Priority data processing method and system of intelligent building automatic control system

Abstract

The invention discloses a priority data processing method and system of a smart building automatic control system, and relates to the technical field of smart buildings, and the method comprises the steps: collecting first-level sub-data in a building, building a data set, determining a data change trend, and building a threshold range according to the first-level sub-data change trend; acquiring the collected first-level sub-data, summarizing the first-level sub-data to form second-level sub-data, and determining a change trend; summarizing the second-level sub-data to form third-level sub-data, comparing the third-level sub-data with a threshold value, and giving an alarm when the third-level sub- After the indoor environment coefficient exceeds the threshold value, whether the smoke coefficient and the temperature and humidity coefficient exceed the threshold value or not is judged, the second-level data and the corresponding first-level data are further judged, and the priority of first-level sub-data processing is determined; according to the method, the difference between each value and the corresponding threshold value is judged, the difference is sorted, and the processing priority is determined, so that disorder and congestion conditions can be avoided when the data is abnormal, and the processing efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart buildings, and in particular to a priority data processing method and system for a smart building automatic control system. Background Art

[0002] Smart buildings are buildings that optimize the structure, system, service and management of buildings according to user needs, thereby providing users with an efficient, comfortable and convenient humanized building environment. According to user needs, the structure, system software, service and management of buildings can be optimally combined to meet the needs of different scenarios, different businesses and different users in digital building spaces, and realize the smart building cloud architecture of the Internet of Things.

[0003] Smart buildings are characterized by intelligence, cost reduction and energy saving, and are the future development direction.

[0004] When the automatic control system in a smart building is in operation, in order to improve the comprehensiveness of the indoor environment management of the building, it is usually necessary to collect more data, such as indoor temperature, humidity, etc. After comprehensive processing of these data, they are used to characterize the internal environmental status of the building.

[0005] However, after collecting data, the existing automatic control system usually adjusts the environment in the building synchronously without any priority. However, this processing method will cause congestion and reduce processing efficiency. Summary of the invention

[0006] 1. Technical issues to be solved

[0007] In view of the deficiencies in the prior art, the present invention provides a priority data processing method and system for a smart building automatic control system, a data acquisition module, a data aggregation module, a first judgment module, an early warning module, and a second judgment module; after the indoor environment coefficient exceeds a threshold, it is judged whether the smoke coefficient and the temperature and humidity coefficient exceed the threshold, and the secondary data and the corresponding primary data are further judged to determine the priority of processing the primary sub-data, thereby avoiding disorder and congestion when the data is abnormal, improving processing efficiency, and solving the problems in the background technology.

[0008] (II) Technical solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A priority data processing method for a smart building automatic control system comprises the following steps:

[0010] Collect the first-level sub-data inside the building, establish a data set, determine the data change trend, and establish a threshold range based on the change trend of the first-level sub-data; obtain the collected first-level sub-data, summarize it to form the second-level sub-data, and determine the change trend; including: based on the data set of smoke data Yw and dust data Hc, obtain the smoke coefficient YC; based on the temperature information T, humidity information S and wind speed information F, obtain the temperature and humidity coefficient WsD;

[0011] Based on the changes in the temperature and humidity coefficient WsD and the smoke coefficient YC, they are compared with the corresponding thresholds. If they are not within the thresholds, an early warning message is issued; the secondary sub-data are aggregated to form tertiary sub-data, which are compared with the thresholds, and an alarm is issued after exceeding the thresholds; after the indoor environment coefficient SN exceeds the threshold, it is determined whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the thresholds, and further the secondary data and the corresponding primary data are judged to determine the priority of the primary sub-data processing;

[0012] Obtain the difference or increase between the first-level sub-data and the corresponding threshold, and determine the ratio of the increase or difference to the upper limit of the corresponding threshold; obtain the above ratios, sort them, form sorting information, and determine the priority based on the obtained sorting information; according to the priority of several values, form a data processing order, and perform corresponding processing on several first-level sub-data.

[0013] Further: obtaining the structural drawings of the building and establishing a three-dimensional model of the building; obtaining the three-dimensional model information and setting a number of detection modules at various locations inside the building; determining the installation location and density of the detection modules according to the distribution of personnel in the building, and completing the installation of the detection modules; wherein the detection modules include a temperature detection unit, a humidity detection unit, and a ventilation detection module;

[0014] Determine a fixed time interval, detect the temperature, humidity and ventilation inside the building at fixed intervals, and obtain several sets of temperature information T, humidity information S and wind speed information F respectively; compare the obtained temperature information T, humidity information S and wind speed information F with the threshold, and issue an alarm if they are outside the threshold range, so that users can make improvements in a timely manner;

[0015] After obtaining the temperature information T, humidity information S and wind speed information F, the data sets are established respectively. After determining the corresponding change trends based on the time series, the functions are fitted respectively.

[0016] Furthermore, according to the three-dimensional model of the building, a number of dust detection modules and smoke detection units are evenly arranged in the building; at fixed time intervals, the smoke data Yw and the dust data Hc in the building are obtained, a data set is established, and the thresholds of the two are determined; from the data set of the smoke data Yw and the dust data Hc, an alarm cycle is determined, the maximum value and the average value in an alarm cycle are determined, and compared with the corresponding thresholds, if at least one of the maximum value or the average value exceeds the threshold, an alarm is issued to the outside.

[0017] Further, a data set of smoke data Yw and dust data Hc is obtained, and a correlation coefficient R1 between the smoke data Yw and the dust data Hc at the same time is determined; the smoke data Yw and the dust data Hc are aggregated and normalized to form a smoke coefficient YC; the aggregation method is as follows:

[0018]

[0019] Among them, 0≤α<<1, 0≤β<<1, and α+β=1, α and β are weights, and their specific values ​​can be adjusted and set by the user.

[0020] Further, the temperature information T, humidity information S and wind speed information F are obtained, and the correlation coefficient R2 between the temperature information T and the humidity information S is determined; the temperature information T, humidity information S and wind speed information F are normalized and summarized to form a temperature and humidity coefficient WsD, and the summary method conforms to the following formula:

[0021]

[0022] Among them, 0≤γ<<1, 0≤θ<<1, and γ+θ=1, γ and θ are weights, and C is a constant correction coefficient, and its specific value can be adjusted and set by the user, or generated by analytical function fitting.

[0023] Further, a plurality of groups of smoke coefficients YC and temperature-humidity coefficients WsD are obtained, and the upper and lower limits of the changes of the smoke coefficients YC and the temperature-humidity coefficients WsD are determined;

[0024] The upper and lower limits of the changes in the smoke coefficient YC and the temperature and humidity coefficient WsD are obtained and determined as the upper threshold and the lower threshold; the current smoke coefficient YC and the temperature and humidity coefficient WsD are obtained and calculated to determine whether they are higher than the upper threshold or the lower threshold. When at least one of the smoke coefficient YC and the temperature and humidity coefficient WsD is not between the upper threshold and the lower threshold, an alarm message is issued to the outside.

[0025] Furthermore, after obtaining the smoke data Yw and the dust data Hc, the data sets are established respectively, and the corresponding change trends are determined according to the time series, and the functions are fitted respectively; along the time axis, several groups of smoke coefficients YC are obtained, and their change trends are determined, and the functions are fitted to form the smoke coefficient fitting function;

[0026] Along the time axis, several groups of temperature and humidity coefficients WsD are obtained, their changing trends are determined, and functions are fitted; users can judge the next changing trend based on the trend function; the changing trends of the smoke coefficient YC and the temperature and humidity coefficient WsD are determined, and the trend function is fitted to judge the next numerical changes of the smoke coefficient YC and the temperature and humidity coefficient WsD; if at least two of the predicted values ​​of the smoke coefficient YC or the temperature and humidity coefficient WsD in the next three moments exceed the upper threshold or the lower threshold, an alarm is issued to the outside.

[0027] Furthermore, the smoke coefficient YC and the temperature and humidity coefficient WsD are obtained, normalized, and summarized to form the indoor environment coefficient SN. The method for obtaining the indoor environment coefficient SN environment conforms to the following formula:

[0028]

[0029] Wherein, ρ is the correlation weight, which is the correlation coefficient between the smoke coefficient YC and the temperature and humidity coefficient WsD, obtained by obtaining several sets of smoke coefficients YC and temperature and humidity coefficients WsD through correlation analysis;

[0030] The indoor environment coefficient SN is obtained and compared with the corresponding threshold to determine whether it is higher than the threshold. If it is higher than the threshold, an external warning is issued.

[0031] Furthermore, when the third-level sub-data exceeds the threshold, the part of the second-level sub-data that exceeds the threshold is determined; if the temperature and humidity coefficient WsD exceeds the corresponding threshold, it is determined whether the temperature information T, humidity information S and wind speed information F exceed the threshold; if the smoke coefficient YC exceeds, it is determined whether the smoke data Yw and the dust data Hc exceed the threshold; it is determined whether the first-level sub-data exceeds the threshold, if it exceeds, the value-added with the threshold is determined; if it does not exceed, the difference with the threshold is determined; the value-added part of the first-level sub-data that exceeds the corresponding threshold is a negative value, and the difference part of the first-level sub-data that is less than the corresponding threshold is a positive value.

[0032] A priority data processing system for a smart building automatic control system, comprising:

[0033] The data collection module collects primary sub-data inside the building, establishes a data set, determines the data change trend, and establishes a threshold range based on the primary sub-data change trend;

[0034] The data aggregation module obtains the collected primary sub-data, aggregates them into secondary sub-data, and determines the change trend; including: obtaining the smoke coefficient YC based on the data set of smoke data Yw and dust data Hc; obtaining the temperature and humidity coefficient WsD based on the temperature information T, humidity information S and wind speed information F;

[0035] The first judgment module compares the changes of the temperature and humidity coefficient WsD and the smoke coefficient YC with the corresponding thresholds. If they are not within the thresholds, an early warning message is issued;

[0036] The early warning module aggregates the secondary sub-data to form tertiary sub-data, compares it with the threshold, and issues an alarm when it exceeds the threshold;

[0037] The second judgment module, after the indoor environment coefficient SN exceeds the threshold, judges whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the threshold, and further judges the secondary data and the corresponding primary data to determine the priority of processing the primary sub-data.

[0038] (III) Beneficial effects

[0039] The present invention provides a priority data processing method and system for a smart building automatic control system. It has the following beneficial effects:

[0040] When processing the environment inside the building, after obtaining the first-level sub-data, it is summarized into second-level sub-data and third-level sub-data in turn. On the basis of the third-level sub-data, an overall judgment is made on the situation inside the building to determine whether to process the building. If necessary, it is judged in turn whether the second-level sub-data and the first-level sub-data exceed the threshold. If the threshold is exceeded, the threshold is processed in a targeted manner. At the same time, the environment inside the building can be predicted, so that users have sufficient reaction time to facilitate early processing.

[0041] After determining that the primary sub-data exceeds the threshold, the gap between each value and the corresponding threshold is determined, and the gap is sorted. The third-level sub-data can be sorted according to the degree of abnormality, and the priority of processing can be determined, so as to avoid disorder and congestion when the data is abnormal, and improve the efficiency of processing. Through the mechanism of multiple early warnings, the abnormal environmental information in the building can be alarmed multiple times at the level of primary sub-data, secondary sub-data and tertiary sub-data, and the attention paid to the environmental monitoring in the building can be improved to improve the safety of the building. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the composition of the indoor environment coefficient of the present invention;

[0043] Figure 2 A schematic diagram of a priority data processing method of an automatic control system of the present invention;

[0044] Figure 3 It is a structural schematic diagram of the priority data processing system of the automatic control system of the present invention.

[0045] In the figure:

[0046] 10. Data collection module; 20. Data aggregation module; 30. First judgment module; 40. Early warning module; 50. Second judgment module. DETAILED DESCRIPTION

[0047] 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.

[0048] Example 1

[0049] See also Figure 1-3 The present invention provides a priority data processing method for a smart building automatic control system, comprising the following steps:

[0050] Step 1: collect primary sub-data inside the building, establish a data set, determine the data change trend, and establish a threshold range based on the primary sub-data change trend; wherein step 1 includes the following contents:

[0051] Step 101: Obtain the structural drawings of the building and establish a three-dimensional model of the building; obtain the three-dimensional model information and set a number of detection modules at various locations inside the building;

[0052] Step 102: Determine the installation location and density of the detection module according to the distribution of people in the building, and complete the installation of the detection module; wherein the detection module includes a temperature detection unit, a humidity detection unit, and a ventilation detection module; which are respectively a temperature sensor, a humidity sensor, and a wind speed sensor;

[0053] Step 103: Determine a fixed time interval, for example, 1 minute or 2 minutes, and detect the temperature, humidity and ventilation inside the building at fixed intervals to obtain several sets of temperature information T, humidity information S and wind speed information F respectively;

[0054] In step 103, the user can judge the change of the internal environment information of the building based on the acquired temperature information T, humidity information S and wind speed information F, and establish corresponding thresholds respectively;

[0055] The acquired temperature information T, humidity information S and wind speed information F are compared with the threshold value. If they are outside the threshold range, an alarm is issued, so that users can make timely improvements.

[0056] Step 104: After obtaining the temperature information T, humidity information S and wind speed information F, respectively establish data sets, determine the corresponding change trends based on the time series, and then fit the functions respectively.

[0057] When in use, the changes in temperature information T, humidity information S and wind speed information F can be effectively displayed in the form of a fitting function, and the next trend of the corresponding information can be determined.

[0058] Step 105: According to the three-dimensional model of the building, a plurality of dust detection modules and smoke detection units are evenly arranged in the building; the dust detection modules and smoke detection units are dust sensors and smoke sensors respectively;

[0059] Step 106: At fixed time intervals, for example, 1 minute or 2 minutes, obtain smoke data Yw and dust data Hc in the building, establish a data set, and determine the thresholds of the two;

[0060] From the data sets of smoke data Yw and dust data Hc, determine the alarm period, for example, 10 minutes or 15 minutes; determine the maximum value and average value in an alarm period and compare them with the corresponding thresholds. If at least one of the maximum value or average value exceeds the threshold, an alarm is issued to the outside.

[0061] When in use, in this step, by obtaining the smoke data Yw and the dust data Hc and monitoring them periodically, it is possible to judge the safety situation in the building and issue an early warning to the outside. When a fire or open flame occurs in the building, an alarm is promptly issued to the user through a ringtone or voice.

[0062] Step 107: After obtaining the smoke data Yw and the dust data Hc, respectively establish data sets, determine the corresponding change trends based on the time series, and then fit the functions respectively.

[0063] When in use, the changes in smoke data Yw and dust data Hc can be effectively displayed in the form of a fitting function, and the next trend of the corresponding information can be determined.

[0064] When in use, in step 1, by respectively setting a temperature detection unit, a humidity detection unit, a ventilation detection module, a dust detection module and a smoke detection unit at various locations in the building, the temperature information T, humidity information S, wind speed information F, smoke data Yw and dust data Hc inside the building are monitored, so that the user can monitor the working environment and fire hazards in the building, and it is convenient for the user to deal with them in time.

[0065] Step 2: Obtain the collected primary sub-data, aggregate them into secondary sub-data, and determine the change trend; including the following:

[0066] Step T1, based on the data set of smoke data Yw and dust data Hc, obtain the smoke coefficient YC; Step T1 includes the following contents:

[0067] Step T101, obtaining a data set of smoke data Yw and dust data Hc, and determining a correlation coefficient R1 between the smoke data Yw and the dust data Hc at the same time; when in use, determining whether the smoke data Yw and the dust data Hc are correlated based on the correlation coefficient R1 between the smoke data Yw and the dust data Hc;

[0068] Step T102: Summarize the smoke data Yw and the dust data Hc, perform normalization processing, and form a smoke coefficient YC; the summarization method is as follows:

[0069]

[0070] Among them, 0≤α<<1, 0≤β<<1, and α+β=1, α and β are weights, and their specific values ​​can be adjusted and set by the user.

[0071] Step T103: Acquire several groups of smoke coefficients YC along the time axis, determine their changing trends, fit functions, and form smoke coefficient fitting functions.

[0072] When in use, it enables users to judge the next changing trend based on its trend function.

[0073] In step T1, by associating the smoke data Yw and the dust data Hc together, the first-level sub-data is divided into two categories, and the second-level sub-data is established. The user can process the fire-related monitoring data in the building according to the second-level sub-data to determine the possibility of a fire inside the building; and further, by using the already formed several groups of smoke coefficient YC data in the form of a fitting function to judge the changes in the subsequent data, it can play a role in time prediction.

[0074] Step T2: based on the temperature information T, the humidity information S and the wind speed information F, obtain the temperature and humidity coefficient WsD; Step T2 includes the following contents:

[0075] Step T201, obtaining temperature information T, humidity information S and wind speed information F, and determining the correlation coefficient R2 between the temperature information T and the humidity information S;

[0076] Step T202: normalize the temperature information T, humidity information S and wind speed information F, and summarize them to form a temperature and humidity coefficient WsD. The summarization method conforms to the following formula:

[0077]

[0078] Among them, 0≤γ<<1, 0≤θ<<1, and γ+θ=1, γ and θ are weights, and C is a constant correction coefficient, and its specific value can be adjusted and set by the user, or generated by analytical function fitting.

[0079] Step T203: along the time axis, obtain several groups of temperature and humidity coefficients WsD, determine their changing trends, and fit functions; the user can judge the next changing trend based on the trend function.

[0080] When in use, in steps T201 to T203, the temperature information T, humidity information S and wind speed information F are aggregated to form a temperature and humidity coefficient WsD, and the working or living environment in the building is characterized by the temperature and humidity coefficient WsD, so that the user can evaluate the working environment in the building as a whole. If the temperature and humidity coefficient WsD changes greatly, or the temperature and humidity coefficient WsD exceeds the threshold after being compared with the corresponding threshold, it means that the internal environment of the building is already poor and no longer mild and suitable, and the user needs to make timely adjustments. Furthermore, through the several groups of temperature and humidity coefficient WsD data that have been formed, the changes in the next data can be judged in the form of a fitting function, which can play a role in time prediction.

[0081] Step 3: Based on the changes of the temperature and humidity coefficient WsD and the smoke coefficient YC, compare them with the corresponding thresholds. If they are not within the thresholds, issue a warning message. Step 3 includes the following contents:

[0082] Step 301, obtain several groups of smoke coefficients YC and temperature and humidity coefficients WsD, determine the upper and lower limits of the smoke coefficient YC and the temperature and humidity coefficient WsD, and the method for determining the upper and lower limits is as follows:

[0083] Take the smoke coefficient YC as an example: determine the middle track curve, upper track curve and lower track curve respectively; among which, the K and N values ​​are determined to be 2 and 20 respectively;

[0084] The middle track curve is the moving average of the first N groups of data of the smoke coefficient YC; the middle track curve is used to represent the change trend of the smoke coefficient YC;

[0085] The upper track curve is: the line connecting the values ​​on several middle track curves plus the standard deviation values ​​of K*N time intervals. The upper track curve is the upper threshold value of the smoke coefficient YC, that is, the upper limit;

[0086] The lower track curve is: the line connecting the values ​​on several middle track curves minus the standard deviation of K*N time intervals. The lower track curve is the lower threshold value, i.e., the lower limit, of the smoke coefficient YC.

[0087] Step 302: Obtain the upper and lower limits of the change of the smoke coefficient YC and the temperature and humidity coefficient WsD, and determine them as the upper threshold and the lower threshold;

[0088] Step 303, obtain and calculate the current smoke coefficient YC and temperature and humidity coefficient WsD, determine whether they are higher than the upper threshold or the lower threshold, and send an alarm message to the outside when at least one of the smoke coefficient YC and the temperature and humidity coefficient WsD is not between the upper threshold and the lower threshold.

[0089] When in use, combined with steps 301 to 303, by determining the upper and lower thresholds of the smoke coefficient YC and the temperature and humidity coefficient WsD, it is determined whether the smoke coefficient YC and the temperature and humidity coefficient WsD are between the upper and lower limits. If not, it can be determined that the smoke coefficient YC or the temperature and humidity coefficient WsD has changed significantly and needs to be processed.

[0090] Step 304, determine the change trend of the smoke coefficient YC and the temperature and humidity coefficient WsD, fit the trend function, and judge the next value change of the smoke coefficient YC and the temperature and humidity coefficient WsD;

[0091] If at least two of the predicted values ​​of the smoke coefficient YC or the temperature-humidity coefficient WsD at the next three moments exceed the upper threshold or the lower threshold, an alarm is issued to the outside.

[0092] When in use, in this step, the subsequent data changes of the smoke coefficient YC and the temperature and humidity coefficient WsD are predicted in the form of establishing a trend function, which can monitor and warn the formation of the indoor environment, and remind the user to take corresponding actions when there are major changes in the indoor environment.

[0093] Step 4: Aggregate the secondary sub-data to form the tertiary sub-data, compare it with the threshold, and issue an alarm when it is higher than the threshold; Step 4 includes the following:

[0094] Step 401: Obtain the smoke coefficient YC and the temperature and humidity coefficient WsD, perform normalization processing, and summarize to form the indoor environment coefficient SN. The method for obtaining the indoor environment coefficient SN environment conforms to the following formula:

[0095]

[0096] Wherein, ρ is the correlation weight, which is the correlation coefficient between the smoke coefficient YC and the temperature and humidity coefficient WsD, and is obtained by obtaining several groups of smoke coefficients YC and temperature and humidity coefficients WsD through correlation analysis.

[0097] Step 402, obtaining the indoor environment coefficient SN, comparing it with the corresponding threshold, and determining whether it is higher than the threshold. If it is higher than the threshold, issuing an external warning.

[0098] When in use, in step 4, by aggregating the smoke coefficient YC and the temperature and humidity coefficient WsD to form the indoor environment coefficient SN, the environment inside the building can be considered as a whole, and the interference caused by some anomalies in the first-level sub-data and the second-level sub-data can be balanced as much as possible. After the indoor environment coefficient SN exceeds the limit, it means that the environment inside the building has undergone a major change. At this time, the user can deal with several factors inside the building one by one according to different priorities.

[0099] Step 5: After the indoor environment coefficient SN exceeds the threshold, determine whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the threshold, and further determine the secondary data and the corresponding primary data to determine the priority of the primary sub-data processing; Step 5 includes the following contents:

[0100] Step 501: when the third-level sub-data exceeds the threshold, determine the portion of the second-level sub-data that exceeds the threshold; the specific contents are as follows:

[0101] If the temperature and humidity coefficient WsD exceeds the corresponding threshold, determine whether the temperature information T, humidity information S and wind speed information F exceed the threshold; if the smoke coefficient YC exceeds, determine whether the smoke data Yw and dust data Hc exceed the threshold.

[0102] When in use, after the third-level sub-data of the indoor environment coefficient SN obtained exceeds the threshold, the second-level sub-data and the first-level sub-coefficient it contains are back-processed, and the reason causing the indoor environment coefficient SN to exceed the threshold is determined while the user processes the building environment.

[0103] Step 502, determine whether the primary sub-data exceeds the threshold, if so, determine the increment compared to the threshold; if not, determine the difference compared to the threshold.

[0104] It should be noted that the first-level sub-data include temperature information T, humidity information S, wind speed information F, smoke data Yw, and dust data Hc. It is necessary to determine whether the five first-level data exceed the threshold or not.

[0105] The added value of the primary sub-data exceeding the corresponding threshold is a negative value, and the difference of the primary sub-data less than the corresponding threshold is a positive value.

[0106] When in use, in step 502, after the indoor environment coefficient SN in the building exceeds the threshold, it can be determined that the overall environment in the building needs to be improved. Therefore, by judging the distance between the first-level sub-data and the corresponding threshold, the distance between several first-level sub-data in the building and the threshold is judged to judge the urgency of improving each first-level sub-data. If the value of the distance is smaller (that is, it is a negative value), the urgency of improvement is greater and priority processing is required.

[0107] Step 503: Obtain the difference or increment between the primary sub-data and the corresponding threshold, and determine the ratio of the increment or difference to the upper limit of the corresponding threshold;

[0108] Step 504: obtain the above ratios, sort them, form sorting information, and determine the priority level based on the obtained sorting information;

[0109] Step 505, according to the priority of several values, a data processing order is formed, and several first-level sub-data are processed accordingly. It should be noted that the corresponding processing of several first-level sub-data, taking the humidity information S as an example, means that when the humidity information S exceeds the threshold, the humidity inside the building is processed.

[0110] When in use, in step 5, the order of data processing is determined by judging the distance between the first-level sub-data and the corresponding threshold value, that is, when the ratio is negative and the smaller it is, it means that the first-level sub-data exceeds the threshold more and needs to be processed immediately, and when the ratio is positive and the larger it is, it means that there is still a certain distance between the first-level sub-data and the threshold and no processing is required for the time being, thereby determining the processing order of several sub-data based on this and judging the priority of the first-level sub-data.

[0111] In the present application, steps 1 to 5 are integrated. When processing the environment inside the building, after obtaining the first-level sub-data, they are successively summarized into second-level sub-data and third-level sub-data. On the basis of the third-level sub-data, an overall judgment is made on the situation inside the building to determine whether to process the building. If necessary, it is judged in turn whether the second-level sub-data and the first-level sub-data exceed the threshold. If they exceed the threshold, the threshold is processed in a targeted manner. At the same time, the environment inside the building can also be predicted, so that the user has sufficient reaction time to facilitate early processing.

[0112] Furthermore, after determining that there are values ​​exceeding the threshold in the first-level sub-data, the gaps between each value and the corresponding threshold are judged and the gaps are sorted. The third-level sub-data can be sorted according to the degree of abnormality and finally the processing priority can be determined, thereby avoiding disorder and congestion when the data is abnormal and improving processing efficiency.

[0113] At the same time, through the mechanism of multiple early warnings, it is possible to issue alarms multiple times for abnormal environmental information in the building at the levels of primary, secondary and tertiary sub-data, thereby increasing the level of attention paid to environmental monitoring in the building and improving the safety of the building.

[0114] Example 2

[0115] See also Figure 1-3 The present invention provides a priority data processing system for a smart building automatic control system, comprising:

[0116] The data collection module 10 collects primary sub-data inside the building, establishes a data set, determines the data change trend, and establishes a threshold range based on the primary sub-data change trend;

[0117] The data aggregation module 20 obtains the collected primary sub-data, aggregates them into secondary sub-data, and determines the change trend; including: obtaining the smoke coefficient YC based on the data set of smoke data Yw and dust data Hc; obtaining the temperature and humidity coefficient WsD based on the temperature information T, humidity information S and wind speed information F;

[0118] The first judgment module 30 compares the changes of the temperature and humidity coefficient WsD and the smoke coefficient YC with the corresponding thresholds, and issues a warning message if they are not within the thresholds;

[0119] The early warning module 40 aggregates the secondary sub-data to form tertiary sub-data, compares it with a threshold value, and issues an alarm when it exceeds the threshold value;

[0120] The second judgment module 50 judges whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the threshold value after the indoor environment coefficient SN exceeds the threshold value, and further judges the secondary data and the corresponding primary data to determine the priority of processing the primary sub-data.

[0121] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0122] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0127] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0129] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A priority data processing method for a smart building automatic control system, characterized in that: The steps include: Collect primary sub-data inside the building, establish a data set, determine the data change trend, and establish a threshold range based on the primary sub-data change trend; Obtain the collected primary sub-data, aggregate them into secondary sub-data, and determine the change trend; including: obtaining the smoke coefficient YC based on the data set of smoke data Yw and dust data Hc; obtaining the temperature and humidity coefficient WsD based on the temperature information T, humidity information S and wind speed information F; Based on the changes in the temperature and humidity coefficient WsD and the smoke coefficient YC, they are compared with the corresponding thresholds. If they are not within the thresholds, an early warning message is issued; Aggregate the secondary sub-data to form tertiary sub-data, compare them with the threshold, and issue an alarm when they are higher than the threshold; After the indoor environment coefficient SN exceeds the threshold, it is determined whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the threshold, and the secondary data and the corresponding primary data are further judged to determine the priority of the primary sub-data processing; Obtain the difference or increase between the first-level sub-data and the corresponding threshold, and determine the ratio of the increase or difference to the upper limit of the corresponding threshold; obtain the above ratios, sort them, form sorting information, and determine the priority based on the obtained sorting information; according to the priority of several values, form a data processing order, and perform corresponding processing on several first-level sub-data.

2. The priority data processing method of a smart building automatic control system according to claim 1 is characterized by: Obtain the structural drawings of the building and establish a three-dimensional model of the building; obtain the three-dimensional model information and set up several detection modules at various locations inside the building; According to the distribution of people in the building, determine the installation position and density of the detection module, and complete the installation of the detection module; wherein the detection module includes a temperature detection unit, a humidity detection unit, and a ventilation detection module; Determine a fixed time interval, detect the temperature, humidity and ventilation inside the building at fixed intervals, and obtain several sets of temperature information T, humidity information S and wind speed information F respectively; Compare the acquired temperature information T, humidity information S and wind speed information F with the threshold value. If they are outside the threshold range, an alarm is issued, so that users can make timely improvements. After obtaining the temperature information T, humidity information S and wind speed information F, the data sets are established respectively. After determining the corresponding change trends based on the time series, the functions are fitted respectively.

3. The priority data processing method of a smart building automatic control system according to claim 2 is characterized in that: According to the three-dimensional model of the building, several dust detection modules and smoke detection units are evenly arranged in the building; At fixed time intervals, the smoke data Yw and dust data Hc in the building are obtained, a data set is established, and the thresholds of the two are determined; From the data sets of smoke data Yw and dust data Hc, determine the alarm cycle, determine the maximum value and average value in an alarm cycle, and compare them with corresponding thresholds. If at least one of the maximum value or average value exceeds the threshold, an alarm is issued to the outside.

4. The priority data processing method of a smart building automatic control system according to claim 1 is characterized in that: Obtain a data set of smoke data Yw and dust data Hc, and determine the correlation coefficient R1 between the smoke data Yw and the dust data Hc at the same time; Smoke data Yw and dust data Hc are aggregated and normalized to form the smoke coefficient YC. The aggregation method is as follows: Among them, 0≤α<<1, 0≤β<<1, and α+β=1, α and β are weights, and their specific values ​​can be adjusted and set by the user.

5. The priority data processing method of a smart building automatic control system according to claim 4 is characterized by: Obtain temperature information T, humidity information S and wind speed information F, and determine the correlation coefficient R2 between the temperature information T and the humidity information S; The temperature information T, humidity information S and wind speed information F are normalized and summarized to form the temperature and humidity coefficient WsD. The summary method conforms to the following formula: Among them, 0≤γ<<1, 0≤θ<<1, and γ+θ=1, γ and θ are weights, and C is a constant correction coefficient, and its specific value can be adjusted and set by the user, or generated by analytical function fitting.

6. The priority data processing method of a smart building automatic control system according to claim 1, characterized in that: Obtain several groups of smoke coefficients YC and temperature-humidity coefficients WsD, and determine the upper and lower limits of the smoke coefficients YC and temperature-humidity coefficients WsD; The upper and lower limits of the smoke coefficient YC and the temperature and humidity coefficient WsD are obtained and determined as the upper threshold and the lower threshold; Obtain and calculate the current smoke coefficient YC and temperature and humidity coefficient WsD, determine whether they are higher than the upper threshold or the lower threshold, and send an alarm message to the outside when at least one of the smoke coefficient YC and the temperature and humidity coefficient WsD is not between the upper threshold and the lower threshold.

7. The priority data processing method of a smart building automatic control system according to claim 1 is characterized by: After obtaining the smoke data Yw and dust data Hc, the data sets are established respectively, and the corresponding change trends are determined according to the time series, and the functions are fitted respectively; Along the time axis, several groups of smoke coefficients YC are obtained, their changing trends are determined, and functions are fitted to form smoke coefficient fitting functions; Along the time axis, several groups of temperature and humidity coefficients WsD are obtained, their changing trends are determined, and the fitting function is performed; Users can judge the next change trend based on its trend function; Determine the change trend of the smoke coefficient YC and the temperature and humidity coefficient WsD, fit the trend function, and judge the next value change of the smoke coefficient YC and the temperature and humidity coefficient WsD; If at least two of the predicted values ​​of the smoke coefficient YC or the temperature-humidity coefficient WsD at the next three moments exceed the upper threshold or the lower threshold, an alarm is issued to the outside.

8. The priority data processing method of a smart building automatic control system according to claim 1 is characterized by: Obtain the smoke coefficient YC and the temperature and humidity coefficient WsD, perform normalization, and summarize to form the indoor environment coefficient SN. The method for obtaining the indoor environment coefficient SN environment conforms to the following formula: Wherein, ρ is the correlation weight, which is the correlation coefficient between the smoke coefficient YC and the temperature and humidity coefficient WsD, obtained by obtaining several sets of smoke coefficients YC and temperature and humidity coefficients WsD through correlation analysis; The indoor environment coefficient SN is obtained and compared with the corresponding threshold to determine whether it is higher than the threshold. If it is higher than the threshold, an external warning is issued.

9. The priority data processing method of a smart building automatic control system according to claim 1, characterized in that: When the third-level sub-data exceeds the threshold, determine the portion of the second-level sub-data that exceeds the threshold; If the temperature and humidity coefficient WsD exceeds the corresponding threshold, it is determined whether any of the temperature information T, humidity information S, and wind speed information F exceeds the threshold; If the smoke coefficient YC exceeds, it is determined whether the smoke data Yw and dust data Hc exceed the threshold; Determine whether the primary sub-data exceeds the threshold, and if so, determine the increment compared to the threshold; If it does not exceed, determine the difference from the threshold; The added value of the primary sub-data exceeding the corresponding threshold is taken as a negative value, and the difference of the primary sub-data less than the corresponding threshold is taken as a positive value.

10. A priority data processing system for a smart building automatic control system, characterized by: include: The data collection module collects primary sub-data inside the building, establishes a data set, determines the data change trend, and establishes a threshold range based on the primary sub-data change trend; The data aggregation module obtains the collected primary sub-data, aggregates them into secondary sub-data, and determines the change trend; including: obtaining the smoke coefficient YC based on the data set of smoke data Yw and dust data Hc; obtaining the temperature and humidity coefficient WsD based on the temperature information T, humidity information S and wind speed information F; The first judgment module compares the changes of the temperature and humidity coefficient WsD and the smoke coefficient YC with the corresponding thresholds. If they are not within the thresholds, an early warning message is issued; The early warning module aggregates the secondary sub-data to form tertiary sub-data, compares it with the threshold, and issues an alarm when it exceeds the threshold; The second judgment module, after the indoor environment coefficient SN exceeds the threshold, judges whether the smoke coefficient YC and the temperature and humidity coefficient WsD exceed the threshold, and further judges the secondary data and the corresponding primary data to determine the priority of processing the primary sub-data.

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