CN116070781B - Electric energy metering equipment demand prediction method and device and computer equipment - Google Patents
Electric energy metering equipment demand prediction method and device and computer equipment Download PDFInfo
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- CN116070781B CN116070781B CN202310201449.3A CN202310201449A CN116070781B CN 116070781 B CN116070781 B CN 116070781B CN 202310201449 A CN202310201449 A CN 202310201449A CN 116070781 B CN116070781 B CN 116070781B
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The application relates to a method and a device for predicting demand of electric energy metering equipment and computer equipment, and relates to the technical field of computers. The method comprises the following steps: determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type; acquiring equipment demand information of a first demand type, and acquiring actual influence information of a second demand type under preset demand influence factors in a prediction time interval and/or a historical time period before the prediction time interval; determining a first equipment demand of a first demand type in a predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in a predicted time interval according to the actual influence information corresponding to the second demand type; and obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand. By adopting the method, accurate demand pre-measurement of the electric energy metering equipment can be obtained.
Description
Technical Field
The present invention relates to the field of computer technology, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for predicting demand of an electric energy metering device.
Background
At present, the demand management of the electric energy metering equipment of each power grid company is mainly that the electric energy metering equipment demand of each city power supply company or power supply institute is predicted to form a demand plan, and the equipment distribution is completed after the electric energy metering equipment demand is reported step by step and approved and balanced by a provincial metering center. Therefore, the accuracy of demand prediction of the electric energy metering equipment directly influences the working quality and efficiency of links such as purchasing, distribution, installation and the like.
In the traditional method, estimation is mainly performed based on annual installation quantity and by means of manual experience. However, the method has randomness and blindness, the demand quantity prediction accuracy is not high, and multi-level summarizing, auditing and reporting are needed, the flow time is long, time and labor are consumed, and the reported demand and the actual demand are easy to generate time difference, so that the cost of purchasing, distributing and storing the follow-up electric energy metering equipment is increased.
Disclosure of Invention
Based on this, there is a need to provide an electric energy metering device demand prediction method, an apparatus, a computer device, a computer readable storage medium and a computer program product for the technical problem of inaccurate electric energy metering device demand prediction.
In a first aspect, the present application provides a method for demand prediction for an electric energy metering device. The method comprises the following steps:
Determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type;
acquiring equipment demand information of the first demand type, and acquiring actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or a historical time period before the prediction time interval;
determining a first equipment demand of the first demand type in the predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
and obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
In one embodiment, the obtaining the device requirement information of the first requirement type includes:
acquiring a current demand work order of the first demand type; the current demand work orders comprise a plurality of work orders;
determining the current processing link of each demand work order according to the current demand work order;
Determining a time point of the completion work order corresponding to each required work order according to the current processing link;
and taking the current requirement work order and the time point of completing the work order as the equipment requirement information of the first requirement type.
In one embodiment, the determining, according to the current processing link, a corresponding completion work order time point when each of the required work orders completes the requirement includes:
acquiring average historical processing time length from each processing link to the time point of completing the work order;
and determining a corresponding time point of the completion work order when each required work order completes the requirement according to the current processing link and the corresponding average historical processing time.
In one embodiment, the determining, according to the device requirement information, a first device requirement amount of the first requirement type in the predicted time interval includes:
screening target demand work orders of the finishing work order time point in the prediction time interval from the current demand work orders;
and determining a first equipment demand of the first demand type in the predicted time interval according to the target demand work order.
In one embodiment, the second demand type includes a device replacement demand type; the requirement influence factors corresponding to the equipment replacement requirement types are equipment periodic replacement plans; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
And determining a second equipment demand of the equipment replacement demand type in the predicted time interval according to an actual equipment periodic replacement plan in the predicted time interval corresponding to the equipment replacement demand type.
In one embodiment, the second demand type further includes a sporadic traffic demand type; the demand influence factors corresponding to the scattered service demand types are scattered service statistical information; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
and inputting the actual scattered business statistical information in the predicted time interval and the historical time interval corresponding to the scattered business demand type into a scattered business demand prediction model to obtain the second equipment demand of the scattered business demand type in the predicted time interval.
In one embodiment, the second demand type further comprises a device failure demand type; the requirement influence factors corresponding to the equipment fault requirement types are equipment fault conditions with different working time durations; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
Acquiring the number of devices still working at the current moment, and taking the number of devices still working in the prediction time interval as the number of devices still working in the prediction time interval;
determining the accumulated working time length when each device still working is working to the predicted time interval;
and determining a second equipment demand of the equipment fault demand type in the predicted time interval according to the equipment quantity, the accumulated working time and the equipment fault conditions of different working time periods in the historical time period corresponding to the equipment fault demand type.
In a second aspect, the present application further provides a device for predicting a demand of an electric energy metering device. The device comprises:
the demand type determining module is used for determining the equipment demand type in the prediction time interval; the device demand type includes a first demand type and a second demand type;
the information acquisition module is used for acquiring the equipment demand information of the first demand type and acquiring the actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or in a historical time period before the prediction time interval;
the demand prediction module is used for determining a first equipment demand of the first demand type in the prediction time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
And the total demand determining module is used for obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type;
acquiring equipment demand information of the first demand type, and acquiring actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or a historical time period before the prediction time interval;
determining a first equipment demand of the first demand type in the predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
and obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type;
acquiring equipment demand information of the first demand type, and acquiring actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or a historical time period before the prediction time interval;
determining a first equipment demand of the first demand type in the predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
and obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
Determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type;
acquiring equipment demand information of the first demand type, and acquiring actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or a historical time period before the prediction time interval;
determining a first equipment demand of the first demand type in the predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
and obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
According to the electric energy metering equipment demand prediction method, the electric energy metering equipment demand prediction device, the computer equipment, the storage medium and the computer program product, equipment demand amounts of different equipment demand types in a prediction time interval are predicted by distinguishing different equipment demand types and pertinently acquiring corresponding influence factor information. Thus, an accurate equipment demand quantity prediction result is obtained, the use efficiency of equipment is improved, and the management cost of the equipment is reduced.
Drawings
FIG. 1 is a flow chart of a method for demand prediction of an electric energy metering device according to one embodiment;
FIG. 2 is a flowchart illustrating a first device requirement information obtaining step of a first requirement type according to one embodiment;
FIG. 3 is a flow diagram of a second device demand determination step of a device failure demand type in one embodiment;
FIG. 4 is a schematic diagram illustrating a complete flow of a method for demand prediction of an electrical energy metering device according to another embodiment;
FIG. 5 is a block diagram of an electrical energy metering device demand prediction apparatus in one embodiment;
fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for predicting demand of an electric energy metering device is provided, where the method is applied to a terminal for illustrating, it is understood that the method may also be applied to a server, and may also be applied to a system including the terminal and the server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the steps of:
Step 101, determining the equipment requirement type in a prediction time interval; the device demand type includes a first demand type and a second demand type.
Wherein the predicted time interval may be a month or a quarter, etc.
Wherein the first demand type may be a bulk business demand type; the second demand type includes a device replacement demand type, a sporadic business demand type, and a device failure demand type.
It should be noted that the first requirement type is a requirement type with a long processing period; the second demand type is a demand type with a short processing cycle.
The user determines a predicted time interval for predicting the equipment demand according to the actual demand, which may be the next month or the next quarter; and determining the equipment demand type for electric energy metering equipment demand prediction in the prediction time interval according to the actual demand.
Step 102, obtaining device demand information of a first demand type, and obtaining actual influence information of a second demand type under a preset demand influence factor in a predicted time interval and/or a historical time period before the predicted time interval.
Wherein the historical time period may be a historical contemporaneous time interval of the predicted time interval. For example, the predicted time interval is 11 months of the year, and the historical time period may be 11 months of the last year.
Illustratively, because the first demand type is a demand type with a long processing period, a prediction of demand completion time based on the current actual device demand of the first demand type is required; and the second demand type is a demand type with a short processing period, and the current actual equipment demand of the second demand type can be processed and completed quickly, so that the prediction of the demand completion time is not needed, but the prediction of the demand is performed. Thus, for a first demand type, it may be that device demand information associated with the current actual device demand is obtained from a business (or marketing) system; for the second demand type, demand influencing factors associated with the predicted time interval and/or the historical time period may be obtained from user input or database queries.
Step 103, determining a first equipment demand of a first demand type in a predicted time interval according to the equipment demand information; and determining the second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type.
Illustratively, according to the equipment demand information associated with the current actual equipment demands, obtaining a demand prediction completion time of each current actual demand of the first demand type, and determining a first equipment demand in a prediction time interval based on the demand prediction completion time of each current actual demand; and determining the second equipment demand of the second demand type in the predicted time interval according to the actual influence information under the demand influence factors related to the demand by a method corresponding to the second demand type.
Step 104, obtaining the total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand.
The first device demand obtained in the above steps is added to the second device demand to obtain a total device demand in a predicted time interval, and the total device demand is displayed to a user to help the user to apply for purchase of the device demand in advance, and the like. It should be noted that, the first device demand and the second device demand may be obtained by respectively counting devices of different categories, or may be obtained by counting the total amount of devices of all categories.
In the electric energy metering equipment demand prediction method, the equipment demand of different equipment demand types in a prediction time interval is predicted by distinguishing the different equipment demand types and pertinently acquiring corresponding influence factor information. Thus, an accurate equipment demand quantity prediction result is obtained, the use efficiency of equipment is improved, and the management cost of the equipment is reduced.
In one embodiment, as shown in fig. 2, the step 102 of obtaining the device requirement information of the first requirement type may further be implemented by the following steps:
step 201, acquiring a current demand work order of a first demand type; the current demand worksheets include a plurality of worksheets;
Step 202, determining the current processing link of each demand work order according to the current demand work order;
step 203, determining a time point of the completion work order corresponding to each required work order according to the current processing link;
and 204, taking the current requirement work order and the time point of finishing the work order as the equipment requirement information of the first requirement type.
Wherein, each required work order has a plurality of processing links from ordering to finishing; further, each required work order only needs to use corresponding equipment when the last work order is completed, and the processing link before the work order is completed can be regarded as the preparation link of the equipment.
For example, the current demand worksheets of the first demand type may be queried and obtained from the service system, and a current processing link of each demand worksheet, such as a service acceptance link or a contract signing link, may be determined. And then calculating the predicted completion work order time point of each demand work order according to the current processing link of each demand work order. And integrating each demand work order with the corresponding predicted completion work order time point, and then taking the integrated demand work order as equipment demand information required by the demand quantity prediction in a predicted time interval of the first demand type.
In this embodiment, the current demand work order of the first demand type with a long processing period is obtained, the demand work order completion time is predicted, and the predicted demand work order is used as data information required by the first demand type to predict the demand in a predicted time interval. The demand prediction method of the first demand type is more practical, and the demand prediction result with more accuracy and practicability is obtained.
In one embodiment, the step 203 determines, according to the current processing link, a time point of the completion work order corresponding to each required work order, which may be further implemented by the following steps:
step one, acquiring average historical processing time length from each processing link to a time point of completing a work order;
step two, according to the current processing link and the corresponding average historical processing time length, determining the corresponding time point of completing the work order when each required work order completes the requirement.
Illustratively, historical demand work order information is queried from a database, and then the average processing time length from each processing link of the demand work order to the time point of completing the work order is obtained based on statistics of the historical demand work order information. For example, the average processing time period from the business acceptance link to the work order completion link is t1, and the average processing time period from the contract signing link to the work order completion link is t2; it should be noted that, assuming that the next link of the service acceptance link is a contract signing link, the processing duration indicated by t1 includes the processing duration indicated by t 2. And then calculating from the current power metering equipment demand forecast execution time serving as a starting point according to the average processing time corresponding to the current processing link of each demand work order, and determining the forecast completion work order time point of each demand work order.
In this embodiment, the average processing time length from each processing link of the required work order to the work order completion link is obtained through statistics of historical data, so as to determine the current predicted completion work order time point of each required work order. The inaccuracy of demand forecast of the first demand type due to the long processing period can be avoided.
In one embodiment, the step 103 determines the first device demand amount of the first demand type in the predicted time interval according to the device demand information, which may further be implemented by the following steps:
step one, screening out a target demand work order in a predicted time interval at the time point of completing the work order from the current demand work order;
and step two, determining the first equipment demand of the first demand type in the predicted time interval according to the target demand work order.
The method includes the steps of selecting a target demand work order of a predicted completion work order time point in a predicted time interval according to a current demand work order of a first demand type and a corresponding predicted completion work order time point, and then accumulating and calculating to obtain a first equipment demand of the first demand type in the predicted time interval according to work order content (i.e. equipment demand number) in the target demand work order. For example, the equipment requirement of the work order a is 3 and the predicted completion time point of the work order is 11 months and 3 days, the equipment requirement of the work order B is 4 and the predicted completion time point of the work order is 12 months and 1, the equipment requirement of the work order C is 5 and the predicted completion time point of the work order is 11 months and 18, and the predicted time interval is 11 months; the a work order and the C work order are target demand work orders, and the first device demand of the first demand type in the predicted time interval is 8 (3+5) stations. It should be noted that, each requirement work order has at least one type of equipment, so that different types of equipment can separately calculate the respective corresponding requirement according to actual conditions, and the total requirement of all types of equipment can also be calculated in an accumulated manner.
In the embodiment, the time point of completing the work order of the current demand work order is predicted, so that the demand time of the current work order is predicted and matched, and accurate demand prediction can be performed according to actual service.
In one embodiment, the second demand type includes a device replacement demand type; the requirement influencing factor corresponding to the equipment replacement requirement type is an equipment periodic replacement plan, and the step 103 determines the second equipment requirement amount of the second requirement type in the predicted time interval according to the actual influencing information corresponding to the second requirement type, and may also be implemented by the following steps:
and determining a second equipment demand of the equipment replacement demand type in the predicted time interval according to the actual equipment periodic replacement plan in the predicted time interval corresponding to the equipment replacement demand type.
The device replacement demand type may be periodic batch replacement of devices in the project, and the corresponding device periodic replacement plan has the characteristic of strong predictability, so that the second device demand of the device replacement demand type in the predicted time interval can be determined directly based on the actual device periodic replacement plan in the predicted time interval. It should be noted that, at least one type of device exists in the device periodic replacement plan, so that different types of devices can separately calculate respective corresponding demand amounts according to actual situations, and total demand amounts of all types of devices can be calculated in an accumulated manner.
In one embodiment, the second demand type further comprises a sporadic traffic demand type; the demand influence factors corresponding to the scattered service demand types are scattered service statistical information; step 103 determines, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type within the predicted time interval, which may also be implemented by the following steps:
and inputting the actual scattered business statistical information in the predicted time interval and the historical time interval corresponding to the scattered business demand type into the scattered business demand prediction model to obtain the second equipment demand of the scattered business demand type in the predicted time interval.
The actual scattered business statistical information comprises the recent searching times of the user, the latest scattered business volume, the seasonal index, the economic index and the like.
The method includes the steps that the number of searching times of scattered business and the latest scattered business quantity of a user in the near term are inquired, meanwhile, a seasonal index is obtained according to seasons in which a predicted time interval is located, and the current economic index can be determined through input of the user or according to related information in a network; and inputting the scattered service statistical information into a scattered service demand prediction model (which can be a published machine learning model, such as a linear regression model, a neural network model and the like) which is selected and trained by a user in advance, wherein the output of the scattered service demand prediction model is the second equipment demand of the scattered service demand type in a prediction time interval. It should be noted that, at least one kind of device exists in the scattered service demand service, so that different kinds of devices can separately calculate the respective corresponding demand according to the actual situation, and also can calculate the total demand of all kinds of devices in an accumulated manner.
In this embodiment, the second device demand of the scattered service demand type in the predicted time interval is obtained by inputting the scattered service statistical information into the scattered service demand prediction model. Because the scattered business statistical information comprises the information of the searching times, the seasonal index, the economic index and the like of the users, the demands of the current users, the social environment and the like can be accurately reflected, and thus the demand can be accurately predicted.
In one embodiment, the second demand type further comprises a device failure demand type; the requirement influence factors corresponding to the equipment fault requirement types are equipment fault conditions with different working time durations; as shown in fig. 3, the step 103 of determining the second device demand of the second demand type in the predicted time interval according to the actual impact information corresponding to the second demand type may further be implemented by the following steps:
The database is queried to determine the number of devices still working at present as a data basis for demand prediction of the device fault demand type in a prediction time interval; meanwhile, the equipment failure rate obtained by statistics based on historical data is required to be obtained. The statistics of the failure rate of the equipment can consider the working time of the equipment, and for example, a user needs to divide the interval of the working time of the equipment in advance, which can be less than 1 year, 1-3 years and more than 3 years; assuming that the predicted time interval is 11 months in the present year, the fault rate of the equipment with different working time durations in the 11 months in the same year or in the fourth quarter in the same year in the last year can be counted, and the fault rate of the equipment with the working time duration of less than 1 year, the fault rate of the equipment with the working time duration of 1-3 years and the fault rate of the equipment with the working time duration of more than 3 years are obtained. And then determining the working time length of each piece of equipment still in operation, determining the number of equipment corresponding to each working time length interval, and calculating the predicted fault number of the equipment corresponding to each working time length interval through the fault rate. And adding the number of the predicted faults corresponding to all the working time intervals to obtain the second equipment demand of the equipment fault demand type in the predicted time interval. It should be noted that, at least one type of equipment exists in the equipment fault demands, so that the corresponding demands of different types of equipment can be calculated separately according to actual conditions, and the total demands of all types of equipment can be calculated in an accumulated manner. Correspondingly, the devices of different categories need to independently count the corresponding fault rate, and meanwhile, the division of the working time intervals of the devices of different categories can be different.
In the embodiment, by considering that the equipment with different working time periods corresponds to different fault rates, the demand of the equipment fault demand type is predicted according to the trend relation between the fault rates and the working time periods, and a prediction result with higher accuracy is obtained.
In another embodiment, as shown in fig. 4, there is provided a method for predicting demand of an electric energy metering device, including the steps of:
step 401, determining a device demand type in a predicted time interval; the device demand type includes a first demand type and a second demand type.
Step 402, acquiring a current demand work order of a first demand type; the current demand worksheet includes a plurality of worksheets.
Step 404, determining a corresponding time point of completing the work order when each required work order completes the requirement according to the current processing link and the corresponding average historical processing time.
And step 405, screening out the target demand work orders in the predicted time interval at the time point of completing the work orders from the current demand work orders.
Step 406, determining a first device demand of the first demand type in the predicted time interval according to the target demand worksheet.
And step 410, inputting the actual scattered service statistical information in the predicted time interval and the historical time interval corresponding to the scattered service demand type into the scattered service demand prediction model to obtain the second equipment demand of the scattered service demand type in the predicted time interval.
Step 411, the second demand type further includes a device failure demand type, for which: and acquiring a demand influence factor corresponding to the equipment fault demand type, namely, equipment fault conditions with different working time lengths.
Step 412, obtaining the number of devices still operating at the current time, as the number of devices still operating in the predicted time interval, and determining the accumulated operating time length when each still operating device is operated to the predicted time interval.
And 413, determining a second equipment demand of the equipment fault demand type in the predicted time interval according to the equipment quantity, the accumulated working time and the equipment fault conditions of different working time periods in the historical time period corresponding to the equipment fault demand type.
Step 414, obtaining a total device demand in the predicted time interval based on the first device demand and the second device demand.
In order to facilitate understanding of embodiments of the present application by those skilled in the art, the present application is described below in connection with specific examples. In this example, the above-described power metering device demand prediction method is applied to power metering device demand prediction in a power grid system, wherein,
the first demand type of the electric energy metering equipment is a batch service demand type, and particularly is a low-voltage batch new installation; the equipment replacement demand type in the second demand type is specifically a metering equipment periodic rotation; the scattered business demand types in the second demand type are specifically other business expansion installation businesses except for low-voltage batch new installations, including high-voltage new installations, high-voltage capacity increase and decrease, class change, temporary power utilization, low-voltage resident new installations, low-voltage non-resident new installations, low-voltage resident capacity increase, low-voltage resident capacity reduction and the like; the equipment failure requirement type of the second requirement type is specifically a metering equipment failure replacement.
Assume that the predicted time interval is 11 months of 2022.
In the above steps 402 and 403, the current low-pressure batch newly installed demand worksheets and the current processing links corresponding to each demand worksheet are queried from the service/marketing management system. The processing links include a business acceptance link, a investigation dispatching link … …, a batch signing power supply contract link and a final batch allocation link of metering equipment (i.e. a link requiring metering equipment for actual use can be considered as work order completion). And then inquiring a finished low-voltage batch newly-installed demand work order in the last year from a business/marketing management system, and counting and calculating the average processing time length from each link to the batch preparation link of the metering equipment, wherein the average processing time length from a business acceptance link to the batch preparation link of the metering device is calculated as L1, and the average processing time length from the investigation dispatching link to the batch preparation link of the metering equipment is calculated as L2 (the average processing time length from the ith link to the batch preparation link of the metering equipment is calculated as Li).
In the step 404, according to the current low-voltage batch new-installed required work orders and the current processing links corresponding to each required work order, the predicted complete work order time point of each required work order is calculated from the current real time and the average processing time between the corresponding links and the batch allocation links of the metering device (for example, the predicted complete work order time point of the work order a is 2022, 11, 25 days, the measured complete work order time point of the work order B is 2022, 12 days, and 2022, 11, 22 days).
In step 405, the target demand work order of the predicted completion work order time point in the predicted time interval is selected by comparing the predicted completion work order time point and the predicted time interval of each demand work order, for example, the work order a and the work order C are target demand work orders, and the work order B is not target demand work order.
In step 406, the device demand of each target demand work order is determined according to the specific content in the target demand work order. It should be noted that, there are various kinds of devices required in the requirement work order, and individual calculation can be performed based on the specific contents of the requirement work order. For example, the content of the work order A indicates that the metering mode is direct access type, the demand of the electric energy meter equipment is 1, and the demand of the low-voltage transformer equipment is 0; the content of the work order C indicates that the metering mode is accessed through a transformer, the electric energy meter equipment demand is 1, and the low-voltage current transformer equipment demand is 3. The first demand of the final low-voltage batch new installation demand can be accumulated by differentiating the device types (the demand of the electric energy meter device is 2, the demand of the low-voltage transformer device is 3), or can be accumulated together (the demand of the electric energy meter device is 5).
In the above steps 407 and 408, the device periodic replacement plan is queried from the database, and the second device demand of the device replacement demand type in the predicted time interval is directly calculated. Wherein the periodic replacement plan may be set in advance by the user. The second device demand of the device replacement demand type may also be accumulated by differentiating the device categories or accumulated all together.
In the above steps 409 and 410, because the processing period of the work order of the scattered service requirement type is short, the prediction meaning of the time for the current work order is not great. It is therefore desirable to derive a second device demand for the sporadic traffic demand type within the predicted time interval by inputting the sporadic traffic statistics associated with the sporadic traffic demand into the machine learning model for prediction. For example, the scattered statistical information is the searching times, seasonal indexes, the number of scattered business worksheets in the last 3 years, economic indexes and the like of the clients at the telegraph package client side in the last 1 month, and the machine learning model can be an index smoothing model, a regression analysis model, a gray prediction model, a neural network model and the like. The device demand of each category may be individually predicted by differentiating the category of the device when model prediction is used, or the device demand total amount may be directly predicted regardless of the category of the device.
In the above step 411, the number of working devices and the number of fault devices in the same year (i.e. 11 months in 2021) are obtained by querying, and the corresponding device fault rates are calculated based on the working time intervals preset by the user, where the working time intervals of the devices in different categories need to be set respectively, and the corresponding fault rates need to be calculated respectively. For example, the operation duration interval of the electric energy meter device is: the working time is less than or equal to 1 year, 1 year is less than or equal to 3 years, 3 years is less than or equal to 5 years, 5 years is less than or equal to 10 years, and the working time is more than 10 years; the working time interval of the metering terminal equipment is as follows: the working time is less than or equal to 1 year, 1 year is less than or equal to 3 years, 3 years is less than or equal to 5 years, 5 years is less than or equal to 6 years, and the working time is more than 6 years; the working time interval of the transformer equipment is as follows: the working time is less than or equal to 5 years, less than or equal to 15 years and more than 15 years.
In step 413, the second device demand of the device failure demand type may be accumulated by differentiating the device types or may be accumulated together.
In this embodiment, the demand prediction is performed by different demand types and different device classes: when the demand of the batch service demand type is predicted, the demand is predicted according to the time length of the links of each demand work order and the batch allocation links of the metering equipment in the system, and the time of the equipment demand can be directly and accurately reflected by the links of the work order, so that the obtained prediction result is more accurate; when the demand of the scattered service demand type is predicted, the searching times of the clients on the telegram package client side for the scattered service are used as the input quantity of a machine learning prediction model, and the service demand of the clients can be predicted to a certain extent due to the searching times, so that the accurate predicted demand can be obtained; when the demand prediction of the fault replacement demand type is carried out, the fault rate of equipment under different working time periods is considered, the number of the equipment for fault replacement is predicted according to the trend relation between the fault rate and the working time period, and the accuracy is higher.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an electric energy metering device demand prediction device for realizing the electric energy metering device demand prediction method. The implementation of the solution provided by the apparatus is similar to the implementation described in the above method, so the specific limitation in the embodiments of the apparatus for predicting a demand for an electric energy metering device provided below may be referred to the limitation of the method for predicting a demand for an electric energy metering device hereinabove, and will not be repeated herein.
In one embodiment, as shown in fig. 5, there is provided an electric energy metering device demand prediction apparatus, including: a demand type determination module 501, an information acquisition module 502, a demand prediction module 503, and a total demand determination module 504, wherein:
a demand type determining module 501, configured to determine a device demand type in a predicted time interval; the device demand type includes a first demand type and a second demand type;
the information obtaining module 502 is configured to obtain device demand information of a first demand type, and obtain actual influence information of a second demand type under a preset demand influence factor in a predicted time interval and/or a historical time period before the predicted time interval;
a demand prediction module 503, configured to determine, according to the device demand information, a first device demand of the first demand type in a predicted time interval; determining a second equipment demand of the second demand type in a predicted time interval according to the actual influence information corresponding to the second demand type;
the total demand determination module 504 is configured to obtain a total device demand in the predicted time interval based on the first device demand and the second device demand.
In one embodiment, the information obtaining module 502 is further configured to obtain a current requirement work order of the first requirement type; the current demand worksheets include a plurality of worksheets; determining the current processing link of each demand work order according to the current demand work order; determining a time point of a completion work order corresponding to each required work order according to the current processing link; and taking the current requirement work order and the time point of completing the work order as the equipment requirement information of the first requirement type.
In one embodiment, the information obtaining module 502 is further configured to obtain an average historical processing duration from each processing link to a time point of completing the work order; and determining a corresponding time point of the work order to be completed when each required work order completes the requirement according to the current processing link and the corresponding average historical processing time.
In one embodiment, the demand prediction module 503 is further configured to screen a target demand work order from the current demand work order, where the time point of completing the work order is within the predicted time interval; and determining the first equipment demand of the first demand type in the predicted time interval according to the target demand work order.
In one embodiment, the second demand type includes a device replacement demand type; the requirement influencing factors corresponding to the equipment replacement requirement types are equipment periodic replacement plans; the demand prediction module 503 is further configured to determine a second device demand of the device replacement demand type in the predicted time interval according to the actual device periodic replacement plan in the predicted time interval corresponding to the device replacement demand type.
In one embodiment, the second demand type further comprises a sporadic traffic demand type; the demand influence factors corresponding to the scattered service demand types are scattered service statistical information; the demand prediction module 503 is further configured to input actual scattered service statistics information in a prediction time interval and a history time interval corresponding to the scattered service demand type into a scattered service demand prediction model, so as to obtain a second device demand of the scattered service demand type in the prediction time interval.
In one embodiment, the second demand type further comprises a device failure demand type; the requirement influence factors corresponding to the equipment fault requirement types are equipment fault conditions with different working time durations; the demand prediction module 503 is further configured to obtain, as the number of devices still operating in the predicted time interval, the number of devices still operating at the current time; determining the accumulated working time length when each still-working device works to a predicted time interval; and determining a second equipment demand of the equipment fault demand type in the predicted time interval according to the equipment quantity, the accumulated working time and the equipment fault conditions of different working time periods in the historical time period corresponding to the equipment fault demand type.
The modules in the electric energy metering device demand prediction device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing the required work order data and the device operation data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of demand prediction for an electric energy metering device.
It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as Static Random access memory (Static Random access memory AccessMemory, SRAM) or dynamic Random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A method of demand prediction for an electrical energy metering device, the method comprising:
determining a device demand type within a predicted time interval; the device demand type includes a first demand type and a second demand type;
acquiring equipment demand information of the first demand type, and acquiring actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or a historical time period before the prediction time interval;
Determining a first equipment demand of the first demand type in the predicted time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
obtaining a total equipment demand in the predicted time interval based on the first equipment demand and the second equipment demand;
the obtaining the device requirement information of the first requirement type includes:
acquiring a current demand work order of the first demand type; the current demand work orders comprise a plurality of work orders; determining the current processing link of each demand work order according to the current demand work order; acquiring average historical processing time length from each processing link to a time point of completing a work order; determining a time point of the completion work order corresponding to each required work order according to the current processing link and the corresponding average historical processing time length; taking the current requirement work order and the time point of completing the work order as the equipment requirement information of the first requirement type;
the second demand type includes a device failure demand type; the requirement influence factors corresponding to the equipment fault requirement types are equipment fault conditions with different working time durations; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
Acquiring the number of devices still working at the current moment, and taking the number of devices still working in the prediction time interval as the number of devices still working in the prediction time interval; determining the accumulated working time length when each device still working is working to the predicted time interval; determining a second equipment demand of the equipment fault demand type in the predicted time interval according to the equipment quantity, the accumulated working time and equipment fault conditions of different working time periods in the historical time period corresponding to the equipment fault demand type;
the second demand type further comprises a scattered service demand type; the demand influence factors corresponding to the scattered service demand types are scattered service statistical information; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
inputting actual scattered business statistical information in the predicted time interval and the historical time interval corresponding to the scattered business demand type into a scattered business demand prediction model to obtain second equipment demand of the scattered business demand type in the predicted time interval; the actual scattered service statistical information at least comprises the number of times that scattered services are searched.
2. The method of claim 1, wherein determining a first device demand for the first demand type within the predicted time interval based on the device demand information comprises:
screening target demand work orders of the finishing work order time point in the prediction time interval from the current demand work orders;
and determining a first equipment demand of the first demand type in the predicted time interval according to the target demand work order.
3. The method of claim 1, wherein the second demand type comprises a device replacement demand type; the requirement influence factors corresponding to the equipment replacement requirement types are equipment periodic replacement plans; the determining, according to the actual impact information corresponding to the second demand type, a second device demand of the second demand type in the predicted time interval includes:
and determining a second equipment demand of the equipment replacement demand type in the predicted time interval according to an actual equipment periodic replacement plan in the predicted time interval corresponding to the equipment replacement demand type.
4. The method according to claim 1, wherein the method further comprises:
The first device demand and the second device demand each include at least one category of electrical energy metering device demand.
5. An electric energy metering device demand prediction apparatus, characterized in that the apparatus comprises:
the demand type determining module is used for determining the equipment demand type in the prediction time interval; the device demand type includes a first demand type and a second demand type;
the information acquisition module is used for acquiring the equipment demand information of the first demand type and acquiring the actual influence information of the second demand type under a preset demand influence factor in the prediction time interval and/or in a historical time period before the prediction time interval;
the demand prediction module is used for determining a first equipment demand of the first demand type in the prediction time interval according to the equipment demand information; determining a second equipment demand of the second demand type in the predicted time interval according to the actual influence information corresponding to the second demand type;
a total demand determination module configured to obtain a total device demand in the predicted time interval based on the first device demand and the second device demand;
The information acquisition module is further used for acquiring a current demand work order of the first demand type; the current demand work orders comprise a plurality of work orders; determining the current processing link of each demand work order according to the current demand work order; acquiring average historical processing time length from each processing link to a time point of completing a work order; determining a time point of the completion work order corresponding to each required work order according to the current processing link and the corresponding average historical processing time length; taking the current requirement work order and the time point of completing the work order as the equipment requirement information of the first requirement type;
the second demand type includes a device failure demand type; the requirement influence factors corresponding to the equipment fault requirement types are equipment fault conditions with different working time durations; the demand prediction module is further configured to obtain, as the number of devices still operating in the prediction time interval, the number of devices still operating at the current time; determining the accumulated working time length when each device still working is working to the predicted time interval; determining a second equipment demand of the equipment fault demand type in the predicted time interval according to the equipment quantity, the accumulated working time and equipment fault conditions of different working time periods in the historical time period corresponding to the equipment fault demand type;
The second demand type further comprises a scattered service demand type; the demand influence factors corresponding to the scattered service demand types are scattered service statistical information; the demand prediction module is further configured to input actual scattered service statistical information in the prediction time interval and the history time interval corresponding to the scattered service demand type into a scattered service demand prediction model, so as to obtain a second device demand of the scattered service demand type in the prediction time interval; the actual scattered service statistical information at least comprises the number of times that scattered services are searched.
6. The apparatus of claim 5, wherein the demand prediction module is further configured to screen the current demand work order for a target demand work order for the completion work order time point within the predicted time interval; and determining a first equipment demand of the first demand type in the predicted time interval according to the target demand work order.
7. The apparatus of claim 5, wherein the second demand type comprises a device replacement demand type; the requirement influence factors corresponding to the equipment replacement requirement types are equipment periodic replacement plans; the demand prediction module is further configured to determine a second device demand of the device replacement demand type in the predicted time interval according to an actual device period replacement plan in the predicted time interval corresponding to the device replacement demand type.
8. The apparatus of claim 5, wherein the first device demand and the second device demand each comprise at least one category of electrical energy metering device demand.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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