CN116485267A - Intelligent building health state evaluation system based on multidimensional data - Google Patents
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Abstract
The invention discloses an intelligent building health state evaluation system based on multidimensional data, and belongs to the technical field of intelligent buildings; the method comprises the steps of carrying out modularized treatment on different positions of a building in the early stage, carrying out fault monitoring analysis on different divided areas in the building after the modularized treatment, and obtaining fault degrees corresponding to faults by integrating aspects of the divided areas in the different positions with data of aspects of faults, and providing reliable data support for overall health state evaluation of subsequent divided areas while carrying out analysis and classification on the fault states based on the fault degrees; the method and the device are used for solving the technical problems that in the existing scheme, modularized monitoring is not carried out on different position areas in a building, faults occurring in the different position areas are evaluated, classified and integrated to carry out targeted inspection, so that the overall effect of building health state evaluation is poor.
Description
Technical Field
The invention relates to the technical field of intelligent buildings, in particular to an intelligent building health state evaluation system based on multidimensional data.
Background
The intelligent building is characterized in that the structure, the system, the service and the management of the building are optimally combined according to the requirements of users, so that an efficient, comfortable and convenient humanized building environment is provided for the users.
When the existing building health state evaluation scheme is implemented, modularization processing is not implemented on a building in the early stage, so that the monitoring effect of a local area of the building is poor, and the overall monitoring effect of the building is influenced; meanwhile, data monitoring and data analysis of different dimensions are not implemented on faults occurring in different areas after division, and the faults are evaluated, classified and integrated according to analysis results to implement targeted inspection, so that the overall effect of building health state evaluation is poor.
Disclosure of Invention
The invention aims to provide an intelligent building health state assessment system based on multidimensional data, which is used for solving the technical problems that in the existing scheme, modularized monitoring is not carried out on different position areas in a building, and faults occurring in the different position areas are assessed, classified and integrated to carry out targeted inspection, so that the overall effect of building health state assessment is poor.
The aim of the invention can be achieved by the following technical scheme:
an intelligent building health state evaluation system based on multidimensional data comprises a regional health monitoring module, a regional health state evaluation module and a regional health state evaluation module, wherein the regional health monitoring module is used for monitoring and processing anomalies occurring at different positions in a building; comprising the following steps: a plurality of dividing areas i, i=1, 2,3, … …, n obtained by dividing areas of different positions in a building; n is a positive integer;
when the regional health monitoring is sequentially carried out on a plurality of divided regions according to a preset monitoring period, the fault types and corresponding maintenance time lengths of the divided regions are obtained and marked as GLi and WSi respectively; matching the fault type with a fault type-weight table prestored in a database to obtain a corresponding type weight and marking the corresponding type weight as LQi; extracting type weights corresponding to a plurality of faults occurring in the divided areas in the monitoring period and numerical values of maintenance time length, integrating the types and the numerical values in parallel, and obtaining fault degrees thetai corresponding to the divided areas through calculation;
the regional health analysis module is used for carrying out matching classification on the fault degree corresponding to each divided region obtained through monitoring and a preset fault threshold value to obtain regional health data comprising a light signal, a medium signal and a heavy signal, and sending the regional health data to the cloud platform and the database;
the health degree evaluation module is used for carrying out integrated evaluation on faults occurring in the monitoring period of the divided areas according to the area health data to obtain health degree data comprising a health area, state evaluation values, corresponding first state signals, a first type of area, second state signals, a second type of area, third state signals and three types of areas;
and the health state prompting module is used for carrying out targeted prompting and dynamic inspection on different partitioned areas in the building according to the health degree data.
Preferably, the calculation formula of the failure degree θi is:
wherein g1 and g2 are preset different proportion coefficients, and g2 is more than 0 and less than 1 and less than g1; GZi is expressed as the total number of different faults occurring in different divided areas; phi is expressed as the position coefficient corresponding to different dividing areas;
the step of acquiring the position coefficient phi corresponding to different dividing regions comprises the following steps:
obtaining area names corresponding to a plurality of divided areas; sequentially matching the obtained divided areas with an area name-weight table pre-stored in a database to obtain corresponding area weights and marking the corresponding area weights as QQi;
acquiring area corresponding to a plurality of divided areas and marking the area as QMi; extracting the region weight corresponding to the divided regions and the numerical value of the region area, integrating in parallel, and obtaining the position coefficient phi corresponding to the divided regions through calculation; the calculation formula of the position coefficient phi is as follows:
wherein y1 and y2 are preset different proportion coefficients, and 0 < y2 < 1 < y1.
Preferably, if the failure degree is smaller than the failure threshold value, generating a failure light signal and adding one to the total number of slight anomalies of the corresponding divided area; if the fault degree is not less than the fault threshold value and is not greater than Y of the fault threshold value, Y is a real number greater than one hundred, generating a middle signal and adding one to the middle abnormal total times of the corresponding divided areas; if the fault degree is greater than Y% of the fault threshold value, generating a heavy signal and adding one to the total number of severe anomalies of the corresponding divided area.
Preferably, the working steps of the regional health analysis module include:
in the monitoring period, counting the total number of faults in the divided areas, setting the total number as a first analysis value, summing the fault degrees corresponding to each fault occurrence to obtain a fault sum value, and setting the fault sum value as a second analysis value; screening the monitoring degree evaluation of the divided areas according to the first analysis value and the second analysis value;
if the first analysis value corresponding to the divided area is not greater than the first analysis threshold value and the second analysis value is not greater than the second analysis threshold value, marking the corresponding divided area as a healthy area;
if the first analysis value corresponding to the divided area is larger than the first analysis threshold value or the second analysis value is larger than the second analysis threshold value, generating a traceable signal, marking the corresponding divided area as a selected area, and evaluating and classifying the whole health state of the divided area.
Preferably, the corresponding total number of mild anomalies, total number of moderate anomalies or total number of severe anomalies is obtained according to the light signals, the medium signals or the heavy signals of the selected area in the monitoring period and marked as QZi, ZZi and ZYi respectively; and extracting the numerical values of the total number of mild anomalies, the total number of moderate anomalies and the total number of severe anomalies corresponding to the selected area, and obtaining the state estimation xi corresponding to the selected area through calculation.
Preferably, the calculation formula of the state estimation degree ζi is:
ζi＝φi×(z1×QZi+z2×ZZi+z3×ZYi)
wherein z1, z2 and z3 are preset different proportion coefficients, and z1 is more than 0 and z2 is more than 0 and less than z3;
and analyzing and evaluating the overall health state of the corresponding selected area according to the state estimation degree, and matching the state estimation degree with a preset state estimation range to obtain a first state signal and an area, a second state signal and an area, and a third state signal and an area.
Preferably, when matching the state estimation degree with a preset state estimation range; if the state estimation degree is smaller than the minimum value of the state estimation range, generating a first state signal and marking the first state signal as a type of region; if the state estimation degree is not smaller than the minimum value of the state estimation range and not larger than the maximum value of the state estimation range, generating a second state signal and marking the second state signal as a second-class area; if the state estimate is greater than the maximum value of the state estimate range, a third state signal is generated and marked as three types of regions.
Preferably, the working steps of the health status prompting module include:
traversing the health degree data corresponding to the plurality of divided areas in sequence, and respectively generating prompts for maintaining the existing inspection scheme, implementing one type of inspection scheme, implementing two types of inspection scheme and implementing three types of inspection scheme according to the health areas, one type of areas, two types of areas or three types of areas obtained through traversing.
Preferably, the first-class inspection scheme, the second-class inspection scheme and the third-class inspection scheme are all used for improving the inspection frequency on the basis of the existing inspection scheme, and the improved inspection frequency is sequentially increased.
Preferably, the method further comprises a data table construction module, wherein the data table construction module is used for constructing a fault type-weight table and a region name-weight table in advance and sending the fault type-weight table and the region name-weight table to a database for storage.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, modularization processing is carried out on different positions of a building in the early stage, fault monitoring analysis is carried out on different division areas in the building after the modularization processing, the fault degree corresponding to the fault is obtained by integrating aspects of the division areas of the different positions with data of each aspect of faults, and reliable data support is provided for overall health state evaluation of subsequent division areas while the fault state is analyzed and classified based on the fault degree; by carrying out matching classification on faults occurring in different division areas, the influence degree corresponding to the faults can be obtained, the specific monitoring and the integrated analysis on the faults occurring in different division areas are realized, and the overall effect of fault monitoring analysis can be effectively improved;
by integrating all fault analysis results of different divided areas in a preset period to evaluate and classify the overall health state, modularized health state analysis can be implemented on different positions in a building, the accuracy and the comprehensiveness of health state monitoring analysis on different positions in the building can be effectively improved, and meanwhile, reliable data support can be provided for subsequent overall health state analysis display of the building;
the method has the advantages that differentiated prompt and inspection are implemented according to health degree data corresponding to the divided areas at different positions in the building, so that the overall efficiency and inspection quality of inspection can be effectively improved, and compared with the prior art, the method has the advantages that the inspection is implemented at fixed time or dynamic inspection is implemented by experience, so that the overall effect of building health state assessment can be effectively improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
FIG. 1 is a block diagram of an intelligent building health status assessment system based on multidimensional data according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the invention provides an intelligent building health state evaluation system based on multidimensional data, which comprises a regional health monitoring module, a data table construction module, a regional health analysis module, a health degree evaluation module, a health state prompt module, a cloud platform and a database;
the regional health monitoring module is used for monitoring and processing anomalies at different positions in the building; comprising the following steps:
dividing regions i, i=1, 2,3, … …, n obtained by dividing regions of different positions in a building through a preset dividing rule; n is a positive integer;
the dividing rules can be customized according to experience, and can be set according to building big data;
in addition, the purpose of carrying out regional division on different positions in the building is used for carrying out modularized monitoring analysis on the building, and the accuracy of monitoring analysis and evaluation on different positions of the building can be effectively improved by carrying out monitoring analysis on local areas of different positions in the building and then integrating the analysis results of the local areas of different positions to evaluate the overall health state of the building.
According to a preset monitoring period, the unit of the monitoring period is day, specifically can be 60 days, and the health states of different positions of a building can be analyzed and evaluated through the monitoring period, so that dynamic inspection can be implemented according to an evaluation result later, and when area health monitoring is implemented on a plurality of divided areas in sequence, the fault types and corresponding maintenance time lengths of the divided areas are obtained and marked as GLi and WSi respectively; the maintenance time is in minutes; matching the fault type with a fault type-weight table prestored in a database to obtain a corresponding type weight and marking the corresponding type weight as LQi;
extracting type weights corresponding to a plurality of faults occurring in the divided areas in the monitoring period and numerical values of maintenance time length, integrating the types and the numerical values in parallel, and obtaining fault degrees thetai corresponding to the divided areas through calculation; the calculation formula of the failure degree thetai is as follows:
wherein g1 and g2 are preset different proportion coefficients, and g2 is more than 0 and less than 1 and less than g1; g1 may take a value of 1.746 and g2 may take a value of 0.533; GZi is the total number of different faults in different dividing areas, and can be 0; phi is expressed as the position coefficient corresponding to different dividing areas;
the failure degree is a numerical value for integrating each item of data of the failure in the divided area to evaluate and classify the failure; the greater the failure degree, the more serious the corresponding failure;
the step of acquiring the position coefficient phi corresponding to different dividing regions comprises the following steps:
obtaining area names corresponding to a plurality of divided areas; sequentially matching the obtained divided areas with an area name-weight table pre-stored in a database to obtain corresponding area weights and marking the corresponding area weights as QQi;
acquiring area corresponding to a plurality of divided areas and marking the area as QMi; the unit of the area is square meter; extracting the region weight corresponding to the divided regions and the numerical value of the region area, integrating in parallel, and obtaining the position coefficient phi corresponding to the divided regions through calculation; the calculation formula of the position coefficient phi is as follows:
wherein y1 and y2 are preset different proportion coefficients, and y2 is more than 0 and less than 1 and less than y1; y1 can take a value of 1.204 and y2 can take a value of 0.037;
the position coefficient is a numerical value for integrating various data of different aspects of the divided area to integrally evaluate the influence of the divided area; the larger the position coefficient is, the larger the influence of the corresponding divided area is;
the data table construction module is used for constructing a fault type-weight table and a region name-weight table in advance and sending the fault type-weight table and the region name-weight table to a database for storage; the fault type-weight table comprises a plurality of different fault types and corresponding type weights, and one corresponding type weight is preset for the different fault types; the specific value of the type weight can be customized according to experience; the construction mode of the area name-weight table is the same as that of the area name-weight table;
in the embodiment of the invention, the fault monitoring analysis is carried out on different divided areas in the building after the modular processing, the fault degree corresponding to the fault is obtained by integrating the aspects of the divided areas at different positions with the data of each aspect of the fault, and the fault state is analyzed and classified based on the fault degree, so that reliable data support can be provided for the whole health state evaluation of the subsequent divided areas.
The regional health analysis module is used for carrying out matching classification on the fault degree corresponding to each divided region obtained through monitoring and a preset fault threshold value to obtain the health state corresponding to the region;
if the fault degree is smaller than the fault threshold value, judging that the fault corresponding to the fault degree belongs to mild abnormality and generating a light signal, and adding one to the total number of mild abnormality corresponding to the divided area according to the light signal;
if the fault degree is not less than the fault threshold value and is not greater than Y of the fault threshold value, wherein Y is a real number greater than one hundred, judging that the fault corresponding to the fault degree belongs to moderate abnormality and generating a middle fault signal, and adding one to the total number of the moderate abnormality of the corresponding divided areas according to the middle fault signal;
if the fault degree is greater than Y of the fault threshold value, judging that the fault corresponding to the fault degree belongs to severe abnormality and generating a severe signal, and adding one to the total number of severe abnormality of the corresponding divided areas according to the severe signal;
the fault degree corresponding to the plurality of divided areas and the associated fault degree, fault degree or fault degree signal form area health data and are sent to the cloud platform and the database;
in the embodiment of the invention, the matching classification is carried out on the faults in different division areas, so that the influence degree corresponding to the faults can be obtained, the specific monitoring and the integration analysis of the faults in different division areas are realized, and the overall effect of fault monitoring analysis can be effectively improved.
The health degree evaluation module is used for carrying out integrated evaluation on faults occurring in the monitoring period of the divided areas according to the area health data; comprising the following steps:
in the monitoring period, counting the total number of faults in the divided areas, setting the total number as a first analysis value, summing the fault degrees corresponding to each fault occurrence to obtain a fault sum value, and setting the fault sum value as a second analysis value; screening the monitoring degree evaluation of the divided areas according to the first analysis value and the second analysis value;
if the first analysis value corresponding to the divided area is not greater than the first analysis threshold value and the second analysis value is not greater than the second analysis threshold value, judging that the whole health state of the divided area is normal and marking the corresponding divided area as a health area;
if the first analysis value corresponding to the divided area is larger than the first analysis threshold value or the second analysis value is larger than the second analysis threshold value, judging that the overall health state of the divided area is abnormal, generating a tracing signal, marking the corresponding divided area as a selected area according to the tracing signal, and evaluating and classifying the overall health state of the divided area;
acquiring corresponding light abnormal total times, medium abnormal total times or heavy abnormal total times according to the light signals, the medium signals or the heavy signals of the selected area in the monitoring period, and marking the light abnormal total times, the medium abnormal total times or the heavy abnormal total times as QZi, ZZi and ZYi respectively; the numerical values of the light abnormal total times, the moderate abnormal total times and the severe abnormal total times corresponding to the selected area are extracted and vertically integrated, and the state estimation xi corresponding to the selected area is obtained through calculation; the calculation formula of the state estimation degree xi is as follows:
ζi＝φi×(z1×QZi+z2×ZZi+z3×ZYi)
wherein z1, z2 and z3 are preset different proportion coefficients, and z1 is more than 0 and z2 is more than 0 and less than z3; z1 may take a value of 1.325, z2 may take a value of 2.647, and z3 may take a value of 3.833;
it should be noted that, the state estimation is a numerical value for integrating analysis results of a plurality of faults in the divided area within a preset period to estimate the overall health state of the divided area; the larger the state estimation is, the poorer the overall health state of the corresponding divided area is;
when analyzing and evaluating the overall health state of the corresponding selected area according to the state estimation, matching the state estimation with a preset state estimation range;
if the state estimation degree is smaller than the minimum value of the state estimation range, judging that the overall health state of the corresponding selected area is slightly abnormal, generating a first state signal and marking the first state signal as an area;
if the state estimation degree is not smaller than the minimum value of the state estimation range and not larger than the maximum value of the state estimation range, judging that the overall health state of the corresponding selected area is moderate abnormal, generating a second state signal and marking the second state signal as a second-class area;
if the state estimation degree is larger than the maximum value of the state estimation range, judging that the overall health state of the corresponding selected area is severely abnormal, generating a third state signal and marking the third state signal as three types of areas; the serious abnormality of the whole health state can be generally understood as high frequency of occurrence of problems or incapability of normal operation and great influence;
the health area, the state estimation degree, the corresponding first state signal, the first type area, the second state signal, the second type area, the third state signal and the three types of areas form health degree data;
in the embodiment of the invention, the whole health state of the building is evaluated and classified by integrating all fault analysis results of different divided areas in a preset period, modularized health state analysis can be implemented on different positions in the building, the accuracy and the comprehensiveness of health state monitoring and analysis on different positions in the building can be effectively improved, and meanwhile, reliable data support can be provided for subsequent whole health state analysis and display of the building.
The health state prompting module is used for carrying out targeted prompting and dynamic inspection on different partitioned areas in the building according to the health degree data; comprising the following steps:
traversing the health degree data corresponding to the plurality of divided areas in sequence, and respectively generating prompts for maintaining the existing inspection scheme, implementing one type of inspection scheme, implementing two types of inspection scheme and implementing three types of inspection scheme according to the health areas, one type of areas, two types of areas or three types of areas obtained through traversing; the first-class inspection scheme, the second-class inspection scheme and the third-class inspection scheme are used for improving the inspection frequency on the basis of the existing inspection scheme, and the improved inspection frequency is sequentially increased.
In the embodiment of the invention, differentiated prompt and inspection are implemented according to the health degree data corresponding to the divided areas at different positions in the building, so that the overall efficiency and the inspection quality of inspection can be effectively improved.
In addition, the formulas related in the above description are all formulas with dimensions removed and numerical values calculated, and are a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and the proportionality coefficient in the formulas and each preset threshold value in the analysis process are set by a person skilled in the art according to the actual situation or are obtained through training of a large amount of data.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. The intelligent building health state evaluation system based on the multidimensional data is characterized by comprising a regional health monitoring module, a regional health state evaluation module and a regional health state evaluation module, wherein the regional health monitoring module is used for monitoring and processing anomalies occurring at different positions in a building; comprising the following steps: a plurality of dividing areas i, i=1, 2,3, … …, n obtained by dividing areas of different positions in a building; n is a positive integer;
when the regional health monitoring is sequentially carried out on a plurality of divided regions according to a preset monitoring period, the fault types and corresponding maintenance time lengths of the divided regions are obtained and marked as GLi and WSi respectively; matching the fault type with a fault type-weight table prestored in a database to obtain a corresponding type weight and marking the corresponding type weight as LQi; extracting type weights corresponding to a plurality of faults occurring in the divided areas in the monitoring period and numerical values of maintenance time length, integrating the types and the numerical values in parallel, and obtaining fault degrees thetai corresponding to the divided areas through calculation;
the regional health analysis module is used for carrying out matching classification on the fault degree corresponding to each divided region obtained through monitoring and a preset fault threshold value to obtain regional health data comprising a light signal, a medium signal and a heavy signal, and sending the regional health data to the cloud platform and the database;
the health degree evaluation module is used for carrying out integrated evaluation on faults occurring in the monitoring period of the divided areas according to the area health data to obtain health degree data comprising a health area, state evaluation values, corresponding first state signals, a first type of area, second state signals, a second type of area, third state signals and three types of areas;
and the health state prompting module is used for carrying out targeted prompting and dynamic inspection on different partitioned areas in the building according to the health degree data.
2. The intelligent building health assessment system based on multidimensional data according to claim 1, wherein the calculation formula of the failure degree θi is:
wherein g1 and g2 are preset different proportion coefficients, and g2 is more than 0 and less than 1 and less than g1; GZi is expressed as the total number of different faults occurring in different divided areas; phi is expressed as the position coefficient corresponding to different dividing areas;
the step of acquiring the position coefficient phi corresponding to different dividing regions comprises the following steps:
obtaining area names corresponding to a plurality of divided areas; sequentially matching the obtained divided areas with an area name-weight table pre-stored in a database to obtain corresponding area weights and marking the corresponding area weights as QQi;
acquiring area corresponding to a plurality of divided areas and marking the area as QMi; extracting the region weight corresponding to the divided regions and the numerical value of the region area, integrating in parallel, and obtaining the position coefficient phi corresponding to the divided regions through calculation; the calculation formula of the position coefficient phi is as follows:
wherein y1 and y2 are preset different proportion coefficients, and 0 < y2 < 1 < y1.
3. The intelligent building health assessment system based on multidimensional data according to claim 1, wherein if the failure degree is smaller than the failure threshold, generating a failure light signal and adding one to the total number of light anomalies of the corresponding divided area; if the fault degree is not less than the fault threshold value and is not greater than Y of the fault threshold value, Y is a real number greater than one hundred, generating a middle signal and adding one to the middle abnormal total times of the corresponding divided areas; if the fault degree is greater than Y% of the fault threshold value, generating a heavy signal and adding one to the total number of severe anomalies of the corresponding divided area.
4. The intelligent building health assessment system based on multidimensional data of claim 1, wherein the operating steps of the regional health analysis module include:
in the monitoring period, counting the total number of faults in the divided areas, setting the total number as a first analysis value, summing the fault degrees corresponding to each fault occurrence to obtain a fault sum value, and setting the fault sum value as a second analysis value; screening the monitoring degree evaluation of the divided areas according to the first analysis value and the second analysis value;
if the first analysis value corresponding to the divided area is not greater than the first analysis threshold value and the second analysis value is not greater than the second analysis threshold value, marking the corresponding divided area as a healthy area;
if the first analysis value corresponding to the divided area is larger than the first analysis threshold value or the second analysis value is larger than the second analysis threshold value, generating a traceable signal, marking the corresponding divided area as a selected area, and evaluating and classifying the whole health state of the divided area.
5. The system of claim 4, wherein the corresponding total number of mild anomalies, total number of moderate anomalies, or total number of severe anomalies is obtained from the light signals, the medium signals, or the heavy signals of the selected area during the monitoring period and is labeled QZi, ZZi, and ZYi, respectively; and extracting the numerical values of the total number of mild anomalies, the total number of moderate anomalies and the total number of severe anomalies corresponding to the selected area, and obtaining the state estimation xi corresponding to the selected area through calculation.
6. The intelligent building health assessment system based on multidimensional data according to claim 5, wherein the calculation formula of the state estimation ζi is:
ζi＝φi×(z1×QZi+z2×ZZi+z3×ZYi)
wherein z1, z2 and z3 are preset different proportion coefficients, and z1 is more than 0 and z2 is more than 0 and less than z3;
and analyzing and evaluating the overall health state of the corresponding selected area according to the state estimation degree, and matching the state estimation degree with a preset state estimation range to obtain a first state signal and an area, a second state signal and an area, and a third state signal and an area.
7. The intelligent building health assessment system based on multidimensional data according to claim 6, wherein when matching the state estimation with a preset state estimation range; if the state estimation degree is smaller than the minimum value of the state estimation range, generating a first state signal and marking the first state signal as a type of region; if the state estimation degree is not smaller than the minimum value of the state estimation range and not larger than the maximum value of the state estimation range, generating a second state signal and marking the second state signal as a second-class area; if the state estimate is greater than the maximum value of the state estimate range, a third state signal is generated and marked as three types of regions.
8. The intelligent building health assessment system based on multidimensional data as recited in claim 1, wherein the working steps of the health alert module include:
traversing the health degree data corresponding to the plurality of divided areas in sequence, and respectively generating prompts for maintaining the existing inspection scheme, implementing one type of inspection scheme, implementing two types of inspection scheme and implementing three types of inspection scheme according to the health areas, one type of areas, two types of areas or three types of areas obtained through traversing.
9. The intelligent building health state evaluation system based on multidimensional data according to claim 8, wherein the first-class inspection scheme, the second-class inspection scheme and the third-class inspection scheme are all capable of increasing inspection frequency based on the existing inspection scheme, and the increased inspection frequency is sequentially increased.
10. The intelligent building health assessment system based on multidimensional data according to claim 1, further comprising a data table construction module for constructing and transmitting fault type-weight tables and zone name-weight tables to a database for storage according to a pre-constructed fault type-weight tables and zone name-weight tables.
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