CN115577035A - Hidden danger data visualization method, device, equipment and storage medium - Google Patents
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Abstract
The invention is applicable to the technical field of computers, and provides a method, a device, equipment and a storage medium for visualizing hidden danger data, wherein the method comprises the following steps: receiving a request for carrying out hidden danger data visualization on a preset monitoring area in a monitoring screen, and acquiring monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request; calculating an importance parameter of the significant maximum value of the monitoring data according to the monitoring data corresponding to each monitoring index, and acquiring an influence degree parameter corresponding to each monitoring index; acquiring hidden danger parameters of each monitoring index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index; and outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters, so that a plurality of indexes of the monitoring data are unified to the same dimensionality through the significance maximum value importance parameters and the influence degree parameters, and the possible hidden dangers in the monitoring area are accurately obtained and visualized.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a hidden danger data visualization method, device, equipment and storage medium.
Background
With the coming of the cloud era, big data attracts more and more attention, the big data can have stronger decision-making power, insight discovery power and process optimization capability only by a new processing mode so as to adapt to massive, high-growth rate and diversified information assets, and data visualization can clearly and effectively transmit and communicate information by means of a graphical mode. Currently, in the research, teaching and development fields, data visualization is an extremely active and critical field, and is positively influenced by data visualization, and various services are also converted into a visualization mode, so that the efficiency of service interaction processing is improved.
In the big data era, in order to further improve the quality of business interaction processing, a big data mining process aiming at visual business is indispensable. However, the existing data large-screen visualization technology is mainly based on surface layer monitoring, and lacks the capability of data mining, namely, for a plurality of indexes of a certain target object, only chart-based visualization is performed on each index of the target object, but the influence degree of the state and the change of the indexes on the target object is difficult to evaluate and compare.
Disclosure of Invention
The invention aims to provide a method, a device, equipment and a storage medium for visualizing hidden danger data, and aims to solve the problem that the hidden danger data visualization effect is poor due to the fact that an effective hidden danger data visualization method cannot be provided in the prior art.
In one aspect, the invention provides a hidden danger data visualization method, which includes the following steps:
receiving a request for carrying out hidden danger data visualization on a preset monitoring area in a monitoring screen, and acquiring monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request;
calculating an importance parameter of the significant maximum value of the monitoring data according to the monitoring data corresponding to each monitoring index, and acquiring an influence degree parameter corresponding to each monitoring index;
acquiring hidden danger parameters of each monitoring index according to the significance maximum value importance parameters and the influence degree parameters corresponding to each monitoring index;
and outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters so as to show the indexes with potential dangers.
In another aspect, the present invention provides a hidden danger data visualization apparatus, including:
the system comprises a data acquisition unit, a data processing unit and a monitoring unit, wherein the data acquisition unit is used for receiving a request for carrying out hidden danger data visualization on a preset monitoring area in a monitoring screen and acquiring monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request;
the first parameter acquisition unit is used for calculating the significance maximum value importance parameter of the monitoring data according to the monitoring data corresponding to each monitoring index and acquiring the influence degree parameter corresponding to each monitoring index;
the second parameter obtaining unit is used for obtaining the hidden danger parameters of each monitoring index according to the significance maximum importance parameters and the influence degree parameters corresponding to each monitoring index; and
and the hidden danger visualization unit is used for outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters so as to display the indexes with potential hidden dangers.
In another aspect, the present invention also provides a visualization apparatus, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method as described above when executing the computer program.
In another aspect, the present invention also provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method as described above.
The method comprises the steps of receiving a request for visualizing the hidden danger data of a preset monitoring area in a monitoring screen, obtaining the monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area, calculating the significance maximum value importance parameter of the monitoring data according to the monitoring data corresponding to each monitoring index, obtaining the influence degree parameter corresponding to each monitoring index, obtaining the hidden danger parameter of each monitoring index according to the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index, outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters to show the indexes with the potential dangers, unifying a plurality of indexes of the monitoring data to the same dimension through the significance maximum value importance parameter and the influence degree parameter, and accurately obtaining and visualizing the potential dangers possibly existing in the monitoring area.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a hidden danger data visualization method according to an embodiment of the present invention;
fig. 2 is a flowchart of an implementation of a hidden danger data visualization method according to a second embodiment of the present invention; (ii) a
Fig. 3 is a schematic structural diagram of a hidden danger data visualization device according to a third embodiment of the present invention; and
fig. 4 is a schematic structural diagram of a visualization apparatus according to a fourth embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The following detailed description of specific implementations of the invention is provided in conjunction with specific embodiments:
the first embodiment is as follows:
fig. 1 shows an implementation flow of a hidden danger data visualization method provided by an embodiment of the present invention, and for convenience of description, only parts related to the embodiment of the present invention are shown, which are detailed as follows:
in step S101, a request for performing hidden danger data visualization on a preset monitoring area in a monitoring screen is received, and monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area is obtained according to the request;
the monitoring or monitoring device provided by the embodiment of the invention is particularly suitable for monitoring or monitoring devices with monitoring screens, and is used for visualizing the hidden danger data of the monitored or monitored area in the monitoring screens, so that the hidden danger possibly existing in the monitored area is displayed specifically and vividly for a user, and the user is reminded of processing the hidden danger possibly existing in time.
In a specific embodiment, a user can select a preset monitoring area in a monitoring screen and then request hidden danger data visualization for the selected monitoring area to input a hidden danger data visualization request, one monitoring area can be provided with different monitoring index groups, and each monitoring index is associated with corresponding monitoring data. As an example, the monitoring index set of the monitored area may be represented as { m } 1 ,m 2 ,…,m n In which m is 1 、m 2 For example, the preset monitoring index set may specifically be { GDP of each province in one year, GDP of each region in one year, GDP of each industry in one year }, where the monitoring data corresponding to GDP of each province in one year may be 900 province a, 130 provinces B, 1100 provinces C, and 300 provinces D.
In step S102, calculating an importance parameter of a significant maximum of the monitoring data according to the monitoring data corresponding to each monitoring index, and obtaining an influence degree parameter corresponding to each monitoring index;
in the embodiment of the invention, the significance maximum importance parameter of the monitoring data indicates the degree of influence of a significance maximum point (maximum monitoring data) in a group of monitoring data corresponding to the monitoring index on potential safety hazards, and the significance maximum point is significantly far away from the positions of other points, so that the significance maximum point can be regarded as an abnormal point in the group of monitoring data, whether the potential hazard data exists in the monitoring data can be accurately determined through the significance maximum importance parameter, and the potential hazards possibly existing in a monitoring area can be accurately determined. In addition, the influence degree parameter corresponding to each monitoring index indicates the influence degree of each monitoring index on potential safety hazards possibly existing in the user center, and the parameter can be defined by the user, and certainly can be set according to knowledge of an expert knowledge base.
In step S103, acquiring a hidden danger parameter of each monitoring index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index;
in the embodiment of the invention, after the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index are obtained, the hidden danger parameter of each monitoring index can be calculated according to the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index, a plurality of monitoring indexes of hidden danger data are unified to the same dimension through the hidden danger parameter, and the probability or hidden danger values of different hidden dangers existing in a monitored area are accurately obtained.
In step S104, each index in the monitoring index set is output in a first visualization manner according to the hidden danger parameter to show the index that may have a hidden danger.
In the embodiment of the invention, the monitoring index group and the hidden danger parameters corresponding to each monitoring index in the monitoring index group can be displayed and output on the monitoring screen in a visualization mode of various charts, videos and the like so as to display the indexes with potential dangers, thereby reminding a user of paying attention to the hidden dangers, further reminding the user of exploring the reasons causing the abnormity of the monitoring indexes, or warning the user that the safety precaution needs to be improved and corrected as soon as possible.
The method and the device for displaying the hidden danger of the monitoring area have the advantages that after a request for visualizing the hidden danger data of the preset monitoring area in the monitoring screen is received, the monitoring data corresponding to each monitoring index in the preset monitoring index group of the monitoring area is obtained, the significance maximum value importance parameter of the monitoring data is calculated according to the monitoring data corresponding to each monitoring index, the influence degree parameter corresponding to each monitoring index is obtained, the hidden danger parameter of each monitoring index is obtained according to the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index, each index in the monitoring index group is output in a first visualization mode according to the hidden danger parameters to display the indexes with the potential dangers, therefore, the multiple monitoring indexes of the hidden danger data are unified to the same dimension through the significance maximum value importance parameter and the influence degree parameter, and the potential dangers possibly existing in the monitoring area are accurately obtained and visualized.
The second embodiment:
fig. 2 shows an implementation flow of a hidden danger data visualization method provided by the second embodiment of the present invention, and for convenience of description, only the parts related to the second embodiment of the present invention are shown, which are detailed as follows:
in step S201, a request for performing hidden danger data visualization on a preset monitoring area in a monitoring screen is received, and monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area is obtained according to the request;
the monitoring or monitoring device provided by the embodiment of the invention is particularly suitable for monitoring or monitoring devices with monitoring screens, and is used for visualizing the hidden danger data of the monitored or monitored area in the monitoring screens, so that the hidden danger possibly existing in the monitored area is displayed to a user specifically and vividly, and the user is reminded to process the hidden danger possibly existing in the monitored area in time.
In a specific embodiment, a user can select a preset monitoring area in a monitoring screen and further request hidden danger data visualization for the selected monitoring area to input a hidden danger data visualization request, one monitoring area can be provided with different monitoring index groups, and each monitoring index is associated with corresponding monitoring data. As an example, the monitoring index set of the monitored area may be represented as { m } 1 ,m 2 ,…,m n In which m is 1 、m 2 For example, the preset monitoring index set may specifically be { GDP of each province in one year, GDP of each region in one year, GDP of each industry in one year }, where the monitoring data corresponding to GDP of each province in one year may be 900 province a, 130 provinces B, 1100 provinces C, and 300 provinces D.
In step S202, calculating an importance parameter of a significant maximum value of the monitoring data according to the monitoring data corresponding to each monitoring index, and obtaining an influence degree parameter corresponding to each monitoring index;
in the embodiment of the invention, the significance maximum importance parameter of the monitoring data indicates the degree of influence of a significance maximum point in a group of monitoring data corresponding to the monitoring index on potential safety hazards, and the significance maximum point is significantly far away from the positions of other points, so that the significance maximum point can be regarded as an abnormal point in the group of monitoring data, whether the potential safety hazards exist in the monitoring data can be accurately determined through the significance maximum importance parameter, and the potential safety hazards possibly existing in a monitoring area can be accurately determined. The influence degree parameter corresponding to each monitoring index indicates the influence degree of each monitoring index on potential safety hazards in the user center, and the parameter can be defined by the user, and can be set according to the knowledge of the expert knowledge base.
In a preferred embodiment, when calculating the significance maximum importance parameter of the monitoring data according to the monitoring data corresponding to each monitoring index, the significance maximum importance parameter can be calculated by the following steps:
(1) Sorting the monitoring data corresponding to each monitoring index in a descending order to obtain sorted monitoring data after sorting in the descending order;
(2) Fitting the sequencing monitoring data with the maximum value removed into a power law distribution function, and performing regression analysis on the sequencing monitoring data with the maximum value removed by using the power law distribution function to obtain a corresponding estimated value;
by way of example, in one embodiment, X \ x max Denotes a set of sorted monitor data with the maximum value removed, χ = { x = 1 ,x 2 ,…,x l Denotes a set of monitoring data corresponding to the monitoring index, x max Represents the maximum value in χ, and has a power law distribution function of α · k -β Wherein, alpha and beta represent constants which are obtained after being fitted into power law distribution and are larger than zero, and k represents variable. After regression analysis is carried out by using a power law distribution function, k is obtained at x i The estimated value (fitted value) of (c).
(3) Calculating a first fitting residual of the sequencing monitoring data with the maximum value removed according to the estimated value, fitting the first fitting residual into normal distribution, and calculating a second fitting residual when the maximum value is fitted through a power law distribution function;
by way of example, in one embodiment, the first fitting residual may be obtained by subtracting the observed (actual) value from the estimated value obtained in the previous step, i.e., using the formula
A first fitting residual is obtained, and the first fitting residual,
represents the estimated value, x i As an observed value, x i ∈χ\{x max And then fitting a first fitted residual corresponding to the monitored data set to a normal distribution N (μ, δ). In another embodiment, the data can be represented by a formula
Calculating a second fit residual when the maximum value is fit by the power law distribution function, wherein,
representing k in x calculated by a power law distribution function max The estimated value of (c).
(4) And calculating the probability that the first fitting residual is larger than the second fitting residual under normal distribution, and obtaining the significance maximum value importance parameter of the monitoring data corresponding to each monitoring index according to the probability.
By way of example, in one embodiment, the value may be calculated by the formula Pr (∈ > ∈ epsilon max N (μ, δ)) calculating the probability that the first fit residual is greater than the second fit residual under normal distribution, and then determining the probability by 1-Pt (∈ >. Epsilon max L N (μ, δ)) calculates a significant maximum importance parameter of the monitoring data corresponding to each monitoring index.
Through the steps, the significance maximum value importance parameter of the monitoring data corresponding to each monitoring index can be accurately obtained, so that a basis is provided for accurately determining whether hidden danger data (hidden danger points) exist in the monitoring data corresponding to each monitoring index subsequently, and further, the potential dangers or the hidden danger points possibly existing in the monitoring area are accurately determined.
In a specific embodiment, when obtaining the influence degree parameter corresponding to each monitoring index, the influence degree parameter corresponding to each monitoring index in the preset monitoring index group is calculated, all the influence degree parameters are normalized to obtain the normalized influence degree parameter, so that the situation that when the influence degree parameter is greatly different from the significance parameter of the significant maximum value, the effect of the parameter with a larger value in the calculation of the hidden danger parameter is prominent, and the effect of the parameter with a smaller value is weakened is avoided, and the hidden danger parameter obtained by subsequent calculation can accurately reflect the severity of the hidden danger of the monitoring data.
In step S203, acquiring a hidden danger parameter of each monitoring index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index;
in the embodiment of the invention, after the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index are obtained, the hidden danger parameter of each monitoring index can be calculated according to the significance maximum value importance parameter and the influence degree parameter corresponding to each monitoring index, a plurality of indexes of hidden danger data are unified to the same dimension through the hidden danger parameter, and the probability or hidden danger values of different hidden dangers existing in a monitored area are accurately obtained.
In a specific embodiment, if the influence degree parameter is the normalized influence degree parameter, the hidden danger parameter of each index is obtained according to the significance maximum importance parameter corresponding to each monitoring index and the normalized influence degree parameter. More specifically, the hidden danger parameter of each monitoring index is calculated by using a formula Si = Imp (mi) × Sig (mi), where Imp (mi) represents a normalized influence degree parameter corresponding to the index mi, and Sig (mi) represents a significant maximum importance parameter corresponding to the index mi.
In step S204, each index in the monitoring index group is output in a first visualization manner according to the hidden danger parameter to show indexes that may have hidden dangers;
in the embodiment of the invention, the monitoring index group and the hidden danger parameters corresponding to each monitoring index in the monitoring index group can be displayed and output on the monitoring screen in a visual mode such as various charts, videos and the like to display the indexes with potential dangers, so that the user can be reminded of paying attention to the hidden dangers, and further, the user can be reminded of exploring the reasons causing the abnormal monitoring indexes or warning the user that the safety precaution should be improved and corrected as soon as possible.
In step S205, a time sequence monitoring index related to the time sequence in the preset monitoring index group is obtained, and time sequence monitoring data corresponding to the time sequence monitoring index is obtained;
in the embodiment of the present invention, the monitoring index related to the time series is used to indicate or measure the change of the monitored area or the monitored object over time, and the monitoring index related to the time series is referred to as a time series monitoring index herein. In order to confirm whether the monitoring data corresponding to the time sequence monitoring index has a variation trend with hidden danger or not, so as to further visually output the time sequence monitoring index possibly having hidden danger on a monitoring screen, the time sequence monitoring index related to the time sequence in a preset monitoring index group is obtained, and the time sequence monitoring data corresponding to the time sequence monitoring index is obtained.
In step S206, calculating an importance parameter of a significant trend of the time sequence monitoring data according to the time sequence monitoring data corresponding to each time sequence monitoring index, and obtaining an influence degree parameter corresponding to each time sequence monitoring index;
in the embodiment of the invention, the significant trend importance parameter of the time sequence monitoring data indicates the influence degree of trend change of a group of time sequence monitoring data corresponding to the time sequence monitoring index on potential safety hazards, whether the time sequence monitoring data changing along with time has the potential safety hazards or not can be accurately determined through the significant trend importance parameter, and the potential safety hazards possibly existing in a monitoring area can be accurately determined. The influence degree parameter corresponding to each time sequence monitoring index indicates the influence degree of each time sequence monitoring index on potential safety hazards possibly existing in the user center, and the parameter can be defined by the user, and certainly can be set according to knowledge of an expert knowledge base.
In a preferred embodiment, when calculating the significant trend importance parameter of the time series monitoring data according to the time series monitoring data corresponding to each time series monitoring index, the method can be implemented by the following steps:
(1) Fitting the time sequence monitoring data to a straight line through linear regression analysis, and acquiring a corresponding fitting degree value and a slope of the straight line;
(2) Mapping the obtained slope through a preset mapping function to obtain a mapped slope, so that the mapped slope is within a preset range;
in a preferred embodiment, use is made of
And mapping the obtained slope to obtain the mapped slope, so that the mapped slope is in the range of [0, 1), thereby avoiding the situation that when the difference between the slope value and the fitting degree is large, the slope value is too high, the effect in the significance calculation of the significant trend is prominent, and the effect of the fitting degree in the calculation is relatively weakened, and ensuring that the significant trend significance parameter obtained by subsequent calculation accurately reflects the significant change trend of the time sequence monitoring data.
(3) And calculating the significant trend importance parameter of the time sequence monitoring data according to the fitting degree value and the mapped slope.
In a specific embodiment, the fitness value and the mapped slope may be multiplied to calculate the significant trend importance parameter of the time series monitoring data, so that the significant trend importance parameter is calculated by combining the accuracy of the trend change and the significance of the trend change, and the reliability of the significant trend importance parameter is ensured.
In a specific embodiment, when the influence degree parameter corresponding to each time sequence monitoring index is obtained, the influence degree parameter corresponding to each time sequence monitoring index is calculated, and normalization processing is performed on all the influence degree parameters to obtain a normalized influence degree parameter.
In step S207, acquiring a hidden danger parameter of each time sequence monitoring index according to the significant trend importance parameter and the influence degree parameter corresponding to each time sequence monitoring index;
in the embodiment of the invention, after the significant trend importance parameter and the influence degree parameter corresponding to each time sequence monitoring index are obtained, the hidden danger parameter of each time sequence monitoring index can be calculated according to the significant trend importance parameter and the influence degree parameter corresponding to each time sequence monitoring index, a plurality of time sequence indexes of hidden danger data are unified to the same dimension through the hidden danger parameter, and the probability or hidden danger values of different hidden dangers existing in a monitored area are accurately obtained.
In a specific embodiment, if the influence degree parameter is the normalized influence degree parameter, the hidden danger parameter of each time sequence monitoring index is obtained according to the significant trend importance parameter corresponding to each time sequence monitoring index and the normalized influence degree parameter. More specifically, the hidden danger parameter of each time-series monitoring index is calculated by using a formula Si = Imp (mi) × Sig (mi), where Imp (mi) represents a normalized influence degree parameter corresponding to the time-series monitoring index mi, and Sig (mi) represents a significant trend importance parameter corresponding to the index mi.
In step S208, the time sequence monitoring index is output in a second visualization manner according to the hidden danger parameter of each time sequence monitoring index, so as to show the time sequence monitoring index that may have hidden danger.
In the embodiment of the invention, the time sequence monitoring index group and the hidden danger parameters corresponding to each time sequence monitoring index in the time sequence monitoring index group can be displayed and output on the monitoring screen in a visual mode of various charts, videos and the like to display the indexes with possible hidden danger trends, so that a user is reminded of paying attention to the hidden danger trends, the user can be reminded of exploring the reasons causing the abnormal change of the time sequence monitoring indexes, or the user is warned to improve the safety and correct the time sequence monitoring indexes as soon as possible.
Example three:
fig. 3 shows a structure of a hidden danger data visualization apparatus provided in a third embodiment of the present invention, and for convenience of description, only the portions related to the third embodiment of the present invention are shown, where the portions include:
the data acquiring unit 31 is configured to receive a request for performing hidden danger data visualization on a preset monitoring area in a monitoring screen, and acquire monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request;
a first parameter obtaining unit 32, configured to calculate, according to the monitoring data corresponding to each monitoring index, a significant maximum importance parameter of the monitoring data, and obtain an influence degree parameter corresponding to each monitoring index;
a second parameter obtaining unit 33, configured to obtain a hidden danger parameter of each monitoring index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index; and
and the hidden danger visualization unit 34 is used for outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters so as to show the indexes with potential hidden dangers.
In a preferred embodiment, the first parameter obtaining unit 32 includes:
the descending sorting unit is used for carrying out descending sorting on the monitoring data corresponding to each monitoring index to obtain sorted monitoring data after the descending sorting;
the regression analysis unit is used for fitting the sequencing monitoring data with the maximum value removed into a power law distribution function, and performing regression analysis on the sequencing monitoring data with the maximum value removed by using the power law distribution function to obtain a corresponding estimation value;
the residual error calculation unit is used for calculating a first fitting residual error of the sequencing monitoring data with the maximum value removed according to the estimation value, fitting the first fitting residual error into normal distribution, and calculating a second fitting residual error when the maximum value is fitted through a power law distribution function; and
and the parameter calculating unit is used for calculating the probability that the first fitting residual is greater than the second fitting residual under normal distribution and obtaining the significance maximum value importance parameter of the monitoring data corresponding to each monitoring index according to the probability.
In the embodiment of the present invention, each unit of the hidden danger data visualization apparatus may be implemented by a corresponding hardware or software unit, and each unit may be an independent software or hardware unit, or may be integrated into a software or hardware unit, which is not limited herein. The detailed implementation of each unit can refer to the description of the foregoing method embodiment, and is not repeated herein.
Example four:
fig. 4 shows a structure of a visualization apparatus provided in a fourth embodiment of the present invention, and for convenience of explanation, only a part related to the embodiment of the present invention is shown.
The visualization apparatus 4 of an embodiment of the invention comprises a processor 40, a memory 41 and a computer program 42 stored in the memory 41 and executable on the processor 40. The processor 40 executes the computer program 42 to implement the steps in the above-described embodiments of the hidden danger data visualization method, such as steps S101 to S104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functionality of the various units in the above-described apparatus embodiments, such as the functionality of the units 31 to 34 shown in fig. 3.
In the embodiment of the invention, a request for visualizing the hidden danger data of a preset monitoring area in a monitoring screen is received, monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area is obtained according to the request, an importance parameter of a significant maximum value of the monitoring data is calculated according to the monitoring data corresponding to each monitoring index, an influence degree parameter corresponding to each monitoring index is obtained, a hidden danger parameter of each monitoring index is obtained according to the importance parameter of the significant maximum value and the influence degree parameter corresponding to each monitoring index, each index in the monitoring index group is output in a first visualization mode according to the hidden danger parameters to show the indexes with the potential dangers, and therefore, a plurality of monitoring indexes of the hidden danger data are unified to the same dimension through the importance parameter of the significant maximum value and the influence degree parameter, and the potential dangers possibly existing in the monitoring area are accurately obtained and visualized.
The visualization device of the embodiment of the present invention may be a computer system having a display screen. The steps implemented when the processor 40 in the visualization apparatus 4 executes the computer program 42 to implement the hidden danger data visualization method may refer to the description of the foregoing method embodiments, and are not described herein again.
Example five:
in an embodiment of the present invention, a computer-readable storage medium is provided, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned hidden danger data visualization method embodiment, for example, the steps S101 to S104 shown in fig. 1. Alternatively, the computer program may be adapted to perform the functions of the units of the above-described apparatus embodiments, such as the functions of the units 31 to 34 shown in fig. 3, when executed by the processor.
In the embodiment of the invention, a request for visualizing the hidden danger data of a preset monitoring area in a monitoring screen is received, monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area is obtained according to the request, an importance parameter of a significant maximum value of the monitoring data is calculated according to the monitoring data corresponding to each monitoring index, an influence degree parameter corresponding to each monitoring index is obtained, a hidden danger parameter of each monitoring index is obtained according to the importance parameter of the significant maximum value and the influence degree parameter corresponding to each monitoring index, each index in the monitoring index group is output in a first visualization mode according to the hidden danger parameters to show the indexes with the potential dangers, and therefore, a plurality of indexes of the hidden danger data are unified to the same dimension through the importance parameter of the significant maximum value and the influence degree parameter, and the potential dangers possibly existing in the monitoring area are accurately obtained and visualized.
The computer readable storage medium of the embodiments of the present invention may include any entity or device capable of carrying computer program code, a recording medium, such as a ROM/RAM, a magnetic disk, an optical disk, a flash memory, or the like.
The above description is intended to be illustrative of the preferred embodiment of the present invention and should not be taken as limiting the invention, but rather, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.
Claims (10)
1. A hidden danger data visualization method is characterized by comprising the following steps:
receiving a request for carrying out hidden danger data visualization on a preset monitoring area in a monitoring screen, and acquiring monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request;
calculating an importance parameter of the significant maximum value of the monitoring data according to the monitoring data corresponding to each monitoring index, and acquiring an influence degree parameter corresponding to each monitoring index;
acquiring hidden danger parameters of each monitoring index according to the significance maximum value importance parameters and the influence degree parameters corresponding to each monitoring index;
and outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters so as to show the indexes with potential dangers.
2. The method of claim 1, wherein the step of calculating the significance maximum importance parameter of the monitored data according to the monitored data corresponding to each monitoring index comprises:
performing descending sorting on the monitoring data corresponding to each monitoring index to obtain sorted monitoring data after descending sorting;
fitting the sequencing monitoring data with the maximum value removed into a power law distribution function, and performing regression analysis on the sequencing monitoring data with the maximum value removed by using the power law distribution function to obtain a corresponding estimation value;
calculating a first fitting residual of the sequencing monitoring data with the maximum value removed according to the estimated value, fitting the first fitting residual into normal distribution, and calculating a second fitting residual when the maximum value is fitted through the power law distribution function;
and calculating the probability that the first fitting residual is larger than the second fitting residual under the normal distribution, and obtaining the significance maximum value importance parameter of the monitoring data corresponding to each monitoring index according to the probability.
3. The method according to claim 1, wherein the step of obtaining the degree of influence parameter corresponding to each monitoring index specifically comprises:
calculating an influence degree parameter corresponding to each monitoring index in the preset monitoring index group, and performing normalization processing on all the influence degree parameters to obtain normalized influence degree parameters;
acquiring hidden danger parameters of each index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index, wherein the steps are as follows:
and acquiring the hidden danger parameters of each index according to the significance maximum value importance parameters corresponding to each monitoring index and the normalized influence degree parameters.
4. The method of claim 3, wherein the step of obtaining the hidden danger parameter of each monitoring index according to the significant maximum importance parameter and the influence degree parameter corresponding to each monitoring index comprises:
and calculating the hidden danger parameter of each monitoring index by using a formula Si = Imp (mi) × Sig (mi), wherein Imp (mi) represents the normalized influence degree parameter corresponding to the index mi, and Sig (mi) represents the significance parameter of the significant maximum value corresponding to the index mi.
5. The method of claim 1, wherein the method further comprises:
acquiring a time sequence monitoring index related to a time sequence in the preset monitoring index group, and acquiring time sequence monitoring data corresponding to the time sequence monitoring index;
calculating the significant trend importance parameter of the time sequence monitoring data according to the time sequence monitoring data corresponding to each time sequence monitoring index, and acquiring the influence degree parameter corresponding to each time sequence monitoring index;
acquiring hidden danger parameters of each time sequence monitoring index according to the significant trend importance parameters and the influence degree parameters corresponding to each time sequence monitoring index;
and outputting the time sequence monitoring indexes in a second visual mode according to the hidden danger parameters of each time sequence monitoring index so as to display the time sequence monitoring indexes with potential hidden dangers.
6. The method of claim 5, wherein the step of calculating the significant trend importance parameter of the time-series monitoring data according to the time-series monitoring data corresponding to each time-series monitoring index comprises:
fitting the time sequence monitoring data to a straight line through linear regression analysis, and acquiring a corresponding fitting degree value and the slope of the straight line;
mapping the slope through a preset mapping function to obtain a mapped slope, so that the mapped slope is in a preset range;
and calculating the significant trend importance parameter of the time sequence monitoring data according to the fitting degree value and the mapped slope.
7. A hidden danger data visualization apparatus, the apparatus comprising:
the system comprises a data acquisition unit, a data processing unit and a monitoring unit, wherein the data acquisition unit is used for receiving a request for carrying out hidden danger data visualization on a preset monitoring area in a monitoring screen and acquiring monitoring data corresponding to each monitoring index in a preset monitoring index group of the monitoring area according to the request;
the first parameter acquisition unit is used for calculating the significance maximum value importance parameter of the monitoring data according to the monitoring data corresponding to each monitoring index and acquiring the influence degree parameter corresponding to each monitoring index;
a second parameter obtaining unit, configured to obtain a hidden danger parameter of each monitoring index according to the significance maximum importance parameter and the influence degree parameter corresponding to each monitoring index; and
and the hidden danger visualization unit is used for outputting each index in the monitoring index group in a first visualization mode according to the hidden danger parameters so as to display the indexes with potential hidden dangers.
8. The apparatus of claim 7, wherein the first parameter obtaining unit comprises:
the descending sorting unit is used for carrying out descending sorting on the monitoring data corresponding to each monitoring index to obtain sorted monitoring data after the descending sorting;
the regression analysis unit is used for fitting the sequencing monitoring data with the maximum value removed into a power law distribution function, and performing regression analysis on the sequencing monitoring data with the maximum value removed by using the power law distribution function to obtain a corresponding estimation value;
a residual calculation unit, configured to calculate a first fitting residual of the sorted monitoring data from which the maximum value is removed according to the estimated value, fit the first fitting residual to a normal distribution, and calculate a second fitting residual when the maximum value is fitted to the power law distribution function; and
and the parameter calculation unit is used for calculating the probability that the first fitting residual is greater than the second fitting residual under the normal distribution and obtaining the significance maximum value importance parameter of the monitoring data corresponding to each monitoring index according to the probability.
9. A visualization apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor realizes the steps of the method according to any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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