viewing module is used to receive the utilization level of potential resources in the preset type of energy storage system from the quantitative analysis module and display the utilization level.

6. A method for evaluating energy storage resources, characterized in that: The evaluation method is implemented based on the energy storage resource evaluation system according to any one of claims 1 to 5, and includes: Collecting log information of preset type energy storage systems, and performing format standardization processing on the log information to obtain target log information; wherein the preset types include: mobile type energy storage systems and / or heterogeneous type energy storage systems; Extracting the operating status data of the preset type of energy storage system from the target log information; The operating status data is compared with the status data stored in the knowledge base, and the potential utilization level of the resources in the energy storage system is determined according to the comparison result, wherein the potential utilization level is used to indicate the probability of the resources being usable.

7. The method according to claim 6, characterized in that After determining the potential utilization level of the resources in the energy storage system according to the comparison result, the method further includes: sending the potential utilization level to a viewing module for display.

8. A non-volatile storage medium, characterized in that: The non-volatile storage medium includes a stored program, wherein when the program is executed, the device where the non-volatile storage medium is located is controlled to execute the energy storage resource evaluation method described in any one of claims 6 to 7.

9. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the energy storage resource evaluation method described in any one of claims 6 to 7.

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