Regional intelligent logistics coordination system

Abstract

The invention provides a regional intelligent logistics coordination system, and relates to the field of intelligent logistics. The regional intelligent logistics coordination system comprises a data acquisition module which acquires various data, including position information, environment information and cargo state information, in a logistics process in real time through Internet of Things equipment; the data storage module is used for storing the acquired logistics data by adopting a distributed database technology and carrying out data cleaning and preprocessing; and the data analysis module performs deep analysis on the stored logistics data by using a big data analysis technology, and extracts valuable information including logistics path optimization and cargo demand prediction. Various data in the logistics process are collected in real time through the Internet of Things equipment, real-time analysis is conducted through the big data technology, the system can quickly respond to logistics demand changes and adjust logistics schemes in time, and the intelligent scheduling system based on the artificial intelligence algorithm can dynamically optimize and configure logistics resources and ensure efficient utilization of the resources.

Description

Technical Field

[0001] The present invention relates to the field of smart logistics technology, and specifically to a regional smart logistics coordination system. Background Art

[0002] The development of smart logistics benefits from the integration and innovative application of multiple advanced technologies, especially the progress in information technology, data analysis and communication technology. With the rapid development of globalization and e-commerce, the application of the Internet of Things has brought revolutionary changes to the logistics industry. Through sensors installed in goods, transportation tools and storage facilities, the Internet of Things technology can realize real-time monitoring and data collection of the entire logistics process, making logistics information more transparent, and each link in the transportation process can be accurately tracked and dynamically scheduled.

[0003] With the rapid development of global e-commerce and the continuous expansion of the logistics industry, logistics management is becoming increasingly complex and challenging. Traditional logistics systems usually rely on manual scheduling and experience-based decision-making, and are unable to cope with the high requirements of modern logistics for efficiency, cost control, real-time and accuracy.

[0004] There are some areas that need to be improved and deficiencies in traditional logistics management methods. The logistics process involves multiple links and participants. Traditional systems often lack an effective information sharing mechanism, resulting in untimely and inaccurate information transmission, which in turn affects the timeliness and accuracy of logistics decisions. The manual scheduling and decision-making process is cumbersome and time-consuming, and it is difficult to cope with large-scale and high-frequency logistics needs, especially during peak hours or when emergencies occur. Manual scheduling often cannot be adjusted quickly, resulting in transportation delays and waste of resources. Due to the lack of real-time monitoring and optimal allocation of logistics resources, traditional systems find it difficult to maximize the utilization of resources such as vehicles, warehouses, and personnel, which not only increases operating costs, but may also lead to idle or over-use of resources. Traditional logistics systems usually rely on experience and simple rules to make decisions, and lack the ability to comprehensively analyze historical data, real-time data, and external data. This makes it difficult for logistics companies to make optimal decisions when facing a complex and changing logistics environment. Therefore, technicians in this field provide a regional intelligent logistics coordination system to solve the problems raised in the above background technology. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the shortcomings of the prior art, the present invention provides a regional intelligent logistics coordination system to solve the problems of information asymmetry, low efficiency, low resource utilization and lack of intelligent decision-making support in the existing system.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a regional intelligent logistics coordination system, comprising:

[0009] The data collection module collects various data in the logistics process in real time through IoT devices, including location information, environmental information, and cargo status information;

[0010] The data storage module uses distributed database technology to store the collected logistics data and perform data cleaning and preprocessing;

[0011] The data analysis module uses big data analysis technology to conduct in-depth analysis of stored logistics data and extract valuable information, including logistics route optimization and cargo demand forecasting;

[0012] The decision support module generates logistics scheduling and route optimization plans based on artificial intelligence algorithms and data analysis results;

[0013] The coordination and control module coordinates and controls logistics resources in real time through the intelligent scheduling system according to the plan generated by the decision support module;

[0014] The user interaction module provides a user-friendly interface for logistics managers to view logistics status, adjust logistics plans, and receive system suggestions.

[0015] Preferably, the data acquisition module comprises the following steps:

[0016] S1. Data collection: obtain the real-time location of transportation vehicles and goods through GPS and Beidou satellite positioning systems; obtain transportation environment data through temperature and humidity sensors and light sensors; obtain cargo status information through RFID tags and barcode scanning, including but not limited to quantity, weight and integrity; obtain vehicle speed, fuel consumption and tire pressure operating status information through vehicle-mounted sensors;

[0017] S2. Equipment deployment and installation: install the GPS module and vehicle-mounted terminal on the means of transport to ensure that the equipment is stable and not easily damaged; install the temperature and humidity sensor and the air pressure sensor at the appropriate position of the transport container to ensure that the environmental parameters can be accurately collected; attach the RFID tag or barcode to the goods to ensure that the tag or barcode is not easy to fall off and is easy to scan; install RFID readers and barcode scanners at the distribution center, the entrance and exit of the warehouse and the key positions of the shelves to ensure that the in and out information of the goods can be accurately collected; then configure the parameters of the IoT device, including but not limited to the sampling frequency, communication frequency and transmission mode, to ensure that the device can work as expected; conduct communication tests to ensure that the IoT device can perform stable data transmission with the data acquisition server; collect initial data, verify the accuracy and completeness of the data, and ensure that the data collected by the device meets expectations;

[0018] S3. Data transmission and integration: select appropriate communication networks, including but not limited to 4G / 5G, LoRa and NB-loT, ensure the real-time and reliability of data transmission, use MQTT, CoAP or HTTP lightweight protocols for data transmission, ensure efficient data transmission and low latency, encrypt data in transmission, use SSL / TLS protocols to ensure data security, design data interfaces, integrate data collected by IoT devices into data storage modules, convert collected data into a unified format to facilitate subsequent data processing and analysis, and perform preliminary cleaning of collected data to remove redundant data, correct erroneous data and fill in missing data;

[0019] S4. Data storage and management: select a suitable database based on data type and data volume, partition and store data based on the attributes of time, region and type, improve data query efficiency, regularly back up data to ensure data security and reliability, create indexes for commonly used query fields to improve data query speed, set data access permissions to ensure that only authorized users can access and operate data, and formulate data lifecycle management strategies;

[0020] S5. System monitoring and maintenance: real-time monitoring of the operating status of IoT devices to ensure the normal operation of the devices; setting up fault alarm mechanisms; timely alarming and processing when the devices fail or are abnormal; monitoring the integrity of data to ensure that the collected data is not missing; monitoring the accuracy of data to ensure that the collected data meets expectations; monitoring the consistency of data to ensure that data from different data sources are consistent; regularly maintaining IoT devices; updating data collection software in a timely manner; fixing known vulnerabilities and bugs; improving the stability and security of the system; optimizing performance according to the system operation status to improve the overall performance of the system.

[0021] Preferably, the data storage module comprises the following steps:

[0022] S1. Demand analysis and planning: clarify the types of data that need to be stored, including structured and unstructured data, estimate the daily, monthly and annual data growth, determine the capacity requirements for data storage, analyze the frequency of data use and access patterns, and determine whether the data is used for real-time query, historical analysis or archival storage;

[0023] S2. Architecture design, select a distributed database, split data horizontally by time or region, store it in different database instances, improve query efficiency, store different types of data in different tables, reduce the amount of data in a single table, improve query performance, formulate a data backup strategy, regularly perform full or incremental backup of data, store backup data in different geographical locations, prevent data loss caused by natural disasters or man-made damage, and establish a data recovery mechanism to ensure rapid recovery when data is lost or damaged;

[0024] S3. Data model design: determine the main entities in the logistics system, clarify the relationship between entities, design the database table structure based on the entity relationship model, determine the field type, length and constraints, create indexes for commonly used query fields to improve query efficiency, create views and stored procedures as needed, and simplify complex queries and business logic;

[0025] S4. Data security and privacy protection. Use multi-factor authentication mechanism to ensure that only authorized users can access the database. Assign different access rights according to user roles to ensure data security and integrity. Use SSL / TLS protocol to encrypt data in transit to prevent data theft. Encrypt sensitive data for storage to prevent data leakage. Record all database operation logs to facilitate post-audit and problem tracking. Use database monitoring tools to monitor database performance and security in real time, and promptly detect and handle abnormal situations.

[0026] Preferably, the data analysis module comprises the following steps:

[0027] S1. Requirements analysis and goal definition, clarifying analysis objectives and data source determination;

[0028] S2. Data collection and preprocessing, through data collection, data cleaning, data conversion and data segmentation;

[0029] S3. Feature engineering: select features with high correlation with the target variable through correlation analysis and chi-square test method, use PCA dimensionality reduction technology to extract the main features, reduce the data dimension, and use time features, geographical features and statistical features for feature construction;

[0030] S4. Model selection and training: select a suitable model and train the model, and select appropriate evaluation indicators to evaluate the model according to the task type.

[0031] Preferably, the decision support module generates logistics scheduling plans and path optimization plans based on data analysis results, provides decision support for logistics management personnel, applies deep learning and reinforcement learning artificial intelligence algorithms to make complex decisions and optimizations, uses linear programming and integer programming optimization algorithms to optimize the configuration of logistics paths and resources, and makes real-time decisions and adjustments based on real-time data and analysis results.

[0032] Preferably, the coordination and control module is based on the solution generated by the decision support module, realizes real-time coordination and control of logistics resources through the intelligent scheduling system, adopts intelligent scheduling algorithm to schedule and optimize logistics resources, monitors the status of logistics resources in real time through Internet of Things devices and sensors, and performs remote control, establishes an emergency handling mechanism to deal with emergencies in the logistics process, integrates the intelligent scheduling system with the coordination and control module to realize data sharing and business collaboration, formulates detailed control strategies and operating procedures to ensure the accuracy and timeliness of coordination and control, conducts system testing to verify the effect of coordination and control, and optimizes and improves according to the test results.

[0033] (III) Beneficial effects

[0034] The present invention provides a regional intelligent logistics coordination system, which has the following beneficial effects:

[0035] 1. In the present invention, various data in the logistics process are collected in real time through Internet of Things devices, and real-time analysis is performed using big data technology. The system can quickly respond to changes in logistics demand and adjust logistics plans in a timely manner. The intelligent scheduling system based on artificial intelligence algorithms can dynamically optimize the configuration of logistics resources to ensure efficient use of resources.

[0036] 2. In the present invention, through refined resource management and optimization algorithms, the system can maximize the use of existing resources and reduce idle and wasted resources. The automated and intelligent decision support system reduces dependence on manual scheduling and decision-making, reduces labor costs, and by optimizing transportation routes and vehicle scheduling, the system can reduce unnecessary mileage and fuel consumption, thereby reducing energy consumption and carbon emissions in logistics operations.

[0037] 3. In the present invention, the system provides real-time cargo tracking and vehicle monitoring functions, so that logistics management personnel can understand the logistics status and cargo location at any time, which not only improves the transparency of the logistics process, but also enhances customers' trust in logistics services. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the structure of the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] like Figure 1 As shown, an embodiment of the present invention provides a regional intelligent logistics coordination system, including:

[0042] The data collection module collects various data in the logistics process in real time through IoT devices, including location information, environmental information, and cargo status information;

[0043] The data storage module uses distributed database technology to store the collected logistics data and perform data cleaning and preprocessing;

[0044] The data analysis module uses big data analysis technology to conduct in-depth analysis of stored logistics data and extract valuable information, including logistics route optimization and cargo demand forecasting;

[0045] The decision support module generates logistics scheduling and route optimization plans based on artificial intelligence algorithms and data analysis results;

[0046] The coordination and control module coordinates and controls logistics resources in real time through the intelligent scheduling system according to the plan generated by the decision support module;

[0047] The user interaction module provides a user-friendly interface for logistics managers to view logistics status, adjust logistics plans, and receive system suggestions.

[0048] The data acquisition module includes the following steps:

[0049] S1. Data collection: obtain the real-time location of transportation vehicles and goods through GPS and Beidou satellite positioning systems; obtain transportation environment data through temperature and humidity sensors and light sensors; obtain cargo status information through RFID tags and barcode scanning, including but not limited to quantity, weight and integrity; obtain vehicle speed, fuel consumption and tire pressure operating status information through vehicle-mounted sensors;

[0050] S2. Equipment deployment and installation: install the GPS module and vehicle-mounted terminal on the means of transport to ensure that the equipment is stable and not easily damaged; install the temperature and humidity sensor and the air pressure sensor at the appropriate position of the transport container to ensure that the environmental parameters can be accurately collected; attach the RFID tag or barcode to the goods to ensure that the tag or barcode is not easy to fall off and is easy to scan; install RFID readers and barcode scanners at the distribution center, the entrance and exit of the warehouse and the key positions of the shelves to ensure that the in and out information of the goods can be accurately collected; then configure the parameters of the IoT device, including but not limited to the sampling frequency, communication frequency and transmission mode, to ensure that the device can work as expected; conduct communication tests to ensure that the IoT device can perform stable data transmission with the data acquisition server; collect initial data, verify the accuracy and completeness of the data, and ensure that the data collected by the device meets expectations;

[0051] S3. Data transmission and integration: select appropriate communication networks, including but not limited to 4G / 5G, LoRa and NB-loT, ensure the real-time and reliability of data transmission, use MQTT, CoAP or HTTP lightweight protocols for data transmission, ensure efficient data transmission and low latency, encrypt data in transmission, use SSL / TLS protocols to ensure data security, design data interfaces, integrate data collected by IoT devices into data storage modules, convert collected data into a unified format to facilitate subsequent data processing and analysis, and perform preliminary cleaning of collected data to remove redundant data, correct erroneous data and fill in missing data;

[0052] S4. Data storage and management: select a suitable database based on data type and data volume, partition and store data based on the attributes of time, region and type, improve data query efficiency, regularly back up data to ensure data security and reliability, create indexes for commonly used query fields to improve data query speed, set data access permissions to ensure that only authorized users can access and operate data, and formulate data lifecycle management strategies;

[0053] S5. System monitoring and maintenance: real-time monitoring of the operating status of IoT devices to ensure the normal operation of the devices; setting up fault alarm mechanisms; timely alarming and processing when the devices fail or are abnormal; monitoring the integrity of data to ensure that the collected data is not missing; monitoring the accuracy of data to ensure that the collected data meets expectations; monitoring the consistency of data to ensure that data from different data sources are consistent; regularly maintaining IoT devices; updating data collection software in a timely manner; fixing known vulnerabilities and bugs; improving the stability and security of the system; optimizing performance according to the system operation status to improve the overall performance of the system.

[0054] The data storage module includes the following steps:

[0055] S1. Demand analysis and planning: clarify the types of data that need to be stored, including structured and unstructured data, estimate the daily, monthly and annual data growth, determine the capacity requirements for data storage, analyze the frequency of data use and access patterns, and determine whether the data is used for real-time query, historical analysis or archival storage;

[0056] S2. Architecture design, select a distributed database, split data horizontally by time or region, store it in different database instances, improve query efficiency, store different types of data in different tables, reduce the amount of data in a single table, improve query performance, formulate a data backup strategy, regularly perform full or incremental backup of data, store backup data in different geographical locations, prevent data loss caused by natural disasters or man-made damage, and establish a data recovery mechanism to ensure rapid recovery when data is lost or damaged;

[0057] S3. Data model design: determine the main entities in the logistics system, clarify the relationship between entities, design the database table structure based on the entity relationship model, determine the field type, length and constraints, create indexes for commonly used query fields to improve query efficiency, create views and stored procedures as needed, and simplify complex queries and business logic;

[0058] S4. Data security and privacy protection. Use multi-factor authentication mechanism to ensure that only authorized users can access the database. Assign different access rights according to user roles to ensure data security and integrity. Use SSL / TLS protocol to encrypt data in transit to prevent data theft. Encrypt sensitive data for storage to prevent data leakage. Record all database operation logs to facilitate post-audit and problem tracking. Use database monitoring tools to monitor database performance and security in real time, and promptly detect and handle abnormal situations.

[0059] The data analysis module includes the following steps:

[0060] S1. Requirements analysis and goal definition, clarifying analysis objectives and data source determination;

[0061] S2. Data collection and preprocessing, through data collection, data cleaning, data conversion and data segmentation;

[0062] S3. Feature engineering: select features with high correlation with the target variable through correlation analysis and chi-square test method, use PCA dimensionality reduction technology to extract the main features, reduce the data dimension, and use time features, geographical features and statistical features for feature construction;

[0063] S4. Model selection and training: select a suitable model and train the model, and select appropriate evaluation indicators to evaluate the model according to the task type.

[0064] The decision support module generates logistics scheduling plans and path optimization plans based on data analysis results, provides decision support for logistics managers, applies deep learning and reinforcement learning artificial intelligence algorithms to make complex decisions and optimizations, and uses linear programming and integer programming optimization algorithms to optimize the allocation of logistics paths and resources. It makes real-time decisions and adjustments based on real-time data and analysis results.

[0065] The coordination and control module is based on the solution generated by the decision support module. Through the intelligent scheduling system, it realizes real-time coordination and control of logistics resources. It adopts intelligent scheduling algorithms to schedule and optimize logistics resources. Through IoT devices and sensors, it monitors the status of logistics resources in real time and performs remote control. It establishes an emergency response mechanism to deal with emergencies in the logistics process. It integrates the intelligent scheduling system with the coordination and control module to realize data sharing and business collaboration, formulates detailed control strategies and operating procedures to ensure the accuracy and timeliness of coordination and control, conducts system testing, verifies the effectiveness of coordination and control, and optimizes and improves according to the test results.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A regional intelligent logistics coordination system, characterized by: include: The data collection module collects various data in the logistics process in real time through IoT devices, including location information, environmental information, and cargo status information; The data storage module uses distributed database technology to store the collected logistics data and perform data cleaning and preprocessing; The data analysis module uses big data analysis technology to conduct in-depth analysis of stored logistics data and extract valuable information, including logistics route optimization and cargo demand forecasting; The decision support module generates logistics scheduling and route optimization plans based on artificial intelligence algorithms and data analysis results; The coordination and control module coordinates and controls logistics resources in real time through the intelligent scheduling system according to the plan generated by the decision support module; The user interaction module provides a user-friendly interface for logistics managers to view logistics status, adjust logistics plans, and receive system suggestions.

2. According to claim 1, a regional intelligent logistics coordination system is characterized by: The data acquisition module comprises the following steps: S1. Data collection: obtain the real-time location of transportation vehicles and goods through GPS and Beidou satellite positioning systems; obtain transportation environment data through temperature and humidity sensors and light sensors; obtain cargo status information through RFID tags and barcode scanning, including but not limited to quantity, weight and integrity; obtain vehicle speed, fuel consumption and tire pressure operating status information through vehicle-mounted sensors; S2. Equipment deployment and installation: install the GPS module and vehicle-mounted terminal on the means of transport to ensure that the equipment is stable and not easily damaged; install the temperature and humidity sensor and the air pressure sensor at the appropriate position of the transport container to ensure that the environmental parameters can be accurately collected; attach the RFID tag or barcode to the goods to ensure that the tag or barcode is not easy to fall off and is easy to scan; install RFID readers and barcode scanners at the distribution center, the entrance and exit of the warehouse and the key positions of the shelves to ensure that the in and out information of the goods can be accurately collected; then configure the parameters of the IoT device, including but not limited to the sampling frequency, communication frequency and transmission mode, to ensure that the device can work as expected; conduct communication tests to ensure that the IoT device can perform stable data transmission with the data acquisition server; collect initial data, verify the accuracy and completeness of the data, and ensure that the data collected by the device meets expectations; S3. Data transmission and integration: select appropriate communication networks, including but not limited to 4G / 5G, LoRa and NB-loT, ensure the real-time and reliability of data transmission, use MQTT, CoAP or HTTP lightweight protocols for data transmission, ensure efficient data transmission and low latency, encrypt data in transmission, use SSL / TLS protocols to ensure data security, design data interfaces, integrate data collected by IoT devices into data storage modules, convert collected data into a unified format to facilitate subsequent data processing and analysis, and perform preliminary cleaning of collected data to remove redundant data, correct erroneous data and fill in missing data; S4. Data storage and management: select a suitable database based on data type and data volume, partition and store data based on the attributes of time, region and type, improve data query efficiency, regularly back up data to ensure data security and reliability, create indexes for commonly used query fields to improve data query speed, set data access permissions to ensure that only authorized users can access and operate data, and formulate data lifecycle management strategies; S5. System monitoring and maintenance: real-time monitoring of the operating status of IoT devices to ensure the normal operation of the devices; setting up fault alarm mechanisms; timely alarming and processing when the devices fail or are abnormal; monitoring the integrity of data to ensure that the collected data is not missing; monitoring the accuracy of data to ensure that the collected data meets expectations; monitoring the consistency of data to ensure that data from different data sources are consistent; regularly maintaining IoT devices; updating data collection software in a timely manner; fixing known vulnerabilities and bugs; improving the stability and security of the system; optimizing performance according to the system operation status to improve the overall performance of the system.

3. The regional intelligent logistics coordination system according to claim 1 is characterized by: The data storage module comprises the following steps: S1. Demand analysis and planning: clarify the types of data that need to be stored, including structured and unstructured data, estimate the daily, monthly and annual data growth, determine the capacity requirements for data storage, analyze the frequency of data use and access patterns, and determine whether the data is used for real-time query, historical analysis or archival storage; S2. Architecture design, select a distributed database, split data horizontally by time or region, store it in different database instances, improve query efficiency, store different types of data in different tables, reduce the amount of data in a single table, improve query performance, formulate a data backup strategy, regularly perform full or incremental backup of data, store backup data in different geographical locations, prevent data loss caused by natural disasters or man-made damage, and establish a data recovery mechanism to ensure rapid recovery when data is lost or damaged; S3. Data model design: determine the main entities in the logistics system, clarify the relationship between entities, design the database table structure based on the entity relationship model, determine the field type, length and constraints, create indexes for commonly used query fields to improve query efficiency, create views and stored procedures as needed, and simplify complex queries and business logic; S4. Data security and privacy protection. Use multi-factor authentication mechanism to ensure that only authorized users can access the database. Assign different access rights according to user roles to ensure data security and integrity. Use SSL / TLS protocol to encrypt data in transit to prevent data theft. Encrypt sensitive data for storage to prevent data leakage. Record all database operation logs to facilitate post-audit and problem tracking. Use database monitoring tools to monitor database performance and security in real time, and promptly detect and handle abnormal situations.

4. The regional intelligent logistics coordination system according to claim 1 is characterized by: The data analysis module includes the following steps: S1. Requirements analysis and goal definition, clarifying analysis objectives and data source determination; S2. Data collection and preprocessing, through data collection, data cleaning, data conversion and data segmentation; S3. Feature engineering: select features with high correlation with the target variable through correlation analysis and chi-square test method, use PCA dimensionality reduction technology to extract the main features, reduce the data dimension, and use time features, geographical features and statistical features for feature construction; S4. Model selection and training: select a suitable model and train the model, and select appropriate evaluation indicators to evaluate the model according to the task type.

5. The regional intelligent logistics coordination system according to claim 1 is characterized by: The decision support module generates logistics scheduling plans and path optimization plans based on data analysis results, provides decision support for logistics managers, applies deep learning and reinforcement learning artificial intelligence algorithms to make complex decisions and optimizations, and uses linear programming and integer programming optimization algorithms to optimize the configuration of logistics paths and resources. It makes real-time decisions and adjustments based on real-time data and analysis results.

6. The regional intelligent logistics coordination system according to claim 1 is characterized by: The coordination and control module is based on the solution generated by the decision support module. Through the intelligent scheduling system, it realizes real-time coordination and control of logistics resources, adopts intelligent scheduling algorithms to schedule and optimize logistics resources, monitors the status of logistics resources in real time through Internet of Things devices and sensors, and performs remote control. It establishes an emergency response mechanism to deal with emergencies in the logistics process, integrates the intelligent scheduling system with the coordination and control module to realize data sharing and business collaboration, formulates detailed control strategies and operating procedures to ensure the accuracy and timeliness of coordination and control, conducts system testing, verifies the effectiveness of coordination and control, and optimizes and improves according to the test results.

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