A visual smart park management system and method based on AI analysis

Abstract

The present invention discloses a visualized smart park management system and method based on AI analysis, which relates to the field of park management technology. The system comprises: a data processing module, a display strategy analysis module, and a visualization management unit; the data processing module divides the park business functions into business units, marks the data of each data sensor with the business unit in correspondence, and completes the missing data of the data sensor according to the correlation between the data; the display strategy analysis module calculates and analyzes the display priority of each display unit in the corresponding business function, sets the display strength of each business function according to the risk assessment value of the display unit in each business function, calculates the display time of each business function data, and monitors the marked risk display unit in real time to perform risk management; the visualization management unit uses the risk display section to display the risk display unit marked in real time, and uses the circular display section to cyclically display the display units in each business function.

Description

Technical Field

[0001] The present invention relates to the field of park management technology, and specifically to a visualized smart park management system and method based on AI analysis. Background Art

[0002] With the popularization of smart parks, their management content covers multiple relatively independent businesses such as park space management, personnel flow monitoring, fire equipment management, facility energy consumption supervision, and park property services;

[0003] However, as the current park management tends to be collaborative and flat, the following technical defects exist in the current smart park management: On the one hand, due to the integration requirements of multiple business functions, the amount of data for park visualization management has increased sharply, and the lack of collaborative display strategies for different business functions makes the park visualization lack of pertinence, and it is easy to ignore key data, resulting in low management efficiency; on the other hand, since the data used by various business functions are shared, and different business functions are relatively independent, there are many redundant processing processes in the system data processing;

[0004] Therefore, a visual smart park management system and method based on AI analysis is needed to solve the above technical problems and realize the collaborative and efficient management of the visual smart park. Summary of the invention

[0005] The purpose of the present invention is to provide a visual smart park management system and method based on AI analysis to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A visual smart park management method based on AI analysis, the method comprising the following steps:

[0008] Step S100: Analyze the requirements of various business functions in the park, divide the business functions into business units, and mark the data sources required for the operation of each business unit;

[0009] Step S200: Analyze the data cycle, group the data collected in the park according to the cycle, construct corresponding slope arrays for the data groups, calculate the correlation between the data groups, and then weightedly calculate the slope between the missing data and the non-missing data in the data cycle, and complete the missing data in the data cycle;

[0010] Step S300: Perform process analysis on each business unit in each business function, evaluate the data risk of each business unit, determine the display unit in the display module of each business function, calculate the display priority of each display unit, and set the display mode of the display unit in each business function;

[0011] Step S400: comprehensively calculating the display strength of each business function according to the display unit risk assessment value in each business function;

[0012] Step S500: Set up a risk display section and a cyclic display section respectively, and divide each business unit into display sections according to the display priority; for the cyclic display section, calculate the display duration of each business function displayed, and for the risk display section, display them in order from large to small according to the display priority value of the display unit, and send a risk feedback signal to the management personnel.

[0013] In the above technical solution, step S100 includes the following contents:

[0014] Analyze the requirements of various comprehensive business functions involved in park management, divide various business functions into business units according to data processing business, and mark each business unit according to the data sources used by each business unit;

[0015] For any business unit n, the business unit u is labeled according to the business function as follows: ;in, is the business function to which business unit u belongs, A collection of data acquisition sensors. are the data acquisition sensor numbers that send the collected data to business unit u, A collection of business units. They are the business unit numbers that send business output data to business unit u. They divide the business functions and mark the data used for operation to break the data coupling between business units and provide a scientific and effective analysis basis for the coordination and unification of subsequent visual management.

[0016] In the above technical solution, step S200 includes the following contents:

[0017] Analyze the cycle of each data, group the data collected in the park according to the cycle, and for any data collection sensor , record the data collected in each cycle as: ;in, The data collection sensor in data group g The data collected;

[0018] Get Sensor The data sampling time interval for the sensor After curve fitting, the data is densely sampled to obtain n sampling data, and the slope of the curve at each sampling point is calculated to construct a slope array ;

[0019] Then calculate the data correlation between each data group. For any data collection sensor Data grouping and , data grouping and data grouping The calculation formula of the correlation degree is as follows:

[0020] ;

[0021] in, Grouping data and data grouping Inter-correlation degree, i is the data acquisition sensor The data number in the slope array, n is the data acquisition sensor The number of data in the slope array, Sensors for data collection The weight of the i-th data item in the slope array of Sensors for data collection Data grouping and The i-th data difference in the corresponding slope array;

[0022] Computational Data Sensors The correlation between all data groups, for any missing data in any data group g, the correlation between data groups is used as the weight, and the corresponding slope of the data in the slope array of all data groups except data group g is weighted to calculate the value of the data in the slope array of data group g. The data completion calculation formula is as follows:

[0023] ;

[0024] in, is the data group g to complete the data, j is the data sensor The data group number, For data sensors The number of data packets, is the sum of the associations between all data groups except data group g and data group g, Grouping data and the correlation degree of data group g, Grouping data The data in the slope array;

[0025] Multiply the slope in the slope array by the sampling frequency of the dense sampling of the slope array to obtain the difference between each data, and calculate and complete the missing data according to the difference between the non-missing data and the missing data;

[0026] By analyzing the data cycle and calculating the slope of the data within the cycle, the changing trend between the data is analyzed to meet the actual needs of various business cycles of the smart park. At the same time, the missing data is supplemented according to the correlation between the data to ensure the rationality and scientificity of the subsequent visualization park data display strategy.

[0027] In the above technical solution, step S300 is divided into the following steps:

[0028] Step S301: Acquire data transmission information used for business operation between business units in each business function;

[0029] Step S302: sorting the processes of the business units according to the data transmission direction between the business units;

[0030] For business units x and y, if the output data of business unit x participates in the business operation of business unit y, then in the process sorting, the sorting order of business unit x is lower than that of business unit y;

[0031] Sort all business units in the current business function by process, assign each business unit to a different process chain, and extract the business unit with the highest ranking in each process chain as the display unit of the current business function;

[0032] Step S303: marking the risk assessment value of the historical data of each display unit, constructing an artificial intelligence risk assessment model for each display unit, evaluating the real-time data of each display unit, and obtaining the risk assessment value of each display unit;

[0033] Step S304: calculating the display priority of each display unit in the business function;

[0034] For any display unit s, the display priority calculation formula of the display unit s in the business function is as follows:

[0035] ;

[0036] ;

[0037] in, is the completeness of the data used in the operation of display unit s, d is the data number used in the operation of display unit s, The amount of data used to run the display unit, is the ratio of the weight of data d in the data cycle to the weight of all data in the entire cycle, The weight ratio and total weight of all data used in the business operation of the display unit s. Display priority for display units, Display risk assessment value for display unit s data, are constant parameters respectively; is the judgment function, when When the data is collected originally, the function output is 1. When the data is completed, the function output is 0; when calculating the completeness of the display unit data, if any data is reused in the process chain to which the display unit belongs, the data will be repeatedly calculated according to the number of times it is used;

[0038] The display unit display priority is calculated and analyzed in combination with data integrity and risk assessment value, ensuring the coordinated management and control of data collection sensor anomalies and business unit data risks, further improving the coordination of system visualization risk management;

[0039] Step S305: when displaying the business function data, the display units are arranged in sequence according to the display priorities from large to small.

[0040] In the above technical solution, step S400 includes the following contents:

[0041] Get the risk assessment value of each display unit in the current business function, set the weight coefficient of each business function, according to the formula: , weighted calculation of the display strength of each business function;

[0042] in, is the display strength of business function m, s is the display unit number in business function m, is the number of display units in business function m, is the risk factor of display unit s, It is the risk assessment value of display unit s.

[0043] In the above technical solution, step S500 includes the following contents:

[0044] If the display priority of all business units is less than the corresponding display priority threshold, obtain the system display module cycle display period , and set the display duration of each business function in each cycle according to the cycle display cycle , according to the formula:

[0045] ;

[0046] in, is the number of business functions, is the shortest display duration, is the display strength of business function m, Display strength values ​​and cycle times for all business functions Requirements: ;

[0047] The business function display intensity at the beginning of each cycle display period is used as a calculation parameter to participate in the calculation of the display duration of each business function mentioned above;

[0048] Set display priority thresholds for business units in each business function, monitor and analyze the display priority of each business unit in real time, and mark display units with display priorities greater than or equal to the corresponding display priority thresholds as risky display units;

[0049] In the system risk display section, all risk display units are displayed in order from large to small according to the display unit display priority values, and a risk feedback signal is simultaneously sent to the park management personnel. The risk feedback signal contains data of all risk display units and each business unit in the process chain to which they belong;

[0050] By cyclically displaying different business functions, the park management personnel can ensure global monitoring of the entire system. By real-time monitoring and labeling of risk display units and using risk display panels for real-time display, the system's risk management capabilities are improved and resource consumption in risk investigation is reduced.

[0051] A visual smart park management system based on AI analysis using a visual smart park management method based on AI analysis in the above technical solution, the system comprising: a data processing module, a display strategy analysis module, and a visual management unit;

[0052] The data processing module divides the business functions of the park into business units, labels the data of each data sensor with the business unit, and completes the missing data of the data sensor according to the correlation between the data; the display strategy analysis module calculates and analyzes the display priority of each display unit in the corresponding business function, sets the display intensity of each business function according to the risk assessment value of the display unit in each business function, calculates the display time of each business function data, and monitors the marked risk display units in real time to perform risk management; the visualization management unit uses the risk display section to display the risk display units marked by real-time monitoring, and uses the circular display section to cyclically display the display units in each business function.

[0053] In the above technical solution, the data processing module includes: a business labeling unit, a data labeling unit, and a data completion unit;

[0054] The business labeling unit is used to divide the business functions of the park into business units; the data labeling unit is used to label the data of each data sensor corresponding to the business unit; and the data completion unit completes the missing data of the data sensor according to the correlation between the data.

[0055] In the above technical solution, the display strategy analysis module includes: a data analysis unit, a display decision unit, and a risk monitoring unit;

[0056] The data analysis unit is used to calculate and analyze the display priority of each display unit in its corresponding business function, and set the display intensity of each business function according to the risk assessment value of the display unit in each business function; the display decision unit is used to calculate the display duration of each business function data in the circular display section; the risk monitoring unit monitors the marked risk display units in real time for risk management.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] In the present invention, a detailed analysis is performed on the requirements of various business functions in the park, and a decision on the collaborative display strategy of multiple business functions is made by calculating and analyzing the display priority of the display units in each business function and the display strength of each business function, thereby realizing one-stop management of multiple business function requirements in visual park management, and greatly improving the system management efficiency; secondly, in the present invention, by synchronously analyzing and processing the park collected data used by different business functions, the data barriers between different business modules in the traditional management method are broken, and at the same time, the missing data is supplemented and analyzed, effectively reducing the redundant pre-data processing of system decision-making; in addition, in the present invention, real-time risk monitoring is performed on each display unit, and visual management is performed through different display modules. While multiple business functions are collaboratively operated and managed, system adaptive risk control is realized, thereby improving the practicality of the overall system and further reducing the redundant resource consumption of risk investigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of a visualized smart park management method based on AI analysis in the present invention;

[0060] Figure 2 This is a visualized smart park management system organizational chart based on AI analysis in the present invention. DETAILED DESCRIPTION

[0061] 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.

[0062] Example: See Figure 1-Figure 2 , the present invention provides a technical solution,

[0063] like Figure 1 As shown, the present invention provides a visual smart park management method based on AI analysis, the method comprising the following steps:

[0064] Step S100: Analyze the requirements of various business functions in the park, divide the business functions into business units, and mark the data sources required for the operation of each business unit;

[0065] Step S200: Analyze the data cycle, group the data collected in the park according to the cycle, construct corresponding slope arrays for the data groups, calculate the correlation between the data groups, and then weightedly calculate the slope between the missing data and the non-missing data in the data cycle, and complete the missing data in the data cycle;

[0066] Step S300: Perform process analysis on each business unit in each business function, evaluate the data risk of each business unit, determine the display unit in the display module of each business function, calculate the display priority of each display unit, and set the display mode of the display unit in each business function;

[0067] Step S400: comprehensively calculating the display strength of each business function according to the display unit risk assessment value in each business function;

[0068] Step S500: Set up a risk display section and a cyclic display section respectively, and divide each business unit into display sections according to the display priority; for the cyclic display section, calculate the display duration of each business function displayed, and for the risk display section, display them in order from large to small according to the display priority value of the display unit, and send a risk feedback signal to the management personnel.

[0069] The step S100 includes the following contents:

[0070] Analyze the requirements of various comprehensive business functions involved in park management, divide various business functions into business units according to data processing business, and mark each business unit according to the data sources used by each business unit;

[0071] For any business unit n, the business unit u is labeled according to the business function as follows: ;in, is the business function to which business unit u belongs, A collection of data acquisition sensors. are the data acquisition sensor numbers that send the collected data to business unit u, A collection of business units. They are the business unit numbers that send business output data to business unit u;

[0072] In the specific implementation, for relatively independent business functions such as equipment energy consumption supervision and fire inspection, their business units and data annotations are divided. Taking the above two business functions as an example, they can be divided into data units such as facility energy consumption data in the park, fire equipment inspection data, facility operation status data, and fire evacuation accessibility data. The above two business functions also include facility operation status data, facility inspection, and facility operation status data. Therefore, through the subdivision of different business functions, the data coupling caused by the separation of traditional system modules can be broken to ensure the coordinated operation of multi-dimensional business functions.

[0073] The step S200 includes the following contents:

[0074] Analyze the cycle of each data, group the data collected in the park according to the cycle, and for any data collection sensor , record the data collected in each cycle as: ;in, The data collection sensor in data group g The data collected;

[0075] In specific implementation, the data used by general park businesses will have data cycles, such as passenger flow data that changes with time spans such as days and weeks, edge and sea facility operation data that changes with equipment life and inspection cycles, and facility energy consumption data that changes with the work and rest of the user group.

[0076] Get Sensor The data sampling time interval for the sensor After curve fitting, the data is densely sampled to obtain n sampling data, and the slope of the curve at each sampling point is calculated to construct a slope array ;

[0077] Then calculate the data correlation between each data group. For any data collection sensor Data grouping and , data grouping and data grouping The calculation formula of the correlation degree is as follows:

[0078] ;

[0079] in, Grouping data and data grouping Inter-correlation degree, i is the data acquisition sensor The data number in the slope array, n is the data acquisition sensor The number of data in the slope array, Sensors for data collection The weight of the i-th data item in the slope array of Sensors for data collection Data grouping and The i-th data difference in the corresponding slope array;

[0080] Computational Data Sensors The correlation between all data groups, for any missing data in any data group g, the correlation between data groups is used as the weight, and the corresponding slope of the data in the slope array of all data groups except data group g is weighted to calculate the value of the data in the slope array of data group g. The data completion calculation formula is as follows:

[0081] ;

[0082] in, is the data group g to complete the data, j is the data sensor The data group number, For data sensors The number of data packets, is the sum of the associations between all data groups except data group g and data group g, Grouping data and the correlation degree of data group g, Grouping data The data in the slope array;

[0083] In the specific implementation, for personnel flow data, the data change trends and values ​​in different periods change synchronously, while for equipment status data, it will show monotonous changes with the service life area. However, in different periods, the change trends of equipment status data are correlated. Therefore, when completing the data, the original collected data of each period and the change trends between the data are comprehensively calculated synchronously to ensure the rationality of the completed data.

[0084] The slope in the slope array is multiplied by the sampling frequency of the dense sampling of the slope array to obtain the difference between each data, and the missing data is calculated and completed according to the difference between the non-missing data and the missing data.

[0085] The step S300 is divided into the following steps:

[0086] Step S301: Acquire data transmission information used for business operation between business units in each business function;

[0087] Step S302: sorting the processes of the business units according to the data transmission direction between the business units;

[0088] For business units x and y, if the output data of business unit x participates in the business operation of business unit y, then in the process sorting, the sorting order of business unit x is lower than that of business unit y;

[0089] Sort all business units in the current business function by process, assign each business unit to a different process chain, and extract the business unit with the highest ranking in each process chain as the display unit of the current business function;

[0090] Step S303: marking the risk assessment value of the historical data of each display unit, constructing an artificial intelligence risk assessment model for each display unit, evaluating the real-time data of each display unit, and obtaining the risk assessment value of each display unit;

[0091] Step S304: calculating the display priority of each display unit in the business function;

[0092] For any display unit s, the display priority calculation formula of the display unit s in the business function is as follows:

[0093] ;

[0094] ;

[0095] in, is the completeness of the data used in the operation of display unit s, d is the data number used in the operation of display unit s, The amount of data used to run the display unit, is the ratio of the weight of data d in the data cycle to the weight of all data in the entire cycle, The weight ratio and total weight of all data used in the business operation of the display unit s. Display priority for display units, Display risk assessment value for display unit s data, are constant parameters respectively; is the judgment function, when When the data is collected originally, the function output is 1. When the data is completed, the function output is 0; when calculating the completeness of the display unit data, if any data is reused in the process chain to which the display unit belongs, the data will be repeatedly calculated according to the number of times it is used;

[0096] In the specific implementation, the missing data of the data collection sensor is highly correlated with the abnormal status of the data collection sensor. At the same time, artificial intelligence is used to conduct risk assessment of business unit data, and the risk probability of each display unit is comprehensively considered, so as to give priority to the high-risk display units and improve the overall risk management and control capabilities of the system.

[0097] Step S305: when displaying the business function data, the display units are arranged in sequence according to the display priorities from large to small.

[0098] The step S400 includes the following contents:

[0099] Get the risk assessment value of each display unit in the current business function, set the weight coefficient of each business function, according to the formula: , weighted calculation of the display strength of each business function;

[0100] in, is the display strength of business function m, s is the display unit number in business function m, is the number of display units in business function m, is the risk factor of display unit s, It is the risk assessment value of display unit s.

[0101] The step S500 includes the following contents:

[0102] If the display priority of all business units is less than the corresponding display priority threshold, obtain the system display module cycle display period , and set the display duration of each business function in each cycle according to the cycle display cycle , according to the formula:

[0103] ;

[0104] in, is the number of business functions, is the shortest display duration, is the display strength of business function m, Display strength values ​​and cycle times for all business functions Requirements: ;

[0105] The business function display intensity at the beginning of each cycle display period is used as a calculation parameter to participate in the calculation of the display duration of each business function mentioned above;

[0106] Set display priority thresholds for business units in each business function, monitor and analyze the display priority of each business unit in real time, and mark display units with display priorities greater than or equal to the corresponding display priority thresholds as risky display units;

[0107] In the system risk display section, all risk display units are displayed in order from large to small according to the display unit display priority values, and a risk feedback signal is simultaneously sent to the park management personnel. The risk feedback signal contains data of all risk display units and each business unit in the process chain to which they belong;

[0108] In the specific implementation, if there is no high-risk situation for all business function data in the overall system, the display priority and display intensity are determined according to the risk assessment, and different display times are used to enable managers to control the degree of attention to different business functions; if there is a high-risk display unit, real-time feedback on the risk is required to ensure the fastest control, so the risk display unit is displayed separately in real time, which ensures the system's synergy while further improving the system's risk management capabilities.

[0109] like Figure 2 As shown, the present invention also provides a visual smart park management system based on AI analysis, the system comprising: a data processing module, a display strategy analysis module, and a visual management unit;

[0110] The data processing module divides the business functions of the park into business units, labels the data of each data sensor with the business unit, and completes the missing data of the data sensor according to the correlation between the data; the display strategy analysis module calculates and analyzes the display priority of each display unit in the corresponding business function, sets the display intensity of each business function according to the risk assessment value of the display unit in each business function, calculates the display time of each business function data, and monitors the marked risk display units in real time to perform risk management; the visualization management unit uses the risk display section to display the risk display units marked by real-time monitoring, and uses the circular display section to cyclically display the display units in each business function.

[0111] The data processing module includes: a business labeling unit, a data labeling unit, and a data completion unit;

[0112] The business labeling unit is used to divide the business functions of the park into business units; the data labeling unit is used to label the data of each data sensor corresponding to the business unit; and the data completion unit completes the missing data of the data sensor according to the correlation between the data.

[0113] The display strategy analysis module includes: a data analysis unit, a display decision unit, and a risk monitoring unit;

[0114] The data analysis unit is used to calculate and analyze the display priority of each display unit in its corresponding business function, and set the display intensity of each business function according to the risk assessment value of the display unit in each business function; the display decision unit is used to calculate the display duration of each business function data in the circular display section; the risk monitoring unit monitors the marked risk display units in real time for risk management.

[0115] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A visual smart park management method based on AI analysis, characterized in that , the method comprises the following steps: Step S100: Analyze the requirements of various business functions in the park, divide the business functions into business units, and mark the data sources required for the operation of each business unit; Step S200: Analyze the data cycle, group the data collected in the park according to the cycle, construct corresponding slope arrays for the data groups, calculate the correlation between the data groups, and then weightedly calculate the slope between the missing data and the non-missing data in the data cycle, and complete the missing data in the data cycle; Step S300: Perform process analysis on each business unit in each business function, evaluate the data risk of each business unit, determine the display unit in the display module of each business function, calculate the display priority of each display unit, and set the display mode of the display unit in each business function; Step S400: comprehensively calculating the display strength of each business function according to the display unit risk assessment value in each business function; Step S500: setting up risk display sections and cyclic display sections respectively, and dividing each business unit into display sections according to display priority; for the cyclic display section, calculating the display duration of each business function displayed; for the risk display section, displaying the display units in descending order according to their display priority values, and sending risk feedback signals to management personnel; Wherein, the step S300 is divided into the following steps: Step S301: Acquire data transmission information used for business operation between business units in each business function; Step S302: sorting the processes of the business units according to the data transmission direction between the business units; For business units x and y, if the output data of business unit x participates in the business operation of business unit y, then in the process sorting, the sorting order of business unit x is lower than that of business unit y; Sort all business units in the current business function by process, assign each business unit to a different process chain, and extract the business unit with the highest ranking in each process chain as the display unit of the current business function; Step S303: marking the risk assessment value of the historical data of each display unit, constructing an artificial intelligence risk assessment model for each display unit, evaluating the real-time data of each display unit, and obtaining the risk assessment value of each display unit; Step S304: calculating the display priority of each display unit in the business function; For any display unit s, the display priority calculation formula of the display unit s in the business function is as follows: ； ； in, is the completeness of the data used in the operation of display unit s, d is the data number used in the operation of display unit s, The amount of data used to run the display unit, is the ratio of the weight of data d in the data cycle to the weight of all data in the entire cycle, The weight ratio and total weight of all data used in the business operation of the display unit s. Display priority for display units, Display risk assessment value for display unit s data, are constant parameters respectively; is the judgment function, when When the data is collected originally, the function output is 1. When the data is completed, the function output is 0; when calculating the completeness of the display unit data, if any data is reused in the process chain to which the display unit belongs, the data will be repeatedly calculated according to the number of times it is used; Step S305: when displaying the business function data, the display units are arranged in sequence according to the display priorities from large to small.

2. According to claim 1, a visual smart park management method based on AI analysis is characterized in that: The step S100 includes the following contents: Analyze the requirements of various comprehensive business functions involved in park management, divide various business functions into business units according to data processing business, and mark each business unit according to the data sources used by each business unit; For any business unit n, the business unit u is labeled according to the business function as follows: ;in, is the business function to which business unit u belongs, A collection of data acquisition sensors. are the data acquisition sensor numbers that send the collected data to business unit u, A collection of business units. They are the business unit numbers that send business output data to business unit u respectively.

3. According to claim 2, a visual smart park management method based on AI analysis is characterized in that: The step S200 includes the following contents: Analyze the cycle of each data, group the data collected in the park according to the cycle, and for any data collection sensor , record the data collected in each cycle as: ;in, The data collection sensor in data group g The data collected; Get Sensor The data sampling time interval for the sensor After curve fitting, the data is densely sampled to obtain n sampling data, and the slope of the curve at each sampling point is calculated to construct a slope array ; Then calculate the data correlation between each data group. For any data collection sensor Data grouping and , data grouping and data grouping The calculation formula of the correlation degree is as follows: ； in, Grouping data and data grouping Inter-correlation degree, i is the data acquisition sensor The data number in the slope array, n is the data acquisition sensor The number of data in the slope array, Sensors for data collection The weight of the i-th data item in the slope array of Sensors for data collection Data grouping and The i-th data difference in the corresponding slope array; Computational Data Sensors The correlation between all data groups, for any missing data in any data group g, the correlation between data groups is used as the weight, and the corresponding slope of the data in the slope array of all data groups except data group g is weighted to calculate the value of the data in the slope array of data group g. The data completion calculation formula is as follows: ； in, is the data group g to complete the data, j is the data sensor The data group number, For data sensors The number of data packets, is the sum of the associations between all data groups except data group g and data group g, Grouping data and the correlation degree of data group g, Grouping data The data in the slope array; The slope in the slope array is multiplied by the sampling frequency of the dense sampling of the slope array to obtain the difference between each data, and the missing data is calculated and completed according to the difference between the non-missing data and the missing data.

4. According to claim 1, a visual smart park management method based on AI analysis is characterized in that: The step S400 includes the following contents: Get the risk assessment value of each display unit in the current business function, set the weight coefficient of each business function, according to the formula: , weighted calculation of the display strength of each business function; in, is the display strength of business function m, s is the display unit number in business function m, is the number of display units in business function m, is the risk factor of display unit s, It is the risk assessment value of display unit s.

5. According to claim 1, a visual smart park management method based on AI analysis is characterized in that: The step S500 includes the following contents: If the display priority of all business units is less than the corresponding display priority threshold, obtain the system display module cycle display period , and set the display duration of each business function in each cycle according to the cycle display cycle , according to the formula: ； in, is the number of business functions, is the shortest display duration, is the display strength of business function m, Display strength values ​​and cycle times for all business functions Requirements: ; The business function display intensity at the beginning of each cycle display period is used as a calculation parameter to participate in the calculation of the display duration of each business function mentioned above; Set display priority thresholds for business units in each business function, monitor and analyze the display priority of each business unit in real time, and mark display units with display priorities greater than or equal to the corresponding display priority thresholds as risky display units; In the system risk display section, all risk display units are displayed in order from large to small according to the display unit display priority values, and a risk feedback signal is sent to the park management personnel simultaneously. The risk feedback signal contains data of all risk display units and each business unit in the corresponding process chain.

6. A visual smart park management system based on AI analysis using a visual smart park management method based on AI analysis as claimed in any one of claims 1 to 5, characterized in that: The system includes: a data processing module, a display strategy analysis module, and a visualization management unit; The data processing module divides the business functions of the park into business units, labels the data of each data sensor with the business unit, and completes the missing data of the data sensor according to the correlation between the data; the display strategy analysis module calculates and analyzes the display priority of each display unit in the corresponding business function, sets the display intensity of each business function according to the risk assessment value of the display unit in each business function, calculates the display time of each business function data, and monitors the marked risk display units in real time to perform risk management; the visualization management unit uses the risk display section to display the risk display units marked by real-time monitoring, and uses the circular display section to cyclically display the display units in each business function.

7. The visual intelligent park management system based on AI analysis according to claim 6 is characterized in that: The data processing module includes: a business labeling unit, a data labeling unit, and a data completion unit; The business labeling unit is used to divide the business functions of the park into business units; the data labeling unit is used to label the data of each data sensor corresponding to the business unit; and the data completion unit completes the missing data of the data sensor according to the correlation between the data.

8. The visual intelligent park management system based on AI analysis according to claim 6 is characterized in that: The display strategy analysis module includes: a data analysis unit, a display decision unit, and a risk monitoring unit; The data analysis unit is used to calculate and analyze the display priority of each display unit in its corresponding business function, and set the display intensity of each business function according to the risk assessment value of the display unit in each business function; the display decision unit is used to calculate the display duration of each business function data in the circular display section; the risk monitoring unit monitors the marked risk display units in real time for risk management.

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