Real estate intelligent facility management method and system based on big data

Abstract

The invention relates to the technical field of intelligent facility management, in particular to a real estate intelligent facility management method and system based on big data, and the method comprises the steps: 1, collecting data in the operation process of an intelligent lighting facility, and obtaining various monitoring parameters for analyzing the fault management of the intelligent lighting facility; the intelligent lighting facility fault management method comprises the steps of 1, obtaining various management indexes of intelligent lighting facility fault management, 2, comparing the various management indexes obtained in the step 2 with a threshold value, identifying and managing a fault according to a comparison result, and generating a fault management strategy, and 4, evaluating a fault management result in the step 3 through a big data analysis technology. Step 5, carrying out man-machine interaction on an evaluation result obtained in the step 4; through fault analysis and management of the intelligent lighting facility, the probability and loss of fault occurrence are reduced, and the reliability of the lighting facility is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent facility management, and in particular to a real estate intelligent facility management method and system based on big data. Background Art

[0002] With the rapid development of the real estate industry, the number and types of smart facilities in real estate are increasing. Among them, smart lighting facilities have been widely used in modern living and working environments. The introduction of big data technology has enabled the massive collection and analysis of operating data of lighting facilities, providing strong support for fault warning and diagnosis. The Internet of Things technology has realized the remote monitoring and intelligent management of lighting facilities, further improving the efficiency and accuracy of fault management.

[0003] With the continuous development of science and technology, various faults will inevitably occur in the use of smart lighting facilities, causing inconvenience to users. Traditional fault management methods include: regular manual inspections of smart lighting facilities, where inspectors will record and judge the faults based on experience, and report them to the maintenance team for repair; through limited equipment monitoring data, when the system detects a fault, it will automatically trigger an alarm mechanism to notify management personnel to handle it.

[0004] However, as the scale and complexity of real estate smart facilities increase, fault management becomes an important challenge, and the shortcomings of traditional fault management methods gradually emerge. For example, manual inspections are not only time-consuming and labor-intensive, but also difficult to achieve comprehensive coverage and timely response, and it is difficult to quickly and accurately locate the fault point, resulting in low maintenance efficiency; limited monitoring equipment has problems such as inaccurate and untimely data and difficulty in making intelligent decisions; there is a lack of intelligent technical means and a lack of effective data collection and analysis. Therefore, in order to improve the management efficiency of smart lighting facilities, big data and Internet of Things technologies are combined to achieve intelligent management of smart lighting facility faults. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a real estate intelligent facility management method and system based on big data to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above purpose, the present invention provides the following technical solution: a real estate intelligent facility management method based on big data, comprising:

[0007] Step 1: Collect data from the operation of smart lighting facilities through sensor technology and monitoring equipment to obtain and analyze various monitoring parameters for fault management of smart lighting facilities;

[0008] Step 2: Analyze the monitoring parameters in step 1 through big data analysis technology to obtain various management indicators of intelligent lighting facility fault management, including operation status fault management analysis unit, environmental adaptability fault management analysis unit and energy consumption fault management analysis unit;

[0009] Step 3: Compare each management indicator obtained in step 2 with the threshold value, identify and manage the fault according to the comparison results, and generate a fault management strategy;

[0010] Step 4: Evaluate the results of the fault management in step 3 through big data analysis technology to obtain the evaluation index of the fault management of intelligent lighting facilities;

[0011] Step 5: Conduct human-computer interaction on the intelligent lighting facility fault management evaluation index of the evaluation result obtained in step 4.

[0012] Preferably, the operation state fault management and analysis unit in step 2: firstly, by using sensor technology, records the current data I and voltage data U of the operation of the intelligent lighting facility n times within the monitoring time t, and obtains the current fluctuation rate CFR, , where i∈n, I act,i represents the actual current value measured for the i-th time, μ(I) represents the average value of all measured current values, and the voltage abnormality rate VAR is obtained at the same time, VAR=Na / n, where Na represents the number of voltage abnormalities, and the number of times the voltage exceeds the normal range is recorded; then, the initial brightness L of the intelligent lighting facility during use is obtained through sensor technology. chu and the current brightness L now , get the brightness decay rate LDR, , where t_u represents the usage time of the intelligent lighting facility; secondly, the initial color temperature C is obtained through sensor technology. chu and the current color temperature C now , get the color temperature drift CSD, CSD=|C now -C chu | / C0, where C0 represents the standard value of color temperature; finally, the comprehensive operation status management index CRSI of intelligent lighting facilities is obtained through big data analysis technology. , where LDR0 represents the reference value of the brightness attenuation rate, and w1, w2, w3 and w4 represent the corresponding weight coefficients respectively.

[0013] Preferably, the environmental adaptability fault management and analysis unit in step 2 obtains the ambient light intensity Li, ambient humidity H, ambient dust accumulation Da, brightness Br of the lighting facility, and actual operating temperature range ΔT through the data recorded by the sensor technology and the intelligent lighting control system, and obtains the lighting adaptability index LA. , Br0 represents the set brightness, humidity adaptability index HA, , H max and H min They represent the maximum and minimum humidity allowed by lighting facilities, respectively. The temperature adaptability index TA, TA=|ΔT0-ΔT| / ΔT0, ΔT0 represents the normal operating temperature range, and the dust adaptability index DA, DA=(Da max -Da) / Da max ,Da max It represents the maximum amount of dust accumulation allowed by lighting facilities. Then, through big data analysis technology, the comprehensive environmental adaptability management index EAI of intelligent lighting facilities is obtained, EAI=a1×LA+a2×HA+a3×TA+a4×DA, where a1, a2, a3 and a4 represent the corresponding weights respectively.

[0014] Preferably, the energy consumption fault management and analysis unit in step 2: obtains the total energy consumption data En, the non-peak period energy consumption En(o), and the brightness Br of the lighting facilities within the monitoring time t through sensor technology, obtains the unit brightness energy consumption ULE, ULE=En / μ(Br), μ(Br) represents the average brightness, and obtains the non-peak period energy consumption ratio OPE, OPE=En(o) / En; obtains the initial energy consumption ratio Eer of the intelligent lighting facilities through time series analysis technology ini Compared with the current energy consumption Eer now , energy consumption ratio E_rate=En / t, and get the energy consumption ratio trend index ERCT, , t_u represents the usage time of the smart lighting facilities; then, through big data analysis technology, the comprehensive energy consumption management index CEI of smart lighting facilities is obtained. , where ERCT0 represents the reference value of the energy consumption ratio trend index, and b1 and b2 represent the corresponding weight coefficients respectively.

[0015] Preferably, the fault management strategy in step 3 comprises the following steps:

[0016] Step 3.1: Compare the comprehensive operation status management index CRSI of the intelligent lighting facilities obtained in the operation status fault management analysis unit with the threshold CRSI0 to obtain the risk coefficient η(CRSI) of the comprehensive operation status management index of the intelligent lighting facilities, η(CRSI)=(CRSI0-CRSI) / CRSI0, if η(CRSI)>0.5, it means that the operation status of the intelligent lighting facilities is poor, a high-risk level fault occurs, and the first-level management mechanism is triggered; if 0.2<η(CRSI)≤0.5, it means that the operation status of the intelligent lighting facilities is poor, a medium-risk level fault occurs, and the second-level management mechanism is triggered; if 0<η(CRSI)≤0.2, it means that the operation status of the intelligent lighting facilities is general, a low-risk level fault occurs, and the third-level management mechanism is triggered. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facilities, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive operation status management of the intelligent lighting facilities is good.

[0017] Preferably, the fault management strategy in step 3 comprises the following steps:

[0018] Step 3.2: Compare the comprehensive environmental adaptability management index EAI of the intelligent lighting facility obtained in the environmental adaptability fault management analysis unit with the threshold EAI0 to obtain the risk coefficient η(EAI) of the comprehensive environmental adaptability management index of the intelligent lighting facility, η(EAI)=(EAI0-EAI) / EAI0. If η(EAI)>0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a high-risk level fault occurs, and the first-level management mechanism is triggered. If 0.2<η(EAI)≤0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a medium-risk level fault occurs, and the second-level management mechanism is triggered. If 0<η(EAI)≤0.2, it means that the environmental adaptability of the intelligent lighting facility is general, a low-risk level fault occurs, and the third-level management mechanism is triggered. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive environmental adaptability management of the intelligent lighting facility is good.

[0019] Preferably, the fault management strategy in step 3 comprises the following steps:

[0020] Step 3.3: Compare the comprehensive energy consumption management index CEI of the intelligent lighting facility obtained in the energy consumption fault management analysis unit with the threshold CEI0 to obtain the risk coefficient η(CEI) of the comprehensive energy consumption management index of the intelligent lighting facility, η(CEI)=(CEI-CEI0) / CEI0, if η(CEI)>0.5, it means that the energy consumption of the intelligent lighting facility is high, and a high-risk level fault occurs, triggering the first-level management mechanism; if 0.2<η(CEI)≤0.5, it means that the energy consumption of the intelligent lighting facility is high, and a medium-risk level fault occurs, triggering the second-level management mechanism; if 0≤η(CEI)≤0.2, it means that the energy consumption of the intelligent lighting facility is average, and a low-risk level fault occurs, triggering the third-level management mechanism; according to the fault risk level, a fault management strategy is generated based on the fault case database, and the fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive energy consumption management of the intelligent lighting facility is good.

[0021] Preferably, in step 4, the intelligent lighting facility fault management evaluation index FEI is: , where T P represents the number of faults correctly diagnosed, T F represents the number of faults with incorrect diagnosis, nq represents the number of faults that meet the target fault handling time within the monitoring time period t, T_nq represents the total number of faults, S P Indicates the number of faults successfully resolved, S F represents the number of unresolved faults, and c1, c2, and c3 represent the corresponding weights.

[0022] Preferably, a real estate intelligent facility management system based on big data includes:

[0023] Real estate intelligent facility management center: through the Internet of Things technology, sensors and intelligent devices are installed on lighting facilities to collect intelligent facility management data in real time, build an intelligent lighting facility fault management database, and transmit fault information to the human-computer interaction module based on data analysis results;

[0024] Intelligent facility fault management data acquisition module: collects data during the operation of intelligent lighting facilities through the intelligent lighting facility fault management database, obtains and analyzes various monitoring parameters of intelligent lighting facility fault management, and transmits them to the intelligent facility fault management data analysis module;

[0025] Intelligent facility fault management data analysis module: used to analyze various monitoring parameters of the data acquisition module, obtain various management indicators of intelligent lighting facility fault management, and transmit them to the intelligent facility fault management module;

[0026] Intelligent facility fault management module: used to compare the various management indicators obtained by the data analysis module with the thresholds, identify faults and manage them according to the comparison results, and transmit the fault handling results to the intelligent facility fault management evaluation module;

[0027] Intelligent facility fault management evaluation module: used to evaluate the fault handling results obtained by the fault management module and transmit the evaluation results to the real estate intelligent facility management human-computer interaction module;

[0028] Real estate intelligent facility management human-computer interaction module: used to transmit the evaluation results obtained by the evaluation module to the management personnel information terminal for human-computer interaction.

[0029] Technical effects and advantages of the present invention:

[0030] 1. The present invention uses the Internet of Things technology and big data analysis technology to process and analyze a large amount of lighting facility operation data, environmental adaptability data and energy consumption data, including voltage, current, power, brightness, etc., to provide comprehensive data support for fault warning and intelligent diagnosis, and realize the refinement and scientificization of facility management;

[0031] 2. The present invention reduces manual intervention and realizes automated management through big data analysis and intelligent decision-making; reduces management costs, and accurate maintenance plans and repair plans reduce unnecessary facility replacement and repair costs; improves the reliability and safety of facilities, timely discovers and handles facility failures and potential risks, and ensures the safety and comfort of residents' lives;

[0032] 3. The present invention provides a scientific basis for real estate intelligent facilities through fault analysis and management of intelligent lighting facilities. Through data analysis and decision support, it can quickly locate the fault point and give maintenance suggestions, thereby reducing the probability of fault occurrence and losses; quickly and accurately diagnose faults and shorten maintenance time; improve the reliability and stability of lighting facilities and enhance user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall process of the present invention.

[0034] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0035] 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.

[0036] See also Figure 1 As shown, the present invention provides a real estate intelligent facility management system based on big data, including: a real estate intelligent facility management center, an intelligent facility fault management data acquisition module, an intelligent facility fault management data analysis module, an intelligent facility fault management module, an intelligent facility fault management evaluation module and a real estate intelligent facility management human-computer interaction module.

[0037] The real estate intelligent facility management center is connected to all modules, the intelligent facility fault management data acquisition module is connected to the intelligent facility fault management data analysis module, the intelligent facility fault management module is respectively connected to the intelligent facility fault management evaluation module and the intelligent facility fault management data analysis module, and the real estate intelligent facility management human-computer interaction module is connected to the intelligent facility fault management evaluation module.

[0038] Real estate intelligent facility management center: through the Internet of Things technology, sensors and intelligent devices are installed on lighting facilities to collect intelligent facility management data in real time, build an intelligent lighting facility fault management database, and transmit fault information to the human-computer interaction module based on data analysis results;

[0039] What needs to be specifically explained in this embodiment is that the intelligent lighting facility fault management database includes but is not limited to operating status, detection data, maintenance data, environmental data, equipment energy consumption data, fault case data, lighting facility location data, etc., and is connected to all modules in the system to receive and store data from each module, and at the same time update the intelligent lighting facility fault management database in real time.

[0040] Intelligent facility fault management data acquisition module: collects data during the operation of intelligent lighting facilities through the intelligent lighting facility fault management database, obtains and analyzes various monitoring parameters of intelligent lighting facility fault management, and transmits them to the intelligent facility fault management data analysis module;

[0041] Intelligent facility fault management data analysis module: used to analyze various monitoring parameters of the data acquisition module, obtain various management indicators of intelligent lighting facility fault management, and transmit them to the intelligent facility fault management module;

[0042] Intelligent facility fault management module: used to compare the various management indicators obtained by the data analysis module with the thresholds, identify faults and manage them according to the comparison results, and transmit the fault handling results to the intelligent facility fault management evaluation module;

[0043] Intelligent facility fault management evaluation module: used to evaluate the fault handling results obtained by the fault management module and transmit the evaluation results to the real estate intelligent facility management human-computer interaction module;

[0044] Real estate intelligent facility management human-computer interaction module: used to transmit the evaluation results obtained by the evaluation module to the management personnel information terminal for human-computer interaction.

[0045] See also Figure 2 As shown, a real estate intelligent facility management method based on big data includes: step 1: collecting data during the operation of intelligent lighting facilities through sensor technology and monitoring equipment, and obtaining and analyzing various monitoring parameters for intelligent lighting facility fault management; step 2: analyzing various monitoring parameters in step 1 through big data analysis technology, and obtaining various management indicators for intelligent lighting facility fault management, including an operation status fault management analysis unit, an environmental adaptability fault management analysis unit, and an energy consumption fault management analysis unit; step 3: comparing various management indicators obtained in step 2 with thresholds respectively, identifying and managing faults according to the comparison results, and generating a fault management strategy; step 4: evaluating the results of the fault management in step 3 through big data analysis technology, and obtaining intelligent lighting facility fault management evaluation indicators; step 5: performing human-computer interaction on the evaluation results obtained in step 4, and obtaining intelligent lighting facility fault management evaluation indicators.

[0046] Step 1: Through sensor technology and monitoring equipment, collect data during the operation of intelligent lighting facilities, and obtain and analyze various monitoring parameters for fault management of intelligent lighting facilities, including administrator identity authentication information, operation status parameters, environmental adaptability parameters and energy consumption parameters. The operation status parameters include current data, voltage data, brightness and color temperature of lighting facilities; environmental adaptability parameters include ambient light intensity, ambient humidity, ambient dust accumulation, brightness of lighting facilities, actual working temperature and dust accumulation; energy consumption parameters include total energy consumption data of lighting facilities, energy consumption during non-peak hours, and brightness;

[0047] Step 2: Analyze the monitoring parameters of step 1 through big data analysis technology to obtain various management indicators of intelligent lighting facility fault management, including operation status fault management analysis unit, environmental adaptability fault management analysis unit and energy consumption fault management analysis unit. The parameter analysis includes the following steps:

[0048] Step 2.1: Operation status fault management and analysis unit: During the operation of the smart lighting facility, firstly, through sensor technology, the current data I and voltage data U of the smart lighting facility operation n times are recorded within the monitoring time t, and the current fluctuation rate CFR is obtained. , where i∈n, I act,irepresents the actual current value measured for the i-th time, μ(I) represents the average value of all measured current values, and the voltage abnormality rate VAR is obtained at the same time, VAR=Na / n, where Na represents the number of voltage abnormalities, and the number of times the voltage exceeds the normal range is recorded; then, the initial brightness L of the intelligent lighting facility during use is obtained through sensor technology. chu and the current brightness L now , get the brightness decay rate LDR, , where t_u represents the usage time of the smart lighting facility, which can be measured in years; secondly, the initial color temperature C is obtained through sensor technology. chu and the current color temperature C now , get the color temperature drift CSD, CSD=|C now -C chu | / C0, where C0 represents the standard value of color temperature; finally, the comprehensive operation status management index CRSI of intelligent lighting facilities is obtained through big data analysis technology. , where LDR0 represents the reference value of the brightness attenuation rate, w1, w2, w3 and w4 represent the corresponding weight coefficients, for example, w1=0.3, w2=0.3, w3=0.2 and w4=0.2;

[0049] What needs to be specifically explained in this embodiment is that by analyzing the operating status parameters of the intelligent lighting facilities and integrating multiple sub-data (current fluctuation rate, voltage abnormality rate, brightness decay rate, color temperature drift), the operating status of the intelligent lighting facilities is fully reflected. By real-time monitoring and recording of these sub-data, CRSI can issue an early warning before the facilities fail.

[0050] Step 2.2: Environmental adaptability fault management and analysis unit: During the operation of intelligent lighting facilities, the data recorded by sensor technology and intelligent lighting control system are used to obtain the ambient light intensity Li, ambient humidity H, ambient dust accumulation Da, brightness Br of lighting facilities, and actual operating temperature range ΔT. In high or low temperature environments, ΔT is obtained from the difference between the upper limit of the temperature and the lower limit of the temperature. High temperature refers to a temperature greater than or equal to the upper limit of the normal temperature, and low temperature refers to a temperature less than or equal to the lower limit of the normal temperature. The light adaptability index LA is obtained. , Br0 represents the set brightness, humidity adaptability index HA, , H max and H min They represent the maximum and minimum humidity allowed by lighting facilities, respectively. The temperature adaptability index TA, TA=|ΔT0-ΔT| / ΔT0, ΔT0 represents the normal operating temperature range, and the dust adaptability index DA, DA=(Da max -Da) / Da max ,Da maxIndicates the maximum dust accumulation allowed by lighting facilities; then, through big data analysis technology, the comprehensive environmental adaptability management index EAI of intelligent lighting facilities is obtained, EAI=a1×LA+a2×HA+a3×TA+a4×DA, a1, a2, a3 and a4 represent the corresponding weights, for example, a1=0.3, a2=0.3, a3=0.2 and a4=0.2;

[0051] What needs to be specifically explained in this embodiment is that by monitoring the environment in which the smart lighting facilities are located, the adaptability of the smart lighting facilities under different environmental conditions can be comprehensively and accurately evaluated; through real-time monitoring and analysis of various sub-data, such as inaccurate light adjustment, too high or too low temperature, excessive humidity or excessive dust accumulation, these abnormal performances are often precursors to failures, so EAI helps to predict and prevent failures in advance and reduce service interruptions and losses caused by failures.

[0052] Step 2.3: Energy consumption fault management and analysis unit: During the operation of intelligent lighting facilities, the total energy consumption data En, non-peak period energy consumption En(o), and brightness Br of the lighting facilities are obtained through sensor technology within the monitoring time t. The time period when the total energy consumption of the lighting facilities is less than the threshold is obtained according to the historical data records during the non-peak period, and the unit brightness energy consumption ULE is obtained, ULE=En / μ(Br), μ(Br) represents the average brightness, and the non-peak period energy consumption ratio OPE is obtained, OPE=En(o) / En; the initial energy consumption ratio Eer of the intelligent lighting facilities is obtained through time series analysis technology ini Compared with the current energy consumption Eer now , energy consumption ratio E_rate=En / t, and get the energy consumption ratio trend index ERCT, , t_u represents the usage time of smart lighting facilities, which can be measured in years; then, through big data analysis technology, the comprehensive energy consumption management index CEI of smart lighting facilities is obtained. , where ERCT0 represents the reference value of the energy consumption ratio trend index, b1 and b2 represent the corresponding weight coefficients, for example, b1=0.5, b2=0.5;

[0053] What needs to be specifically explained in this embodiment is that by analyzing the energy consumption parameters, abnormal energy consumption of the intelligent lighting facilities can be discovered in time, and possible fault points can be predicted, thereby reminding the management personnel to perform preventive maintenance.

[0054] Step 3: Compare each management indicator obtained in step 2 with the threshold value, identify and manage the fault according to the comparison result, and generate a fault management strategy, including the following steps:

[0055] Step 3.1: Compare the comprehensive operation status management index CRSI of the intelligent lighting facility obtained in the operation status fault management analysis unit with the threshold CRSI0 to obtain the risk coefficient η(CRSI) of the comprehensive operation status management index of the intelligent lighting facility, η(CRSI)=(CRSI0-CRSI) / CRSI0. If η(CRSI)>0.5, it means that the operation status of the intelligent lighting facility is poor, a high-risk level fault is triggered, and the first-level management mechanism is triggered. If 0.2<η(CRSI)≤0.5, it means that the operation status of the intelligent lighting facility is poor, a medium-risk level fault is triggered, and the second-level management mechanism is triggered. If 0<η(CRSI) SI)≤0.2, indicating that the intelligent lighting facility is in general operation state, with a low-risk level fault, triggering the three-level management mechanism. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures. The fault management strategy is interactively communicated through the management center, and the location of the fault lighting facility is obtained from the management center. For example, the fault risk level is high risk, the fault content is a circuit fault, the management mechanism is a first-level comprehensive inspection, and the management measure is to check the power circuit. Otherwise, it means that the comprehensive operation state of the intelligent lighting facility is well managed.

[0056] Step 3.2: Compare the comprehensive environmental adaptability management index EAI of the intelligent lighting facility obtained in the environmental adaptability fault management analysis unit with the threshold EAI0 to obtain the risk coefficient η(EAI) of the comprehensive environmental adaptability management index of the intelligent lighting facility, η(EAI)=(EAI0-EAI) / EAI0. If η(EAI)>0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a high-risk level fault is triggered, and the first-level management mechanism is triggered. If 0.2<η(EAI)≤0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a medium-risk level fault is triggered, and the second-level management mechanism is triggered. If 0<η(EAI)≤0.2, it means that the intelligent lighting facility has poor environmental adaptability, a medium-risk level fault is triggered, and the second-level management mechanism is triggered. The environmental adaptability of the intelligent lighting facilities is general, and the low-risk level fault triggers the three-level management mechanism. According to the fault risk level, the fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures. The fault management strategy is interactively communicated through the management center, and the location of the fault lighting facility is obtained according to the management center. For example, if the fault risk level is medium risk, the fault content is that the light source of the intelligent lighting facility responds slowly, the management mechanism is the second-level inspection of the sensor components, and the management measure is to replace the faulty light sensor; otherwise, it means that the comprehensive environmental adaptability management of the intelligent lighting facilities is good;

[0057] Step 3.3: Compare the comprehensive energy consumption management index CEI of the intelligent lighting facility obtained in the energy consumption fault management analysis unit with the threshold CEI0 to obtain the risk coefficient η(CEI) of the comprehensive energy consumption management index of the intelligent lighting facility, η(CEI)=(CEI-CEI0) / CEI0. If η(CEI)>0.5, it means that the energy consumption of the intelligent lighting facility is high, a high-risk level fault, triggering the first-level management mechanism; if 0.2<η(CEI)≤0.5, it means that the energy consumption of the intelligent lighting facility is high, a medium-risk level fault, triggering the second-level management mechanism; if 0≤η(CEI≤0.2, it means that the energy consumption of the intelligent lighting facility is average, low Risk level faults trigger a three-level management mechanism. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures. The fault management strategy is interactively communicated through the management center. The location of the fault lighting facility is obtained from the management center. For example, if the fault risk level is low risk and the fault content is that the equipment is not turned off or adjusted to low power consumption mode during non-essential periods, the management mechanism is a three-level inspection of the light source configuration, and the management measure is to reduce energy consumption during non-essential periods through switch control. Otherwise, it means that the comprehensive energy consumption management of the intelligent lighting facility is good.

[0058] Step 4: Use big data analysis technology to evaluate the results of fault management in step 3 and obtain the intelligent lighting facility fault management evaluation index FEI. , where T P represents the number of faults correctly diagnosed, T F represents the number of faults with incorrect diagnosis, nq represents the number of faults that meet the target fault handling time within the monitoring period t, and the handling time refers to the time from the discovery of the fault to the start of fault handling. The target handling time is set according to business needs, such as 1 hour or 5 hours, T_nq represents the total number of faults, and S P Indicates the number of faults successfully resolved, S F represents the number of unresolved faults, c1, c2, and c3 represent the corresponding weights, for example, c1=0.4, c2=0.3, and c3=0.3;

[0059] Step 5: Perform human-computer interaction on the intelligent lighting facility fault management evaluation index of the evaluation result obtained in step 4. If the intelligent lighting facility fault management evaluation index belongs to the set allowable range, it means that the intelligent facility fault management is good. Otherwise, the administrator is prompted to take timely measures to automatically adjust the switch status according to the operating status and load conditions of the intelligent lighting facility to ensure the normal operation of the facility and extend its service life; according to the intelligent facility fault risk level, ensure that key faults are handled first.

[0060] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0061] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A real estate intelligent facility management method based on big data, characterized in that: include: Step 1: Collect data from the operation of smart lighting facilities through sensor technology and monitoring equipment to obtain and analyze various monitoring parameters for fault management of smart lighting facilities; Step 2: Analyze the monitoring parameters in step 1 through big data analysis technology to obtain various management indicators of intelligent lighting facility fault management, including operation status fault management analysis unit, environmental adaptability fault management analysis unit and energy consumption fault management analysis unit; Step 3: Compare each management indicator obtained in step 2 with the threshold value, identify and manage the fault according to the comparison results, and generate a fault management strategy; Step 4: Evaluate the results of the fault management in step 3 through big data analysis technology to obtain the evaluation index of the fault management of intelligent lighting facilities; Step 5: Conduct human-computer interaction on the intelligent lighting facility fault management evaluation index of the evaluation result obtained in step 4.

2. A real estate intelligent facility management method based on big data according to claim 1, characterized in that: The operation state fault management and analysis unit in step 2: firstly, through sensor technology, records the current data I and voltage data U of the operation of the intelligent lighting facility n times within the monitoring time t, and obtains the current fluctuation rate CFR, , where i∈n, I act,i represents the actual current value measured for the i-th time, μ(I) represents the average value of all measured current values, and the voltage abnormality rate VAR is obtained at the same time, VAR=Na / n, where Na represents the number of voltage abnormalities, and the number of times the voltage exceeds the normal range is recorded; then, the initial brightness L of the intelligent lighting facility during use is obtained through sensor technology. chu and the current brightness L now , get the brightness decay rate LDR, , where t_u represents the usage time of the intelligent lighting facility; secondly, the initial color temperature C is obtained through sensor technology. chu and the current color temperature C now , get the color temperature drift CSD, CSD=|C now -C chu | / C0, where C0 represents the standard value of color temperature; finally, the comprehensive operation status management index CRSI of intelligent lighting facilities is obtained through big data analysis technology. , where LDR0 represents the reference value of the brightness attenuation rate, and w1, w2, w3 and w4 represent the corresponding weight coefficients respectively.

3. The real estate intelligent facility management method based on big data according to claim 1, characterized in that: The environmental adaptability fault management and analysis unit in step 2 obtains the ambient light intensity Li, ambient humidity H, ambient dust accumulation Da, brightness Br of the lighting facility, and actual operating temperature range ΔT through the data recorded by the sensor technology and the intelligent lighting control system, and obtains the lighting adaptability index LA. , Br0 represents the set brightness, humidity adaptability index HA, , H max and H min They represent the maximum and minimum humidity allowed by lighting facilities, respectively. The temperature adaptability index TA, TA=|ΔT0-ΔT| / ΔT0, ΔT0 represents the normal operating temperature range, and the dust adaptability index DA, DA=(Da max -Da) / Da max ,Da max It represents the maximum amount of dust accumulation allowed by lighting facilities. Then, through big data analysis technology, the comprehensive environmental adaptability management index EAI of intelligent lighting facilities is obtained, EAI=a1×LA+a2×HA+a3×TA+a4×DA, where a1, a2, a3 and a4 represent the corresponding weights respectively.

4. The real estate intelligent facility management method based on big data according to claim 1, characterized in that: The energy consumption fault management and analysis unit in step 2: obtains the total energy consumption data En, the non-peak period energy consumption En(o), and the brightness Br of the lighting facilities within the monitoring time t through sensor technology, obtains the unit brightness energy consumption ULE, ULE=En / μ(Br), μ(Br) represents the average brightness, and obtains the non-peak period energy consumption ratio OPE, OPE=En(o) / En; obtains the initial energy consumption ratio Eer of the intelligent lighting facilities through time series analysis technology ini Compared with the current energy consumption Eer now , energy consumption ratio E_rate=En / t, and get the energy consumption ratio trend index ERCT, , t_u represents the usage time of the smart lighting facilities; then, through big data analysis technology, the comprehensive energy consumption management index CEI of smart lighting facilities is obtained. , where ERCT0 represents the reference value of the energy consumption ratio trend index, and b1 and b2 represent the corresponding weight coefficients respectively.

5. The real estate intelligent facility management method based on big data according to claim 1 is characterized by: The fault management strategy in step 3 includes the following steps: Step 3.1: Compare the comprehensive operation status management index CRSI of the intelligent lighting facilities obtained in the operation status fault management analysis unit with the threshold CRSI0 to obtain the risk coefficient η(CRSI) of the comprehensive operation status management index of the intelligent lighting facilities, η(CRSI)=(CRSI0-CRSI) / CRSI0, if η(CRSI)>0.5, it means that the operation status of the intelligent lighting facilities is poor, a high-risk level fault occurs, and the first-level management mechanism is triggered; if 0.2<η(CRSI)≤0.5, it means that the operation status of the intelligent lighting facilities is poor, a medium-risk level fault occurs, and the second-level management mechanism is triggered; if 0<η(CRSI)≤0.2, it means that the operation status of the intelligent lighting facilities is general, a low-risk level fault occurs, and the third-level management mechanism is triggered. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facilities, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive operation status management of the intelligent lighting facilities is good.

6. The real estate intelligent facility management method based on big data according to claim 1, characterized in that: The fault management strategy in step 3 includes the following steps: Step 3.2: Compare the comprehensive environmental adaptability management index EAI of the intelligent lighting facility obtained in the environmental adaptability fault management analysis unit with the threshold EAI0 to obtain the risk coefficient η(EAI) of the comprehensive environmental adaptability management index of the intelligent lighting facility, η(EAI)=(EAI0-EAI) / EAI0. If η(EAI)>0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a high-risk level fault occurs, and the first-level management mechanism is triggered. If 0.2<η(EAI)≤0.5, it means that the environmental adaptability of the intelligent lighting facility is poor, a medium-risk level fault occurs, and the second-level management mechanism is triggered. If 0<η(EAI)≤0.2, it means that the environmental adaptability of the intelligent lighting facility is general, a low-risk level fault occurs, and the third-level management mechanism is triggered. According to the fault risk level, a fault management strategy is generated based on the fault case database. The fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive environmental adaptability management of the intelligent lighting facility is good.

7. The real estate intelligent facility management method based on big data according to claim 1, characterized in that: The fault management strategy in step 3 includes the following steps: Step 3.3: Compare the comprehensive energy consumption management index CEI of the intelligent lighting facility obtained in the energy consumption fault management analysis unit with the threshold CEI0 to obtain the risk coefficient η(CEI) of the comprehensive energy consumption management index of the intelligent lighting facility, η(CEI)=(CEI-CEI0) / CEI0, if η(CEI)>0.5, it means that the energy consumption of the intelligent lighting facility is high, and a high-risk level fault occurs, triggering the first-level management mechanism; if 0.2<η(CEI)≤0.5, it means that the energy consumption of the intelligent lighting facility is high, and a medium-risk level fault occurs, triggering the second-level management mechanism; if 0≤η(CEI)≤0.2, it means that the energy consumption of the intelligent lighting facility is average, and a low-risk level fault occurs, triggering the third-level management mechanism; according to the fault risk level, a fault management strategy is generated based on the fault case database, and the fault management strategy includes the fault risk level, the location of the fault lighting facility, the fault content, the management mechanism and the management measures, and the fault management strategy is interacted with the human-computer through the management center; otherwise, it means that the comprehensive energy consumption management of the intelligent lighting facility is good.

8. The real estate intelligent facility management method based on big data according to claim 1 is characterized by: In step 4, the intelligent lighting facility fault management evaluation index FEI is: , where T P represents the number of faults correctly diagnosed, T F represents the number of faults with incorrect diagnosis, nq represents the number of faults that meet the target fault handling time within the monitoring time period t, T_nq represents the total number of faults, S P Indicates the number of faults successfully resolved, S F represents the number of unresolved faults, and c1, c2, and c3 represent the corresponding weights.

9. A real estate intelligent facility management system based on big data, used to use the real estate intelligent facility management method based on big data described in any one of claims 1 to 8, comprising: Real estate intelligent facility management center: through the Internet of Things technology, sensors and intelligent devices are installed on lighting facilities to collect intelligent facility management data in real time, build an intelligent lighting facility fault management database, and transmit fault information to the human-computer interaction module based on data analysis results; Intelligent facility fault management data acquisition module: collects data during the operation of intelligent lighting facilities through the intelligent lighting facility fault management database, obtains and analyzes various monitoring parameters of intelligent lighting facility fault management, and transmits them to the intelligent facility fault management data analysis module; Intelligent facility fault management data analysis module: used to analyze various monitoring parameters of the data acquisition module, obtain various management indicators of intelligent lighting facility fault management, and transmit them to the intelligent facility fault management module; Intelligent facility fault management module: used to compare the various management indicators obtained by the data analysis module with the thresholds, identify faults and manage them according to the comparison results, and transmit the fault handling results to the intelligent facility fault management evaluation module; Intelligent facility fault management evaluation module: used to evaluate the fault handling results obtained by the fault management module and transmit the evaluation results to the real estate intelligent facility management human-computer interaction module; Real estate intelligent facility management human-computer interaction module: used to transmit the evaluation results obtained by the evaluation module to the management personnel information terminal for human-computer interaction.

Images