Smart park property operation and maintenance management system based on big data

Abstract

The invention discloses a smart park property operation and maintenance management system based on big data, and the system comprises a big data collection and processing module which collects the classified data of a smart park, including energy consumption data, crowded people data, equipment operation and maintenance data, and park environment maintenance data; the intelligent lighting and energy management module is used for acquiring intelligent lighting energy-saving and energy optimization operation data and updating energy consumption data in real time; the building self-control and security module is used for monitoring cameras, access control and elevator operation of the park and updating crowded data and equipment operation and maintenance data in real time; the park environment maintenance module is used for updating park environment maintenance data in real time; and the cloud management platform is used for establishing a live-action space big data model, analyzing each classification data in the monitoring period of each target park, and evaluating a property operation and maintenance management index in the monitoring period of each target park. According to the invention, precise quantitative management of the smart park can be realized, and the operation efficiency and service level of the park are greatly improved.

Description

Technical Field

[0001] The present invention relates to a park property management system, and in particular to a smart park property operation and maintenance management system based on big data. Background Art

[0002] Smart parks have gradually become a new engine for urban development. They not only carry the aggregation and innovation of high-tech industries, but also represent the advanced concepts and technical applications of urban management. Various industrial parks, service parks, and logistics parks are emerging in an endless stream under the name of intelligence and smartness. However, many parks may only add temperature and humidity sensors, a certain degree of data visualization, and park IT projects, and are named smart parks. Therefore, it is particularly important to improve the operation and management of smart parks.

[0003] In the daily operation and management of smart parks, how to improve the property management level of the park, improve resource utilization efficiency, and optimize service experience has become an important issue facing managers. The existing operation and maintenance of smart parks mainly have the following problems:

[0004] 1. How to improve the processing capabilities of various types of data

[0005] Although the digital middle platform has solved the problem of internal information integration in the company to a good extent, there is a lack of a unified platform for data integration for the entire park; and the ability for secondary development and utilization of data is insufficient.

[0006] 2. Park lighting monitoring and energy management

[0007] There is a lack of effective monitoring and management of the park's energy planning information and actual operation optimization data; the energy consumption of asset operation and maintenance is high, and there is no real-time monitoring method; the start and stop control management of high-energy-consuming equipment is extensive, and the difference in tiered electricity prices is large, resulting in unnecessary energy waste.

[0008] 3. Building automation and security module

[0009] It is difficult to classify and inventory building automation and security equipment; there are many blind spots in the operation and maintenance of various equipment; and there is no visualization tool for operation and maintenance information.

[0010] 4. Park environment maintenance

[0011] The existing park environment maintenance is relatively simple and extensive, and mainly relies on manual labor, without achieving accurate assessment.

[0012] As can be seen from the above, the existing parks have not been able to use technologies such as the Internet of Things, big data, and artificial intelligence for operation and maintenance management, and their property management level and system efficiency need to be improved. Therefore, it is necessary to provide a smart park property operation and maintenance management system based on big data. Summary of the invention

[0013] The technical problem to be solved by the present invention is to provide a smart park property operation and maintenance management system based on big data, which can realize intelligent and refined management of the park and improve the operation efficiency and service level of the park.

[0014] The technical solution adopted by the present invention to solve the above technical problems is to provide a smart park property operation and maintenance management system based on big data, including a big data collection and processing module: collecting various classified data of the smart park, including energy consumption data, crowd density data, equipment operation and maintenance data and park environment maintenance data; intelligent lighting and energy management module: obtaining intelligent lighting energy-saving and energy optimization operation data, and updating energy consumption data in real time; building automation and security module: monitoring park cameras, access control and elevator operations, capturing abnormal behaviors and intrusion events in real time, performing alarm processing through face recognition and behavior analysis, and updating crowd density data and equipment operation and maintenance data in real time; park environment maintenance module: carrying out environmental maintenance quality assessment within the monitoring period of each target park from three dimensions: green environment maintenance data, road environment maintenance data and building environment maintenance data, and updating the park environment maintenance data in real time; cloud management platform: establishing a real-life space big data model, analyzing the crowd density data within the monitoring period of each target park, and combining the real-time updated energy consumption data, equipment operation and maintenance data and park environment maintenance data to evaluate the property operation and maintenance management index within the monitoring period of each target park.

[0015] Furthermore, the big data collection and processing module obtains multi-source heterogeneous data sources from the archive platform, data middle platform and Internet of Things platform, and uses conversion factors to convert the data obtained from the source data source into the form required by the destination data source according to business needs, and cleans and processes the erroneous and inconsistent data during the conversion; and uses a fusion integration platform to connect the data of various systems in the park, puts all structured data into the NoSQL database Hyperbase, and manages them uniformly through metadata; the data sources include relational data, time series data, message data and request data.

[0016] Furthermore, the real-life space big data model selects each category data of benchmark parks that meet the conditions as samples, and calculates the initial data of each category as a benchmark value; then the real-time updated data of each category is divided by the corresponding benchmark value and multiplied by 100 to obtain each category index, and the weighted sum of each category index is performed to obtain the property operation and maintenance management index; the category indexes include a crowd density index, an energy consumption data index, an equipment operation and maintenance index, and a park environment maintenance index.

[0017] Furthermore, the cloud management platform inputs real-time updated energy consumption data, crowd density data, equipment operation and maintenance data, and park environment maintenance data into the real-life space big data model for calculation; and converts the output content of the real-life space big data model into an index trend chart and corresponding analysis report through an index data model output module.

[0018] Furthermore, the crowd density data obtained by the real-life space big data model include campus bus service indicators and in-and-out vehicle flow indicators, and the talent apartment overview, tenant statistics, monthly access control alarm statistics and monthly entry and exit abnormality statistics are included in the crowd density index; at the same time, it provides human-vehicle binding, key equipment and relevant personnel binding functions, and pushes system alarms, patrol abnormality manual alarms and inspection abnormality manual alarms to the mobile terminals of relevant personnel in real time on the integrated alarm platform for reminders.

[0019] Furthermore, the real-life space big data model maps all enterprises and various equipment in the park to a real-life space for unified management. The specific processing process is as follows: S1: Provide visual configuration of the equipment object model. The visual configuration realizes the full life cycle management of equipment data from input, data processing to output through interface drag and drop, and provides SQL execution, field conversion, and dynamic script rule engine controls. At the same time, it associates equipment, projects and park enterprises to realize scene linkage; S2: Display the location distribution of park enterprises through a map, and display enterprise points through map dot method; S3: Import the park enterprise list, display the enterprise list and main business data; click on the enterprise name It is said that you can jump to the enterprise details page to view the information data of each enterprise; S4: Real-time monitoring of changes in enterprise information data, dynamic acquisition and identification of changes in various types of enterprise information data, and display them in page form; S5: Display the number of equipment and online rate forms in each park on the map. After clicking on a park, the equipment name, system name, equipment location, and equipment status form of the park will pop up. After clicking on a device, the detailed information of the device will pop up; S6: Dynamically present the current rental and sales status and charging status of operating assets on the map, and realize cross-regional resource coordination and refined spatial information management through room status sharing. The operating assets include hotels, offices, venues and conference rooms in the park.

[0020] Furthermore, the intelligent lighting and energy management module realizes energy consumption forecasting, energy efficiency analysis and energy consumption benchmarking management through real-time edge energy management and control; after comprehensive calculation and scoring, updated energy consumption data is generated and sent to the real-life space big data model; specifically includes: energy billing fine management: using smart meters in conjunction with remote meter reading systems to achieve accurate billing of energy consumption in public areas of the park and tenants to avoid inverted electricity bills; energy conservation and emission reduction anomaly tracking: establishing dynamic benchmarks based on historical data, comparing and analyzing predicted data, dynamic benchmarks and real-time data, and optimizing the operating energy consumption of air conditioners, lighting, and office equipment by identifying abnormal electricity consumption behaviors, so that electricity prediction Match with demand forecast; Use energy model tree to accurately predict energy consumption: Establish its own energy management tree for each enterprise, realize real-time collection of energy consumption data, automatic classification statistics, abnormal tracking and chart display; VRV energy saving optimization: Coordinate indoor traffic and temperature sensors, identify indoor environmental status, calculate indoor and outdoor enthalpy values ​​based on climate characteristics, and predict VRV preset temperature, wind speed and operation mode; VRV panoramic control energy saving: Add air quality sensors to areas where VRV air conditioners are used on site, and adjust the equipment operation according to outdoor temperature, indoor temperature and indoor air quality parameters to achieve VRV energy-saving operation.

[0021] Furthermore, the intelligent lighting and energy management module adopts zoning, timing and sensing to perform energy-saving control on the lighting of public areas, specifically including: adopting time scheduling control to control the start and stop of lighting fixtures according to the office hours of each area; setting human infrared sensing sensors in public areas to perform sensing control on lighting fixtures; separating the lighting control of the lighting area from the lighting control of other areas; monitoring the illuminance of the lighting area so that artificial lighting is automatically adjusted as the natural light illuminance changes; presetting a variety of commonly used lighting scenes and making matching selections.

[0022] Furthermore, the building automation and security module obtains equipment model visualization configuration, equipment status monitoring and equipment fault diagnosis data from the Internet of Things platform, stores and obtains maintenance information, elevator information and annual inspection information on the archive platform, monitors the working status and energy consumption of the equipment in real time through sensors and data acquisition equipment, uses big data and artificial intelligence algorithms to conduct equipment health assessment and fault prediction, and performs maintenance and repair in advance; obtains equipment operation monitoring indicators after calculation and scoring and incorporates them into equipment operation and maintenance data; and provides a real-time monitoring page for the selected elevator in the real-life space big data model.

[0023] Furthermore, the real-life space big data model also includes that when the change range of a certain classification index exceeds a preset threshold, the updated data corresponding to the classification index is handed over to the abnormal data receiving module for recording and processing, and the classification index is included after the increase or decrease range is limited, or the updated data that causes the abnormality is stripped and the classification index is recalculated.

[0024] Compared with the prior art, the present invention has the following beneficial effects: the cloud management platform of the smart park property operation and maintenance management system based on big data provided by the present invention can integrate security systems, building automation systems, intelligent lighting systems, energy management systems, intelligent parking systems, etc. At the same time, a real-life space big data model is established using the cloud management platform, and with the help of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent and refined management of various facilities and equipment in the park is achieved, thereby improving the park's operating efficiency and service level. The present invention can intuitively measure the park's property management level, solve the problem of comprehensive quantitative reference evaluation of property management in various smart parks, provide effective data support for the evaluation of smart park operation and maintenance management, and effectively improve the park's management quality and efficiency, thereby enhancing the vitality of park management development and achieving high-quality sustainable development of the park. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is the architecture diagram of the smart park property operation and maintenance management system based on big data of the present invention;

[0026] Figure 2 A preview of the electricity model selection and forecast data generation interface of the present invention;

[0027] Figure 3 Generate a comparison chart for the predicted data and actual data of the present invention;

[0028] Figure 4 It is a schematic diagram of the integrated alarm platform of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0030] Figure 1 This is the architecture diagram of the smart park property operation and maintenance management system based on big data of the present invention.

[0031] See also Figure 1 The smart park property operation and maintenance management system based on big data provided by the present invention includes:

[0032] Big data collection and processing module: collects various types of data of the smart park, including energy consumption data, crowd density data, equipment operation and maintenance data, and park environment maintenance data;

[0033] Intelligent lighting and energy management module: obtain intelligent lighting energy saving and energy optimization operation data, and update energy consumption data in real time;

[0034] Building automation and security module: monitors campus cameras, access control, and elevator operations, captures abnormal behaviors and intrusions in real time, issues alarms through face recognition and behavior analysis, and updates crowd density data and equipment operation and maintenance data in real time;

[0035] Park environment maintenance module: Carry out environmental maintenance quality assessment of each target park during the monitoring period from three dimensions: greening environment maintenance data, road environment maintenance data and building environment maintenance data, and update the park environment maintenance data in real time;

[0036] Cloud management platform: Establish a real-life space big data model to analyze the crowd density data of each target park during the monitoring period, and combine it with real-time updated energy consumption data, equipment operation and maintenance data, and park environment maintenance data to evaluate the property operation and maintenance management index of each target park during the monitoring period.

[0037] The big data-based smart park property operation and maintenance management system provided by the present invention utilizes a cloud management platform to integrate security systems, building automation systems, intelligent lighting systems, energy management systems, intelligent parking systems, etc., to achieve intelligent and refined management of various facilities and equipment in the park, and to improve the operation efficiency and service level of the park; the following describes in detail the selection and processing of each module and key data of the present invention.

[0038] In order to ensure the accuracy of the property operation and maintenance management index, it is particularly important to ensure the integrity, accuracy and uniformity of the data source. The big data acquisition and processing module of the present invention mainly extracts archival data from the archive platform, and the archival data includes equipment asset data, space asset data and space rental data; and extracts engineering development management system data, asset operation management system data, apartment management system data and property expenditure control system data from the data center according to the unique data identifier. The data center of the present invention can access and output the massive data collected from multiple sources and heterogeneity: each data source component represents a type of database; through a simple address and username and password configuration, different data sources can be accessed to the platform; the data source includes four categories: relational data, time series data, message data, and request data. The big data acquisition and processing module of the present invention uses a conversion factor to convert the data obtained from the source data source into the form required by the destination data source according to business needs, and cleans and processes the wrong and inconsistent data. Specifically, it includes processing methods such as statistics, filtering, standard value verification, alias setting, precision setting, data supplementation, attribute addition, and function calculation. In addition, the present invention includes a variety of machine learning factors, covering common algorithm types such as regression algorithm, decision tree algorithm, Bayesian algorithm, neural network algorithm, etc., which greatly reduces the operational threshold of professional data mining and improves modeling efficiency.

[0039] After acquiring data, the big data acquisition and processing module of the present invention provides a unified data classification and coding standard, a unified interface standard and a unified database structure standard; and uses a fusion integration platform to connect the data of various systems in the park, puts all structured data into the NoSQL database Hyperbase, and manages them uniformly through metadata; for example, three types of metadata, monitoring indicators, monitoring assets and monitoring thresholds, are used; unique asset type codes are assigned to different monitored assets, and the logical relationship between monitoring indicators and monitored assets is constructed through the asset type code, forming a structured database based on metadata for system use.

[0040] 1. Real-life space big data model

[0041] In the face of massive equipment and data in the park, in order to solve the problem of inventory difficulties and avoid blind spots in the operation and maintenance of equipment, it is necessary to provide data visualization and report analysis to help managers gain in-depth insights into the park's operating conditions, discover potential problems, and make corresponding decisions and improvement measures. To this end, the present invention uses a real-life space big data model for visualization and report analysis. The specific processing process is as follows:

[0042] S1: Provides visual configuration of equipment object models. The visual configuration realizes the full life cycle management of equipment data from input, data processing to output through drag-and-drop interface, and provides SQL execution, field conversion, and dynamic script rule engine controls. At the same time, it associates equipment, projects, and park enterprises to realize scene linkage;

[0043] S2: Display the location distribution of enterprises in the park through maps, and display the enterprise points through map dot method;

[0044] S3: Import the park enterprise list and display the enterprise list and main business data; click the enterprise name to jump to the enterprise details page to view the information data of each enterprise, including the industry link, enterprise name, registered address, listing status and establishment date;

[0045] S4: Monitor the changes of enterprise information data in real time, dynamically obtain and identify the changes of various types of enterprise information data, and display them in the form of pages;

[0046] S5: Display the number of devices and online rate of each park on the map. Click a park to pop up the device name, system name, device location, and device status of the park. Click a device to pop up the detailed information of the device.

[0047] S6: The real-life space big data model can be used to facilitate operational asset management: inventory of existing space, expansion of space to be built, support for multiple space products such as hotels, offices, venues, and conference rooms, and refined space information management. The current rental and sales status and charging status of assets can be dynamically presented by clicking, and cross-regional resource coordination and management can be achieved through room status sharing.

[0048] In this way, all enterprises and various equipment in the park can be mapped to a real space for unified management, and the operation and management of the park can be intuitively displayed through visualization means, so as to achieve comprehensive situation and decision-making analysis, event monitoring and linkage command based on "one picture", and realize the layer-by-layer connection of various types of data from equipment, projects to park enterprises, so as to achieve full connection, full perception and full transparency of data.

[0049] 2. Intelligent lighting and energy management module

[0050] Obtain the park's electricity consumption data, power supply and distribution data, and lighting equipment operation data, and use the real-life space big data model to view the details of each device and grasp the overall operation of the park. The real-time operating status parameters of all relevant equipment and the data analysis performed are reflected above, including indicators such as voltage and current. The collection time interval can be very short, in minutes, hours, and days. For some key indicators, if the terminal collection equipment itself does not have the corresponding level of intelligence, thresholds can be set on the platform. For example, if there are signs of over-limit in the three temperatures of the transformer, the platform will issue an early warning and automatically intervene to prevent accidents. Analysis of indicators such as power and electricity can also be flexibly configured if you need to view year-on-year or month-on-month charts.

[0051] 2.1 Intelligent lighting module

[0052] The lighting system of the present invention displays project information, total lamp quantity, electricity consumption for the current month, electricity consumption yesterday, and the status of each lighting circuit. In the monitoring page, after selecting the start time and end time, you can search and view the garage lighting electricity consumption at the corresponding time. The lighting power density value of the main functional rooms should not be higher than the current value specified in the current national standard "Architectural Lighting Design Standard" GB50034; the lighting system in the public area should adopt energy-saving control such as zoning, timing, and induction; lighting energy saving is about 3%-5%. The specific processing is as follows:

[0053] iUse time scheduling control to control the start and stop of lighting fixtures according to the office hours of each area;

[0054] ii. Human infrared sensors are installed in public areas to sense and control lighting fixtures;

[0055] iii The lighting control of the lighting area should be independent of the lighting control of other areas; the lighting area should be monitored for illuminance so that artificial lighting can be automatically adjusted as the natural light intensity changes, making full use of natural light sources to save energy;

[0056] iv Multiple scene presets and matching selections, fade in and fade out.

[0057] 2.2 Power supply and distribution system

[0058] The power supply and distribution system includes five modules: substation project information table, MD value real-time monitoring and early warning curve (short-term prediction), MD value real-time prediction curve, electricity real-time prediction curve, and electricity value benchmarking analysis.

[0059] In the early warning curve, if the MD value reaches the early warning standard, an alarm will be issued in time. MD prediction function: Electricity charges can be charged according to demand, and early warning can be achieved to avoid fines by real-time monitoring of MD values; and combined with historical MD values ​​and related factors, the model parameters can be dynamically adjusted through the model's own learning and training to achieve the best prediction effect and guide users to declare demand.

[0060] Electricity consumption forecast: Establish electricity consumption models, evaluate the effects of each model online and automatically correct the model to achieve the best forecast results, obtain reasonable and accurate forecast data, and prepare for users to participate in direct power purchase (bilateral transactions, centralized bidding, etc.) and deviation assessment.

[0061] The present invention provides project overview and energy and carbon overview indicators, and provides budget trend charts, benchmarking analysis and rankings.

[0062]

[0063]

[0064] The present invention fully considers the energy-saving effect brought by the energy operation optimization solution. The intelligent lighting and energy management module of the present invention realizes energy consumption prediction, energy efficiency analysis and energy consumption benchmarking management through real-time edge-side energy control, thereby obtaining energy operation optimization data; the real-time edge-side energy control includes unit electricity consumption sub-item energy consumption analysis, horizontal comparison of similar unit energy consumption, benchmark unit energy consumption comparison, historical comparison of building classification energy heating energy consumption and non-heating energy consumption, proportion of professional energy consumption of each system, comparison of HVAC energy consumption water, electricity and gas, daily year-on-year data of HVAC energy consumption and daily month-on-month data of HVAC energy consumption; after comprehensive calculation and scoring, the updated energy consumption data is generated and sent to the real-life space big data model.

[0065] 2.3 Intelligent HVAC System

[0066] In a building, HVAC is an important energy-consuming equipment. For innovative green office buildings, the intelligent application of HVAC plays an extraordinary role. The biggest highlight of the HVAC system of the present invention is the use of renewable energy and energy storage devices. By making full use of renewable energy, cost-saving and energy-saving, low-carbon emissions can be achieved. By using energy storage devices, the difference in peak and valley electricity prices can be reasonably utilized to achieve a high energy-price ratio, ultimately presenting an optimal solution for consumption and comfort.

[0067] The present invention selects the intelligent HVAC system in a picture provided by the real-life space big data model, and first displays the building dynamic parameters, static parameters, floor overview, floor monitoring and regional load forecast in the terminal management page. On the left side of the page, you can select a floor, and the floor plan information summary will be displayed, including the status of the fresh air unit and pipeline pump.

[0068] Continue to click on the air conditioning preheating / heating page to display the temperature and humidity of each floor, the status of the fresh air unit, the status of the pipeline pump, the status of the fresh air main and fresh air regulating pipe, and the status of the floor radiation main and floor radiation regulating pipe. Click on the air conditioning precooling page to display the operation control status of the air conditioning unit, the temperature and humidity of each floor, the status of the fresh air unit, the status of the pipeline pump, the status of the fresh air main and fresh air regulating pipe, and the summer temperature and winter temperature settings. Click on the air conditioning cooling page to display the temperature and humidity of each floor, the status of the floor radiation main and floor radiation regulating pipe, and the temperature status from 7:00-23:00 and 23:00-7:00 in the floor radiation control.

[0069] The intelligent lighting and energy management module of the present invention implements various energy-saving and low-carbon strategies based on big data and deep learning. It needs to predict future air conditioning loads, and needs to choose between different modes to maximize efficiency, and choose the best solution and AI strategy. It can make strategic decisions in the background through big data analysis, real-time weather forecasts, and indoor temperature and humidity conditions, judge the most reasonable operation mode, and output it to the IEBA automatic control system on the edge side. It can also adjust the operation strategy and correct the operation parameters at any time, thereby realizing the fully automatic and intelligent operation of the HVAC system. It specifically includes the following functional modules:

[0070] Fine management of energy billing: The construction of smart meters and remote meter reading systems can realize accurate billing of energy consumption in the park's public areas and tenants, avoiding inverted electricity bills and saving electricity expenses in the park.

[0071] Abnormal tracking of energy conservation and emission reduction: The system establishes a dynamic benchmark based on historical data, compares and analyzes the predicted data, dynamic benchmark and real-time data, effectively identifies abnormal electricity consumption through AI, optimizes the operating energy consumption of loads such as air conditioning, lighting, and office equipment, and assists enterprises in energy conservation management. The present invention also analyzes the power deviation and demand deviation through historical data analysis, such as MD value daily demand, MD value monthly demand, MD value annual demand, daily power, monthly power, annual power, and load rate; and then performs forecast analysis to match the power forecast with the demand forecast.

[0072] Use the energy model tree to accurately predict energy consumption: Enterprises can establish their own energy management tree according to their own management model, and support subsequent continuous transformation and access. The system energy tree runs through all functional interfaces of the system, realizing real-time collection of energy consumption data, automatic classification statistics, abnormal tracking, chart display, etc., providing data basis for the company's refined energy management.

[0073] VRV (Variable Refrigerant Volume, multi-split) energy-saving optimization: coordinate indoor human flow and temperature sensors, identify indoor environmental conditions, and provide corresponding operation; air conditioning non-cooling heating demand 219 days, summer 26℃±2℃, winter 20℃±2℃, non-cooling heating demand time, air conditioning low-carbon mode operation, calculate indoor and outdoor enthalpy values ​​based on climate characteristics, and predict VRV preset temperature, wind speed and operation mode.

[0074] VRV panoramic control and energy saving: For areas where VRV air conditioners are used on site, IAQ air quality sensors can also be added. According to parameters such as outdoor temperature, indoor temperature, and indoor air quality, the equipment can be operated and regulated through the VRV efficient operation algorithm to achieve VRV energy-saving operation and save 6-9% of energy. VRV is connected to the park's digital platform for panoramic management, which can enhance the park's ESG environment and the value of social factors.

[0075] The present invention is beneficial to historical time period data, and can be combined with other industry-specific influencing factors to generate energy training data and generate new models. Select to use the new model to generate forecast data for a period of time, and generate a comparison chart with the actual data. Use the curve chart to determine whether to use the new model or keep using the old model. When operating, select the source model for forecast data from the drop-down menu, enter the start date and end date of the training data set, such as Figure 2 As shown; click the Generate Data button, click the Predict Data button, and generate a comparison chart with the actual data, as shown Figure 3 as shown, and then choose to keep the old model or use the new model based on the comparison chart.

[0076] 3. Building automation and security module

[0077] Obtain fire alarm data, advertising screen operation data, BA system operation data, and vending machine operation data. You can also use the real-life space big data model to view the details of each device and understand the overall operation of the park.

[0078]

[0079]

[0080] 3.1 Elevator Monitoring Module

[0081] In the real-time monitoring page provided by the real-life space big data model, the specific location of the elevator and equipment information are displayed on the left, the elevator operating status is displayed on the right, and you can choose to open the elevator video to observe the elevator status in real time, and display the elevator location, operating status, operating speed, and number of operations.

[0082] Store and obtain maintenance information, elevator information and annual inspection information on the archive platform.

[0083] 3.2 Fire Module

[0084] The real-time operation status of each terminal is reflected on the system platform. If a fire occurs or a device fails, causing communication interruption, there will be an alarm message for abnormal situations, avoiding the problem of equipment linkage failure during emergency due to equipment problems. In the real-time monitoring page provided by the real-time space big data model, you can select the time on the upper right side and the floor on the left side to monitor the fire status.

[0085] 3.3 Crowd Counting Module

[0086] Display the building population statistics and visitor information, display the real-time building population, traffic flow and tomorrow's population forecast, and display the browsing concentration of people on each floor in real time. For data on dense traffic, you can consider the park bus service indicators and in-and-out traffic flow indicators;

[0087] Park bus service indicators:

[0088] time 2023.11 2023.12 2024.01 2024.02 2024.03 2024.04 2024.05 2024.06 Number of people served 16117 15995 18130 11492 16955 16169 14799 13953 Full load rate 73.85％ 74.21％ 72.20％ 61.83％ 69.90％ 67.16％ 64.48％ 68.60％

[0089] In addition to the number of passengers served and the load factor of the park bus, it can also provide further charts such as bus route overview, QR code scanning passenger statistics, timetables, and provide interactive functions.

[0090]

[0091] Incoming and outgoing traffic indicators:

[0092] time 2024.01 2024.02 2024.03 2024.04 2024.05 2024.06 Incoming traffic 69812 49655 69055 67544 65068 107041 Traffic flow 119894 83197 114946 109812 104888 108675

[0093] In addition, parking management indicators can be combined with the crowd counting module to record the vehicles entering the underground garage, including license plate number, entry time, entry channel, pass type, and vehicle type; and can be searched by license plate number, entry time, and entry channel at the top of the monitoring page. The specific parking lot monitoring indicators are as follows:

[0094]

[0095] For parks that provide talent apartments, the following apartment abnormality warning indicators can also be considered:

[0096] time 2023.11 2023.12 2024.01 2024.02 2024.03 2024.04 2024.05 2024.06 Number of exceptions 515 473 486 518 387 542 528 371

[0097] More specifically, the present invention also incorporates indicators such as talent apartment overview, tenant statistics, monthly access control alarm statistics, and monthly entry and exit abnormal statistics into the crowd density index.

[0098]

[0099]

[0100] 3.4 Equipment Operation and Maintenance Module

[0101] The present invention provides intelligent equipment management and maintenance, and realizes intelligent management and maintenance of various types of equipment in the building through the Internet of Things technology. Specifically, through sensors and data acquisition equipment, the working status and energy consumption of the equipment are monitored in real time, and big data and artificial intelligence algorithms are used to evaluate the health of the equipment and predict faults, so that maintenance and repair can be carried out in advance to reduce the equipment failure rate and maintenance costs. For equipment operation monitoring indicators, the present invention also includes an equipment operation and maintenance module, which obtains the equipment model visualization configuration, equipment status monitoring and equipment fault diagnosis data from the Internet of Things platform, calculates the equipment operation monitoring indicators and incorporates them into the equipment operation and maintenance data.

[0102] 3.5 Intelligent security and access control:

[0103] The present invention uses security monitoring equipment and intelligent analysis algorithms to achieve real-time monitoring and early warning of park security, thereby ensuring the safety and order of the park. Specifically, building automation and security modules are used to monitor the operation of park cameras, access control and elevators, capture abnormal behaviors and intrusion events in real time, perform alarm processing through face recognition and behavior analysis, and update equipment operation and maintenance data in real time. The present invention connects the access control gate systems of various regions, conducts unified visitor management, and establishes safe and efficient visitor access process specifications.

[0104] 3.6 Electronic Patrol System

[0105] The electronic inspection system status is displayed in this system module, which are: scheduled inspection, historical track, electronic fence, tag, base station. The present invention uses equipment operation templates to form a unified standard specification and knowledge base for the inspection tasks of various equipment in the park; set equipment inspection plans, automatically generate inspection plan tasks and remind overdue tasks, and count the completion of inspection tasks in the background; add a system field "equipment status" to customize the equipment status during the inspection, select the equipment status when filling in the inspection form, and update the status information in the equipment ledger through inspection reporting of the equipment status; for equipment maintenance records, set the maintenance frequency according to different types of equipment, and then give warning information according to the frequency to enter the warning management.

[0106] 4. Park environment maintenance module

[0107] The park environment maintenance data is mainly developed from three dimensions: greening environment maintenance data, road environment maintenance data, and building environment maintenance data. The cumulative values ​​of the three-dimensional indicators within the monitoring period of each target park are used as the environmental maintenance data of each target park. The greening environment maintenance data mainly includes green area, greening rate, number of new vegetation types, number of days between vegetation maintenance, and cleanliness and health of vegetation before and after maintenance; road environment data includes road network density, road length and width, road surface flatness, cumulative road damage area ratio (including cracks, potholes, subsidence, delamination, undulations and other forms), maximum road damage depth ratio, minimum street lamp brightness, and cumulative street lamp lighting area; building environment data includes building area, businessable area, housing supporting area, number of public supporting parking spaces, number of elevators, and water supply, power supply, cooling, heating, and elevator operation monitoring indicators of each building.

[0108] The present invention provides a smart park property operation and maintenance management system based on big data. The cloud management platform inputs each classification data into the real-life space big data model for calculation; and converts the output content of the real-life space big data model into an index trend chart and a corresponding analysis report through an index data model output module. Specifically, the real-life space big data model selects various types of data from benchmark parks that meet the conditions as samples, calculates the initial data of each classification as a benchmark value, which is also a standard line for measuring the operation and development of the park; then divides the real-time updated data of each classification by the corresponding benchmark value and multiplies it by 100 to obtain each classification index, and performs weighted summation of each classification index to obtain a property operation and maintenance management index. It can also directly provide each classification index as needed, including a crowd density index, an energy consumption data index, an equipment operation and maintenance index, and a park environment maintenance index.

[0109] The sample data used for each classification index can be added, deleted or adjusted according to actual needs. The real-life space big data model can map each classification data to a continuous value space, score it from 0 to 100 points, normalize and standardize the scoring results, and then multiply it by the preset weight and include it in the classification index. Different preset weights can be set for different indicators, and the weights can be adjusted according to actual needs. In order to avoid large fluctuations in the property operation and maintenance management index, if the change range of a certain classification index exceeds the preset threshold, such as a rise or fall of 10%, the update data corresponding to the classification index will be handed over to the abnormal data receiving module for recording and processing, and the classification index will be included after the limit of the rise or fall, or the update data that caused the abnormality will be stripped and the classification index will be recalculated.

[0110] 5. Integrated alarm platform

[0111] The present invention centrally manages alarm information, pushes reminders in real time, integrates system alarms and manual alarms caused by patrol anomalies and inspection anomalies, such as Figure 4 As shown, each subsystem can be alerted separately, which will result in high labor costs and low efficiency in receiving alarms. By combining the functions of binding people and vehicles, and binding key equipment and relevant personnel, the integrated alarm platform can easily push system alarms, patrol abnormal manual alarms, and inspection abnormal manual alarms to the mobile terminals of relevant personnel in real time for reminders, thus achieving precise management.

[0112] In summary, the cloud management platform of the smart park property operation and maintenance management system based on big data provided by the present invention can integrate security systems, building automation systems, intelligent lighting systems, energy management systems, intelligent parking systems, etc. At the same time, a real-life space big data model is established using the cloud management platform, and with the help of technologies such as the Internet of Things, big data, and artificial intelligence, intelligent and refined management of various facilities and equipment in the park can be achieved, thereby improving the park's operating efficiency and service level. The present invention can intuitively measure the property management level of the park, solve the problem of comprehensive quantitative reference evaluation of property management in various smart parks, provide effective data support for the operation and maintenance management evaluation of smart parks, and effectively improve the management quality and efficiency of the park, thereby improving the vitality of park management development and achieving high-quality sustainable development of the park.

[0113] Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art may make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be based on the definition of the claims.

Claims

1. A smart park property operation and maintenance management system based on big data, characterized in that: include: Big data collection and processing module: collects various types of data of the smart park, including energy consumption data, crowd density data, equipment operation and maintenance data, and park environment maintenance data; Intelligent lighting and energy management module: obtain intelligent lighting energy saving and energy optimization operation data, and update energy consumption data in real time; Building automation and security module: monitors campus cameras, access control, and elevator operations, captures abnormal behaviors and intrusions in real time, issues alarms through face recognition and behavior analysis, and updates crowd density data and equipment operation and maintenance data in real time; Park environment maintenance module: Carry out environmental maintenance quality assessment of each target park during the monitoring period from three dimensions: greening environment maintenance data, road environment maintenance data and building environment maintenance data, and update the park environment maintenance data in real time; Cloud management platform: Establish a real-life space big data model to analyze the crowd density data of each target park during the monitoring period, and combine it with real-time updated energy consumption data, equipment operation and maintenance data, and park environment maintenance data to evaluate the property operation and maintenance management index of each target park during the monitoring period.

2. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The big data collection and processing module obtains multi-source heterogeneous data sources from the archive platform, data middle platform and Internet of Things platform, and uses conversion factors to convert the data obtained from the source data source into the form required by the destination data source according to business needs. During the conversion, the erroneous and inconsistent data are cleaned and processed; and a fusion integration platform is used to connect the data of various systems in the park, and all structured data are placed in the NoSQL database Hyperbase, and unified management is performed through metadata; the data sources include relational data, time series data, message data and request data.

3. The smart park property operation and maintenance management system based on big data as claimed in claim 1, characterized in that: The real-life space big data model selects each category data of benchmark parks that meet the conditions as samples, calculates the initial data of each category as a benchmark value; then divides the real-time updated data of each category by the corresponding benchmark value and multiplies by 100 to obtain each category index, and performs weighted summation on each category index to obtain the property operation and maintenance management index; the category indexes include a crowd density index, an energy consumption data index, an equipment operation and maintenance index, and a park environment maintenance index.

4. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The cloud management platform inputs the real-time updated energy consumption data, crowd density data, equipment operation and maintenance data and park environment maintenance data into the real-life space big data model for calculation; and converts the output content of the real-life space big data model into an index trend chart and a corresponding analysis report through an index data model output module.

5. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The crowd density data acquired by the real-life space big data model include campus bus service indicators and in-and-out vehicle flow indicators, and incorporate talent apartment overview, tenant statistics, monthly access control alarm statistics and monthly entry and exit abnormality statistics indicators into the crowd density index; at the same time, it provides functions for binding people and vehicles, key equipment and relevant personnel, and pushes system alarms, patrol abnormality manual alarms and inspection abnormality manual alarms to the mobile terminals of relevant personnel in real time on the integrated alarm platform for reminders.

6. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The real-life space big data model maps all enterprises and various equipment in the park to a real-life space for unified management. The specific processing process is as follows: S1: Provides visual configuration of equipment object models. The visual configuration realizes the full life cycle management of equipment data from input, data processing to output through drag-and-drop interface, and provides SQL execution, field conversion, and dynamic script rule engine controls. At the same time, it associates equipment, projects, and park enterprises to realize scene linkage; S2: Display the location distribution of enterprises in the park through maps, and display the enterprise points through map dot method; S3: Import the park enterprise list and display the enterprise list and main business data; Click on the company name to jump to the company details page and view the information data of each company; S4: Monitor the changes of enterprise information data in real time, dynamically obtain and identify the changes of various types of enterprise information data, and display them in the form of pages; S5: Display the number of devices and online rate of each park on the map. Click a park to pop up the device name, system name, device location, and device status of the park. Click a device to pop up the detailed information of the device. S6: The current rental and sales status and charging situation of operating assets are dynamically presented on the map. Through room status sharing, cross-regional resource coordination and refined spatial information management are achieved. The operating assets include hotels, offices, venues and conference rooms in the park.

7. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The intelligent lighting and energy management module realizes energy consumption prediction, energy efficiency analysis and energy consumption benchmarking management through real-time edge energy management and control; after comprehensive calculation and scoring, it generates updated energy consumption data and sends it to the real-life space big data model; specifically includes: Fine management of energy billing: Smart meters are used in conjunction with remote meter reading systems to accurately bill energy consumption in the park's public areas and tenants, avoiding inverted electricity bills. Abnormal tracking of energy conservation and emission reduction: Establish dynamic benchmarks based on historical data, compare and analyze predicted data, dynamic benchmarks and real-time data, and optimize the operating energy consumption of air conditioners, lighting and office equipment by identifying abnormal power consumption behaviors, so that power forecasts match demand forecasts; Use energy model trees to accurately predict energy consumption: Establish an energy management tree for each enterprise to achieve real-time collection of energy consumption data, automatic classification statistics, abnormal tracking and chart display; VRV energy-saving optimization: Coordinate with indoor human flow and temperature sensors to identify indoor environmental conditions, calculate indoor and outdoor enthalpy values ​​based on climate characteristics, and predict VRV preset temperature, wind speed, and operating mode; VRV panoramic control and energy saving: Add air quality sensors to areas where VRV air conditioners are used on site, and control the operation of the equipment according to outdoor temperature, indoor temperature and indoor air quality parameters to achieve VRV energy-saving operation.

8. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The intelligent lighting and energy management module uses zoning, timing and sensing to control the lighting of public areas for energy saving, specifically including: Use time scheduling control to control the start and stop of lighting fixtures according to the office hours of each area; Human infrared sensors are installed in public areas to perform sensing control on lighting fixtures; Separate the lighting control of the lighting area from that of other areas; monitor the illuminance of the lighting area so that artificial lighting can be automatically adjusted as the natural light intensity changes; Preset a variety of commonly used lighting scenes and select them to match.

9. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The building automation and security module obtains the visual configuration of equipment models, equipment status monitoring and equipment fault diagnosis data from the Internet of Things platform, stores and obtains maintenance information, elevator information and annual inspection information on the archive platform, monitors the working status and energy consumption of equipment in real time through sensors and data acquisition equipment, and uses big data and artificial intelligence algorithms to conduct equipment health assessment and fault prediction, and perform maintenance and repair in advance; After calculating and scoring, the equipment operation monitoring indicators are obtained and included in the equipment operation and maintenance data; And provide a real-time monitoring page for the selected elevator in the real-life space big data model.

10. The big data-based smart park property operation and maintenance management system according to claim 1, characterized in that: The real-life space big data model also includes the following: when the change range of a certain classification index exceeds a preset threshold, the updated data corresponding to the classification index is handed over to the abnormal data receiving module for recording and processing, and the classification index is included after the increase or decrease range is limited, or the updated data that causes the abnormality is stripped and the classification index is recalculated.

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