Method for adjusting resource allocation and related equipment

Abstract

The invention provides a method for adjusting resource allocation, and the method comprises the steps: responding to a resource allocation data exception event, carrying out the analysis of multi-dimensional parameters of resource allocation, and obtaining the contribution degree of each parameter to the resource allocation data exception event; and analyzing the parameters corresponding to the contribution degree greater than the preset threshold to obtain an analysis result of the resource allocation data exception event, and readjusting resource allocation based on the analysis result. Based on the method for adjusting resource allocation, the invention further provides a device for adjusting resource allocation, electronic equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the technical field of Internet resource adjustment, and in particular, to a method for adjusting resource allocation and related devices. Background Art

[0002] With the development of Internet technology, resource allocation has gradually shifted from non-automation to automation to improve the efficiency and effectiveness in the allocation process. Fully automated resource allocation can effectively save human resources and enhance the allocation effect. For fully automated resource allocation, monitoring data anomalies that occur during the automated resource allocation process, promptly alerting, attributing, promoting the adjustment of resource allocation strategies, and enhancing the timeliness of the adjustment are important aspects of fully automated resource allocation and are also key capabilities for fully automated resource allocation to move towards intelligent allocation.

[0003] However, in the prior art, a series of operations such as monitoring data anomalies during the automated resource allocation process, promptly alerting, attributing, and reallocating resources based on the results are all non-automated and cannot be completed independently of humans. They still rely heavily on human experience, resulting in highly subjective final results and poor timeliness. Summary of the Invention

[0004] In view of this, the purpose of the present disclosure is to propose a method for adjusting resource allocation and related devices, which can effectively monitor data anomalies that occur during the automated resource allocation process, promptly alert, attribute, promote the adjustment of resource allocation strategies, and enhance the timeliness of the adjustment. The overall process can be automatically completed with low human dependence, effectively avoiding the subjectivity of the results and effectively improving the timeliness of resource allocation adjustment.

[0005] According to some embodiments of the present disclosure, the method for adjusting resource allocation may include: in response to a resource allocation data anomaly event, analyzing multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data anomaly event; analyzing the parameters corresponding to the contribution degree greater than a preset threshold to obtain an analysis result of the resource allocation data anomaly event, and readjusting the resource allocation based on the analysis result.

[0006] Based on the above attribution method for applications, embodiments of the present disclosure provide a device for adjusting resource allocation, including:

[0007] An analysis module, configured to, in response to a resource allocation data anomaly event, analyze multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data anomaly event;

[0008] An allocation module, configured to analyze the parameters corresponding to the contribution degree greater than a preset threshold, obtain an analysis result of the resource allocation data anomaly event, and re-adjust the resource allocation based on the analysis result.

[0009] In addition, an embodiment of the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the above method is implemented.

[0010] An embodiment of the present disclosure further provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above method.

[0011] An embodiment of the present disclosure further provides a computer program product including computer program instructions that, when run on a computer, cause the computer to execute the above method.

[0012] The above method and related devices for adjusting resource allocation include: in response to a resource allocation data anomaly event, analyzing multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data anomaly event; analyzing the parameters corresponding to the contribution degree greater than a preset threshold to obtain an analysis result of the resource allocation data anomaly event, and re-adjusting the resource allocation based on the analysis result. When adjusting the resource allocation, the embodiment of the present disclosure can first monitor the data anomaly event of the resource allocation, then analyze the multi-dimensional parameters affecting the resource allocation to obtain the parameter with the highest contribution degree to the resource allocation data anomaly event, then perform further detailed analysis on this parameter to obtain the final analysis result, and finally use this analysis result to implement the adjustment of the resource allocation. It can be seen that in the entire process of the present disclosure, the monitoring of resource allocation anomalies, the analysis of multi-dimensional parameters, the calculation of contribution degrees, the detailed analysis of the parameter with the highest contribution degree, and the provision of relevant adjustment strategies based on the analysis results can all be automated without manual intervention, and there is no need to wait for manual empirical analysis. Therefore, based on the foregoing statements, the embodiment of the present disclosure can effectively achieve the monitoring of data anomalies occurring in the automated resource allocation process, timely warning, attribution, promoting the adjustment of the resource allocation strategy and improving the timeliness of the adjustment. The overall process can be automatically completed, with low dependence on manual labor, effectively avoiding the subjectivity of the results, and effectively improving the timeliness of the resource allocation adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] To more clearly illustrate the technical solutions in the present disclosure or related technologies, the following will briefly introduce the drawings required for use in the embodiments or the description of related technologies. Obviously, the drawings in the following description are only embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 Shows the implementation process of the method for adjusting resource allocation according to some embodiments of the present disclosure;

[0015] Figure 2 Shows the schematic diagram of the box plot according to some embodiments of the present disclosure;

[0016] Figure 3 Shows the schematic diagram of the device for adjusting resource allocation according to some embodiments of the present disclosure;

[0017] Figure 4 Shows the schematic diagram of the hardware structure of the electronic device according to the embodiments of the present disclosure. Detailed implementation manners

[0018] To make the purpose, technical solutions and advantages of the present disclosure clearer and more understandable, the following will further elaborate on the present disclosure in detail with reference to specific embodiments and the accompanying drawings.

[0019] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the ordinary meaning understood by those of ordinary skill in the art to which the present disclosure belongs. The "first", "second" and similar terms used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. The terms such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right" are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0020] It can be understood that before using the technical solutions of the various embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner and the user's authorization will be obtained.

[0021] For example, when responding to an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application, a server, or a storage medium that performs the operations of the present disclosure's technical solution based on the prompt message.

[0022] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0023] It can be understood that the above notification and obtaining user authorization process is only illustrative and does not limit the implementation manner of the present disclosure. Other manners that comply with relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0024] As mentioned above, with the development of Internet technology, resource allocation has gradually shifted from non-automation to automation to improve the efficiency and effectiveness in the allocation link. Full-process automated resource allocation can effectively save human resources and improve the allocation effect. For full-process automated resource allocation, monitoring data anomalies that occur during the automated resource allocation process, promptly alarming, attributing, promoting adjustments to the resource allocation strategy, and enhancing the timeliness of the adjustments are an important part of full-process automated resource allocation and are also the key capabilities for full-process automation to move towards intelligent allocation. However, in the prior art, currently, a series of operations such as monitoring data anomalies during the automated resource allocation process, promptly alarming, attributing, and reallocating resources based on the results all need to be non-automated and cannot be completed independently without human intervention. It still relies heavily on human experience, resulting in a very subjective final result and poor timeliness.

[0025] To this end, some embodiments of the present disclosure provide a method for adjusting resource allocation. When adjusting resource allocation, first, a data anomaly event of resource allocation can be monitored. Then, by analyzing multi-dimensional parameters that affect resource allocation, the parameter with the highest contribution degree to the resource allocation data anomaly event is obtained. After that, further refinement analysis is performed on this parameter to obtain the final analysis result. Finally, the analysis result is used to implement the adjustment of resource allocation. It can be seen that in the entire process of the present disclosure, the monitoring of resource allocation anomalies, the analysis of multi-dimensional parameters, the calculation of contribution degrees, the refinement analysis of the parameter with the highest contribution degree, and the provision of relevant adjustment strategies based on the analysis results can all be automated without manual intervention, and there is no need to wait for manual empirical analysis. Therefore, the embodiments of the present disclosure can effectively achieve the automation of monitoring data anomalies that occur during the resource allocation process, timely alarm, attribution, promote the adjustment of resource allocation strategies, and improve the timeliness of adjustment. The overall process can be automatically completed with low dependence on manual labor, effectively avoiding the subjectivity of results, and effectively improving the timeliness of resource allocation adjustment.

[0026] Figure 1 shows the implementation process of the method for adjusting resource allocation according to some embodiments of the present disclosure, as Figure 1 shown, the method may include the following steps:

[0027] In some embodiments of the present disclosure, before analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event, it further includes: obtaining daily resource allocation data; using a preset algorithm to determine whether there are outliers in the daily resource allocation data; in response to the existence of outliers in the daily resource allocation data, readjusting resource allocation.

[0028] In the embodiments of the present disclosure, before explaining the resource allocation data anomaly event, it is necessary to first detect the daily resource allocation data. The resource allocation data here may include automatic resource allocation data, non-automatic resource allocation data, automatic resource allocation data at the first time, and automatic resource allocation data at the second time.

[0029] First, it is necessary to obtain the daily resource allocation data, and then use a preset algorithm to determine whether there are outliers in the daily resource allocation data.

[0030] Specifically, the judgment of outliers can be made through a threshold. If it is higher than the upper threshold or lower than the lower threshold, it indicates that the data of that day is abnormal.

[0031] In some embodiments of the present disclosure, the lower threshold is calculated by the following formula:

[0032] T1 = P1 - 1.5 × IQR

[0033] Wherein, T1 represents the lower threshold, P1 represents the 25th percentile, IQR represents the difference between P3 and P1, and P3 represents the 75th percentile.

[0034] In some embodiments of the present disclosure, the upper threshold is calculated by the following formula:

[0035] T2 = P3 + 1.5 × IQR

[0036] Wherein, T2 represents the upper threshold, IQR represents the difference between P3 and P1, P3 represents the 75th percentile, and P1 represents the 25th percentile.

[0037] After calculating the upper and lower thresholds, the data of the current day can be judged. In the calculation process, the box plot method can also be used to visually judge the data.

[0038] Figure 2 Shows a schematic diagram of the box plot according to some embodiments of the present disclosure.

[0039] As Figure 2 shown, the left box plot is the box plot of the automatic resource allocation data of the embodiments of the present disclosure, and the right box plot is the box plot of the non-automatic resource allocation data of the embodiments of the present disclosure.

[0040] The middle line of the box is the median of the data, representing the average level of the sample data. The upper and lower limits of the box are the upper quartile and the lower quartile of the data respectively. This means that the box contains 50% of the data. Therefore, the width of the box reflects the degree of data fluctuation to a certain extent.

[0041] Above and below the box, there is another line each. Representing the maximum and minimum values, the data points above the left box plot are abnormal data.

[0042] Obviously, in Figure 2 , there are abnormal data in the automatic resource allocation data. Therefore, it is necessary to first locate the situation of the current day and analyze whether there is an error in resource allocation on the current day, resulting in the appearance of abnormal data. If it is found that there is indeed an error, it can be corrected. If it is found that there is no error, the following steps can be used to continue analyzing the data.

[0043] In step 102, in response to the resource allocation data exception event, analyze the multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data exception event.

[0044] In the embodiments of the present disclosure, the problem of resource allocation in two scenarios is mainly to be solved. One is that the first target parameter of automatic resource allocation is higher than that of non-automatic resource allocation, and the other is that the first target parameter of automatic resource allocation at the first time is higher than that of automatic resource allocation at the second time. In the first scenario, the two resource allocation methods of automatic resource allocation and non-automatic resource allocation coexist. In this scenario, the present disclosure needs to adjust the resource allocation so that the first target parameter of automatic resource allocation is lower than that of non-automatic resource allocation. When the first target parameter of automatic resource allocation is higher than that of non-automatic resource allocation, it is determined at this time that a resource allocation data anomaly event occurs. For the scenarios of automatic resource allocation and non-automatic resource allocation, the present disclosure hopes that this scenario can gradually transition to only the case of automatic resource allocation, because in essence, it still hopes to achieve full-process automation. Therefore, during the transition process, it is necessary to optimize the effect of automatic resource allocation. So the anomaly event is set as the first target parameter of automatic resource allocation being higher than that of non-automatic resource allocation, so that the technical solution of automatic resource allocation can be continuously optimized. When its data gradually starts to stabilize, it can be considered to transition to the scenario of all automatic resource allocation. In the second scenario, this scenario already only has the technical solution of automatic resource allocation, but it still needs to be continuously optimized during the implementation process to make its first target parameter develop towards a lower value. Therefore, in the second scenario, the anomaly event is set as the first target parameter of automatic resource allocation at the first time being higher than that of automatic resource allocation at the second time, so as to achieve continuous optimization of automatic resource allocation.

[0045] The following description of the present disclosure details the specific implementation process of the present disclosure in two branches.

[0046] First, the case where the resource allocation data anomaly event is that the first target parameter of automatic resource allocation is higher than the first target parameter of non-automatic resource allocation is described.

[0047] In some embodiments of the present disclosure, the resource allocation data anomaly event includes: the first target parameter of automatic resource allocation is higher than the first target parameter of non-automatic resource allocation; in response to the resource allocation data anomaly event, the multi-dimensional parameters of resource allocation are analyzed to obtain the contribution degree of each parameter to the resource allocation data anomaly event, including: in response to the first target parameter of automatic resource allocation being higher than the first target parameter of non-automatic resource allocation, analyzing the first difference between the multi-dimensional parameters of the automatic resource allocation and the non-automatic resource allocation; based on the first difference, analyzing the contribution degree of each parameter to the first difference.

[0048] In an embodiment of the present disclosure, the multi-dimensional parameters refer to some parameters that have an impact on resource allocation. In the prior art, generally when a resource allocation data anomaly event is detected, all dimensions of parameters are carefully analyzed. However, this consumes a lot of manpower and material resources, and the prior art also uses manual means when analyzing parameters, which further affects the timeliness of obtaining the analysis results and adjusting resource allocation according to the analysis results. In the embodiment of the present disclosure, the contribution degree of the multi-dimensional parameters is analyzed first, so that the parameters that contribute the most to the current resource allocation data anomaly event can be quickly determined, and then targeted and detailed analysis is performed on this parameter. Obviously, this can quickly find the root cause of the problem and can quickly achieve effective adjustment of resource allocation.

[0049] In step 104, analyze the parameters corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and re-adjust the resource allocation based on the analysis result.

[0050] In some embodiments of the present disclosure, wherein the multi-dimensional parameters include materials; the analyzing the parameters corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and re-adjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being a material, determining whether the materials used in the automatic resource allocation and the non-automatic resource allocation are the same; in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being different, analyzing the characteristics of the material to obtain the analysis result; analyzing the material used in the non-automatic resource allocation to obtain available materials; re-adjusting the allocation of the material based on the analysis result and the available materials.

[0051] In the embodiment of the present disclosure, when calculating the contribution degree, the weighted proportion method can be used. Specifically, in the actual calculation process, it is necessary to first calculate the contribution degree within the category and the contribution degree between categories, and then calculate the sum of the contribution degree within the category and the contribution degree between categories, which is the final comprehensive contribution degree.

[0052] In some embodiments of the present disclosure, the contribution degree within the category is calculated by the following formula:

[0053] C1 = w i2 (q i2 -q i1 )

[0054] Wherein, C1 represents the contribution degree within the category, w i2 represents the denominator proportion of dimension i in this period, q i2 represents the index of dimension i in this period, qi1 Indicates the index of the base period dimension i.

[0055] In the embodiments of the present disclosure, when analyzing the parameter of the material, dimension i represents the material parameter dimension, the current period represents automatic resource allocation, the base period represents non-automatic resource allocation, and the denominator ratio of dimension i in the current period represents the quantity of the second resource obtained. The meaning of the second resource expressed in the embodiments of the present disclosure is that after the resources are allocated, other resources can be obtained, and the second resource is included among the other resources obtained above. The reason for using the expression of the denominator ratio here is that in the embodiments of the present disclosure, the first target parameter is a division operation, and for the case where the target parameter is a division operation, it is more appropriate to use the weighted ratio method to calculate the contribution degree. The denominator ratio here refers to the parameter located in the denominator when calculating the first target parameter, that is, the aforementioned second resource, and the denominator ratio refers to the ratio of the obtained second resource to the whole.

[0056] In some embodiments of the present disclosure, the contribution value between categories is calculated by the following formula:

[0057] C2 = (q i1 - Q1)(w i2 - w i1 )

[0058] Wherein, C2 represents the contribution value between categories, q i1 represents the index of the base period dimension i, Q1 represents the total base period index, w i2 represents the denominator ratio of dimension i in the current period, and w i1 represents the denominator ratio of dimension i in the base period.

[0059] In the embodiments of the present disclosure, when analyzing the parameter of the material, the total base period index represents the total value of the first target parameter in non-automatic resource allocation, and the denominator ratio of dimension i in the current period represents the quantity of the second resource obtained in the case of automatic resource allocation. The remaining parameters are as shown above and will not be elaborated here.

[0060] After that, the contribution degree within the category and the contribution degree between categories are added together to obtain the contribution degree of dimension i, that is, the contribution degree corresponding to the parameter of the material.

[0061] In the above steps, the contribution degree corresponding to each material has been calculated, and the contribution degree reflects the contribution value of the material to the generated first difference.

[0062] Further, after calculating the contribution degree corresponding to each parameter, obtain the parameter corresponding to the contribution degree greater than the preset threshold.

[0063] In the embodiments of the present disclosure, it should be noted that, for the convenience of description, only one parameter is taken here. In the actual application process, there may be multiple parameters whose contribution degrees are higher than the preset threshold. For each parameter whose contribution degree is higher than the preset threshold, corresponding detailed analysis needs to be carried out.

[0064] In the embodiments of the present disclosure, taking the parameter as the material as an example, it is necessary to determine whether the materials used for automatic resource allocation and non-automatic resource allocation are the same at this time. If the materials used are not the same, it is necessary to analyze the characteristics of the material to obtain the corresponding analysis result. For example, it is necessary to analyze whether there are problems with the channels from which the material comes. If there are indeed problems, further adjustment of the channels from which the material comes is required to enable adjustment of the repeatedly allocated materials, so that the first target parameter can be correspondingly optimized.

[0065] After that, it is also possible to analyze the corresponding material categories for automatic resource allocation to check whether there are corresponding impacts on the material categories. If it is found that there are impacts after analysis, the selection of material categories in this dimension is adjusted.

[0066] At the same time, it is also possible to analyze the corresponding materials for non-automatic resource allocation simultaneously or in parallel. For example, it can be compared with the materials used for automatic resource allocation to check whether there are excellent materials that have not been selected by automatic resource allocation. If there are such materials, a corresponding list of the materials is listed, and it is checked whether they need to be added to the material library for automatic resource allocation during the subsequent adjustment process.

[0067] In the embodiments of the present disclosure, when adjusting the channels from which the materials come, it is also possible to continue calculating the contribution degree corresponding to each channel from which the materials come. For example, the dimensions corresponding to the channels from which the materials come may include the sources recycled using a preset model, the sources recycled manually, and the sources of repeated material allocation, etc. In the previous steps, the contribution degrees corresponding to these three dimensions are calculated respectively. Assuming that taking the dimension of the source of material reallocation as an example, that is, its corresponding contribution degree is higher than the preset threshold, then further detailed analysis of the materials in this dimension is carried out. Specifically, it can be determined whether there are problems when the materials are repeatedly allocated in the dimension of material reallocation. If there are indeed problems, further adjustment of the channels from which the materials come is required. For example, the algorithm for material repeated allocation is adjusted to enable adjustment of the repeatedly allocated materials, so that the first target parameter can be correspondingly optimized.

[0068] In some embodiments of the present disclosure, the method further includes: in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same, analyzing the creation parameters of the materials to obtain the analysis result; and readjusting the allocation of the materials based on the analysis result.

[0069] In an embodiment of the present disclosure, if the materials used by both are the same, then analyze the creation parameters corresponding to the materials. For example, the number of times of creation and the number of days of creation of the materials can be analyzed. If there are significant differences in the number of times of creation or the number of days of creation between the automatic resource allocation and the non-automatic resource allocation, then adjust the number of times of creation or the number of days of creation of the materials used in the automatic resource allocation accordingly.

[0070] In addition, it is also possible to obtain the number of times of creation or the number of days of creation of the materials corresponding to the lower first target parameter value under the non-automatic resource allocation, and use it as a reference value to make corresponding adjustments to the relevant parameters of the automatic resource allocation.

[0071] In some embodiments of the present disclosure, the method further includes: in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same, determining whether the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation are the same; in response to the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation being different, adjusting the objects of the automatic resource allocation.

[0072] In an embodiment of the present disclosure, if the materials used by both are the same, but the objects allocated to them are different, it is also possible to adjust the allocation objects of the materials. In the actual adjustment process, the objects corresponding to the automatic resource allocation under this dimension parameter can be modified, or the objects corresponding to the non-automatic resource allocation can be added to the objects corresponding to the automatic resource allocation.

[0073] And it is also possible to analyze whether there is a mismatch between the parameter and its corresponding allocation object. If such a situation exists, then similarly, make appropriate adjustments to the corresponding allocation object.

[0074] In some embodiments of the present disclosure, the multi-dimensional parameter includes the object of resource allocation; analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data exception event, and readjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being the object, analyzing the contribution degree of each object to the first difference; in response to the contribution degree of the object to the first difference being higher than the preset threshold, adjusting the object.

[0075] In an embodiment of the present disclosure, the parameter can also be an object of resource allocation. The object has different types, and the contribution degrees of different types of objects to the first difference can be calculated. When the contribution degree of a certain object is greater than a preset threshold, the object in this dimension can be adjusted accordingly. The adjustment method can refer to the adjustment method of the object in the material, which will not be elaborated here. It should be noted that although the adjustment of the object is involved in both here and the aforementioned adjustment method of the material, the actual objects being adjusted are not exactly the same. Because under the dimension corresponding to the material, the adjustment of the object is based on the premise of the existence of the dimension of the material and has this constraint condition, while the technical solution described here directly analyzes different dimensions of the object, and there are still some differences between the two.

[0076] In some embodiments of the present disclosure, the multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated for the resource allocation; analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and readjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being the allocation parameter, readjusting the allocation parameter.

[0077] In an embodiment of the present disclosure, when allocating resources, the first resource needs to be consumed. The first resource here and the resource are two different resources. The allocation parameter mentioned here is the adjustment of the specific allocation of the first resource. Adjusting the first resource will affect the ability of the resource allocation itself, and thus affect the value of the final first target parameter. Therefore, when considering the parameter, the first resource also needs to be taken into account. When the contribution degree of the allocation parameter is greater than the preset threshold, the relationships between the adjustment of the first resource and the change of the first target parameter are respectively obtained in the cases of automatic resource allocation and non-automatic resource allocation. Based on the aforementioned relationships, relevant suggestions for the adjustment of the first resource are analyzed.

[0078] When the resource allocation data anomaly event is that the first target parameter of the automatic resource allocation at the first time is higher than the first target parameter of the automatic resource allocation at the second time, the specific implementation steps are similar to the case where the resource allocation data anomaly event is that the first target parameter of the automatic resource allocation is higher than the first target parameter of the non-automatic resource allocation. Just replace the automatic resource allocation with the automatic resource allocation at the first time and replace the non-automatic resource allocation with the automatic resource allocation at the second time. Specifically, it is as follows.

[0079] In step 102, in response to the resource allocation data anomaly event, analyze the multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data anomaly event.

[0080] In some embodiments of the present disclosure, the resource allocation data anomaly event includes: the first target parameter of the automatic resource allocation at the first time is higher than the first target parameter of the automatic resource allocation at the second time; in response to the resource allocation data anomaly event, analyzing multi-dimensional parameters of the resource allocation to obtain the contribution degree of each parameter to the resource allocation data anomaly event, including: in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time, analyzing a second difference between the automatic resource allocation at the first time and the first target parameter of the automatic resource allocation at the second time; based on the second difference, analyzing to obtain the contribution degree of each parameter to the second difference.

[0081] In the embodiments of the present disclosure, the multi-dimensional parameters refer to some parameters that affect resource allocation. In the prior art, generally when a resource allocation data anomaly event is detected, all-dimensional parameters are carefully analyzed. However, this will consume a lot of human and material resources, and the prior art also uses manual means when analyzing parameters, which further affects the timeliness of obtaining the analysis results and adjusting resource allocation according to the analysis results. In the embodiments of the present disclosure, the contribution degree of the multi-dimensional parameters is analyzed first, so that the parameters that contribute the most to the current resource allocation data anomaly event can be quickly determined, and then targeted and detailed analysis is performed on this parameter. Obviously, this can quickly find the root cause of the problem and can quickly achieve effective adjustment of resource allocation.

[0082] In step 104, analyze the parameters corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and re-adjust the resource allocation based on the analysis result.

[0083] In some embodiments of the present disclosure, the multi-dimensional parameters include materials; analyzing the parameters corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and re-adjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being a material, determining whether the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same; in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being different, analyzing the characteristics of the material to obtain an analysis result; and / or analyzing the material used in the automatic resource allocation at the second time to obtain available materials; re-adjusting the allocation of the material based on the analysis result and / or the available materials.

[0084] In an embodiment of the present disclosure, when calculating the contribution degree, the weighted proportion method can be used for calculation. Specifically, in the actual calculation process, it is necessary to first calculate the contribution degree within the category and the contribution degree between categories, and then calculate the sum of the contribution degree within the category and the contribution degree between categories, which is the final comprehensive contribution degree.

[0085] In some embodiments of the present disclosure, the contribution degree within the category is calculated by the following formula:

[0086] C1 = w i2 (q i2 -q i1 )

[0087] Wherein, C1 represents the contribution degree within the category, and w i2 represents the denominator proportion of dimension i in the current period, q i2 represents the index of dimension i in the current period, and q i1 represents the index of dimension i in the base period.

[0088] In an embodiment of the present disclosure, when analyzing the parameter of material, dimension i represents the material parameter dimension, the current period represents the automatic resource allocation at the first time, the base period represents the automatic resource allocation at the second time, and the denominator proportion of dimension i in the current period represents the quantity of the second resource obtained. The meaning of the second resource in the embodiment of the present disclosure is that after the resources are allocated, other resources can be obtained, and the second resource is included among the other resources obtained above. The reason for using the expression of denominator proportion here is that in the embodiment of the present disclosure, the first target parameter is a division operation, and for the case where the target parameter is a division operation, it is more appropriate to use the weighted proportion method to calculate the contribution degree. The denominator proportion here refers to the parameter located in the denominator when calculating the first target parameter, that is, the aforementioned second resource, and the denominator proportion refers to the proportion of the obtained second resource in the whole.

[0089] In some embodiments of the present disclosure, the contribution value between categories is calculated by the following formula:

[0090] C2 = (q i1 -Q1)(w i2 -w i1 )

[0091] Wherein, C2 represents the contribution value between categories, q i1 represents the index of dimension i in the base period, Q1 represents the total base period index, w i2 represents the denominator proportion of dimension i in the current period, and w i1 represents the denominator proportion of dimension i in the base period.

[0092] In an embodiment of the present disclosure, when analyzing the parameter of material, the total base period index represents the total value of the first target parameter of automatic resource allocation at the second time, and the denominator proportion of the i-th dimension in the current period represents the quantity of the second resource obtained under the condition of automatic resource allocation at the first time. The remaining parameters are as shown above and will not be elaborated here.

[0093] After that, the contribution within the category and the contribution between categories are summed up to obtain the contribution of dimension i, that is, the contribution corresponding to the parameter of material.

[0094] In the foregoing steps, the contribution corresponding to each material has been calculated, and this contribution reflects the contribution value of the material to the generated first difference.

[0095] Furthermore, after calculating the contribution corresponding to each parameter, obtain the parameters corresponding to the contributions greater than the preset threshold.

[0096] In an embodiment of the present disclosure, it should be noted that, for the convenience of description, only one parameter is taken here. In the actual application process, there may be multiple parameters whose contributions are higher than the preset threshold. For each parameter with a contribution higher than the preset threshold, corresponding refined analysis needs to be carried out.

[0097] In an embodiment of the present disclosure, taking the parameter of material as an example, it is necessary to determine whether the materials used for automatic resource allocation at the first time and the second time are the same. If the materials used are not the same, it is necessary to analyze the characteristics of the material to obtain the corresponding analysis result. For example, analyze whether there is a problem with the channel from which the material comes. If there is indeed a problem, further adjustment of the channel from which the material comes is required to enable the adjustment of the repeatedly allocated materials, so that the first target parameter can be correspondingly optimized.

[0098] After that, it is also possible to analyze the corresponding material category of the automatic resource allocation at the first time to check whether there is an impact on the material category. If it is found that there is an impact after analysis, adjust the selection of the material category under this dimension.

[0099] At the same time, it is also possible to analyze the corresponding materials of the automatic resource allocation at the second time simultaneously or in parallel. For example, compare with the materials used for the automatic resource allocation at the first time to check whether there are excellent materials that have not been selected by the automatic resource allocation. If there are such materials, list the corresponding materials in a list, and check whether they need to be added to the material library of the automatic resource allocation at the first time during the subsequent adjustment process.

[0100] In an embodiment of the present disclosure, when adjusting the channels of material sources, the contribution degrees corresponding to each material source channel can also be continuously calculated. For example, the dimensions corresponding to the channels of material sources can include the sources recovered by using a preset model, the sources recovered manually, and the sources of material reallocation, etc. In the foregoing steps, the contribution degrees corresponding to these three dimensions will be calculated respectively. Assuming that, taking the dimension of the source of material reallocation as an example, its corresponding contribution degree is higher than a preset threshold, then further detailed analysis will be performed on the materials under this dimension. Specifically, it can be determined whether there are problems when analyzing the materials during reallocation in the dimension of material reallocation. If there are indeed problems, then further adjustment needs to be made to the channels of the material sources accordingly. For example, adjust the algorithm during material reallocation to enable the adjustment of the reallocated materials, so that the first target parameter can be correspondingly optimized.

[0101] In some embodiments of the present disclosure, the method further includes: in response to the materials used in the automatic resource allocation at the first time being the same as those used in the automatic resource allocation at the second time, analyzing the creation parameters of the materials to obtain the analysis result; and re-adjusting the allocation of the materials based on the analysis result.

[0102] In an embodiment of the present disclosure, if the materials used are the same, then analyze the creation parameters corresponding to the materials. For example, the number of creation times and the number of days of creation of the materials can be analyzed. If there are significant differences in the number of creation times or the number of days of creation between the automatic resource allocation and the non-automatic resource allocation, then make corresponding adjustments to the number of creation times or the number of days of creation of the materials used in the automatic resource allocation.

[0103] In addition, it is also possible to obtain the number of creation times or the number of days of creation of the materials corresponding to the lower first target parameter value under the automatic resource allocation at the second time, and use it as a reference value to make corresponding adjustments to the relevant parameters of the automatic resource allocation at the first time.

[0104] In some embodiments of the present disclosure, the method further includes: in response to the materials used in the automatic resource allocation at the first time being the same as those used in the automatic resource allocation at the second time, determining whether the objects to which the materials in the automatic resource allocation at the first time and the automatic resource allocation at the second time are allocated are the same; and in response to the objects of the automatic resource allocation at the first time and the automatic resource allocation at the second time being different, adjusting the objects of the automatic resource allocation.

[0105] In an embodiment of the present disclosure, if the materials used by both are the same but the allocated objects are different, the allocated objects of the material can also be adjusted. During the actual adjustment process, the object corresponding to the automatic resource allocation at the first time under this dimensional parameter can be modified, or the object corresponding to the automatic resource allocation at the second time can be added to the object corresponding to the automatic resource allocation at the first time.

[0106] And it is also possible to analyze whether there is a mismatch between the parameter and its corresponding allocated object. If such a situation exists, then similarly, the corresponding allocated object is appropriately adjusted.

[0107] In some embodiments of the present disclosure, the multi-dimensional parameter includes the object of resource allocation; analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data abnormal event, and readjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being the object, analyzing the contribution degree of each object to the second difference; in response to the contribution degree of the object to the second difference being higher than the preset threshold, adjusting the object.

[0108] In an embodiment of the present disclosure, the parameter can also be the object of resource allocation, and the objects have different types. The contribution degrees of different types of objects to the first difference can be calculated. When the contribution degree of a certain object is greater than the preset threshold, the object in this dimension can be adjusted accordingly. The adjustment method can refer to the adjustment method of the object in the material, which will not be elaborated here. It should be noted that although both the adjustment method here and the adjustment method of the material mentioned above involve the adjustment of the object, the actual adjustment objects are not exactly the same. Because under the dimension corresponding to the material, the adjustment of the object is based on the premise of the dimension of the material and has this constraint condition, while the technical solution described here directly analyzes different dimensions of the object, and there are still some differences between the two.

[0109] In some embodiments of the present disclosure, the multi-dimensional parameter includes the allocation parameter; the allocation parameter is the quantity of the first resource allocated for the resource allocation; analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data abnormal event, and readjusting the resource allocation based on the analysis result includes: in response to the parameter corresponding to the contribution degree greater than the preset threshold being the allocation parameter, adjusting the allocation parameter.

[0110] In an embodiment of the present disclosure, when allocating resources, a first resource needs to be consumed. Here, the first resource and the resource are two different resources. The allocation parameter mentioned here is an adjustment for the specific allocation of the first resource. Adjusting the first resource will affect the ability of resource allocation itself, and thus affect the value of the final first target parameter. Therefore, when considering parameters, the first resource also needs to be taken into account. When the contribution degree of the allocation parameter is greater than a preset threshold, the relationship between the adjustment of the first resource and the change of the first target parameter is obtained under the conditions of automatic resource allocation at the first time and automatic resource allocation at the second time respectively. Based on the foregoing relationship, relevant suggestions for the adjustment of the first resource are analyzed.

[0111] In some embodiments of the present disclosure, before analyzing the multi-dimensional parameters of resource allocation in response to a resource allocation data exception event to obtain the contribution degree of each parameter to the resource allocation data exception event, it further includes: in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time, analyzing the influence of external factors.

[0112] In an embodiment of the present disclosure, for the case of a resource allocation data exception event where the first target parameter of the automatic resource allocation at the first time is higher than the first target parameter of the automatic resource allocation at the second time, because there are time differences between the compared data, the influence brought by different times should be considered when considering influencing factors. Because some special events can occur during some special time periods, and of course, it can also directly cause the occurrence of some special events, which are called external factors. These external factors can cause the first target parameter to change. Therefore, the relevant influence of external factors also needs to be considered in this scenario. For example, if some external factors cause the overall first target parameter to double at the second time compared to the first time, and the first target parameter of the automatic resource allocation at the second time also doubles compared to the first target parameter of the automatic resource allocation at the first time, then this event may not be determined as a resource allocation data exception event because the increase amplitude is the same. Of course, the same applies to a decrease.

[0113] The above method for adjusting resource allocation analyzes multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event; analyzes the parameters corresponding to the contribution degree greater than a preset threshold to obtain the analysis result of the resource allocation data anomaly event, and re-adjusts the resource allocation based on the analysis result. When adjusting the resource allocation in the embodiments of the present disclosure, first, a data anomaly event of resource allocation can be monitored, and then by analyzing multi-dimensional parameters affecting resource allocation, the parameter with the highest contribution degree to the resource allocation data anomaly event is obtained. Then, further refined analysis is performed on this parameter to obtain the final analysis result. Finally, the analysis result is used to adjust the resource allocation. It can be seen that in the entire process of the present disclosure, monitoring resource allocation anomalies, analyzing multi-dimensional parameters, calculating contribution degrees, performing refined analysis on the parameter with the highest contribution degree, and providing relevant adjustment strategies according to the analysis result can all be automated without manual intervention, and there is no need to wait for manual empirical analysis. Therefore, based on the foregoing statements, the embodiments of the present disclosure can effectively achieve monitoring of data anomalies occurring in the automated resource allocation process, timely warning, attribution, promoting the adjustment of resource allocation strategies, and improving the timeliness of adjustment. The overall process can be automatically completed with low manual dependence, effectively avoiding the subjectivity of the results, and effectively improving the timeliness of resource allocation adjustment.

[0114] It should be noted that the method of the embodiments of the present disclosure can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In this distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiments of the present disclosure, and these multiple devices will interact with each other to complete the described method.

[0115] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be executed in a different order than in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the figures do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] Corresponding to the above method for adjusting resource allocation, an embodiment of the present disclosure also discloses a device for adjusting resource allocation. Figure 3 Shows the internal structure of the device for adjusting resource allocation according to the embodiments of the present disclosure. As Figure 3 shown, the device may include:

[0117] An analysis module 302, configured to analyze multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event, and obtain the contribution degree of each parameter to the resource allocation data anomaly event;

[0118] An allocation module 304, configured to analyze the parameters corresponding to the contribution degree greater than a preset threshold, obtain an analysis result of the resource allocation data anomaly event, and readjust resource allocation based on the analysis result.

[0119] In some embodiments of the present disclosure, the resource allocation data anomaly event includes: a first target parameter of automatic resource allocation is higher than the first target parameter of non-automatic resource allocation;

[0120] The analysis module 302 includes:

[0121] A first analysis unit, configured to analyze a first difference between the multi-dimensional parameters of the automatic resource allocation and the non-automatic resource allocation in response to the first target parameter of the automatic resource allocation being higher than the first target parameter of the non-automatic resource allocation;

[0122] A first contribution degree calculation unit, configured to analyze the contribution degree of each parameter to the first difference based on the first difference.

[0123] In some embodiments of the present disclosure, the resource allocation data anomaly event includes: a first target parameter of automatic resource allocation at a first time is higher than a first target parameter of automatic resource allocation at a second time;

[0124] The analysis module 302 includes:

[0125] A second analysis unit, configured to analyze a second difference between the multi-dimensional parameters of the automatic resource allocation at the first time and the automatic resource allocation at the second time in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time;

[0126] A second contribution degree calculation unit, configured to analyze the contribution degree of each parameter to the second difference based on the second difference.

[0127] In some embodiments of the present disclosure, it further includes:

[0128] An acquisition module, configured to acquire daily resource allocation data;

[0129] A first judgment module, configured to use a preset algorithm to judge whether there are outliers in the daily resource allocation data;

[0130] An adjustment module, configured to re-adjust resource allocation in response to the presence of outliers in the daily resource allocation data.

[0131] In some embodiments of the present disclosure, it further includes:

[0132] An external factor analysis module, configured to analyze the influence of external factors in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time.

[0133] In some embodiments of the present disclosure, the multi-dimensional parameter includes material;

[0134] The allocation module 304 includes:

[0135] A first judgment unit, configured to judge whether the materials used in the automatic resource allocation and the non-automatic resource allocation are the same in response to the parameter corresponding to the contribution degree being greater than a preset threshold being material;

[0136] A third analysis unit, configured to analyze the characteristics of the material to obtain the analysis result in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being different; and / or, analyze the materials used in the non-automatic resource allocation to obtain available materials;

[0137] A first material adjustment unit, configured to re-adjust the allocation of the material based on the analysis result and / or the available materials.

[0138] In some embodiments of the present disclosure, it further includes:

[0139] A first parameter analysis module, configured to analyze the creation parameters of the material to obtain the analysis result in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same;

[0140] A first material adjustment module, configured to re-adjust the allocation of the material based on the analysis result.

[0141] In some embodiments of the present disclosure, it further includes:

[0142] A second judgment module, configured to judge whether the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation are the same in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same;

[0143] A first object adjustment module, configured to adjust the object of the automatic resource allocation in response to the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation being different.

[0144] In some embodiments of the present disclosure, the multi-dimensional parameter includes the object of resource allocation;

[0145] The allocation module 304 includes:

[0146] A first object contribution degree calculation unit, configured to analyze the contribution degree of each object to the first difference in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the object;

[0147] A first object adjustment unit, configured to adjust the object in response to the contribution degree of the object to the first difference being higher than a preset threshold.

[0148] In some embodiments of the present disclosure, the multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated for the resource allocation;

[0149] The allocation module 304 includes:

[0150] A first allocation parameter adjustment unit, configured to readjust the allocation parameter in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the allocation parameter.

[0151] In some embodiments of the present disclosure, the multi-dimensional parameter includes materials;

[0152] The allocation module 304 includes:

[0153] A second judgment unit, configured to judge whether the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same in response to the parameter corresponding to the contribution degree being greater than a preset threshold being materials;

[0154] A fourth analysis unit, configured to analyze the characteristics of the materials to obtain an analysis result in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being different; and / or analyze the materials used in the automatic resource allocation at the second time to obtain available materials;

[0155] A second material adjustment unit, configured to readjust the allocation of the materials based on the analysis result and the available materials.

[0156] In some embodiments of the present disclosure, the method further includes:

[0157] A second parameter analysis module, configured to analyze the creation parameters of the materials to obtain the analysis result in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being the same;

[0158] The second material adjustment module is used to re-adjust the allocation of the material based on the analysis result.

[0159] In some embodiments of the present disclosure, it further includes:

[0160] The third judgment module is used to judge whether the objects allocated to the materials of the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being the same;

[0161] The second object adjustment module is used to adjust the objects of the automatic resource allocation in response to the objects of the automatic resource allocation at the first time and the automatic resource allocation at the second time being different.

[0162] In some embodiments of the present disclosure, the multi-dimensional parameter includes the object of resource allocation;

[0163] The allocation module 304 includes:

[0164] The second object contribution degree calculation unit is used to analyze the contribution degree of each object to the second difference in response to the parameter corresponding to the contribution degree being greater than the preset threshold being the object;

[0165] The second object adjustment unit is used to adjust the object in response to the contribution degree of the object to the second difference being higher than the preset threshold.

[0166] In some embodiments of the present disclosure, the multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated by the resource allocation;

[0167] The allocation module 304 includes:

[0168] The second allocation parameter adjustment unit is used to adjust the allocation parameter in response to the parameter corresponding to the contribution degree being greater than the preset threshold being the allocation parameter.

[0169] For the convenience of description, the above device is described by dividing it into various modules according to functions. Of course, when implementing the present disclosure, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0170] The device in the above embodiments is used to implement the corresponding method for adjusting resource allocation in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0171] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method for adjusting resource allocation described in any one of the above embodiments is implemented.

[0172] Figure 4 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0173] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0174] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0175] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0176] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0177] The bus 1050 includes a path for transmitting information among various components of the device, such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040.

[0178] It should be noted that although only the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050 are shown in the above device, in the specific implementation process, the device may further include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0179] The electronic device of the above embodiment is used to implement the corresponding method for adjusting resource allocation in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0180] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the method for adjusting resource allocation as described in any of the foregoing embodiments.

[0181] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0182] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the method for adjusting resource allocation as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0183] Based on the same inventive concept, corresponding to the method for adjusting resource allocation described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the method for adjusting resource allocation. Corresponding to the execution subjects corresponding to the respective steps in the respective embodiments of the method for adjusting resource allocation, the processors executing the corresponding steps can belong to the corresponding execution subjects.

[0184] The computer program product of the above embodiment is used to cause the computer and / or the processor to execute the method for adjusting resource allocation described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0185] Those of ordinary skill in the art should understand that: the discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples; under the concept of the present disclosure, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present disclosure as described above, and they are not provided in detail for the sake of brevity.

[0186] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of the present disclosure difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in the form of a block diagram in order to avoid making the embodiments of the present disclosure difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure will be implemented (that is, these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0187] Although the present disclosure has been described in conjunction with specific embodiments of the present disclosure, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) can be used with the embodiments discussed.

[0188] Embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for adjusting resource allocation, comprising: Analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event; Analyzing the parameters corresponding to the contribution degree greater than a preset threshold to obtain an analysis result of the resource allocation data anomaly event, and readjusting resource allocation based on the analysis result.

2. The method according to claim 1, wherein, The resource allocation data anomaly event includes: the first target parameter of automatic resource allocation is higher than the first target parameter of non-automatic resource allocation; The analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event includes: Analyzing a first difference between the first target parameter of automatic resource allocation and the first target parameter of non-automatic resource allocation in response to the first target parameter of automatic resource allocation being higher than the first target parameter of non-automatic resource allocation; Analyzing the contribution degree of each parameter to the first difference based on the first difference.

3. The method according to claim 1, wherein The resource allocation data anomaly event includes: the first target parameter of automatic resource allocation at a first time is higher than the first target parameter of automatic resource allocation at a second time; The analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event includes: Analyzing a second difference between the first target parameter of automatic resource allocation at the first time and the first target parameter of automatic resource allocation at the second time in response to the first target parameter of automatic resource allocation at the first time being higher than the first target parameter of automatic resource allocation at the second time; Analyzing the contribution degree of each parameter to the second difference based on the second difference.

4. The method according to claim 1, wherein, Before the analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event, it further includes: Obtaining daily resource allocation data; Using a preset algorithm to determine whether there are outliers in the daily resource allocation data; Readjusting resource allocation in response to there being outliers in the daily resource allocation data.

5. The method according to claim 3, wherein, Before the analyzing multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event to obtain the contribution degree of each parameter to the resource allocation data anomaly event, it further includes: Analyzing the influence of external factors in response to the first target parameter of automatic resource allocation at the first time being higher than the first target parameter of automatic resource allocation at the second time.

6. The method according to claim 2, wherein The multi-dimensional parameters include materials; The analyzing the parameters corresponding to the contribution degree greater than a preset threshold to obtain an analysis result of the resource allocation data anomaly event, and readjusting resource allocation based on the analysis result includes: Judging whether the materials used for automatic resource allocation and non-automatic resource allocation are the same in response to the parameter corresponding to the contribution degree greater than the preset threshold being materials; Analyze the characteristics of the material in response to the difference in the materials used for the automatic resource allocation and the non-automatic resource allocation to obtain the analysis result; and / or analyze the material used for the non-automatic resource allocation to obtain the available material; Readjust the allocation of the material based on the analysis result and / or the available material.

7. The method according to claim 6, the method further comprising: Analyze the creation parameters of the material in response to the same material being used for the automatic resource allocation and the non-automatic resource allocation to obtain the analysis result; Readjust the allocation of the material based on the analysis result.

8. The method according to claim 6, the method further comprising: Judge whether the objects allocated to the materials for the automatic resource allocation and the non-automatic resource allocation are the same in response to the same material being used for the automatic resource allocation and the non-automatic resource allocation; Adjust the object of the automatic resource allocation in response to the objects allocated to the materials for the automatic resource allocation and the non-automatic resource allocation being different.

9. The method according to claim 2, wherein The multi-dimensional parameter includes the object of resource allocation; The analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data abnormal event, and readjusting the resource allocation based on the analysis result includes: Analyze the contribution degree of each object to the first difference in response to the parameter corresponding to the contribution degree greater than the preset threshold being the object; Adjust the object in response to the contribution degree of the object to the first difference being higher than the preset threshold.

10. The method according to claim 2, wherein, The multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated by the resource allocation; The analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data abnormal event, and readjusting the resource allocation based on the analysis result includes: Readjust the allocation parameter in response to the parameter corresponding to the contribution degree greater than the preset threshold being the allocation parameter.

11. The method according to claim 3, the multi-dimensional parameter includes the material; The analyzing the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data abnormal event, and readjusting the resource allocation based on the analysis result includes: Judge whether the materials used for the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same in response to the parameter corresponding to the contribution degree greater than the preset threshold being the material; Analyze the characteristics of the material in response to the materials used for the automatic resource allocation at the first time and the automatic resource allocation at the second time being different to obtain the analysis result; and / or analyze the material used for the automatic resource allocation at the second time to obtain the available material; Readjust the allocation of the material based on the analysis result and / or the available material.

12. The method according to claim 11, the method further comprising: Analyze the creation parameters of the material in response to the same material being used in the automatic resource allocation at the first time and the automatic resource allocation at the second time, and obtain the analysis result; Readjust the allocation of the material based on the analysis result.

13. The method according to claim 11, wherein the method further comprises: In response to the same material being used in the automatic resource allocation at the first time and the automatic resource allocation at the second time, determine whether the objects to which the materials in the automatic resource allocation at the first time and the automatic resource allocation at the second time are allocated are the same; In response to the objects in the automatic resource allocation at the first time and the automatic resource allocation at the second time being different, adjust the objects of the automatic resource allocation.

14. The method according to claim 3, wherein The multi-dimensional parameter includes the object of resource allocation; The analysis of the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and readjust the resource allocation based on the analysis result, includes: In response to the parameter corresponding to the contribution degree greater than the preset threshold being the object, analyze the contribution degree of each object to the second difference; In response to the contribution degree of the object to the second difference being higher than the preset threshold, adjust the object.

15. The method according to claim 3, wherein, The multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated by the resource allocation; The analysis of the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and readjust the resource allocation based on the analysis result, includes: In response to the parameter corresponding to the contribution degree greater than the preset threshold being the allocation parameter, adjust the allocation parameter.

16. An apparatus for adjusting resource allocation, comprising: An analysis module, configured to analyze multi-dimensional parameters of resource allocation in response to a resource allocation data anomaly event, and obtain the contribution degree of each parameter to the resource allocation data anomaly event; An allocation module, configured to analyze the parameter corresponding to the contribution degree greater than the preset threshold to obtain the analysis result of the resource allocation data anomaly event, and readjust the resource allocation based on the analysis result.

17. The apparatus according to claim 16, wherein, The resource allocation data anomaly event includes: the first target parameter of the automatic resource allocation is higher than the first target parameter of the non-automatic resource allocation; The analysis module includes: A first analysis unit, configured to analyze the first difference between the multi-dimensional parameters of the automatic resource allocation and the non-automatic resource allocation in response to the first target parameter of the automatic resource allocation being higher than the first target parameter of the non-automatic resource allocation; A first contribution degree calculation unit, configured to analyze and obtain the contribution degree of each parameter to the first difference based on the first difference.

18. The apparatus according to claim 16, wherein The resource allocation data anomaly event includes: the first target parameter of the automatic resource allocation at the first time is higher than the first target parameter of the automatic resource allocation at the second time; The analysis module includes: A second analysis unit, configured to analyze a second difference between the multi-dimensional parameters of the automatic resource allocation at the first time and the automatic resource allocation at the second time in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time; A second contribution degree calculation unit, configured to analyze the contribution degree of each parameter to the second difference based on the second difference.

19. The apparatus according to claim 16, further comprising: An acquisition module, configured to acquire daily resource allocation data; A first judgment module, configured to use a preset algorithm to judge whether there are outliers in the daily resource allocation data; An adjustment module, configured to re-adjust the resource allocation in response to the presence of outliers in the daily resource allocation data.

20. The apparatus according to claim 18, further comprising: An external factor analysis module, configured to analyze the influence of external factors in response to the first target parameter of the automatic resource allocation at the first time being higher than the first target parameter of the automatic resource allocation at the second time.

21. The apparatus according to claim 17, wherein The multi-dimensional parameters include materials; The allocation module includes: A first judgment unit, configured to judge whether the materials used in the automatic resource allocation and the non-automatic resource allocation are the same in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the material; A third analysis unit, configured to analyze the characteristics of the material to obtain the analysis result in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being different; and / or, analyze the materials used in the non-automatic resource allocation to obtain available materials; A first material adjustment unit, configured to re-adjust the allocation of the materials based on the analysis result and / or the available materials.

22. The apparatus according to claim 21, further comprising: A first parameter analysis module, configured to analyze the creation parameters of the material to obtain the analysis result in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same; A first material adjustment module, configured to re-adjust the allocation of the materials based on the analysis result.

23. The apparatus according to claim 21, further comprising: A second judgment module, configured to judge whether the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation are the same in response to the materials used in the automatic resource allocation and the non-automatic resource allocation being the same; A first object adjustment module, configured to adjust the object of the automatic resource allocation in response to the objects allocated to the materials in the automatic resource allocation and the non-automatic resource allocation being different.

24. The apparatus according to claim 17, wherein, The multi-dimensional parameters include the objects of resource allocation; The allocation module includes: A first object contribution degree calculation unit, configured to analyze the contribution degree of each object to the first difference in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the object; A first object adjustment unit, configured to adjust the object in response to the contribution degree of the object to the first difference being higher than a preset threshold.

25. The apparatus according to claim 17, wherein, The multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated for the resource allocation. The allocation module includes: A first allocation parameter adjustment unit, configured to re-adjust the allocation parameter in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the allocation parameter.

26. The apparatus according to claim 18, wherein, The multi-dimensional parameter includes materials. The allocation module includes: A second determination unit, configured to determine whether the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the materials. A fourth analysis unit, configured to analyze the characteristics of the materials to obtain an analysis result in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being different; and / or analyze the materials used in the automatic resource allocation at the second time to obtain available materials. A second material adjustment unit, configured to re-adjust the allocation of the materials based on the analysis result and the available materials.

27. The apparatus according to claim 26, wherein the method further includes: A second parameter analysis module, configured to analyze the creation parameters of the materials to obtain the analysis result in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being the same. A second material adjustment module, configured to re-adjust the allocation of the materials based on the analysis result.

28. The apparatus according to claim 26, further including: A third determination module, configured to determine whether the objects allocated for the materials in the automatic resource allocation at the first time and the automatic resource allocation at the second time are the same in response to the materials used in the automatic resource allocation at the first time and the automatic resource allocation at the second time being the same. A second object adjustment module, configured to adjust the object of the automatic resource allocation in response to the objects in the automatic resource allocation at the first time and the automatic resource allocation at the second time being different.

29. The apparatus according to claim 18, wherein The multi-dimensional parameter includes the object of the resource allocation. The allocation module includes: A second object contribution degree calculation unit, configured to analyze the contribution degree of each object to the second difference in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the object. A second object adjustment unit, configured to adjust the object in response to the contribution degree of the object to the second difference being higher than a preset threshold.

30. The device according to claim 18, wherein, The multi-dimensional parameter includes an allocation parameter; the allocation parameter is the quantity of the first resource allocated for the resource allocation. The allocation module includes: A second allocation parameter adjustment unit, configured to adjust the allocation parameter in response to the parameter corresponding to the contribution degree being greater than a preset threshold being the allocation parameter.

31. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the method described in any one of claims 1 to 15 is implemented.

32. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause a computer to execute the method described in any one of claims 1 to 15.

33. A computer program product comprising computer program instructions which, when run on a computer, cause the computer to perform the method according to any one of claims 1 to 15.

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