CN116227961B - Resource allocation method, device, equipment and computer readable storage medium - Google Patents
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- 238000013468 resource allocation Methods 0.000 title claims abstract description 74
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Abstract
The invention provides a resource allocation method, a device, equipment and a computer readable storage medium. Determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service resources; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the allocation strategy of the initial service resources according to the service adaptation degree data. According to the embodiment of the invention, the allocation strategy for allocating the initial service resources is dynamically adjusted according to the service adaptation data obtained by verification and calculation, so that the problem of resource waste caused by unbalanced service resource allocation is solved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a computer readable storage medium for allocating resources.
Background
In the related art, when evaluating the business effect of the offline operation authority of a city (such as the store floor maintenance effect), the adaptation degree of the city and the business is usually ignored, so that the problem of uneven distribution of business resources in each city is caused.
Therefore, how to determine the adaptation degree between the service and the cities so as to determine that the service resources are reasonably allocated in each city according to the adaptation degree, thereby overcoming the defect of resource waste caused by unbalanced resource allocation in the prior art.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a resource allocation method, apparatus, device, and computer-readable storage medium. The method and the device are used for overcoming the defect of resource waste caused by unbalanced resource allocation in the prior art, realizing verification calculation processing of data allocated by an allocation strategy, and dynamically adjusting the allocation strategy according to the obtained service adaptation data, thereby avoiding the problem of resource waste caused by unbalanced service resource allocation.
In a first aspect, the present invention provides a resource allocation method, applied to a resource allocation device, where the resource allocation method includes:
determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service resources;
preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
extracting target business indexes from the target model according to a preset extraction rule;
calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior;
and adjusting the allocation strategy of the initial service resources according to the service adaptation degree data.
According to the resource allocation method provided by the invention, before the allocation strategy of pre-allocating the initial service resources is determined, the resource allocation method further comprises the following steps:
the initial traffic index at least comprises: a dependent variable index, an independent variable index;
the dependent variable index comprises at least: maintaining a competition index;
The argument index at least comprises: stock house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special job broker number index, floor number index.
According to the resource allocation method provided by the invention, the pre-processing is performed on a preset initial model according to the initial service index and the initial service resource data to obtain a target model, and the method specifically comprises the following steps:
acquiring an initial function corresponding to the initial model; wherein the intercept term of the initial function is a preset constant value;
determining a function model according to the maintenance competition index and the initial function;
and calculating the initial business resource data by using the function model to obtain the target model.
According to the resource allocation method provided by the invention, the calculation processing is performed on the initial service resource data by using the function model to obtain the target model, and the method specifically comprises the following steps:
inputting a plurality of independent variable indexes into the function model respectively to obtain a plurality of business models corresponding to the independent variable indexes;
calculating the initial business resource data corresponding to the independent variable indexes based on each business model to obtain a plurality of information criterion data corresponding to each business model;
Comparing the numerical values corresponding to the information criterion data with preset standard thresholds respectively to obtain comparison results;
screening out the information criterion data with the value not exceeding the preset standard threshold according to the comparison result, and determining the business model corresponding to the information criterion data with the minimum value as an optimal model; determining an optimal independent variable index corresponding to the optimal model;
judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable indexes into the optimal model for calculation processing to obtain an updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold.
According to the resource allocation method provided by the invention, the calculation processing is performed on the target service resource data corresponding to the target service index to obtain the service adaptation degree data corresponding to the pre-allocation behavior, and the method comprises the following steps:
acquiring the initial service resource data corresponding to the target service index;
Performing data conversion processing on the initial service resource data to obtain target service resource data corresponding to the target service index;
and calculating the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior.
According to the resource allocation method provided by the invention, the calculation processing is performed on the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior, and the method comprises the following steps:
acquiring a weight value preset for the target service index;
and carrying out weighted calculation processing on the target service resource data by using the adaptation formula and the weight value to obtain the service adaptation degree data.
In a second aspect, the present invention further provides a resource allocation apparatus, including:
the acquisition module is used for determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city and acquiring a plurality of initial service resource data corresponding to the initial service resources;
the preprocessing module is used for preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
The extraction module is used for extracting target business indexes from the target model according to preset extraction rules;
the computing module is used for computing the target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior;
and the adjustment module is used for adjusting the allocation strategy of the initial service resources according to the service adaptation degree data.
In a third aspect, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the resource allocation methods described above when the program is executed.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the resource allocation methods described above.
In a fifth aspect, the invention also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of any of the resource allocation methods described above.
The invention provides a resource allocation method, a device, equipment and a computer readable storage medium, which are used for determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city and acquiring a plurality of initial service resource data corresponding to the initial service resources; preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the allocation strategy of the initial service resources according to the service adaptation degree data. The method and the device realize the verification calculation processing of the data allocated by the allocation policy, and dynamically adjust the allocation policy of the initial service resource allocation according to the service adaptation data obtained by the verification calculation processing, thereby avoiding the problem of resource waste caused by unbalanced service resource allocation.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a resource allocation method according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of initial service resource data provided by the present invention;
FIG. 3 is a schematic diagram of a corresponding embodiment of service adaptation data provided by the present invention;
FIG. 4 is a schematic flow chart of step S200 in FIG. 1 according to the present invention;
FIG. 5 is a flow chart of step S230 in FIG. 4 according to the present invention;
FIG. 6 is a schematic flow chart of step S400 in FIG. 1 according to the present invention;
FIG. 7 is a flow chart of step S430 of FIG. 6 according to the present invention;
fig. 8 is a schematic structural diagram of a resource allocation device according to the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the related art, when evaluating the business effect of the offline operation authority of a city (such as the store floor maintenance effect), the adaptation degree of the city and the business is generally ignored, so that the problem of uneven distribution of business resources in each city is caused.
Based on the above, the embodiments of the present invention provide a resource allocation method, apparatus, device, and computer readable storage medium, by performing verification calculation processing on data allocated by an allocation policy, an allocation policy for allocating initial service resources is dynamically adjusted according to service adaptation data obtained by the verification calculation processing, so as to avoid a problem of resource waste caused by unbalanced service resource allocation.
The following embodiments are specifically described, and a resource allocation method in the embodiments of the present invention is first described.
Fig. 1 is a schematic flow chart of an implementation of a resource allocation method according to an embodiment of the present invention, and the resource allocation method may include, but is not limited to, steps S100 to S500.
S100, determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service resources;
S200, preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
s300, extracting target business indexes from the target model according to a preset extraction rule;
s400, calculating and processing the target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior;
s500, according to the service adaptation degree data, the allocation strategy of the initial service resource is adjusted.
In step S100 of some embodiments, an allocation policy for pre-allocating initial service resources is determined based on a plurality of initial service indexes of a preset city, and a plurality of initial service resource data corresponding to the initial service resources are acquired. It can be understood that the resource allocation device obtains a preset allocation policy, which is used for allocating resources to the initial service resources corresponding to the initial service indexes of the preset city according to the allocation policy, and the terminal device obtains a plurality of initial service resource data corresponding to the initial service indexes according to the obtaining instruction.
Further, the initial traffic index at least includes: a dependent variable index, an independent variable index;
The dependent variable index comprises at least: maintaining a competition index;
the maintenance competitive index is an index for measuring the competitive degree of the market when maintaining the house source.
The argument index at least comprises: stock house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special job broker number index, floor number index.
It will be appreciated that referring to fig. 2, the preset city may include at least: cities such as Guiyang city, nan Ning city, huohe city, leshan city, zhuhai city and the like;
and the initial traffic index includes at least: the system comprises a maintenance competition index, an inventory house number index, a second hand broker number index, a store number ratio index, a house broker number ratio index, a special post broker number index, a building number index and a maintenance house number index.
In some embodiments of the present invention, the initial traffic index is divided into a dependent variable index and an independent variable index, and the maintenance competitiveness index is taken as the dependent variable index.
Initial traffic resource data: for example, the initial service resource data corresponding to the maintenance room number index (maintenance disc room number) distributed in Guiyang is 50000.
In step S200 of some embodiments, a preset initial model is preprocessed according to the initial service index and the initial service resource data, so as to obtain a target model. It may be understood that the specific implementation step may be that, after the implementation of step S100 is completed and based on a plurality of initial service indexes of a preset city, an allocation policy of pre-allocated initial service resources is determined, and after a plurality of initial service resource data corresponding to the initial service resources are obtained, a resource allocation device first performs obtaining an initial function corresponding to the initial model; and determining a function model according to the maintenance competition index and the initial function, and calculating the initial business resource data by using the function model to obtain the target model.
In step S300 of some embodiments, a target business index is extracted from the target model according to a preset extraction rule. It may be appreciated that after the resource allocation apparatus performs the preprocessing on the preset initial model according to the initial service index and the initial service resource data in step S200 to obtain the target model, the resource allocation apparatus extracts the target service index from the target model according to the received extraction instruction by using the extraction rule.
Further, in some embodiments of the present invention, the target traffic index extracted according to the extraction rule includes at least: store number ratio index, special post broker number index, maintenance house number index.
In step S400 of some embodiments, calculation processing is performed on the target service resource data corresponding to the target service index, so as to obtain service adaptation degree data corresponding to the pre-allocation behavior. It may be appreciated that after the resource allocation apparatus performs the step S300 according to the preset extraction rule, after extracting the target service index from the target model, the resource allocation apparatus first performs data conversion processing on the initial service resource data to obtain the target service resource data corresponding to the target service index, and then performs calculation processing on the target service resource data according to the preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior.
In step S500 of some embodiments, the allocation policy of the initial service resource is adjusted according to the service adaptation degree data. It may be understood that after the step S400 is performed to calculate the target service resource data corresponding to the target service index, and service adaptation data corresponding to the pre-allocation behavior is obtained, the resource allocation device adjusts the allocation policy of the initial service index according to the service adaptation degree data obtained in the step S400.
Further, in some embodiments, referring to fig. 3, the calculated service adaptation data are ordered in order from large to small, and in fig. 3 of the embodiment of the present invention, the adaptation data are city score data, which further includes a corresponding target service index and a corresponding weight value.
According to the sorting result in fig. 3, it can be intuitively seen that, for example, the Chongqing city and the Sian city have higher adaptation degree of distributing the initial business index data according to the pre-distributed distribution strategy in the step S100, while the Huizhou city and the gallery city have lower adaptation degree of distributing the initial business index data according to the pre-distributed distribution strategy in the step S100, which further indicates that resources distributed to the cities by using the original distribution strategy cannot be fully utilized, and a large amount of resource waste exists. Therefore, at this time, it is necessary to further adjust allocation policies for cities with relatively low adaptation data so as to avoid resource waste allocated to these cities.
In some embodiments, referring to fig. 4, step S200 may further include, but is not limited to, steps S210 to S230.
S210, acquiring an initial function corresponding to the initial model; wherein the intercept term of the initial function is a preset constant value;
S220, determining a function model according to the maintenance competition index and the initial function;
s230, calculating the initial business resource data by using the function model to obtain the target model.
In step S210 of some embodiments, an initial function corresponding to the initial model is obtained; wherein the intercept term of the initial function is a preset constant value. It can be understood that the resource allocation device receives the instruction for acquiring the initial function, and acquires the initial function corresponding to the initial model from the preset database according to the received instruction for acquiring the initial function.
Further, the intercept term of the initial function is a preset constant value, that is, when the intercept term is the preset constant value, the function value corresponding to the initial function is f (b) =b, where b is the preset constant value, and f (b) is the function value corresponding to the initial function.
In step S220 of some embodiments, a function model is determined according to the maintenance competition index and the initial function. It may be understood that, after the step S210 is performed to obtain the initial function corresponding to the initial model, the resource allocation device obtains the function model according to the maintenance competitiveness index in the argument index and the initial function obtained in the step S210.
Further, the maintenance competition index is input into the initial function to obtain a function model, wherein the function model is f (x) =k×x+b, f (x) is a function value of the initial function, b is a preset constant value, x is a dependent variable index to be input, k is a coefficient, and at the moment, x is 0, namely no dependent variable index is input.
In step S230 of some embodiments, the function model is used to perform calculation processing on the initial service resource data, so as to obtain the target model. It may be understood that after the step S220 is performed and the function model is determined according to the maintenance competitive index and the initial function, the resource allocation device performs calculation processing on the initial service resource data acquired in the step S100 by using the function model obtained in the step S220, so as to obtain a target model.
Further, step S230 may be specifically executed by first inputting a plurality of independent variable indexes into the function model respectively to obtain a plurality of service models corresponding to the independent variable indexes, then performing calculation processing on the initial service resource data corresponding to the independent variable indexes based on each service model to obtain a plurality of information criterion data corresponding to each service model, comparing a plurality of values corresponding to the information criterion data with a preset standard threshold respectively to obtain a comparison result, screening out the information criterion data with a value not exceeding the preset standard threshold according to the comparison result, and determining that the service model corresponding to the information criterion data with the minimum value is an optimal model; and determining an optimal independent variable index corresponding to the optimal model, judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable index into the optimal model for calculation processing to obtain an updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold.
In some embodiments, referring to fig. 5, step S230 may further include, but is not limited to, steps S231 to S235.
S231, inputting a plurality of independent variable indexes into the function model respectively to obtain a plurality of business models corresponding to the independent variable indexes;
s232, calculating the initial business resource data corresponding to the independent variable indexes based on each business model to obtain a plurality of information criterion data corresponding to each business model;
s233, comparing the numerical values corresponding to the information criterion data with a preset standard threshold value respectively to obtain a comparison result;
s234, screening out the information criterion data with the value not exceeding the preset standard threshold according to the comparison result, and determining the business model corresponding to the information criterion data with the minimum value as an optimal model; determining an optimal independent variable index corresponding to the optimal model;
s235, judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable indexes into the optimal model for calculation processing to obtain the updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold.
In step S231 of some embodiments, a plurality of the argument indexes are respectively input into the function models, so as to obtain a plurality of business models corresponding to the argument indexes. It may be understood that after the step S230 is performed to perform calculation processing on the initial business resource data by using the function model to obtain the target model, the resource allocation device inputs a plurality of independent variable indexes into the function model obtained in the step S220, and then obtains a plurality of business models corresponding to the input independent variable indexes.
Further, the argument index includes at least: stock house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special job broker number index, floor number index.
A plurality of argument indexes may be defined as A, B, C, which is only an example, and actually each letter represents an argument index, i.e., a is a store number ratio index, B is a building number index, and C is a special job broker number index.
The method may further include inputting a plurality of the argument indexes into the function model, and obtaining a plurality of service models corresponding to the argument indexes may be, for example, inputting a into f (x) =k×x+b, to obtain an A1 service model: f (x) =k×a+b, and B is input f (x) =k×x+b to obtain a B1 service model: f (x) =k×b+b, and C input f (x) =k×x+b, to obtain a C1 traffic model: f (x) =k×c+b.
In step S232 of some embodiments, based on each of the service models, the initial service resource data corresponding to the argument index is calculated, so as to obtain a plurality of information criterion data corresponding to each of the service models. It may be understood that after the step S231 is performed to input a plurality of the argument indexes into the function models, respectively, to obtain a plurality of service models corresponding to the argument indexes, the resource allocation apparatus performs calculation processing on the initial service resource data corresponding to the argument indexes according to the plurality of service models obtained in the step S231, thereby obtaining information criterion data corresponding to each service model.
Further, in some embodiments, taking the B1 service model as an example, initial service resource data corresponding to B (which is a building number index) in the B1 service model is calculated, that is, data corresponding to a column of the number of maintenance rooms shown in fig. 2 is calculated, and a residual square sum is calculated.
The sum of squares of residuals (Residual Sum of Squares, i.e., RSS), also called the sum of squares of the residuals. Statistically, the difference between a data point and its corresponding position on the regression line is called the residual, and each residual squared is then added up to be called the sum of the residual squares, which represents the effect of random errors. The calculation steps of the sum of squares of the residuals are as follows:
Firstly, finding out the abscissa of each data point, substituting the abscissa into a regression model equation, calculating a theoretical ordinate value, subtracting the calculated theoretical ordinate value from the ordinate of the data point to obtain the difference between the data point and the theoretical ordinate value, calculating the square of the difference between the data point and the theoretical ordinate value, adding all squares, and finally obtaining the result as the sum of squares of the residual errors. Wherein, the difference value is calculated by subtracting the theoretical value and the actual value corresponding to the same abscissa.
It should be further noted that the regression model equation is a general mathematical model of multiple linear regression, and is not further limited herein.
The information criterion data calculation formula is:AIC is information criterion data, p represents variable number in service model, n represents observed quantity number, RSS represents residual square sum.
According to the information criterion data, the method is used for judging the service model with the optimal interpretation effort, and the AIC value is the model with the lowest interpretation effort, namely the model with the optimal current interpretation effort, so that the problems that in a traditional linear model, all variable indexes are directly introduced for fitting, the model interpretation effort is not necessarily optimal, and therefore a selected evaluation variable is possibly not optimal, when the variable is not representative, the evaluation based on the variable is inaccurate, and the resource allocation is unreasonable can be solved.
In step S233 of some embodiments, the values corresponding to the plurality of information criterion data are compared with a preset standard threshold value, so as to obtain a comparison result. It may be appreciated that, after the step S232 is performed, the initial service resource data corresponding to the argument index is calculated based on each service model, so as to obtain a plurality of information criterion data corresponding to each service model, and then the resource allocation device compares the values corresponding to the information criterion data corresponding to the plurality of service models with preset standard thresholds, so as to obtain a comparison result.
In step S234 of some embodiments, the information criterion data with the value not exceeding the preset standard threshold is screened according to the comparison result, and the service model corresponding to the information criterion data with the minimum value is determined to be an optimal model; and determining an optimal independent variable index corresponding to the optimal model. It can be understood that after the step S233 is performed to compare the values corresponding to the plurality of information criterion data with the preset standard threshold, and obtain a comparison result, according to the comparison result obtained in the step S233, the information criterion data whose value does not exceed the preset standard threshold is first filtered, and the information criterion data whose value exceeds the preset standard threshold is discarded. And sorting the information criterion data with the screened values not exceeding the preset standard threshold value, determining the information criterion data with the minimum value, and then obtaining the service model corresponding to the information criterion data with the minimum value as the optimal model.
Further, the corresponding optimal independent variable index is determined according to the optimal model, it can be understood that the optimal model is actually a model determined from a plurality of service models, and according to step S231, a plurality of independent variable indexes are respectively input into the function model, so as to obtain a plurality of service models corresponding to the independent variable index, and each service model has a unique corresponding independent variable index, so that the independent variable index corresponding to the optimal model is the optimal independent variable index.
In step S235 of some embodiments, it is determined whether the number of updates of the optimal model reaches a set number of times threshold, if the number of times threshold is not reached, other independent variable indexes except the optimal independent variable index are input to the optimal model for calculation processing, so as to obtain an updated optimal model, until the number of updates of the optimal model reaches the set number of times threshold, and the finally updated optimal model is used as the target model. It can be understood that, after step S234 is performed, the information criterion data whose value does not exceed the preset standard threshold is screened out according to the comparison result, and the service model corresponding to the information criterion data with the minimum value is determined to be the optimal model; after determining the optimal independent variable index corresponding to the optimal model, if in the embodiment of the present invention, the threshold of the number of times is set to three when determining the optimal model, which is only an example, the specific implementation steps may be as follows:
In some embodiments, the first step of determining the optimal model is:
and respectively inputting a plurality of independent variable indexes into an initial model to obtain a plurality of first business models corresponding to the independent variable indexes, wherein each first business model corresponds to each independent variable index, and based on each first business model, performing first calculation processing on the initial business resource data corresponding to the independent variable indexes to obtain a plurality of first information criterion data corresponding to each first business model.
Comparing the numerical value of the first information criterion data with a preset standard threshold value, screening out the first information criterion data with the numerical value not exceeding the preset standard threshold value, determining the first business model corresponding to the first information criterion data with the minimum numerical value as a first optimal model, screening out a first optimal independent variable index corresponding to the first optimal model from the independent variable indexes according to the first optimal model, and determining the target model according to the first optimal model and the independent variable indexes after the first optimal index is removed.
In some embodiments, the step of determining the optimal model a second time is:
And screening the first optimal index from the plurality of independent variable indexes to obtain a plurality of second independent variable indexes, respectively inputting the plurality of second independent variable indexes into the first optimal model to obtain a plurality of second service models, and performing second calculation processing on the initial service resource data corresponding to the second independent variable indexes based on each second service model to obtain second information criterion data corresponding to each second service model.
The specific implementation steps of the method can be as follows: let a be the first optimal index, the first optimal model be f (A1) =k1×x+b, where k1=k1×a, f (A1) be the first optimal model function value, where k1=k1×a corresponds to the new coefficient, and b is a constant. And respectively inputting a plurality of second independent variable indexes into the first optimal model to obtain a plurality of second service models, and performing second calculation processing on the initial service resource data corresponding to the second independent variable indexes based on each second service model to obtain second information criterion data corresponding to each second service model.
And comparing the numerical value of the second information criterion data with a preset standard threshold value, screening out the second information criterion data with the numerical value not exceeding the preset standard threshold value, determining the second business model corresponding to the second information criterion data with the minimum numerical value as a second optimal model, screening out a second optimal independent variable index corresponding to the second optimal model from the second independent variable indexes according to the second optimal model, and determining the target model according to the second optimal model and the second independent variable index after the second optimal index is removed.
In some embodiments, the third step of determining the optimal model is:
and screening the second optimal index from the second independent variable indexes to obtain a plurality of third independent variable indexes, respectively inputting the third independent variable indexes into the second optimal model to obtain a plurality of third service models, and carrying out third calculation processing on the initial service resource data corresponding to the third independent variable indexes based on each third service model to obtain third information criterion data corresponding to each third service model.
And comparing the numerical value of the third information criterion data with a preset standard threshold value, screening out the third information criterion data with the numerical value not exceeding the preset standard threshold value, determining the third business model corresponding to the third information criterion data with the minimum numerical value as a third optimal model, screening out a third optimal independent variable index corresponding to the third optimal model from the third independent variable indexes according to the third optimal model, and determining a target model according to the third optimal model and the third independent variable index after the third optimal index is removed.
In some embodiments, referring to fig. 6, step S400 may further include, but is not limited to, steps S410 to S430.
S410, acquiring the initial business resource data corresponding to the target business index;
s420, performing data conversion processing on the initial business resource data to obtain the target business resource data corresponding to the target business index;
and S430, calculating the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior.
In step S410 of some embodiments, the initial traffic resource data corresponding to the target traffic index is acquired. It may be appreciated that after the step S300 is performed and the target service index is extracted from the target model according to the preset extraction rule, the resource allocation device obtains, according to the received obtaining instruction, initial service resource data corresponding to the target service index from the database.
For example, in some embodiments of the present invention, the target traffic index extracted according to the extraction rule includes at least: store number ratio index, special post broker number index, maintenance house number index.
Then, referring to fig. 2, taking the maintenance room source number index as an example, the maintenance room source number indexes corresponding to all preset cities stored in the database are selected first, and then the initial service resource data corresponding to the maintenance room source number indexes are extracted for further data processing.
In step S420 of some embodiments, data conversion processing is performed on the initial service resource data, so as to obtain the target service resource data corresponding to the target service index. It may be appreciated that, after the step S410 is performed to obtain the initial service resource data corresponding to the target service index, the resource allocation device performs a data conversion process on the obtained initial service resource data to obtain the target service resource data.
Further, the formula for obtaining the target business resource data is as follows:
target traffic resource data= (median of x_i-x index)/(lower part of x index). Wherein x represents a certain target service index, i represents a certain city, the median of the x index is the median of the initial service resource data corresponding to the target service index, and the lower dividing point of the x index is the lower quarter point value of the initial service resource data corresponding to the target service index.
Further, median (Median), also known as Median, is a statistically proper term that is a Median number in a set of data arranged in sequence, representing a numerical value in a sample, population, or probability distribution, that divides a set of values into equal upper and lower parts. For a finite set of numbers, one in the middle can be found by ordering all observations high and low. If there are an even number of observations, the average of the two values in the middle is usually taken as the median.
The Quantile (Quantile), also called Quantile, refers to a numerical point that divides the probability distribution range of a random variable into several equal parts, and there are usually median (i.e., bipartite), quartile, percentile, etc. The quantile refers to a point in the continuous distribution function, which corresponds to the probability p. If the probability 0< p <1, the random variable X or the quantile Za of its probability distribution refers to a real number satisfying the condition p (x+.za) =α.
Wherein, quartile (Quartile) is one of the quartiles in statistics, i.e. all values are arranged from small to large and divided into four equal parts, and the value at the position of three division points is the Quartile.
The following sub-points: the first quartile (Q1), also known as the "smaller quartile", is equal to the 25% number after all values in the sample are arranged from small to large.
In step S430 of some embodiments, the target service resource data is calculated according to a preset adaptation formula, so as to obtain the service adaptation degree data corresponding to the pre-allocation behavior. It may be understood that after the step S420 is performed to perform data conversion processing on the initial service resource data to obtain the target service resource data corresponding to the target service index, the resource allocation device first obtains a preset adaptation formula, and then further performs calculation processing on the target service resource data obtained by the calculation in the step S420 according to the adaptation formula, so as to obtain service adaptation degree data.
Further, the specific implementation steps of the method can be as follows: firstly, obtaining a weight value preset for the target service index, and then carrying out weighted calculation processing on the target service resource data by utilizing the adaptation formula and the weight value to obtain the service adaptation degree data.
In some embodiments, referring to fig. 7, step S430 may further include, but is not limited to, steps S431 to S432.
S431, obtaining a weight value preset for the target business index;
s432, weighting calculation processing is carried out on the target business resource data by utilizing the adaptation formula and the weight value, and the business adaptation degree data is obtained.
In step S431 of some embodiments, a weight value preset for the target traffic index is acquired. It can be understood that the resource allocation device obtains the target service resource data corresponding to the target service index according to the obtaining instruction, and sets the weight value corresponding to the target service index according to the actual scene requirement of the user.
In step S432 of some embodiments, the service adaptation degree data is obtained by performing a weighted calculation process on the target service resource data using the adaptation formula and the weight value. It can be understood that, after step S431 is performed to obtain the weight value preset for the target service index, the adaptation formula and the weight value are used to perform weighted calculation processing on the target service resource data, so as to obtain service adaptation degree data.
Further, for example, as shown in fig. 3, the weight values of the target traffic indexes are preset to 0.5, 1, and 1, respectively. For example, in Chongqing city in fig. 3, the weight values are multiplied by the target service resource data corresponding to the target service indexes respectively, and then the adaptation data corresponding to a plurality of target service indexes in the same city are added and summed, so as to obtain the final service adaptation data. After obtaining the service adaptation data corresponding to each preset city, dynamically adjusting the allocation strategy of the initial service resources corresponding to the initial service indexes according to the service adaptation data so as to solve the problem of resource waste allocated according to the existing unreasonable allocation strategy.
According to the resource allocation method provided by the embodiment of the invention, the allocation strategy of pre-allocating a plurality of initial service indexes to the preset city is determined, and a plurality of initial service resource data corresponding to the initial service indexes are obtained; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the distribution strategy of the initial service index according to the service adaptation degree data. The distribution strategy of the initial distribution business resources can be dynamically adjusted according to the business adaptation data obtained by the verification calculation processing, so that the problem of resource waste caused by unbalanced distribution of the business resources is solved.
The following describes a resource allocation device provided by the present invention, and a resource allocation device described below and a resource allocation method described above may be referred to correspondingly to each other.
Referring to fig. 8, the present invention also provides a resource allocation apparatus, including:
an obtaining module 810, configured to determine an allocation policy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city, and obtain a plurality of initial service resource data corresponding to the initial service resources;
a preprocessing module 820, configured to preprocess a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
the extracting module 830 is configured to extract a target service index from the target model according to a preset extracting rule;
the calculation module 840 is configured to perform calculation processing on the target service resource data corresponding to the target service index, so as to obtain service adaptation degree data corresponding to the pre-allocation behavior;
and an adjustment module 850, configured to adjust the allocation policy of the initial service resource according to the service adaptation degree data.
According to the resource allocation device provided by the present invention, the obtaining module 810 is specifically further configured to at least include: a dependent variable index, an independent variable index;
the dependent variable index comprises at least: maintaining a competition index;
The argument index at least comprises: stock house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special job broker number index, floor number index.
According to the resource allocation device provided by the invention, the preprocessing module 820 is specifically further configured to obtain an initial function corresponding to the initial model; wherein the intercept term of the initial function is a preset constant value;
determining a function model according to the maintenance competition index and the initial function;
and calculating the initial business resource data by using the function model to obtain the target model.
According to the resource allocation device provided by the invention, the preprocessing module 820 is specifically further configured to input a plurality of independent variable indexes into the function model respectively, so as to obtain a plurality of service models corresponding to the independent variable indexes;
calculating the initial business resource data corresponding to the independent variable indexes based on each business model to obtain a plurality of information criterion data corresponding to each business model;
comparing the numerical values corresponding to the information criterion data with preset standard thresholds respectively to obtain comparison results;
Screening out the information criterion data with the value not exceeding the preset standard threshold according to the comparison result, and determining the business model corresponding to the information criterion data with the minimum value as an optimal model; determining an optimal independent variable index corresponding to the optimal model;
judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable indexes into the optimal model for calculation processing to obtain an updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold.
According to the resource allocation device provided by the invention, the calculation module 840 is specifically configured to obtain the initial service resource data corresponding to the target service index;
performing data conversion processing on the initial service resource data to obtain target service resource data corresponding to the target service index;
and calculating the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior.
According to the resource allocation device provided by the invention, the calculation module 840 is specifically configured to obtain a weight value preset for the target service index;
and carrying out weighted calculation processing on the target service resource data by using the adaptation formula and the weight value to obtain the service adaptation degree data.
The invention discloses a resource allocation device provided by an embodiment, which is characterized in that an allocation strategy for pre-allocating a plurality of initial service indexes to a preset city is determined, and a plurality of initial service resource data corresponding to the initial service indexes are obtained; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the distribution strategy of the initial service index according to the service adaptation degree data. The distribution strategy of the initial distribution business resources can be dynamically adjusted according to the business adaptation data obtained by the verification calculation processing, so that the problem of resource waste caused by unbalanced distribution of the business resources is solved.
Fig. 9 illustrates a physical schematic diagram of an electronic device, as shown in fig. 9, which may include: processor 910, communication interface (Communications Interface), memory 930, and communication bus 940, wherein processor 910, communication interface 920, and memory 930 communicate with each other via communication bus 940. Processor 910 can invoke logic instructions in memory 930 to perform a resource allocation method comprising: determining an allocation strategy for pre-allocating a plurality of initial service indexes to a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service indexes; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the distribution strategy of the initial service index according to the service adaptation degree data.
Further, the logic instructions in the memory 930 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, is capable of performing a resource allocation method comprising: determining an allocation strategy for pre-allocating a plurality of initial service indexes to a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service indexes; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the distribution strategy of the initial service index according to the service adaptation degree data.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform a method of resource allocation provided by the above methods, the method comprising: determining an allocation strategy for pre-allocating a plurality of initial service indexes to a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service indexes; preprocessing a preset initial model according to initial service indexes and initial service resource data to obtain a target model; extracting target business indexes from the target model according to a preset extraction rule; calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior; and adjusting the distribution strategy of the initial service index according to the service adaptation degree data.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A resource allocation method, characterized by being applied to a resource allocation apparatus, the resource allocation method comprising:
determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city, and acquiring a plurality of initial service resource data corresponding to the initial service resources;
preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
extracting target business indexes from the target model according to a preset extraction rule;
calculating target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior;
According to the service adaptation degree data, the allocation strategy of the initial service resources is adjusted;
before the determining the allocation policy for pre-allocating the initial service resources, the resource allocation method further includes:
the initial traffic index at least comprises: a dependent variable index, an independent variable index;
the dependent variable index comprises at least: maintaining a competition index;
the argument index at least comprises: inventory house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special post broker number index, floor number index;
the preprocessing of the preset initial model according to the initial service index and the initial service resource data to obtain a target model specifically comprises the following steps:
acquiring an initial function corresponding to the initial model; wherein the intercept term of the initial function is a preset constant value;
determining a function model according to the maintenance competition index and the initial function;
calculating the initial business resource data by using the function model to obtain the target model;
the calculating processing is performed on the initial business resource data by using the function model to obtain the target model, and the method specifically comprises the following steps:
Inputting a plurality of independent variable indexes into the function model respectively to obtain a plurality of business models corresponding to the independent variable indexes;
calculating the initial business resource data corresponding to the independent variable indexes based on each business model to obtain a plurality of information criterion data corresponding to each business model;
comparing the numerical values corresponding to the information criterion data with preset standard thresholds respectively to obtain comparison results;
screening out the information criterion data with the value not exceeding the preset standard threshold according to the comparison result, and determining the business model corresponding to the information criterion data with the minimum value as an optimal model; determining an optimal independent variable index corresponding to the optimal model;
judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable indexes into the optimal model for calculation processing to obtain an updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold.
2. The resource allocation method according to claim 1, wherein the calculating the target service resource data corresponding to the target service index to obtain service fitness data corresponding to the pre-allocation behavior includes:
acquiring the initial service resource data corresponding to the target service index;
performing data conversion processing on the initial service resource data to obtain target service resource data corresponding to the target service index;
and calculating the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior.
3. The method for allocating resources according to claim 2, wherein the calculating the target service resource data according to a preset adaptation formula to obtain the service adaptation degree data corresponding to the pre-allocation behavior includes:
acquiring a weight value preset for the target service index;
and carrying out weighted calculation processing on the target service resource data by using the adaptation formula and the weight value to obtain the service adaptation degree data.
4. A resource allocation apparatus, characterized by being applied to a resource allocation device, the resource allocation apparatus comprising:
The acquisition module is used for determining an allocation strategy of pre-allocated initial service resources based on a plurality of initial service indexes of a preset city and acquiring a plurality of initial service resource data corresponding to the initial service resources;
the preprocessing module is used for preprocessing a preset initial model according to the initial service index and the initial service resource data to obtain a target model;
before the determining of the allocation policy for pre-allocating the initial service resources, the method further comprises:
the initial traffic index at least comprises: a dependent variable index, an independent variable index;
the dependent variable index comprises at least: maintaining a competition index;
the argument index at least comprises: inventory house source number index, second hand broker number index, store number ratio index, house broker number ratio index, special post broker number index, floor number index;
the preprocessing of the preset initial model according to the initial service index and the initial service resource data to obtain a target model specifically comprises the following steps:
acquiring an initial function corresponding to the initial model; wherein the intercept term of the initial function is a preset constant value;
Determining a function model according to the maintenance competition index and the initial function;
calculating the initial business resource data by using the function model to obtain the target model;
the calculating processing is performed on the initial business resource data by using the function model to obtain the target model, and the method specifically comprises the following steps:
inputting a plurality of independent variable indexes into the function model respectively to obtain a plurality of business models corresponding to the independent variable indexes;
calculating the initial business resource data corresponding to the independent variable indexes based on each business model to obtain a plurality of information criterion data corresponding to each business model;
comparing the numerical values corresponding to the information criterion data with preset standard thresholds respectively to obtain comparison results;
screening out the information criterion data with the value not exceeding the preset standard threshold according to the comparison result, and determining the business model corresponding to the information criterion data with the minimum value as an optimal model; determining an optimal independent variable index corresponding to the optimal model;
judging whether the update times of the optimal model reach a set time threshold, if not, inputting other independent variable indexes except the optimal independent variable indexes into the optimal model for calculation processing to obtain an updated optimal model, and taking the finally updated optimal model as the target model until the update times of the optimal model reach the set time threshold;
The extraction module is used for extracting target business indexes from the target model according to preset extraction rules;
the computing module is used for computing the target service resource data corresponding to the target service index to obtain service adaptation degree data corresponding to the pre-allocation behavior;
and the adjustment module is used for adjusting the allocation strategy of the initial service resources according to the service adaptation degree data.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the resource allocation method according to any one of claims 1 to 3 when the program is executed by the processor.
6. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the resource allocation method according to any of claims 1 to 3.
7. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the resource allocation method according to any of claims 1 to 3.
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