CN112395351A - Visual identification group complaint risk method, device, computer equipment and medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of big data, and relates to a visual risk identification method for group complaints. The application also provides a visual group complaint identification risk device, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and the association relation can be stored in the block chain. The method and the device can realize accurate positioning of the group complaint risk of the user, and are beneficial to timely predicting and discovering the group complaint risk.
Description
Technical Field
The application relates to the technical field of big data processing, in particular to a visual method, device, computer equipment and medium for identifying a group complaint risk.
Background
Currently, most enterprises set up client service departments specially for clients to receive and process the matters of the clients, and perform post-event analysis and risk monitoring on the matters of the clients so as to predict and discover risks in time and take further preventive or coping measures.
In the event of group complaint, a leader generally causes public opinion, then people following the public opinion are organized into a group and initiate complaints, or a plurality of people make a call continuously to make complaints, once the group complaints occur, the group complaints are easy to influence the reputation of an enterprise, further loss is caused to the enterprise, and the prior art cannot monitor and analyze the group complaint risks in advance, so that how to identify the group complaints so as to predict and find the group complaint risks of clients in time is a problem to be solved at present.
Disclosure of Invention
The embodiment of the application aims to provide a visual method, a visual device, a visual computer device and a visual medium for identifying a group complaint risk, so as to solve the problem that the group complaint risk cannot be monitored and analyzed in advance in the related technology, so that the user group complaint risk cannot be predicted and found in time.
In order to solve the above technical problem, an embodiment of the present application provides a visual method for identifying a risk of a group complaint, which adopts the following technical solutions:
acquiring all associated users having association with a target user, and constructing an association map between the target user and all the associated users by taking the target user as a center;
scoring the target user and all the associated users according to a preset scoring rule, and reconstructing the associated map according to a scoring result;
and performing group-complaint risk early warning scoring on the reconstructed association map to obtain a group-complaint risk early warning scoring result, and displaying the group-complaint risk early warning scoring result in a visual display area.
Further, the step of obtaining all associated users having an association relationship with a target user and constructing an association map between the target user and all associated users with the target user as a center includes:
determining an association factor according to the association relationship, and finding out an associated user of a preset level of the target user based on the association strength of the association factor, wherein the associated user is used as the associated user having the association relationship with the target user;
and associating the target user with each associated user by adopting a shortest path algorithm to construct the associated map.
Further, the step of associating the target user with each associated user by using a shortest path algorithm to construct the associated map includes:
calculating the shortest path from the target user to each associated user by adopting the shortest path algorithm;
and associating the target user with each associated user through the shortest path to form an associated map.
Further, the step of calculating the shortest path from the target user to each of the associated users by using a shortest path algorithm includes:
step 0, taking the target user as an initial node V0Each of the associated users serves as a termination node ViWhere i ∈ [1, n ]]N is the number of the associated users;
step 1, starting node V0And said termination node ViForming a node set V, and establishing an adjacency matrix A according to the connection relation between nodes;
step 2, establishing a set S and a set U, wherein the set S only initially comprises the starting node V0Said set U comprising said start node V0Other end nodes are searched for the initial node V in the adjacency matrix A0In the row, if the V-th row in the adjacent matrix A is0Line ViIf the column value is 1, the length distance of the connecting route is obtained as the termination node V in the set UiTo the starting node V0The distance weight of (2); if Vth in the adjacency matrix A0Line ViThe column value is 0, the termination node V is setiTo the starting node V0The distance weight of (2) is infinity;
step 3, selecting a termination node V with the minimum distance weight value from the set UkConnecting the termination node VkAdding into the set S, the termination node VkTo the starting node V0Distance L ofkAs the shortest path length;
step 4, searching the termination node V in the adjacency matrix AkTermination node V with element 1 in rowmAnd said termination node VmIn the set U, the termination node V is usedkModifying the termination node V for newly considered intermediate pointsmTo the starting node V0The distance of (d);
step 5, repeating steps 3 and 4 until all nodes are contained in the set S, based on the starting node V0To the termination node ViAs the shortest path between two points, the nodes and lines that the distance generation process of (2) goes through.
Further, the step of scoring the target user and all the associated users according to a preset scoring rule and reconstructing the associated map according to a scoring result includes:
determining a scoring factor of the target user and each associated user according to the associated map;
scoring the target user and each associated user according to the scoring factors;
and reconstructing the association map by taking the object with the highest score as a center, wherein the object is a target user or an associated user.
Further, the step of reconstructing the associated atlas centering on the object with the highest score includes:
determining whether the object with the highest score is an associated user;
if so, reconstructing the associated map by taking the associated user with the highest score as a center;
if not, keeping the associated map unchanged.
Further, the step of scoring the reconstructed associated graph for the risk pre-warning of the complaints comprises:
configuring different weights for preset group appeal factors;
scoring each of the pair of population appetitions based on a preset factor scoring rule;
and calculating to obtain the score result of the group complaint risk early warning according to the score of each group complaint factor and the weight corresponding to each group complaint factor.
In order to solve the above technical problem, an embodiment of the present application further provides a visual apparatus for identifying a risk of a group complaint, which adopts the following technical solutions:
the system comprises a construction module, a correlation module and a processing module, wherein the construction module is used for acquiring all correlation users having correlation with a target user and constructing a correlation map between the target user and all the correlation users by taking the target user as a center;
the reconstruction module is used for scoring the target user and all the associated users according to a preset scoring rule and reconstructing the associated map according to a scoring result;
and the grading display module is used for grading the reconstructed association map for the group-complaint risk early warning to obtain a group-complaint risk early warning grading result and displaying the group-complaint risk early warning grading result in a visual display area.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
the computer device comprises a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the visualized method for identifying risk of crowd sourcing as described above.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
the computer readable storage medium has stored thereon computer readable instructions which, when executed by a processor, implement the steps of the visualized method of identifying a risk of a complaint as described above.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
according to the method, all associated users having an associated relationship with a target user are obtained, an associated graph between the target user and all associated users centering on the target user is constructed, the target user and all associated users are scored according to a preset scoring rule, the associated graph is reconstructed according to a scoring result, the reconstructed associated graph is subjected to group-complaint risk early warning scoring to obtain a group-complaint risk early warning scoring result, and the group-complaint risk early warning scoring result is displayed in a visual display area; by constructing the association graph with the target user as the center, scoring the target user and all the association users in the association graph, wherein the higher the score is, the higher the risk of group complaint of the corresponding user is, the user is used as a key monitoring object, the accurate positioning of the group complaint risk of the user can be realized, the association graph is reconstructed according to the scoring result, the group complaint risk early warning scoring is performed on the reconstructed association graph, and the group complaint risk early warning scoring can be helpful for predicting and discovering the group complaint risk in time.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a visual method of identifying a risk of a complaint according to the present application;
FIG. 3 is a flowchart of one embodiment of step S201 in FIG. 2;
FIG. 4 is a flowchart of one embodiment of step S203 in FIG. 2;
FIG. 5 is a schematic diagram of an embodiment of a visual complaint risk identification device according to the present application;
FIG. 6 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
In order to solve the problem that the group complaint risk cannot be monitored and analyzed in advance in the related art, so that the user group complaint risk cannot be predicted and found in time, the present application provides a visual method for identifying the group complaint risk, which is applied to a system architecture 100 shown in fig. 1, where the system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture Experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the visual risk identification method for group complaints provided in the embodiments of the present application is generally executed by a server, and accordingly, the visual risk identification device for group complaints is generally disposed in the server.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow diagram of one embodiment of a method of visually identifying risk of a complaint according to the present application is shown. The method comprises the following steps:
step S201, all the associated users having the association relationship with the target user are obtained, and an association map between the target user and all the associated users with the target user as the center is constructed.
The associative map is a visual intelligent analysis product, and objects and relations are determined based on a map database. In this embodiment, a group complaint risk relationship network is implemented based on the association map. The target user is a target complaint user determined from the complaint users. The construction of the associated map is to perform post analysis and risk monitoring on the complaint matters of the user so as to predict and find the risk in time and avoid loss.
The associated users are all associated users having an association relation with the target user. The association relationship is a set including a plurality of association relationships. The association relationship includes, but is not limited to, a call relationship association, an address list relationship association, a short message relationship association, a mobile phone number association, a home address association, a work unit association, a device fingerprint association, an account association and a bank account association.
It should be emphasized that, to further ensure the privacy and security of the association relationship, the association relationship may also be stored in a node of a block chain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In some optional manners of this embodiment, the constructing the association map between the target user and all corresponding associated users specifically includes the following steps:
step S301, determining a correlation factor according to the correlation relationship, and finding out a correlation user of a preset level of the target user as a correlation user having a correlation relationship with the target user based on the correlation strength of the correlation factor.
The association factor is an intermediary between the target user and the associated user, including but not limited to work unit name, GPS location, mobile phone number, home address, IP address. The association factor is obtained according to the association relationship, for example, if the association relationship is mobile phone number association, the corresponding association factor is mobile phone number; if the association relationship is family address association, the corresponding association factor is a family address; the association relationship is the association of the working unit, and the association factor is the name of the working unit.
The association strength includes strong association, weak association, and indirect association, and the association factors corresponding to the association relationship may be classified according to the influence degree of the association relationship in the relationship map, for example, if the call relationship association is an important influence factor, the association factor "mobile phone number" corresponding to the call relationship association is strong association.
It should be noted that the association strength of the association factor is preset.
And finding out the associated users of the preset hierarchy of the target user, for example, the target user is A, A is the first hierarchy, finding out the associated users B and C which have the association relation with A according to the association factor, wherein B and C are the second hierarchy, and so on. In this embodiment, the hierarchy is set according to actual conditions.
Step S302, a shortest path algorithm is adopted to associate the target user with each associated user, and an associated map is constructed.
The association relationship between the target user and the associated user is drawn into an association map, and the association relationship is graphically displayed, so that the association relationship between the target user and the associated user can be more intuitively seen.
Specifically, a shortest path algorithm is adopted to calculate the shortest path from a target user to each associated user; and associating the target user with each associated user through the shortest path to form an associated map.
In this embodiment, the shortest path algorithm is Dijkstra algorithm, which is a greedy algorithm, and the algorithm can find the shortest path from one point to the other points in the network, and specifically includes the following steps:
step 0, taking the target user as an initial node V0Each associated user is used as a termination node ViWhere i ∈ [1, n ]]Wherein n is the number of found and target usersThe number of associated users of the association relationship;
step 1, starting node V0And a termination node ViAnd forming a node set V, and establishing an adjacency matrix A according to the connection relation among the nodes.
The adjacency matrix can clearly express the connection relation between the nodes, namely the connection relation between the target user and the associated user; the ith row and jth column elements in the matrix represent the connection relationship between the node i and the node j, and when the node i can directly reach the node j from a certain edge in the graph, the ith row and jth column elements in the matrix are 1; otherwise the element is 0. The nodes in the adjacent matrix A are respectively V0,V1,V2,V3,V4。
Step 2, sets S and U are established, and initially, the set S only includes an initial node, i.e., S ═ V0}, start node V0To the start node V0Is 0, the set U contains a division start node V0Other nodes, i.e., U ═ the rest nodes, look up V in the adjacency matrix a0In row, if in A, V0Line ViIf the column value is 1, the length of the connecting route is obtained as the termination node V in the set UiTo the start V0The distance weight of (2); if in A is at V0Line ViThe column value is 0, the termination node V is setiTo the start node V0The distance weight of (d) is ∞.
The above-described established adjacency matrix a is taken as an example for explanation. Initially, the set S contains only the start node, i.e., S ═ V0At a distance of 0, the set U contains the division V0Other nodes than the above, i.e. U ═ V1,V2,V3,V4}; finding V in adjacency matrix A0At row, node V1，V2，V4If the corresponding column value is 1, the node V1，V2，V4Distances have weights of 100, 30 and 10 respectively, and the distance weights of the rest nodes are infinity.
It should be noted that, the weight is preset, and the stronger the correlation strength of the correlation factor between two nodes is, the shorter the length of the connected line is.
Step 3, selecting a termination node V with the minimum distance weight from the set UkNode V of terminationkAdding to the set S, selected distances LkIs the starting node V0To the termination node VkThe shortest path length of (2).
For example, the start node V0To the termination node V4If the distance weight is minimum, the termination node V is set4Join set S, terminate node V4To the start node V0Has a shortest path length of 10.
Step 4, searching for a termination node V in the adjacency matrix AkThe termination node with element 1 in the row is VmAnd terminates node VmIn the set U, the node V is terminatedkModifying the termination node V for the newly considered intermediate pointmTo the start node V0The distance of (d); setting a termination node VkAnd the start node VmThe length of the connecting line is LkmIf from the start node V0To the termination node VmDistance L ofmVia the termination node Vk，Lm＝Lk+LkmThan original termination node VmTo the start node V0If the distance is short, the termination node V is modifiedmTo the start node V0Is a shorter value.
In particular, find node V in matrix A4The node whose element in the row is 1 is node V3. By node V4Modifying node V for newly considered intermediate points3A distance; node V4And node V3The length of the connection line is 50 from the start node V0To node V3The distance (50 + 10) ∞ 60 is shorter than the original distance weight, and the node V is modified3Is 60.
Step 5, repeating steps 3 and 4 until all nodes are contained in S, at which point the starting node V0To the termination node ViThe distance is two sectionsLength of shortest path between points; the nodes and lines that the distance generation process passes through are the shortest path between two points.
The Dijkstra algorithm has good expandability and can expand outwards layer by taking the starting point as the center until the end point is reached, so that the associated map is more accurate and complete.
In the embodiment, the shortest path and the distance can be accurately determined by adopting the shortest path algorithm, the operation speed can also be improved, the target user and the associated user are further connected according to the association strength, and the strength of the risk influence can be visually seen.
And step S202, scoring the target user and all associated users according to a preset scoring rule, and reconstructing an associated map according to a scoring result.
Specifically, the scoring factors of the target user and each associated user are determined according to the associated map, and the target user and each associated user are scored according to the scoring factors; and reconstructing the associated map by taking the object with the highest score as the center.
It should be noted that the object is a target user or an associated user.
The scoring factors comprise the number of associated persons of each node, the complaint level, the identity of the associated persons and the like, the scoring rules of different scoring factors are different, the scoring rules are preset, and the target user and each associated user are scored according to the scoring rules of the scoring factors; for example, the number of people associated, with several scores; the grade of complaint is grade one 4, grade two 3, grade three 2, grade four 1, if there is more complaint by one person, only the highest grade of complaint is taken for calculation; the identity of the associated person is directly divided into 5 points if the identity is present and 0 point if the identity is not present.
It should be noted that the associated person identity includes information such as professional positions of the associated user, and if the associated person identity is more critical, the risk of the group complaint is higher, for example, a person in the associated user who has a supervision industry may attach importance to the event, or may quickly utilize social resources of the person to investigate and process the event, and the risk of the group complaint is higher, and for the associated user with the identity, the customer service staff needs to attach importance to and process the complaint event as soon as possible.
And if the highest score indicates that the group complaint risk of the user is highest, the user is taken as the center to reconstruct the association map, so that the association relationship of the user can be conveniently known, and the group complaint risk can be predicted and found in time.
In some optional implementations, centering on the associated user with the highest score, the step of reconstructing the associated map specifically includes the following steps:
determining whether the object with the highest score is the associated user;
if so, reconstructing a correlation map by taking the correlation user with the highest score as a center;
if not, keeping the association map unchanged.
When the object with the highest score is determined as the associated user, the associated map is reconstructed by taking the associated user as the center, so that the reconstruction can be accurately performed, and unnecessary operation is avoided.
And step S203, performing group-complaint risk early warning scoring on the reconstructed association diagram to obtain a group-complaint risk early warning scoring result, and displaying the group-complaint risk early warning scoring result in a visual display area.
In this embodiment, the reconstructed association graph is scored for the group-complaint risk early warning, and the scoring result of the group-complaint risk early warning is displayed in the visual display area, so that the relevant service personnel can visually see the severity of the group-complaint risk, and the content of the scale, time, location coordinates, the association relationship and hierarchy of each group-complaint personnel, the case state and the like, thereby forming a visual relationship network graph for identifying the group-complaint risk.
In some optional implementations, the step of performing a crowd-sourcing risk early warning scoring on the reconstructed association graph specifically includes the following steps:
step S401, configuring different weights for the preset group apperceives.
The group appearancer is preset, and may be set by a service person according to past experience or an internal familiar parameter, or may be obtained through model training. The group complaint factors include a group complaint scale, a complaint level, a correlation strength, a transaction dispersion, and the like, and the weights are set in advance, and for example, the weight arrangement is 60% of the group complaint scale, 20% of the complaint level, 10% of the correlation strength, 10% of the transaction dispersion, and the like.
The scale of the group complaints is the number of all complaints related to the risk of the group complaints;
the complaint grade is the complaint grade, different complaint grades are represented by different color blocks, the colors can be represented by the same color system from deep to light, the first grade is the deepest, and the fourth grade is the lightest;
the correlation strength comprises strong correlation, weak correlation and indirect correlation;
the event dispersion is the dispersion of events or problem subclasses, the events are keywords of complaint events, the keywords can be set according to the complaint events, and particularly the dispersion analysis method can be adopted to calculate the proportion of the different members of the event keyword data to obtain the event keyword.
It should be noted that the proportion of the outliers is mainly suitable for measuring the dispersion degree of the classification data, and means the proportion of the frequency of the non-mode array in the total frequency, and the proportion of the outliers is larger, which indicates that the proportion of the frequency of the non-mode array in the total frequency is larger, the representativeness of the mode is worse, and the dispersion is larger; the smaller the proportion of the abnormal number is, the smaller the proportion of the frequency of the non-mode group in the total frequency is, the better the representativeness of the mode is, and the smaller the dispersion is.
In step S402, each group appeal factor is scored based on a preset factor scoring rule.
In this embodiment, the preset factor score rule is preset by a service, and X may be used to represent each group of appeal factor scores.
For example, X1The concentration coefficient represents the scale of the cluster complaints, and is the proportion of the current scale of the cluster complaints, namely the number of the associated people in the region of all the complaints, (20 percent, 40 percent)]The coefficient is 1.2, when the associated people number interval is [5,10 ], the score is 60, and then X1 is 60 × 1.2 is 72 points;
X2representative complaint grades: the number of the first-level complaints is 100 points (0,1000), and if no first-level complaint exists, the second-level complaint is 3 points; grade 2 complaints; grade four complaints score 1; x2 ═ 3Pieces +2 pieces +1 pieces)/3 total pieces 100;
X3representative strength of association: strong correlation 4 points, strong correlation 3 points, weak correlation 2 points, X3 ═ 3X lines + 2X lines + 1X lines)/3X total lines]*100；
X4 represents the event dispersion, each keyword is added with 20 points when finding a keyword, the highest point is added with 100 points (the keyword can be added, modified and deleted), the calculated event dispersion is (0, 100% ], and the corresponding score of 0-100 points can be configured according to the event dispersion correspondingly.
And step S403, calculating to obtain a group complaint risk early warning score result according to the score of each group complaint factor and the weight corresponding to each group complaint factor.
In the present embodiment, the formula Z ═ Σ X × Y is used;
i.e. Z ═ X1*Y1+X2*Y2+X3*Y3+X4*Y4+…+Xn*YnCalculating the group complaint risk early warning score, wherein X represents the score of each group complaint factor, Y represents the weight of each group complaint factor, and n is the number of the group complaint factors.
By means of the calculated group complaint risk early warning scores, the client group complaint risks can be accurately positioned, and timely prediction and discovery of the group complaint risks are facilitated.
The method comprises the steps of taking a target user as a center, constructing an association graph between the target user and all corresponding association users, scoring the target user and all the association users according to a preset scoring rule, reconstructing the association graph according to a scoring result, performing group-complaint risk early warning scoring on the reconstructed association graph to obtain a group-complaint risk early warning scoring result, and displaying the group-complaint risk early warning scoring result in a visual display area; the method comprises the steps of constructing a correlation map with a target user as the center, scoring the target user and all correlation users in the correlation map, wherein the higher the score is, the higher the risk of group complaints of the corresponding user is, using the user as a key monitoring object, realizing accurate positioning of the group complaint risk of the user, reconstructing the correlation map according to the scoring result, and finally performing group complaint risk early warning scoring on the reconstructed correlation map, wherein the group complaint risk early warning scoring can be beneficial to timely predicting and discovering the group complaint risk.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 5, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a visual apparatus for identifying a risk of a complaint, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be applied to various electronic devices.
As shown in fig. 5, the visual apparatus 500 for identifying a risk of a crowd complaining described in this embodiment includes: a construction module 501, a reconstruction module 502 and a calculation presentation module 503. Wherein:
the building module 501 is configured to obtain all associated users having an association relationship with a target user, and build an association map between the target user and all associated users with the target user as a center;
the reconstruction module 502 is configured to score the target user and the associated user according to a preset scoring rule, and reconstruct an associated map according to a scoring result;
the calculation and display module 503 is configured to perform group-complaint risk early-warning scoring on the reconstructed association graph spectrum, and display a group-complaint risk early-warning scoring result in a visual display area.
It should be emphasized that, to further ensure the privacy and security of the association relationship, the association relationship may also be stored in a node of a block chain.
According to the visual group complaint risk identification device, the target user and all associated users in the associated map are scored, the higher the score is, the higher the risk of group complaint of the corresponding user is, the user serves as a key monitoring object, the accurate positioning of the group complaint risk of the user can be realized, the associated map is reconstructed according to the scoring result, the group complaint risk early warning scoring is performed on the reconstructed associated map, the group complaint risk early warning scoring can be helpful for timely predicting and discovering the group complaint risk, and meanwhile, the accurate positioning of the group complaint risk of the user is realized according to the scoring result and the importance.
In some optional implementation manners of this embodiment, the building module 501 includes a query submodule and a building submodule, where the query submodule is configured to determine a correlation factor according to the correlation relationship, and find out a correlated user at a preset level of the target user based on the correlation strength of the correlation factor; and the construction submodule is used for associating the target user with each associated user by adopting a shortest path algorithm to construct an associated map.
In some optional implementations, the building submodule includes a computing unit and a building unit, and the computing unit is configured to compute, by using a shortest path algorithm, a shortest path from each target user to each associated user; the construction unit is used for associating the target user with each associated user through a shortest path to form an associated map.
The incidence relation between the target user and the incidence user is drawn into the incidence map, and the incidence relation is graphically displayed, so that the incidence relation between the target user and the incidence user can be more intuitively seen.
Specifically, the computing unit is further configured to:
step 0, taking the target user as an initial node V0With associated users as termination nodes ViWhere i ∈ [1, n ]]，
Step 1, forming a node set V by all nodes, forming a set E by connecting lines of the nodes, forming a network structure diagram by the node set V and the line set E, marking as G (V, E), and establishing an adjacency matrix A;
step 2, sets S and U are established, and initially, S only includes an initial node, i.e., S ═ V { (V)0}，V0Is 0, U includes dividing V0Other nodes, i.e., U ═ the rest nodes, look up V in the adjacency matrix a0In row, if in A, V0Line ViIf the column value is 1, the length of the connected route is obtained as the node V in UiTo V0The distance weight of (2); if in A is at V0Line ViThe column value is 0, then set ViTo V0The distance weight is infinity;
step 3, selecting a node V with the minimum distance weight from the UkHandle VkAdding to S, selecting distance LkIs exactly V0To VkThe shortest path length of (2);
step 4, searching V in the adjacent matrix AkNode V with element 1 in rowmAnd node VmIn the set U, then VkFor newly considered intermediate points, modify VmTo V0The distance of (d);
step 5, repeating steps 3 and 4 until all nodes are contained in S, based on V0To ViAs the shortest path between two points, the nodes and lines that the distance generation process of (2) goes through.
The Dijkstra algorithm has good expandability, and can expand outwards layer by taking a starting point as a center until the point is expanded to a terminal point, so that the associated map is more accurate and complete. In some optional implementation manners of this embodiment, the reconstruction module 502 includes a scoring submodule and a reconstruction submodule, and the scoring submodule is configured to score the target user and all the associated users according to scoring rules of different scoring factors; and the reconstruction submodule is used for reconstructing the associated map by taking the object with the highest score as the center, wherein the object is the target user or the associated user.
After the target user and all the associated users are scored, the associated map is reconstructed by taking the object with the highest score as the center, the highest score indicates that the group complaint risk of the user is highest, and then the associated map is reconstructed by taking the user as the center, so that the association relationship of the user is conveniently known, and the group complaint risk is predicted and found in time.
In some optional implementations, the reconstruction module 502 further includes a judgment sub-module, where the judgment sub-module is configured to determine whether the object with the highest score is the associated user; if so, reconstructing a correlation map by taking the correlation user with the highest score as a center; if not, keeping the association map unchanged.
By determining whether the object with the highest score is the associated user or not, and when the object with the highest score is determined to be the associated user, the associated map is reconstructed by taking the associated user as the center, so that the reconstruction can be accurately performed, and unnecessary operation is avoided.
In some optional implementation manners of this embodiment, the calculation and presentation module 503 includes a configuration submodule and a scoring submodule, where the configuration submodule is configured to configure different weights for different group apperceives; the scoring submodule is used for scoring the group appeal factors based on a preset factor scoring rule; and calculating the score of the group complaint risk early warning according to the score of the group complaint factor and the weight of the group complaint factor.
By means of the calculated group complaint risk early warning scores, the user group complaint risks can be accurately positioned, and timely prediction and discovery of the group complaint risks are facilitated.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 6, fig. 6 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6, such as a hard disk or a memory of the computer device 6. In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system and various application software installed on the computer device 6, such as a visual computer readable instruction for identifying a risk of a complaint. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device 6. In this embodiment, the processor 62 is configured to execute computer readable instructions stored in the memory 61 or process data, such as executing the visualized method for identifying risk of a complaint.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
In this embodiment, when the processor executes the computer readable instructions stored in the memory, the steps of the demonstration method of the product according to the above embodiment are implemented, by constructing the association map centering on the target user, and scoring the target user and all associated users in the association map, where a higher score indicates a higher risk of a group complaint occurring to the corresponding user, and the user is used as a key monitoring object, the accurate positioning of the group complaint risk of the user can be implemented, the association map is reconstructed according to the scoring result, and finally, the group complaint risk early warning scoring is performed on the reconstructed association map, so that the group complaint risk early warning scoring can be helpful for predicting and discovering the group complaint risk in time.
The present application further provides another embodiment, which is a computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by at least one processor, to cause the at least one processor to perform the steps of the visual method of identifying a risk of a complaint as described above, by constructing an associated map with a target user as a center and scoring the target user and all associated users in the associated map, the higher the score is, the higher the risk of group complaints of the corresponding user is, the user is used as a key monitoring object, the accurate positioning of the user group complaint risk can be realized, the associated graph spectrum is reconstructed according to the grading result, and performing group-complaint risk early warning scoring on the reconstructed association graph, wherein the group-complaint risk early warning scoring can be helpful for predicting and discovering the group-complaint risk in time.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A visual method for identifying a risk of a complaint, comprising the steps of:
acquiring all associated users having association with a target user, and constructing an association map between the target user and all the associated users by taking the target user as a center;
scoring the target user and all the associated users according to a preset scoring rule, and reconstructing the associated map according to a scoring result;
and performing group-complaint risk early warning scoring on the reconstructed association map to obtain a group-complaint risk early warning scoring result, and displaying the group-complaint risk early warning scoring result in a visual display area.
2. The visual method for identifying risk of complaints of claim 1, wherein the step of obtaining all associated users having an association relationship with a target user and constructing an association map between the target user and all associated users centered on the target user comprises:
determining an association factor according to the association relationship, and finding out an associated user of a preset level of the target user based on the association strength of the association factor, wherein the associated user is used as the associated user having the association relationship with the target user;
and associating the target user with each associated user by adopting a shortest path algorithm to construct the associated map.
3. The visual method for identifying risk of a complaint according to claim 2, wherein the step of associating the target user with each of the associated users by using a shortest path algorithm to construct the association map comprises:
calculating the shortest path from the target user to each associated user by adopting the shortest path algorithm;
and associating the target user with each associated user through the shortest path to form an associated map.
4. The visual method for identifying risk of complaints of claim 3, wherein the step of calculating the shortest path from the target user to each of the associated users using the shortest path algorithm comprises:
step 0, taking the target user as an initial node V0Each of the associated users serves as a termination node ViWhere i ∈ [1, n ]]N is the number of the associated users;
step 1, starting node V0And said termination node ViForming a node set V, and establishing an adjacency matrix A according to the connection relation between nodes;
step 2, establishing a set S and a set U, wherein the set S only initially comprises the starting node V0Said set U comprising said start node V0Other end nodes are searched for the initial node V in the adjacency matrix A0In the row, if the V-th row in the adjacent matrix A is0Line ViIf the column value is 1, the length distance of the connecting route is obtained as the termination node V in the set UiTo the starting node V0The distance weight of (2); if Vth in the adjacency matrix A0Line ViThe column value is 0, the termination node V is setiTo the aboveStarting node V0The distance weight of (2) is infinity;
step 3, selecting a termination node V with the minimum distance weight value from the set UkConnecting the termination node VkAdding into the set S, the termination node VkTo the starting node V0Distance L ofkAs the shortest path length;
step 4, searching the termination node V in the adjacency matrix AkTermination node V with element 1 in rowmAnd said termination node VmIn the set U, the termination node V is usedkModifying the termination node V for newly considered intermediate pointsmTo the starting node V0The distance of (d);
step 5, repeating steps 3 and 4 until all nodes are contained in the set S, based on the starting node V0To the termination node ViAs the shortest path between two points, the nodes and lines that the distance generation process of (2) goes through.
5. The visual method for identifying the risk of a crowd complaint according to claim 1, wherein the step of scoring the target user and all the associated users according to a preset scoring rule and reconstructing the associated map according to the scoring result comprises:
determining a scoring factor of the target user and each associated user according to the associated map;
scoring the target user and each associated user according to the scoring factors;
and reconstructing the association map by taking the object with the highest score as a center, wherein the object is a target user or an associated user.
6. The visual method for identifying risk of complaints of claim 5, wherein the step of reconstructing the associated map, centered on the object with the highest score, comprises:
determining whether the object with the highest score is an associated user;
if so, reconstructing the associated map by taking the associated user with the highest score as a center;
if not, keeping the associated map unchanged.
7. The visual method for identifying the risk of a group complaint according to claim 1, wherein the step of performing the risk early warning scoring on the reconstructed associated graph to obtain a risk early warning scoring result of the group complaint comprises:
configuring different weights for preset group appeal factors;
scoring each of the group appeal factors based on a preset factor scoring rule;
and calculating to obtain the score result of the group complaint risk early warning according to the score of each group complaint factor and the weight corresponding to each group complaint factor.
8. A visual apparatus for identifying a risk of a complaint, comprising:
the system comprises a construction module, a correlation module and a processing module, wherein the construction module is used for acquiring all correlation users having correlation with a target user and constructing a correlation map between the target user and all the correlation users by taking the target user as a center;
the reconstruction module is used for scoring the target user and all the associated users according to a preset scoring rule and reconstructing the associated map according to a scoring result;
and the grading display module is used for grading the reconstructed association map for the group-complaint risk early warning to obtain a group-complaint risk early warning grading result and displaying the group-complaint risk early warning grading result in a visual display area.
9. A computer device comprising a memory having stored therein computer readable instructions which, when executed by a processor, carry out the steps of the visualized method of identifying a risk of a complaint according to any of claims 1-7.
10. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor, carry out the steps of the visualized method of identifying a risk of a complaint of any one of claims 1-7.
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