CN114565114B - Computer data safety monitoring method and system based on big data technology - Google Patents
Computer data safety monitoring method and system based on big data technology Download PDFInfo
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Abstract
The invention discloses a computer data safety monitoring method and system based on a big data technology, and belongs to the technical field of data safety monitoring. The system comprises a network vehicle operation big data module, an order distribution module and a time prediction module; the output end of the order distribution module is connected with the input end of the network vehicle operation big data module; the output end of the network vehicle operation big data module is connected with the input end of the time prediction module. The system also comprises a tone analysis module and a blockchain module; the output end of the tone analysis module is connected with the input end of the blockchain module. The invention can solve the taxi taking problem of the user in the network taxi-taking peak period, further save the user time, provide a transfer scheme for the user, monitor the data in the network taxi-taking, and ensure the security of passengers and drivers on the premise of ensuring the privacy of the passengers and drivers.
Description
Technical Field
The invention relates to the technical field of data security monitoring, in particular to a computer data security monitoring method and system based on a big data technology.
Background
With the increasing value of data, data security has become a major concern in government, business and personal information security. Data security refers to the security protection of techniques and management established and employed for data processing systems, protecting computer hardware, software, and data from accidental and malicious damage, alteration, and leakage. Computer data security can be understood as: by adopting various technologies and management measures, the network system is enabled to normally operate, so that the availability, the integrity and the confidentiality of network data are ensured.
With the development of society, the network about car becomes a common trip mode, and problems caused by the network about car in recent years, such as carrying problems in the peak period of the network about car and malignant events of the network about car, and various network about car platforms are provided with multiple means such as a network about car audio-video system, a self-service alarm system and the like aiming at the problems, but the means can play a certain role in the malignant events of the network about car, but the malignant events of the network about car are very few events, and the audio-video severely infringes the privacy of passengers and drivers in most cases; in addition, at specific moments, such as at night, in rainy and snowy days, the network vehicle enters a carrying peak, so that passengers have a long driving time, a long waiting time and high price adding and dispatching cost, and the problems of carrying and data safety in the network vehicle are solved, but no technical measures exist at present.
Disclosure of Invention
The invention aims to provide a computer data safety monitoring method and system based on big data technology, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a computer data security monitoring method based on big data technology, the method comprising the following steps:
s1, acquiring network vehicle order information of a user P, wherein the network vehicle order information comprises an order starting address A, an order arriving address B, an order queuing serial number C and order starting time D;
s2, constructing a region radius R1, acquiring network vehicle operation history data in a region taking an order starting address A as a circle center and taking R1 as a radius, constructing a first time prediction model, and predicting the arrival time T of the network vehicle when the order queuing sequence number is C 0 ；
S3, constructing an area radius R2, wherein R2 is smaller than R1, acquiring a public transportation station in an area taking an order departure address A as a circle center and R2 as a radius, wherein the public transportation station can reach a temporary place E, and the temporary place E is transferred through the temporary place E, and the temporary place E meets the following conditions:
the departure time of the user P riding the network about car is less than T 0 ；
The difference value between the fare of the user P at the temporary station E at the network contract car arrival address B and the fare of the user P at the order departure address A at the network contract car arrival order arrival address B is smaller than a threshold value M, wherein M is a fare threshold value;
s4, acquiring voice monitoring data in the network vehicle, constructing a tone analysis model, setting blockchain recorded voice monitoring data, constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, retrieving the voice monitoring data in the blockchain and feeding the voice monitoring data back to a port of the network vehicle system to give an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port.
According to the technical scheme, the network vehicle operation history data comprise the time interval of queuing the vehicle by the user, the order starting time, the order starting address and the period;
the period comprises a first period and a second period;
the first period is 8 hours in Monday morning to 12 days in friday noon; the second period is 13 pm on friday to 7 am on monday;
in the period, X represents a time period [ X, X+1] in hours;
i.e. 8 in monday morning, represents the 8 o 'clock to 9 o' clock time period in the morning;
according to the above technical solution, the first temporal prediction model includes:
acquiring network vehicle operation history data in an area taking an order starting address A as a circle center and R1 as a radius;
taking the interval waiting time length as a dependent variable, and taking the time area, the queuing number and the weather as independent variables to construct a first time prediction model:
wherein u is 0 、u 1 、u 2 、u 3 Regression coefficients for the first temporal prediction model;
wherein t is 0 Representing an interval waiting time length, wherein the interval waiting time length refers to a user waiting time length between two adjacent queuing numbers, and the two adjacent queuing numbers are marked as c 1 、c 2 Then t 0 Representing queuing number c 2 User at c 1 、c 2 Waiting time between;
m 1 representing a time zone, which is a whole point time number, defining a time zone m 1 Representative time period [ m ] 1 ，m 1 +1]；
Here, as defined for the period, for example, between 7 and 8 pm is denoted as time zone 7;
m 2 representing queuing sequence numbers;
m 3 representing weather, wherein the weather comprises extreme weather and general weather, and the extreme weather comprises rainy days, snowy days and haze weather; in extreme weather, m 3 ＝k 1 Under ordinary weather, m 3 ＝k 2 Wherein k is 1 、k 2 All are settable constants;
in the actual situation, the network is generally difficult to drive in the late peak period, in the application, the system is used for 24 hours, namely X is a value of 0-23, the system is generally difficult to drive in the peak period from 6 to 11 o ' clock at night, the value of X is 18-23 from 6 to 11 o ' clock at night, the corresponding influence error is larger, after 0 o ' clock is passed, a large number of people are fewer, although vehicles are fewer, the driving is relatively easier, and therefore, under the condition that the value of X is smaller, the corresponding influence error is also reduced;
acquiring network about vehicle order information of a user P;
acquiring a period and a time region of order time of a user P, acquiring a first order queuing number in the time region of the user P, and acquiring predicted waiting time of the user P according to a first time prediction model:
wherein T is 3 Predicted wait time for user P; i is the first order queuing number in the time region of the user P, and j is the order queuing number of the user P;
at T 3 Less than 60min or T 3 When=60 min, output T 3 Predicted wait time for user P;
at T 3 At > 60min, the loop steps were constructed:
s3-1.1, leading in order queuing sequence number j 1 ；
Wherein j is 1 Satisfy i<j 1 <j, and satisfy
At the same time satisfy
Is at a maximum value;
s3-1.2, obtaining:
wherein T is 4 Queuing orders for sequence numbers j 1 Is a predicted wait time for a program;
s3-1.3, the predicted waiting time of the user P is as follows:
wherein T is 5 Predicted wait time for new user P;
if present
Output T 5 Predicted wait time for new user P;
if present
Repeating steps S3-1.1 to S3-1.3 and introducing a new order queuing sequence number j n ；
Wherein j is n Satisfy j 1 <j n <j, and satisfy
At the same time satisfy
Is at a maximum value;
outputting the predicted waiting time of the new user P, which is marked as T, until the predicted waiting time of the user P in the new time zone is less than 60 minutes 0 。
In this step, mainly because the waiting time of the network bus may exceed the time area, for example, the waiting time is 7 to 8 half, and 90 minutes, the waiting time is 7 to 8 half, and the waiting time is one time area, and the waiting time is 8 to 8 half, and the waiting time is another time area, and the represented first time prediction model curves are different, so that the model accuracy can be further enhanced by performing the segment analysis.
According to the above technical solution, the temporary location E includes:
constructing an area radius R2, wherein R2 is less than R1, and acquiring public transportation stations in the area taking an order starting address A as a circle center and R2 as a radius;
detecting public traffic running at public traffic stations, acquiring stations, in which the difference between the fare meeting the network-based order arrival address B of the user P and the fare meeting the network-based order arrival address B of the user P at the order departure address A is smaller than a threshold value M, and marking the stations as stations E θ ；
Acquiring arrival of user P at public transportation site and taking public transportation to arrival site E θ The time of (2) is denoted as T 6 ；
Acquisition site E θ The order placed is started at the time of D+T 6 Order queuing number of
Acquisition of site E from a first temporal prediction model θ Is denoted as T 7 ；
If present, T 6 +T 7 <T 0 Site E is then θ Marked as temporary location E and pushed to user P.
According to the above technical solution, the pitch analysis model includes:
acquiring voice monitoring data of a user P in a network appointment vehicle, and constructing a tone analysis model:
the tone analysis model comprises sound curve monitoring and time threshold T construction 8 If T is present 8 If the tone is increased for F times in the time, judging that the bad behavior occurs in the network vehicle, retrieving voice monitoring data in the blockchain, feeding back to a network vehicle system port, and sending out an alarm; if there is no T 8 And if the tone is increased for F times in time, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port.
In extreme emotions, such as fear, anger, etc., the tone may increase involuntarily, become sharp, the response may be a peak on the sound curve, the sound curve inside the net car may be always in a gentle state after the passenger gets on the car, if at T 8 The occurrence of F times of peak in time indicates that passengers or vehicles are in the netThe emotion of the driver is excited, so that the tone is changed, and the tone is fed back to the system end, so that chat data of the driver and passengers can be prevented from being stolen by the mode, the privacy safety of the driver and the passengers is protected, and meanwhile, the contradiction between the driver and the passengers can be found at the highest speed.
A big data technology based computer data security monitoring system, the system comprising: the system comprises a network vehicle operation big data module, an order distribution module and a time prediction module;
the network vehicle operation big data module user obtains network vehicle operation history data; the order distribution module is used for acquiring order information of the network about vehicle, wherein the order information of the network about vehicle comprises an order starting address, an order arrival address, an order queuing serial number and order starting time; the time prediction module is used for constructing a first time prediction model and predicting the arrival time of the network about vehicle;
the output end of the order distribution module is connected with the input end of the network vehicle operation big data module; the output end of the network vehicle operation big data module is connected with the input end of the time prediction module.
According to the technical scheme, the order distribution module comprises a user unit and an order information unit;
the user unit is used for issuing order information through a user port; the order information unit is used for receiving order information data issued by the user port;
the output end of the user unit is connected with the input end of the order information unit; the output end of the order information unit is connected with the input end of the network vehicle operation big data module.
According to the technical scheme, the network about vehicle operation big data module comprises an order place monitoring unit and a historical data calling unit;
the order place monitoring unit is used for monitoring the order place and establishing an order place area; the historical data calling unit is used for calling the operation historical data of the network about vehicle at the order place obtained by monitoring in the order place monitoring unit;
the output end of the order place monitoring unit is connected with the input end of the historical data calling unit; the output end of the historical data calling unit is connected with the input end of the time prediction module.
According to the technical scheme, the time prediction module comprises a model construction unit and a time prediction unit;
the model construction unit is used for constructing a first time prediction model according to the data information of the network vehicle operation big data module; the time prediction unit is used for predicting and obtaining the predicted time of the network contract vehicle arrival according to the first time prediction model;
the output end of the model building unit is connected with the input end of the time prediction unit.
According to the technical scheme, the system further comprises a tone analysis module and a blockchain module;
the tone analysis module is used for acquiring the voice monitoring data in the network vehicle and constructing a tone analysis model; the block chain module is used for storing and recording voice monitoring data;
the pitch analysis model includes:
constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, invoking voice monitoring data in the blockchain and feeding back to a network bus system port to send out an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port;
the output end of the tone analysis module is connected with the input end of the blockchain module.
Compared with the prior art, the invention has the following beneficial effects:
the invention obtains the historical data of the network vehicle operation through the big data module of the network vehicle operation; acquiring order information of the network about vehicle by using an order distribution module, wherein the order information of the network about vehicle comprises an order starting address, an order arrival address, an order queuing sequence number and order starting time; constructing a first time prediction model by using a time prediction module, and predicting the arrival time of the network about vehicle; simultaneously, a tone analysis module is utilized to acquire voice monitoring data in the net appointment vehicle, and a tone analysis model is constructed; the blockchain module is used for storing and recording voice monitoring data.
The invention can solve the problem of taxi taking of the user in the rush hour of the network taxi, further save the user time, provide the transit scheme for the user, can provide the linkage system of the bus and the network taxi; meanwhile, the data in the network bus can be monitored, and the security of passengers and drivers can be guaranteed on the premise of guaranteeing the privacy of the passengers and drivers.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method and system for monitoring computer data security based on big data technology;
fig. 2 is a schematic diagram of steps of a computer data security monitoring method based on big data technology according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a computer data security monitoring method based on big data technology, the method comprising the following steps:
s1, acquiring network vehicle order information of a user P, wherein the network vehicle order information comprises an order starting address A, an order arriving address B, an order queuing serial number C and order starting time D;
s2, constructing a region radius R1, and acquiring a region taking an order starting address A as a circle center and taking R1 as a radiusThe network appointment vehicle operation historical data in the network appointment vehicle is used for constructing a first time prediction model, and predicting the arrival time T of the network appointment vehicle when the order queuing serial number is C 0 ；
S3, constructing an area radius R2, wherein R2 is smaller than R1, acquiring a public transportation station in an area taking an order departure address A as a circle center and R2 as a radius, wherein the public transportation station can reach a temporary place E, and the temporary place E is transferred through the temporary place E, and the temporary place E meets the following conditions:
the departure time of the user P riding the network about car is less than T 0 ；
The difference value between the fare of the user P at the temporary station E at the network contract car arrival address B and the fare of the user P at the order departure address A at the network contract car arrival order arrival address B is smaller than a threshold value M, wherein M is a fare threshold value;
s4, acquiring voice monitoring data in the network vehicle, constructing a tone analysis model, setting blockchain recorded voice monitoring data, constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, retrieving the voice monitoring data in the blockchain and feeding the voice monitoring data back to a port of the network vehicle system to give an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port.
The network vehicle operation history data comprise time intervals for queuing the user on a workshop, order starting time, order starting address and period;
the period comprises a first period and a second period;
the first period is 8 hours in Monday morning to 12 days in friday noon; the second period is 13 pm on friday to 7 am on monday;
in the period, X represents a time period [ X, X+1] in hours;
the first temporal prediction model includes:
acquiring network vehicle operation history data in an area taking an order starting address A as a circle center and R1 as a radius;
taking the interval waiting time length as a dependent variable, and taking the time area, the queuing number and the weather as independent variables to construct a first time prediction model:
wherein u is 0 、u 1 、u 2 、u 3 Regression coefficients for the first temporal prediction model;
wherein t is 0 Representing an interval waiting time length, wherein the interval waiting time length refers to a user waiting time length between two adjacent queuing numbers, and the two adjacent queuing numbers are marked as c 1 、c 2 Then t 0 Representing queuing number c 2 User at c 1 、c 2 Waiting time between;
m 1 representing a time zone, which is a whole point time number, defining a time zone m 1 Representative time period [ m ] 1 ，m 1 +1]；
m 2 Representing queuing sequence numbers;
m 3 representing weather, wherein the weather comprises extreme weather and general weather, and the extreme weather comprises rainy days, snowy days and haze weather; in extreme weather, m 3 ＝k 1 Under ordinary weather, m 3 ＝k 2 Wherein k is 1 、k 2 All are settable constants;
acquiring network about vehicle order information of a user P;
acquiring a period and a time region of order time of a user P, acquiring a first order queuing number in the time region of the user P, and acquiring predicted waiting time of the user P according to a first time prediction model:
wherein T is 3 Predicted wait time for user P; i is the first order queuing number in the time region of the user P, and j is the order queuing number of the user P;
at T 3 Less than 60min or T 3 When=60 min, output T 3 Predicted wait time for user P;
at T 3 At > 60min, the loop steps were constructed:
s3-1.1, leading in order queuing sequence number j 1 ；
Wherein j is 1 Satisfy i<j 1 <j, and satisfy
At the same time satisfy
Is at a maximum value;
s3-1.2, obtaining:
wherein T is 4 Queuing orders for sequence numbers j 1 Is a predicted wait time for a program;
s3-1.3, the predicted waiting time of the user P is as follows:
wherein T is 5 Predicted wait time for new user P;
if present
Output T 5 Predicted wait time for new user P;
if present
Repeating steps S3-1.1 to S3-1.3 and introducing a new order queuing sequence number j n ；
Wherein j is n Satisfy j 1 <j n <j, and satisfy
At the same time satisfy
Is at a maximum value;
outputting the predicted waiting time of the new user P, which is marked as T, until the predicted waiting time of the user P in the new time zone is less than 60 minutes 0 。
The temporary location E includes:
constructing an area radius R2, wherein R2 is less than R1, and acquiring public transportation stations in the area taking an order starting address A as a circle center and R2 as a radius;
detecting public traffic running at public traffic stations, acquiring stations, in which the difference between the fare meeting the network-based order arrival address B of the user P and the fare meeting the network-based order arrival address B of the user P at the order departure address A is smaller than a threshold value M, and marking the stations as stations E θ ；
Acquiring arrival of user P at public transportation site and multiplyingSit public transport to station E θ The time of (2) is denoted as T 6 ；
Acquisition site E θ The order placed is started at the time of D+T 6 Order queuing number of
Acquisition of site E from a first temporal prediction model θ Is denoted as T 7 ；
If present, T 6 +T 7 <T 0 Site E is then θ Marked as temporary location E and pushed to user P.
The pitch analysis model includes:
acquiring voice monitoring data of a user P in a network appointment vehicle, and constructing a tone analysis model:
the tone analysis model comprises sound curve monitoring and time threshold T construction 8 If T is present 8 If the tone is increased for F times in the time, judging that the bad behavior occurs in the network vehicle, retrieving voice monitoring data in the blockchain, feeding back to a network vehicle system port, and sending out an alarm; if there is no T 8 And if the tone is increased for F times in time, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port.
A big data technology based computer data security monitoring system, the system comprising: the system comprises a network vehicle operation big data module, an order distribution module and a time prediction module;
the network about vehicle operation big data module is used for acquiring network about vehicle operation historical data; the order distribution module is used for acquiring order information of the network about vehicle, wherein the order information of the network about vehicle comprises an order starting address, an order arrival address, an order queuing serial number and order starting time; the time prediction module is used for constructing a first time prediction model and predicting the arrival time of the network about vehicle;
the output end of the order distribution module is connected with the input end of the network vehicle operation big data module; the output end of the network vehicle operation big data module is connected with the input end of the time prediction module.
The order distribution module comprises a user unit and an order information unit;
the user unit is used for issuing order information through a user port; the order information unit is used for receiving order information data issued by the user port;
the output end of the user unit is connected with the input end of the order information unit; the output end of the order information unit is connected with the input end of the network vehicle operation big data module.
The network about vehicle operation big data module comprises an order place monitoring unit and a historical data calling unit;
the order place monitoring unit is used for monitoring the order place and establishing an order place area; the historical data calling unit is used for calling the operation historical data of the network about vehicle at the order place obtained by monitoring in the order place monitoring unit;
the output end of the order place monitoring unit is connected with the input end of the historical data calling unit; the output end of the historical data calling unit is connected with the input end of the time prediction module.
The time prediction module comprises a model construction unit and a time prediction unit;
the model construction unit is used for constructing a first time prediction model according to the data information of the network vehicle operation big data module; the time prediction unit is used for predicting and obtaining the predicted time of the network contract vehicle arrival according to the first time prediction model;
the output end of the model building unit is connected with the input end of the time prediction unit.
The system also comprises a tone analysis module and a blockchain module;
the tone analysis module is used for acquiring the voice monitoring data in the network vehicle and constructing a tone analysis model; the block chain module is used for storing and recording voice monitoring data;
the pitch analysis model includes:
constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, invoking voice monitoring data in the blockchain and feeding back to a network bus system port to send out an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port;
the output end of the tone analysis module is connected with the input end of the blockchain module.
In this embodiment:
acquiring network vehicle order information of a user P, wherein the network vehicle order information comprises an order starting address A, an order arrival address B, an order queuing serial number C and an order starting time D;
wherein c=20, time D is 8 points 25 minutes on friday;
constructing an area radius R1, acquiring network vehicle operation history data in an area taking an order starting address A as a circle center and taking R1 as a radius, constructing a first time prediction model, and predicting the arrival time T of the network vehicle when the order queuing sequence number is C 0 ；
Acquiring network vehicle operation history data in an area taking an order starting address A as a circle center and R1 as a radius;
taking the interval waiting time length as a dependent variable, and taking the time area, the queuing number and the weather as independent variables to construct a first time prediction model:
wherein u is 0 、u 1 、u 2 、u 3 Regression coefficients for the first temporal prediction model;
wherein t is 0 Representing an interval waiting time length, wherein the interval waiting time length refers to a user waiting time length between two adjacent queuing numbers, and the two adjacent queuing numbers are marked as c 1 、c 2 Then t 0 Representing queuing number c 2 User at c 1 、c 2 Waiting time between;
m 1 representing a time zone, which is a whole point time number, defining a time zone m 1 Representative time period [ m ] 1 ，m 1 +1]；m 1 ＝20；
m 2 Representing queuing sequence numbers; m is m 2 ＝20；
m 3 Representing weather, wherein the weather comprises extreme weather and general weather, and the extreme weather comprises rainy days, snowy days and haze weather; in extreme weather, m 3 ＝k 1 Under ordinary weather, m 3 ＝k 2 Wherein k is 1 、k 2 All are settable constants;
the current weather is general weather, m 3 ＝k 2 ；
the current time is a second period of time,
acquiring network about vehicle order information of a user P;
acquiring a period and a time region of order time of a user P, acquiring a first order queuing number in the time region of the user P, and acquiring predicted waiting time of the user P according to a first time prediction model:
wherein T is 3 Predicted wait time for user P; i is the first order queuing number in the time region of the user P, and j is the order queuing number of the user P; i=16, j=20;
discovery of T 3 ＝70min；
At T 3 At > 60min, the loop steps were constructed:
s3-1.1, leading in order queuing sequence number j 1 ；
Wherein j is 1 Satisfy i<j 1 <j, and satisfy
At the same time satisfy
Is at a maximum value;
can get j 1 ＝18；
S3-1.2, obtaining:
wherein T is 4 Queuing orders for sequence numbers j 1 Is a predicted wait time for a program;
calculated to obtain T 4 30min;
s3-1.3, the predicted waiting time of the user P is as follows:
wherein T is 5 Predicted wait time for new user P;
wherein the method comprises the steps of
50 < 60; output T 5 Predicted wait time for new user P;
T 5 ＝30+50＝80
so the new predicted waiting time of the user is 80min;
the predicted time varies mainly because it varies in each time zone, and the interval time of the same sequence number in different time zones is different.
Constructing an area radius R2, wherein R2 is less than R1, and acquiring public transportation stations in the area taking an order starting address A as a circle center and R2 as a radius;
detecting public traffic running at public traffic stations, acquiring stations, in which the difference between the fare meeting the network-based order arrival address B of the user P and the fare meeting the network-based order arrival address B of the user P at the order departure address A is smaller than a threshold value M, and marking the stations as stations E θ ；
In which there are 3 sites E θ ；
Acquiring arrival of user P at public transportation site and taking public transportation to arrival site E θ The time of (2) is denoted as T 6 ；
Acquisition site E θ The order placed is started at the time of D+T 6 Order queuing number of
Acquisition of site E from a first temporal prediction model θ Is denoted as T 7 ；
Calculated, there are 3 sites E θ Two of them satisfy T 6 +T 7 <T 0 Two sites E θ Marked as temporary location E and pushed to user P.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A computer data safety monitoring method based on big data technology is characterized in that: the method comprises the following steps:
s1, acquiring network vehicle order information of a user P, wherein the network vehicle order information comprises an order starting address A, an order arriving address B, an order queuing serial number C and order starting time D;
s2, constructing a region radius R1, acquiring network vehicle operation history data in a region taking an order starting address A as a circle center and taking R1 as a radius, constructing a first time prediction model, and predicting the arrival time T of the network vehicle when the order queuing sequence number is C 0 ；
S3, constructing an area radius R2, wherein R2 is smaller than R1, acquiring a public transportation station in an area taking an order departure address A as a circle center and R2 as a radius, wherein the public transportation station can reach a temporary place E, and the temporary place E is transferred through the temporary place E, and the temporary place E meets the following conditions:
the departure time of the user P riding the network about car is less than T 0 ；
The difference value between the fare of the user P at the temporary station E at the network contract car arrival address B and the fare of the user P at the order departure address A at the network contract car arrival order arrival address B is smaller than a threshold value M, wherein M is a fare threshold value;
s4, acquiring voice monitoring data in the network vehicle, constructing a tone analysis model, setting blockchain recorded voice monitoring data, constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, retrieving the voice monitoring data in the blockchain and feeding the voice monitoring data back to a port of the network vehicle system to give an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port;
the first temporal prediction model includes:
acquiring network vehicle operation history data in an area taking an order starting address A as a circle center and R1 as a radius;
taking the interval waiting time length as a dependent variable, and taking the time area, the queuing number and the weather as independent variables to construct a first time prediction model:
wherein u is 0 、u 1 、u 2 、u 3 Regression coefficients for the first temporal prediction model;
wherein t is 0 Representing an interval waiting time length, wherein the interval waiting time length refers to a user waiting time length between two adjacent queuing numbers, and the two adjacent queuing numbers are marked as c 1 、c 2 Then t 0 Representing queuing number c 2 User at c 1 、c 2 Waiting time between;
m 1 representing a time zone, which is a whole point time number, defining a time zone m 1 Representative time period [ m ] 1 ，m 1 +1]；
m 2 Representing queuing sequence numbers;
m 3 representing weather including extreme weather including rainy, snowy, and hazy days, and general weatherA gas; in extreme weather, m 3 ＝k 1 Under ordinary weather, m 3 ＝k 2 Wherein k is 1 、k 2 All are settable constants;
acquiring network about vehicle order information of a user P;
acquiring a period and a time region of order time of a user P, acquiring a first order queuing number in the time region of the user P, and acquiring predicted waiting time of the user P according to a first time prediction model:
wherein T is 3 Predicted wait time for user P; i is the first order queuing number in the time region of the user P, and j is the order queuing number of the user P;
at T 3 Less than 60min or T 3 When=60 min, output T 3 Predicted wait time for user P;
at T 3 At > 60min, the loop steps were constructed:
s3-1.1, leading in order queuing sequence number j 1 ；
Wherein j is 1 Satisfy i<j 1 <j, and satisfy
At the same time satisfy
Is at a maximum value;
s3-1.2, obtaining:
wherein T is 4 Queuing orders for sequence numbers j 1 Is a predicted wait time for a program;
s3-1.3, the predicted waiting time of the user P is as follows:
wherein T is 5 Predicted wait time for new user P;
if present
Output T 5 Predicted wait time for new user P;
if present
Repeating steps S3-1.1 to S3-1.3 and introducing a new order queuing sequence number j n ；
Wherein j is n Satisfy j 1 <j n <j, and satisfy
At the same time satisfy
Is at a maximum value;
outputting the predicted waiting time of the new user P, which is marked as T, until the predicted waiting time of the user P in the new time zone is less than 60 minutes 0 。
2. The method for monitoring the safety of computer data based on big data technology according to claim 1, wherein the method comprises the following steps: the network vehicle operation history data comprise time intervals for queuing the user on a workshop, order starting time, order starting address and period;
the period comprises a first period and a second period;
the first period is 8 hours in Monday morning to 12 days in friday noon; the second period is 13 pm on friday to 7 am on monday;
in the period, X represents a period of time [ X, x+1] in hours.
3. The method for monitoring the safety of computer data based on big data technology according to claim 2, wherein the method comprises the following steps: the temporary location E includes:
constructing an area radius R2, wherein R2 is less than R1, and acquiring public transportation stations in the area taking an order starting address A as a circle center and R2 as a radius;
detecting public traffic running at public traffic stations, acquiring stations, in which the difference between the fare meeting the network-based order arrival address B of the user P and the fare meeting the network-based order arrival address B of the user P at the order departure address A is smaller than a threshold value M, and marking the stations as stations E θ ；
Acquiring arrival of user P at public transportation site and taking public transportation to arrival site E θ The time of (2) is denoted as T 6 ；
Acquisition site E θ The order placed is started at the time of D+T 6 Order queuing number of
Acquisition of site E from a first temporal prediction model θ Is denoted as T 7 ；
If present, T 6 +T 7 <T 0 Site E is then θ Marked as temporary location E and pushed to user P.
4. The method for monitoring the safety of computer data based on big data technology according to claim 1, wherein the method comprises the following steps: the pitch analysis model includes:
acquiring voice monitoring data of a user P in a network appointment vehicle, and constructing a tone analysis model:
the tone analysis model comprises sound curve monitoring and time threshold T construction 8 If T is present 8 If the tone is increased for F times in the time, judging that the bad behavior occurs in the network vehicle, retrieving voice monitoring data in the blockchain, feeding back to a network vehicle system port, and sending out an alarm; if there is no T 8 And if the tone is increased for F times in time, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port.
5. A computer data security monitoring system based on big data technology applying a computer data security monitoring method based on big data technology as claimed in claim 1, characterized in that: the system comprises: the system comprises a network vehicle operation big data module, an order distribution module and a time prediction module;
the network about vehicle operation big data module is used for acquiring network about vehicle operation historical data; the order distribution module is used for acquiring order information of the network about vehicle, wherein the order information of the network about vehicle comprises an order starting address, an order arrival address, an order queuing serial number and order starting time; the time prediction module is used for constructing a first time prediction model and predicting the arrival time of the network about vehicle;
the output end of the order distribution module is connected with the input end of the network vehicle operation big data module; the output end of the network vehicle operation big data module is connected with the input end of the time prediction module.
6. The big data technology based computer data security monitoring system of claim 5, wherein: the order distribution module comprises a user unit and an order information unit;
the user unit is used for issuing order information through a user port; the order information unit is used for receiving order information data issued by the user port;
the output end of the user unit is connected with the input end of the order information unit; the output end of the order information unit is connected with the input end of the network vehicle operation big data module.
7. The big data technology based computer data security monitoring system of claim 6, wherein: the network about vehicle operation big data module comprises an order place monitoring unit and a historical data calling unit;
the order place monitoring unit is used for monitoring the order place and establishing an order place area; the historical data calling unit is used for calling the operation historical data of the network about vehicle at the order place obtained by monitoring in the order place monitoring unit;
the output end of the order place monitoring unit is connected with the input end of the historical data calling unit; the output end of the historical data calling unit is connected with the input end of the time prediction module.
8. A big data technology based computer data security monitoring system according to claim 7, wherein: the time prediction module comprises a model construction unit and a time prediction unit;
the model construction unit is used for constructing a first time prediction model according to the data information of the network vehicle operation big data module; the time prediction unit is used for predicting and obtaining the predicted time of the network contract vehicle arrival according to the first time prediction model;
the output end of the model building unit is connected with the input end of the time prediction unit.
9. A big data technology based computer data security monitoring system according to claim 8, wherein: the system also comprises a tone analysis module and a blockchain module;
the tone analysis module is used for acquiring the voice monitoring data in the network vehicle and constructing a tone analysis model; the block chain module is used for storing and recording voice monitoring data;
the pitch analysis model includes:
constructing a tone analysis threshold F, and if the analysis result of the tone analysis model exceeds the threshold F, invoking voice monitoring data in the blockchain and feeding back to a network bus system port to send out an alarm; if the analysis result of the tone analysis model does not exceed the threshold F, the tone data is fed back to the network bus system port, and the voice monitoring data is recorded to the blockchain port;
the output end of the tone analysis module is connected with the input end of the blockchain module.
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