A prediction method, device, equipment and medium for budget approval

Abstract

The present application discloses a prediction method, device, equipment and medium for budget approval. The method includes: constructing automatic approval parameters; determining budget approval data based on user operations and judging whether to enable the automatic approval parameters; if so, obtaining historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results; training an initial support vector machine model through the historical budget approval data to construct a support vector machine model; predicting whether the budget approval data is approved through the support vector machine model and the budget approval data. It can realize autonomous inspection of the approval of budget submission, improve the accuracy of prediction, form a scientific approval standard at the same time, and improve the efficiency of the approval process.

Description

Technical Field

[0001] This application relates to the technical field of data management, and in particular to a prediction method, device, equipment and medium for budget approval. Background Art

[0002] A comprehensive budget is an application method for overall management of business indicators, various expenses, asset status, etc. In order to improve the customer's control over the project, various costs such as resources and personnel are adjusted according to the progress of the enterprise project to provide a more reasonable guiding role for the enterprise.

[0003] Currently, after an enterprise prepares a budget, it will submit the prepared report to the superior leader for approval. With the continuous development of the enterprise, the number of reviewers has increased accordingly, and the management decision-making levels have also increased accordingly, which will lead to a large amount of time being occupied by daily preparation, review and approval, etc. At the same time, when conducting approval, the superior leader usually only approves the received current budget data by manually reading the budget approval data of previous years. Over-reliance on experience results in a low accuracy rate of budget approval results. Summary of the Invention

[0004] Embodiments of this application provide a prediction method, device, equipment and medium for budget approval, which are used to solve the problem of low accuracy rate of budget approval results.

[0005] Embodiments of this application adopt the following technical solutions:

[0006] On the one hand, embodiments of this application provide a prediction method for budget approval, and the method includes: constructing automatic approval parameters; determining budget approval data based on the user's operation, and determining whether to enable the automatic approval parameters; if so, obtaining historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results; training a support vector machine initial model with the historical budget approval data to construct a support vector machine model; predicting whether the budget approval data is approved through the support vector machine model and the budget approval data.

[0007] In an example, the training of the support vector machine initial model with the historical budget approval data to construct a support vector machine model specifically includes: determining a regularization term expression and a loss function term expression; performing a Lagrangian transformation on the regularization term expression and the loss function term expression to construct the support vector machine initial model; determining an initial prediction function according to the support vector machine initial model; inputting the historical budget approval data into the support vector machine initial model for training to determine the values corresponding to the normal vector and the bias in the initial prediction function respectively; inputting the values corresponding to the normal vector and the bias respectively into the initial prediction function to generate a prediction function.

[0008] In one example, predicting whether the budget approval data is approved through the support vector machine model and the budget approval data specifically includes: inputting the budget approval data into the prediction function to generate a prediction value; determining whether the prediction value meets the preset approval passing condition, and if so, determining that the budget approval data is approved.

[0009] In one example, the method further includes: if the prediction value does not meet the preset approval passing condition, calculating the similarity between the budget approval data and the historical budget approval data; using the approval result corresponding to the historical budget approval data with the highest similarity to the budget approval data as the reference approval result for the budget approval data; if the budget approval data is inconsistent with the reference approval result, verifying with the user whether the budget approval data is abnormal; if not, rejecting the budget approval data.

[0010] In one example, the method further includes: if the budget approval data is abnormal, obtaining the updated budget approval data sent by the user; re-inputting the updated budget approval data into the prediction function to generate an updated prediction value; determining whether the updated prediction value meets the preset approval passing condition; if not, rejecting the budget approval data.

[0011] In one example, after rejecting the budget approval data, the method further includes: adding the budget approval data to the rejection result queue; in the rejection result queue, within the preset time period, grouping multiple rejected budget approval data according to the budget period to obtain multiple groups of rejected budget approval data; extracting the budget amounts corresponding to the same budget organization for each group of rejected budget approval data; in the budget organization, determining the average value of the corresponding budget amounts, comparing multiple budget organizations according to the average value, and determining the budget organization with the highest average value in each group of rejected budget approval data; summarizing the budget organization with the highest average value to determine the budget organization with the highest occurrence frequency; and feeding back the budget organization with the highest occurrence frequency to the user for the user to verify the operation status of the budget organization with the highest occurrence frequency.

[0012] In one example, the method further includes: if the automatic approval parameter is not enabled, determining the budget organization corresponding to the budget approval data; retrieving the mapping rule table corresponding to the budget organization in the pre-set approval library; reviewing the budget approval data through the mapping rule table to determine the abnormal data in the budget approval data; and sending the abnormal data to the user for the user to review the budget approval data based on the abnormal data.

[0013] On the other hand, an embodiment of the present application provides a prediction device for budget approval, the device comprising: a construction module for constructing automatic approval parameters; a judgment module for determining budget approval data based on a user's operation and judging whether to enable the automatic approval parameters; an acquisition module for, if so, acquiring historical budget approval data within a preset time period; the historical budget approval data including historical budget dimensions, historical budget amounts, and historical approval results; a training module for training an initial support vector machine model with the historical budget approval data to construct a support vector machine model; and a prediction module for predicting whether the budget approval data is approved or not through the support vector machine model and the budget approval data.

[0014] On the other hand, an embodiment of the present application provides a prediction device for budget approval, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is capable of: constructing automatic approval parameters; determining budget approval data based on a user's operation and judging whether to enable the automatic approval parameters; if so, acquiring historical budget approval data within a preset time period; the historical budget approval data including historical budget dimensions, historical budget amounts, and historical approval results; training an initial support vector machine model with the historical budget approval data to construct a support vector machine model; and predicting whether the budget approval data is approved or not through the support vector machine model and the budget approval data.

[0015] On the other hand, an embodiment of the present application provides a non-volatile computer storage medium for budget approval prediction, storing computer-executable instructions, the computer-executable instructions being configured to: construct automatic approval parameters; determine budget approval data based on a user's operation and judge whether to enable the automatic approval parameters; if so, acquire historical budget approval data within a preset time period; the historical budget approval data including historical budget dimensions, historical budget amounts, and historical approval results; train an initial support vector machine model with the historical budget approval data to construct a support vector machine model; and predict whether the budget approval data is approved or not through the support vector machine model and the budget approval data.

[0016] The above at least one technical solution adopted by the embodiment of the present application can achieve the following beneficial effects:

[0017] In the embodiments of the present application, by constructing automatic approval parameters, it is possible to enter the automatic approval mode, train an initial support vector machine model with historical budget approval data, construct a support vector machine model, and thus automatically approve the budget approval data through the support vector machine model and the budget approval data. It can realize the independent inspection of the approval of budget submission, improve the accuracy of prediction, and at the same time form a scientific approval standard, and improve the efficiency of the approval process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings. In the drawings:

[0019] Figure 1 is a schematic flowchart of a prediction method for budget approval provided by an embodiment of the present application;

[0020] Figure 2 is a schematic structural diagram of a prediction device for budget approval provided by an embodiment of the present application;

[0021] Figure 3 is a schematic structural diagram of a prediction device for budget approval provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific embodiments and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0023] Some embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0024] Figure 1 is a schematic flowchart of a prediction method for budget approval provided by an embodiment of the present application. Some input parameters or intermediate results in this process allow manual intervention and adjustment to help improve accuracy.

[0025] The implementation of the analysis method involved in the embodiments of the present application can be a terminal device or a server, and the present application does not make special restrictions on this. For the convenience of understanding and description, the following embodiments will be described in detail using the server as an example.

[0026] It should be noted that this server can be a single device or a system composed of multiple devices, that is, a distributed server, and the present application does not make specific limitations on this.

[0027] Figure 1 The process in

[0028] S101: Construct automatic approval parameters.

[0029] In some embodiments of the present application, based on budget approval on the server, the user adds a new parameter, i.e., automatic approval parameter. After the approval manager, i.e., the user, turns on the function of the automatic approval parameter, the automatic approval mode will be entered. If the user does not turn on the function of the automatic approval parameter, the manual approval mode will be entered.

[0030] S102: Based on the user's operation, determine budget approval data and judge whether to enable the automatic approval parameter.

[0031] In some embodiments of the present application, when the user conducts budget approval, since the budget approval data is a multi-dimensional data and the dimensions of different table formats are different. For example, (budget organization, budget indicator, budget period, budget amount). According to this structure, for example, (office, US dollar, 2019, 200) means that the budget of the office in 2019 is 200 US dollars. Therefore, the user will pre-define a budget approval plan, and when defining the budget approval plan, determine whether to enable the automatic approval parameter. For example, when the user defines the budget approval plan and clicks save, the user will select whether to enable the automatic approval parameter.

[0032] S103: If so, obtain historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results.

[0033] In some embodiments of the present application, if the user selects to start the automatic approval parameter, then the server will determine to start the automatic approval parameter and enter the automatic approval mode. Since it is the automatic approval mode, the budget approval model needs to automatically approve the budget approval data to automatically judge whether the current budget approval data can pass the approval.

[0034] Based on this, after the server enters the automatic approval mode, it will start to obtain historical budget approval data within a preset time period and generate a budget approval model by training the historical budget approval data.

[0035] Furthermore, if the user does not select to start the automatic approval parameter, the server will enter the manual approval mode and conduct a preliminary review of the budget approval data to reduce the cost of manual review by the user.

[0036] Specifically, first determine the budget organization corresponding to the budget approval data, and retrieve the mapping rule table corresponding to the budget organization in the pre-set approval library. Among them, the mapping rule table includes the restrictive conditions of the budget organization. For example, the budget amount of Office 2019 shall not exceed $200.

[0037] Review the budget approval data through the mapping rule table to determine the abnormal data in the budget approval data. Finally, send the abnormal data to the user so that the user can review the budget approval data based on the abnormal data. That is, the user can refer to the review result and then review the budget approval data.

[0038] S104: Train the initial support vector machine model through the historical budget approval data to construct a support vector machine model.

[0039] In some embodiments of the present application, since the support vector machine is an algorithm for finite samples, it can be trained by minimizing the actual output value and the predicted output value under finite samples, expecting to obtain good training parameters, and finally obtaining good results during prediction to achieve accurate prediction results. In addition, the support vector machine is a quadratic convex problem, so it can be solved quickly and obtain a unique optimal value, avoiding the situation of local optimal values.

[0040] Based on this, the support vector machine can obtain an optimal hyperplane through training, and use this hyperplane to judge whether a new decision is approved. Its structure is mainly divided into two parts: a regularization term and a loss function term. The support vector machine reduces the structural risk of the model by minimizing the regularization term and reduces the empirical risk by using the loss function term.

[0041] Therefore, the support vector machine can be trained through the historical budget approval data of previous years. Since its loss function is the Hinge loss, which is a convex function, it can be solved relatively quickly and a unique value can be obtained. When this model is applied to comprehensive budget review, it can more simply and accurately predict whether the budget approval data meets the conditions, so as to automatically approve or reject the review.

[0042] It should be noted that the basic framework of the support vector machine is as follows:

[0043]

[0044] s.t, y i (w T X i +b)≥1-L i 、L i ≥0

[0045] Among them, is a regular term expression, is a loss function term expression, L i = max[0, 1 - y i (ω T X i + b)] is a specific loss function.

[0046] Among them, X i is the data corresponding to the historical budget dimension, y i is the historical approval result, w is the normal vector, b is the bias, and C′ is a constant greater than 0.

[0047] Furthermore, perform Lagrangian transformation on the regular term expression and the loss function term expression to construct an initial support vector machine model.

[0048] The initial support vector machine model is as follows:

[0049]

[0050] Among them, ξ, λ, and μ are Lagrange multipliers.

[0051] Through the above initial vector machine model, an initial prediction function can be obtained. In the initial prediction function, the values corresponding to the normal vector and the bias are unknown. The initial prediction function is as follows:

[0052] w T x + b = 0

[0053] Substitute the historical budget approval data into the initial support vector machine model for training, so that the values corresponding to the normal vector and the bias in the initial prediction function can be obtained respectively.

[0054] Input the values corresponding to the normal vector and the bias into the initial prediction function respectively, and a specific prediction function can be generated, thereby constructing a support vector machine model for budget approval.

[0055] S105: Predict whether the budget approval data is approved through the support vector machine model and the budget approval data.

[0056] In some implementations of this application, by inputting the budget approval data into the prediction function to generate a prediction value, and then determining whether the prediction value meets the preset approval passing condition. If so, it is determined that the budget approval data is approved. For example, if the prediction value is +1, it represents approval, and if the prediction value is -1, it represents non - approval.

[0057] Furthermore, since the user may input the budget approval data incorrectly, if the approval is directly rejected, the automatic approval parameters need to be restarted, and the process takes a long time. To improve the efficiency of budget approval, it is possible to verify in a timely manner whether the budget approval data is abnormal for the user.

[0058] Specifically, when the predicted value does not meet the preset approval passing condition, the server calculates the similarity between the budget approval data and the historical budget approval data, and uses the approval result corresponding to the historical budget approval data with the highest similarity to the budget approval data as the reference approval result of the budget approval data. If the budget approval data is inconsistent with the reference approval result, the server verifies whether the budget approval data is abnormal with the user. If not, the budget approval data is rejected.

[0059] Furthermore, when the budget approval data is abnormal, the server obtains the updated budget approval data sent by the user, re-enters the updated budget approval data into the prediction function to generate an updated predicted value. Then, it determines whether the updated predicted value meets the preset approval passing condition.

[0060] To improve security, when the updated predicted value does not meet the preset approval passing condition, no verification is performed, and the budget approval data is directly rejected.

[0061] Even further, when the budget approval data is rejected, since the budget approval data after prediction includes the budget amounts of each budget organization in each period and the corresponding approval results. Then, there may be a situation where a department develops rapidly due to certain needs. For example, the department needs to expand its scale. However, during this process, it is not possible to monitor the actual progress of the department's development in real time, and there may be a situation of excessive expansion.

[0062] Therefore, it is possible to extract the budget organizations with budget overruns in each budget period as sensitive objects to balance the development of each budget organization in a timely manner.

[0063] Specifically, the budget approval data is added to the rejection result queue. In the rejection result queue, within a preset time period, multiple rejected budget approval data are grouped according to the budget period to obtain multiple groups of rejected budget approval data.

[0064] Then, for each group of rejected budget approval data, the budget amounts corresponding to the same budget organization are extracted. In the budget organization, the average value of the corresponding budget amounts is determined, and multiple budget organizations are compared according to the average value to determine the budget organization with the highest average value in each group of rejected budget approval data.

[0065] Finally, the budget organizations with the highest average value are summarized to determine the budget organization with the highest frequency of occurrence, and the budget organization with the highest frequency of occurrence is fed back to the user so that the user can verify the operation status of the budget organization with the highest frequency of occurrence.

[0066] It should be noted that although the embodiments of the present application are referred to Figure 1The steps S101 to S105 will be introduced and described in sequence hereinafter. However, this does not mean that the steps S101 to S105 must be executed in a strict sequence. The reason why the embodiments of the present application introduce and describe the steps S101 to S105 in the Figure 1 sequence shown is to facilitate those skilled in the art to understand the technical solutions of the embodiments of the present application. In other words, in the embodiments of the present application, the sequence among the steps S101 to S105 can be appropriately adjusted according to actual needs.

[0067] By Figure 1 this method, by constructing automatic approval parameters, it is possible to enter the automatic approval mode, train the initial support vector machine model with historical budget approval data, and construct a support vector machine model. Thus, through the support vector machine model and the budget approval data, the budget approval data can be automatically approved, which can realize the independent inspection of the approval of budget submission, improve the prediction accuracy, and at the same time form a scientific approval standard, and can also improve the efficiency of the approval process.

[0068] Based on the same idea, some embodiments of the present application also provide a device, a device and a non-volatile computer storage medium corresponding to the above method.

[0069] Figure 2 FIG. is a schematic structural diagram of a prediction device for budget approval provided by an embodiment of the present application. The device includes:

[0070] A construction module 201 for constructing automatic approval parameters;

[0071] A judgment module 202 for determining budget approval data based on a user's operation and judging whether to enable the automatic approval parameters;

[0072] An acquisition module 203, if so, acquiring historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results;

[0073] A training module 204 for training an initial support vector machine model with the historical budget approval data to construct a support vector machine model;

[0074] A prediction module 205 for predicting whether the budget approval data is approved through the support vector machine model and the budget approval data.

[0075] Figure 3 FIG. is a schematic structural diagram of a prediction device for budget approval provided by an embodiment of the present application. The device includes:

[0076] At least one processor; and,

[0077] A memory communicatively connected to the at least one processor; wherein,

[0078] The memory stores instructions executable by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor is enabled to:

[0079] Construct automatic approval parameters;

[0080] Based on the user's operation, determine budget approval data and determine whether to enable the automatic approval parameters;

[0081] If so, obtain historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results;

[0082] Train a support vector machine initial model with the historical budget approval data to construct a support vector machine model;

[0083] Predict whether the budget approval data is approved through the support vector machine model and the budget approval data.

[0084] A non-volatile computer storage medium for predicting budget approval provided by an embodiment of the present application stores computer-executable instructions, and the computer-executable instructions are set to:

[0085] Construct automatic approval parameters;

[0086] Based on the user's operation, determine budget approval data and determine whether to enable the automatic approval parameters;

[0087] If so, obtain historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results;

[0088] Train a support vector machine initial model with the historical budget approval data to construct a support vector machine model;

[0089] Predict whether the budget approval data is approved through the support vector machine model and the budget approval data.

[0090] The embodiments in the present application are all described in a progressive manner. For the same or similar parts between the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the embodiments of the device, equipment, and medium, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0091] The device, equipment, medium, and method provided by the embodiments of the present application are in one-to-one correspondence. Therefore, the device, equipment, and medium also have beneficial technical effects similar to those of their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the device, equipment, and medium will not be elaborated here.

[0092] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer-usable program code.

[0093] The present invention is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0094] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0096] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0097] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0098] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information 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 cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0099] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0100] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the technical principle of the present application shall fall within the protection scope of the present application.

Claims

1. A prediction method for budget approval, characterized in that, the method includes: Constructing automatic approval parameters; Based on the user's operations, determining budget approval data and judging whether to enable the automatic approval parameters; If so, obtaining historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results; Training an initial support vector machine model with the historical budget approval data to construct a support vector machine model; Predicting whether the budget approval data is approved through the support vector machine model and the budget approval data; The training of the initial support vector machine model with the historical budget approval data to construct a support vector machine model specifically includes: Determining a regularization term expression and a loss function term expression; Performing Lagrangian transformation on the regularization term expression and the loss function term expression to construct the initial support vector machine model; Determining an initial prediction function according to the initial support vector machine model; Inputting the historical budget approval data into the initial support vector machine model for training to determine the values corresponding to the normal vector and the bias in the initial prediction function respectively; Inputting the values corresponding to the normal vector and the bias respectively into the initial prediction function to generate a prediction function; The predicting whether the budget approval data is approved through the support vector machine model and the budget approval data specifically includes: Inputting the budget approval data into the prediction function to generate a predicted value; Judging whether the predicted value meets the preset approval passing condition, if so, determining that the budget approval data is approved; The method further includes: If the predicted value does not meet the preset approval passing condition, calculating the similarity between the budget approval data and the historical budget approval data; Taking the approval result corresponding to the historical budget approval data with the highest similarity to the budget approval data as the reference approval result of the budget approval data; If the budget approval data is inconsistent with the reference approval result, verifying whether the budget approval data is abnormal to the user; If not, rejecting the budget approval data.

2. The method according to claim 1, characterized in that, the method further includes: If the budget approval data is abnormal, obtaining updated budget approval data sent by the user; Re-inputting the updated budget approval data into the prediction function to generate an updated predicted value; Judging whether the updated predicted value meets the preset approval passing condition; If not, rejecting the budget approval data.

3. The method according to claim 1, characterized in that, after rejecting the budget approval data, the method further includes: Adding the budget approval data to a rejection result queue; In the rejection result queue, within the preset time period, grouping multiple rejected budget approval data according to the budget period to obtain multiple groups of rejected budget approval data; Extracting the budget amounts corresponding to the same budget organization for each group of rejected budget approval data; In the budget organization, determine the mean value of the corresponding budget amount, compare multiple budget organizations according to the mean value, and determine the budget organization with the highest mean value among the rejection budget approval data of each group; Summarize the budget organization with the highest mean value and determine the budget organization with the highest frequency of occurrence; Feed back the budget organization with the highest frequency of occurrence to the user so that the user can verify the operation status of the budget organization with the highest frequency of occurrence.

4. The method according to claim 1, wherein, the method further includes: If the automatic approval parameter is not enabled, determine the budget organization corresponding to the budget approval data; In a pre-set approval library, retrieve the mapping rule table corresponding to the budget organization; Examine the budget approval data through the mapping rule table to determine the abnormal data in the budget approval data; Send the abnormal data to the user so that the user can review the budget approval data based on the abnormal data.

5. A prediction device for budget approval, which can implement the method described in any one of claims 1 to 4, wherein, the device includes: A construction module for constructing an automatic approval parameter; A judgment module for determining budget approval data based on the user's operation and judging whether to enable the automatic approval parameter; An acquisition module, if so, acquire historical budget approval data within a preset time period; the historical budget approval data includes historical budget dimensions, historical budget amounts, and historical approval results; A training module for training an initial support vector machine model through the historical budget approval data to construct a support vector machine model; A prediction module for predicting whether the budget approval data is approved through the support vector machine model and the budget approval data.

6. A prediction device for budget approval, wherein, the device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a prediction method for budget approval described in any one of claims 1 to 4.

7. A non-volatile computer storage medium for budget approval prediction, storing computer-executable instructions, wherein, The computer-executable instructions are configured to be able to execute a prediction method for budget approval described in any one of claims 1 to 4.

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