CN116822727B - Smart community cloud platform-based refined community management method and device - Google Patents

Smart community cloud platform-based refined community management method and device Download PDF

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CN116822727B
CN116822727B CN202310718088.XA CN202310718088A CN116822727B CN 116822727 B CN116822727 B CN 116822727B CN 202310718088 A CN202310718088 A CN 202310718088A CN 116822727 B CN116822727 B CN 116822727B
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community
node
matrix
vector
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CN116822727A (en
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李家才
李全彬
严凤英
丁研研
陆胜海
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Shenzhen Huiruitong Intelligent Technology Co ltd
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Shenzhen Huiruitong Intelligent Technology Co ltd
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Abstract

The invention relates to the technical field of community resource scheduling, and discloses a smart community cloud platform-based method and device for managing a smart community, wherein the method comprises the following steps: receiving a task to be processed, determining task types contained in the task to be processed, performing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix, acquiring all community managers currently available for use according to an intelligent community cloud platform, determining the number of personnel groups based on the number of lines of the multidimensional task matrix, performing grouping on all the community managers currently available for use by utilizing the number of personnel groups to obtain one or more groups of community grouping personnel, filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix, dispatching the corresponding community manager based on the task response matrix to process the task to be processed, and completing community management. The invention mainly aims to solve the problem of unbalanced scheduling of personnel resources for responding to tasks to be processed.

Description

Smart community cloud platform-based refined community management method and device
Technical Field
The invention relates to a smart community cloud platform-based refined community management method and device, and belongs to the technical field of community resource scheduling.
Background
Along with the continuous development of science and technology, the intelligent degree of community management is higher and higher, and more community management is introduced into an intelligent community cloud platform to respond to the problem that the amount of tasks to be processed is more and more caused by the continuous increase of the community scale. How to efficiently distribute community managers to respond to each task to be processed by utilizing the intelligent community cloud platform is a technical problem which needs to be solved urgently.
Generally, the intelligent community cloud platform receives a task processing instruction and identifies a task to be processed, then traverses all community managers available currently, and selects one or more community managers from all community managers available for use to respond to the task to be processed.
The method can realize task response of communities, but does not consider the task quantity of the task to be processed, or simply quantizes the task quantity of the task to be processed, and further directly dispatches relevant community managers to respond to the task to be processed, so that the problem of unbalanced scheduling of personnel resources for processing the task is extremely easy to cause.
Disclosure of Invention
The invention provides a smart community cloud platform-based refined community management method and device and a computer-readable storage medium, and mainly aims to solve the problem of unbalanced personnel resource scheduling for responding to a task to be processed.
In order to achieve the above purpose, the invention provides a smart community cloud platform-based refined community management method, which comprises the following steps:
receiving a community management instruction of an intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end;
determining task types contained in the task to be processed, and performing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix, wherein the determining the task types contained in the task to be processed comprises the following steps:
determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
Acquiring all community managers available for use currently according to an intelligent community cloud platform, determining the grouping number of the personnel based on the number of lines of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the grouping number of the personnel to obtain one or more groups of community grouping personnel, wherein the grouping number of the personnel is smaller than or equal to the number of lines of the multidimensional task matrix;
filling each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix;
executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises:
executing constant item folding operation on the task response matrix to obtain a task folding matrix;
acquiring all task history matrixes of the intelligent community cloud platform in a specified time period;
constructing a task operator set according to all the task history matrixes, wherein the task operator set comprises a plurality of task operators, and each task operator is different from each other;
calculating the difference value between the row vector of each row in the task folding matrix and each task operator, and determining the task operator with the minimum difference value as a reference operator;
if the difference value is 0, generating an index address of a reference operator, and replacing a corresponding row vector in the task folding matrix by the index address;
If the difference value is not 0, only the data which are different from the reference operator in the corresponding row vector are reserved;
until the row vector of each row in the task folding matrix completes the operation, obtaining a task optimization matrix;
and dispatching a corresponding community manager to process the task to be processed based on the task optimization matrix to complete community management.
Optionally, the performing parameter description on the task to be processed according to the task type to obtain a multidimensional task matrix includes:
according to the task types of the task processing nodes, sequencing the task processing node sets to obtain task sequence node sets;
traversing each task sequence node from the task sequence node set in turn, and executing the following operations on each task sequence node:
generating a single-dimensional empty vector for each task processing node in turn, and executing marking operation for the single-dimensional empty vector based on the task type corresponding to each task processing node to obtain a type empty vector;
acquiring node description corresponding to a task processing node from a task to be processed, wherein the node description comprises an occurrence time, a participant, a place and an event description text;
quantizing the node description to obtain a plurality of groups of node quantized values, and sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain a node vector;
And constructing the multidimensional task matrix according to the node vector corresponding to each task sequence node.
Optionally, the sorting the task processing node set execution nodes according to the task types of the task processing nodes to obtain a task sequence node set includes:
and sequencing the execution of different task types to obtain a task priority chain, wherein the task priority chain sequentially comprises the following steps according to the sequence of the processed tasks: the first priority chain is a home medical service and an outdoor medical service, the second priority chain is a home care service and a resident parking service, and the third priority chain is a property public service, an environment remediation service and a community entertainment service;
and sequencing the task processing nodes according to the task priority chain to obtain a task sequence node set.
Optionally, the method for representing the type null vector is as follows:
wherein,and representing a type null vector corresponding to the ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to the jth task type, and cn represents an nth column null parameter in the type null vector.
Optionally, the quantizing the node description to obtain a plurality of sets of node quantized values includes:
Converting the occurrence time in the node description into a standard time format to obtain standard occurrence time;
sequentially judging whether each participant in the node description belongs to a registrant of the intelligent community cloud platform;
if the participant does not belong to the registrant of the intelligent community cloud platform, marking the participant as a person with information to be confirmed;
if the participant belongs to a registrant of the intelligent community cloud platform, or the intelligent community cloud platform is utilized to collect information of the information to-be-confirmed person again, marking the participant or the information to-be-confirmed person as the information confirmed person;
determining whether a place in the node description belongs to a community range, if the place does not belong to the community range, marking the place as an out-of-jurisdiction place, and if the place belongs to the community range, marking the place as an in-jurisdiction place;
removing stop words of event description texts in the node descriptions to obtain concise description texts;
and performing encryption operation on the standard occurrence time, the information confirmed person, the jurisdictional place or the jurisdictional place and the brief description text to obtain a plurality of groups of node quantized values, wherein the node quantized values comprise the encryption occurrence time, the encryption confirmed person, the encryption place or the encryption place and the encryption description text.
Optionally, the sequentially filling the plurality of sets of node quantized values into the type empty vector to obtain a node vector includes:
adding encryption occurrence time to a first column of null parameters of the null-type vector, and filling the encrypted confirmed person to a second column of null parameters of the null-type vector;
filling the encrypted outer place or the encrypted inner place into a third column space parameter of the type space vector;
determining the text length of the encryption description text, and if the text length is smaller than a text threshold value, directly filling the encryption description text into a fourth column null parameter of the type null vector;
if the text length is greater than or equal to the text threshold value, splitting the encryption description text to obtain a plurality of groups of encryption segmentation texts;
filling each group of encrypted segmented texts into a fourth column, a fifth column, … and an nth column of null parameters of the type null vectors in sequence to obtain node vectors, wherein the node vectors are represented by the following steps:
wherein,representing a node vector corresponding to an ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to a jth task type, p n A parameter indicating that the n-th column of the node vector has been filled with the node quantization value.
Optionally, the constructing according to the node vector corresponding to each task sequence node to obtain the multidimensional task matrix includes:
Executing priority labeling for each node vector according to the task priority chain to obtain a node sequence vector;
each node sequence vector is built according to the priority sequence to obtain a multi-dimensional task matrix, wherein the multi-dimensional task matrix is represented by the following steps:
wherein A represents a multidimensional task matrix, e-nodeS i o E represents the priority chain number of the node sequence vector in the task priority chain, l is the total number of the priority chains of the task priority chain, and l is less than or equal to m, j, o and r each represent any oneAnd the task type number corresponding to the task sequence node.
Optionally, the determining the number of person groups based on the number of rows of the multidimensional task matrix, and grouping all community managers currently available to use by using the number of person groups to obtain one or more groups of community group persons includes:
determining the total number of people of all community managers currently available;
judging whether the total number of the personnel is larger than the number of rows of the multi-dimensional task matrix;
if the total number of the personnel is greater than or equal to the number of the lines of the multidimensional task matrix, grouping community management personnel according to the number of the lines of the multidimensional task matrix to obtain a plurality of groups of community grouping personnel, wherein the number of the groups of the community grouping personnel is the same as the number of the lines of the multidimensional task matrix;
If the total number of people is smaller than the number of lines of the Yu Duowei task matrix, determining the number of node vectors with the highest priority chain level in the multidimensional task matrix, and grouping community management personnel according to the number of the node vectors with the highest priority chain level to obtain a plurality of groups of community grouping personnel, wherein the number of the community grouping personnel is smaller than or equal to the number of lines of the multidimensional task matrix.
Optionally, the filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix includes:
constructing personnel information vectors of grouping personnel of each group of communities, wherein the personnel information vectors are represented by the following steps:
maxP i ＝{m 1 ，m 2 ，…，m h wherein, manP i Personnel information vector, m, representing group personnel of group i community h Personnel information of an h community manager in the ith group of community grouping personnel is represented, wherein the personnel information comprises personnel name, sex, current position and contact mode;
filling the personnel information vector into a multidimensional task matrix to obtain a task response matrix, wherein the task response matrix is represented by the following steps:
b is a task response matrix corresponding to the number of human groups equal to the number of rows of the multidimensional task matrix, and e-modeS i o -manP i And the node sequence vector corresponding to the ith task sequence node with the priority chain number of e is represented, and the personnel information vector corresponding to the node sequence vector is processed.
In order to solve the above problems, the present invention further provides a smart community cloud platform-based refined community management device, which includes:
the community management instruction receiving module is used for receiving a community management instruction of the intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent equipment end;
the multi-dimensional task matrix construction module is used for determining task types contained in the task to be processed, executing parameter description on the task to be processed according to the task types to obtain a multi-dimensional task matrix, wherein the determining of the task types contained in the task to be processed comprises the following steps:
determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
the manager grouping module is used for acquiring all community managers available for use currently according to the intelligent community cloud platform, determining the number of person groupings based on the number of rows of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the number of person groupings to obtain one or more groups of community groupings, wherein the number of person groupings is smaller than or equal to the number of rows of the multidimensional task matrix;
The task response module is used for filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix, and executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises the following steps: performing constant item folding operation on the task response matrix to obtain a task folding matrix, obtaining all task history matrixes of the intelligent community cloud platform within a specified time period, and constructing a task calculation subset according to all the task history matrixes, wherein a plurality of task operators are included in a task operator set, each task operator is different from each other, a difference value between a row vector of each row in the task folding matrix and each task operator is calculated, the task operator with the minimum difference value is determined to be a reference operator, if the difference value is 0, an index address of the reference operator is generated, the corresponding row vector in the task folding matrix is replaced by the index address, if the difference value is not 0, only data which are different from the reference operator in the corresponding row vector is reserved until the row vector of each row in the task folding matrix is completed, a task optimization matrix is obtained, and community manager corresponding to dispatch the task to be processed based on the task optimization matrix to complete community management.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to implement the smart community cloud platform-based method of smart community management described above.
In order to solve the above problems, the present invention further provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the smart community cloud platform-based refined community management method described above.
Compared with the problems in the background art, the method and the device for identifying the task to be processed in the intelligent community cloud platform receive the community management instruction of the intelligent community cloud platform, and identify the task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end, and it is to be explained that in order to guarantee timeliness of response of the task to be processed, the method and the device for identifying the task to be processed in the intelligent community cloud platform immediately after receiving the community management instruction. Further, determining a task type contained in the task to be processed, and performing parameter description on the task to be processed according to the task type to obtain a multidimensional task matrix, wherein the determining the task type contained in the task to be processed comprises the following steps: determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow; the task type of each task processing node in the task processing node set is judged in sequence, wherein the task type comprises property public service, home medical service, outdoor medical service, home care service, resident parking service, environment improvement service and community entertainment service, obviously, the embodiment of the invention does not directly and simply evaluate the task amount of the task to be processed and distribute community personnel for processing the task according to the task amount, but determines which task processing nodes are formed by the task to be processed first, and determines the task type of each task processing node. Further, according to the intelligent community cloud platform, all community managers which can be used currently are obtained, the number of person groups is determined based on the number of rows of the multidimensional task matrix, the person groups are utilized to carry out grouping on all the community managers which can be used currently, one group or a plurality of groups of community group personnel are obtained, wherein the number of person groups is smaller than or equal to the number of rows of the multidimensional task matrix, and therefore the embodiment of the invention fully utilizes the matrix characteristics of the multidimensional task matrix, comprises dividing all the community managers which can be used currently based on the number of rows of the multidimensional task matrix, and further improves the resource scheduling efficiency of the community managers. Finally, each group of community grouping personnel is filled into a multidimensional task matrix to obtain a task response matrix, data optimization processing is carried out on the task response matrix to obtain a task optimization matrix, and corresponding community management personnel are dispatched based on the task optimization matrix to process the task to be processed to complete community management.
Drawings
FIG. 1 is a flow chart of a method for managing a refined community based on a smart community cloud platform according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of a smart community cloud platform-based refined community management device according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the smart community cloud platform-based method for managing a smart community according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a refined community management method based on an intelligent community cloud platform. The execution subject of the smart community cloud platform-based method for managing a smart community includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the smart community cloud platform-based refined community management method may be performed by software or hardware installed in a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
referring to fig. 1, a flow chart of a smart community cloud platform-based method for managing a smart community according to an embodiment of the present invention is shown. In this embodiment, the smart community cloud platform-based method for managing a smart community includes:
and SS1, receiving a community management instruction of the intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end.
It can be explained that the task to be processed in the embodiment of the invention is initiated by community users with specific requirements at the intelligent equipment end, wherein the task to be processed generally comprises property public service, home care service, resident parking service, environment remediation service and the like. Illustratively, the sheetlet is an ordinary user of community A, and it is found that fitness equipment in community public facilities is damaged due to old and old equipment, so that the sheetlet initiates a task to be processed at the intelligent equipment end, namely the purpose of the task to be processed is to transmit maintenance requirements for the public facilities to a management department of the community.
In addition, the community management instruction in the embodiment of the invention is generally initiated by a manager of the community. Illustratively, xiao Li is a manager of community a, whose job is to perform management activities on user demands by reasonably allocating community resources, so xiao Li initiates community management instructions for timely responding to pending tasks initiated by community users and quickly executing task processing decisions.
S2, determining task types contained in the task to be processed, and executing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix.
In detail, the determining the task type contained in the task to be processed includes:
determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
and sequentially judging the task type of each task processing node in the task processing node set, wherein the task type comprises a property public service, a home medical service, an outdoor medical service, a home care service, a resident parking service, an environment remediation service and a community entertainment service.
It is understood that a task to be processed is typically made up of a plurality of task processing nodes. The method comprises the steps that an example, a small sheet is an ordinary user in a community A, when fitness equipment in community public facilities is used, the small sheet falls from the fitness equipment to the ground due to the fact that the equipment is old, raw material residues are scattered on the ground due to the fact that the equipment is cracked, therefore, a task to be processed at this time comprises three task processing nodes, namely the fitness equipment is old, the small sheet falls to be injured, the raw material residues of the equipment are scattered on the ground, and task types corresponding to the three task processing nodes are property public service, community medical service and environment improvement service.
Thus, in summary, the task to be processed is classified into n task types, where the task types include, but are not limited to, property public service, home medical service, outdoor medical service, home care service, resident parking service, environment improvement service, and community entertainment service, and each task to be processed is composed of 1 or more task processing nodes, i.e., corresponding, a task to be processed initiated by a community user may include multiple task types.
In detail, the performing parameter description on the task to be processed according to the task type to obtain a multidimensional task matrix includes:
according to the task types of the task processing nodes, sequencing the task processing node sets to obtain task sequence node sets;
traversing each task sequence node from the task sequence node set in turn, and executing the following operations on each task sequence node:
generating a single-dimensional empty vector for each task processing node in turn, and executing marking operation for the single-dimensional empty vector based on the task type corresponding to each task processing node to obtain a type empty vector;
acquiring node description corresponding to a task processing node from a task to be processed, wherein the node description comprises an occurrence time, a participant, a place and an event description text;
Quantizing the node description to obtain a plurality of groups of node quantized values, and sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain a node vector;
and constructing the multidimensional task matrix according to the node vector corresponding to each task sequence node.
In detail, the step of ordering the task processing node set execution nodes according to the task types of the task processing nodes to obtain a task sequence node set includes:
and sequencing the execution of different task types to obtain a task priority chain, wherein the task priority chain sequentially comprises the following steps according to the sequence of the processed tasks: the first priority chain is a home medical service and an outdoor medical service, the second priority chain is a home care service and a resident parking service, and the third priority chain is a property public service, an environment remediation service and a community entertainment service;
and sequencing the task processing nodes according to the task priority chain to obtain a task sequence node set.
The task to be processed initiated by the sheetlet includes three task processing nodes, namely: the fitness equipment is old, the small pieces of fitness equipment are fallen and injured, raw material residues of equipment are scattered on the ground, and task types corresponding to the three task processing nodes are property public service, community medical service and environment remediation service respectively. Obviously, when the task processing node is community medical service, it indicates that personnel in the jurisdiction of the community need to provide medical service for the community, and the priority of the task processing node is higher than that of other task types due to the fact that the personnel safety is involved, so that the priority of the task processing node corresponding to the small-sized fall injury needs to be processed is highest. Therefore, obviously, the task sequence nodes corresponding to the tasks to be processed initiated by the small sheets are concentrated, and the sequence of the nodes is as follows: the small pieces fall to the injured node, the body-building equipment is old and the raw material residues of the equipment are scattered on the ground (parallel).
Further, the representation method of the type null vector comprises the following steps:
wherein,and representing a type null vector corresponding to the ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to the jth task type, and cn represents an nth column null parameter in the type null vector.
It should be explained that the type null vector is preset with a plurality of null parameters, and the null parameters are used for facilitating the subsequent filling of the quantized values of the plurality of groups of nodes into the type null vector.
It is emphasized that each task processing node corresponds to a node description. The task to be processed initiated by the sheetlet includes three task processing nodes, namely, the old fitness equipment, the sheetlet falling to be injured, equipment raw material residues scatter on the ground, and the sheetlet can simultaneously execute expressions on the old fitness equipment, the sheetlet falling to be injured, the equipment raw material residues scatter on the ground when initiating the task to be processed, wherein the expressions can be in a text or voice form and the like, and the voice form is still finally converted into a text form, so that subsequent processing is convenient, for example, when the sheetlet starts an intelligent equipment end (such as a mobile phone and a tablet) to describe falling to be injured, the address, time and injury degree of the falling injury are determined. The method comprises the steps of determining the address, time and degree of injury of falling, namely node description corresponding to task processing nodes, wherein participants comprise small pieces, event description comprises the degree of injury, the cause of injury, the development trend of the current moment and the like.
Further, the quantizing the node description to obtain a plurality of sets of node quantized values includes:
converting the occurrence time in the node description into a standard time format to obtain standard occurrence time;
sequentially judging whether each participant in the node description belongs to a registrant of the intelligent community cloud platform;
if the participant does not belong to the registrant of the intelligent community cloud platform, marking the participant as a person with information to be confirmed;
if the participant belongs to a registrant of the intelligent community cloud platform, or the intelligent community cloud platform is utilized to collect information of the information to-be-confirmed person again, marking the participant or the information to-be-confirmed person as the information confirmed person;
determining whether a place in the node description belongs to a community range, if the place does not belong to the community range, marking the place as an out-of-jurisdiction place, and if the place belongs to the community range, marking the place as an in-jurisdiction place;
removing stop words of event description texts in the node descriptions to obtain concise description texts;
and performing encryption operation on the standard occurrence time, the information confirmed person, the jurisdictional place or the jurisdictional place and the brief description text to obtain a plurality of groups of node quantized values, wherein the node quantized values comprise the encryption occurrence time, the encryption confirmed person, the encryption place or the encryption place and the encryption description text.
It should be explained that, in the embodiment of the present invention, in order to improve the processing of the processing message of each node, the node description corresponding to each task sequence node is normalized to a vector, so as to obtain a node vector convenient for subsequent calculation, and before the node vector is constructed, the information in the node description needs to be sequentially quantized, so that the quantized value of the node obtained by quantization is conveniently filled into a type null vector. In the embodiment of the invention, the node description comprises the time of occurrence, participants, places and event description text, and further, the time of occurrence is converted into a standard time format, such as 2023-05-05.
Further, the encryption operation can be selected from symmetric encryption, asymmetric encryption, hash algorithm and the like, and the encryption operation on information such as succinct description text and the like can be realized through the encryption algorithm, so that a plurality of groups of node quantized values are obtained.
In detail, the sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain a node vector includes:
adding encryption occurrence time to a first column of null parameters of the null-type vector, and filling the encrypted confirmed person to a second column of null parameters of the null-type vector;
Filling the encrypted outer place or the encrypted inner place into a third column space parameter of the type space vector;
determining the text length of the encryption description text, and if the text length is smaller than a text threshold value, directly filling the encryption description text into a fourth column null parameter of the type null vector;
if the text length is greater than or equal to the text threshold value, splitting the encryption description text to obtain a plurality of groups of encryption segmentation texts;
filling each group of encrypted segmented texts into a fourth column, a fifth column, a third column and an nth column of null parameters of the type of null vectors in sequence to obtain node vectors, wherein the node vectors are represented by the following steps:
wherein,representing a node vector corresponding to an ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to a jth task type, p n A parameter indicating that the n-th column of the node vector has been filled with the node quantization value.
It can be understood that the minimum n columns of the node vector is 4, and when the text length is greater than or equal to the text threshold, the number of columns of the n columns is determined according to the number of the multiple groups of encrypted segmented texts obtained after splitting.
Further, the constructing the multidimensional task matrix according to the node vector corresponding to each task sequence node includes:
Executing priority labeling for each node vector according to the task priority chain to obtain a node sequence vector;
each node sequence vector is built according to the priority sequence to obtain a multi-dimensional task matrix, wherein the multi-dimensional task matrix is represented by the following steps:
wherein A represents a multidimensional task matrix, e-nodeS i o Representing an ith task order node pair including a priority chain numberThe corresponding node sequence vector, e, represents the number of the priority chains of the node sequence vector in the task priority chains, l is the total number of the priority chains of the task priority chains, and l is smaller than or equal to m, j, o and r, and each represents the number of the task type corresponding to the task sequence node.
It should be explained that, the task priority chain may include a first priority chain, a second priority chain, a third priority chain, and the like sequentially according to the order in which the tasks are processed, where in the task to be processed initiated by the sheetlet, the first priority chain is a home care service and an outdoor medical service, the second priority chain is a home care service and a resident parking service, the third priority chain is a property public service, an environment remediation service, and a community entertainment service, and then the e values of the home care service and the outdoor medical service are all 1, the e values of the home care service and the resident parking service are all 2, i.e., the l value is 3, and the m value is 7.
S, acquiring all community managers available for use currently according to the intelligent community cloud platform, determining the grouping number of the personnel based on the number of lines of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the grouping number of the personnel to obtain one or more groups of community grouping personnel, wherein the grouping number of the personnel is smaller than or equal to the number of lines of the multidimensional task matrix.
It can be understood that according to the description of step S2, the task execution node to be processed can be split to obtain a multi-dimensional task matrix, and the multi-dimensional task matrix shows the processed sequence of each task processing node and the node description of each task processing node, wherein the node description and the processed sequence are both performed through e-nodeS i o Shows that due to e-nodeS i o Important information is intuitively expressed, so that the intelligent community is more beneficial to dispatching community management personnel to respond to the task to be processed.
In detail, the determining the grouping number of people based on the number of rows of the multidimensional task matrix, and grouping all community managers currently available to use by using the grouping number of people to obtain one or more groups of community grouping people includes:
determining the total number of people of all community managers currently available;
Judging whether the total number of the personnel is larger than the number of rows of the multi-dimensional task matrix;
if the total number of the personnel is greater than or equal to the number of the lines of the multidimensional task matrix, grouping community management personnel according to the number of the lines of the multidimensional task matrix to obtain a plurality of groups of community grouping personnel, wherein the number of the groups of the community grouping personnel is the same as the number of the lines of the multidimensional task matrix;
if the total number of people is smaller than the number of lines of the Yu Duowei task matrix, determining the number of node vectors with the highest priority chain level in the multidimensional task matrix, and grouping community management personnel according to the number of the node vectors with the highest priority chain level to obtain a plurality of groups of community grouping personnel, wherein the number of the community grouping personnel is smaller than or equal to the number of lines of the multidimensional task matrix.
For example, after a sheet initiates a task to be processed, a multi-dimensional task matrix is constructed according to the task to be processed initiated by the sheet to obtain 7 rows, which means that 7 total task processing nodes need to be processed, and traversing e-nodeSio finds that the value of l is 2, which means that 2 total priority chains of a first priority chain and a second priority chain are shared, and at the moment, the total number of people of all community managers available for use is less, so that the number of node vectors of the first priority chain is determined first, and the community managers are divided into 3 groups for responding to the task processing nodes of the first priority chain first, provided that the number of node vectors of the first priority chain is 3.
S4, filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix.
In detail, the step of filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix comprises the following steps:
constructing personnel information vectors of grouping personnel of each group of communities, wherein the personnel information vectors are represented by the following steps:
maxP i ＝{m 1 ，m 2 ，…，m h }
wherein, manP i Personnel representing group I community grouping personnelInformation vector, m h Personnel information of an h community manager in the ith group of community grouping personnel is represented, wherein the personnel information comprises personnel name, sex, current position and contact mode;
filling the personnel information vector into a multidimensional task matrix to obtain a task response matrix, wherein the task response matrix is represented by the following steps:
b is a task response matrix corresponding to the number of the personnel groups equal to the number of the rows of the multidimensional task matrix, and e-nodeS i o -manP i And the node sequence vector corresponding to the ith task sequence node with the priority chain number of e is represented, and the personnel information vector corresponding to the node sequence vector is processed.
In the embodiment of the present invention, after obtaining the task response matrix, the method further includes performing data optimization processing on the task response matrix, and in detail, performing data optimization processing on the task response matrix to obtain a task optimization matrix, including:
Executing constant item folding operation on the task response matrix to obtain a task folding matrix;
acquiring all task history matrixes of the intelligent community cloud platform in a specified time period;
constructing a task operator set according to all the task history matrixes, wherein the task operator set comprises a plurality of task operators, and each task operator is different from each other;
calculating the difference value between the row vector of each row in the task folding matrix and each task operator, and determining the task operator with the minimum difference value as a reference operator;
if the difference value is 0, generating an index address of a reference operator, and replacing a corresponding row vector in the task folding matrix by the index address;
if the difference value is not 0, only the data which are different from the reference operator in the corresponding row vector are reserved;
and (3) finishing the operation until the row vector of each row in the task folding matrix to obtain a task optimization matrix.
It should be explained that the main purpose of the constant term folding operation is to reduce the data storage amount, prevent matrix redundancy caused by data writing mode, display mode and other reasons, and further cause the problem of resource waste caused by subsequent storage of the matrix. For example, there may be numbers of community group personnel in the task response matrix B, where the numbers of the community group personnel start with SQ110, then the SQ110 is directly folded, and only S is reserved. In addition, the task response matrix B may further involve an update operation of a part of the vectors, for example, a row vector a exists in the task response matrix B, but the row vector a needs to continue to add personnel information, so in order to ensure the simplicity of the matrix, the embodiment of the invention folds the personnel information existing in the row vector a.
It can be understood that each time a community management instruction is received, a corresponding task response matrix is generated, so when the community management instruction is received in the history time, the generated matrix is the task history matrix, a large number of task history matrices are generated in the history time, and in most cases, the task history matrix has higher similarity with the current task response matrix, so in order to save computing resources and avoid the problem of resource waste, the embodiment of the invention constructs a task calculation subset according to all the task history matrices.
It is emphasized that the task operators are single-dimensional vectors that are different from each other constructed from all the task history matrices. In other words, the task operators are elements forming the task history matrix, and a plurality of same or different task operators are combined according to the matrix combination rule, so that the task history matrix can be obtained. The method for representing the task response matrix is as follows:
1-nodeS 1 j -manP 1 、2-nodeS 2 o -manP 2 Etc. can be referred to as a task operator, but it is understood that if 1-n is not the caseodeS 1 j -manP 1 、2-nodeS 2 o -manP 2 And the like, a large number of different task operators need to be constructed, and resource loss is caused, so that the embodiment of the invention replaces the solidified sequence numbers or the customed numbers and the like in the task operators based on the wild card.
Exemplary, the task operator is expressed in the form of × -nodeS * * -manP * Wherein, the symbol is a wild card, which indicates that when other vectors and the expression form of the task operator are the same, replacing the symbol with any character does not affect the differential calculation of other vectors and the task operator. Furthermore, it should be emphasized that in nodeS * * 、manP * There are also a plurality of wildcards, and where a wildcard occurs, there is typically some sort of cured sequential number or custom number, etc.
Further, the calculating the difference value between the row vector of each row in the task folding matrix and each task operator includes:
according to the positions of the wildcards, splitting the task operator and the row vectors to respectively obtain a plurality of groups of operator sub-vectors and a plurality of groups of row sub-vectors;
respectively calculating operator integration vectors corresponding to a plurality of groups of operator sub-vectors and row integration vectors corresponding to a plurality of groups of row sub-vectors, wherein the calculation method comprises the following steps:
generating an operator empty matrix and a row empty matrix, wherein the dimensions of the operator empty matrix and the row empty matrix are the same;
calculating a dimension vector of each dimension of each operator empty matrix or each row empty matrix, wherein the calculation method of the dimension vector of the ith dimension is as follows:
wherein H is i A dimension vector representing the ith dimension of the operator or row empty matrix, d i An ith operator sub-vector or row sub-vector representing a plurality of sets of operator sub-vectors or a plurality of sets of row sub-vectors,representing its transpose, A represents the correspondence of all d's according to all task history matrices i The average vector generated;
will H i Sequentially filling the operator empty matrix or the row empty matrix to obtain an operator integration vector and a row integration vector;
and calculating the similarity of the operator integration vector and the line integration vector by using a vector similarity calculation method to obtain a difference value.
It should be explained that, in the embodiment of the present invention, the difference value between the row vector and the task operator is calculated not directly by using the vector similarity calculation method, because in most cases, multiple sets of data of different types are embedded in the row vector, and there is often relevance between the data, if the similarity between the row vector and the task operator is calculated directly, the accuracy of the obtained difference value is not high, so that the above-mentioned splitting operation is performed first, that is, the row vector and the task operator are split by using the position of the wild card, so that the data of different types are all placed in different dimensions of the same matrix, and the problem of inaccurate calculation caused by multiple nesting and relevance of the data can be effectively avoided. It can be understood that the data volume of the task response matrix can be effectively reduced by the method, and the task optimization matrix with smaller data dimension and volume can be obtained.
And S5, dispatching corresponding community management personnel based on the task optimization matrix to process the task to be processed, and completing community management.
It can be understood that the task optimization matrix directly integrates each time of task to be processed and personnel information responding to the task to be processed into one matrix, and the matrix backtracking and the searching are more convenient due to the fact that the data volume occupied by the matrix is smaller, so that the smooth operation of the intelligent community cloud platform is simplified, and the problem of the clamping phenomenon caused by the fact that the intelligent community cloud platform has too many tasks to be processed is solved.
Compared with the problems in the background art, the method and the device for identifying the task to be processed in the intelligent community cloud platform receive the community management instruction of the intelligent community cloud platform, and identify the task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end, and it is to be explained that in order to guarantee timeliness of response of the task to be processed, the method and the device for identifying the task to be processed in the intelligent community cloud platform immediately after receiving the community management instruction. Further, determining a task type contained in the task to be processed, and performing parameter description on the task to be processed according to the task type to obtain a multidimensional task matrix, wherein the determining the task type contained in the task to be processed comprises the following steps: determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow; the task type of each task processing node in the task processing node set is judged in sequence, wherein the task type comprises property public service, home medical service, outdoor medical service, home care service, resident parking service, environment improvement service and community entertainment service, obviously, the embodiment of the invention does not directly and simply evaluate the task amount of the task to be processed and distribute community personnel for processing the task according to the task amount, but determines which task processing nodes are formed by the task to be processed first, and determines the task type of each task processing node. Further, according to the intelligent community cloud platform, all community managers which can be used currently are obtained, the number of person groups is determined based on the number of rows of the multidimensional task matrix, the person groups are utilized to carry out grouping on all the community managers which can be used currently, one group or a plurality of groups of community group personnel are obtained, wherein the number of person groups is smaller than or equal to the number of rows of the multidimensional task matrix, and therefore the embodiment of the invention fully utilizes the matrix characteristics of the multidimensional task matrix, comprises dividing all the community managers which can be used currently based on the number of rows of the multidimensional task matrix, and further improves the resource scheduling efficiency of the community managers. Finally, each group of community grouping personnel is filled into a multidimensional task matrix to obtain a task response matrix, data optimization processing is carried out on the task response matrix to obtain a task optimization matrix, and corresponding community management personnel are dispatched based on the task optimization matrix to process the task to be processed to complete community management.
Example 2:
fig. 2 is a functional block diagram of a smart community cloud platform-based refined community management device according to an embodiment of the present invention.
The smart community cloud platform-based refined community management device 100 can be installed in electronic equipment. According to the implemented functions, the smart community cloud platform-based refined community management device 100 may include a community management instruction receiving module 101, a multidimensional task matrix building module 102, a manager grouping module 103 and a task response module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The community management instruction receiving module 101 is configured to receive a community management instruction of the smart community cloud platform, and identify a task to be processed according to the community management instruction, where the task to be processed is initiated by a community user at the smart device end;
the multidimensional task matrix construction module 102 is configured to determine a task type included in the task to be processed, perform parameter description on the task to be processed according to the task type, and obtain a multidimensional task matrix, where the determining the task type included in the task to be processed includes:
Determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
the manager grouping module 103 is configured to obtain all community managers currently available for use according to a smart community cloud platform, determine a person grouping number based on the number of rows of the multidimensional task matrix, and perform grouping on all community managers currently available for use by using the person grouping number to obtain one or more groups of community grouping personnel, where the person grouping number is less than or equal to the number of rows of the multidimensional task matrix;
the task response module 104 is configured to fill each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix, and perform data optimization processing on the task response matrix to obtain a task optimization matrix, where the data optimization processing includes: performing constant item folding operation on the task response matrix to obtain a task folding matrix, obtaining all task history matrixes of the intelligent community cloud platform within a specified time period, and constructing a task calculation subset according to all the task history matrixes, wherein a plurality of task operators are included in a task operator set, each task operator is different from each other, a difference value between a row vector of each row in the task folding matrix and each task operator is calculated, the task operator with the minimum difference value is determined to be a reference operator, if the difference value is 0, an index address of the reference operator is generated, the corresponding row vector in the task folding matrix is replaced by the index address, if the difference value is not 0, only data which are different from the reference operator in the corresponding row vector is reserved until the row vector of each row in the task folding matrix is completed, a task optimization matrix is obtained, and community manager corresponding to dispatch the task to be processed based on the task optimization matrix to complete community management.
In detail, the modules in the smart community cloud platform-based refined community management apparatus 100 in the embodiment of the present invention use the same technical means as the smart community cloud platform-based refined community management method described in fig. 1, and can produce the same technical effects, which are not described herein.
Example 3:
fig. 3 is a schematic structural diagram of an electronic device for implementing a smart community cloud platform-based method for managing a smart community according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as a smart community management program based on a smart community cloud platform.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 1111 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 11. The memory 1111 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SmartMediaCard, SMC), a secure digital (SecureDigital, SD) card, a flash card (FlashCard) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of a smart community management program based on a smart community cloud platform, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (CentralProcessingunit, CPU), microprocessors, digital processing chips, graphics processors, a combination of various control chips, and the like. The processor 10 is a control unit (control unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device 1 and processes data by running or executing programs or modules (e.g., a smart community cloud platform-based smart community management program, etc.) stored in the memory 11, and calling data stored in the memory 11.
The bus may be an Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be a lle display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (organic light-emitting diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The smart community cloud platform-based smart community management program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can implement:
receiving a community management instruction of an intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end;
Determining task types contained in the task to be processed, and performing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix, wherein the determining the task types contained in the task to be processed comprises the following steps:
determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
acquiring all community managers available for use currently according to an intelligent community cloud platform, determining the grouping number of the personnel based on the number of lines of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the grouping number of the personnel to obtain one or more groups of community grouping personnel, wherein the grouping number of the personnel is smaller than or equal to the number of lines of the multidimensional task matrix;
filling each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix;
executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises:
Executing constant item folding operation on the task response matrix to obtain a task folding matrix;
acquiring all task history matrixes of the intelligent community cloud platform in a specified time period;
constructing a task operator set according to all the task history matrixes, wherein the task operator set comprises a plurality of task operators, and each task operator is different from each other;
calculating the difference value between the row vector of each row in the task folding matrix and each task operator, and determining the task operator with the minimum difference value as a reference operator;
if the difference value is 0, generating an index address of a reference operator, and replacing a corresponding row vector in the task folding matrix by the index address;
if the difference value is not 0, only the data which are different from the reference operator in the corresponding row vector are reserved;
until the row vector of each row in the task folding matrix completes the operation, obtaining a task optimization matrix;
and dispatching a corresponding community manager to process the task to be processed based on the task optimization matrix to complete community management.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
receiving a community management instruction of an intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end;
determining task types contained in the task to be processed, and performing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix, wherein the determining the task types contained in the task to be processed comprises the following steps:
Determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
acquiring all community managers available for use currently according to an intelligent community cloud platform, determining the grouping number of the personnel based on the number of lines of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the grouping number of the personnel to obtain one or more groups of community grouping personnel, wherein the grouping number of the personnel is smaller than or equal to the number of lines of the multidimensional task matrix;
filling each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix;
executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises:
executing constant item folding operation on the task response matrix to obtain a task folding matrix;
acquiring all task history matrixes of the intelligent community cloud platform in a specified time period;
Constructing a task operator set according to all the task history matrixes, wherein the task operator set comprises a plurality of task operators, and each task operator is different from each other;
calculating the difference value between the row vector of each row in the task folding matrix and each task operator, and determining the task operator with the minimum difference value as a reference operator;
if the difference value is 0, generating an index address of a reference operator, and replacing a corresponding row vector in the task folding matrix by the index address;
if the difference value is not 0, only the data which are different from the reference operator in the corresponding row vector are reserved;
until the row vector of each row in the task folding matrix completes the operation, obtaining a task optimization matrix;
and dispatching a corresponding community manager to process the task to be processed based on the task optimization matrix to complete community management.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (8)

1. A smart community cloud platform-based refined community management method is characterized by comprising the following steps:
receiving a community management instruction of an intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent device end;
Determining task types contained in the task to be processed, and performing parameter description on the task to be processed according to the task types to obtain a multidimensional task matrix, wherein the determining the task types contained in the task to be processed comprises the following steps:
determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
the task to be processed is subjected to parameter description according to the task type to obtain a multidimensional task matrix, which comprises the following steps:
according to the task types of the task processing nodes, sequencing the task processing node sets to obtain task sequence node sets;
traversing each task sequence node from the task sequence node set in turn, and executing the following operations on each task sequence node:
generating a single-dimensional empty vector for each task processing node in turn, and executing marking operation for the single-dimensional empty vector based on the task type corresponding to each task processing node to obtain a type empty vector;
Acquiring node description corresponding to a task processing node from a task to be processed, wherein the node description comprises an occurrence time, a participant, a place and an event description text;
quantizing the node description to obtain a plurality of groups of node quantized values, and sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain a node vector;
constructing and obtaining the multidimensional task matrix according to the node vector corresponding to each task sequence node;
the step of sequencing the task processing node set execution nodes according to the task types of the task processing nodes to obtain a task sequence node set comprises the following steps:
and sequencing the execution of different task types to obtain a task priority chain, wherein the task priority chain sequentially comprises the following steps according to the sequence of the processed tasks: the first priority chain is a home medical service and an outdoor medical service, the second priority chain is a home care service and a resident parking service, and the third priority chain is a property public service, an environment remediation service and a community entertainment service;
sequencing the task processing nodes according to the task priority chain to obtain a task sequence node set;
acquiring all community managers available for use currently according to an intelligent community cloud platform, determining the grouping number of the personnel based on the number of lines of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the grouping number of the personnel to obtain one or more groups of community grouping personnel, wherein the grouping number of the personnel is smaller than or equal to the number of lines of the multidimensional task matrix;
Filling each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix;
executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises:
executing constant item folding operation on the task response matrix to obtain a task folding matrix;
acquiring all task history matrixes of the intelligent community cloud platform in a specified time period;
constructing a task operator set according to all the task history matrixes, wherein the task operator set comprises a plurality of task operators, and each task operator is different from each other;
calculating the difference value between the row vector of each row in the task folding matrix and each task operator, and determining the task operator with the minimum difference value as a reference operator;
if the difference value is 0, generating an index address of a reference operator, and replacing a corresponding row vector in the task folding matrix by the index address;
if the difference value is not 0, only the data which are different from the reference operator in the corresponding row vector are reserved;
until the row vector of each row in the task folding matrix completes the operation, obtaining a task optimization matrix;
and dispatching a corresponding community manager to process the task to be processed based on the task optimization matrix to complete community management.
2. The smart community cloud platform-based refined community management method as claimed in claim 1, wherein the type null vector representation method is as follows:
wherein, nodeE i j Representing the type null vector corresponding to the ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to the jth task type, c n Representing the nth column null parameter in the type null vector.
3. The smart community cloud platform-based refined community management method of claim 2, wherein the quantizing the node description to obtain a plurality of groups of node quantized values comprises:
converting the occurrence time in the node description into a standard time format to obtain standard occurrence time;
sequentially judging whether each participant in the node description belongs to a registrant of the intelligent community cloud platform;
if the participant does not belong to the registrant of the intelligent community cloud platform, marking the participant as a person with information to be confirmed;
if the participant belongs to a registrant of the intelligent community cloud platform, or the intelligent community cloud platform is utilized to collect information of the information to-be-confirmed person again, marking the participant or the information to-be-confirmed person as the information confirmed person;
determining whether a place in the node description belongs to a community range, if the place does not belong to the community range, marking the place as an out-of-jurisdiction place, and if the place belongs to the community range, marking the place as an in-jurisdiction place;
Removing stop words of event description texts in the node descriptions to obtain concise description texts;
and performing encryption operation on the standard occurrence time, the information confirmed person, the jurisdictional place or the jurisdictional place and the brief description text to obtain a plurality of groups of node quantized values, wherein the node quantized values comprise the encryption occurrence time, the encryption confirmed person, the encryption place or the encryption place and the encryption description text.
4. The smart community cloud platform-based refined community management method as claimed in claim 3, wherein the sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain the node vector comprises:
adding encryption occurrence time to a first column of null parameters of the null-type vector, and filling the encrypted confirmed person to a second column of null parameters of the null-type vector;
filling the encrypted outer place or the encrypted inner place into a third column space parameter of the type space vector;
determining the text length of the encryption description text, and if the text length is smaller than a text threshold value, directly filling the encryption description text into a fourth column null parameter of the type null vector;
if the text length is greater than or equal to the text threshold value, splitting the encryption description text to obtain a plurality of groups of encryption segmentation texts;
Filling each group of encrypted segmented texts into a fourth column, a fifth column, … and an nth column of null parameters of the type null vectors in sequence to obtain node vectors, wherein the node vectors are represented by the following steps:
nodeS i j ＝{p 1 ，p 2 ，p 3 ，…，p n }
wherein, modeS i j Representing a node vector corresponding to an ith task sequence node in the task sequence node set, wherein the ith task sequence node corresponds to a jth task type, p n A parameter indicating that the n-th column of the node vector has been filled with the node quantization value.
5. The smart community cloud platform-based refined community management method as claimed in claim 4, wherein the constructing the multidimensional task matrix according to the node vector corresponding to each task sequence node comprises:
executing priority labeling for each node vector according to the task priority chain to obtain a node sequence vector;
each node sequence vector is built according to the priority sequence to obtain a multi-dimensional task matrix, wherein the multi-dimensional task matrix is represented by the following steps:
wherein A represents a multidimensional task matrix, e-nodeS i o The method comprises the steps of representing a node sequence vector corresponding to an ith task sequence node comprising the number of the priority chains, e representing the number of the priority chains of the node sequence vector in the task priority chains, l representing the total number of the priority chains of the task priority chains, and l being smaller than or equal to m, j, o and r representing the number of the task type corresponding to the task sequence node.
6. The smart community cloud platform-based refined community management method as claimed in claim 5, wherein the determining the grouping number of people based on the number of rows of the multi-dimensional task matrix, and grouping all community management people currently available for use by using the grouping number of people to obtain one or more groups of community grouping people comprises:
determining the total number of people of all community managers currently available;
judging whether the total number of the personnel is larger than the number of rows of the multi-dimensional task matrix;
if the total number of the personnel is greater than or equal to the number of the lines of the multidimensional task matrix, grouping community management personnel according to the number of the lines of the multidimensional task matrix to obtain a plurality of groups of community grouping personnel, wherein the number of the groups of the community grouping personnel is the same as the number of the lines of the multidimensional task matrix;
if the total number of people is smaller than the number of lines of the Yu Duowei task matrix, determining the number of node vectors with the highest priority chain level in the multidimensional task matrix, and grouping community management personnel according to the number of the node vectors with the highest priority chain level to obtain a plurality of groups of community grouping personnel, wherein the number of the community grouping personnel is smaller than or equal to the number of lines of the multidimensional task matrix.
7. The smart community cloud platform-based refined community management method as claimed in claim 6, wherein the step of filling each group of community grouping personnel into a multidimensional task matrix to obtain a task response matrix comprises the steps of:
Constructing personnel information vectors of grouping personnel of each group of communities, wherein the personnel information vectors are represented by the following steps:
ManP t ＝{M 1 ，M 2 ，…，M h }
wherein, manP t Personnel information vector representing group personnel of group t community, M h Personnel information of an h community manager in the t group community grouping personnel is represented, wherein the personnel information comprises personnel names, sexes, current positions and contact modes;
filling the personnel information vector into a multidimensional task matrix to obtain a task response matrix, wherein the task response matrix is represented by the following steps:
b is a task response matrix corresponding to the number of the personnel groups equal to the number of the rows of the multidimensional task matrix, and e-nodeS i o -ManP t And the node sequence vector corresponding to the ith task sequence node with the priority chain number of e is represented, and the personnel information vector corresponding to the node sequence vector is processed.
8. An intelligent community cloud platform-based refined community management device, which is characterized by comprising:
the community management instruction receiving module is used for receiving a community management instruction of the intelligent community cloud platform, and identifying a task to be processed according to the community management instruction, wherein the task to be processed is initiated by a community user at an intelligent equipment end;
the multi-dimensional task matrix construction module is used for determining task types contained in the task to be processed, executing parameter description on the task to be processed according to the task types to obtain a multi-dimensional task matrix, wherein the determining of the task types contained in the task to be processed comprises the following steps:
Determining the processing flow of the task to be processed, and constructing a task processing node set according to the processing flow;
sequentially judging task types of each task processing node in the task processing node set, wherein the task types comprise property public service, home medical service, outdoor medical service, home care service, resident parking service, environment remediation service and community entertainment service;
the task to be processed is subjected to parameter description according to the task type to obtain a multidimensional task matrix, which comprises the following steps:
according to the task types of the task processing nodes, sequencing the task processing node sets to obtain task sequence node sets;
traversing each task sequence node from the task sequence node set in turn, and executing the following operations on each task sequence node:
generating a single-dimensional empty vector for each task processing node in turn, and executing marking operation for the single-dimensional empty vector based on the task type corresponding to each task processing node to obtain a type empty vector;
acquiring node description corresponding to a task processing node from a task to be processed, wherein the node description comprises an occurrence time, a participant, a place and an event description text;
Quantizing the node description to obtain a plurality of groups of node quantized values, and sequentially filling the plurality of groups of node quantized values into the type empty vector to obtain a node vector;
constructing and obtaining the multidimensional task matrix according to the node vector corresponding to each task sequence node;
the step of sequencing the task processing node set execution nodes according to the task types of the task processing nodes to obtain a task sequence node set comprises the following steps:
and sequencing the execution of different task types to obtain a task priority chain, wherein the task priority chain sequentially comprises the following steps according to the sequence of the processed tasks: the first priority chain is a home medical service and an outdoor medical service, the second priority chain is a home care service and a resident parking service, and the third priority chain is a property public service, an environment remediation service and a community entertainment service;
sequencing the task processing nodes according to the task priority chain to obtain a task sequence node set;
the manager grouping module is used for acquiring all community managers available for use currently according to the intelligent community cloud platform, determining the number of person groupings based on the number of rows of the multidimensional task matrix, and grouping all the community managers available for use currently by utilizing the number of person groupings to obtain one or more groups of community groupings, wherein the number of person groupings is smaller than or equal to the number of rows of the multidimensional task matrix;
The task response module is used for filling each group of community grouping personnel into the multidimensional task matrix to obtain a task response matrix, and executing data optimization processing on the task response matrix to obtain a task optimization matrix, wherein the data optimization processing comprises the following steps: performing constant item folding operation on the task response matrix to obtain a task folding matrix, obtaining all task history matrixes of the intelligent community cloud platform within a specified time period, and constructing a task calculation subset according to all the task history matrixes, wherein a plurality of task operators are included in a task operator set, each task operator is different from each other, a difference value between a row vector of each row in the task folding matrix and each task operator is calculated, the task operator with the minimum difference value is determined to be a reference operator, if the difference value is 0, an index address of the reference operator is generated, the corresponding row vector in the task folding matrix is replaced by the index address, if the difference value is not 0, only data which are different from the reference operator in the corresponding row vector is reserved until the row vector of each row in the task folding matrix is completed, a task optimization matrix is obtained, and community manager corresponding to dispatch the task to be processed based on the task optimization matrix to complete community management.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109840619A (en) * 2018-12-29 2019-06-04 罗海洋 A kind of express delivery network optimized approach with community structure and daughter nucleus
KR20200082338A (en) * 2018-12-28 2020-07-08 주식회사 데이터몬스터즈 Community Point Market Place System for Allocation and Management of Resources
CN112380425A (en) * 2020-10-23 2021-02-19 华南理工大学 Community recommendation method, system, computer equipment and storage medium
CN114357312A (en) * 2022-03-16 2022-04-15 中南大学 Community discovery method and personality recommendation method based on automatic modeling of graph neural network
CN115271635A (en) * 2022-06-29 2022-11-01 深圳市双银科技有限公司 Intelligent community service platform system
CN115330056A (en) * 2022-08-12 2022-11-11 重庆邮电大学 Topic network influence user prediction method based on deep propagation and breadth propagation

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7186149B2 (en) * 2019-08-29 2022-12-08 株式会社日立製作所 RESOURCE MANAGEMENT SYSTEM, INFORMATION PROCESSING DEVICE, AND RESOURCE MANAGEMENT METHOD

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200082338A (en) * 2018-12-28 2020-07-08 주식회사 데이터몬스터즈 Community Point Market Place System for Allocation and Management of Resources
CN109840619A (en) * 2018-12-29 2019-06-04 罗海洋 A kind of express delivery network optimized approach with community structure and daughter nucleus
CN112380425A (en) * 2020-10-23 2021-02-19 华南理工大学 Community recommendation method, system, computer equipment and storage medium
CN114357312A (en) * 2022-03-16 2022-04-15 中南大学 Community discovery method and personality recommendation method based on automatic modeling of graph neural network
CN115271635A (en) * 2022-06-29 2022-11-01 深圳市双银科技有限公司 Intelligent community service platform system
CN115330056A (en) * 2022-08-12 2022-11-11 重庆邮电大学 Topic network influence user prediction method based on deep propagation and breadth propagation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于双曲空间嵌入的极小值社区划分算法;谢菁;羿舒文;张毅;;计算机工程(06);全文 *

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