CN110807546A - Community grid population change early warning method and system - Google Patents
Community grid population change early warning method and system Download PDFInfo
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- CN110807546A CN110807546A CN201911006158.9A CN201911006158A CN110807546A CN 110807546 A CN110807546 A CN 110807546A CN 201911006158 A CN201911006158 A CN 201911006158A CN 110807546 A CN110807546 A CN 110807546A
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Abstract
The invention provides a community grid population change early warning method, which comprises the following steps: acquiring a service data set uploaded by each community grid, and performing cluster analysis on energy consumption data contained in the service data set to obtain a population structure model; setting buried points for each grid resident of a community to be detected, wherein the buried points acquire monthly energy consumption data of corresponding residents, and the monthly energy consumption data are imported into the population structure model to obtain the population structure of the corresponding residents; and comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt. The community grid population change early warning method provided by the embodiment of the invention overcomes the defect of large time cost in the conventional census mode.
Description
Technical Field
The embodiment of the invention relates to the field of data early warning, in particular to a method and a system for early warning of community grid population change.
Background
At present, population registration is always a key work of community and even public security management, which affects regional safety and related governance, then in the actual population registration process, grid managers of the community, namely street management personnel, need to check information of each resident in the grid every month or half a month, and in the checking process, various problems such as no door of the resident, no home for pretending, low information checking efficiency, too long time consumption, and how to carry out targeted population checking are one of the problems which need to be solved urgently at present.
Disclosure of Invention
In order to solve the above problems, an embodiment of the present invention provides a method for warning a change in a community grid population, including the following steps:
acquiring a service data set uploaded by each community grid, and performing cluster analysis on energy consumption data contained in the service data set to obtain a population structure model;
setting buried points for each grid resident of a community to be detected, wherein the buried points acquire monthly energy consumption data of corresponding residents, and the monthly energy consumption data are imported into the population structure model to obtain the population structure of the corresponding residents;
and comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt.
Preferably, the energy consumption data includes monthly electricity consumption, monthly water consumption and monthly natural gas consumption.
Preferably, the step of performing cluster analysis on the energy consumption data included in the service data set to obtain a population structure model includes:
respectively taking monthly electricity consumption, monthly water consumption and monthly natural gas consumption in the energy consumption data as coordinate axes to generate a three-dimensional coordinate system, and converting each element in the energy consumption data into a three-dimensional sample coordinate point;
dividing each sample coordinate point into five clusters by using a clustering algorithm, and continuously performing iterative operation until the iterative difference of each cluster data accords with a preset threshold, wherein the clusters correspond to different population structures;
and forming a population structure model according to each cluster and the corresponding coordinate point set thereof.
Preferably, the step of dividing each sample coordinate point into five clusters by using a clustering algorithm and continuously performing iterative operation until the iterative difference of each cluster data meets a preset threshold includes:
randomly selecting five sample points as a mass center, and calculating the distances of the rest sample points relative to the mass center;
dividing each sample point into clusters to which centroids closest to each other belong to obtain five clusters;
and adjusting a certain centroid coordinate to serve as one iteration, recalculating the cluster group and updating until the iteration difference of each cluster data meets a preset threshold value.
Recalculating the cluster group to generate a new cluster group;
detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the last iteration, wherein if the number of the non-identical elements is less than one, the iteration difference of each cluster data accords with a preset threshold value.
Preferably, the detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the last iteration, and if the number of non-identical elements is less than one, after the step of the iteration difference of each cluster data meeting a preset threshold, further includes:
traversing all elements in each cluster to obtain the minimum element of each cluster group;
calculating the sum of three coordinate values of the minimum elements of each cluster group, sequencing the minimum elements according to the calculated sum value, and sequencing and mapping according to the cluster group to which each minimum element belongs to obtain a cluster group sequence;
and assigning corresponding population structure labels to the cluster sequences, and performing associated storage on the population structure labels and the corresponding clusters to form a population structure model.
Preferably, the step of setting a burial point for each grid resident of the community to be detected, the burial point acquiring monthly energy consumption data of the corresponding resident, importing the monthly energy consumption data into the population structure model, and obtaining the population structure of the corresponding resident includes:
the method comprises the steps that a point burying program is correspondingly arranged for each resident in each grid of a community to be detected, and the point burying program periodically calls a preset interface to pull the monthly energy consumption data of the corresponding resident;
and matching the monthly energy consumption data with each model in the population structure model, and if the monthly energy consumption data belongs to a numerical value interval contained in one model, judging that the model is the attributive model of the monthly energy consumption data.
Preferably, the step of comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by calculation in the previous month, and if the value changes, sending an early warning message to the grid manager for early warning prompt includes:
pulling the resident population structure obtained by calculation in the previous month from the server;
and comparing the calculated population structure of the resident with the population structure of the resident calculated in the previous month, and if the numerical value changes, sending a preset message text to the grid management personnel along the registered network address for early warning prompt.
The embodiment of the invention also provides a community grid population change early warning system, which comprises:
the analysis module is used for acquiring the service data sets uploaded by the community grids, and performing cluster analysis on the energy consumption data contained in the service data sets to obtain a population structure model;
the system comprises a buried point module, a population structure model and a detection module, wherein the buried point module is used for setting buried points for each grid resident of a community to be detected, the buried points acquire monthly energy consumption data of corresponding residents, and the monthly energy consumption data are imported into the population structure model to obtain the population structure of the corresponding residents;
and the early warning module is used for comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt. The embodiment of the present invention further provides a computer device, where the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is implemented by the processor to implement the method for warning the change in the population of the community grid as described above.
Embodiments of the present invention further provide a computer storage medium, in which a computer program is stored, where the computer program is executable by at least one processor, so as to cause the at least one processor to execute the steps of the community grid demographic change warning method as described above.
The community grid population change early warning method provided by the embodiment of the invention can detect the grid residents with changed population structures through the energy use condition of each household in the community grid and the preset corresponding model of the population structures and the energy use, and further send early warning messages to a grid manager to remind the grid manager to check the resident information at home, thereby overcoming the defect of large time cost in the conventional general survey mode.
Drawings
FIG. 1 is a flow chart illustrating steps of a community grid population change warning method according to the present invention;
FIG. 2 is a schematic diagram of a program module of a community grid population change warning system according to the present invention;
fig. 3 is a schematic diagram of a hardware structure of the computer device of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that although the terms first, second, etc. may be used to describe the designated key in embodiments of the present invention, the designated key should not be limited to these terms. These terms are only used to distinguish specified keywords from each other. For example, the first specified keyword may also be referred to as the second specified keyword, and similarly, the second specified keyword may also be referred to as the first specified keyword, without departing from the scope of embodiments of the present invention.
The word "if" as used herein may be interpreted as referring to "at … …" or "when … …" or "corresponding to a determination" or "in response to a detection", depending on the context. Similarly, the phrase "if determined" or "if detected (a stated condition or time)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
Referring to fig. 1, an embodiment of the present invention provides a method for warning a change in a community grid population, including:
step S100, acquiring a service data set uploaded by each community grid, and performing cluster analysis on energy consumption data contained in the service data set to obtain a population structure model.
Specifically, the community grid is a management node of each community, is a regional division performed according to geographic environment, population situation and the like in a specific city community range, and is a basic unit in the whole community grid management system. Once the grid is defined and covered to the whole city community, the setting of the grid and the management content thereof are fixed. Therefore, the grid in the urban community is a physical grid, and is divided to replace natural area boundaries or artificial administrative divisions, so that the fine glass scraping is realized in a smaller regional range, the management object is clear, and different management subjects do not use the whole urban community as a working range but manage and provide services according to different grid requirements.
A community grid is composed of divided households, and illustratively, the whole golden garden is divided into a grid, 2451 households are arranged under the grid, and the grid is composed of 2451 households.
The business data set is a population structure and energy usage data, wherein the population structure comprises population number or population composition of a household. Exemplary, the flower garden, B, No. 204, population 5, 2 middle-aged people, 2 young people, 1 child. The energy use data in 7 months is 520 degrees of monthly electricity, 25 cubic months of monthly water and 70 square months of monthly natural gas.
The data is only data of one family, usually the service interface uploads a service data set or an array of the whole cell, generally in a database file form or directly in an excel file form in a windows system, and the following examples are given:
TABLE 1
Related personnel upload data such as the data in the table 1 on the business interface, so that a population structure data table with a large base number is obtained, more than thirty million households of population structure data are covered in terms of a constant community, the data are used as original data before cluster analysis, and the finally obtained population structure model is more accurate due to the large base number.
With the total sample data of more than thirty million of the permanent community, the sample data of more than three hundred million is subjected to cluster analysis, 5 classes are preferably set in the embodiment of the invention, the number of the residents is represented by 1-2 population, 3-4 population, 4-5 population, 5-6 population and more than 7 population, and the population number of the residents is also represented by the population of a common family, so that 5 cluster group data are finally obtained, the 5 cluster group data are used as a final population structure model for carrying out population analysis on a newly-built community, namely a community to be matched, and therefore, the invasion of the privacy of the residents and the labor cost registered by the property personnel one by one are avoided, and the population number prediction of each household of one community can be completed.
Step S200, setting buried points for each grid resident of the community to be detected, wherein the buried points acquire monthly energy consumption data of corresponding residents, and importing the monthly energy consumption data into the population structure model to obtain the population structure of the corresponding residents.
Specifically, each grid resident of the community to be detected is provided with a buried point, one or more computer devices can be configured, each resident is provided with a buried point program, one resident corresponds to one buried point program, and the buried point program is used for pulling monthly energy consumption of monitored residents corresponding to the buried point program to interfaces provided by district hydropower offices and district natural gas service providers, for example, and leading the pulled data into the population structure model, so as to output and obtain the population structure of the resident calculated by the month.
Step S300 compares the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the value changes, sends an early warning message to the grid manager for early warning prompt.
Specifically, following step S200, after the resident population structure calculated in this month is output, the stored resident population structure in the previous month is pulled. Because the preferred population structure calculation cycle of the present invention is once a month, the population structure comparison is performed by storing the calculation results each time, and the storage path may be a local device or a remote server, which is not limited in the present invention.
And the burdening program draws the calculation result of the population structure of the previous month, compares the calculation of the population structure of the previous month with the population structure obtained by calculation of the current month, judges that the population structure is changed if the numerical values are different, and sends a preset message text to a grid manager.
Illustratively, if the analysis result of the last month of the user in the last month grid golden Bill garden 203 is 1-2 population, and the calculation result of the current month is 3-4 population, and the values of the two are different, the population structure is determined to be changed.
The community grid population change early warning method provided by the embodiment of the invention can detect the grid residents with changed population structures through the energy use condition of each household in the community grid and the preset corresponding model of the population structures and the energy use, and further send early warning messages to a grid manager to remind the grid manager to check the resident information at home, thereby overcoming the defect of large time cost in the conventional general survey mode
Optionally, the energy consumption data includes monthly electricity consumption, monthly water consumption and monthly natural gas consumption.
The energy consumption data may be data in units of years or in quarterly, and the present embodiment is preferably monthly data not including holidays.
Optionally, the step of performing cluster analysis on the energy consumption data included in the service data set to obtain a population structure model includes:
step S110 is to generate a three-dimensional coordinate system by using monthly electricity usage, monthly water usage, and monthly natural gas usage in the energy usage data as coordinate axes, and convert each element in the energy usage data into a three-dimensional sample coordinate point.
Specifically, the energy consumption data includes monthly electricity consumption. The monthly water usage amount and the monthly natural gas amount are represented by the coordinate axes, which may be arbitrary, for example, the x axis may be the monthly water usage point or the monthly water usage amount.
Step S120, dividing each sample coordinate point into five clusters by using a clustering algorithm, and continuously performing iterative operation until the iterative difference of each cluster data accords with a preset threshold, wherein the clusters correspond to different population structures.
Specifically, in the embodiment of the present invention, it is preferable that the k value of the clustering algorithm is five, that is, it is desirable that 5 sets are obtained by clustering the data set, where the sets are clusters, and the text uses the clusters to perform literal identification of the sample set. And then, clustering and iterating all the sample points until the difference between the set in each cluster and each element in the cluster generated by the last iteration meets a preset threshold value.
In addition, each cluster corresponds to different population structures, wherein the population structures are 1-2 population, 3-4 population, 4-5 population, 5-6 population and more than 7 population.
Step S130 forms a population structure model according to each cluster and its corresponding coordinate point set.
Specifically, after the iteration is completed, the maximum value and the minimum value in the generated five clusters are analyzed, and a data interval is recorded, which is exemplarily as follows:
(1-2 persons) Cluster A (No. 204, monthly electricity consumption 100; water consumption 10; natural gas consumption 10)
No. B, No. 209, monthly electricity consumption of 70, water consumption of 12 and natural gas consumption of 11
Number B, electricity consumption in month of 90, water consumption of 7 and natural gas consumption of 2
The population structure model of 1-2 people is characterized as:
the monthly electricity consumption is 70-100
The monthly water consumption is 7-11
The amount of the natural gas used in the month is 2-11
The above is a form of data that uses numerical intervals to characterize population 1-2 people.
Optionally, in step S120, the step of dividing each sample coordinate point into five clusters by using a clustering algorithm, and continuously performing iterative operation until an iterative difference of each cluster data meets a preset threshold includes:
step S121 randomly selects five sample points as a centroid, and calculates the distances of the other sample points relative to the centroid.
Specifically, the centroid is a clustering center, five sample points are randomly selected as an initial clustering center, the distance from each sample point to each clustering center is calculated, and the class of the clustering center where the data object track is closest to the data object track is located.
Step S122 divides each sample point into clusters to which centroids closest to each other belong, to obtain five clusters.
Step S123 uses a certain centroid coordinate to perform adjustment processing as an iteration, and recalculates and updates the cluster until the iteration difference of each cluster data meets the preset threshold.
Continuing with the explanatory description of the step S121, after the classification is completed, a new centroid is selected again for the classified sample cluster, and the clustering step is repeatedly performed.
Optionally, the step S123 of recalculating and updating the cluster group until the iteration difference of each cluster data meets the preset threshold includes:
step S123A recalculates the cluster, and generates a new cluster.
Step S123B detects the number of non-identical elements of the newly generated cluster and the cluster generated in the previous iteration, and if the number of non-identical elements is less than one, the iteration difference of each cluster data meets a preset threshold.
Specifically, whether the classification of each sample is correct or not is examined in each iteration, if not, adjustment is needed, if all data objects are correctly classified in one iteration, no adjustment is needed, and no change is found in the clustering center, which indicates that convergence is completed and the calculation is finished.
Optionally, the step S123B, after the step of detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the previous iteration, and if the number of non-identical elements is less than one, the iteration difference of each cluster data meets a preset threshold, further includes:
step S123B-1, traversing all elements in each cluster to obtain the minimum element of each cluster group;
step S123B-2, the sum of three coordinate values of the minimum elements of each cluster is calculated, the minimum elements are sorted according to the calculated sum, and sorting mapping is carried out according to the cluster to which each minimum element belongs, so that a cluster sequence is obtained.
And assigning corresponding population structure labels to the cluster sequences, and performing associated storage on the population structure labels and the corresponding clusters to form a population structure model.
Specifically, after the clusters are generated, there is no actual definition or label, and thus only five clusters are generated, and these five clusters do not represent any significance, so that the data interval of each cluster is sorted, and each cluster is defined according to the rank of each cluster, and the definitions are defined as five definitions, namely 1-2 population, 3-4 population, 4-5 population, 5-6 population, and 7 population. The order bits are generated by comparing the minimum values of the data in each cluster interval.
Optionally, the step of setting a burial point for each grid resident of the community to be detected, the burial point acquiring monthly energy consumption data of the corresponding resident, importing the monthly energy consumption data into the population structure model, and obtaining the population structure of the corresponding resident includes:
and correspondingly setting a point burying program for each resident in each grid of the community to be detected, wherein the point burying program periodically calls a preset interface to pull the monthly energy consumption data of the corresponding resident.
And matching the monthly energy consumption data with each model in the population structure model, and if the monthly energy consumption data belongs to a numerical value interval contained in one model, judging that the model is the attributive model of the monthly energy consumption data.
Optionally, the step of comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning and prompting includes:
pulling the resident population structure obtained by calculation in the previous month from the server;
and comparing the calculated population structure of the resident with the population structure of the resident calculated in the previous month, and if the numerical value changes, sending a preset message text to the grid management personnel along the registered network address for early warning prompt.
The embodiment of the invention also provides a community grid population change early warning system, which comprises:
the analysis module 100 is configured to obtain a service data set uploaded by each community grid, and perform cluster analysis on energy consumption data included in the service data set to obtain a population structure model.
And the point burying module 200 is configured to set a buried point for each grid resident of the community to be detected, where the buried point obtains monthly energy consumption data of a corresponding resident, and the monthly energy consumption data is imported into the population structure model to obtain the population structure of the corresponding resident.
The early warning module 300 is configured to compare the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the value changes, send an early warning message to a grid manager to perform early warning prompt
Optionally, the energy consumption data in the analysis module includes monthly electricity consumption, monthly water consumption and monthly natural gas consumption.
Optionally, the analysis module 100 comprises
A coordinate unit 110, configured to generate a three-dimensional coordinate system by using monthly electricity consumption, monthly water consumption, and monthly natural gas consumption in the energy consumption data as coordinate axes, and convert each element in the energy consumption data into a three-dimensional sample coordinate point.
The clustering unit 120 is configured to divide each sample coordinate point into five clusters by using a clustering algorithm, and continuously perform iterative operation until an iterative difference of each cluster data meets a preset threshold, where the clusters correspond to different population structures;
and a model unit 130, configured to form a population structure model according to each cluster and its corresponding coordinate point set.
Optionally, the clustering unit 120 further includes:
a distance unit 121, configured to randomly select five sample points as a centroid, and calculate distances of the remaining sample points with respect to the centroid;
the dividing unit 122 is configured to divide each sample point into clusters to which centroids closest to each other belong, so as to obtain five clusters;
and the iteration unit 123 is configured to perform adjustment processing on a certain centroid coordinate as one iteration, recalculate the cluster group, and update the cluster group until the iteration difference of each cluster data meets a preset threshold.
Optionally, the iteration unit 123 is used for
Recalculating the cluster group to generate a new cluster group;
detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the last iteration, wherein if the number of the non-identical elements is less than one, the iteration difference of each cluster data accords with a preset threshold value.
Optionally, the iteration unit 123 is further configured to:
traversing all elements in each cluster to obtain the minimum element of each cluster group;
calculating the sum of three coordinate values of the minimum elements of each cluster group, sequencing the minimum elements according to the calculated sum value, and sequencing and mapping according to the cluster group to which each minimum element belongs to obtain a cluster group sequence;
and assigning corresponding population structure labels to the cluster sequences, and performing associated storage on the population structure labels and the corresponding clusters to form a population structure model.
Please refer to fig. 3, which is a schematic diagram of a hardware architecture of a computer device according to an embodiment of the present invention. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a preset or stored instruction. The computer device 2 may be a personal computer, a tablet computer, a mobile phone, a smartphone, or a rack server, a blade server, a tower server, or a cabinet server (including an independent server or a server cluster composed of a plurality of servers), and the like, and is configured to provide a virtual client. As shown, the computer device 2 includes, but is not limited to, at least a memory 21, a processor 22, a network interface 23, and a community grid demographic change warning system 20, which are communicatively connected to each other via a system bus, wherein:
in this embodiment, the memory 21 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the storage 21 may be an internal storage unit of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 21 may also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (Secure Digital) SD Card, a Flash memory Card (Flash Card), etc. provided on the computer device 20, and of course, the memory 21 may also include both an internal storage unit and an external storage device of the computer device 2. In this embodiment, the memory 21 is used for storing an operating system installed in the computer device 2 and various application software, such as a program code of the community grid population change warning system 20. Further, the memory 21 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 2. In this embodiment, the processor 22 is configured to execute the program code stored in the memory 21 or process data, for example, execute the community grid population change warning system 20, so as to implement the community grid population change warning method.
The network interface 23 may comprise a wireless network interface or a limited network interface, and the network interface 23 is typically used for establishing a communication connection between the computer device 2 and other electronic apparatuses. For example, the network interface 23 is used to connect the computer device 2 with an external terminal necklace, establish a data transmission channel and a communication connection between the computer device 2 and an external interrupt, and the like via a network. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
In this embodiment, the community grid demographic change warning system 20 stored in the memory 21 may be further divided into one or more program modules, and the one or more program modules are stored in the memory 21 and executed by one or more processors (in this embodiment, the processor 22) to complete the present invention.
In addition, the present embodiment also provides a computer-readable storage medium, such as a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor implements a corresponding function. The computer-readable storage medium of the embodiment is used for the community grid population change early-warning system 20, and when being executed by a processor, the computer-readable storage medium implements the community grid population change early-warning method of the invention.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A community grid population change early warning method is characterized by comprising the following steps:
acquiring a service data set uploaded by each community grid, and performing cluster analysis on energy consumption data contained in the service data set to obtain a population structure model;
setting buried points for each grid resident of a community to be detected, wherein the buried points acquire monthly energy consumption data of corresponding residents, and the monthly energy consumption data are imported into the population structure model to obtain the population structure of the corresponding residents;
and comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt.
2. The community grid population change early warning method as claimed in claim 1, wherein the energy usage data comprises monthly electricity usage, monthly water usage and monthly natural gas usage.
3. The community grid population change early warning method according to claim 2, wherein the step of performing cluster analysis on the energy consumption data contained in the service data set to obtain a population structure model comprises:
respectively taking monthly electricity consumption, monthly water consumption and monthly natural gas consumption in the energy consumption data as coordinate axes to generate a three-dimensional coordinate system, and converting each element in the energy consumption data into a three-dimensional sample coordinate point;
dividing each sample coordinate point into five clusters by using a clustering algorithm, and continuously performing iterative operation until the iterative difference of each cluster data accords with a preset threshold, wherein the clusters correspond to different population structures;
and forming a population structure model according to each cluster and the corresponding coordinate point set thereof.
4. The community grid population change early warning method according to claim 3, wherein the step of dividing each sample coordinate point into five clusters by using a clustering algorithm and continuously performing iterative operation until the iterative difference of each cluster data meets a preset threshold value comprises:
randomly selecting five sample points as a mass center, and calculating the distances of the rest sample points relative to the mass center;
dividing each sample point into clusters to which centroids closest to each other belong to obtain five clusters;
and adjusting a certain centroid coordinate to serve as one iteration, recalculating the cluster group and updating until the iteration difference of each cluster data meets a preset threshold value.
Recalculating the cluster group to generate a new cluster group;
detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the last iteration, wherein if the number of the non-identical elements is less than one, the iteration difference of each cluster data accords with a preset threshold value.
5. The community grid population change early warning method according to claim 4, wherein the step of detecting the number of non-identical elements of the newly generated cluster and the cluster generated in the last iteration, and if the number of non-identical elements is less than one, the iteration difference of each cluster data is in accordance with a preset threshold value, further comprises:
traversing all elements in each cluster to obtain the minimum element of each cluster group;
calculating the sum of three coordinate values of the minimum elements of each cluster group, sequencing the minimum elements according to the calculated sum value, and sequencing and mapping according to the cluster group to which each minimum element belongs to obtain a cluster group sequence;
and assigning corresponding population structure labels to the cluster sequences, and performing associated storage on the population structure labels and the corresponding clusters to form a population structure model.
6. The community grid population change early warning method according to claim 1, wherein the step of setting a buried point for each grid resident of the community to be detected, the buried point acquiring monthly energy usage data of the corresponding resident, importing the monthly energy usage data into the population structure model, and acquiring the population structure of the corresponding resident comprises the steps of:
the method comprises the steps that a point burying program is correspondingly arranged for each resident in each grid of a community to be detected, and the point burying program periodically calls a preset interface to pull the monthly energy consumption data of the corresponding resident;
and matching the monthly energy consumption data with each model in the population structure model, and if the monthly energy consumption data belongs to a numerical value interval contained in one model, judging that the model is the attributive model of the monthly energy consumption data.
7. The community grid population change early warning method as claimed in claim 1, wherein the step of comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt comprises:
pulling the resident population structure obtained by calculation in the previous month from the server;
and comparing the calculated population structure of the resident with the population structure of the resident calculated in the previous month, and if the numerical value changes, sending a preset message text to the grid management personnel along the registered network address for early warning prompt.
8. A community grid population change early warning system, comprising:
the analysis module is used for acquiring the service data sets uploaded by the community grids, and performing cluster analysis on the energy consumption data contained in the service data sets to obtain a population structure model;
the system comprises a buried point module, a population structure model and a detection module, wherein the buried point module is used for setting buried points for each grid resident of a community to be detected, the buried points acquire monthly energy consumption data of corresponding residents, and the monthly energy consumption data are imported into the population structure model to obtain the population structure of the corresponding residents;
and the early warning module is used for comparing the population structure of the resident obtained by the calculation with the population structure of the resident obtained by the calculation in the previous month, and if the numerical value changes, sending an early warning message to a grid manager for early warning prompt.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the community grid demographic change warning method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program executable by at least one processor to cause the at least one processor to perform the steps of the community grid demographic change warning method as claimed in any one of claims 1 to 7.
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Application publication date: 20200218 |