CN116319567B - Resource allocation method, resource allocation device, and readable storage medium - Google Patents
Resource allocation method, resource allocation device, and readable storage medium Download PDFInfo
- Publication number
- CN116319567B CN116319567B CN202310203648.8A CN202310203648A CN116319567B CN 116319567 B CN116319567 B CN 116319567B CN 202310203648 A CN202310203648 A CN 202310203648A CN 116319567 B CN116319567 B CN 116319567B
- Authority
- CN
- China
- Prior art keywords
- determining
- service
- resource allocation
- value
- service identifier
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000013468 resource allocation Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000003860 storage Methods 0.000 title claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 48
- 238000013441 quality evaluation Methods 0.000 claims abstract description 43
- 238000012545 processing Methods 0.000 claims abstract description 34
- 238000004364 calculation method Methods 0.000 claims abstract description 23
- 238000006243 chemical reaction Methods 0.000 claims abstract description 19
- 230000002596 correlated effect Effects 0.000 claims abstract description 7
- 238000001303 quality assessment method Methods 0.000 claims abstract description 6
- 230000000875 corresponding effect Effects 0.000 claims description 107
- 238000013507 mapping Methods 0.000 claims description 69
- 238000009826 distribution Methods 0.000 claims description 23
- 230000003993 interaction Effects 0.000 claims description 13
- 230000004913 activation Effects 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 5
- 230000000694 effects Effects 0.000 abstract description 10
- 238000005516 engineering process Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 7
- 238000004891 communication Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
- H04L47/125—Avoiding congestion; Recovering from congestion by balancing the load, e.g. traffic engineering
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/76—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions
- H04L47/762—Admission control; Resource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/78—Architectures of resource allocation
- H04L47/783—Distributed allocation of resources, e.g. bandwidth brokers
Abstract
The application discloses a resource allocation method, a resource allocation device and a computer readable storage medium, wherein the method comprises the following steps: when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform; calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results; determining a difference between the quality assessment parameter and the assessment reference value; and establishing an association relation between the flow data and the target service identifier according to the difference value. The technical problem of low traffic yield of drainage clients caused by uneven flow quality in the related technology is solved, the labor cost is effectively reduced, and the technical effect of improving the conversion rate of the drainage clients is achieved.
Description
Technical Field
The present application relates to the field of the internet, and in particular, to a resource allocation method, a resource allocation device, and a computer readable storage medium.
Background
With the continuous development of the internet, the internet platform has rich media content and huge user groups, and the rapid development of the internet drainage service is promoted on the basis of the rich media content. The service provider puts advertisements on the internet platform, so that the user can jump to a transaction page corresponding to the service provider through the advertisement link, and the drainage effect is realized. Based on the drainage effect achieved by the mode, the internet platform can not screen the quality of the user group, so that the quality of the flow is uneven. The conversion rate from lead customers to deal customers is low.
Disclosure of Invention
The embodiment of the application solves the technical problem of low yield of the drainage clients caused by uneven flow quality in the related technology by providing the resource distribution method, the resource distribution equipment and the computer readable storage medium, and achieves the technical effects of effectively reducing the labor cost and improving the conversion rate of the drainage clients.
The embodiment of the application provides a resource allocation method, which comprises the following steps:
when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform;
Calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
determining a difference between the quality assessment parameter and the assessment reference value;
and establishing an association relation between the flow data and the target service identifier according to the difference value.
Optionally, when receiving the flow data sent by the non-identification drainage platform, determining a quality evaluation parameter of the flow data includes:
determining a plan group identifier according to the platform identifier, the advertisement account, the advertisement group, the advertisement plan and the amount corresponding to the flow data;
acquiring historical transaction data corresponding to the plan group identifier, wherein indexes of the historical transaction data comprise an addition rate, an activation rate, an interaction rate, a lesson completion rate, a conversion rate and a profit margin;
and carrying out weighted calculation or region mapping on the indexes to determine the quality evaluation parameters.
Optionally, before the establishing the association relationship between the traffic data and the target service identifier according to the difference value, the method further includes:
determining the ratio of the flow rate in each preset time period to the historical flow rate in the corresponding preset time period in the data acquired on the same day, and forming a ratio sequence based on the ratio;
Fitting a flow rate advance prediction value corresponding to each preset time period in the future according to the ratio sequence;
and determining the instantaneous satisfaction degree according to the flow advance speed predicted value.
Optionally, the establishing an association relationship between the traffic data and the target service identifier according to the difference value includes:
mapping the difference value into a region mapping value according to a preset mapping rule;
determining a service identifier corresponding to the area mapping value which accords with a preset allocation standard;
and when the instantaneous satisfaction degree is smaller than the preset flow satisfaction degree, determining the target service identifier based on the distribution priority corresponding to the service identifier.
Optionally, the determining the target service identifier based on the allocation priority corresponding to the service identifier includes:
determining the distribution priority corresponding to the service identifier;
determining an allocation weight corresponding to the service identifier according to the area mapping value corresponding to the service identifier and the allocation priority;
and determining the target service identification based on the allocation weight.
Optionally, after determining the service identifier corresponding to the area mapping value meeting the preset allocation standard, the method further includes:
When the instantaneous satisfaction is greater than the preset flow satisfaction, determining the service capacity balance corresponding to the service identifier;
determining the distribution weight of the service identifier according to the service capacity balance;
and determining the target service identification and the corresponding allocation quantity according to the allocation weight.
Optionally, the calculating the historical work order processing information associated with the service identifier according to a preset rule, and determining the evaluation reference value associated with each service identifier according to the calculation result includes:
acquiring the historical work order processing information corresponding to the service identifier, wherein the historical work order processing information comprises personal service proportion, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin;
and carrying out weighted calculation or region mapping on each item of the historical work order processing information, and determining the evaluation reference value.
Optionally, the resource allocation method further includes:
when receiving flow data sent by an identified drainage platform, determining a service volume balance corresponding to a service identifier associated with the identified drainage platform;
when the service volume balance is zero, determining historical transaction data associated with the identified drainage platform, and determining quality evaluation parameters of the identified drainage platform according to the historical transaction data;
Calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
and establishing an association relation between the flow data and the target service identifier according to the difference value between the quality evaluation parameter and each evaluation reference value.
In addition, the application also provides a resource allocation device, which comprises a memory, a processor and a resource allocation program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the resource allocation method when executing the resource allocation program.
Furthermore, the present application proposes a computer readable storage medium having stored thereon a resource allocation program which, when executed by a processor, implements the steps of the resource allocation method as described above.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
1. when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform; calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results; determining a difference between the quality assessment parameter and the assessment reference value; and establishing an association relation between the flow data and the target service identifier according to the difference value, so that the technical problem of low traffic yield of the drainage clients caused by uneven flow quality in the related technology is effectively solved, and the technical effects of effectively reducing labor cost and improving conversion rate of the drainage clients are realized.
Drawings
Fig. 1 is a schematic flow chart of a first embodiment of a resource allocation method of the present application;
FIG. 2 is a detailed schematic diagram of steps in a second embodiment of a resource allocation method according to the present application;
fig. 3 is a detailed schematic diagram of step S140 in the second embodiment of the resource allocation method of the present application;
fig. 4 is a schematic diagram of a hardware structure related to an embodiment of a resource allocation device of the present application.
Detailed Description
In the related art, a service provider puts advertisements on an internet platform to guide clients, but the internet platform cannot screen the quality of a user group, so that the quality of guide flow is uneven, the conversion rate of the flow is lower, and the labor cost is improved. The main technical scheme adopted by the embodiment of the application is as follows: when receiving the flow data sent by the non-identification drainage platform, determining a quality evaluation parameter of the flow data; determining an evaluation reference value of the service identifier according to the historical work order processing information associated with the service identifier; and determining a target service identifier according to the quality evaluation parameter and the difference value between each evaluation reference value, and further establishing an association relationship between the flow data and the target service identifier. Thereby realizing the effective improvement of the conversion rate, reducing the labor cost and further improving the income.
In order to better understand the above technical solution, exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example 1
An embodiment of the present application discloses a resource allocation method, referring to fig. 1, the resource allocation method includes:
step S110, when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform;
in this embodiment, the drainage platform is a platform for delivering advertisements, and the service provider delivers advertisements on the drainage platform to drain users to purchase services of the service provider through the advertisements. Wherein the service provider can mark each drainage platform so that users drained through the drainage platform are allocated to the specified service mark. The no-mark drainage platform refers to a drainage platform without a specified service mark. The quality evaluation parameter is used to evaluate the quality of the flow coming in through the drainage platform. Alternatively, the value index is a yield. The flow data is the user information coming in through the drainage platform.
Optionally, step S110 includes:
step S111, determining a plan group identifier according to the platform identifier, the advertisement account, the advertisement group, the advertisement plan and the amount corresponding to the flow data;
in this embodiment, after the drainage platform triggers the advertisement, the user is guided to input information such as the service and the contact information that the user expects to purchase, so that the flow data is attached with the platform identifier of the drainage platform, the advertisement account, the advertisement group and the advertisement plan of the advertisement put in the drainage platform, and the identifier such as the amount of the expected service.
Step S112, historical transaction data corresponding to the plan group identification is obtained, wherein indexes of the historical transaction data comprise addition rate, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin;
and step S113, performing weighted calculation or region mapping on the indexes to determine the quality evaluation parameters.
As an alternative implementation manner, historical transaction data corresponding to the plan group identifier is obtained, wherein the historical transaction data comprises, but is not limited to, an addition rate, an activation rate, an interaction rate, a lesson completion rate, a conversion rate and a profit margin; and calculating the index according to the data corresponding to the index and the weight value or the region mapping rule corresponding to the index, and determining the quality evaluation parameter corresponding to the flow data. The weight values or the region mapping rules can be preset or adaptively optimized according to the overall service condition.
The process of obtaining the "platform identifier +b advertisement account +c advertisement group +d advertisement plan +e amount" corresponding to the flow data, generating the plan group identifier, and calculating the quality evaluation parameter according to the historical transaction data associated with the plan group identifier is as follows: the rate of addition of the flow rate data is 85% in the near 30 days, the evaluation value thereof is=85% > 100% (index weight), the activation rate thereof is 95%, the evaluation value thereof is=95% > 80% (index weight), the interaction rate thereof is 81%, the evaluation value thereof is=81% > 70% (index weight), the finishing rate thereof is 72%, the evaluation value thereof is=72% > 100% (index weight), the conversion rate thereof is 5% and the evaluation value thereof is based on the regional map=75%, the profit rate thereof is 2%, and the evaluation value thereof is based on the regional map=50%, and then the quality evaluation parameter of the flow rate data is=the average value of the evaluation values of the above-mentioned indexes.
Step S120, calculating the historical work order processing information associated with the service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
in this embodiment, each salesman has a corresponding service identifier, and the historical work order processing information is associated with the service identifier, including the performance information of the salesman history corresponding to the service identifier. The evaluation reference value is used for measuring the service capability level of the corresponding service personnel of the service identifier.
Optionally, step S120 includes:
step S121, acquiring the historical work order processing information corresponding to the service identifier, wherein the historical work order processing information comprises personal service proportion, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin;
in this embodiment, the personal service proportion is the number of users that the service identifies in a batch of service plans.
And step S122, carrying out weighted calculation or area mapping on each item of the historical work order processing information, and determining the evaluation reference value.
As an optional implementation manner, historical work order processing information corresponding to the service identifier is obtained, wherein the historical work order processing information comprises, but is not limited to, personal service proportion, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin; and calculating the historical processing work order information according to the corresponding weight value or the region mapping rule, and determining an evaluation reference value corresponding to the service identifier.
Optionally, after each evaluation reference value is determined, weighted average calculation can be performed again to eliminate interference of other data and feed back the truest service capability. After the calculation of each quality evaluation parameter is completed, a weighted average calculation may be performed.
Illustratively, the evaluation reference value is calculated for the service identifier "0039C": in the batch of service plans closest to the current time, the personal service proportion 150 has an evaluation value based on the regional map=60%, and in the batch of service plans closest to the current time, the three batches of personal service proportion 180 has an evaluation value based on the regional map=80%, an activation rate 95%, an evaluation value=95% > 80% (index weight), an interaction rate 81%, an evaluation value=81% > 70% (index weight), a class completion rate 72%, an evaluation value=72% > 100% (index weight), a conversion rate 5%, an evaluation value based on the regional map=75%, a profit margin 2%, and an evaluation value based on the regional map=50%, i.e. the evaluation reference value of the service identifier is the average value of the evaluation values of the indexes.
Step S130 of determining a difference between the quality evaluation parameter and the evaluation reference value;
and step S140, establishing the association relation between the flow data and the target service identifier according to the difference value.
In this embodiment, the service identifier finally allocated by the traffic data is the target service identifier.
As an optional implementation manner, determining a quality evaluation parameter and a difference value between each evaluation reference value, forming a difference value sequence according to the difference value, selecting the difference value with the smallest absolute value in the difference value sequence as a target difference value, determining a service identifier corresponding to the target difference value as a target service identifier, and establishing an association relationship between flow data and the target service identifier.
Optionally, after the batch of flow data is distributed to the target service identifier, removing the target difference value from the difference value sequence, and if the service capacity of the target service identifier is full but the batch of flow data has a balance, determining the difference value with the smallest absolute value in the updated difference value sequence as a new target difference value; and distributing the residual flow data to a target service identifier corresponding to the target difference value. When traffic data still has a balance, and so on.
As another alternative implementation manner, after the difference sequence is formed, selecting the difference value in the preset interval as a target difference value, determining the distribution percentage of the target difference value according to the weight value of the target difference value, and distributing the flow data to each target service identifier corresponding to the target difference value according to the distribution percentage.
The service capacity balance of the target service identifier corresponding to the target difference value is obtained, and the weight value is determined based on each service capacity balance. And carrying out weighted average calculation according to the weight value to determine the distribution percentage corresponding to each target service identifier.
Optionally, the resource allocation method further includes:
step S10, when receiving flow data sent by an identification drainage platform, determining a service volume balance corresponding to a service identification associated with the identification drainage platform;
Optionally, after step S10, the method further includes:
and when the service volume balance corresponding to the associated service identifier is not zero, distributing the flow data to the service identifier.
Step S20, when the service volume balance is zero, determining historical transaction data associated with the identified drainage platform, and determining quality evaluation parameters of the identified drainage platform according to the historical transaction data;
step S30, calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
and step S40, establishing an association relationship between the flow data and the target service identifier according to the difference value between the quality evaluation parameter and each evaluation reference value.
As an alternative embodiment, when the service volume balance of the service identifier is zero, it indicates that the service has reached the preset maximum received traffic limit, and no more traffic can be received. At this time, quality evaluation needs to be performed on the identified drainage platform in order to optimize the subsequent recommendation strategies. And determining the quality evaluation parameters of the mark drainage platform, and further determining the difference value between the quality evaluation parameters and each evaluation reference value by determining the evaluation reference value of the service mark with the balance of the rest service capacity not being zero.
Optionally, step S40 includes:
step S41, performing difference operation on the quality evaluation parameters and each evaluation reference value, and determining the difference value;
step S42, mapping the difference value into a region mapping value according to a preset mapping rule;
step S43, determining the service identifier corresponding to the area mapping value which accords with a preset allocation standard;
and step S44, determining a target service identifier and the distribution quantity corresponding to the target service identifier according to the service volume balance corresponding to the service identifier.
In this embodiment, the mapping rule is predetermined, and may be adjusted according to adjustment of the service structure or change of the quality evaluation parameter of the traffic data. The preset allocation rule is a rule for determining service identification based on the area mapping value. Wherein the mapping rules are associated with allocation criteria, each mapping rule corresponding to an allocation criteria.
As an optional implementation manner, after determining the difference between the quality evaluation parameter and each evaluation reference value, mapping each difference into a corresponding region mapping value according to a preset mapping rule; and selecting an area mapping value which accords with the allocation standard according to a preset allocation standard matched with the mapping rule, and further determining a corresponding service identifier.
The mapping rule is an absolute value, that is, the area mapping value is an absolute value of each difference value, and the allocation standard is to select a service identifier corresponding to a difference value with an absolute value smaller than a preset threshold.
As an optional implementation manner, when the instantaneous satisfaction is greater than the preset flow satisfaction, determining a service capacity balance corresponding to the service identifier, determining an allocation weight of the service identifier according to the service capacity balance, and determining the allocation quantity corresponding to each service identifier according to the allocation weight in proportion.
Illustratively, the larger the service capacity balance, the larger its assigned weight.
When the instantaneous satisfaction is greater than the preset flow satisfaction, determining that the area mapping rule is an absolute value, and taking the service identifier corresponding to the difference value with the minimum area mapping value as the target service identifier; when there are a plurality of minimum area mapping values, the service identifications corresponding to the plurality of equal area mapping values are used as target service identifications, the service capacity balance of the target service identifications is determined, the allocation weight of the target service identifications is determined according to the service capacity balance, and the flow data is allocated according to the allocation weight. The distribution weight is the distributed flow data, and accounts for the percentage of the total flow data.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages:
when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform; calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results; determining a difference between the quality assessment parameter and the assessment reference value; and establishing an association relation between the flow data and the target service identifier according to the difference value, so that the technical problem of low traffic yield of the drainage clients caused by uneven flow quality in the related technology is effectively solved, and the technical effects of effectively reducing labor cost and improving conversion rate of the drainage clients are realized.
Example two
Based on the first embodiment, a second embodiment of the present application proposes a resource allocation method, referring to fig. 2, before step S130, further including:
step S210, determining the ratio of the flow rate in each preset time period to the historical flow rate in the corresponding preset time period in the acquired data of the same day, and forming a ratio sequence based on the ratio;
In this embodiment, the flow rate at each time point in the day is stored in association with the time node, and the acquired data in the day is the information of the flow rate acquired in the day. The flow rate of advance may be the instantaneous rate of flow data entering the system, or may be the average rate of advance over a current preset period of time. The historical flow rate of approach is the flow rate of approach in the historical data. The duration of each preset time period may be equal or unequal.
Optionally, the flow rate of advance and the historical flow rate of advance directly generate a flow rate of advance table, wherein the flow rate of advance table is based on the same preset time period, the corresponding flow rate of advance and the historical flow rate of advance are in one-to-one correspondence, and according to the lapse of time, the flow rate of advance in each preset time period is supplemented according to newly acquired flow data.
As an alternative implementation manner, the data acquired on the same day are stored in association with preset time periods, the flow speed in each preset time period is determined, and the historical flow data associated with each preset time period is determined; and determining the ratio of the flow rate and the historical flow rate associated with the same preset time period, and determining a ratio sequence based on each ratio.
The preset time period is one hour, and the current time is divided into twelve time periods from seven am to seven pm of each day, namely, eleven am of the same three months, namely, the flow rate inflow of each preset time period is determined according to the flow rate data collected from seven am to eleven am, namely, four data are available for the flow rate inflow of the same day; acquiring the flow inflow speed of each preset time period in March and one day, namely twelve data of historical flow inflow speed; and generating a ratio sequence according to the ratio of the flow rate in the same preset time period to the historical flow rate.
Step S220, fitting out flow advance prediction values corresponding to the preset time periods in the future according to the ratio sequence;
and step S230, determining the instantaneous satisfaction according to the flow rate advance speed predicted value.
In this embodiment, the predicted value of the flow rate advance is a predicted value of the flow rate advance corresponding to each preset time period in the future on the same day. The predicted value of the flow rate advance will change with the change of the collected data on the same day. The instantaneous satisfaction is a predicted value of the current day residual flow which is determined by predicting the flow speed of each preset time period in the future at the current moment.
As an optional real-time manner, according to the ratio sequence, fitting a ratio predicted value corresponding to each time period in the future by using a fitting algorithm (such as linear regression, a neural network, etc.); calculating a flow rate advance predicted value corresponding to each time period in the future according to the ratio predicted value and the historical flow rate advance; and summing according to the predicted value of the flow rate advance corresponding to each time period, and determining the instantaneous satisfaction, namely, the predicted value of the total amount of the future flow from the current moment to the end of the daily business plan.
By way of example, assuming that the current time period is 9:00-9:15, we want to predict the flow rate for this time period 9:15-9:30 of the day. We can acquire current flow rate data for this period (say 100/min) and historical flow rate data for this period (say 80/min). We then calculated a ratio of 100/80=1.25. Next we fit a predicted value of the ratio (assumed to be 1.2) for this time period in the future from the sequence of ratios known nine points ago using a fitting algorithm. Finally, according to the ratio predicted value and the historical flow speed data, the flow speed predicted value corresponding to the time period in the future is calculated to be 1.2x80=96/min. And the like, the flow rate advance prediction value of each time period in the future of the current day can be calculated.
Alternatively, the historical flow rate corresponding to the preset time period may be flow related data of the previous day of the current day, an average value of the historical flow rate of the previous three days, or a weighted average value of the historical flow rate data of the previous three days.
Optionally, referring to fig. 3, step S140 includes:
step S240, mapping the difference value into a region mapping value according to a preset mapping rule;
Step S250, determining a service identifier corresponding to the area mapping value which accords with a preset allocation standard;
in this embodiment, the mapping rule is predetermined, and may be adjusted according to adjustment of the service structure or change of the quality evaluation parameter of the traffic data. The preset allocation rule is a rule for determining service identification based on the area mapping value. Wherein the mapping rules are associated with allocation criteria, each mapping rule corresponding to an allocation criteria.
As an optional implementation manner, after determining the difference between the quality evaluation parameter and each evaluation reference value, mapping each difference into a corresponding region mapping value according to a preset mapping rule; and selecting an area mapping value which accords with the allocation standard according to a preset allocation standard matched with the mapping rule, and further determining a corresponding service identifier.
The mapping rule is an absolute value, that is, the area mapping value is an absolute value of each difference value, and the allocation standard is to select a service identifier corresponding to a difference value with an absolute value smaller than a preset threshold.
And step S260, when the instantaneous satisfaction degree is smaller than the preset flow satisfaction degree, determining the target service identifier based on the distribution priority corresponding to the service identifier.
In this embodiment, the preset traffic satisfaction may be a service capacity balance associated with all current service identifiers.
Optionally, step S260 includes:
step S261, determining the distribution priority corresponding to the service identifier;
step S262, determining the distribution weight corresponding to the service identifier according to the area mapping value corresponding to the service identifier and the distribution priority;
step S263, determining the target service identifier based on the allocation weight.
In this embodiment, the allocation priority is a coefficient of the service identifier, which may be associated with the historical performance of the service identifier, or may be associated with the balance of the service capacity of the service identifier.
As an optional implementation manner, determining the allocation priority corresponding to the service identifier, determining the allocation weight corresponding to the service identifier according to the area mapping value corresponding to the service identifier and the allocation priority, and determining the target service identifier based on the allocation weight.
Illustratively, the allocation weight corresponding to each service identifier is determined according to the product of the area mapping value and the allocation priority, and the target service identifier and the allocation quantity of each target service identifier are determined based on the allocation weight.
The method comprises the steps of determining an allocation weight corresponding to each service identifier according to a product of a region mapping value and allocation priority, and preferentially allocating traffic data to the service identifier with the largest allocation weight based on the allocation weight, namely, a target service identifier; when the service capacity balance of the target service identifier is zero, selecting the rest service identifiers, and allocating the service identifier with the largest weight as the target service identifier, and so on.
Optionally, after step S250, the method further includes:
step S251, when the instantaneous satisfaction is greater than the preset flow satisfaction, determining the service capacity balance corresponding to the service identifier;
step S252, determining the distribution weight of the service identifier according to the service capacity balance;
step S253, determining the target service identifier and the corresponding allocation number according to the allocation weight.
As an optional implementation manner, when the instantaneous satisfaction is greater than the preset flow satisfaction, determining a service capacity balance corresponding to the service identifier, determining an allocation weight of the service identifier according to the service capacity balance, and determining the allocation quantity corresponding to each service identifier according to the allocation weight in proportion.
Illustratively, the larger the service capacity balance, the larger its assigned weight.
As another optional implementation manner, when the instantaneous satisfaction is greater than the preset flow satisfaction, determining that the area mapping rule is an absolute value, and taking the service identifier corresponding to the difference value with the minimum area mapping value as the target service identifier; when there are a plurality of minimum area mapping values, the service identifications corresponding to the plurality of equal area mapping values are used as target service identifications, the service capacity balance of the target service identifications is determined, the allocation weight of the target service identifications is determined according to the service capacity balance, and the flow data is allocated according to the allocation weight. The distribution weight is the distributed flow data, and accounts for the percentage of the total flow data.
The technical scheme in the embodiment of the application at least has the following technical effects or advantages:
because the ratio of the flow rate in each preset time period to the historical flow rate in the corresponding preset time period is determined in the acquired data of the same day, a ratio sequence is formed based on the ratio; fitting a flow rate advance prediction value corresponding to each preset time period in the future according to the ratio sequence; the instantaneous satisfaction degree is determined according to the flow speed predicted value, so that the technical problem that a flow data distribution mechanism is inaccurate in the related technology is solved, the flow data is distributed to matched service identifiers, and the technical effect of reducing labor cost is achieved.
The application further provides a resource allocation device, referring to fig. 4, and fig. 4 is a schematic structural diagram of the resource allocation device of the hardware running environment related to the embodiment of the application.
As shown in fig. 4, the resource allocation apparatus may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WIreless-FIdelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) Memory or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 4 is not limiting of the resource allocation apparatus and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
Optionally, the memory 1005 is electrically connected to the processor 1001, and the processor 1001 may be configured to control operation of the memory 1005, and may also read data in the memory 1005 to implement resource allocation.
Alternatively, as shown in fig. 4, an operating system, a data storage module, a network communication module, a user interface module, and a resource allocation program may be included in the memory 1005 as one storage medium.
Optionally, in the resource allocation device shown in fig. 4, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the resource allocation apparatus of the present application may be provided in the resource allocation apparatus.
As shown in fig. 4, the resource allocation apparatus calls, by the processor 1001, a resource allocation program stored in the memory 1005, and performs the relevant step operations of the resource allocation method provided in the embodiment of the present application:
when receiving flow data sent by an unidentified drainage platform, determining a quality evaluation parameter of the flow data, wherein the quality evaluation parameter is positively correlated with a value index associated with the unidentified drainage platform;
Calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
determining a difference between the quality assessment parameter and the assessment reference value;
and establishing an association relation between the flow data and the target service identifier according to the difference value.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
determining a plan group identifier according to the platform identifier, the advertisement account, the advertisement group, the advertisement plan and the amount corresponding to the flow data;
acquiring historical transaction data corresponding to the plan group identifier, wherein indexes of the historical transaction data comprise an addition rate, an activation rate, an interaction rate, a lesson completion rate, a conversion rate and a profit margin;
and carrying out weighted calculation or region mapping on the indexes to determine the quality evaluation parameters.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
determining the ratio of the flow rate in each preset time period to the historical flow rate in the corresponding preset time period in the data acquired on the same day, and forming a ratio sequence based on the ratio;
Fitting a flow rate advance prediction value corresponding to each preset time period in the future according to the ratio sequence;
and determining the instantaneous satisfaction degree according to the flow advance speed predicted value.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
mapping the difference value into a region mapping value according to a preset mapping rule;
determining a service identifier corresponding to the area mapping value which accords with a preset allocation standard;
and when the instantaneous satisfaction degree is smaller than the preset flow satisfaction degree, determining the target service identifier based on the distribution priority corresponding to the service identifier.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
determining the distribution priority corresponding to the service identifier;
determining an allocation weight corresponding to the service identifier according to the area mapping value corresponding to the service identifier and the allocation priority;
and determining the target service identification based on the allocation weight.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
when the instantaneous satisfaction is greater than the preset flow satisfaction, determining the service capacity balance corresponding to the service identifier;
Determining the distribution weight of the service identifier according to the service capacity balance;
and determining the target service identification and the corresponding allocation quantity according to the allocation weight.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
acquiring the historical work order processing information corresponding to the service identifier, wherein the historical work order processing information comprises personal service proportion, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin;
and carrying out weighted calculation or region mapping on each item of the historical work order processing information, and determining the evaluation reference value.
Optionally, the processor 1001 may call a resource allocation program stored in the memory 1005, and also perform the following operations:
when receiving flow data sent by an identified drainage platform, determining a service volume balance corresponding to a service identifier associated with the identified drainage platform;
when the service volume balance is zero, determining historical transaction data associated with the identified drainage platform, and determining quality evaluation parameters of the identified drainage platform according to the historical transaction data;
calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
And establishing an association relation between the flow data and the target service identifier according to the difference value between the quality evaluation parameter and each evaluation reference value.
In addition, the embodiments of the present application further provide a computer readable storage medium, where a resource allocation program is stored, where the resource allocation program, when executed by a processor, implements the relevant steps of any of the embodiments of the resource allocation method as described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (5)
1. A resource allocation method, characterized in that the resource allocation method comprises:
when receiving flow data sent by an unidentified drainage platform, determining a plan group identifier according to a platform identifier, an advertisement account, an advertisement group, an advertisement plan and an amount corresponding to the flow data;
acquiring historical transaction data corresponding to the plan group identifier, wherein indexes of the historical transaction data comprise an addition rate, an activation rate, an interaction rate, a lesson completion rate, a conversion rate and a profit margin;
performing weighted calculation or region mapping on the indexes to determine quality evaluation parameters of the flow data, wherein the quality evaluation parameters are positively correlated with the value indexes associated with the non-identification drainage platform;
Acquiring historical work order processing information corresponding to a service identifier, wherein the historical work order processing information comprises personal service proportion, activation rate, interaction rate, lesson completion rate, conversion rate and profit margin;
performing weighted calculation or region mapping on each piece of historical work order processing information, and determining an evaluation reference value associated with each service identifier;
determining a difference between the quality assessment parameter and the assessment reference value;
determining the ratio of the flow rate in each preset time period to the historical flow rate in the corresponding preset time period in the data acquired on the same day, and forming a ratio sequence based on the ratio;
fitting a flow rate predicted value corresponding to each preset time period in the future according to the ratio sequence, wherein the flow rate predicted value is a predicted value of the flow rate corresponding to each preset time period in the future on the current day, and the flow rate predicted value changes along with the change of the acquired data on the current day;
determining an instantaneous satisfaction degree according to the predicted value of the flow speed, wherein the instantaneous satisfaction degree is a predicted value of the residual flow on the same day, which is determined by predicting the flow speed of each preset time period in the future at the current moment;
Mapping the difference value into a region mapping value according to a preset mapping rule;
determining a service identifier corresponding to the area mapping value which accords with a preset allocation standard;
when the instantaneous satisfaction is smaller than the preset flow satisfaction, determining a target service identifier based on the distribution priority corresponding to the service identifier;
when the instantaneous satisfaction is greater than the preset flow satisfaction, determining the service capacity balance corresponding to the service identifier;
determining the distribution weight of the service identifier according to the service capacity balance;
and determining the target service identification and the corresponding allocation quantity according to the allocation weight.
2. The resource allocation method according to claim 1, wherein said determining the target service identity based on the allocation priority corresponding to the service identity comprises:
determining the distribution priority corresponding to the service identifier;
determining an allocation weight corresponding to the service identifier according to the area mapping value corresponding to the service identifier and the allocation priority;
and determining the target service identification based on the allocation weight.
3. The resource allocation method according to claim 1, wherein the resource allocation method further comprises:
When receiving flow data sent by an identified drainage platform, determining a service volume balance corresponding to a service identifier associated with the identified drainage platform;
when the service volume balance is zero, determining historical transaction data associated with the identified drainage platform, and determining quality evaluation parameters of the identified drainage platform according to the historical transaction data;
calculating historical work order processing information associated with service identifiers according to preset rules, and determining evaluation reference values associated with the service identifiers according to calculation results;
and establishing an association relation between the flow data and the target service identifier according to the difference value between the quality evaluation parameter and each evaluation reference value.
4. A resource allocation device comprising a memory, a processor and a resource allocation program stored on the memory and executable on the processor, the processor implementing the steps of the resource allocation method according to any one of claims 1 to 3 when the resource allocation program is executed.
5. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a resource allocation program, which, when executed by a processor, implements the steps of the resource allocation method according to any of claims 1 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310203648.8A CN116319567B (en) | 2023-03-03 | 2023-03-03 | Resource allocation method, resource allocation device, and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310203648.8A CN116319567B (en) | 2023-03-03 | 2023-03-03 | Resource allocation method, resource allocation device, and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116319567A CN116319567A (en) | 2023-06-23 |
| CN116319567B true CN116319567B (en) | 2024-02-20 |
Family
ID=86786386
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310203648.8A Active CN116319567B (en) | 2023-03-03 | 2023-03-03 | Resource allocation method, resource allocation device, and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116319567B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010262369A (en) * | 2009-04-30 | 2010-11-18 | Ntt Docomo Inc | Recommendation information generating device, terminal device, recommendation information generating method, and recommendation information presenting system |
| CN105809365A (en) * | 2016-03-22 | 2016-07-27 | 武汉斗鱼网络科技有限公司 | User drainage management system |
| CN113298637A (en) * | 2021-04-30 | 2021-08-24 | 上海淇玥信息技术有限公司 | User diversion method, device and system of service platform |
| CN113849731A (en) * | 2021-09-22 | 2021-12-28 | 平安银行股份有限公司 | Information pushing method, device, equipment and medium based on natural language processing |
-
2023
- 2023-03-03 CN CN202310203648.8A patent/CN116319567B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010262369A (en) * | 2009-04-30 | 2010-11-18 | Ntt Docomo Inc | Recommendation information generating device, terminal device, recommendation information generating method, and recommendation information presenting system |
| CN105809365A (en) * | 2016-03-22 | 2016-07-27 | 武汉斗鱼网络科技有限公司 | User drainage management system |
| CN113298637A (en) * | 2021-04-30 | 2021-08-24 | 上海淇玥信息技术有限公司 | User diversion method, device and system of service platform |
| CN113849731A (en) * | 2021-09-22 | 2021-12-28 | 平安银行股份有限公司 | Information pushing method, device, equipment and medium based on natural language processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116319567A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11272059B2 (en) | Exclusive agent pool allocation method, electronic device, and computer readable storage medium | |
| AU2017404110B2 (en) | Agent allocation method and device, server, and storage medium | |
| CN108958939B (en) | Service resource allocation method and device and server | |
| CN107679740A (en) | Business personnel's screening and activating method, electronic installation and computer-readable recording medium | |
| CN107517190B (en) | Service recommendation method and device | |
| WO2019062000A1 (en) | Incoming call assignment method for attendants, electronic device and computer readable storage medium | |
| CN111566684B (en) | Method and system for optimizing user grouping of advertisements | |
| CN111260382A (en) | Prediction processing method and device for loss probability | |
| CN111242473B (en) | Resource scheduling method and device, electronic equipment and readable storage medium | |
| US20160283888A1 (en) | Systems and methods for use in marketing | |
| CN111598632B (en) | Method and device for determining equity shares and equity share sequence | |
| CN111652471B (en) | List distribution control method and device, electronic equipment and storage medium | |
| CN116319567B (en) | Resource allocation method, resource allocation device, and readable storage medium | |
| CN113411453A (en) | Intelligent management method and device of outbound object, medium and computing equipment | |
| CN112967091A (en) | Intelligent distribution method, system and storage medium for marketing service event | |
| CN110070392B (en) | User loss early warning method and device | |
| CN113139826A (en) | Method and device for determining distribution authority of advertisement space and computer equipment | |
| CN115002049B (en) | Resource allocation method and device | |
| CN111401772A (en) | Customer service request distribution method, device and equipment | |
| García | The impact of information and communication technologies on economic growth in Mexico | |
| CN111738837A (en) | Credit card limit processing method and device | |
| CN113205391B (en) | Historical order matching degree based order dispatching method, electronic equipment and computer readable medium | |
| CN114493113A (en) | Task allocation method, device, electronic device and storage medium | |
| CN112508631A (en) | User policy distribution method and device and electronic equipment | |
| CN113129098A (en) | Order allocation method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |