CN112699435A

CN112699435A - Building safety detection method and building safety detection system

Info

Publication number: CN112699435A
Application number: CN202011432276.9A
Authority: CN
Inventors: 张涛; 杜东东; 贾芳苗
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-12-09
Filing date: 2020-12-09
Publication date: 2021-04-23

Abstract

When the absence between the building sensing detection information of the building object and each building safety detection network reaches a preset matching parameter threshold value, combining a plurality of model matching attributes corresponding to each building object and a model attribute node loaded in each model matching attribute with the safety detection configuration magnitude of each building safety detection network relative to the building object, associating a safety attribute influence factor corresponding to each model matching attribute into a second target building safety detection network corresponding to the model attribute node loaded in each model matching attribute, and carrying out safety detection on the building object according to the safety attribute influence factor, so that the safety detection precision can be improved in real time, and the problem that the safety detection precision is low due to the fact that the parameter configuration process of the building safety detection network cannot be effectively updated and continuously updated in a short time is avoided The case (1).

Description

Building safety detection method and building safety detection system

Technical Field

The application relates to the technical field of safety detection, in particular to a building safety detection method and a building safety detection system.

Background

For a building security detection platform, a built building security detection network is usually limited, and in an actual model matching process, the building security detection network with the best matching degree is preferably associated to a corresponding building object. However, when the matching degree is low (lower than a certain matching degree threshold), even if the building security detection network with the best matching degree is adopted, it is difficult to achieve a relatively accurate security detection precision, and the parameter configuration process of the building security detection network cannot be effectively updated in a short time, so that the security detection precision is continuously low.

Inventive arrangements

In view of the above, an object of the present invention is to provide a building security detection method and a building security detection system, which can improve security detection accuracy in real time, and avoid a situation of low security detection accuracy caused by a parameter configuration process of a building security detection network being unable to be updated continuously in a short time.

In a first aspect, the present application provides a building security detection method, which is applied to a security detection platform, where the security detection platform is in communication connection with a plurality of monitoring sensing nodes, and the method includes:

building sensing detection information corresponding to a building object is obtained from each monitoring sensing node, and safety index data corresponding to the building sensing detection information are extracted, wherein the building sensing detection information is obtained by the safety detection platform according to safety detection strategy information of the monitoring sensing node corresponding to the building object and a safety detection feedback mode between the monitoring sensing node and the safety detection platform;

acquiring a safety index influence factor of a building safety detection network of each building object and a safety detection reference parameter corresponding to a safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and calculating a matching parameter between the building sensing detection information and each building safety detection network according to an index factor weight corresponding to index factor information in the safety index data, the safety index influence factor of each building safety detection network and a safety detection reference parameter corresponding to the safety detection strategy under the corresponding safety index influence factor;

when target matching parameters reaching a preset matching parameter threshold exist in all the determined matching parameters, the building object is associated to a first target building safety detection network corresponding to the target matching parameters, so that the first target building safety detection network carries out safety detection on the building object to obtain a first building safety detection result;

when target matching parameters reaching the preset matching parameter threshold do not exist in all the determined matching parameters, determining a safety detection configuration magnitude of each building safety detection network relative to the building object, performing data feature identification on the building sensing detection information of the building object through each corresponding building safety detection network according to the magnitude sequence of the safety detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node corresponding to the safety detection configuration magnitude of each building safety detection network in each model matching attribute, determining a safety attribute influence factor corresponding to each model matching attribute from safety index data corresponding to the building object according to each model matching attribute, and associating the safety attribute influence factor corresponding to each model matching attribute with the model attribute node loaded in each model matching attribute And in the corresponding second target building safety detection network, carrying out safety detection on the building object through each second target building safety detection network according to the safety attribute influence factor to obtain a second building safety detection result.

In a possible example of the first aspect, the step of calculating a matching parameter between the building sensing detection information and each building security detection network according to an index factor weight corresponding to index factor information in the security index data, a security index influence factor of each building security detection network, and a security detection reference parameter corresponding to the security index influence factor in a security detection policy under the corresponding security index influence factor includes:

calculating index factor conversion weights of index factor weights corresponding to the index factor information in the safety index data under the safety index influence factors of each building safety detection network;

calculating the absolute value of the difference value between each index factor conversion weight and the corresponding safety detection reference parameter in the safety detection strategy under the corresponding safety index influence factor;

and adding the absolute values of the calculated difference values to obtain matching parameters between the building sensing detection information and each building safety detection network.

In one possible example of the first aspect, the step of determining a security detection configuration level of each building security detection network with respect to the building object includes:

the method comprises the steps of obtaining a plurality of network weight units corresponding to each building safety detection network, determining a configuration vector list corresponding to safety detection configuration features in each network weight unit and a plurality of configuration reference weights, wherein the configuration vector list is used for representing safety detection configuration features and the safety detection tendency behaviors in the safety detection process, and the configuration reference weights are used for representing influence weights of the safety detection configuration features on the safety detection process;

when determining that each network weight unit contains a first index factor dimension vector list according to the configuration vector list, determining a first matching parameter between each configuration reference weight of each network weight unit under a second index factor dimension vector list and each configuration reference weight of each network weight unit under the first index factor dimension vector list according to the configuration reference weight and the position information of each network weight unit under the first index factor dimension vector list, wherein the first index factor dimension vector list represents a vector list for learning configuration index factors, and the second index factor dimension vector list represents a vector list for safety indexes;

when determining that each network weight unit contains a first index factor dimension vector list according to the configuration vector list, determining a first matching parameter between each configuration reference weight of each network weight unit under a second index factor dimension vector list and each configuration reference weight of each network weight unit under the first index factor dimension vector list according to the configuration reference weight and the position information of each network weight unit under the first index factor dimension vector list;

transferring the configuration reference weight of each network weight unit to the first index factor dimension vector list when the first matching parameter between the configuration reference weight of each network weight unit in the second index factor dimension vector list and the configuration reference weight in the first index factor dimension vector list reaches a preset parameter range;

when each network weight unit contains a plurality of configuration reference weights under the second index factor dimension vector list, determining a second matching parameter between the configuration reference weights of each network weight unit under the second index factor dimension vector list according to the configuration reference weight and the position information of each network weight unit under the first index factor dimension vector list, and screening each configuration reference weight under the second index factor dimension vector list according to the second matching parameter between the configuration reference weights;

setting an influence node grade for the screened target configuration reference weight according to the configuration reference weight of each network weight unit in the first index factor dimension vector list and the position information thereof, and transferring the target configuration reference weight to a list interval corresponding to the influence node grade in the first index factor dimension vector list;

and after weighting processing is respectively carried out according to all the configuration reference weights in the first index factor dimension vector list, weighting the matching parameters corresponding to the building safety detection networks corresponding to the network weight units, and determining the safety detection configuration magnitude of the building safety detection network corresponding to each network weight unit relative to the building object.

In a possible example of the first aspect, the step of performing data feature recognition on the building object according to the magnitude order of the security detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node loaded in each model matching attribute and corresponding to the security detection configuration magnitude of each building security detection network includes:

listing the safety detection configuration nodes corresponding to the safety detection configuration orders to establish a building safety detection network node sequence, wherein the building safety detection network node sequence is a subentry processing list, each network unit corresponds to one group of list features, each group of list features is provided with at least one safety detection configuration node, and each network unit of the building safety detection network node sequence has a progressive relationship from high to low;

determining preset configuration attribute information of the building object, and extracting at least one safety detection configuration node in the building safety detection network node sequence contained in the preset configuration attribute information of the building object;

establishing an influence relation between the safety detection configuration node and the building safety detection network node sequence, and generating a target safety detection strategy according to the influence relation;

generating a target security detection strategy according to the influence relationship, wherein the generating of the target security detection strategy comprises the following steps: converting the corresponding building safety detection network node sequence into model node data according to each safety detection configuration node, respectively generating at least one safety detection unit of each model node data, then obtaining the safety detection units with the non-repetitive safety detection configuration magnitude to form a safety detection unit group, and influencing each safety detection unit in the safety detection unit group into the building safety detection network node sequence to form a target safety detection strategy, wherein each safety detection unit corresponds to one safety detection project configuration one by one;

traversing and comparing the safety detection configuration nodes contained in the preset configuration attribute information of the building object with each safety detection configuration node in the target safety detection strategy, and recording a safety detection unit as a model matching attribute direction of the building object if all the safety detection configuration nodes of the safety detection unit are contained in the preset configuration attribute information of the building object in the traversing and comparing process;

and determining a plurality of matching positions corresponding to the building object according to the model matching attribute directions of the building object, performing data feature identification on the building object according to each matching position to obtain a corresponding model matching attribute, and determining a model attribute node of the safety detection configuration magnitude according to at least one safety detection unit of loaded model node data of the safety detection configuration magnitude included in each model matching attribute.

In one possible example of the first aspect, the step of determining preset configuration attribute information of the building object includes:

performing item processing on the building object to obtain a plurality of pieces of specification configuration project information based on a plurality of pieces of building specification configuration information formed by specification configuration nodes of the building object stored in the safety detection platform and currently used nodes of the building object corresponding to the building object;

acquiring information before and information after specification updating of each specification configuration item information;

establishing a specification updating behavior set of the specification updating record corresponding to the building object according to the information before and after specification updating of each specification configuration project information;

acquiring a plurality of specification updating units corresponding to the specification updating records corresponding to the building object, and counting target specification updating units in the plurality of specification updating units, wherein the target specification updating units have specification updating character code characteristics;

judging whether an associated specification updating object exists between two adjacent target specification updating units, if so, counting the number of the associated specification updating objects, implanting the specification updating behavior set into the specification configuration data information of each building when the number does not exceed a set numerical value, acquiring the updated specification updating behavior set when the specification updating behavior set implanted into the specification configuration data information of each building is updated, and counting the specification updating operation characteristic and the specification configuration item information deviation information corresponding to each acquired updated specification updating behavior set;

determining the specification updating weight of each updated specification updating behavior set according to the specification updating operation characteristics and the specification configuration item information deviation information corresponding to each updated specification updating behavior set;

and modifying the specification updating behavior set which is obtained in real time and completes updating according to the specification updating weight to obtain a building object block specification updating sequence, extracting safety index data in each building specification configuration data information according to list features in the building object block specification updating sequence, and determining preset configuration attribute information of the building object according to the extracted safety index data.

In a possible example of the first aspect, the step of performing data feature recognition on the building object according to each matching position to obtain a corresponding model matching attribute includes:

acquiring current operation characteristics of the building object and positioning first matching characteristics corresponding to each matching position from the current operation characteristics;

judging whether a first matching feature corresponding to each matching position in the current running features has a matched feature value relative to a second matching feature in the current running features, wherein the second matching feature is a feature except the first matching feature in the current running features;

if so, determining a first matching feature corresponding to each matching position located from the current running features as an effective matching feature of the current running features, otherwise, performing weighted summation on the first matching feature corresponding to each matching position located from the current running features and a second matching feature in the current running features, and determining the weighted summation result as the effective matching feature of the current running features;

aiming at each matching position, extracting a first thread node of the matching position implanted into a cloud computing control of the security detection platform, and fusing partial features in the effective matching features of the current running features with the first thread node to obtain a second thread node;

respectively operating the first thread node and the second thread node in a mirror thread corresponding to the cloud computing control to obtain first cloud computing information and second cloud computing information which respectively correspond to each other;

judging whether the similarity of the first cloud computing information and the second cloud computing information reaches a preset threshold value, starting the matching position to operate the second thread node when the similarity of the first cloud computing information and the second cloud computing information reaches the preset threshold value, obtaining third cloud computing information corresponding to the second thread node, extracting feature classification information in the third cloud computing information, obtaining model matching attributes corresponding to the matching position according to the feature classification information, and returning to the step of fusing partial features in effective matching features of the current operating features and the first thread node to obtain the second thread node when the similarity of the first cloud computing information and the second cloud computing information does not reach the preset threshold value.

In one possible example of the first aspect, the method further comprises:

obtaining a second building safety detection result obtained by each second target building safety detection network performing safety detection on the building object according to the safety attribute influence factor;

according to the safety detection configuration items of each second building safety detection result, obtaining the safety detection index factor characteristics of the safety detection index factors corresponding to each second building safety detection result;

acquiring a safety detection screening result according to the characteristic screening range of the preset safety detection index factors of the building object and the safety detection index factor characteristics of the safety detection index factors corresponding to the second building safety detection result, wherein the safety detection screening result comprises a plurality of index factor characteristic sets corresponding to the safety detection index factor characteristics in the characteristic screening range of the preset safety detection index factors;

obtaining screening feature configuration information of any one first screening feature configuration of different index factor features included in the safety detection screening result, determining a screening feature configuration attribute of the first screening feature configuration according to the screening feature configuration information of the first screening feature configuration, and determining a target building operation environment corresponding to the first screening feature configuration based on a screening feature configuration scene in the screening feature configuration information of the first screening feature configuration;

determining security detection service information matched with the screening feature configuration attribute of the first screening feature configuration, and selecting security detection service matched with the security detection service information;

according to the screening feature configuration attribute of the first screening feature configuration and the security marks of a plurality of security detection services with the security detection service information in the operating environment of the target building, selecting a target security detection service matched with the first screening feature configuration from the plurality of security detection services with the security detection service information, wherein the target security detection service is also required to be matched with a second screening feature configuration associated with the first screening feature configuration;

acquiring security label information of the first screening feature configuration included in screening feature configuration information of the first screening feature configuration, and acquiring security label information of the second screening feature configuration included in screening feature configuration information of the second screening feature configuration;

and generating a corresponding third building safety detection result according to the safety mark information configured by the first screening characteristic and the safety mark information configured by the second screening characteristic.

In a second aspect, an embodiment of the present application further provides a building safety detection device, which is applied to a safety detection platform, where the safety detection platform is in communication connection with a plurality of monitoring sensing nodes, and the device includes:

the extraction module is used for acquiring building sensing detection information corresponding to a building object from each monitoring sensing node and extracting safety index data corresponding to the building sensing detection information, wherein the building sensing detection information is obtained by the safety detection platform according to safety detection strategy information of the monitoring sensing node corresponding to the building object and a safety detection feedback mode between the monitoring sensing node and the safety detection platform;

the computing module is used for acquiring a safety index influence factor of a building safety detection network of each building object and a safety detection reference parameter corresponding to a safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and computing a matching parameter between the building sensing detection information and each building safety detection network according to an index factor weight corresponding to index factor information in the safety index data, a safety index influence factor of each building safety detection network and a safety detection reference parameter corresponding to the safety index influence factor in the safety detection strategy under the corresponding safety index influence factor;

the first safety detection module is used for associating the building object to a first target building safety detection network corresponding to a preset matching parameter when the target matching parameter reaching a preset matching parameter threshold exists in all the determined matching parameters so that the first target building safety detection network can carry out safety detection on the building object to obtain a first building safety detection result;

a second security detection module, configured to determine a security detection configuration magnitude of each building security detection network when there is no target matching parameter reaching the preset matching parameter threshold among all the determined matching parameters, perform data feature identification on the building sensing detection information of the building object through each corresponding building security detection network according to a magnitude order of the security detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node corresponding to the security detection configuration magnitude of each building security detection network in each model matching attribute, determine a security attribute influence factor corresponding to each model matching attribute from security index data corresponding to the building object according to each model matching attribute, and associate the security attribute influence factor corresponding to each model matching attribute with the model attribute node loaded in each model matching attribute And in the corresponding second target building safety detection network, carrying out safety detection on the building object through each second target building safety detection network according to the safety attribute influence factor to obtain a second building safety detection result.

In a third aspect, an embodiment of the present application further provides a building security detection system, where the building security detection system includes a security detection platform and a plurality of monitoring sensing nodes communicatively connected to the security detection platform;

each monitoring sensing node is used for sending building sensing detection information corresponding to a building object to the safety detection platform;

the safety detection platform is used for acquiring building sensing detection information corresponding to a building object from each monitoring sensing node and extracting safety index data corresponding to the building sensing detection information, wherein the building sensing detection information is acquired by the safety detection platform according to safety detection strategy information of the monitoring sensing node corresponding to the building object and a safety detection feedback mode between the monitoring sensing node and the safety detection platform;

the safety detection platform is used for acquiring a safety index influence factor of a building safety detection network of each building object and a safety detection reference parameter corresponding to a safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and calculating a matching parameter between the building sensing detection information and each building safety detection network according to an index factor weight corresponding to index factor information in the safety index data, the safety index influence factor of each building safety detection network and the safety detection reference parameter corresponding to the safety detection strategy under the corresponding safety index influence factor;

when a target matching parameter reaching a preset matching parameter threshold value exists in all the determined matching parameters, the safety detection platform is used for associating the building object to a first target building safety detection network corresponding to the target matching parameter so that the first target building safety detection network carries out safety detection on the building object to obtain a first building safety detection result;

when the target matching parameters reaching the preset matching parameter threshold do not exist in all the determined matching parameters, the safety detection platform is used for determining the safety detection configuration magnitude of each building safety detection network, performing data feature identification on the building sensing detection information of the building object through each corresponding building safety detection network according to the magnitude sequence of the safety detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node corresponding to the safety detection configuration magnitude of each building safety detection network in each model matching attribute, determining the safety attribute influence factor corresponding to each model matching attribute from the safety index data corresponding to the building object according to each model matching attribute, and associating the safety attribute influence factor corresponding to each model matching attribute with the model attribute node loaded in each model matching attribute And in the corresponding second target building safety detection network, carrying out safety detection on the building object through each second target building safety detection network according to the safety attribute influence factor to obtain a second building safety detection result.

In a fourth aspect, the present invention further provides a security detection platform, where the security detection platform includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one monitoring sensor node, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or code in the machine-readable storage medium to perform the building security detection method in the first aspect or any one of the possible examples in the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform the building security detection method in the first aspect or any one of the possible examples of the first aspect.

According to any one of the aspects, by combining the safety index data corresponding to the building sensing detection information of the building object, when the non-existence between each building safety detection network and each building safety detection network reaches the preset matching parameter threshold, combining a plurality of model matching attributes corresponding to each building safety detection network and the model attribute node loaded in each model matching attribute corresponding to the safety detection configuration magnitude of the building object, thereby associating the safety attribute influence factor corresponding to each model matching attribute to the second target building safety detection network corresponding to the model attribute node loaded in each model matching attribute, and performing safety detection on the building object according to the safety attribute influence factor through each second target building safety detection network, thereby improving the safety detection precision in real time, the condition that the safety detection precision is low due to the fact that the parameter configuration process of the building safety detection network cannot be effectively updated continuously in a short time is avoided.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a schematic view of an application scenario of a building security detection system according to an embodiment of the present application;

fig. 2 is a schematic flow chart of a building security detection method according to an embodiment of the present application;

fig. 3 is a functional block diagram of a building safety detection device provided in an embodiment of the present application;

fig. 4 is a block diagram schematically illustrating a structure of a security detection platform for implementing the building security detection method according to an embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interactive schematic diagram of a building security detection system 10 according to an embodiment of the present application. The building security detection system 10 may include a security detection platform 100 and a monitoring sensing node 200 communicatively connected to the security detection platform 100 via a network, the building security detection system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the building security detection system 10 may include only one of the components shown in fig. 1 or may also include other components.

To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of a building security detection method provided in an embodiment of the present application, which can be executed by the security detection platform 100 shown in fig. 1, and the building security detection method is described in detail below.

Step S110, building sensing detection information corresponding to the building object is obtained from each monitoring sensing node 200, and safety index data corresponding to the building sensing detection information is extracted.

In this embodiment, the building sensing detection information may be obtained by the security detection platform 100 according to the security detection policy information of the monitoring sensing node 200 corresponding to the building object and a security detection feedback manner between the monitoring sensing node 200 and the security detection platform 100. For example, the security detection policy information may be used to represent security detection policies of building objects, and for different security detection policies of users, acquisition policies of building sensing detection information may be different, and may be flexibly adjusted according to actual design requirements, which is not limited herein. For another example, the safety detection feedback method may be a feedback method between the building object and the building safety detection platform (safety detection platform 100), and is not particularly limited herein.

Step S120, obtaining the safety index influence factor of the building safety detection network of each building object and the safety detection reference parameter corresponding to the safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and calculating the matching parameter between the building sensing detection information and each building safety detection network according to the index factor weight corresponding to the index factor information in the safety index data, the safety index influence factor of each building safety detection network and the safety detection reference parameter corresponding to the safety detection strategy under the corresponding safety index influence factor.

In this embodiment, the safety index impact factor may refer to an impact factor label occupied by a specific safety index, and the safety detection strategies corresponding to different safety index impact factors are different, whereas for different building safety detection networks, the safety detection reference parameters corresponding to the safety detection strategies corresponding to different safety index impact factors are also different, and may be specifically configured and trained in advance, which is not described in detail herein.

Step S130, when the target matching parameters reaching the preset matching parameter threshold exist in all the determined matching parameters, the building object is associated to a first target building safety detection network corresponding to the target matching parameters, so that the first target building safety detection network carries out safety detection on the building object to obtain a first building safety detection result.

Step S140, when there is no target matching parameter reaching the preset matching parameter threshold value in all the determined matching parameters, determining a security detection configuration magnitude of each building security detection network relative to the building object, performing data feature identification on building sensing detection information of the building object through each corresponding building security detection network according to the magnitude sequence of the security detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node corresponding to the security detection configuration magnitude of each building security detection network in each model matching attribute, determining a security attribute influence factor corresponding to each model matching attribute from security index data corresponding to the building object according to each model matching attribute, and associating the security attribute influence factor corresponding to each model matching attribute with a second target building corresponding to the model attribute node loaded in each model matching attribute And in the safety detection network, carrying out safety detection on the building object through each second target building safety detection network according to the safety attribute influence factor to obtain a second building safety detection result.

Based on the above design, in this embodiment, when the absence between the building sensing detection information of the building object and each building security detection network reaches the preset matching parameter threshold, the security detection configuration magnitude of each building security detection network relative to the building object is combined to match the multiple model matching attributes corresponding to the building object and the model attribute node loaded in each model matching attribute, so that the security attribute impact factor corresponding to each model matching attribute is associated to the second target building security detection network corresponding to the model attribute node loaded in each model matching attribute, and the building object is security detected by each second target building security detection network according to the security attribute impact factor, so that the security detection accuracy can be improved in real time, and the situation that the security detection accuracy is higher due to the fact that the parameter configuration process of the building security detection network cannot be effectively updated and continuously updated in a short time is avoided Low case.

In a possible example, for step S120, the embodiment may calculate an index factor conversion weight of an index factor weight corresponding to index factor information in the security index data under the security index influence factor of each building security detection network, then calculate an absolute value of a difference value between each index factor conversion weight and a corresponding security detection reference parameter in the security detection policy under the corresponding security index influence factor, and thereby add the calculated absolute values of the difference values to obtain a matching parameter between the building sensing detection information and each building security detection network.

For example, assuming that the safety index influencing factor of the building safety detection network Q includes an influencing factor Q1, an influencing factor Q2, and an influencing factor Q3, and the index factor weight corresponding to the index factor information in the safety index data is B, the index factor conversion weight B1, the index factor conversion weight B2, and the index factor conversion weight B3 of the index factor weight B under the influencing factor Q1, the influencing factor Q2, and the influencing factor Q3, respectively, may be calculated, and then the matching parameter between the building sensing detection information and the building safety detection network Q may be | Q1-Q2| + | Q1-Q2| + | Q1-Q2 |.

In a possible example, based on the foregoing description, when there is a target matching parameter that reaches a preset matching parameter threshold in all the determined matching parameters, the building object may be associated with a first target building security detection network corresponding to the target matching parameter, so that the first target building security detection network performs security detection on the building object to obtain a first building security detection result. The key point of the present application is how to improve the security detection accuracy in real time when the target matching parameter that reaches the preset matching parameter threshold does not exist in all the determined matching parameters, so as to avoid a situation that the security detection accuracy is low due to the fact that the parameter configuration process of the building security detection network cannot be updated effectively and continuously in a short time, and therefore, the present embodiment will focus on the detailed explanation of step S140.

For example, in one possible example, for step S140, in the process of determining the security detection configuration magnitude of each building security detection network relative to the building object, the embodiment may acquire a plurality of network weight units corresponding to each building security detection network, and determine a configuration vector list corresponding to the security detection configuration feature in each network weight unit and a plurality of configuration reference weights.

It should be noted that the configuration vector list may be used to represent a security detection tendency behavior of the security detection configuration feature in the security detection invoking process, and the configuration reference weight may be used to represent an influence weight of the security detection configuration feature on the security detection invoking process.

On this basis, when it is determined that each network weight unit includes the first index factor dimension vector list according to the configuration vector list, a first matching parameter between each configuration reference weight of each network weight unit under the second index factor dimension vector list and each configuration reference weight of each network weight unit under the first index factor dimension vector list can be determined according to the configuration reference weight and the position information of each network weight unit under the first index factor dimension vector list.

It should be noted that the first index factor dimension vector list may represent a vector list of learning configuration index factors, and the second index factor dimension vector list may represent a vector list of safety indexes.

Therefore, when the fact that each network weight unit comprises the first index factor dimension vector list is determined according to the configuration vector list, a first matching parameter between each configuration reference weight of each network weight unit under the second index factor dimension vector list and each configuration reference weight of each network weight unit under the first index factor dimension vector list can be determined according to the configuration reference weight and the position information of each network weight unit under the first index factor dimension vector list, and then the configuration reference weight of each network weight unit, of which the first matching parameter between the configuration reference weight of each network weight unit under the second index factor dimension vector list and under the first index factor dimension vector list reaches a preset parameter range, is transferred to the first index factor dimension vector list. And then, when each network weight unit contains a plurality of configuration reference weights under the second index factor dimension vector list, determining second matching parameters among the configuration reference weights of each network weight unit under the second index factor dimension vector list according to the configuration reference weights of each network weight unit under the first index factor dimension vector list and position information of each network weight unit, and screening each configuration reference weight under the second index factor dimension vector list according to the second matching parameters among the configuration reference weights.

In this way, the influence node level can be set for the target configuration reference weight obtained by screening according to the configuration reference weight of each network weight unit in the first index factor dimension vector list and the position information thereof, and the target configuration reference weight is transferred to the list section corresponding to the influence node level in the first index factor dimension vector list, so that after weighting processing is respectively performed according to all the configuration reference weights in the first index factor dimension vector list, the matching parameters corresponding to the building safety detection network corresponding to each network weight unit are weighted, and the safety detection configuration level of the building safety detection network corresponding to each network weight unit relative to the building object is determined.

For another example, in a possible example, in step S140, in a process of performing data feature identification on the building object according to the magnitude sequence of the security detection configuration levels to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node loaded in each model matching attribute and corresponding to the security detection configuration level of each building security detection network, the embodiment may list security detection configuration nodes corresponding to each security detection configuration level, and establish a building security detection network node sequence.

Optionally, the building security detection network node sequence may be a itemized processing list, each network element corresponds to one group of list features, each group of list features has at least one security detection configuration node, and each network element of the building security detection network node sequence has a progressive relationship from high to low.

Meanwhile, in this embodiment, the preset configuration attribute information of the building object may be determined, and the security detection configuration node in at least one building security detection network node sequence included in the preset configuration attribute information of the building object may be extracted.

And then, establishing an influence relation between the safety detection configuration node and the building safety detection network node sequence, and generating a target safety detection strategy according to the influence relation.

For example, generating the target security detection policy according to the influence relationship may specifically be: converting the corresponding building safety detection network node sequence into model node data according to each safety detection configuration node, respectively generating at least one safety detection unit of each model node data, then obtaining safety detection units with non-repetition safety detection configuration orders to form a safety detection unit group, and influencing each safety detection unit in the safety detection unit group into the building safety detection network node sequence to form a target safety detection strategy, wherein each safety detection unit corresponds to one safety detection project configuration.

Therefore, the safety detection configuration nodes contained in the preset configuration attribute information of the building object can be compared with each safety detection configuration node in the target safety detection strategy in a traversing way, in the traversal comparison process, if all the safety detection configuration nodes of one safety detection unit are contained in the preset configuration attribute information of the building object, recording the safety detection unit as the model matching attribute direction of the building object, then determining a plurality of matching positions corresponding to the building object according to the model matching attribute directions of the building object, carrying out data characteristic identification on the building object according to each matching position to obtain a corresponding model matching attribute, and determining the model attribute node of the safety detection configuration magnitude according to at least one safety detection unit of the loaded model node data of the safety detection configuration magnitude included in each model matching attribute.

In a possible example, in the process of determining the preset configuration attribute information of the building object, the present embodiment may perform a itemization process on the building object to obtain a plurality of specification configuration item information based on a plurality of pieces of building specification configuration data information formed by specification configuration nodes of the building object stored in the security inspection platform 100 corresponding to the building object and currently used nodes of the building object, and obtain pre-specification update information and post-specification update information of each specification configuration item information, and then establish a specification update behavior set of the specification update record corresponding to the building object according to the pre-specification update information and the post-specification update information of each specification configuration item information.

Next, a plurality of specification update units corresponding to the specification update records corresponding to the building object may be acquired, and a target specification update unit in the plurality of specification update units may be counted, where a specification update character feature exists in the target specification update unit.

Therefore, whether the associated specification updating objects exist between two adjacent target specification updating units can be judged, if yes, the number of the associated specification updating objects is counted, and when the number does not exceed a set numerical value, the specification updating behavior set is implanted into the specification configuration data information of each building, when the specification updating behavior set implanted into the specification configuration data information of each building is updated, the updated specification updating behavior set is obtained, and the specification updating operation characteristics and the specification configuration item information deviation information corresponding to each obtained updated specification updating behavior set are counted.

And then, determining the specification updating weight of each updated specification updating behavior set according to the specification updating operation characteristic and the specification configuration item information deviation information corresponding to each updated specification updating behavior set, correcting the updated specification updating behavior set obtained in real time according to the specification updating weight to obtain a building object block specification updating sequence, extracting safety index data in each building specification configuration information according to the list characteristic in the building object block specification updating sequence, and determining preset configuration attribute information of the building object according to the extracted safety index data.

In a possible example, for example, in the process of performing data feature recognition on the building object according to each matching position to obtain the corresponding model matching attribute, the embodiment may obtain the current operating feature of the building object and locate the first matching feature corresponding to each matching position from the current operating feature, and then determine whether the first matching feature corresponding to each matching position in the current operating feature has a matching feature value with respect to the second matching feature in the current operating feature. Wherein the second matching feature is a feature other than the first matching feature in the current operating feature.

If the first matching feature corresponding to each matching position in the current running features has a matched feature value relative to the second matching feature in the current running features, the first matching feature corresponding to each matching position located in the current running features can be determined as an effective matching feature of the current running features, otherwise, the first matching feature corresponding to each matching position located in the current running features and the second matching feature in the current running features are subjected to weighted summation, and the weighted summation result is determined as the effective matching feature of the current running features.

On this basis, for each matching position, a first thread node in a cloud computing control of the security detection platform 100 is extracted from the matching position, partial features in effective matching features of current operating features are fused with the first thread node to obtain a second thread node, and then the first thread node and the second thread node are respectively operated in a mirror thread corresponding to the cloud computing control to obtain first cloud computing information and second cloud computing information which respectively correspond to each other.

Therefore, whether the similarity of the first cloud computing information and the second cloud computing information reaches a preset threshold value or not can be judged, when the similarity of the first cloud computing information and the second cloud computing information reaches the preset threshold value, the matching position is started to operate the second thread node to obtain third cloud computing information corresponding to the second thread node, feature classification information in the third cloud computing information is extracted, model matching attributes corresponding to the matching position are obtained according to the feature classification information, and when the similarity of the first cloud computing information and the second cloud computing information does not reach the preset threshold value, the step of fusing partial features in effective matching features of current operating features and the first thread node to obtain the second thread node is returned.

It should be noted that, on the basis of the above, for example, the inventor of the present application further considers that each second target building security detection network may have different security detection methods, in order to avoid the excessive screening experience brought to the building object by the complicated security detection, the embodiment may further obtain the second building security detection result obtained by performing the security detection on the building object by each second target building security detection network according to the security attribute influence factor, and then obtain the security detection index factor characteristic of the security detection index factor corresponding to each second building security detection result according to the security detection configuration item of each second building security detection result, so as to screen the range according to the preset security detection index factor characteristic of the building object and the security detection index factor characteristic of the security detection index factor corresponding to the second building security detection result, and obtaining a safety detection screening result.

It should be noted that the safety detection screening result may include a plurality of index factor feature sets corresponding to the safety detection index factor features within the feature screening range of the preset safety detection index factor.

Then, screening feature configuration information of any one first screening feature configuration of different index factor features contained in the security detection screening result can be acquired, screening feature configuration attributes of the first screening feature configuration are determined according to the screening feature configuration information of the first screening feature configuration, a target building operation environment corresponding to the first screening feature configuration is determined based on a screening feature configuration scene in the screening feature configuration information of the first screening feature configuration, meanwhile, security detection service information matched with the screening feature configuration attributes of the first screening feature configuration is determined, and security detection services matched with the security detection service information are selected, so that security marks of a plurality of security detection services with the security detection service information in the target building operation environment can be obtained according to the screening feature configuration attributes of the first screening feature configuration, and selecting a target security detection service matched with the first screening feature configuration from the plurality of security detection services with the security detection service information, wherein the target security detection service is also required to be matched with a second screening feature configuration associated with the first screening feature configuration.

Then, the security mark information of the first screening feature configuration included in the screening feature configuration information of the first screening feature configuration may be acquired, and the security mark information of the second screening feature configuration included in the screening feature configuration information of the second screening feature configuration may be acquired, so that a corresponding third building security detection result may be generated according to the security mark information of the first screening feature configuration and the security mark information of the second screening feature configuration.

The third building safety detection result obtained by integrating and screening based on the design can avoid excessive screening experience brought to building objects by too complicated safety detection.

Fig. 3 is a schematic functional module diagram of a building safety detection apparatus 300 according to an embodiment of the present application, and the present embodiment may divide the functional module of the building safety detection apparatus 300 according to the foregoing method embodiment. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the present application is schematic, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the building security detection apparatus 300 shown in fig. 3 is only a schematic diagram of an apparatus. The building security detection apparatus 300 may include an extraction module 310, a calculation module 320, a first security detection module 330, and a second security detection module 340, and the functions of the functional modules of the building security detection apparatus 300 are described in detail below.

The extracting module 310 is configured to obtain building sensing detection information corresponding to the building object from each monitoring sensing node 200, and extract safety index data corresponding to the building sensing detection information, where the building sensing detection information is obtained by the safety detection platform 100 according to safety detection policy information of the monitoring sensing node 200 corresponding to the building object and a safety detection feedback manner between the monitoring sensing node 200 and the safety detection platform 100.

The calculating module 320 is configured to obtain a security index impact factor of the building security detection network of each building object and a security detection reference parameter corresponding to the security detection policy of each building security detection network under the corresponding security index impact factor, and calculate a matching parameter between the building sensing detection information and each building security detection network according to an index factor weight corresponding to index factor information in the security index data, the security index impact factor of each building security detection network and the security detection reference parameter corresponding to the security detection policy under the corresponding security index impact factor.

The first safety detection module 330 is configured to, when a target matching parameter reaching a preset matching parameter threshold exists in all the determined matching parameters, associate the building object with a first target building safety detection network corresponding to the target matching parameter, so that the first target building safety detection network performs safety detection on the building object to obtain a first building safety detection result.

A second security detection module 340, configured to, when there is no target matching parameter reaching a preset matching parameter threshold in all the determined matching parameters, determine a security detection configuration magnitude of each building security detection network, perform data feature identification on building sensing detection information of the building object through each corresponding building security detection network according to a magnitude order of the security detection configuration magnitude to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node corresponding to the security detection configuration magnitude of each building security detection network in each model matching attribute, determine a security attribute impact factor corresponding to each model matching attribute from security index data corresponding to the building object according to each model matching attribute, and associate the security attribute impact factor corresponding to each model matching attribute with a second target building corresponding to the model attribute node loaded in each model matching attribute And in the safety detection network, carrying out safety detection on the building object through each second target building safety detection network according to the safety attribute influence factor to obtain a second building safety detection result.

Further, fig. 4 is a schematic structural diagram of a security detection platform 100 for performing the above-mentioned building security detection method according to an embodiment of the present application. As shown in FIG. 4, the security detection platform 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The processor 130 may be one or more, and one processor 130 is illustrated in fig. 4 as an example. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified by the connection by the bus 140 in fig. 4.

The machine-readable storage medium 120 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the building security detection method in the embodiment of the present application (for example, the extraction module 310, the calculation module 320, the first security detection module 330, and the second security detection module 340 of the building security detection apparatus 300 shown in fig. 3). The processor 130 executes various functional applications and data processing of the terminal device by detecting the software programs, instructions and modules stored in the machine-readable storage medium 120, that is, the above-mentioned building security detection method is implemented, and details are not described herein.

The machine-readable storage medium 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like.

The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130.

The security detection platform 100 may interact with other devices (e.g., the monitoring sensing node 200) via the network interface 110. Network interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may send and receive information using network interface 110.

In the above embodiments, the implementation may be wholly or partially implemented by software, hardware, firmware, or any pair thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device.

Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present application without departing from the spirit and scope of the application. Thus, to the extent that such expressions and modifications of the embodiments of the application fall within the scope of the claims and their equivalents, the application is intended to embrace such alterations and modifications.

Claims

1. A building safety detection method is applied to a safety detection platform, the safety detection platform is in communication connection with a plurality of monitoring sensing nodes, and the method comprises the following steps:

obtaining a safety index influence factor of a building safety detection network of each building object and a safety detection reference parameter corresponding to a safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and calculating a matching parameter between the building sensing detection information and each building safety detection network according to an index factor weight corresponding to index factor information in the safety index data, the safety index influence factor of each building safety detection network and a safety detection reference parameter corresponding to the safety detection strategy under the corresponding safety index influence factor, wherein the safety index influence factor refers to an influence factor label occupied by a specific safety index, the safety detection strategies corresponding to different safety index influence factors are different, and for different building safety detection networks, the safety detection reference parameters corresponding to the safety detection strategies corresponding to different safety index influence factors are different;

2. The building security detection method according to claim 1, wherein the step of calculating the matching parameter between the building sensing detection information and each building security detection network according to the index factor weight corresponding to the index factor information in the security index data, the security index influence factor of each building security detection network, and the security detection reference parameter corresponding to the security detection network in the security detection policy under the corresponding security index influence factor comprises:

3. The building security detection method of claim 1, wherein the step of determining the security detection configuration level of each building security detection network relative to the building object comprises:

4. The building security detection method according to claim 1, wherein the step of performing data feature recognition on the building object according to the magnitude sequence of the security detection configuration levels to obtain a plurality of model matching attributes corresponding to the building object and a model attribute node loaded in each model matching attribute and corresponding to the security detection configuration level of each building security detection network includes:

5. The building security detection method according to claim 4, wherein the step of determining the preset configuration attribute information of the building object comprises:

6. A building safety detection system is characterized by comprising a safety detection platform and a plurality of monitoring sensing nodes in communication connection with the safety detection platform;

the safety detection platform is used for acquiring a safety index influence factor of a building safety detection network of each building object and a safety detection reference parameter corresponding to a safety detection strategy of each building safety detection network under the corresponding safety index influence factor, and calculating a matching parameter between the building sensing detection information and each building safety detection network according to an index factor weight corresponding to index factor information in the safety index data, the safety index influence factor of each building safety detection network and a safety detection reference parameter corresponding to the safety detection strategy under the corresponding safety index influence factor, wherein the safety index influence factor is an influence factor label occupied by a specific safety index, and the safety detection strategies corresponding to different safety index influence factors are different, for different building safety detection networks, safety detection reference parameters corresponding to safety detection strategies corresponding to different safety index influence factors are different;