Monitoring management method and system based on intelligent building construction process information

Abstract

The invention discloses a monitoring management method and system based on intelligent building construction process information, relates to the technical field of data information management, and solves the problems that data processing is not comprehensive and a good data classification effect is not achieved. According to the method, numerous and jumbled monitoring data are classified and managed according to the time characteristics of different sensors, the primary and secondary sensors are innovatively selected, and the primary packet, the secondary packet and even the primary packet of the data are divided in combination with the timeline, so that the hierarchical and tidy arrangement of the data is realized. By means of the ordered classification, the workload of searching and checking in mass data by operators is greatly reduced, data query and feature confirmation become convenient and rapid, the overall efficiency of construction information management is remarkably improved, and it is ensured that key information can be obtained and utilized in time.

Description

Technical Field

[0001] The present invention relates to the technical field of data information management, and specifically to a monitoring and management method and system based on smart building construction process information. Background Art

[0002] Smart construction data covers massive, multi-type information generated in all aspects of the entire construction process. These data are key elements for achieving intelligent management, accurate decision-making, and ensuring construction quality, progress, and safety.

[0003] The application with publication number CN118469241A discloses a large-scale steel structure construction safety management method based on BIM technology, including six steps: in-depth design of steel structure, pre-assembly simulation of steel structure, prediction and elimination of safety risks, optimization of construction plan, industrialized production of steel structure components, and collaborative safety management of information-based construction process. BIM technology is applied from the design deepening stage to predict and correct safety issues in advance. Based on BIM technology combined with information technologies such as the Internet of Things, intelligent algorithms, smart monitoring, intelligent monitoring, and the Internet, joint modules are combined with on-site smart equipment, mobile devices, construction robots, intelligent monitoring and other equipment control systems. By building an overall smart construction management platform, functional modules such as epidemic safety informatization control, construction fall risk management, and steel structure hoisting safety management are realized. The safety of on-site construction of large steel structures is managed by means of informatization, the efficiency and quality of safety management are improved, and the safety needs of the epidemic are met.

[0004] During the construction of smart buildings, monitoring is required based on data related to a large number of different monitoring sensors. Therefore, a large amount of monitoring data will be generated during monitoring. However, in the actual management process, only the data of the same type of sensors are stored in a unified manner, and the data of multiple groups of different sensors are not classified and stored. The original data classification processing method is not comprehensive in data processing, and does not achieve good data classification effects. It is not convenient for subsequent related operators to identify data, and its data supervision effect needs to be improved. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a monitoring and management method and system based on smart building construction process information, which solves the problem that data processing is not comprehensive and does not achieve good data classification effect.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A monitoring and management method based on smart building construction process information includes the following steps:

[0007] Step 1: Define a set of supervision cycles, collect monitoring data from different sensors in the construction site, and perform preliminary processing on the monitoring data belonging to the same sensor, confirm the missing data of the abnormal monitoring data, and fill the missing position with data blanks in the following way:

[0008] S11. Based on the monitoring data of different sensors collected during the supervision period, the monitoring data belonging to the same group of sensors are divided into similar data sets, and according to the different monitoring data associated with different timestamps in the similar data sets, several groups of monitoring data are sorted according to the time sequence to confirm the monitoring data sequence;

[0009] S12. Confirm the time difference between adjacent monitoring data in the monitoring data sequence, and mark the timestamp of the previous group of monitoring data in the adjacent monitoring data as SJ1. i , the timestamp of the latter set of monitoring data is marked as SJ2 i , where i represents different adjacent monitoring data, and its time difference = |SJ1 i -SJ2 i |Confirm the time difference associated with adjacent monitoring data, and confirm the number of identical time differences based on several groups of time differences associated with several groups of monitoring data G k , where k represents different identical time differences, G is selected k The time difference associated with max is taken as the specific time difference T;

[0010] Based on the determined specific time difference T, identify whether there is missing data between adjacent monitoring data: mark the adjacent monitoring data that satisfy the time difference equal to the characteristic time difference T as a normal data column, otherwise, mark the corresponding adjacent monitoring data as an error data column, based on the time difference of the error data column, use time difference ÷ T = Q to confirm the number of missing data, fill (Q-1) data spaces in the error data column, and mark the error data column after the data space filling as a missing data column;

[0011] S13, for the calibrated missing data column, according to the determined specific time difference T and the timestamps associated with the first and last data of the missing data column, the timestamps associated with the data spaces are confirmed in sequence;

[0012] Step 2: After filling in the missing data of several groups associated with this supervision cycle, classify and manage several groups of monitoring data according to the time characteristics associated with different sensors, classify several groups of monitoring data with the same time characteristics, and determine the classified data packets. The sub-steps are:

[0013] S21. Based on different monitoring data associated with different sensors in the current monitoring period, the current monitoring data includes the missing data spaces, and the data spaces have associated timestamps;

[0014] S22, based on different sets of data of the same type associated with different sensors, confirm the specific time difference associated with the corresponding sets of data of the same type, select the maximum value from the confirmed sets of specific time differences, and calibrate the sensor associated with the maximum specific time difference as the main sub-sensor;

[0015] Based on the different monitoring data associated with different timestamps of the main and sub-sensors, the supervision period is divided into multiple sub-periods according to the time node of the timestamp, and the timeline associated with each different sub-period is confirmed. The data initial package is divided based on this timeline: each data initial package includes the monitoring data associated with the corresponding timestamp, and synchronously includes the monitoring data associated with the corresponding timestamps of other sensors, and several groups of monitoring data belonging to the corresponding sub-period are determined for initial package;

[0016] S23, classify the several groups of monitoring data associated in the initial package, classify the data belonging to the same type of sensors, confirm the specific time difference of sensors with two or more groups of monitoring data, and the number of sensors is not less than two groups, and synchronously select the sensor associated with the maximum specific time difference, use the monitoring data associated with this sensor as feature data, and divide the timeline associated with the initial package again based on the time node of the timestamp of the feature data, and confirm multiple segmented timelines;

[0017] The same type of sensors with two or more groups of monitoring data are used as sensors to be divided, the monitoring data of the sensors to be divided are re-divided, the monitoring data belonging to each different segmentation timeline is divided, and a number of sub-packets are confirmed;

[0018] S24, re-marking the determined sub-package as the main package, and then dividing in sequence in the same manner as step S23, dividing in sequence several groups of different monitoring data that are time-related, until the monitoring data contained in the last sub-package does not meet the division condition;

[0019] Step 3: Based on the determined groups of classified data packets and the different timelines associated with each different classified data packet, the monitoring data associated with the classified data packets are sorted, and the sorted groups of classified data packets are stored. The specific method is as follows:

[0020] S31, based on the confirmed several classified data packets, confirm the timestamps of the monitoring data included in the classified data packets, and mark the timestamps of the confirmed different monitoring data as JJ m-q, where q represents different monitoring data in this classification data packet, and m represents different classification data packets;

[0021] S32, confirm the timeline associated with the corresponding classification data packet, whose time length is the length of this timeline, and then mark the first time point of this timeline as JD m , using: JJ q -JD m =SC q Confirm the time difference SC between each different monitoring data and the first time point q ;

[0022] S33, different time differences SC confirmed based on different monitoring data q , re-arrange the associated monitoring data in a descending order of time values, and mark the classified data packets after the sorting process as sorted data packets;

[0023] S34. Based on the confirmed classified data packets, the same method as steps S31-S33 is used to process them in sequence until all the classified data packets are marked as sorted data packets, and the sorted data packets are stored. The classified data packets include several primary packets and corresponding secondary packets, and the data packets with data spaces are marked.

[0024] Preferably, the timeline associated with the sub-periods is [X1, X2), including the previous group of time points X1 but excluding the next group of time points X2.

[0025] Preferably, the monitoring and management system based on the construction process information of smart buildings includes:

[0026] The data missing confirmation end defines a set of supervision cycles, collects monitoring data from different sensors in the construction site, and performs preliminary processing on the monitoring data belonging to the same sensor, confirms the missing data for abnormal monitoring data, and fills the missing position with data blanks;

[0027] At the data classification processing end, after filling in the missing data of several groups associated with this supervision cycle, several groups of monitoring data are classified and managed according to the time characteristics associated with different sensors, and several groups of monitoring data with the same time characteristics are specifically classified, and the classified data packets are determined;

[0028] The data packet optimization processing end, based on the determined groups of classified data packets and the different timelines associated with each different classified data packet, associates and sorts the monitoring data associated with the classified data packets, and stores the sorted groups of classified data packets.

[0029] The present invention provides a monitoring and management method and system based on smart building construction process information. Compared with the prior art, it has the following beneficial effects:

[0030] Through a rigorous and meticulous missing data confirmation mechanism, it is possible to accurately identify anomalies and missing data in the monitoring data sequence. Based on the classification set of different sensor data within the supervision period and the time stamp correlation analysis, the specific time difference is accurately calculated, and then the number of missing data is accurately determined and the data gaps are reasonably filled, which effectively ensures the integrity of the data, avoids analysis deviations and decision-making errors caused by missing data, and lays a solid foundation for the accurate judgment of subsequent construction status;

[0031] The complex and diverse monitoring data is classified and managed according to the time characteristics of different sensors. Innovatively, by selecting the main and sub-sensors and combining the timeline to divide the data into primary packages, secondary packages and even main packages, the data is organized in a hierarchical and organized manner. This orderly classification greatly reduces the workload of operators in searching and checking massive data, making data query and feature confirmation convenient and fast, significantly improving the overall efficiency of construction information management, and ensuring that key information can be obtained and used in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of the present invention;

[0033] Figure 2 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0034] 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.

[0035] First embodiment

[0036] See also Figure 1 , this application provides a monitoring and management method based on smart building construction process information, including the following steps:

[0037] Step 1: Define a set of monitoring cycles, which are preset cycles, which are prepared by relevant operators in advance based on experience, and are generally 12 hours or 24 hours. Collect monitoring data from different sensors in the construction site, and perform preliminary processing on monitoring data belonging to the same sensor. Confirm missing data for abnormal monitoring data, and fill in the missing position with data blanks. Specifically, within the corresponding monitoring cycle, there are several groups of different monitoring data associated with different sensors, and the monitoring data of the same sensor are displayed according to corresponding features. Then, based on the relevant feature trends of the corresponding monitoring data, specific confirmation of data missing can be performed, which is convenient for the subsequent specific data management process.

[0038] Among them, the specific methods for confirming missing data for monitoring data are:

[0039] S11. Based on the monitoring data of different sensors collected during the supervision period, the monitoring data belonging to the same group of sensors are divided into similar data sets, and according to the different monitoring data associated with different timestamps in the similar data sets, several groups of monitoring data are sorted according to the time sequence to confirm the monitoring data sequence;

[0040] S12. Confirm the time difference between adjacent monitoring data in the monitoring data sequence, and mark the timestamp of the previous group of monitoring data in the adjacent monitoring data as SJ1. i , the timestamp of the latter set of monitoring data is marked as SJ2 i , where i represents different adjacent monitoring data, and its time difference = |SJ1 i -SJ2 i |Confirm the time difference associated with adjacent monitoring data, and confirm the number of identical time differences based on several groups of time differences associated with several groups of monitoring data G k , where k represents different identical time differences (that is, there may be multiple identical time differences), select G k The time difference associated with max is taken as the specific time difference T;

[0041] Based on the determined specific time difference T, identify whether there is missing data between adjacent monitoring data: mark the adjacent monitoring data that satisfy the time difference equal to the characteristic time difference T as a normal data column, otherwise, mark the corresponding adjacent monitoring data as an error data column, based on the time difference of the error data column, use time difference ÷ T = Q to confirm the number of missing data, fill (Q-1) data spaces in the error data column, and mark the error data column after the data space filling as a missing data column (specifically, when the normal time difference associated with the data is 5 seconds, if there is a time difference of 10 seconds between two data, the confirmed Q = 2, then the number of missing data in this missing data column is (Q-1), that is, 1);

[0042] S13. For the calibrated missing data column, according to the determined specific time difference T and the timestamps associated with the first and last data of the missing data column, the timestamps associated with the data spaces are confirmed in sequence. Specifically, it is proposed that there are two groups of missing data in the missing data column, and the associated specific time difference T is 5 seconds. The timestamp associated with the first data of the missing data column is 11:37:43, the timestamp associated with the second group of missing data is 11:37:48, the timestamp associated with the third group of missing data is 11:37:53, and the timestamp associated with the last data of the missing data column is 11:37:58.

[0043] Step 2: After filling in the missing data of several groups associated with this supervision cycle, classify and manage several groups of monitoring data according to the time characteristics associated with different sensors, classify several groups of monitoring data with the same time characteristics, and determine the classified data packets. Specifically, because the data monitored by different construction sensors are large and complex, when performing subsequent data comparison and analysis, the data are not well classified, resulting in great difficulty in subsequent verification, and operators need to perform a large number of searches and verifications, so it is necessary to effectively classify several groups of monitoring data;

[0044] The specific sub-steps of determining the classified data packets are as follows:

[0045] S21. Based on different monitoring data associated with different sensors in the current monitoring period, the current monitoring data includes the missing data spaces, and the data spaces have associated timestamps;

[0046] S22, based on different sets of data of the same type associated with different sensors, confirm the specific time difference associated with the corresponding sets of data of the same type, select the maximum value from the confirmed sets of specific time differences, and calibrate the sensor associated with the maximum specific time difference as the main sub-sensor;

[0047] Based on the different monitoring data associated with different timestamps of the main and sub-sensors, the supervision period is divided into multiple sub-periods according to the time node of the timestamp, and the time line associated with each different sub-period is confirmed. The data initial package is divided based on this time line: each data initial package includes the monitoring data associated with the corresponding timestamp, and synchronously includes the monitoring data associated with the corresponding timestamps of other sensors. The time line associated with the sub-period is [X1, X2), including the previous group of time points X1 but excluding the next group of time points X2. Several groups of monitoring data belonging to the corresponding sub-period are initially packaged (that is, the determined supervision period is 12h, if there is a sensor with a waiting time difference of 3h, then a group of data is generated every 3h, and this supervision cycle is divided into 4 sub-periods, each of which is 3h long. The time lines associated with the 4 sub-periods are: 0-3h, 3h-6h, 6h-9h, 9h-12h, then in this stage, the associated groups of monitoring data are located in 0-3h and the first group of initial packages are divided, and the data of 3h-6h are divided into another group of initial packages. Each monitoring data has a related timestamp, and the corresponding monitoring data can be determined based on the time line to which the timestamp belongs)

[0048] S23, classify the several groups of monitoring data associated in the initial package, classify the data belonging to the same type of sensors, confirm the specific time difference of sensors with two or more groups of monitoring data, and the number of sensors is not less than two groups, and synchronously select the sensor associated with the maximum specific time difference, use the monitoring data associated with this sensor as feature data, and divide the timeline associated with the initial package again based on the time node of the timestamp of the feature data, and confirm multiple segmented timelines;

[0049] The same type of sensors with two or more groups of monitoring data are used as sensors to be divided, the monitoring data of the sensors to be divided are re-divided, the monitoring data belonging to each different segmentation timeline is divided, and a number of sub-packets are confirmed;

[0050] S24, re-marking the determined sub-package as the main package, and then dividing in sequence in the same manner as step S23, dividing in sequence several groups of different monitoring data that are time-related, until the monitoring data contained in the last sub-package does not meet the division condition;

[0051] For example: there are three sensors A, B and C, where the data generated by A during the supervision period is: A1-A2-A3-A4, the data generated by B is B1-B2-B3, and the data generated by C is C1-C2. The supervision period is 6 hours, and the specific time difference associated with C is 3 hours. The timeline is divided into 0-3 hours and 3 hours-6 hours first. Two initial packages are confirmed, each of which includes a set of C data (C1 or C2). The specific time difference of A is 1.5 hours, and the specific time difference of B is 2 hours. h, then the data associated with the first group of initial packages are {C1, B1, A1, A2}, and the data associated with the second group of initial packages are {C2, B2, B3, A3, A4}. The first group of initial packages here do not meet the requirements of step S23: "Confirm the specific time difference of sensors with two or more groups of monitoring data, and there are no less than two groups of sensors". The sensors with two or more groups of monitoring data in the first group of initial packages are only sensor A, and there are no more than two groups. The sensors that meet this division condition are the second group of initial packages;

[0052] The second group of initial packets is divided again, where there are two or more groups of monitoring data sensors B and A, where the specific time difference of B is 2h, and the pending time difference of A is 1.5h. Then, according to the timestamps of the corresponding monitoring data, the timeline associated with this initial packet is divided again, and the expression form of the second group of initial packets after division is: {C2, [B2-A3], [B3-A4]}. It can be understood that there is a data C2 in a large packet, and there are two small packets synchronously, and each small packet contains different monitoring data;

[0053] By adopting this method of monitoring data division, several groups of monitoring data can be effectively distinguished in sequence, and better displayed based on the actual data distinction results, which is convenient for relevant operators to query data and confirm features, etc., to achieve better data information supervision effect;

[0054] Step 3: Based on the determined groups of classified data packets and the different timelines associated with each of the different classified data packets, the monitoring data associated with the classified data packets are sorted, and the sorted groups of classified data packets are stored, wherein the specific method of sorting is as follows:

[0055] S31. Based on the confirmed several classified data packets (including several different data packets such as primary packets and secondary packets), the monitoring data included in the classified data packets are timestamped (excluding the secondary packets included in the corresponding data packets, only for the monitoring data), and the timestamps of the confirmed different monitoring data are marked as JJ m-q , where q represents different monitoring data in this classification data packet, and m represents different classification data packets;

[0056] S32, confirm the timeline associated with the corresponding classification data packet, whose time length is the length of this timeline, and then mark the first time point of this timeline as JD m , using: JJ q -JD m =SC q Confirm the time difference SC between each different monitoring data and the first time point q ;

[0057] S33, different time differences SC confirmed based on different monitoring data q , re-arrange the associated monitoring data in a descending order of time values, and mark the classified data packets after the sorting process as sorted data packets;

[0058] S34. Based on the confirmed several classified data packets, the same method as steps S31-S33 is adopted to process them in sequence until the classified data packets are all marked as sorted data packets, and the sorted data packets are stored (the stored large packets are generally the initial packets determined, each of which includes different sub-packets, and the primary packets and sub-packets need to be determined in sequence). The classified data packets include several primary packets and corresponding sub-packets, and the data packets with data spaces are marked to facilitate the subsequent review and query by relevant operators.

[0059] Second embodiment

[0060] Combination Figure 2 , a monitoring and management system based on smart building construction process information, including:

[0061] The data missing confirmation end defines a set of supervision cycles, collects monitoring data from different sensors in the construction site, and performs preliminary processing on the monitoring data belonging to the same sensor, confirms the missing data for abnormal monitoring data, and fills the missing position with data blanks;

[0062] At the data classification processing end, after filling in the missing data of several groups associated with this supervision cycle, several groups of monitoring data are classified and managed according to the time characteristics associated with different sensors, and several groups of monitoring data with the same time characteristics are specifically classified, and the classified data packets are determined;

[0063] The data packet optimization processing end, based on the determined groups of classified data packets and the different timelines associated with each different classified data packet, associates and sorts the monitoring data associated with the classified data packets, and stores the sorted groups of classified data packets.

[0064] Third embodiment

[0065] The specific implementation process of this embodiment includes the entire implementation process of the above two groups of embodiments.

[0066] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0067] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A monitoring and management method based on smart building construction process information, characterized in that: The following steps are involved: Step 1: Define a set of supervision cycles, collect monitoring data from different sensors in the construction site, and perform preliminary processing on the monitoring data belonging to the same sensor, confirm the missing data of abnormal monitoring data, and fill in the missing positions with data blanks; Step 2: After filling in the missing data of several groups associated with this supervision cycle, classify and manage several groups of monitoring data according to the time characteristics associated with different sensors, classify several groups of monitoring data with the same time characteristics, and determine the classified data packets; Step three: based on the determined groups of classified data packets and based on different timelines associated with each different classified data packet, the monitoring data associated with the classified data packets are sorted, and the sorted groups of classified data packets are stored.

2. The monitoring and management method based on smart building construction process information according to claim 1 is characterized in that: In step 1, the method of confirming data missing of monitoring data is as follows: S11. Based on the monitoring data of different sensors collected during the supervision period, the monitoring data belonging to the same group of sensors are divided into similar data sets, and according to the different monitoring data associated with different timestamps in the similar data sets, several groups of monitoring data are sorted according to the time sequence to confirm the monitoring data sequence; S12. Confirm the time difference of adjacent monitoring data in the monitoring data sequence, and mark the timestamp of the previous group of monitoring data in the adjacent monitoring data as SJ1. i , the timestamp of the latter set of monitoring data is marked as SJ2 i , where i represents different adjacent monitoring data, and its time difference = |SJ1 i -SJ2 i |Confirm the time difference associated with adjacent monitoring data, and confirm the number of identical time differences based on several groups of time differences associated with several groups of monitoring data G k , where k represents different identical time differences, G is selected k The time difference associated with max is taken as the specific time difference T; Based on the determined specific time difference T, identify whether there is missing data between adjacent monitoring data: adjacent monitoring data that satisfy the time difference equal to the characteristic time difference T are calibrated as normal data columns, otherwise, the corresponding adjacent monitoring data are calibrated as error data columns. Based on the time difference of the error data column, the time difference ÷ T = Q is used to confirm the number of missing data, and (Q-1) data spaces are filled in the error data column. The error data column after the data spaces are filled is calibrated as a missing data column.

3. The monitoring and management method based on smart building construction process information according to claim 1 is characterized in that: The step one also includes: S13. For the marked missing data column, based on the determined specific time difference T and the timestamps associated with the first and last data of the missing data column, the timestamps associated with the data spaces are confirmed in sequence.

4. The monitoring and management method based on smart building construction process information according to claim 1 is characterized in that: In step 2, the sub-steps of determining the classified data packets are: S21. Based on different monitoring data associated with different sensors in the current monitoring period, the current monitoring data includes the missing data spaces, and the data spaces have associated timestamps; S22, based on different sets of data of the same type associated with different sensors, confirm the specific time difference associated with the corresponding sets of data of the same type, select the maximum value from the confirmed sets of specific time differences, and calibrate the sensor associated with the maximum specific time difference as the main sub-sensor; Based on the different monitoring data associated with different timestamps of the main and sub-sensors, the supervision period is divided into multiple sub-periods according to the time node of the timestamp, and the timeline associated with each different sub-period is confirmed. The data initial package is divided based on this timeline: each data initial package includes the monitoring data associated with the corresponding timestamp, and synchronously includes the monitoring data associated with the corresponding timestamps of other sensors, and several groups of monitoring data belonging to the corresponding sub-period are determined for initial package; S23, classify the several groups of monitoring data associated in the initial package, classify the data belonging to the same type of sensors, confirm the specific time difference of sensors with two or more groups of monitoring data, and the number of sensors is not less than two groups, and synchronously select the sensor associated with the maximum specific time difference, use the monitoring data associated with this sensor as feature data, and divide the timeline associated with the initial package again based on the time node of the timestamp of the feature data, and confirm multiple segmented timelines; The same type of sensors with two or more groups of monitoring data are used as sensors to be divided, the monitoring data of the sensors to be divided are re-divided, the monitoring data belonging to each different segmentation timeline is divided, and a number of sub-packets are confirmed; S24, re-calibrate the determined sub-package as the main package, and then divide it in sequence in the same way as step S23, and divide several groups of different monitoring data that are time-related in sequence until the monitoring data contained in the last sub-package does not meet the division condition.

5. The monitoring and management method based on smart building construction process information according to claim 4 is characterized in that: In the step S22, the timeline associated with the sub-periods is [X1, X2), including the previous group of time points X1 but excluding the next group of time points X2.

6. The monitoring and management method based on smart building construction process information according to claim 4 is characterized in that: In step 3, the specific method of associating and sorting the monitoring data in the classified data packet is: S31, based on the confirmed several classified data packets, confirm the timestamps of the monitoring data included in the classified data packets, and mark the timestamps of the confirmed different monitoring data as JJ m-q , where q represents different monitoring data in this classification data packet, and m represents different classification data packets; S32, confirm the timeline associated with the corresponding classification data packet, whose time length is the length of this timeline, and then mark the first time point of this timeline as JD m , using: JJ q -JD m =SC q Confirm the time difference SC between each different monitoring data and the first time point q ; S33, different time differences SC confirmed based on different monitoring data q , re-arrange the associated monitoring data in a descending order of time values, and mark the classified data packets after the sorting process as sorted data packets; S34. Based on the confirmed classified data packets, the same method as steps S31-S33 is used to process them in sequence until all the classified data packets are marked as sorted data packets, and the sorted data packets are stored. The classified data packets include several primary packets and corresponding secondary packets, and the data packets with data spaces are marked.

7. A monitoring and management system based on smart building construction process information, the system is operated according to the monitoring and management method based on smart building construction process information according to any one of claims 1 to 6, characterized in that: include: The data missing confirmation end defines a set of supervision cycles, collects monitoring data from different sensors in the construction site, and performs preliminary processing on the monitoring data belonging to the same sensor, confirms the missing data for abnormal monitoring data, and fills the missing position with data blanks; At the data classification processing end, after filling in the missing data of several groups associated with this supervision cycle, several groups of monitoring data are classified and managed according to the time characteristics associated with different sensors, and several groups of monitoring data with the same time characteristics are specifically classified, and the classified data packets are determined; The data packet optimization processing end, based on the determined groups of classified data packets and the different timelines associated with each different classified data packet, associates and sorts the monitoring data associated with the classified data packets, and stores the sorted groups of classified data packets.

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