Data processing method and device

Abstract

The invention provides a data processing method and device, and relates to the field of big data. The method comprises the following steps: collecting statistical data of a target project; according to the project field set and the processing node field set, determining a field coincidence degree between each processing node in the processing node set and the target project and a coincidence field set; according to the domain coincidence degree between each processing node in the processing node set and the target item, the proportion of each coincidence domain in the coincidence domain set corresponding to each processing node in the processing node set in the target item, and the weight value of each processing node in the processing node set in each coincidence domain in the corresponding coincidence domain set; determining a first score coefficient of the target item; and determining a quantitative score of the target item according to the statistical data of the target item and the first score coefficient. Through the scheme, the quantitative score of the target project can be accurately determined, the target project is accurately evaluated, and decision support is provided for production guidance, technical improvement and risk management.

Description

Technical Field

[0001] This specification belongs to the field of big data, and in particular, relates to a data processing method and device. Background Art

[0002] After the project is completed, the project party needs to evaluate the project in order to improve the project decision-making level, optimize resource allocation, and improve work performance. Through comprehensive, objective and scientific evaluation, problems in the project can be discovered and corrected in a timely manner, lessons learned can be summarized, and strong support can be provided for future projects or work. At the same time, post-project evaluation can also help enhance the transparency and credibility of the project or work, and improve user satisfaction and recognition.

[0003] However, due to the inability to fully understand the external environment and difficulties in data collection during the evaluation process, the evaluation results are biased and inaccurate, affecting the quality of the evaluation work. In addition, since different evaluators may have different backgrounds, experiences, and positions, when different evaluation conclusions are given for the same project or work, the evaluation criteria and evaluation methods are often subjective, leading to differences and uncertainties in the evaluation results, affecting the fairness and credibility of the evaluation results.

[0004] There is currently no effective solution for how to conduct accurate quantitative evaluation of projects. Summary of the invention

[0005] The present application provides a data processing method and device to accurately determine the quantitative score of a target project.

[0006] A data processing method and device provided by the present application is implemented as follows:

[0007] A data processing method, comprising:

[0008] Collect statistical data of target projects;

[0009] taking at least one field associated with the target project as a project field set;

[0010] Taking at least one processing node that determines the score of the target project as a processing node set, and establishing an association relationship between each processing node and a field in the processing node set as a processing node field set;

[0011] Determine, according to the project domain set and the processing node domain set, the domain overlap degree and the overlap domain set between each processing node in the processing node set and the target project;

[0012] Acquire demand data of the target project;

[0013] According to the demand data, respectively determine the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project;

[0014] Respectively determine the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0015] Determine the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0016] Determine a first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set;

[0017] A quantitative score of the target project is determined according to the statistical data of the target project and the first scoring coefficient.

[0018] In one embodiment, the first score coefficient of the target project is determined according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set, including:

[0019] The first score coefficient of the target item is calculated according to the following formula:

[0020]

[0021] Among them, α is the first score coefficient, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set, is the proportion of the overlapped domain k in the target project in the overlapped domain set corresponding to the processing node j in the processing node set, K is the weight value of processing node j in the corresponding overlap area k in the processing node set, j is the size of the set of overlapping domains corresponding to processing node j in the processing node set, and N is the number of processing nodes.

[0022] In one embodiment, determining the quantitative score of the target project according to the statistical data of the target project and the first score coefficient includes:

[0023] Determine multiple dimensional data of the target project according to the statistical data of the target project;

[0024] According to the multiple dimension data and the first score coefficient, the quantitative score of the target project is calculated according to the following formula:

[0025]

[0026] Wherein, G is the quantitative score, α is the first score coefficient, M is the first dimension data, X is the second dimension data, and D is the third dimension data.

[0027] In one embodiment, determining multiple dimensional data of the target project according to the statistical data of the target project includes:

[0028] Determine direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators of the target project based on the statistical data of the target project;

[0029] According to the direct positive statistical data, indirect positive statistical data, impact indicators and impact indicator correction values ​​of the target project, the first dimension data is calculated according to the following formula:

[0030] M=A(i)+L(i)+βI(i)

[0031] Wherein, M is the first dimension data, i is the target time period for collecting statistical data of the target project, A(i) is the direct positive statistical data of the target project in the target time period, L(i) is the indirect positive statistical data of the target project in the target time period, I(i) is the impact indicator of the target project in the target time period, and β is the correction value of the impact indicator.

[0032] In one embodiment, it further comprises:

[0033] Determine the necessary reduction items, influencing factors of the necessary reduction items, and optimization items of the second dimension data of the target project according to the statistical data of the target project;

[0034] According to the required reduction items of the target project, the influencing factors of the required reduction items, and the optimization items of the second dimension data, the second dimension data is calculated according to the following formula:

[0035] X=BT-R

[0036] Among them, X is the second dimension data, B is the must-reduced item, T is the influencing factor of the must-reduced item, and R is the optimization item.

[0037] In one embodiment, it further comprises:

[0038] Determine direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project according to the statistical data of the target project;

[0039] According to the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project, the third dimension data is calculated according to the following formula:

[0040] D=E+P(i)+O(i)

[0041] Wherein, D is the third dimension data, E is the indirect negative statistical data of the target project, P(i) is the indirect negative statistical data of the target project in the target time period, and O(i) is the continuous negative statistical data of the target project in the target time period.

[0042] A data processing device, comprising:

[0043] The collection module is used to collect statistical data of the target project;

[0044] A first associating module, configured to use at least one field associated with the target project as a project field set;

[0045] A second association module is used to take at least one processing node for determining the score of the target project as a processing node set, and establish an association relationship between each processing node in the processing node set and a field as a processing node field set;

[0046] A first determination module is used to determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project according to the project domain set and the processing node domain set;

[0047] An acquisition module, used to acquire demand data of the target project;

[0048] A second determination module is used to determine, according to the demand data, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project;

[0049] A third determination module is used to respectively determine the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0050] A fourth determination module, for determining the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0051] A first calculation module is used to determine a first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set;

[0052] A second calculation module is used to determine a quantitative score of the target project according to the statistical data of the target project and the first score coefficient.

[0053] In one embodiment, the first score coefficient of the target project is determined according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set, including:

[0054] The first score coefficient of the target item is calculated according to the following formula:

[0055]

[0056] Among them, α is the first score coefficient, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set, is the proportion of the overlapped domain k in the target project in the overlapped domain set corresponding to the processing node j in the processing node set, K is the weight value of processing node j in the corresponding overlap area k in the processing node set, j is the size of the set of overlapping domains corresponding to processing node j in the processing node set, and N is the number of processing nodes.

[0057] An electronic device comprises a processor and a memory for storing instructions executable by the processor, wherein the processor implements the steps of any one of the above methods when executing the instructions.

[0058] A computer-readable storage medium stores computer instructions, which, when executed by a processor, implement the steps of any of the above methods.

[0059] A computer program product comprises a computer program / instruction, wherein when the computer program / instruction is executed by a processor, the steps of any one of the above methods are implemented.

[0060] The present application provides a data processing method and device, which determines the domain overlap and the overlap domain set between each processing node in the processing node set and the target project through the project domain set of the target project, the processing node set and the processing node domain set for determining the score of the target project; and further determines the weight value of each processing node in the processing node set in each overlap domain of the corresponding overlap domain set, thereby determining the first score coefficient of the target project; and finally determines the quantitative score of the target project through the statistical data of the target project and the first score coefficient. That is, the evaluation of the target project is quantified through the above scheme, and an accurate quantitative score is determined, so that the accurate evaluation of the target project can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 is a method flow chart of an embodiment of the data processing method provided by the present application;

[0063] Figure 2 It is a flowchart of an embodiment of the financial project data processing method provided by the present application;

[0064] Figure 3 It is a hardware structure block diagram of an electronic device of a data processing method provided by the present application;

[0065] Figure 4 It is a schematic diagram of the module structure of an embodiment of the data processing device provided by the present application. DETAILED DESCRIPTION

[0066] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0067] In view of the problem that the quantitative score of the project cannot be accurately determined and the project cannot be accurately evaluated in the existing project data processing process, a data processing method is provided in the embodiments of the present application. Although the present application provides method operation steps or device structures as shown in the following embodiments or drawings, more or fewer operation steps or module units may be included in the method or device based on routine or no creative labor. In the steps or structures where there is no necessary causal relationship logically, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure described in the embodiments of the present application and shown in the drawings. When the method or module structure is applied to a device or terminal product in practice, it can be connected according to the method or module structure shown in the embodiments or drawings for sequential execution or parallel execution (for example, a parallel processor or a multi-threaded processing environment, or even a distributed processing environment).

[0068] Specifically, Figure 1 As shown, the above data processing method may include the following steps:

[0069] S101: Collect statistical data of the target project.

[0070] Specifically, the above statistical data may be various qualitative indicators of the target project. When actually implemented, the statistical data may be determined based on the nature of the project and actual needs. For example, in financial projects, the statistical data may be actual project results and benefits, labor cost savings, taxable income, tax rates, tax exemptions, tax credits, labor development costs, operation and maintenance costs, etc.

[0071] S102: Taking at least one field associated with the target project as a project field set.

[0072] Specifically, the target project may be associated with one or more different fields, and each field associated with the target project may be recorded in a project field set.

[0073] S103: taking at least one processing node for determining the score of the target item as a processing node set, and establishing an association relationship between each processing node in the processing node set and a domain, as a processing node domain set.

[0074] Specifically, one or more processing nodes for determining the score of the target project are taken as a processing node set, each processing node in the processing node set is associated with one or more different fields, and a processing node field set corresponding to each processing node is established based on the association between each processing node and the field.

[0075] S104: Determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project according to the project domain set and the processing node domain set.

[0076] Specifically, in order to measure the occurrence of the same fields between the fields associated with the target project and the fields associated with each processing node, the same fields associated with each processing node and the target project can be recorded in the corresponding overlapped field set according to the project field set and the processing node field set corresponding to each processing node, and the field overlap between each processing node in the processing node set and the target project can be calculated according to the following formula:

[0077]

[0078] in, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set.

[0079] S105: Acquire demand data of the target project.

[0080] S106: According to the demand data, respectively determine the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project.

[0081] Specifically, since the target project is not equally associated with the various fields in the project field set, the importance of the various fields in the project field set to the target project is different. Therefore, the degree of association between the various fields in the project field set in the target project can be determined based on the project requirements of the target project. Furthermore, based on the degree of association between the various fields in the project field set in the target project, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the target project can be determined.

[0082] S107: respectively determining the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set.

[0083] S108: Determine the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set.

[0084] Specifically, since the degree of association between each processing node in the processing node set and each field in the corresponding processing node field set is inconsistent, the weight level of each processing node in the processing node set in each overlapping field in the corresponding overlapping field set can be determined first, and then the weight value of each processing node in the processing node set in each overlapping field in the corresponding overlapping field set can be determined according to the corresponding weight level.

[0085] S109: Determine the first score coefficient of the target project based on the degree of field overlap between each processing node in the processing node set and the target project, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project, and the weight value of each overlapping field in the corresponding overlapping field set of each processing node in the processing node set.

[0086] Specifically, in order to reduce the differences and uncertainties in determining the scores of the target items by the processing nodes in the processing node set, the first score coefficient of the target item can be calculated according to the following formula based on the degree of overlap between the fields of the processing nodes in the processing node set and the target item, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target item, and the weight value of each processing node in the corresponding overlapping field set in the processing node set:

[0087]

[0088] Among them, α is the first score coefficient, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set, is the proportion of the overlapped domain k in the target project in the overlapped domain set corresponding to the processing node j in the processing node set, K is the weight value of processing node j in the corresponding overlap area k in the processing node set, j is the size of the set of overlapping domains corresponding to processing node j in the processing node set, and N is the number of processing nodes.

[0089] S110: Determine a quantitative score of the target project according to the statistical data of the target project and the first scoring coefficient.

[0090] Specifically, multiple dimensional data of the target project may be determined according to the statistical data of the target project, and then the quantitative score of the target project may be calculated according to the following formula:

[0091]

[0092] Wherein, G is the quantitative score, α is the first score coefficient, M is the first dimension data, X is the second dimension data, and D is the third dimension data.

[0093] Specifically, the multi-dimensional data can be qualitative indicators, evaluation indicators, etc. of the target project. When actually implemented, the multi-dimensional data can be determined according to the nature and actual needs of the target project. For example, in a financial project, the first dimensional data can be income, the second dimensional data can be taxes, and the third dimensional data can be costs.

[0094] Furthermore, when the multidimensional data cannot be directly quantified, it is necessary to determine the multidimensional data based on statistical data. Specifically, the direct positive statistical data, indirect positive statistical data, influencing indicators and revised values ​​of the influencing indicators of the target project can be determined based on the statistical data of the target project, and then the first dimension data can be determined based on the direct positive statistical data, indirect positive statistical data, influencing indicators and revised values ​​of the influencing indicators of the target project.

[0095] Specifically, the first dimension data can be calculated according to the following formula:

[0096] M=A(i)+L(i)+βI(i)

[0097] Wherein, M is the first dimension data, i is the target time period for collecting statistical data of the target project, A(i) is the direct positive statistical data of the target project in the target time period, L(i) is the indirect positive statistical data of the target project in the target time period, I(i) is the impact indicator of the target project in the target time period, and β is the correction value of the impact indicator.

[0098] Specifically, the direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators of the target project are data that can describe the value or positive influence of the target project. When actually implemented, the direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators can be determined according to the nature and actual needs of the target project. For example, in a financial project, the direct positive statistical data can be the actual return of the target financial project, the indirect positive statistical data can be the cost-optimized return of the target financial project, and the impact indicator can be the impact rate of return of the target financial project.

[0099] Furthermore, the necessary reduction items, influencing factors of the necessary reduction items, and optimization items of the target project can be determined based on the statistical data of the target project, and then the second dimension data can be calculated according to the following formula based on the necessary reduction items, influencing factors of the necessary reduction items, and optimization items of the target project:

[0100] X=BT-R

[0101] Among them, X is the second dimension data, B is the data of the must-reduced item, T is the influencing factor of the data of the must-reduced item, and R is the optimization item.

[0102] During actual implementation, the necessary reduction items, the influencing factors of the necessary reduction items and the optimization items can be determined according to the nature of the target project and actual needs. For example, in financial projects, the necessary reduction items can be the amount of tax payable, the influencing factors of the necessary reduction items can be the tax rate, and the optimization items can be tax exemptions, tax credits, etc.

[0103] Furthermore, the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project can be determined according to the statistical data of the target project, and then the third dimension data can be calculated according to the following formula according to the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project:

[0104] D=E+P(i)+O(i)

[0105] Wherein, D is the third dimension data, E is the direct negative statistical data of the target project, P(i) is the indirect negative statistical data of the target project in the target time period, and O(i) is the continuous negative statistical data of the target project in the target time period.

[0106] Specifically, the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project are data that can describe the negative impact on the value or positive influence of the target project. When actually implemented, the direct negative statistical data, indirect negative statistical data and continuous negative statistical data can be determined according to the nature and actual needs of the target project. For example, in financial projects, the direct negative statistical data can be labor costs, the indirect negative statistical data can be physical resource input costs, and the continuous negative statistical data can be operation and maintenance costs.

[0107] The above method is described below in conjunction with a specific embodiment. However, it should be noted that this specific embodiment is only for better illustrating the present application and does not constitute an improper limitation on the present application.

[0108] In this example, considering that the existing post-project evaluation methods cannot accurately determine the quantitative scores of the projects and cannot accurately evaluate the projects, a post-project evaluation method based on statistics and data processing is proposed. This method determines the first score coefficient of each processing node for scoring the target project through the associated fields of the target project and the associated fields of each processing node for scoring the target project. According to the first score coefficient, the multi-dimensional data of the target project and the relationship between the multi-dimensional data, the optimal quantitative score of the target project is accurately determined, and the target project is accurately evaluated, providing decision support for production guidance, technical improvement and risk management.

[0109] Specifically, the above-mentioned post-project evaluation method based on statistics and data processing may include the following steps:

[0110] S1: Collect various statistical data of the target items entered by the user.

[0111] S2: Determine multi-dimensional data of the target project based on the statistical data.

[0112] S3: Determine the first score coefficient of the target item.

[0113] S4: Calculate the quantitative score of the target project according to the multi-dimensional data and the first score coefficient.

[0114] Specifically, the quantitative score of the target project can be calculated according to the following formula:

[0115]

[0116] Wherein, G is the quantitative score, α is the first score coefficient, M is the first dimension data, X is the second dimension data, and D is the third dimension data.

[0117] The multidimensional data of the target project can be determined by the following steps:

[0118] Determine direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators of the target project based on the statistical data of the target project;

[0119] According to the direct positive statistical data, indirect positive statistical data, impact indicators and impact indicator correction values ​​of the target project, the first dimension data is calculated according to the following formula:

[0120] M=A(i)+L(i)+βI(i)

[0121] Wherein, M is the first dimension data, i is the target time period for collecting the statistical data of the target project, A(i) is the direct positive statistical data of the target project in the target time period, L(i) is the indirect positive statistical data of the target project in the target time period, I(i) is the impact index of the target project in the target time period, and β is the impact index correction value;

[0122] Determine the necessary reduction items, influencing factors of the necessary reduction items, and optimization items of the second dimension data of the target project according to the statistical data of the target project;

[0123] According to the required reduction items of the target project, the influencing factors of the required reduction items, and the optimization items of the second dimension data, the second dimension data is calculated according to the following formula:

[0124] X=BT-R

[0125] Among them, X is the second dimension data, B is the must-reduced item, T is the influencing factor of the must-reduced item, and R is the optimization item;

[0126] Determine direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project according to the statistical data of the target project;

[0127] According to the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project, the third dimension data is calculated according to the following formula:

[0128] D=E+P(i)+O(i)

[0129] Wherein, D is the third dimension data, E is the indirect negative statistical data of the target project, P(i) is the indirect negative statistical data of the target project in the target time period, and O(i) is the continuous negative statistical data of the target project in the target time period.

[0130] Furthermore, the first score coefficient of the target item can be determined by the following steps:

[0131] Label the relevant fields of the target project as the project label set;

[0132] Label the associated fields corresponding to each processing node for determining the score of the target project as a processing node label set for each node;

[0133] According to the project demand data, determine the proportion of each field in the project tag set;

[0134] Determine the weight value of each processing node in each field in the corresponding processing node label set according to the weight level of each processing node in each field in the corresponding processing node label set;

[0135] Determine the overlap degree and overlap domain set of the processing node label set corresponding to each processing node and the project label set according to the project label set and the processing node label set corresponding to each processing node;

[0136] Specifically, the overlap between the processing node label set and the project label set corresponding to each processing node can be determined according to the following formula:

[0137]

[0138] in, To process the overlap between node j and the target item, B P is the project tag set, is the label set for processing node j;

[0139] Determine the first score coefficient of the target project according to the overlap degree and overlap field set of the processing node label set and the project label set corresponding to each processing node, the proportion of each field in the project label set, and the weight value of each processing node in each field in the corresponding processing node label set;

[0140] Specifically, the first score coefficient of the target item is calculated according to the following formula:

[0141]

[0142] Among them, α is the first score coefficient, To process the overlap between node j and the target item, B P is the project tag set, is the processing node label set corresponding to processing node j, The proportion of the coincident area k in the target project in the coincident area set corresponding to the processing node j is: K is the weight value of processing node j in the corresponding overlap area k of the overlap area set. j is the size of the overlapping domain set corresponding to processing node j, and N is the number of processing nodes.

[0143] The above-mentioned post-project evaluation method is explained with a specific example as follows:

[0144] Specifically, Figure 2 As shown, applying the above-mentioned post-project evaluation method to the post-evaluation of financial projects may include the following steps:

[0145] S1: Determine various statistical data of the target financial project entered by the user.

[0146] Among them, the statistical data include the actual project results benefits of the target financial projects, labor cost savings benefits, IF project impact rate of return, enterprise market value correction value, taxable income, tax rate, tax exemption, tax credit, labor development costs, physical resource input costs and operation and maintenance costs.

[0147] S2: Determine the multi-dimensional data of the target financial project based on the statistical data, and determine the expert scoring coefficient of the target financial project.

[0148] Specifically, the multi-dimensional data may be revenue data, tax data, and cost data.

[0149] S3: Calculate the quantitative score of the target financial project based on the multi-dimensional data and the expert scoring coefficient;

[0150] Specifically, the quantitative score of the target project can be calculated according to the following formula:

[0151]

[0152] Wherein, G is the quantitative score, α is the expert scoring coefficient, M is the revenue data, X is the tax data, and D is the cost data.

[0153] S4: Output the quantitative score, and evaluate the financial project according to the quantitative score.

[0154] Specifically, the revenue data, tax data and cost data of the target financial project can be determined by the following steps:

[0155] According to the actual project results of the target financial project, the labor cost savings, the IF project impact rate of return and the enterprise market value correction value, the target financial project's income data is calculated according to the following formula:

[0156] M=A(i)+L(i)+βI(i)

[0157] Wherein, M is the income data, i is the period from the completion of the target financial project to the start of the post-evaluation, recorded as the target time period, A(i) is the actual project results income of the target financial project in the target time period, L(i) is the labor cost saving income of the target financial project in the target time period, I(i) is the IF project impact rate of return of the target financial project in the target time period, and β is the revised value of the enterprise market value;

[0158] Based on the taxable income, tax rate, tax exemption and tax credit of the target financial project, the tax data of the target financial project is calculated according to the following formula:

[0159] X=BT-RC

[0160] Wherein, X is the tax data, B is the taxable income, T is the tax rate, R is the tax reduction and exemption amount, and C is the tax credit amount;

[0161] According to the labor development cost, physical resource input cost and operation and maintenance cost of the target financial project, the cost data of the target financial project is calculated according to the following formula:

[0162] D=E+P(i)+O(i)

[0163] Wherein, D is the cost data, E is the labor development cost, P(i) is the physical resource investment cost of the target financial project in the target time period, and O(i) is the operation and maintenance cost of the target financial project in the target time period.

[0164] Furthermore, the expert scoring coefficient of the target project can be determined by the following steps:

[0165] Label the relevant fields of the target project as the project label set;

[0166] Label the corresponding related fields of each expert who scores the target project as the expert label set;

[0167] According to the project demand data, determine the proportion of each field in the project tag set;

[0168] According to the functional level of each expert in each field in the corresponding expert tag set, the weight value of each expert in each field in the corresponding expert tag set is determined;

[0169] According to the project label set and the expert label set of each expert, determine the overlap degree and overlap field set of the expert label set and the project label set corresponding to each expert;

[0170] Specifically, the overlap between the expert label set and the project label set corresponding to each expert can be determined according to the following formula:

[0171]

[0172] in, is the overlap between expert j and the target item, B P is the project tag set, is the label set of expert j;

[0173] Determine the expert scoring coefficient of the target project according to the overlap degree and overlap field set of the expert label set and the project label set corresponding to each expert, the proportion of each field in the project label set, and the weight value of each expert in each field in the corresponding expert label set;

[0174] Specifically, the expert scoring coefficient of the target project is calculated according to the following formula:

[0175]

[0176] Among them, α is the expert scoring coefficient, is the overlap between expert j and the target item, B P is the project tag set, is the expert label set corresponding to expert j, is the proportion of the overlapped field k in the target project corresponding to expert j, is the weight value of expert j in the corresponding overlapping field set within the overlapping field k, K j is the size of the overlapping domain set corresponding to expert j, and N is the number of expert digits.

[0177] In the above example, by determining the first scoring coefficient for each processing node to determine the score of the target project, the optimal quantitative score of the target project is accurately determined based on the first scoring coefficient, the multi-dimensional data of the target project and the relationship between the multi-dimensional data, and the target project is accurately evaluated to provide decision support for production guidance, technological improvement and risk management.

[0178] The method embodiments provided in the above embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on an electronic device as an example, Figure 3 This is a hardware structure block diagram of an electronic device of a data processing method provided by this application. Figure 3 As shown, the electronic device 10 may include one or more (only one is shown in the figure) processors 02 (the processor 02 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 04 for storing data, and a transmission module 06 for communication functions. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown.

[0179] The memory 04 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the data processing method in the embodiment of the present application. The processor 02 executes various functional applications and data processing by running the software programs and modules stored in the memory 04, that is, the data processing method of the above-mentioned application is realized. The memory 04 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 04 may further include a memory remotely arranged relative to the processor 02, and these remote memories may be connected to the electronic device 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0180] The transmission module 06 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 10. In one example, the transmission module 06 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission module 06 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0181] At the software level, the data processing device can be Figure 4 As shown, including:

[0182] A collection module 401 is used to collect statistical data of a target project;

[0183] A first associating module 402, configured to use at least one field associated with the target project as a project field set;

[0184] The second association module 403 is used to take at least one processing node that determines the score of the target project as a processing node set, and establish an association relationship between each processing node in the processing node set and a field as a processing node field set;

[0185] A first determination module 404 is used to determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project according to the project domain set and the processing node domain set;

[0186] The acquisition module 405 is used to acquire the demand data of the target project;

[0187] A second determination module 406 is used to determine, according to the demand data, the proportion of each overlapped field in the overlapped field set corresponding to each processing node in the processing node set in the target project;

[0188] The third determination module 407 is used to respectively determine the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0189] A fourth determination module 408, for determining the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0190] A first calculation module 409 is used to determine a first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set;

[0191] The second calculation module 410 is used to determine the quantitative score of the target project according to the statistical data of the target project and the first score coefficient.

[0192] In one embodiment, the first calculation module 409 determines the first score coefficient of the target project according to the degree of overlap between the fields of each processing node in the processing node set and the target project, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping field in the corresponding overlapping field set, including:

[0193] The first score coefficient of the target item is calculated according to the following formula:

[0194]

[0195] Among them, α is the first score coefficient, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set, is the proportion of the overlapped domain k in the target project in the overlapped domain set corresponding to the processing node j in the processing node set, is the weight value K of processing node j in the corresponding overlap area k in the processing node set jis the size of the set of overlapping domains corresponding to processing node j in the processing node set, and N is the number of processing nodes.

[0196] In one embodiment, the second calculation module 410 determines the quantitative score of the target project according to the statistical data of the target project and the first score coefficient, including:

[0197] Determine multiple dimensional data of the target project according to the statistical data of the target project;

[0198] According to the multiple dimension data and the first score coefficient, the quantitative score of the target project is calculated according to the following formula:

[0199]

[0200] Wherein, G is the quantitative score, α is the first score coefficient, M is the first dimension data, X is the second dimension data, and D is the third dimension data.

[0201] In one embodiment, the above-mentioned determining the multiple dimensional data of the target project according to the statistical data of the target project includes:

[0202] Determine direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators of the target project based on the statistical data of the target project;

[0203] According to the direct positive statistical data, indirect positive statistical data, impact indicators and impact indicator correction values ​​of the target project, the first dimension data is calculated according to the following formula:

[0204] M=A(i)+L(i)+βI(i)

[0205] Wherein, M is the first dimension data, i is the target time period for collecting statistical data of the target project, A(i) is the direct positive statistical data of the target project in the target time period, L(i) is the indirect positive statistical data of the target project in the target time period, I(i) is the impact indicator of the target project in the target time period, and β is the correction value of the impact indicator.

[0206] In one embodiment, the determining of the plurality of dimensional data of the target project according to the statistical data of the target project further includes:

[0207] Determine the necessary reduction items, influencing factors of the necessary reduction items, and optimization items of the second dimension data of the target project according to the statistical data of the target project;

[0208] According to the required reduction items of the target project, the influencing factors of the required reduction items, and the optimization items of the second dimension data, the second dimension data is calculated according to the following formula:

[0209] X=BT-R

[0210] Among them, X is the second dimension data, B is the must-reduced item, T is the influencing factor of the must-reduced item, and R is the optimization item.

[0211] In one embodiment, the determining of the plurality of dimensional data of the target project according to the statistical data of the target project further includes:

[0212] Determine direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project according to the statistical data of the target project;

[0213] According to the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project, the third dimension data is calculated according to the following formula:

[0214] D=E+P(i)+O(i)

[0215] Wherein, D is the third dimension data, E is the indirect negative statistical data of the target project, P(i) is the indirect negative statistical data of the target project in the target time period, and O(i) is the continuous negative statistical data of the target project in the target time period.

[0216] The embodiments of the present application also provide a specific implementation of an electronic device capable of implementing all the steps in the data processing method in the above embodiments, wherein the electronic device specifically includes the following contents: a processor, a memory, a communications interface, and a bus; wherein the processor, the memory, and the communications interface communicate with each other through the bus; the processor is used to call a computer program in the memory, and when the processor executes the computer program, all the steps in the data processing method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0217] Step 1: Collect statistical data of the target project;

[0218] Step 2: taking at least one field associated with the target project as a project field set;

[0219] Step 3: taking at least one processing node that determines the score of the target project as a processing node set, and establishing an association relationship between each processing node and a field in the processing node set as a processing node field set;

[0220] Step 4: According to the project domain set and the processing node domain set, determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project;

[0221] Step 5: Obtain demand data of the target project;

[0222] Step 6: According to the demand data, respectively determine the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project;

[0223] Step 7: respectively determining the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0224] Step 8: According to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set, respectively determine the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0225] Step 9: Determine the first score coefficient of the target project according to the degree of overlap between the fields of each processing node in the processing node set and the target project, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the corresponding overlapping field set in the processing node set;

[0226] Step 10: Determine the quantitative score of the target project based on the statistical data of the target project and the first scoring coefficient.

[0227] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all the steps of the data processing method in the above embodiments. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the data processing method in the above embodiments are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0228] Step 1: Collect statistical data of the target project;

[0229] Step 2: taking at least one field associated with the target project as a project field set;

[0230] Step 3: taking at least one processing node that determines the score of the target project as a processing node set, and establishing an association relationship between each processing node and a field in the processing node set as a processing node field set;

[0231] Step 4: According to the project domain set and the processing node domain set, determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project;

[0232] Step 5: Obtain demand data of the target project;

[0233] Step 6: According to the demand data, respectively determine the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project;

[0234] Step 7: respectively determining the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0235] Step 8: According to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set, respectively determine the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set;

[0236] Step 9: Determine the first score coefficient of the target project according to the degree of overlap between the fields of each processing node in the processing node set and the target project, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the corresponding overlapping field set in the processing node set;

[0237] Step 10: Determine the quantitative score of the target project based on the statistical data of the target project and the first scoring coefficient.

[0238] From the above description, it can be seen that the embodiment of the present application determines the domain overlap and the overlap domain set between each processing node in the processing node set and the target project through the project domain set of the target project, the processing node set and the processing node domain set for determining the score of the target project; and further determines the weight value of each processing node in the processing node set in each overlap domain in the corresponding overlap domain set, thereby determining the first score coefficient of the target project; and finally determines the quantitative score of the target project through the statistical data of the target project and the first score coefficient. That is, the evaluation of the target project is quantified through the above scheme, and an accurate quantitative score is determined, so that the accurate evaluation of the target project can be achieved.

[0239] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the hardware + program embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0240] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0241] Although the present application provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the order of the method shown in the embodiments or the drawings or in parallel (for example, in a parallel processor or multi-threaded processing environment).

[0242] Although the present specification embodiment provides the method operation steps as described in the embodiment or flow chart, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiment is only one way in the order of execution of many steps, and does not represent a unique execution order. When the device or terminal product in practice is executed, it can be executed in sequence or in parallel (such as a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment) according to the method shown in the embodiment or the accompanying drawings. The term "include", "comprise" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or equipment including a series of elements not only includes those elements, but also includes other elements not clearly listed, or also includes elements inherent to such process, method, product or equipment. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or equipment including the elements.

[0243] For the convenience of description, the above devices are described in various modules according to their functions. Of course, when implementing the embodiments of this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the module implementing the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0244] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0245] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0246] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0247] The present specification embodiments may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present specification embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0248] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, in the absence of contradiction, a person skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0249] The above is only an example of the embodiment of the present specification and is not intended to limit the embodiment of the present specification. For those skilled in the art, the embodiment of the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiment of the present specification shall be included in the scope of the claims of the embodiment of the present specification.

Claims

1. A data processing method, characterized in that: include: Collect statistical data of target projects; taking at least one field associated with the target project as a project field set; Taking at least one processing node that determines the score of the target project as a processing node set, and establishing an association relationship between each processing node and a field in the processing node set as a processing node field set; Determine, according to the project domain set and the processing node domain set, the domain overlap degree and the overlap domain set between each processing node in the processing node set and the target project; Acquiring demand data of the target project; According to the demand data, respectively determine the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project; Respectively determine the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set; Determine the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set; Determine a first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set; A quantitative score of the target project is determined according to the statistical data of the target project and the first scoring coefficient.

2. The method according to claim 1, characterized in that: Determining the first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set, including: The first score coefficient of the target item is calculated according to the following formula: Among them, α is the first score coefficient, To process the domain overlap between node j and the target project, B P is the project domain set, is the domain set of processing node j in the processing node set, is the proportion of the overlapped domain k in the target project in the overlapped domain set corresponding to the processing node j in the processing node set, K is the weight value of processing node j in the corresponding overlap area k in the processing node set, j is the size of the set of overlapping domains corresponding to processing node j in the processing node set, and N is the number of processing nodes.

3. The method according to claim 1, characterized in that Determining a quantitative score of the target project according to the statistical data of the target project and the first score coefficient includes: Determine multiple dimensional data of the target project according to the statistical data of the target project; According to the multiple dimension data and the first score coefficient, the quantitative score of the target project is calculated according to the following formula: Wherein, G is the quantitative score, α is the first score coefficient, M is the first dimension data, X is the second dimension data, and D is the third dimension data.

4. The method according to claim 3, characterized in that Determine multiple dimensional data of the target project according to the statistical data of the target project, including: Determine direct positive statistical data, indirect positive statistical data, impact indicators and revised values ​​of impact indicators of the target project based on the statistical data of the target project; According to the direct positive statistical data, indirect positive statistical data, impact indicators and impact indicator correction values ​​of the target project, the first dimension data is calculated according to the following formula: M=A(i)+L(i)+βI(i) Wherein, M is the first dimension data, i is the target time period for collecting statistical data of the target project, A(i) is the direct positive statistical data of the target project in the target time period, L(i) is the indirect positive statistical data of the target project in the target time period, I(i) is the impact indicator of the target project in the target time period, and β is the correction value of the impact indicator.

5. The method according to claim 3, characterized in that: Determine multiple dimensional data of the target project according to the statistical data of the target project, including: Determine the necessary reduction items, influencing factors of the necessary reduction items and optimization items of the target project according to the statistical data of the target project; According to the required reduction items, the influencing factors of the required reduction items and the optimization items of the target project, the second dimension data is calculated according to the following formula: X=BT-R Among them, X is the second dimension data, B is the must-reduced item, T is the influencing factor of the must-reduced item, and R is the optimization item.

6. The method according to claim 3, characterized in that Determine multiple dimensional data of the target project according to the statistical data of the target project, including: Determine direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project according to the statistical data of the target project; According to the direct negative statistical data, indirect negative statistical data and continuous negative statistical data of the target project, the third dimension data is calculated according to the following formula: D=E+P(i)+O(i) Wherein, D is the third dimension data, E is the indirect negative statistical data of the target project, P(i) is the indirect negative statistical data of the target project in the target time period, and O(i) is the continuous negative statistical data of the target project in the target time period.

7. A data processing device, characterized in that: include: The collection module is used to collect statistical data of the target project; A first associating module, configured to use at least one field associated with the target project as a project field set; A second association module is used to take at least one processing node for determining the score of the target project as a processing node set, and establish an association relationship between each processing node in the processing node set and a field as a processing node field set; A first determination module is used to determine the domain overlap degree and overlap domain set between each processing node in the processing node set and the target project according to the project domain set and the processing node domain set; An acquisition module, used to acquire demand data of the target project; A second determination module is used to determine, according to the demand data, the proportion of each overlapping field in the overlapping field set corresponding to each processing node in the processing node set in the target project; A third determination module is used to respectively determine the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set; A fourth determination module, for determining the weight value of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set according to the weight level of each processing node in the processing node set in each overlapped area in the corresponding overlapped area set; A first calculation module is used to determine a first score coefficient of the target project according to the degree of domain overlap between each processing node in the processing node set and the target project, the proportion of each overlapping domain in the overlapping domain set corresponding to each processing node in the processing node set in the target project, and the weight value of each processing node in the processing node set in each overlapping domain in the corresponding overlapping domain set; A second calculation module is used to determine a quantitative score of the target project according to the statistical data of the target project and the first score coefficient.

8. An electronic device comprising a processor and a memory for storing instructions executable by the processor, characterized in that: When the processor executes the instructions, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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