CN117540067A - Social governance-oriented big data visualization method, device and medium - Google Patents
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Abstract
The invention provides a big data visualization method, a device and a medium for social management, and relates to the technical field of social management data visualization.
Description
Technical Field
The invention relates to the technical field of social treatment data visualization, in particular to a social treatment-oriented big data visualization method, device and medium.
Background
At present, social management comprises various aspects of coordinating social relationship, standardizing social behavior, solving social problems, resolving social contradictions and the like, and in order to better perform social management work, a social management center generally performs patrol work on streets according to fixed time shifts and fixed patrol routes by each community center, reflects whether the social management of the community is good or not by reporting events existing on the streets, and informs related departments of solving the existing social hidden trouble.
At present, for the patrol event reported by patrol personnel, different labels are usually adopted to mark different types of patrol events so as to display the result of the patrol operation, but the method can only obtain the type of the event through different labels, can not reflect the possible hazard influence degree of the event, can not well reflect the treatment result of the community, and can not provide assistance for the subsequent related departments to process the event.
Disclosure of Invention
The invention aims to provide a big data visualization method, a device and a medium for social administration, which are characterized in that a patrol result diagram A containing identification blocks or a patrol result diagram B containing extended identification blocks is constructed by adopting patrol information to represent information of a patrol event, corresponding identification blocks are constructed in the patrol result diagram, and possible hazard influence degrees of the event are reflected according to the length of the identification blocks to be displayed correspondingly.
The following scheme is adopted in particular:
s1, acquiring a patrol event database, wherein the patrol event database comprises n event sets Q { Q1, Q2, … and Qn } with different event labels, one event set comprises j event subsets, namely Qn { Qn1, qn2, … and Qnj }, the j event subsets have the same event labels, and 'Qn 1, qn2, … and Qnj' are in one-to-one correspondence with patrol events of different patrol information under j same event labels;
s2, acquiring a patrol diary reported by a patrol personnel when the patrol personnel performs patrol according to a street patrol route, and preprocessing the patrol diary to obtain patrol information of a patrol event reported by the patrol personnel, wherein the patrol information comprises an event reporting time point and an event existence time period;
and S3, matching the patrol information reported by the patrol personnel with the patrol information of the event subset in the patrol database, and constructing a patrol result diagram A containing the identification block or a patrol result diagram B containing the expansion identification block for the patrol event according to the matching result.
Further, the abscissa of the inspection result graph a is a time period axis from left to right, in which the starting time point to the ending time point of the event existence time period become larger in sequence, and the ordinate is a time point axis from top to bottom, in which the event reporting time point becomes larger in sequence;
and the abscissa of the inspection result diagram B is a time period axis from the starting time point to the ending time point of the extended event existence time period to be sequentially enlarged from left to right, and the ordinate is a time point axis from top to bottom to be sequentially enlarged from the event reporting time point.
Further, the step S3 specifically includes the following steps:
s31, matching the patrol information reported by the patrol personnel with event subsets in a patrol database, if the event subsets are not matched, turning to step S32, and if the event subsets are matched, turning to step S33;
s32, constructing a patrol result graph A containing an identification block according to an event reporting time point and an event existence time period of a patrol event, wherein the identification block comprises a panel frame, a first attribute bar vertically connecting the left side of the panel frame with an ordinate, a second attribute bar vertically connecting the lower edge starting point of the panel frame with an abscissa, and a third attribute bar vertically connecting the lower edge end point of the panel frame with the abscissa, and the length of the panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the event existence time period;
s33, expanding the event existence time period of the patrol event according to the event existence time period of the patrol event in the matched event subset to obtain an expanded event existence time period, and constructing a patrol result diagram B containing an expansion identification block according to the event reporting time point of the patrol event and the expanded event existence time period, wherein the expansion identification block comprises an expansion panel frame, a first attribute bar vertically connecting the left side of the expansion panel frame with the ordinate, a second attribute bar vertically connecting the lower side starting point of the expansion panel frame with the abscissa, and a third attribute bar vertically connecting the lower side end point of the expansion panel frame with the abscissa, and the length of the expansion panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the expanded event existence time period.
Further, the step S33 specifically further includes the following steps:
s331, obtaining an event existence time period of a patrol event in the matched event subset, wherein the event existence time period is a time period from an event starting time point to an event ending time point;
s332, comparing the magnitude of the event starting time point of the patrol event in the event subset with the magnitude of the event starting time point of the patrol event to obtain a minimum event starting time point, and comparing the magnitude of the event ending time point of the patrol event in the event subset with the magnitude of the event ending time point of the patrol event to obtain a maximum time ending time point;
s333, taking a time period from a minimum event starting time point to a maximum time ending time point as an extended event existence time period of the patrol event;
s334, establishing a coordinate axis diagram with an abscissa being a time period axis where the event existence time period is located and an ordinate being a time point axis where the event reporting time point is located;
s335, an expansion panel frame is established on the coordinate axis diagram, a first attribute bar is formed by vertically connecting from the left central point of the expansion panel frame to the event reporting time point of the inspection event on the abscissa, a second attribute bar is formed by vertically connecting from the lower starting point of the expansion panel frame to the minimum event starting time point on the ordinate, and a third attribute bar is formed by vertically connecting from the lower end point of the expansion panel frame to the maximum time ending time point on the ordinate.
Further, the patrol information includes an event type and an event occurrence address, and the panel frame or the extended panel frame includes the event type and the event occurrence address.
Further, in S31, the process of determining that the patrol information reported by the patrol personnel is matched with the subset of events in the patrol database specifically includes:
if the event type and the event occurrence address reported by the patrol personnel are consistent with the event type and the event occurrence address of the event subset in the patrol database, and the time difference between the event reporting time point reported by the patrol personnel and the event reporting time point of the event subset in the patrol database is within a preset range, judging that the patrol information reported by the patrol personnel is matched with the event subset in the patrol database.
Further, in S2, the specific process of obtaining the patrol diary reported by the patrol personnel when the patrol personnel performs the patrol according to the street patrol route, and preprocessing the patrol diary to obtain the patrol information of the patrol event reported by the patrol personnel is:
the method comprises the steps of obtaining an event proving picture, an event reporting time point, an event occurrence address and an inspection diary of an event existence time period uploaded by inspection personnel, carrying out image recognition on the event proving picture, obtaining a shooting main body of the event proving picture through feature extraction, and judging the type of the shooting main body to obtain the event type.
Further, the event existence time period uploaded by the patrol personnel comprises an event reporting time point uploaded by the patrol personnel.
A social governance oriented big data visualization device, comprising:
a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer execution instructions stored in the memory to realize the big data visualization method facing social governance.
A computer readable storage medium, wherein computer executable instructions are stored in the computer readable storage medium, and the computer executable instructions are used for realizing the big data visualization method facing social governance when being executed by a processor.
The invention has the beneficial effects that:
the invention provides a social management-oriented big data visualization method, a social management-oriented big data visualization device and a social management-oriented big data visualization medium.
The identification block consists of a panel frame for representing inspection information and three attribute bars connected with the panel frame, the identification block is correspondingly displayed on an inspection result graph, the possible hazard influence degree of the event can be intuitively displayed, inspection result graphs of the same inspection event at different reporting time points are compared, and whether social management work is effective or not can be obtained according to the length of the identification block on the inspection result graph.
Drawings
Fig. 1 is a flow chart of a big data visualization method for social governance in embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of constructing a patrol result map A1 and a patrol result map A2 in embodiment 1 of the present invention.
Fig. 3 is a schematic diagram of a patrol result diagram B1 constructed in embodiment 1 of 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. The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. 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.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
In addition, descriptions of well-known structures, functions and configurations may be omitted for clarity and conciseness. Those of ordinary skill in the art will recognize that various changes and modifications of the examples described herein can be made without departing from the spirit and scope of the present disclosure.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
The invention is described in detail below by reference to the attached drawings and in connection with the embodiments:
example 1
As shown in fig. 1, a big data visualization method for social governance specifically includes the following steps:
s1, acquiring a patrol event database, wherein the patrol event database comprises n event sets Q { Q1, Q2, … and Qn } with different event labels, one event set comprises j event subsets, namely Qn { Qn1, qn2, … and Qnj }, the j event subsets have the same event labels, and 'Qn 1, qn2, … and Qnj' are in one-to-one correspondence with patrol events of different patrol information under j same event labels;
s2, acquiring a patrol diary reported by a patrol personnel when the patrol personnel performs patrol according to a street patrol route, and preprocessing the patrol diary to obtain patrol information of a patrol event reported by the patrol personnel, wherein the patrol information comprises an event reporting time point and an event existence time period;
and S3, matching the patrol information reported by the patrol personnel with the patrol information of the event subset in the patrol database, and constructing a patrol result diagram A containing the identification block or a patrol result diagram B containing the expansion identification block for the patrol event according to the matching result.
Preferably, the abscissa of the inspection result graph a is a time period axis from left to right, in which the starting time point to the ending time point of the event existence time period become larger in sequence, and the ordinate is a time point axis from top to bottom, in which the event reporting time point becomes larger in sequence;
and the abscissa of the inspection result diagram B is a time period axis from the starting time point to the ending time point of the extended event existence time period to be sequentially enlarged from left to right, and the ordinate is a time point axis from top to bottom to be sequentially enlarged from the event reporting time point.
Because at present, different types of patrol events are marked by different labels for the patrol events reported by patrol personnel, so that the result of the patrol work is displayed, but the method can only obtain the types of the events through different labels, can not reflect the possible harm influence degree of the events, can not well reflect the treatment result of the community, and can not provide assistance for the subsequent related departments to process the events.
Therefore, in order to solve the above problems, the present invention constructs a patrol result graph a including the identification block or a patrol result graph B including the extended identification block by using the patrol information to represent information of the patrol event, constructs a corresponding identification block in the patrol result graph, and reflects the possible hazard influence degree of the event according to the length of the identification block to display the hazard influence degree accordingly. The possible harm of the patrol event can be intuitively represented through the display of the patrol result diagram A and the patrol result diagram B, wherein the patrol result diagram A can indicate that the patrol event is a less harmful hidden event through the identification block, and the patrol result diagram B can indicate that the patrol event is a more harmful hidden event through the expanded identification block.
In the inspection result map a or the inspection result map B, the abscissa axis represents a time axis, but the abscissa axis represents a different meaning, and the abscissa axis represents a time period in which the event existence time period of the identification block is represented, so that the abscissa axis from left to right sequentially represents a time period axis in which the starting time point to the ending time point of the event existence time period after expansion sequentially become larger, and the ordinate axis represents a time point axis in which the event reporting time point of the identification block sequentially becomes larger from top to bottom.
Preferably, the step S3 specifically includes the following steps:
s31, matching the patrol information reported by the patrol personnel with event subsets in a patrol database, if the event subsets are not matched, turning to step S32, and if the event subsets are matched, turning to step S33;
s32, constructing a patrol result graph A containing an identification block according to an event reporting time point and an event existence time period of a patrol event, wherein the identification block comprises a panel frame, a first attribute bar vertically connecting the left side of the panel frame with an ordinate, a second attribute bar vertically connecting the lower edge starting point of the panel frame with an abscissa, and a third attribute bar vertically connecting the lower edge end point of the panel frame with the abscissa, and the length of the panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the event existence time period;
s33, expanding the event existence time period of the patrol event according to the event existence time period of the patrol event in the matched event subset to obtain an expanded event existence time period, and constructing a patrol result diagram B containing an expansion identification block according to the event reporting time point of the patrol event and the expanded event existence time period, wherein the expansion identification block comprises an expansion panel frame, a first attribute bar vertically connecting the left side of the expansion panel frame with the ordinate, a second attribute bar vertically connecting the lower side starting point of the expansion panel frame with the abscissa, and a third attribute bar vertically connecting the lower side end point of the expansion panel frame with the abscissa, and the length of the expansion panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the expanded event existence time period.
Preferably, the step S33 specifically further includes the following steps:
s331, obtaining an event existence time period of a patrol event in the matched event subset, wherein the event existence time period is a time period from an event starting time point to an event ending time point;
s332, comparing the magnitude of the event starting time point of the patrol event in the event subset with the magnitude of the event starting time point of the patrol event to obtain a minimum event starting time point, and comparing the magnitude of the event ending time point of the patrol event in the event subset with the magnitude of the event ending time point of the patrol event to obtain a maximum time ending time point;
s333, taking a time period from a minimum event starting time point to a maximum time ending time point as an extended event existence time period of the patrol event;
s334, establishing a coordinate axis diagram with an abscissa being a time period axis where the event existence time period is located and an ordinate being a time point axis where the event reporting time point is located;
s335, an expansion panel frame is established on the coordinate axis diagram, a first attribute bar is formed by vertically connecting from the left central point of the expansion panel frame to the event reporting time point of the inspection event on the abscissa, a second attribute bar is formed by vertically connecting from the lower starting point of the expansion panel frame to the minimum event starting time point on the ordinate, and a third attribute bar is formed by vertically connecting from the lower end point of the expansion panel frame to the maximum time ending time point on the ordinate.
Specifically, since one event subset has one event tag, it indicates that the content included in one event subset is the patrol information of one patrol event, so if the patrol information reported by the patrol personnel is matched with the event subset in the patrol database, it indicates that the report of the patrol event is recorded twice in the patrol database before the patrol personnel reports the patrol event.
Because, in the actual inspection process, if the same event is reported for multiple times, the event may often exist at the event occurrence address as the event which often occurs, so the event has a great influence of damage, and if the hidden damage of the event is not found, the event cannot be accurately and timely processed.
In addition, in the process of the patrol personnel, if the event is a frequent event, in most cases, the time point when the patrol personnel discovers the event and reports the event cannot be used as the occurrence time point when the event exists, which can cause inaccurate judgment of the event, and in the actual case, the reporting time point of the patrol personnel is far later than the occurrence time point when the event exists, so that the invention considers the event existence time period, sequentially deduces the event existence time period according to the occurrence times of the event by taking the frequent occurrence characteristic of the event into consideration, and correspondingly displays the possible hazard influence degree of the event according to the event existence time period.
In one embodiment, as shown in fig. 2, the patrol information of the patrol event 3 reported by the patrol personnel includes: event reporting time point: 2023, 11, 3, 8:00; event occurrence address: subway exit A; event type: the sharing bicycle is parked in disorder; event presence time period: 7:40-8:10. Matching the patrol information of the patrol event with event subsets in a patrol database to obtain two event subsets, wherein the patrol information of the first event subset comprises: event reporting time point: 2023, 11, 1, 7:30; event occurrence address: subway exit A; event type: the sharing bicycle is parked in disorder; event presence time period: 7:20-7:40, the patrol information for the second subset of events includes: event reporting time point: 2023, 11, 2, 7:20; event occurrence address: subway exit A; event type: the sharing bicycle is parked in disorder; event presence time period: 7:10-7:40.
When the event 1 represented by the first event subset is reported by a patrol personnel, the event subset is not matched, so that a patrol result graph A1 containing an identification block is constructed according to patrol information of the event 1, the abscissa of the patrol result graph A1 is a time period axis where an event exists time period, the ordinate is a time point axis where the event exists time period, the construction of the patrol result graph A1 does not need an extended event exists time period, because the event subset is not matched, the event 1 is an occasional event, therefore, only the point where the event exists time period 7:30 is found on the ordinate of the patrol result graph A1, the identification block is positioned, a first attribute bar is formed, then the left lower point of the identification block is positioned to the point where the event exists time period 7:30 on the abscissa, a second attribute bar is formed, the length of the identification block is 7:20-7:40, then the right lower point of the identification block is positioned to the third attribute bar, the third attribute bar is formed, and the third attribute bar is connected to the position of the event exists time period 7:20-7:40, and the third attribute bar is formed, and the third attribute bar is connected to the third attribute bar, and the position of the third attribute bar is formed.
Then, when the event 2 represented by the second event subset is reported by the patrol personnel, the event 2 can be matched with the first event subset, the event existence time period 7:10-7:40 of the event 2 represented by the second event subset is compared with the event existence time period 7:20-7:40 of the first event subset, the minimum event starting time point is 7:10, and the maximum event ending time point is 7:40, namely, the extended time period is the event existence time period of the event 2, and a patrol result graph A2 containing identification blocks can be built without extension in the same way as the patrol result graph A1 is built by the event 1, and the patrol result graph A2 is added into patrol information and stored as the event subset of the event 2.
When the patrol personnel reports the patrol event 3, extracting the event existence time period of the matched subset of the events in the patrol database, and in the event existence time period: 7:40-8:10, event presence time period: 7:20-7:40, event presence time period: the size comparison between 7:10-7:40 can obtain a minimum event start time point 7:10 and a maximum event end time point 8:10, and the time period from the minimum event start time point 7:10 to the maximum event end time point 8:10 is used as the event existence time period after the expansion of the patrol event, namely the event existence time period after the expansion of the patrol event is: 7:10-8:10.
According to the data reported twice, the event is seen to happen frequently in the time period, so that the event existence period of the inspection event reported by the inspection personnel is expanded according to the historical event existence period, the event existence period of the event is 7:10-8:10, and the event existence period is seen to be expanded step by step along with the increase of the reporting times, so that the greater the event reporting times, the greater the event existence period, the longer the length of the identification block, and the corresponding display is carried out according to the length of the identification block to reflect the possible hazard influence degree of the event.
As shown in fig. 3, a patrol result diagram B1 including an extended identifier block is constructed, where the abscissa of the patrol structure diagram B is a time period axis where an extended event exists, the ordinate is a time point axis where an event reporting time point exists, the meanings of the abscissa and the ordinate of the patrol structure diagram are different, the ordinate is to represent the event reporting time point, and the abscissa is to represent the event exists, so that the present invention provides the following steps according to the event reporting time point: 8:00, the extended event existence time period is: 7:10-8:10, firstly, finding a point with an event reporting time point of 8:00 on the ordinate of the inspection result diagram B1, positioning the identification block, forming a first attribute bar, then positioning the left lower point of the identification block to a point with an event starting time point of 7:10 on the abscissa, forming a second attribute bar, performing transverse expansion extension according to the expanded event existence time period of 7:10-8:10, enabling the length of the expanded identification block to be 7:10-8:10, positioning the right lower point of the identification block to a point with an event starting time point of 8:10 on the abscissa, forming a third attribute bar, connecting the second attribute bar with the third attribute bar, and displaying the expanded event existence time period on the abscissa by the length of the expanded identification block to reflect the possible hazard influence degree of the event.
Preferably, the patrol information includes event type and event occurrence address, and the panel frame or the extended panel frame includes event type and event occurrence address. Specifically, the panel frame can display specific information of the event, including but not limited to event type, event occurrence address and event description text, so that a worker can more intuitively know the event to a certain extent.
Preferably, in S31, the process of determining that the patrol information reported by the patrol personnel is matched with the subset of events in the patrol database specifically includes:
if the event type and the event occurrence address reported by the patrol personnel are consistent with the event type and the event occurrence address of the event subset in the patrol database, and the time difference between the event reporting time point reported by the patrol personnel and the event reporting time point of the event subset in the patrol database is within a preset range, judging that the patrol information reported by the patrol personnel is matched with the event subset in the patrol database.
Specifically, the time difference between the event reporting time point reported by the patrol personnel and the event reporting time point of the event subset in the patrol database is within a preset range, where the preset range may be set according to actual situations, for example, the preset range may be set to 0-10 minutes or 0-20 minutes according to the patrol rate of the patrol personnel, the patrol area size of the patrol personnel, and the number of events in the patrol area.
Preferably, in S2, the specific process of obtaining the patrol diary reported by the patrol personnel when the patrol personnel performs the patrol according to the street patrol route, and preprocessing the patrol diary to obtain the patrol information of the patrol event reported by the patrol personnel is:
the method comprises the steps of obtaining an event proving picture, an event reporting time point, an event occurrence address and an inspection diary of an event existence time period uploaded by inspection personnel, carrying out image recognition on the event proving picture, obtaining a shooting main body of the event proving picture through feature extraction, and judging the type of the shooting main body to obtain the event type.
Specifically, when an inspection person shoots an event proof picture, the inspection person shoots an event main body so as to prove that the event occurs, for example, when the inspection person inspects that a dustbin on a street is disordered, the inspection person shoots a photograph with the disordered dustbin and uploads the photograph as the event proof picture, when the photograph is subjected to image recognition and feature extraction, the shooting main body of the event proof picture is the disordered dustbin, and the type of the event is judged to be the disordered dustbin.
In one embodiment, the patrol personnel can upload event proof text, and the event type, the event reporting time point, the event occurrence address and the event existence time period can be obtained by extracting keywords from the event proof text.
Preferably, the event existence time period uploaded by the patrol personnel comprises an event reporting time point uploaded by the patrol personnel.
Specifically, after the patrol personnel uploads the event reporting time point, the system automatically generates a preset range, wherein the preset range comprises the event reporting time point, and then the preset range is expanded forwards and backwards to obtain a time period with the time range being the preset range, and the event existence time period uploaded by the patrol personnel must be within the time period of the preset range so as to ensure the credibility of the event existence time period.
Example 2
A social governance oriented big data visualization device, comprising:
a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer execution instructions stored in the memory to realize the big data visualization method facing social governance.
A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to implement the social governance oriented big data visualization method.
The foregoing description of the preferred embodiment of the invention is not intended to limit the invention in any way, but rather to cover all modifications, equivalents, improvements and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. The big data visualization method for social governance is characterized by comprising the following steps of:
s1, acquiring a patrol event database, wherein the patrol event database comprises n event sets Q { Q1, Q2, … and Qn } with different event labels, one event set comprises j event subsets, namely Qn { Qn1, qn2, … and Qnj }, the j event subsets have the same event labels, and 'Qn 1, qn2, … and Qnj' are in one-to-one correspondence with patrol events of different patrol information under j same event labels;
s2, acquiring a patrol diary reported by a patrol personnel when the patrol personnel performs patrol according to a street patrol route, and preprocessing the patrol diary to obtain patrol information of a patrol event reported by the patrol personnel, wherein the patrol information comprises an event reporting time point and an event existence time period;
and S3, matching the patrol information reported by the patrol personnel with the patrol information of the event subset in the patrol database, and constructing a patrol result diagram A containing the identification block or a patrol result diagram B containing the expansion identification block for the patrol event according to the matching result.
2. The social governance-oriented big data visualization method according to claim 1, wherein the abscissa of the inspection result graph a is a time period axis in which starting time points to ending time points of an event existence time period become larger in sequence from left to right, and the ordinate is a time point axis in which event reporting time points become larger in sequence from top to bottom;
and the abscissa of the inspection result diagram B is a time period axis from the starting time point to the ending time point of the extended event existence time period to be sequentially enlarged from left to right, and the ordinate is a time point axis from top to bottom to be sequentially enlarged from the event reporting time point.
3. The social governance-oriented big data visualization method according to claim 2, wherein the step S3 specifically comprises the following steps:
s31, matching the patrol information reported by the patrol personnel with event subsets in a patrol database, if the event subsets are not matched, turning to step S32, and if the event subsets are matched, turning to step S33;
s32, constructing a patrol result graph A containing an identification block according to an event reporting time point and an event existence time period of a patrol event, wherein the identification block comprises a panel frame, a first attribute bar vertically connecting the left side of the panel frame with an ordinate, a second attribute bar vertically connecting the lower edge starting point of the panel frame with an abscissa, and a third attribute bar vertically connecting the lower edge end point of the panel frame with the abscissa, and the length of the panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the event existence time period;
s33, expanding the event existence time period of the patrol event according to the event existence time period of the patrol event in the matched event subset to obtain an expanded event existence time period, and constructing a patrol result diagram B containing an expansion identification block according to the event reporting time point of the patrol event and the expanded event existence time period, wherein the expansion identification block comprises an expansion panel frame, a first attribute bar vertically connecting the left side of the expansion panel frame with the ordinate, a second attribute bar vertically connecting the lower side starting point of the expansion panel frame with the abscissa, and a third attribute bar vertically connecting the lower side end point of the expansion panel frame with the abscissa, and the length of the expansion panel frame represents the relative distance between the second attribute bar and the third attribute bar on the abscissa, namely the expanded event existence time period.
4. The social governance-oriented big data visualization method according to claim 3, wherein the step S33 specifically further comprises the steps of:
s331, obtaining an event existence time period of a patrol event in the matched event subset, wherein the event existence time period is a time period from an event starting time point to an event ending time point;
s332, comparing the magnitude of the event starting time point of the patrol event in the event subset with the magnitude of the event starting time point of the patrol event to obtain a minimum event starting time point, and comparing the magnitude of the event ending time point of the patrol event in the event subset with the magnitude of the event ending time point of the patrol event to obtain a maximum time ending time point;
s333, taking a time period from a minimum event starting time point to a maximum time ending time point as an extended event existence time period of the patrol event;
s334, establishing a coordinate axis diagram with an abscissa being a time period axis where the event existence time period is located and an ordinate being a time point axis where the event reporting time point is located;
s335, an expansion panel frame is established on the coordinate axis diagram, a first attribute bar is formed by vertically connecting from the left central point of the expansion panel frame to the event reporting time point of the inspection event on the abscissa, a second attribute bar is formed by vertically connecting from the lower starting point of the expansion panel frame to the minimum event starting time point on the ordinate, and a third attribute bar is formed by vertically connecting from the lower end point of the expansion panel frame to the maximum time ending time point on the ordinate.
5. A social governance oriented big data visualization method in accordance with claim 3, wherein the patrol information comprises event type, event occurrence address, and the panel frame or the extended panel frame comprises event type, event occurrence address.
6. The social governance-oriented big data visualization method according to claim 5, wherein in S31, the determining process for matching the patrol information reported by the patrol personnel with the subset of events in the patrol database is specifically:
if the event type and the event occurrence address reported by the patrol personnel are consistent with the event type and the event occurrence address of the event subset in the patrol database, and the time difference between the event reporting time point reported by the patrol personnel and the event reporting time point of the event subset in the patrol database is within a preset range, judging that the patrol information reported by the patrol personnel is matched with the event subset in the patrol database.
7. The social governance-oriented big data visualization method according to claim 1, wherein in S2, the specific process of obtaining the patrol diary reported by the patrol personnel when the patrol personnel performs the patrol according to the street patrol route and preprocessing the patrol diary to obtain the patrol information of the patrol event reported by the patrol personnel is as follows:
the method comprises the steps of obtaining an event proving picture, an event reporting time point, an event occurrence address and an inspection diary of an event existence time period uploaded by inspection personnel, carrying out image recognition on the event proving picture, obtaining a shooting main body of the event proving picture through feature extraction, and judging the type of the shooting main body to obtain the event type.
8. The social governance oriented big data visualization method of claim 7, wherein the event presence time period uploaded by the patrolling personnel comprises an event reporting time point uploaded by the patrolling personnel.
9. A social governance-oriented big data visualization device, comprising:
a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement a social governance oriented big data visualization method as defined in any of claims 1-8.
10. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are for implementing a social governance oriented big data visualization method as claimed in any of claims 1 to 8.
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Denomination of invention: A big data visualization method, device, and medium for social governance Granted publication date: 20240430 Pledgee: Industrial and Commercial Bank of China Limited Chengdu Chunxi Branch Pledgor: SICHUAN SANSIDE TECHNOLOGY Co.,Ltd. Registration number: Y2024980028599 |