Building operation monitoring and scheduling system and method based on big data analysis

Abstract

The invention provides a building operation monitoring and scheduling system and method based on big data analysis. The system comprises a monitoring module, a task issuing module and a scheduling module. Wherein the monitoring module monitors each device in a building; the task issuing module issues tasks based on the monitored data and an event monitoring library obtained through big data analysis; and the scheduling module carries out scheduling processing on the released tasks. According to the building operation monitoring and scheduling system based on big data analysis, when an event occurs, scheduling processing can be carried out in time.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and particularly to a building operation monitoring and scheduling system and method based on big data analysis. Background Art

[0002] One of the characteristics of intelligent buildings is building automation; building automation uses computer distributed control, which means centralized management of decentralized control. Usually, a direct digital controller (DDC) is used, and a host computer is used for monitoring and management of the screen. The main means include animation, curves, text, databases, scripts, and various special controls, etc. Building automation includes: air conditioning and ventilation monitoring, water supply and drainage monitoring, lighting monitoring, power supply monitoring, elevator operation monitoring, integrated security, fire monitoring, and structured integrated wiring; however, the existing building automation still stays at the monitoring stage, and the scheduling mainly relies on manual operation, which is quite dependent on the experience of personnel and cannot respond to events emergently, that is, cannot achieve timely scheduling and processing. Summary of the Invention

[0003] One of the purposes of the present invention is to provide a building operation monitoring and scheduling system based on big data analysis, which can perform timely scheduling and processing when an event occurs.

[0004] A building operation monitoring and scheduling system based on big data analysis provided by an embodiment of the present invention includes: a monitoring module, a task publishing module, and a scheduling module; wherein, the monitoring module monitors each device in the building; the task publishing module publishes tasks based on the monitored data and an event monitoring library obtained through big data analysis; the scheduling module performs scheduling and processing on the published tasks.

[0005] Preferably, the task publishing module performs the following operations:

[0006] Screen relevant data from the monitored data according to the corresponding data requirements of each monitoring item in the event monitoring library;

[0007] When the relevant data meets the trigger condition, generate a task corresponding to the monitoring item and publish it.

[0008] Preferably, the scheduling module performs the following operations:

[0009] Place the published task into the task queue;

[0010] When the tasks in the task queue are not unique, determine the priority value of each task based on a pre-configured priority analysis library;

[0011] Process the event tasks in descending order of the priority value;

[0012] During the emergency pre - processing of a task, monitor the changes in the monitoring data related to other tasks in the task queue, perform quantitative analysis on the changes according to the pre - configured first correlation analysis library, and determine the first correlation value;

[0013] Perform quantitative analysis on the differences in the occurrence locations and event types between the task and other tasks in the task queue according to the pre - configured second correlation analysis library, and determine the second correlation value;

[0014] Merge other tasks whose sum of the first correlation value and the second correlation value is greater than or equal to the preset threshold to generate a task to be processed;

[0015] Send the task to be processed to the pre - configured task group.

[0016] Preferably, based on the pre - configured priority analysis library, determine the priority values of each task, including:

[0017] Extract features of the task, and based on the extracted feature parameters, construct a description parameter set;

[0018] Retrieve the priority value corresponding to the description parameter set from the priority set analysis library;

[0019] Among them, the feature parameters include: parameters representing the type of the task, parameters representing the affected area of the task, and parameters representing the type and quantity of equipment in the building corresponding to the task.

[0020] Preferably, the scheduling module also performs the following operations:

[0021] When scheduling for elevator busy, perform an analysis of the elevator ride demand for the elevator group corresponding to the task to obtain elevator ride demand data;

[0022] Analyze the elevator operation data to obtain elevator operation data;

[0023] Generate an elevator scheduling plan based on the elevator operation data and the elevator ride demand data.

[0024] The present invention also provides a building operation monitoring and scheduling method based on big data analysis, including:

[0025] Monitor each device in the building;

[0026] Based on the monitored data and the event monitoring library obtained through big data analysis, issue tasks;

[0027] Perform scheduling processing on the issued tasks.

[0028] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description and drawings.

[0029] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Brief Description of the Drawings

[0030] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0031] Figure 1 It is a schematic diagram of a building operation monitoring and scheduling system based on big data analysis in an embodiment of the present invention;

[0032] Figure 2 It is a schematic diagram of a building operation monitoring and scheduling method based on big data analysis in an embodiment of the present invention. Detailed Embodiments

[0033] The preferred embodiments of the present invention will be described below with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not intended to limit the present invention.

[0034] An embodiment of the present invention provides a building operation monitoring and scheduling system based on big data analysis, as Figure 1 shown, including: a monitoring module 1, a task publishing module 2, and a scheduling module 3; wherein, the monitoring module 1 monitors each device in the building; the task publishing module 2 publishes tasks based on the monitored data and the event monitoring library obtained through big data analysis; the scheduling module 3 performs scheduling processing on the published tasks.

[0035] The building operation monitoring and scheduling system based on big data analysis in this embodiment analyzes the data (usually data on the operation status) of each device in the building monitored by the monitoring module through big data analysis to obtain an event monitoring library, so as to determine whether an event occurs. When an event occurs, a corresponding task is generated; then the scheduling module performs scheduling processing on the task; the tasks include: emergency control tasks for each device, regional drainage tasks, regional water supply control tasks, regional power control tasks, elevator busy-time scheduling tasks, etc.; the event monitoring library is used to monitor and analyze the operation of each device in the building to automatically discover problems and timely detect the events that occur; when an event occurs, timely scheduling processing is realized through the scheduling module; for simple events, such as emergency control tasks for each device, they can be directly controlled by remote control; however, for complex tasks that require manual coordination, such as regional drainage, the relevant devices can be automatically and emergently controlled in the first step to avoid the expansion of the impact, and then the task team is notified to handle it, thus realizing the whole-process emergency handling.

[0036] Among them, the monitoring of the operation status of each device can be realized through a data acquisition device and a status monitoring device associated with each device; the data acquisition device is directly connected to the controller of the device and is used to collect the data of the controller of the device, so as to realize the analysis of the operation status of the device; the status monitoring device includes: various monitoring sensors (such as current, voltage, angle, water depth, flow rate, etc.) for monitoring the status of the pipeline and each component corresponding to the device; the technology in the aspect of device monitoring is already quite mature and will not be elaborated here.

[0037] Among them, the task publishing module 2 performs the following operations:

[0038] According to the corresponding data requirements of each monitoring item in the event monitoring library, relevant data are screened out from the monitored data; when configuring the event monitoring library, the types of required data corresponding to each monitoring item and the target objects (devices) corresponding to the data are specified, so only the monitored data needs to be associated with the monitoring items;

[0039] When the relevant data meet the triggering conditions, a task corresponding to the monitoring item is generated and published. The triggering conditions are also configured in the event monitoring library, and different monitoring items correspond to different triggering conditions. For example: some are threshold triggers, and some are trend triggers; among them, the threshold trigger means that the monitored data is greater than or equal to the preset threshold, and at this time the triggering conditions are met. The trend trigger is to analyze a preset number of data, and when the obtained change trend conforms to the set trend, the release of the task is triggered.

[0040] Generally, tasks are generated one by one, and in this case, they only need to be processed in the order of generation. However, there are still situations where multiple tasks are generated in an instant. In this case, how to process the tasks in an orderly manner can ensure the processing efficiency of the tasks. Therefore, the scheduling module 3 performs the following operations:

[0041] Place the released tasks into the task queue;

[0042] When the tasks in the task queue are not unique, based on the pre-configured priority analysis library, determine the priority values of each task. The priority analysis library is pre-analyzed and configured to analyze the priority values of each task, that is, to quantify the specific situations of each task, which is convenient for the orderly processing of tasks;

[0043] Process the tasks in descending order of the priority values. The larger the priority value, the more important the event task is and needs to be processed first. The importance is reflected in the impacts on losses, safety, etc. that can be brought;

[0044] During the emergency preprocessing of the tasks, monitor the changes in the monitoring data related to other tasks in the task queue, and perform quantitative analysis on the changes based on the pre-configured first association analysis library to determine the first association value. The changes can be manifested as the change direction and change speed of the monitoring data. The change direction can be positive or negative, and the change speed is the change amount per unit time;

[0045] Based on the pre-configured second association analysis library, perform quantitative analysis on the differences in the occurrence locations and event types between the task and other tasks in the task queue to determine the second association value. Here, the occurrence location of the task is essentially the affected area corresponding to the task (taking the drainage task as an example, the corresponding affected area is the area that needs to be drained). The determination of the location difference first involves determining parameters such as whether the areas overlap, the distance between the center points of the areas of the two tasks, and the proportion of the areas corresponding to each event in the overlapping area. Then, arrange the type codes corresponding to the types of the two tasks and the determined parameters in a fixed order to form an analysis parameter set. Retrieve the corresponding second association value from the second association analysis library based on the analysis parameter set. Among them, the second association value in the second association analysis library is in one-to-one correspondence with the analysis parameter set;

[0046] Merge other tasks whose sum of the first association value and the second association value is greater than or equal to the preset threshold to generate tasks to be processed. The first association value represents the correlation between two tasks analyzed from the mutual influence, and the second association value represents the correlation between two tasks analyzed from the spatial association. Combining the two can more accurately judge the correlation between two tasks;

[0047] Send the task to be processed to a pre-configured task team; sending relevant event tasks to the task team together can facilitate the task team to better process the event tasks.

[0048] Among them, based on a pre-configured priority analysis library, determine the priority values of each task, including:

[0049] Extract features from the task, and based on the extracted feature parameters, construct a description parameter set;

[0050] Retrieve the priority value corresponding to the description parameter set from the priority set analysis library;

[0051] Among them, the feature parameters include: parameters representing the type of the task, parameters representing the impact area of the task, and parameters representing the type and quantity of the equipment in the building corresponding to the task. Comprehensively analyzing the type of the task, the impact area, and the type and quantity of the involved equipment can accurately control the importance of the task and better arrange the processing order of the task.

[0052] In order to achieve the reasonable utilization of elevators, in one embodiment, the scheduling module also performs the following operations:

[0053] When the task is elevator busy scheduling, analyze the elevator riding demand for the elevator group corresponding to the task to obtain elevator riding demand data;

[0054] Analyze the elevator operation data to obtain elevator operation data;

[0055] Generate an elevator scheduling plan based on the elevator operation data and the elevator riding demand data.

[0056] During the elevator busy scheduling process, comprehensively analyzing the elevator operation data and the elevator riding demand data can better perform elevator scheduling, improve the utilization of elevators, and effectively meet the elevator usage requirements during peak hours such as commuting.

[0057] In the event monitoring library, the monitoring item corresponding to the elevator busy scheduling task is elevator busy analysis; the monitored data is: time data and / or trigger data of the buttons on the elevator side of each floor of the building; the trigger condition is that the time corresponding to the time data is within a preset time interval range and / or the number of floors corresponding to the trigger data reaches a preset quantity threshold.

[0058] Among them, the elevator riding demand data mainly depends on the analysis of the first image in front of the elevator captured by the camera configured on each floor of the building; the elevator operation data mainly depends on the analysis of the elevator operation data collected by the data acquisition device associated with the elevator controller and the second image captured by the camera configured in the car;

[0059] The specific analysis process of elevator ride demand data is as follows: Extract and identify the human body contours from the first image corresponding to each floor to obtain the basic parameters of the human body contours (quantity, estimated age, estimated weight, etc.); then fill them into the preset elevator ride demand data template according to the floor to obtain the first data set corresponding to the elevator ride demand data; among them, each row of the data set corresponds to the basic parameters of the human body contours on one floor; the first data in each row corresponds to the quantity of the human body contours waiting for the elevator on that floor, and the second to N+1th data correspond to the estimated ages corresponding to each human body contour, and the N+2th to 2N+1th data correspond to the estimated weight data corresponding to each human body contour; when the number of waiting human body contours corresponding to the floor is less than N, fill in the preset numerical values;

[0060] The elevators in the building are all linked in multiple units (2, 4, etc.), so the analysis of elevator operation data is more complex than that of a single car. The specific analysis process of elevator operation data is as follows: Analyze the second image in each car to determine the remaining space; analyze the weight sensor data of each car in the elevator operation data to determine the weight margin; use the position data and moving direction of each car in the elevator operation data as the position parameter and the movement direction parameter respectively; fill the remaining space, weight margin, position parameter and movement direction parameter of each car into the preset operation data template respectively to obtain the second data set corresponding to the operation data. Each row of this data set corresponds to one car, and the data in each row corresponds to the remaining space, weight margin, position parameter and movement direction parameter in sequence;

[0061] During the process of generating an elevator dispatching plan based on elevator operation data and elevator ride demand data, it is necessary to apply a dispatching plan determination library pre-configured for this elevator. Match the first data set of elevator ride demand data and the second data set of elevator operation data with the first standard data set and the second standard data set in the dispatching plan determination library respectively, so as to retrieve the elevator dispatching plan associated with the first standard data set and the second standard data set in the dispatching plan determination library.

[0062] In addition, when the task is the abnormal dispatching of an overweight elevator, after the alarm sounds again, if it is determined that the alarm occurs before the door opens, then the second image inside the car corresponding to the overweight alarm is analyzed to determine whether to execute the elevator dispatching plan for direct operation; among them, the monitoring item corresponding to the abnormal dispatching of an overweight elevator in the event monitoring library is the analysis of the overweight alarm of the elevator; the monitored corresponding data is: the alarm data of the overweight alarm of the elevator; the triggering condition is that the number of overweight alarm triggers is greater than or equal to the preset number within the preset time and the elevator has run at least one floor; the scenario corresponding to this task is: due to the sensitivity of the weight monitoring device of the elevator, when the elevator is going down, under the action of inertia, the detected value by the sensor will fluctuate and exceed the alarm threshold, at this time, the overweight alarm will be triggered. People near the door in the car often deal with it by getting out and then getting back in, which will eliminate the alarm. At this time, the elevator will trigger the alarm every time it stops at a floor, and passengers need to get out and get back in every time it stops, which is extremely inconvenient. People getting in and out are also prone to feeling embarrassed and delaying the operation of the elevator.

[0063] Among them, the analysis of the second image inside the car corresponding to the overweight alarm specifically includes:

[0064] Identify the human body contours (mainly the contours of the head) in the second image to determine the number of people.

[0065] When the number of people is less than or equal to the preset threshold (any value from 2 to 20) and there are no other objects, an abnormal elevator maintenance task is generated. The dispatching of the dispatching module for executing the abnormal elevator maintenance task includes: controlling the elevator to stop at the next floor and opening the elevator door to let the passengers get out of the elevator. After the passengers get out, the elevator door closes and notifies the pre-configured task team responsible for elevator maintenance to handle it; the threshold is generally configured as the maximum load divided by twice the configured average weight of a person, and then the integer value obtained.

[0066] When the number of people is greater than or equal to the preset threshold, determine the change in the number of people; when the change in the number of people = the number of people after the door opens minus one and / or the number of people increases by one before the door closes and the overweight alarm is not triggered, execute the elevator dispatching plan for direct operation, directly reaching the next stop floor on the elevator inner panel without stopping at intermediate floors.

[0067] The present invention also provides a building operation monitoring and dispatching method based on big data analysis, as Figure 2 shown, including:

[0068] Step 1: Monitor each device in the building;

[0069] Step 2: Based on the monitored data and the event monitoring library obtained through big data analysis, issue tasks;

[0070] Step 3: Dispatch and process the issued tasks.

[0071] Among them, based on the monitored data and the event monitoring library obtained through big data analysis, tasks are issued, including:

[0072] Filter relevant data from the monitored data according to the corresponding data requirements of each monitoring item in the event monitoring library;

[0073] When the relevant data meets the trigger condition, generate tasks corresponding to the monitoring items and issue them.

[0074] Among them, the scheduling process for the issued tasks includes:

[0075] Place the issued tasks into the task queue;

[0076] When the tasks in the task queue are not unique, determine the priority values of each task based on the pre-configured priority analysis library;

[0077] Process the event tasks in descending order of the priority values;

[0078] During the emergency preprocessing of the tasks, monitor the changes in the monitoring data related to other tasks in the task queue, and conduct quantitative analysis on the changes according to the pre-configured first correlation analysis library to determine the first correlation value;

[0079] Conduct quantitative analysis on the differences in the occurrence locations and event types between the tasks and other tasks in the task queue according to the pre-configured second correlation analysis library to determine the second correlation value;

[0080] Merge other tasks whose sum value of the first correlation value and the second correlation value is greater than or equal to the preset threshold to generate tasks to be processed;

[0081] Send the tasks to be processed to the pre-configured task team.

[0082] Among them, determining the priority values of each task based on the pre-configured priority analysis library includes:

[0083] Extract features of the tasks, and construct a description parameter set based on the extracted feature parameters;

[0084] Retrieve the priority values corresponding to the description parameter set from the priority set analysis library;

[0085] Among them, the feature parameters include: parameters representing the type of the task, parameters representing the affected area of the task, and parameters representing the type and quantity of the equipment in the building corresponding to the task.

[0086] Among them, the scheduling process for the issued tasks also includes:

[0087] When the task is elevator busy-time scheduling, analyze the elevator ride demands of the elevator group corresponding to the task to obtain elevator ride demand data;

[0088] Analyze the elevator operation data to obtain elevator operation data;

[0089] Generate an elevator scheduling plan based on the elevator operation data and the elevator ride demand data.

[0090] Among them, the monitoring item corresponding to the elevator busy-time scheduling task in the event monitoring library is elevator busy-time analysis; the monitored data are: time data and / or trigger data of the buttons on the elevator side of each floor of the building; the trigger condition is that the time corresponding to the time data is within a preset time interval range and / or the number of floors corresponding to the trigger data reaches a preset quantity threshold.

[0091] Among them, the elevator ride demand data mainly depends on the analysis of the first image in front of the elevator captured by the cameras configured on each floor of the building on the elevator side; the elevator operation data mainly depends on the analysis of the elevator operation data collected by the data acquisition device associated with the elevator controller and the second image captured by the camera configured in the car.

[0092] Among them, the specific analysis process of the elevator ride demand data is as follows: extract and identify the human body contour from the first image corresponding to each floor to obtain the basic parameters of the human body contour; then fill in the preset elevator ride demand data template according to the floor to obtain the first data set corresponding to the elevator ride demand data.

[0093] Among them, the specific analysis process of the elevator operation data is as follows: analyze the second images in each car to determine the remaining space; analyze the weight sensor data of each car in the elevator operation data to determine the weight margin; use the position data and moving direction of each car in the elevator operation data as the position parameter and the movement direction parameter respectively; fill in the preset operation data template with the remaining space, weight margin, position parameter and movement direction parameter of each car to obtain the data set corresponding to the operation data.

[0094] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A building operation monitoring and dispatching system based on big data analysis, characterized in that: include: Monitoring module, task release module and scheduling module; among them, the monitoring module monitors each device in the building; The task publishing module publishes tasks based on the monitored data and the event monitoring library obtained through big data analysis; the scheduling module schedules and processes the published tasks.

2. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 1, characterized in that: The task publishing module performs the following operations: According to the corresponding data requirements of each monitoring project in the event monitoring library, relevant data are filtered out from the monitored data; When the relevant data meets the trigger conditions, a task corresponding to the monitoring project is generated and released.

3. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 1, characterized in that: The scheduling module performs the following operations: Put the published tasks into the task queue; When the tasks in the task queue are not unique, the priority value of each task is determined based on the pre-configured priority analysis library; Process event tasks in descending order of priority value; In the process of emergency preprocessing of the task, the changes of the monitoring data related to other tasks in the task queue are monitored, and the changes are quantitatively analyzed according to the pre-configured first correlation analysis library to determine the first correlation value; Performing quantitative analysis on the differences in occurrence locations and event types between the task and other tasks in the task queue according to a pre-configured second correlation analysis library to determine a second correlation value; Merge other tasks whose sum of the first correlation value and the second correlation value is greater than or equal to a preset threshold to generate a task to be processed; Send pending tasks to pre-configured task groups.

4. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 3, characterized in that: Determine the priority value of each task based on a pre-configured priority analysis library, including: Extract features of the task and build a description parameter set based on the extracted feature parameters; Retrieving a priority value corresponding to a description parameter set from a priority set analysis library; The characteristic parameters include: parameters indicating the type of task, parameters indicating the impact area of ​​the task, and parameters indicating the type and quantity of equipment in the building corresponding to the task.

5. The building operation monitoring and dispatching system based on big data analysis according to claim 1, characterized in that: The scheduling module also performs the following operations: When the task is elevator busy-hour scheduling, the elevator demand analysis is performed on the elevator group corresponding to the task to obtain the elevator demand data; Analyze the elevator operation data and obtain the elevator operation data; Generate elevator dispatching plan based on elevator operation data and elevator demand data.

6. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 5, characterized in that: In the event monitoring library, the monitoring item corresponding to the elevator busy-hour scheduling task is the elevator busy-hour analysis; the corresponding monitoring data is: time data and / or the trigger data of the buttons on the elevator side of each floor of the building; the trigger condition is that the time corresponding to the time data is within the preset time interval and / or the number of floors corresponding to the trigger data reaches the preset number threshold.

7. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 5, characterized in that: The elevator demand data mainly relies on the analysis of the first image in front of the elevator taken by the camera on the elevator side of each floor of the building; the elevator operation data mainly relies on the analysis of the elevator operation data collected by the data acquisition device associated with the elevator controller and the second image collected by the camera installed in the car.

8. The building operation monitoring and dispatching system based on big data analysis as claimed in claim 7, characterized in that: The specific analysis process of the elevator demand data is as follows: extract and identify the human body contour of the first image corresponding to each floor to obtain the basic parameters of the human body contour; then fill in the preset elevator demand data template according to the floor to obtain the first data set corresponding to the elevator demand data.

9. The building operation monitoring and dispatching system based on big data analysis according to claim 7, characterized in that: The specific analysis process of the elevator operation data is as follows: analyze the second image in each car to determine the remaining space; analyze the weight sensor data of each car in the elevator operation data to determine the weight margin; use the position data and moving direction of each car in the elevator operation data as position parameters and movement direction parameters respectively; fill the remaining space, weight margin, position parameters and movement direction parameters of each car into the preset operation data template to obtain the data set corresponding to the operation data.

10. A building operation monitoring and scheduling method based on big data analysis, characterized in that: include: Monitor each device in the building; Issue tasks based on monitored data and the event monitoring library obtained through big data analysis; Schedule the published tasks.

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