CN109740935B - Building legacy monitoring system and monitoring method - Google Patents
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Abstract
The invention discloses a building legacy monitoring system, comprising: a temperature sensor for monitoring the ambient temperature of the legacy of the building to be monitored; a humidity sensor for monitoring the environmental humidity of the legacy of the building to be monitored; the wind power measurement sensing device is used for monitoring the wind power level of the heritage of the building to be monitored; the soil compactness detector is used for monitoring the soil compactness of the heritage of the building to be monitored. The invention discloses a method for monitoring architecture heritage.
Description
Technical Field
The invention relates to the field of building heritage protection, in particular to a building heritage monitoring system and a monitoring method.
Background
The architecture heritage belongs to the following cultural heritage of the cultural heritage in the world heritage: (1) cultural relics: from the historical, artistic or scientific perspective, the building, the carving and the drawing with outstanding and general value, the components or the structure with archaeological significance, the inscription, the cave, the residential area and the complex of various cultural relics; (2) building group: from a historical, artistic or scientific perspective, individual or interconnected groups of buildings of outstanding, general value due to their form, identity and their status in the landscape; (3) site of heritage: artificial engineering or joint human and natural endeavors with outstanding, general value from a historical, aesthetic, anthropological or anthropological standpoint, as well as archaeological site zones.
Therefore, a monitoring system is needed to reasonably monitor the architecture legacy, and monitor the risk problem of the architecture legacy.
Disclosure of Invention
The invention designs and develops a building legacy monitoring system, and aims to judge the risk state of a building legacy through data acquisition so as to protect the building legacy.
The invention designs and develops a building heritage monitoring method, and aims to carry out risk judgment on the building heritage by calculating preset risk evaluation indexes and risk evaluation indexes.
The invention also aims to carry out risk judgment on the building heritage through the BP neural network so as to effectively protect the building heritage.
The technical scheme provided by the invention is as follows:
a building legacy monitoring system, comprising:
a temperature sensor for monitoring the ambient temperature of the legacy of the building to be monitored;
a humidity sensor for monitoring the environmental humidity of the legacy of the building to be monitored;
the wind power measurement sensing device is used for monitoring the wind power level of the heritage of the building to be monitored;
the soil compactness detector is used for monitoring the soil compactness of the heritage of the building to be monitored.
Preferably, the method further comprises the following steps:
the front-end acquisition module is simultaneously connected with the temperature sensor, the humidity sensor, the wind power measurement sensing device and the soil compactness detector and is used for acquiring environmental index information data of the heritage of the building to be monitored;
the data input module is used for inputting and acquiring index information data of the heritage of the building to be monitored;
the data receiving and storing module is used for receiving the information sent by the acquisition module and the data input module;
the service module receives the data sent by the data receiving and storing module, calculates the index information data and outputs a risk state;
and the display module is connected with the service module and is used for displaying the risk state.
The building legacy monitoring method using the monitoring system comprises the following steps:
acquiring data of a target building heritage, acquiring a preset risk assessment index tau corresponding to the target building heritage according to the data of the target building heritage, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the target building heritage; wherein, tauSComparing the risk assessment indicators;
acquiring the environment temperature, the environment humidity, the wind power level and the soil compactness, processing the preset risk evaluation index according to the environment temperature, the environment humidity, the wind power level and the soil compactness to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, the risk state of the target building heritage is judged; wherein ξSComparing the risk assessment indices;
and thirdly, judging the risk state of the building heritage according to the environment temperature, the environment humidity, the wind power level, the soil compactness and the risk evaluation index so as to monitor the building heritage.
Preferably, in the step one, the preset risk assessment indicator τ is calculated by:
wherein, kappa is a correction coefficient, and when the building age of the building heritage is not more than 80 years, the value of kappa is 102-1.09, when the building age of the building heritage is more than 80 years, taking the value of kappa as 0.93-0.98, taking H as the building height, and taking S as the building heightUFor the area of the above-ground buildings, SDFor the area of the underground structure, SAIs the total area of the building, delta1Correction of the coefficient, delta, for the area of the superstructure2Correction of the coefficient, delta, for the area of the underlying building1+δ2＝1，δ3The value of the correction coefficient is 0.55-0.59.
Preferably, in the step one, when the building age of the building heritage is not more than 80 years, the risk assessment index τ is comparedSThe value is 1.05; and
comparing the risk assessment index tau when the building age of the building heritage is more than 80 yearsSThe value was 0.95.
Preferably, in the second step, the risk assessment index ξ is calculated as:
wherein RH is the ambient humidity, T is the ambient temperature, F is the wind power level, P is the soil compactness, RH0For comparison of ambient humidity, T0To compare the ambient temperatures, F0For comparison of wind power classes, P0To compare the soil compaction, e is the base of the natural logarithm.
Preferably, in the third step, the determining the risk state by establishing a BP neural network model includes the following steps:
step 1, according to a sampling period, acquiring the environmental temperature T of the building heritage to be monitored through a temperature sensor, acquiring the environmental humidity RH of the building heritage to be monitored through a humidity sensor, acquiring the wind power level F of the building heritage to be monitored through a wind power measurement sensing device, acquiring the soil compactness P of the building heritage to be monitored through a soil compactness detector, and determining the risk evaluation index xi;
step 2, normalizing the parameters in sequence to determine three-layer BP neural networkInput layer neuron vector x ═ x1,x2,x3,x4,x5In which x1Is the ambient temperature coefficient, x2Is the ambient humidity coefficient, x3Is the wind power class coefficient, x4Is the coefficient of soil compactness, x5Evaluating an index coefficient for risk;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer neuron vector o ═ o1,o2,o3,o4}; wherein o is1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o isk1, when the building heritage to be monitored is okA corresponding risk level;
step 5, the service module judges according to the output security level, and the display module displays the risk state; the 1 st risk level is a safety state, protective measures are not needed for the building heritage, the 2 nd risk level is a warning state, monitoring and early warning are needed for the building heritage, the 3 rd risk level is a dangerous state, protective measures are needed for the building heritage, the 4 th risk level is a high-risk level, and emergency protective measures are needed for the building heritage.
Preferably, in the third step, the number m of hidden nodes satisfies:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
Preferably, in the step 2, the environmental temperature T, the environmental humidity RH, the wind power level F, the soil firmness P, and the risk assessment index ψ are normalized by the formula:
wherein x isjFor parameters in the input layer vector, XjMeasurement parameters T, RH, F, P, ξ, j ═ 1,2,3,4,5, respectively; xjmaxAnd XjminRespectively, a maximum value and a minimum value in the corresponding measured parameter.
Preferably, the excitation functions of the hidden layer and the output layer both adopt S-shaped functions fj(x)＝1/(1+e-x)。
Compared with the prior art, the invention has the following beneficial effects: according to the invention, the building legacy monitoring system is established, the risk state is judged by calculating the preset risk evaluation index and the risk evaluation index and based on the BP neural network, and the building legacy is effectively and reasonably protected according to the risk level.
Detailed Description
The present invention is described in further detail below to enable those skilled in the art to practice the invention with reference to the description.
The present invention provides a building legacy monitoring system, including: the temperature sensor is used for monitoring the environmental temperature of the heritage of the building to be monitored; the humidity sensor is used for monitoring the environmental humidity of the heritage of the building to be monitored; the wind power measurement sensing device is used for monitoring the wind power level of the heritage of the building to be monitored; the soil compactness detector is used for monitoring the soil compactness of the heritage of the building to be monitored.
The invention also comprises: the front-end acquisition module is simultaneously connected with the temperature sensor, the humidity sensor, the wind power measurement sensing device and the soil compactness detector and is used for acquiring environmental index information data of the heritage of the building to be monitored; the data input module is used for inputting and acquiring index information data of the heritage of the building to be monitored; the data receiving and storing module receives the information sent by the acquisition module and the data input module; the service module receives the data sent by the data receiving and storing module, calculates the index information data and outputs a risk state; and the display module is connected with the service module and used for displaying the risk state.
The invention also provides a building legacy monitoring method, which comprises the following steps:
acquiring data of a target building heritage, acquiring a preset risk assessment index tau corresponding to the target building heritage according to the data of the target building heritage, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the target building heritage; wherein, tauSComparing the risk assessment indicators;
acquiring the environment temperature, the environment humidity, the wind power level and the soil compactness, processing the preset risk evaluation index according to the acquired environment temperature, the environment humidity, the wind power level and the soil compactness to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, the risk state of the target building heritage is judged; wherein ξSComparing the risk assessment indices;
and thirdly, judging the risk state of the building heritage according to the environment temperature, the environment humidity, the wind power level, the soil compactness and the risk evaluation index so as to monitor the building heritage.
In another embodiment, the preset risk assessment indicator τ is calculated by:
wherein kappa is a correction coefficient, when the building age of the building heritage is not more than 80 years, the value of kappa is 1.02-1.09, when the building age of the building heritage is more than 80 years, the value of kappa is 0.93-0.98, H is the building height, and S is the building heightUFor the area of the above-ground buildings, SDFor the area of the underground structure, SAIs the total area of the building, delta1Correction of the coefficient, delta, for the area of the superstructure2Correction of the coefficient, delta, for the area of the underlying building1+δ2＝1，δ3The value of the correction coefficient is 0.55-0.59; preferably, in the present embodiment, δ1The value is 0.55, delta2The value is 0.45, delta3The value is 0.58.
In another embodiment, the risk assessment indicator τ is compared when the building age of the building legacy is not greater than 80 yearsSThe value is 1.05; comparing the risk assessment index tau when the building age of the building heritage is more than 80 yearsSThe value was 0.95.
In another embodiment, the risk assessment index ξ is calculated as:
wherein RH is the ambient humidity, T is the ambient temperature, F is the wind power level, P is the soil compactness, RH0For comparison of ambient humidity, T0To compare the ambient temperatures, F0For comparison of wind power classes, P0To compare the soil compaction, e is the base of the natural logarithm.
In another embodiment, in step three, the determining the risk state by building a BP neural network model includes the following steps:
step 1, establishing a BP neural network model.
Fully interconnected connections are formed among neurons of each layer on the BP model, the neurons in each layer are not connected, and the output and the input of neurons in an input layer are the same, namely oi＝xi. The operating characteristics of the neurons of the intermediate hidden and output layers are
opj＝fj(netpj)
Where p represents the current input sample, ωjiIs the connection weight from neuron i to neuron j, opiIs the current input of neuron j, opjIs the output thereof; f. ofjIs a non-linear, slightly non-decreasing function, generally taken as a sigmoid function, i.e. fj(x)＝1/(1+e-x)。
The BP network system structure adopted by the invention consists of three layers, wherein the first layer is an input layer, n nodes are provided in total, n detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module; the second layer is a hidden layer, and has m nodes which are determined by the training process of the network in a self-adaptive mode; the third layer is an output layer, p nodes are provided in total, and the output is determined by the response actually needed by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xn)T
Intermediate layer vector: y ═ y1,y2,...,ym)T
Outputting a vector: o ═ o (o)1,o2,...,op)T
In the invention, the number of nodes of the input layer is n equals to 5, the number of nodes of the output layer is p equals to 4, and the number of nodes of the hidden layer m is estimated by the following formula:
the input layer 5 parameters are respectively expressed as: x is the number of1Is the ambient temperature coefficient, x2Is the ambient humidity coefficient, x3Is the wind power class coefficient, x4Is the coefficient of soil compactness, x5Evaluating an index coefficient for risk;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
The normalized formula is
Wherein x isjFor parameters in the input layer vector, XjAre respectively a measurement parameterThe numbers T, RH, F, P, ξ, j ═ 1,2,3,4, 5; xjmaxAnd XjminAnd respectively adopting S-shaped functions for the maximum value and the minimum value in the corresponding measurement parameters.
Specifically, the ambient temperature T measured by the temperature sensor is normalized to obtain an ambient temperature coefficient x1：
Wherein, TminAnd TmaxRespectively, a minimum ambient temperature and a maximum ambient temperature measured by the temperature sensor.
Similarly, the ambient humidity RH measured by the humidity sensor is normalized by the following equation to obtain the ambient humidity coefficient x2：
Wherein RH isminAnd RHmaxRespectively, the minimum humidity and the maximum humidity measured by the humidity sensor.
Measuring by using a wind power measuring and sensing device to obtain a wind power grade F, and normalizing to obtain a wind power grade coefficient x3：
Wherein, FminAnd FmaxThe minimum wind power level and the maximum wind power level are respectively measured by the wind power measuring and sensing device.
Measuring by using a soil compactness detector to obtain a soil compactness P, and normalizing to obtain a soil compactness coefficient x4：
Wherein, PminAnd PmaxThe minimum soil compactness and the maximum soil compactness measured by the soil compactness detector are respectively.
Normalizing according to the calculated risk evaluation index xi to obtain a risk evaluation index coefficient x5：
Wherein ξminAnd ximaxThe minimum risk assessment index and the maximum risk assessment index which can be obtained by calculation are respectively.
The output layer 4 parameters are respectively expressed as: o1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o isk1, when the building heritage to be monitored is okThe corresponding risk level.
And 2, training the BP neural network.
After the BP neural network node model is established, the training of the BP neural network can be carried out. And obtaining a training sample according to historical experience data of the product, and giving a connection weight between the input node i and the hidden layer node j and a connection weight between the hidden layer node j and the output layer node k.
(1) Training method
Each subnet adopts a separate training method; when training, firstly providing a group of training samples, wherein each sample consists of an input sample and an ideal output pair, and when all actual outputs of the network are consistent with the ideal outputs of the network, the training is finished; otherwise, the ideal output of the network is consistent with the actual output by correcting the weight; the output samples for each subnet training are shown in table 1.
TABLE 1 output samples for network training
(2) Training algorithm
The BP network is trained by using a back Propagation (Backward Propagation) algorithm, and the steps can be summarized as follows:
the first step is as follows: and selecting a network with a reasonable structure, and setting initial values of all node thresholds and connection weights.
The second step is that: for each input sample, the following calculations are made:
(a) forward calculation: for j unit of l layer
In the formula (I), the compound is shown in the specification,
for the weighted sum of the j unit information of the l layer at the nth calculation,
is the connection weight between the j cell of the l layer and the cell i of the previous layer (i.e. the l-1 layer),
is the previous layer (i.e. l-1 layer, node number n)l-1) The operating signal sent by the unit i; when i is 0, order
Is the threshold of the j cell of the l layer.
If the activation function of the unit j is a sigmoid function, then
And is
If neuron j belongs to the first hidden layer (l ═ 1), then there are
If neuron j belongs to the output layer (L ═ L), then there are
(b) And (3) calculating the error reversely:
for output unit
Pair hidden unit
(c) Correcting the weight value:
The third step: inputting a new sample or a new period sample until the network converges, and randomly re-ordering the input sequence of the samples in each period during training.
The BP algorithm adopts a gradient descent method to solve the extreme value of a nonlinear function, and has the problems of local minimum, low convergence speed and the like. A more effective algorithm is a Levenberg-Marquardt optimization algorithm, which enables the network learning time to be shorter and can effectively inhibit the network from being locally minimum. The weight adjustment rate is selected as
Δω＝(JTJ+μI)-1JTe
Wherein J is a Jacobian (Jacobian) matrix of error to weight differentiation, I is an input vector, e is an error vector, and the variable mu is a scalar quantity which is self-adaptive and adjusted and is used for determining whether the learning is finished according to a Newton method or a gradient method.
When the system is designed, the system model is a network which is only initialized, the weight needs to be learned and adjusted according to data samples obtained in the using process, and therefore the self-learning function of the system is designed. Under the condition of appointing learning samples and quantity, the system can carry out self-learning so as to continuously improve the network performance.
Step 3, the service module judges according to the output security level, and the display module displays the risk state; the 1 st risk level is a safety state, protective measures are not needed to be taken for the building heritage, the 2 nd risk level is a warning state, monitoring and early warning are needed to be taken for the building heritage, the 3 rd risk level is a dangerous state, protective measures are needed for the building heritage, the 4 th risk level is a high-risk level, and emergency protective measures are needed to be taken for the building heritage.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (6)
1. The method for monitoring the architecture heritage is characterized by comprising the following steps:
acquiring data of a target building heritage, acquiring a preset risk assessment index tau corresponding to the target building heritage according to the data of the target building heritage, and when tau is larger than or equal to tauSThen, carrying out risk assessment on the target building heritage; wherein the content of the first and second substances,τScomparing the risk assessment indicators;
acquiring the environment temperature, the environment humidity, the wind power level and the soil compactness, processing the preset risk evaluation index according to the environment temperature, the environment humidity, the wind power level and the soil compactness to obtain a risk evaluation index xi, and when xi is more than or equal to xiSThen, the risk state of the target building heritage is judged; wherein ξSComparing the risk assessment indices;
thirdly, judging the risk state of the building heritage according to the environment temperature, the environment humidity, the wind power level, the soil compactness and the risk evaluation index so as to monitor the building heritage;
in the first step, the calculation process of the preset risk assessment index τ is as follows:
wherein kappa is a correction coefficient, when the building age of the building heritage is not more than 80 years, the value of kappa is 1.02-1.09, when the building age of the building heritage is more than 80 years, the value of kappa is 0.93-0.98, H is the building height, and S is the building heightUFor the area of the above-ground buildings, SDFor the area of the underground structure, SAIs the total area of the building, delta1Correction of the coefficient, delta, for the area of the superstructure2Correction of the coefficient, delta, for the area of the underlying building1+δ2＝1，δ3The value of the correction coefficient is 0.55-0.59;
the risk assessment index xi calculation process is as follows:
wherein RH is the ambient humidity, T is the ambient temperature, F is the wind power level, P is the soil compactness, RH0For comparison of ambient humidity, T0To compare environmentsTemperature, F0For comparison of wind power classes, P0To compare the soil compaction, e is the base of the natural logarithm.
2. The building legacy monitoring method according to claim 1, wherein in the step one, the risk assessment index τ is compared when the building age of the building legacy is not more than 80 yearsSThe value is 1.05; and
comparing the risk assessment index tau when the building age of the building heritage is more than 80 yearsSThe value was 0.95.
3. The building legacy monitoring method according to claim 2, wherein in the third step, the determination of the risk state by building a BP neural network model comprises the following steps:
step 1, according to a sampling period, acquiring the environmental temperature T of a building heritage to be monitored through a temperature sensor, acquiring the environmental humidity RH of the building heritage to be monitored through a humidity sensor, acquiring the wind power grade F of the building heritage to be monitored through a wind power measurement sensing device, acquiring the soil compactness P of the building heritage to be monitored through a soil compactness detector, and determining a risk evaluation index xi through the risk evaluation index xi calculation process;
step 2, normalizing the parameters in sequence, and determining an input layer neuron vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5In which x1Is the ambient temperature coefficient, x2Is the ambient humidity coefficient, x3Is the wind power class coefficient, x4Is the coefficient of soil compactness, x5Evaluating an index coefficient for risk;
and 3, mapping the input layer vector to a hidden layer, wherein the hidden layer vector y is { y ═ y1,y2,…,ymM is the number of hidden nodes;
and 4, obtaining an output layer neuron vector o ═ o1,o2,o3,o4}; wherein o is1To a set 1 st risk level, o2To a set 2 nd risk level, o3To a set risk level of 3, o4For a set 4 th risk level, the output layer neuron value is
k is the output layer neuron sequence number, k is {1,2,3,4}, i is the set ith risk level, i is {1,2,3,4}, and when o isk1, when the building heritage to be monitored is okA corresponding risk level;
step 5, the service module judges according to the output security level, and the display module displays the risk state; the 1 st risk level is a safety state, protective measures are not needed for the building heritage, the 2 nd risk level is a warning state, monitoring and early warning are needed for the building heritage, the 3 rd risk level is a dangerous state, protective measures are needed for the building heritage, the 4 th risk level is a high-risk level, and emergency protective measures are needed for the building heritage.
4. The building heritage monitoring method according to claim 3, wherein in the third step, the number m of hidden nodes satisfies:
wherein n is the number of nodes of the input layer, and p is the number of nodes of the output layer.
5. The building heritage monitoring method according to claim 4, wherein in the step 2, the environmental temperature T, the environmental humidity RH, the wind power level F, the soil firmness P and the risk assessment index ξ are normalized by the formula:
wherein x isjFor parameters in the input layer vector, XjRespectively are parameters T, RH, F, P,ξ，j＝1,2,3,4,5；XjmaxAnd XjminRespectively, a maximum value and a minimum value in the corresponding parameters.
6. The method for monitoring the heritage of buildings according to claim 5, wherein the excitation functions of the hidden layer and the output layer adopt S-shaped functions fj(x)＝1/(1+e-x)。
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