Project progress analysis system based on artificial intelligence statistics

Abstract

The invention relates to a project progress analysis system based on artificial intelligence statistics, and the system comprises a data collection group, a plurality of data monitoring points which collect project progress data and collect the data at the same project progress stage, and the data monitoring points are uniformly distributed at key progress points of the progress stage through a preset logic rule. The range of the key progress area is indicated through the index signal in the project progress; forming a progress analysis group by a plurality of analysis engines; under the indication of an index signal of the data acquisition group, focusing the progress analysis group to a key progress area; a plurality of analysis engines of the progress analysis group establish a master-slave structure, and under the control of a master-slave controller, the structure is expanded and transformed, and an in-depth analysis task of the project progress is executed. According to the method, the project progress can be efficiently analyzed, and the key area can be accurately positioned, so that the project management process is optimized, and the accuracy and the real-time performance of project progress analysis are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the fields of project management and data analysis, and in particular to a project progress analysis system based on artificial intelligence statistics. Background Art

[0002] In the field of project management, traditional progress analysis methods rely on manual data collection and analysis, which is not only time-consuming and labor-intensive, but also difficult to achieve real-time monitoring and accurate evaluation of project progress. Existing technologies usually use static data monitoring points that collect data at different stages of the project progress, but lack flexibility and adaptability and cannot effectively respond to dynamic changes in project progress. In addition, traditional analysis methods often require a lot of manual intervention, which limits the efficiency and accuracy of analysis. In a complex project environment, how to quickly and accurately identify key progress areas and conduct in-depth analysis is the main challenge facing existing technologies.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the existing project progress analysis system cannot effectively integrate artificial intelligence and statistical methods to achieve real-time and dynamic analysis of project progress data; the existing data monitoring points and analysis engines lack flexibility and adaptability, and it is difficult to cope with the complexity and variability of project progress; in addition, the existing system has problems with insufficient efficiency and accuracy in the identification and analysis of key progress areas, which limits the optimization of project management and the effective control of project progress. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] The purpose of the embodiment of the present invention is to provide a project progress analysis system based on artificial intelligence statistics, which, through a series of innovative training methods and technical strategies, can achieve efficient analysis of project progress and precise positioning of key areas, optimize project management processes, and improve the accuracy and real-time performance of project progress analysis.

[0006] The present invention provides a project progress analysis system based on artificial intelligence statistics, comprising: A data collection group is composed of multiple data monitoring points; the data collection group collects project progress data during the project management process, wherein each data monitoring point is deployed in the same progress stage of the same project, and the data monitoring points are evenly distributed at the key progress points of the progress stage through preset logical rules; each data monitoring point characterizes the importance of the progress point data it monitors according to the strength of the indicator signal it sends; the scope of the key progress area is indicated by the indicator signal in the project progress; The progress analysis group is composed of multiple analysis engines. Under the guidance of the indicator signal of the data collection group, the progress analysis group is focused on the key progress area through data analysis navigation. After reaching the critical progress area, multiple analysis engines of the progress analysis group establish a master-slave structure, and under the control of the master-slave controller, perform structural expansion and transformation to execute in-depth analysis tasks of the project progress.

[0007] Furthermore, in the established master-slave structure, one of the analysis engines is designated as the master analysis engine, and the other analysis engines are designated as slave analysis engines; and a corresponding virtual slave analysis engine is set for each slave analysis engine in the structure, and the position of the virtual slave analysis engine is the expected position to be achieved by the corresponding slave analysis engine in the structure to maintain the formation; Under the control of the master-slave controller, the master analysis engine tracks the trajectory point position of a given analysis reference trajectory in real time, and the slave analysis engine tracks the real-time position of the virtual slave analysis engine.

[0008] Furthermore, the master-slave controller is a master-slave controller that adopts a dual closed-loop adaptive adjustment mechanism; the master-slave controller includes a dual closed-loop adjustment method that adopts adjustment based on a statistical model and adaptive adjustment based on a machine learning model.

[0009] Furthermore, in the dual closed-loop adaptive adjustment of the main analysis engine, a statistical model and a machine learning model of the main analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the main analysis engine, so that the position of the main analysis engine converges to the trajectory point position of the given analysis reference trajectory; In the dual closed-loop adaptive adjustment of the slave analysis engine, a statistical model and a machine learning model of the slave analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the slave analysis engine so that the position of the slave analysis engine converges to the position of the corresponding virtual slave analysis engine; The position of the virtual slave analysis engine is obtained by performing coordinate transformation according to the position of the master analysis engine and the analysis depth data.

[0010] Furthermore, the main analysis engine in the progress analysis group is equipped with a data collection tool, which tracks the indicator signal indication of the data collection group to obtain the data analysis signal, performs data analysis positioning, and guides the progress analysis group to focus on the key progress area; The main analysis engine also establishes a data communication link with the data monitoring points in the key progress area; after the progress analysis group data signal is lost, the signal strength indication distance measurement is performed through the signal strength of the data communication link; and the progress analysis group is secondary positioned based on the distance measurement of multiple data monitoring points in the key progress area.

[0011] Furthermore, the method for the data collection group to collect project progress data during the project management process includes: Multiple data monitoring points within the data collection group are randomly distributed in advance during the project management process. Each data monitoring point has a data perception function and identifies the perceived data through the indicator intensity value; The data monitoring point perceives the importance of the data at the progress point where it is located, and updates the indicator intensity value that identifies the perceived data; The data monitoring point converts the indicator strength value into the indicator strength distribution amount according to the logical association with other data monitoring points in the group, and distributes it to the corresponding data monitoring point. The lower the association, the smaller the indicator strength distribution amount. Each data monitoring point pairs the monitoring points according to the indicator strength value obtained by its own perceived data importance and the indicator strength distribution received from other data monitoring points in the group, and determines the data monitoring point paired with itself; After determining the data monitoring point that is paired with itself, update the logical state of the monitoring point itself; By updating the logical status of the data monitoring points in the group, the logic distribution of multiple data monitoring points in the group is spread over the key progress areas of the project.

[0012] Furthermore, the data monitoring point senses the importance of the data through the data sensor carried by the data monitoring point; and converts the sensed data importance value into an indicator strength value; The indicator intensity value updated by the i-th data monitoring point that detects the key progress point at the current time t for: In the formula, is the indicator intensity value of the i-th data monitoring point at the previous moment, is the data importance value detected by the i-th data monitoring point at the current time t; is a constant and satisfies .

[0013] Furthermore, the indicator intensity distribution of the i-th data monitoring point to the j-th data monitoring point in the group at the current time t is for: In the formula, is the logical correlation between the i-th data monitoring point and the j-th data monitoring point, is the attenuation coefficient, is the number of monitoring points in the group.

[0014] Furthermore, the data monitoring point is a data sensing unit that can be flexibly deployed in a project environment; The data sensing unit has a built-in data sensor, and a data importance indicator is set at the output end of the unit; the data sensing unit is provided with a wireless communication module for establishing a data communication link with other data monitoring points in the group and with the main analysis engine.

[0015] Furthermore, the progress analysis group includes a plurality of data processing units and a data management center; Multiple data processing units and data management centers are connected together in sequence, the first data processing unit is the main analysis engine in the progress analysis group, and the remaining data processing units and data management centers are slave analysis engines; The master analysis engine is connected to the first slave analysis engine via a data link, the remaining data processing units are connected in sequence via data links, and the last data processing unit is connected to the data management center via a data link; The main analysis engine is used to guide the slave analysis engine to focus on the key progress area under the guidance of the data collection group; in the process of executing the analysis task, the main analysis engine selects a suitable analysis structure according to the project characteristics, and provides the expected position and expected angle for the structure expansion and structure transformation of the slave analysis engine; The data processing units serving as slave analysis engines are all equipped with data analysis tools and are connected to the data management center through data links. In the process of executing analysis tasks, the slave analysis engines deployed according to the analysis structure use data analysis tools to conduct in-depth analysis of key progress points, conduct early control of project progress, and optimize project progress management.

[0016] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: the present invention can significantly improve the efficiency of collecting and analyzing project progress data by adopting a project progress analysis system based on artificial intelligence statistics. The system can collect project progress data in real time through a data collection group composed of multiple data monitoring points, and characterize the importance of the progress point data where the monitoring point is located according to the strength of the indicator signal emitted by the data monitoring point. This design enables the system to automatically identify key progress areas, and guide the progress analysis group to focus on these key areas through logical allocation and indicator signal indications of data monitoring points. In addition, the progress analysis group composed of multiple analysis engines in the system can establish a master-slave structure and perform structural expansion and transformation under the control of the master-slave controller, and perform in-depth analysis tasks of the project progress, thereby achieving precise control and optimization of the project progress.

[0017] Furthermore, the system can precisely control the position and analysis depth of the master and slave analysis engines by using a master-slave controller with a dual closed-loop adaptive adjustment mechanism. This adjustment mechanism combines the adjustment based on the statistical model with the adaptive adjustment based on the machine learning model, so that the analysis engine can track and analyze the reference trajectory in real time, improving the accuracy and real-time performance of the analysis. At the same time, the system can also perform secondary positioning of the progress analysis group through data communication links and signal strength indication ranging, ensuring that even in the case of data signal loss, the key progress area can be accurately located, thereby achieving continuous and stable control of the project progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic diagram of the process of a project progress analysis system based on artificial intelligence statistics provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0019] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0020] Figure 1 The project progress analysis system based on artificial intelligence statistics provided by one embodiment of the present invention shown in FIG. 1 performs the following steps 100: Step 101, a data collection group is formed by a plurality of data monitoring points; the data collection group collects project progress data in the process of project management, wherein each data monitoring point is deployed in the same progress stage of the same project, and the data monitoring points are evenly distributed at the key progress points of the progress stage through preset logical rules; each data monitoring point characterizes the importance of the progress point data it monitors according to the strength of the indicator signal it sends; the scope of the key progress area is indicated by the indicator signal in the project progress; Step 102, a progress analysis group is formed by multiple analysis engines; under the indication of the indicator signal of the data collection group, the progress analysis group is focused on the key progress area through data analysis navigation; Step 103, after reaching the critical progress area, multiple analysis engines of the progress analysis group establish a master-slave structure, and under the control of the master-slave controller, perform structure expansion and transformation to execute the in-depth analysis task of the project progress.

[0021] It should be noted that key progress points refer to specific time points or milestones that are critical to the success of a project during the project management process. These points usually mark important stages of a project, such as the start of a project, key decision points, and the completion of important deliverables. The determination of key progress points is based on project management experience and project characteristics. They are nodes that require special attention and monitoring in project progress management. Key progress areas refer to a continuous period of time or work scope surrounding key progress points. This area contains multiple key progress points and is an area that requires special attention in project progress management. In the key progress area, the success of a project depends largely on the completion of these key points. Through in-depth analysis of key progress areas, project progress can be more effectively controlled and managed to ensure that the project proceeds as planned. Preset logical rules are used to ensure that data monitoring points can be evenly distributed, dynamically adjusted, and perform effective correlation analysis during the key progress stages of project management. Preset logical rules include rules that ensure that data monitoring points are evenly distributed in the key progress area of ​​the project, rules that allow the location of data monitoring points to be dynamically adjusted according to project progress and changes in the external environment, and rules that consider the logical correlation between data monitoring points and adjust the working status according to the correlation with other monitoring points.

[0022] The system constructs a data collection group consisting of multiple data monitoring points, which are responsible for collecting project progress data during the project management process. These monitoring points not only collect data, but also characterize the importance of their monitoring point data by the strength of the indicator signals they send, thereby helping the system identify key progress areas in the project.

[0023] Specifically, data monitoring points can be various sensors, software loggers or other data collection tools, which are deployed at key nodes of the project. These monitoring points can sense real-time data of project progress, such as resource usage, task completion, etc., and convert these data into indicator signals through preset algorithms. The strength of the indicator signal can be quantified by the signal strength value, which can be determined based on the importance, urgency or impact of the data on the project goals.

[0024] Preferably, in order to more accurately reflect the importance of the monitoring point data, a machine learning algorithm can be used to analyze the data collected by the monitoring points and automatically adjust the strength value of the indicator signal. In addition, the system can set up a dynamic adjustment mechanism to continuously optimize the distribution of monitoring points and the calculation method of indicator signals according to changes in project progress and historical data analysis results, so as to improve the accuracy and response speed of the system in identifying key progress areas.

[0025] In some embodiments, in the established master-slave structure, one of the analysis engines is designated as the master analysis engine, and the remaining analysis engines are slave analysis engines; and a corresponding virtual slave analysis engine is set for each slave analysis engine in the structure, and the position of the virtual slave analysis engine is the expected position that the corresponding slave analysis engine needs to reach in order to maintain the formation in the structure; Under the control of the master-slave controller, the master analysis engine tracks the trajectory point position of a given analysis reference trajectory in real time, and the slave analysis engine tracks the real-time position of the virtual slave analysis engine.

[0026] It should be noted that in this embodiment, the system establishes a master-slave structure, in which one analysis engine is designated as the master analysis engine and the remaining analysis engines are slave analysis engines. This structure allows the master analysis engine to track the analysis reference track of the project progress in real time, while the slave analysis engines track the positions of their respective virtual slave analysis engines to maintain the coordination and efficiency of the entire analysis group.

[0027] Specifically, the master analysis engine can be an advanced data processing unit with strong computing and analysis capabilities, responsible for processing the most critical data and guiding the work of the entire analysis group. The slave analysis engine can be a data processing unit with simpler functions, which operates according to the instructions and data analysis results of the master analysis engine. The virtual slave analysis engine is a conceptual entity that represents the ideal position of the slave analysis engine in the master-slave structure and is used to guide the movement and analysis tasks of the slave analysis engine.

[0028] Preferably, in order to improve the performance and flexibility of the system, multiple virtual slave analysis engines can be set for each slave analysis engine, and these virtual entities can be dynamically adjusted according to the different stages of the project progress and the analysis requirements. In addition, the master-slave controller can use advanced algorithms, such as genetic algorithms or particle swarm optimization algorithms, to optimize the coordination and task allocation between the master analysis engine and the slave analysis engine. Such a design can make the entire analysis group respond more flexibly to changes in the project progress and improve the accuracy and efficiency of the analysis.

[0029] In some embodiments, the master-slave controller is a master-slave controller that adopts a dual closed-loop adaptive adjustment mechanism; the master-slave controller includes a dual closed-loop adjustment method that adopts statistical model-based adjustment and machine learning model-based adaptive adjustment.

[0030] Specifically, the inner loop of the dual closed-loop control mechanism can be based on a statistical model, which predicts and adjusts the behavior of the analysis engine based on the statistical characteristics of historical and current data. The outer loop can be based on a machine learning model, which continuously optimizes the control strategy by learning from past analysis results and changing trends in project progress. Under this mechanism, the control parameters for the location and analysis depth of the main analysis engine can be set based on the specific needs and historical performance of the project. For example, thresholds can be set to determine when the analysis depth needs to be adjusted, or weights can be set to balance the control effects of statistical and machine learning models.

[0031] Preferably, in order to further improve the accuracy and adaptability of control, the concept of adaptive learning rate can be introduced so that the machine learning model can dynamically adjust the learning speed according to the actual situation of the current project progress. In addition, a multi-model fusion method can be used to combine different machine learning algorithms to improve the accuracy and robustness of predictions. In practical applications, statistical models and machine learning models can also be regularly retrained and optimized according to the characteristics of the project and historical data to ensure that the control system is always in the best state.

[0032] In some embodiments, in the dual closed-loop adaptive adjustment of the main analysis engine, a statistical model and a machine learning model of the main analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the main analysis engine so that the position of the main analysis engine converges to the trajectory point position of a given analysis reference trajectory; In the dual closed-loop adaptive adjustment of the slave analysis engine, a statistical model and a machine learning model of the slave analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the slave analysis engine so that the position of the slave analysis engine converges to the position of the corresponding virtual slave analysis engine; The position of the virtual slave analysis engine is obtained by performing coordinate transformation according to the position of the master analysis engine and the analysis depth data.

[0033] Specifically, statistical models can be used to build prediction models based on historical data analysis to estimate possible changes in project progress. Machine learning models can use algorithms such as decision trees, random forests, or neural networks to learn and predict complex patterns in project progress. The inner loop in the dual closed-loop adaptive adjustment mechanism is responsible for adjusting the position of the analysis engines in real time to ensure that they can respond quickly to changes in project progress; the outer loop is responsible for adjusting the analysis depth to ensure that the depth of analysis matches the actual needs of the project. In this process, a series of control parameters can be set, such as learning rate, prediction window size, model update frequency, etc., to optimize the performance of the model.

[0034] Preferably, in order to improve the prediction accuracy of the model and the response speed of the system, online learning or incremental learning methods can be used to enable the machine learning model to update and adapt to new data in real time. In addition, a multi-objective optimization algorithm can be introduced to optimize multiple control parameters at the same time to achieve better control effects. During the implementation process, it is also possible to consider introducing an anomaly detection mechanism. When the project progress is abnormal, the system can issue an alarm in time and adjust the analysis strategy to reduce potential risks. These detailed operation steps and alternatives can further improve the flexibility and robustness of the system.

[0035] In some embodiments, the main analysis engine in the progress analysis group is equipped with a data collection tool, which tracks the indicator signal indication of the data collection group to obtain the data analysis signal, performs data analysis positioning, and guides the progress analysis group to focus on the key progress area; The main analysis engine also establishes a data communication link with the data monitoring points in the key progress area; after the progress analysis group data signal is lost, the signal strength indication distance measurement is performed through the signal strength of the data communication link; and the progress analysis group is secondary positioned based on the distance measurement of multiple data monitoring points in the key progress area.

[0036] Specifically, the data acquisition tool carried by the main analysis engine can be a series of sensors or data interfaces for real-time capture and analysis of indicator signals from data monitoring points. Data analysis positioning is to determine the key areas of project progress by analyzing the pattern and strength of these signals. The data communication link can be wireless or wired and is used to transmit data between the main analysis engine and the data monitoring point. Signal strength indication ranging can be achieved by calculating signal attenuation or delay, which requires setting appropriate signal processing algorithms and parameters, such as signal attenuation threshold, time synchronization accuracy, etc.

[0037] Preferably, in order to improve the accuracy of signal strength indication ranging, Doppler effect measurement technology can be used to more accurately estimate the speed and direction of the signal. In addition, a redundant communication link design can be introduced so that the system can still maintain continuous transmission and analysis of data even when some links fail. In terms of data analysis and positioning, advanced data fusion technology can be used to combine information from multiple data sources to improve the accuracy and robustness of positioning. These refined operating steps and alternatives help improve the performance and reliability of the system in complex environments.

[0038] In some embodiments, the method for the data collection group to collect project progress data during the project management process includes: Multiple data monitoring points within the data collection group are randomly distributed in advance during the project management process. Each data monitoring point has a data perception function and identifies the perceived data through the indicator intensity value; The data monitoring point perceives the importance of the data at the progress point where it is located, and updates the indicator intensity value that identifies the perceived data; The data monitoring point converts the indicator strength value into the indicator strength distribution amount according to the logical association with other data monitoring points in the group, and distributes it to the corresponding data monitoring point. The lower the association, the smaller the indicator strength distribution amount. Each data monitoring point pairs the monitoring points according to the indicator strength value obtained by its own perceived data importance and the indicator strength distribution received from other data monitoring points in the group, and determines the data monitoring point paired with itself; After determining the data monitoring point that is paired with itself, update the logical state of the monitoring point itself; By updating the logical status of the data monitoring points in the group, the logic distribution of multiple data monitoring points in the group is spread over the key progress areas of the project.

[0039] Specifically, data monitoring points can be various sensors, software loggers or other data collection tools, which are deployed at key nodes of the project. These monitoring points can sense real-time data of project progress, such as resource usage, task completion, etc., and convert these data into indicator intensity values ​​through preset algorithms. Indicator intensity values ​​can be determined based on the importance, urgency or impact of the data on project goals. In this process, a series of parameters can be set, such as data collection frequency, data type, calculation formula of indicator intensity value, etc., to optimize the efficiency and accuracy of data collection.

[0040] Preferably, in order to improve the flexibility and adaptability of data collection, an adaptive data collection strategy can be adopted to dynamically adjust the distribution of data monitoring points and the parameters of data collection according to changes in project progress. In addition, data preprocessing and cleaning techniques can be introduced to improve the quality and availability of data. In terms of the logical association of data monitoring points and the calculation of indicator intensity distribution, graph theory or network analysis methods can be used to more accurately evaluate the association between data monitoring points and the importance of data. These detailed operation steps and alternatives help improve the performance of the data collection group and the management efficiency of the project.

[0041] In some embodiments, the data monitoring point senses the importance of the data through the data sensor carried by the data monitoring point; and converts the sensed data importance value into an indicator strength value; The indicator intensity value updated by the i-th data monitoring point that detects the key progress point at the current time t for: In the formula, is the indicator intensity value of the i-th data monitoring point at the previous moment, is the data importance value detected by the i-th data monitoring point at the current time t; is a constant and satisfies .

[0042] Specifically, data sensors can be various types of sensors or data collection modules that are responsible for collecting data related to project progress. These data may include timestamps, resource consumption, task completion, etc. The calculation of the indicator strength value can be achieved through a predefined formula that maps the data importance value to a quantitative indicator. For example, a weighted algorithm can be used, in which the importance of the data is weighted according to its impact on the project goal. Parameter settings may include weight assignment, data update frequency, attenuation factor of historical data, etc.

[0043] Preferably, in order to improve the perception accuracy of data monitoring points and the accuracy of indicator intensity values, machine learning algorithms can be used to dynamically adjust the parameters of data sensors. These algorithms can be continuously optimized based on historical data and real-time feedback to adapt to changes in project progress. In addition, anomaly detection mechanisms can be introduced to timely adjust the calculation method of indicator intensity values ​​when data anomalies occur. During implementation, multi-source data fusion technology can also be considered to combine information from different data monitoring points to provide a more comprehensive assessment of data importance. These refined operational steps and alternatives help improve the performance of data monitoring points and the efficiency of the entire data collection group.

[0044] In some embodiments, the indicator strength distribution amount of the i-th data monitoring point to the j-th data monitoring point in the group at the current time t is for: In the formula, is the logical correlation between the i-th data monitoring point and the j-th data monitoring point, is the attenuation coefficient, is the number of monitoring points in the group.

[0045] Specifically, the logical association can be determined based on the physical distance between data monitoring points, data correlation or other predefined rules. The attenuation coefficient is a key parameter that determines the degree of reduction of the indicator intensity value when it is transmitted between data monitoring points. This coefficient can be set according to the specific needs of the project and the environmental characteristics to ensure the efficiency and accuracy of information transmission. The number of monitoring points in the group is another important parameter, which directly affects the calculation of the indicator intensity distribution and the operation of the entire data collection group.

[0046] Preferably, in order to improve the calculation accuracy and adaptability of the indicator intensity distribution, an adaptive algorithm can be used to dynamically adjust the attenuation coefficient. These algorithms can optimize the value of the attenuation coefficient based on the analysis results of real-time data and historical data to adapt to changes in project progress. In addition, a multi-factor analysis method can be introduced to comprehensively consider the various logical associations between data monitoring points to more comprehensively evaluate the indicator intensity distribution of each monitoring point. During the implementation process, network analysis techniques, such as social network analysis, can also be considered to more accurately model and analyze the complex relationships between data monitoring points. These refined operating steps and alternatives help to improve the collaboration efficiency between data monitoring points and the data analysis capabilities of the entire system.

[0047] In some embodiments, the data monitoring point is a data sensing unit that can be flexibly deployed in a project environment; The data sensing unit has a built-in data sensor, and a data importance indicator is set at the output end of the unit; the data sensing unit is provided with a wireless communication module for establishing a data communication link with other data monitoring points in the group and with the main analysis engine.

[0048] It should be noted that this embodiment describes the deployment and function of data monitoring points as data perception units. Data perception units can be flexibly deployed in a project environment. Their built-in data sensors are used to perceive the importance of data and display it through the data importance indicator at the output end. In addition, the data perception unit is also equipped with a wireless communication module for establishing data communication links with other data monitoring points and the main analysis engine.

[0049] Specifically, the data sensing unit can be various forms of devices, such as RFID tags, smart sensors or mobile devices, which are configured to collect data at different stages and locations of the project. The data sensor can be a temperature sensor, a pressure sensor, an image recognition system, etc., which is used to capture environmental or operational data. The data importance indicator can be an LED light, a display screen or other forms of output devices to display the importance level of the data to the operator or system. The wireless communication module can use Wi-Fi, Bluetooth, ZigBee or other wireless communication technologies to achieve wireless transmission of data.

[0050] Preferably, in order to improve the deployment flexibility and communication efficiency of data monitoring points, a modular design can be adopted so that data sensing units can be quickly configured and redeployed according to project requirements. In addition, low-power wide area network LPWAN technology, such as LoRaWAN, can be introduced to achieve low-power, long-distance communication in a wide area. In the selection and configuration of data sensors, multi-sensor fusion technology can be used to combine different types of sensor data to improve the accuracy and reliability of data perception. These detailed operation steps and alternatives help to improve the performance of data monitoring points and the data processing capabilities of the entire system.

[0051] In some embodiments, the progress analysis group includes a plurality of data processing units and a data management center; Multiple data processing units and data management centers are connected together in sequence, the first data processing unit is the main analysis engine in the progress analysis group, and the remaining data processing units and data management centers are slave analysis engines; The master analysis engine is connected to the first slave analysis engine via a data link, the remaining data processing units are connected in sequence via data links, and the last data processing unit is connected to the data management center via a data link; The main analysis engine is used to guide the slave analysis engine to focus on the key progress area under the guidance of the data collection group; in the process of executing the analysis task, the main analysis engine selects a suitable analysis structure according to the project characteristics, and provides the expected position and expected angle for the structure expansion and structure transformation of the slave analysis engine; The data processing units serving as slave analysis engines are all equipped with data analysis tools and are connected to the data management center through data links. In the process of executing analysis tasks, the slave analysis engines deployed according to the analysis structure use data analysis tools to conduct in-depth analysis of key progress points, conduct early control of project progress, and optimize project progress management.

[0052] It should be noted that this implementation method involves the composition and operation of the progress analysis group. The progress analysis group includes multiple data processing units and a data management center, which are connected in sequence through data links to form a hierarchical data processing and management center structure. The main analysis engine, as the first data processing unit, is responsible for guiding the slave analysis engine to focus on the key progress area and select the appropriate analysis structure when performing the analysis task.

[0053] Specifically, the data processing unit can be a high-performance computing device equipped with specialized software and algorithms to process and analyze project schedule data. The data link can be a physical or virtual connection used to transmit data and instructions between the data processing unit and the data management center. The data management center is responsible for coordinating the work of the entire schedule analysis group, storing analysis results, and providing decision support. When setting parameters, it is necessary to consider the processing power of the data processing unit, the transmission rate and stability of the data link, and the storage and processing capacity of the data management center.

[0054] Preferably, in order to improve the efficiency and accuracy of data processing, a distributed computing architecture can be adopted so that multiple data processing units can work in parallel and share the computing load. In addition, cloud computing technology can be introduced to migrate some data processing tasks to the cloud to utilize more powerful computing resources. In the design of data links, high-bandwidth, low-latency communication technologies can be used to ensure rapid data transmission. In the construction of the data management center, big data analysis and machine learning technologies can be used to improve the level of intelligence in data processing. These detailed operating steps and alternatives help improve the performance of the entire progress analysis group and the quality of project management.

[0055] The above-mentioned embodiments of the present invention have the following beneficial effects: The project progress analysis system based on artificial intelligence statistics of the present invention can improve the accuracy and response speed of project progress monitoring. Through the intelligent monitoring and logical allocation of data acquisition groups, the system can capture key progress areas in real time, and conduct in-depth analysis of these areas through the collaborative work of the analysis engine. This integrated and intelligent analysis method can not only reduce human intervention and reduce the error rate, but also improve the efficiency and effectiveness of project management. In addition, the adaptive adjustment mechanism of the system can dynamically adjust the analysis strategy according to the real-time changes in the project progress to ensure the accuracy and practicality of the analysis results.

[0056] Furthermore, the system can achieve refined management of project progress through the master-slave structure and dual closed-loop adaptive adjustment mechanism. The collaborative work of the master analysis engine and the slave analysis engine can ensure that key progress areas receive full attention. At the same time, the introduction of the virtual slave analysis engine further improves the flexibility and adaptability of the system. The random distribution and logical association mechanism of the data collection group enable the system to fully cover the key nodes of the project and achieve comprehensive monitoring of the project progress. These beneficial effects work together to not only improve the accuracy of project progress analysis, but also provide strong decision-making support for project management.

[0057] The storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0058] It should be noted that in some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0059] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0060] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0061] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0062] The units described in some embodiments of the present disclosure may be implemented in software or in hardware. The units described may also be provided in a processor, and the functions described above herein may be at least partially performed by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0063] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure.

Claims

1. A project progress analysis system based on artificial intelligence statistics, characterized in that: include: A data collection group is composed of multiple data monitoring points; The data collection group collects project progress data during the project management process; each data monitoring point is deployed in the same progress stage of the same project, and the data monitoring points are evenly distributed at the key progress points of the progress stage through preset logical rules; each data monitoring point characterizes the importance of the progress point data it monitors according to the strength of the indicator signal it sends; the scope of the key progress area is indicated by the indicator signal in the project progress; The progress analysis group is composed of multiple analysis engines. Under the guidance of the indicator signal of the data collection group, the progress analysis group is focused on the key progress area through data analysis navigation. After reaching the critical progress area, multiple analysis engines of the progress analysis group establish a master-slave structure, and under the control of the master-slave controller, perform structural expansion and transformation to execute in-depth analysis tasks of the project progress.

2. The project progress analysis system based on artificial intelligence statistics according to claim 1 is characterized in that: In the established master-slave structure, one of the analysis engines is designated as the master analysis engine, and the other analysis engines are designated as slave analysis engines; and a corresponding virtual slave analysis engine is set for each slave analysis engine in the structure, and the position of the virtual slave analysis engine is the expected position to be achieved by the corresponding slave analysis engine in the structure to maintain the formation; Under the control of the master-slave controller, the master analysis engine tracks the trajectory point position of a given analysis reference trajectory in real time, and the slave analysis engine tracks the real-time position of the virtual slave analysis engine.

3. The project progress analysis system based on artificial intelligence statistics according to claim 2 is characterized in that: The master-slave controller is a master-slave controller that adopts a dual closed-loop adaptive adjustment mechanism; the master-slave controller includes a dual closed-loop adjustment method that adopts an adjustment based on a statistical model and an adaptive adjustment based on a machine learning model.

4. The project progress analysis system based on artificial intelligence statistics according to claim 3 is characterized in that: The dual closed-loop regulation method in the master-slave controller includes regulation based on statistical models and adaptive regulation based on machine learning models, including: In the dual closed-loop adaptive adjustment of the main analysis engine, a statistical model and a machine learning model of the main analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the main analysis engine so that the position of the main analysis engine converges to the trajectory point position of the given analysis reference trajectory; In the dual closed-loop adaptive adjustment of the slave analysis engine, a statistical model and a machine learning model of the slave analysis engine are established, and a master-slave controller of the dual closed-loop adaptive adjustment mechanism is used to control the position and analysis depth of the slave analysis engine so that the position of the slave analysis engine converges to the position of the corresponding virtual slave analysis engine; The position of the virtual slave analysis engine is obtained by performing coordinate transformation according to the position of the master analysis engine and the analysis depth data.

5. The project progress analysis system based on artificial intelligence statistics according to claim 2 is characterized in that: The main analysis engine is equipped with a data collection tool, which tracks the indicator signal indication of the data collection group to obtain the data analysis signal, performs data analysis positioning, and guides the progress analysis group to focus on the key progress area; The main analysis engine also establishes a data communication link with the data monitoring points in the key progress area; after the progress analysis group data signal is lost, the signal strength indication distance measurement is performed through the signal strength of the data communication link; and the progress analysis group is secondary positioned based on the distance measurement of multiple data monitoring points in the key progress area.

6. The project progress analysis system based on artificial intelligence statistics according to any one of claims 1 to 5, characterized in that: The method for the data collection group to collect project progress data during the project management process includes: Multiple data monitoring points within the data collection group are randomly distributed in advance during the project management process. Each data monitoring point has a data perception function and identifies the perceived data through the indicator intensity value; The data monitoring point senses the importance of the data at the progress point where it is located, and updates the indicator strength value that identifies the sensed data; The data monitoring point converts the indicator strength value into the indicator strength distribution amount according to the logical association with other data monitoring points in the group, and distributes it to the corresponding data monitoring point. The lower the association, the smaller the indicator strength distribution amount. Each data monitoring point pairs the monitoring points according to the indicator strength value obtained by its own perceived data importance and the indicator strength distribution received from other data monitoring points in the group, and determines the data monitoring point paired with itself; After determining the data monitoring point that is paired with itself, update the logical state of the monitoring point itself; By updating the logical status of the data monitoring points in the group, the logic distribution of multiple data monitoring points in the group is spread over the key progress areas of the project.

7. The project progress analysis system based on artificial intelligence statistics according to claim 6 is characterized in that: The data monitoring point senses the importance of the data through the data sensor carried by the data monitoring point; and converts the sensed data importance value into an indicator strength value; The indicator intensity value updated by the i-th data monitoring point that detects the key progress point at the current time t for: In the formula, is the indicator intensity value of the i-th data monitoring point at the previous moment, is the data importance value detected by the i-th data monitoring point at the current time t; is a constant and satisfies .

8. The project progress analysis system based on artificial intelligence statistics according to claim 7 is characterized in that: The indicator intensity distribution of the i-th data monitoring point to the j-th data monitoring point in the group at the current time t for: In the formula, is the logical correlation between the i-th data monitoring point and the j-th data monitoring point, is the attenuation coefficient, is the number of monitoring points in the group.

9. The project progress analysis system based on artificial intelligence statistics according to claim 6 is characterized in that: The data monitoring point is a data sensing unit that can be flexibly deployed in a project environment; The data sensing unit has a built-in data sensor, and a data importance indicator is set at the output end of the unit; the data sensing unit is provided with a wireless communication module for establishing a data communication link with other data monitoring points in the group and with the main analysis engine.

10. The project progress analysis system based on artificial intelligence statistics according to claim 6 is characterized in that: The progress analysis group includes a plurality of data processing units and a data management center; Multiple data processing units and data management centers are connected together in sequence, the first data processing unit is the main analysis engine in the progress analysis group, and the remaining data processing units and data management centers are slave analysis engines; The master analysis engine is connected to the first slave analysis engine via a data link, the remaining data processing units are connected in sequence via data links, and the last data processing unit is connected to the data management center via a data link; The main analysis engine is used to guide the slave analysis engine to focus on the key progress area under the guidance of the data collection group; In the process of executing the analysis task, the main analysis engine selects the appropriate analysis structure according to the project characteristics, and provides the expected position and expected angle for the structure expansion and structure transformation of the slave analysis engine; The data processing units serving as slave analysis engines are all equipped with data analysis tools and are connected to the data management center through data links. In the process of executing analysis tasks, the slave analysis engines deployed according to the analysis structure use data analysis tools to conduct in-depth analysis of key progress points, conduct early control of project progress, and optimize project progress management.

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