Project evaluation method and device and electronic equipment

Abstract

The embodiment of the invention discloses a project evaluation method and device and electronic equipment, and the method comprises the steps: determining evidence information related to a project evaluation index according to a government affair data development and utilization case; determining an index evaluation score corresponding to the project evaluation index based on the evidence information; performing clustering processing on the index evaluation score corresponding to the project evaluation index to obtain a plurality of classes, and determining a first evaluation score corresponding to each class according to the index evaluation score corresponding to each class; determining a target score corresponding to the project evaluation index according to the number of the index evaluation scores corresponding to the project evaluation index, the number of the index evaluation scores corresponding to each class and the first evaluation score corresponding to each class; and according to the target score corresponding to the project evaluation index, performing development and utilization value and / or development and utilization risk evaluation processing on a to-be-evaluated government affair data development and utilization project to obtain a project evaluation result.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a project evaluation method, apparatus, and electronic device. Background Technology

[0002] With the increasing number of government data development and utilization projects, how to effectively evaluate these projects in order to improve the utilization rate of government data has become a focus of public attention.

[0003] When evaluating government data development and utilization projects, manual evaluation can be conducted from the perspective of the entire data lifecycle. However, due to the difficulty in accurately assessing factors such as the costs of government data generation and processing, and the complexity and variability of the methods and scenarios adopted in government data development and utilization projects, the above-mentioned manual evaluation method suffers from low evaluation efficiency and effectiveness. Therefore, a technical solution is needed to effectively evaluate development and utilization projects in order to improve the utilization rate of government data. Summary of the Invention

[0004] The purpose of this invention is to provide a technical solution that can effectively evaluate development and utilization projects in order to improve the development and utilization rate of government data.

[0005] To solve the above-mentioned technical problems, the embodiments of the present invention are implemented as follows: In a first aspect, an embodiment of the present invention provides a project evaluation method, the method comprising: Based on cases of government data development and utilization, determine the evidentiary information related to project evaluation indicators; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators; Based on the evidence information, determine the evaluation scores of the corresponding project evaluation indicators; Cluster the evaluation scores of the project evaluation indicators to obtain multiple classes, and determine the first evaluation score for each class based on the evaluation scores of the indicators corresponding to each class. The target score corresponding to the project evaluation index is determined based on the number of evaluation scores for each project evaluation index, the number of evaluation scores for each category, and the first evaluation score for each category. Based on the target scores corresponding to the project evaluation indicators, the development and utilization value and / or development and utilization risk assessment of the government data development and utilization project to be evaluated is carried out to obtain the project evaluation results for the government data development and utilization project.

[0006] Secondly, embodiments of the present invention provide a project evaluation apparatus, the apparatus comprising: The evidence determination module is used to determine evidence information related to project evaluation indicators based on cases of government data development and utilization; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators. The first determining module is used to determine the indicator evaluation score corresponding to the project evaluation indicator based on the evidence information. The score clustering module is used to cluster the indicator evaluation scores corresponding to the project evaluation indicators to obtain multiple classes, and determine the first evaluation score corresponding to each class based on the indicator evaluation scores corresponding to each class. The second determining module is used to determine the target score corresponding to the project evaluation index based on the number of indicator evaluation scores corresponding to the project evaluation index, the number of indicator evaluation scores corresponding to each category, and the first evaluation score corresponding to each category. The project evaluation module is used to conduct development and utilization value and / or development and utilization risk assessments on the government data development and utilization projects to be evaluated based on the target scores corresponding to the project evaluation indicators, and to obtain the project evaluation results for the government data development and utilization projects.

[0007] Thirdly, embodiments of the present invention provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the project evaluation method provided in the above embodiments.

[0008] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the project evaluation method provided in the above embodiments.

[0009] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the steps of the project evaluation method provided in the above embodiments. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a flowchart illustrating a project evaluation method according to the present invention; Figure 2 This is a flowchart illustrating a method for obtaining evidence information according to the present invention. Figure 3 This is a flowchart illustrating a method for determining the evaluation scores of project evaluation indicators according to the present invention. Figure 4 This is a schematic diagram illustrating the process of determining the evaluation score of a project evaluation indicator according to the present invention. Figure 5 This is a flowchart illustrating a method for determining the target score corresponding to a project evaluation indicator according to the present invention. Figure 6 This is a flowchart illustrating a project evaluation process according to the present invention; Figure 7 This is a schematic diagram of the structure of a project evaluation device according to the present invention; Figure 8 This is a schematic diagram of the structure of an electronic device according to the present invention. Detailed Implementation

[0012] This invention provides a project evaluation method, apparatus, and electronic device.

[0013] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0014] This specification provides project evaluation methods, apparatus, and equipment. With the increasing number of government data development and utilization projects, how to effectively evaluate these projects to improve the utilization rate of government data has become a focus of public attention. While manual evaluation of government data development and utilization projects can be conducted from the perspective of the entire data lifecycle, this method suffers from low efficiency and effectiveness due to the difficulty in accurately assessing factors such as the costs of data generation and processing, and the complexity and variability of the methods and scenarios employed in these projects. Therefore, a technical solution is needed to effectively evaluate development and utilization projects and improve the utilization rate of government data. In this scheme, evidence information related to project evaluation indicators can be determined based on cases of government data development and utilization. These indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the evaluation scores corresponding to the project evaluation indicators are determined. The evaluation scores are then clustered to obtain multiple classes. Based on the evaluation scores of each class, a first evaluation score is determined for each class. The target score for each project evaluation indicator is determined based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class. Based on the target score, the development and utilization value and / or development and utilization risk assessments are performed on the government data development and utilization project to be evaluated, resulting in the project evaluation results for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence, we can effectively evaluate the project evaluation indicators, obtain the corresponding indicator scores, and then, through clustering, determine the target scores for each indicator. Clustering can yield more effective evaluation results even in cases of conflicting evidence or significant differences in indicator scores. Thus, by using the target scores for project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be assessed, thereby improving the utilization rate of government data. Specific processing details can be found in the following embodiments.

[0015] like Figure 1 As shown, this embodiment of the invention provides a project evaluation method. The execution subject of this method can be a terminal device or a server. The terminal device can be a mobile terminal device such as a mobile phone, tablet computer, or smartwatch, or a terminal device such as a computer. The server can be an independent server or a server cluster composed of multiple servers. Specifically, the method may include the following steps: In step S102, based on the case of government data development and utilization, evidence information related to project evaluation indicators is determined.

[0016] The project evaluation indicators may include development and utilization risk indicators and / or development and utilization value indicators. Development and utilization value indicators may include commercial value indicators, social value indicators, political value indicators, etc. Development and utilization risk indicators may include privacy and security indicators, data security indicators, technical security indicators, social order and security indicators, enterprise security indicators, social security indicators, and national security indicators, etc. Evidence information may include evidence corresponding to the project evaluation indicators. For example, if the project evaluation indicator is a commercial value indicator, the evidence information may include evidence to prove that a certain government data development and utilization project has generated commercial value.

[0017] During implementation, the server can obtain project evaluation indicators. For example, the server can determine the value and risk indicators of government data development and utilization based on the data security management requirements, sharing and opening regulations and related indicators in the "National Integrated Government Affairs Data Classification and Grading Standard" and the "National Integrated Government Affairs Data Sharing and Opening Standard".

[0018] The server can acquire government data development and utilization cases within a preset data acquisition period (such as the past three months, the past six months, etc.), and extract evidence information related to project evaluation indicators based on the government data development and utilization cases through a pre-trained evidence extraction model. The evidence extraction model can be a model built based on deep learning algorithms.

[0019] In step S104, the evaluation scores of the project evaluation indicators are determined based on the evidence information.

[0020] In implementation, the server can use multiple pre-trained score determination models to determine the evaluation scores of the project evaluation indicators based on evidence information. The score determination models can be models built based on deep learning algorithms, and different score determination models can have different network structures. In this way, the server can use multiple score determination models with different network structures to evaluate the project evaluation indicators from different dimensions based on evidence information and obtain multiple indicator evaluation scores.

[0021] Alternatively, the server can utilize a large language model to determine the evaluation scores of project evaluation indicators across different evaluation dimensions based on evidence information.

[0022] Furthermore, the method for determining the evaluation scores of the aforementioned project evaluation indicators is an optional and feasible method. In practical application scenarios, there can be a variety of different methods. Different methods can be selected according to different practical application scenarios. This specification does not specifically limit this method in the embodiments.

[0023] In step S106, the evaluation scores of the project evaluation indicators are clustered to obtain multiple classes, and the first evaluation score corresponding to each class is determined based on the evaluation scores of each class.

[0024] In implementation, the server can cluster the evaluation scores of the project evaluation indicators based on a preset clustering algorithm to obtain multiple classes. For example, the server can use the k-means algorithm to cluster the evaluation scores of the project evaluation indicators according to a preset number of clusters (such as 3 clusters) to obtain 3 classes.

[0025] The server can use Dempster's rule of composition in the DS evidence theory to fuse and calculate the evaluation scores of indicators in each class, so as to determine the first evaluation score corresponding to each class.

[0026] Alternatively, the server can determine the first evaluation score for each class based on one or more of the mean, maximum, minimum, mode, and median of the evaluation scores for each class's corresponding indicators.

[0027] Furthermore, the method for determining the first evaluation score corresponding to each of the above categories is an optional and implementable method. In actual application scenarios, there can be a variety of different methods. Different methods can be selected according to different actual application scenarios. This specification does not specifically limit this embodiment.

[0028] In step S108, the target score corresponding to the project evaluation index is determined based on the number of evaluation scores corresponding to the project evaluation index, the number of evaluation scores corresponding to each category, and the first evaluation score corresponding to each category.

[0029] In implementation, the server can perform weighted processing based on the number of evaluation scores corresponding to the project evaluation indicators, the number of evaluation scores corresponding to each category, and the first evaluation score corresponding to each category, so as to determine the target score corresponding to the project evaluation indicators based on the weighted processing result.

[0030] In step S110, based on the target scores corresponding to the project evaluation indicators, the development and utilization value and / or development and utilization risk assessment of the government data development and utilization project to be evaluated is carried out to obtain the project evaluation results for the government data development and utilization project.

[0031] In practice, the server can filter project evaluation indicators based on the target scores corresponding to the project evaluation indicators to obtain target evaluation indicators. Then, based on the target evaluation indicators, the server can conduct development and utilization value and / or development and utilization risk assessments on the government data development and utilization projects to be evaluated, and obtain project evaluation results for the government data development and utilization projects.

[0032] Furthermore, the server can send the evidence information and target scores corresponding to the project evaluation indicators to a preset evaluator, and receive feedback from the preset evaluator regarding the target scores. Based on the feedback, the server can then correct the target scores to obtain corrected target scores. Then, based on the corrected target scores, the server can perform development and utilization value and / or development and utilization risk assessments on the government data development and utilization project to be evaluated, thus obtaining the project evaluation results for the government data development and utilization project.

[0033] This invention provides a project evaluation method that, based on cases of government data development and utilization, determines evidence information related to project evaluation indicators. These indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the method determines the evaluation scores for each project evaluation indicator. These scores are then clustered to obtain multiple classes. A first evaluation score is determined for each class based on its corresponding evaluation score. A target score is determined for each project evaluation indicator based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class. Finally, based on the target score, the method performs development and utilization value and / or development and utilization risk assessments on the government data development and utilization project to be evaluated, resulting in a project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0034] In practical applications, the processing methods for evidence information related to project evaluation indicators determined in step S102 above, based on cases of government data development and utilization, can vary. One optional processing method is provided below, such as... Figure 2 As shown, the specific process may include the following steps S1022 to S1024.

[0035] In step S1022, preset data security requirements are obtained, and an evidence knowledge base is constructed based on the data security requirements and cases of government data development and utilization.

[0036] In practical applications, the evidence knowledge base is constructed in step S1022 above based on data security requirements and cases of government data development and utilization. The processing methods can vary; the following provides one optional method, which may specifically include the processing in step A1.

[0037] In step A1, a pre-set large language model is used to extract knowledge from data security requirements and government data development and utilization cases to obtain evidence corresponding to project evaluation indicators, development and utilization methods, development and utilization scenario descriptions, and correlation descriptions between project evaluation indicators and corresponding evidence.

[0038] In implementation, the server can utilize the deep understanding capabilities of the pre-set large language model to perform knowledge extraction processing on data security requirements and government data development and utilization cases, and obtain evidence e, development and utilization method m, development and utilization scenario description s, and association description d between project evaluation indicators and corresponding evidence for constructing evidence knowledge base k, i.e., evidence knowledge base K={m, s, n, e, d}, where n is the project evaluation indicator.

[0039] For example, K = {"commercial development and utilization" m, "the logistics service company's development and utilization of the agency's logistics data" s, "development and utilization of commercial value" n, "the agency's logistics data creates economic benefits for the service company" e, "through the agency's logistics data, the service company can accurately grasp its needs, enabling it to optimize resource allocation and reduce costs, thereby generating economic benefits and commercial value" d}.

[0040] Based on the constructed evidence knowledge base, and according to the design of the thought chain based on description d, input m, s, n, e, and prompt the training on the association between n and . After the model training is completed, the following questions can be input into the large language model: Q1. What other scenarios and methods exist for developing and utilizing government affairs data? Q2. What value might the above development and utilization scenarios and methods generate, and how can they generate value? Q3. What risks might the above development and utilization scenarios and methods bring, and how do these risks arise? Because large language models can aggregate a wide range of information and knowledge and have strong generalization capabilities, they can fully explore potential application scenarios, value, and risks. The server can extract relevant s, m, n, e, and d based on the answers from the large language model and supplement them into the constructed associated knowledge base.

[0041] By employing large language models to acquire social knowledge and public awareness of the value and risks of developing and utilizing government data, we can more fully grasp its potential development and utilization methods, application scenarios, and various risks that may be faced, and generate relevant evidence to improve the efficiency and accuracy of subsequent project evaluations.

[0042] In step S1024, evidence information related to the project evaluation indicators is obtained based on the evidence knowledge base.

[0043] In practice, based on the aforementioned knowledge base, the server can organize the evidence into a set Ei(si,mi,ei) based on social knowledge and public cognition, in accordance with the requirements of the DS evidence theory; where i is the evidence number, and si,mi,ei are the development and utilization scenarios, development and utilization methods, and related evidence content of the corresponding number, respectively.

[0044] In practical applications, the evidentiary information related to project evaluation indicators can include evidence corresponding to each project evaluation indicator, development and utilization methods, development and utilization scenarios, and a description of the relationship between the project evaluation indicators and their corresponding evidence. The processing method for determining the indicator evaluation score corresponding to the project evaluation indicator based on the evidentiary information in step S104 can be varied. The following provides one optional processing method, such as... Figure 3 As shown, the specific process may include the following steps, S1042.

[0045] In step S1042, the evidence, development and utilization methods, development and utilization scenario descriptions, and correlation descriptions between the project evaluation indicators and the corresponding evidence are sent to multiple preset evaluation parties. Based on the score returned by the multiple preset evaluation parties for each project evaluation indicator, the indicator evaluation score corresponding to the project evaluation indicator is determined.

[0046] In implementation, such as Figure 4 As shown, the server can perform indicator evaluation processing based on DS evidence theory. First, the server can construct a propositional framework. For example, the server can construct an indicator propositional framework according to the given government agency affairs data development and utilization value and risk assessment indicators (i.e., project evaluation indicators):

[0047] in, For k project evaluation indicators, for example, ={Business Value Indicators, Social Value Indicators, National Security Indicators, Social Order Security Indicators, Privacy Security Indicators, ...,}.

[0048] It is possible to construct a subset A = {"Completely Conforms", "Mostly Conforms", "Does Not Conforms"} of the proposition regarding the credibility assessment of evidence information for each project evaluation indicator, where subset A1 = {"Completely Conforms"}, set A2 = {"Mostly Conforms"}, and set A3 = {"Does Not Conforms"}.

[0049] Then, each piece of evidence can be randomly selected from the evidence set Ei(mi,si,ei) (using random sampling can avoid the bias that may be caused by the correlation between evidence before and after). The n and d information of the above evidence can be extracted from the evidence knowledge base, and sent to the preset evaluator along with the m and s information of the evidence. The preset evaluator will give the evaluation index scores of "fully compliant", "basically compliant" and "non-compliant" for each item in the proposition based on the preset continuous interval of [0-1] for each piece of evidence information in the same time period. The sum of the evaluation index scores of the above three items is 1.

[0050] Among them, the preset evaluators can be experts and scholars in relevant fields, and the number of preset evaluators can be greater than the preset number threshold. For example, the preset evaluators can include no less than 18 experts in relevant fields.

[0051] Since different experts may have significantly different levels of knowledge and cognitive abilities, the average method is insufficient to reflect the impact of these differences on the final score. Therefore, the evaluation scores of the project evaluation indicators can be determined based on the DS evidence theory method.

[0052] First, clustering algorithms such as k-means can be used to cluster the evaluation scores of the project evaluation indicators to obtain multiple classes, such as Ci classes, where i is the i-th class.

[0053] The number of classes can be determined by dividing the number of pre-defined evaluators (experts) by 6. For example, if there are 18 experts, i can be 3, and if there are 19 experts, i can be 4.

[0054] Then, the server can use Dempster's composition algorithm to merge and calculate the evaluation scores of the indicators in each class:

[0055] The first evaluation score corresponding to each class is obtained, where, The credibility fusion score is obtained by calculating the indicator evaluation scores in the i-th class using the Dempster synthesis method. This is the first evaluation score corresponding to the i-th class, where p is the number of indicator evaluation scores corresponding to the i-th class, and k can be used to characterize the degree of conflict between pieces of evidence. The calculation is as follows:

[0056] In practical applications, the processing method for determining the target score (i.e., the final score) of the project evaluation indicators in step S108 above, based on the number of indicator evaluation scores corresponding to the project evaluation indicators, the number of indicator evaluation scores corresponding to each category, and the first evaluation score corresponding to each category, can be varied. The following provides one optional processing method, such as... Figure 5 As shown, the specific process may include the following steps, S1082.

[0057] In step S1082, the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each category, and the first evaluation score for each category are substituted into the following formula.

[0058] The target scores corresponding to the project evaluation indicators are obtained, where m(A) is the target score, H is the number of indicator evaluation scores corresponding to the project evaluation indicators, t is the number of classes, and NCI is the number of indicator evaluation scores corresponding to the i-th class. This is the first evaluation score corresponding to the i-th class.

[0059] For example, taking the project evaluation indicator "citizen privacy risk" as an example, the target score corresponding to this project evaluation indicator can be shown in Table 1 below.

[0060] Table 1

[0061] The target score value for each indicator obtained above can be within the preset range [0,1], or it can be converted into different scores (such as 10 points or 100 points) according to the actual application scenario.

[0062] By employing an evaluation method based on the DS evidence theory, external evidence obtained from large language models can be combined with expert experience, enabling a more comprehensive and scientific evaluation of various project evaluation indicators. Furthermore, by introducing clustering methods, existing DS evidence theory methods can be improved. Based on experience, clustering into i classes allows for the weighted calculation of expert evaluation scores after clustering, yielding the target score for each project evaluation indicator. This approach can achieve more effective evaluation results and improve computational efficiency, even in cases of conflicting evidence or significant cognitive differences among evaluation experts.

[0063] In practical applications, step S110 above involves assessing the development and utilization value and / or development and utilization risk of the government data development and utilization project based on the target scores corresponding to the project evaluation indicators. The processing methods for obtaining the project evaluation results for the government data development and utilization project can vary. One optional processing method is provided below, such as... Figure 6 As shown, the specific process may include the following steps S1102 to S1106.

[0064] In step S1102, the project information corresponding to the government data development and utilization project to be evaluated is obtained.

[0065] The project information should include at least the development and utilization methods and the development and utilization scenarios.

[0066] In step S1104, the evaluation result corresponding to each project evaluation indicator is determined based on the project evaluation indicators and project information.

[0067] In practice, the server can evaluate each project evaluation indicator based on the project evaluation metrics and project information, and obtain the evaluation result corresponding to each project evaluation indicator.

[0068] In step S1106, the project evaluation results for the government data development and utilization project are determined based on the evaluation results and target scores corresponding to each project evaluation indicator.

[0069] In practice, the server can use the target score as the indicator weight corresponding to the project evaluation indicator, and perform weighted processing based on the indicator weight and evaluation results to obtain the project evaluation result for the government data development and utilization project.

[0070] By leveraging the social knowledge and public perception advantages gathered by large language models, as well as the scientific methods provided by DS evidence theory, a more comprehensive and scientific approach can be adopted for the value and risk assessment of government data development and utilization from the perspective of combining social cognition and objective evidence with the subjective experience of experts.

[0071] This specification provides a project evaluation method that, based on cases of government data development and utilization, determines evidence information related to project evaluation indicators. These indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the method determines the evaluation scores for each project evaluation indicator. These scores are then clustered to obtain multiple classes. A first evaluation score is determined for each class based on its corresponding evaluation score. A target score is determined for each project evaluation indicator based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class. Finally, based on the target score, the method performs development and utilization value and / or development and utilization risk assessments on the government data development and utilization project to be evaluated, resulting in a project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0072] The above describes the project evaluation method provided in the embodiments of this specification. Based on the same idea, the embodiments of this specification also provide a project evaluation device, such as... Figure 7 As shown.

[0073] The project evaluation device includes: an evidence determination module 701, a first determination module 702, a score clustering module 703, a second determination module 704, and a project evaluation module 705, wherein: The evidence determination module 701 is used to determine evidence information related to project evaluation indicators based on cases of government data development and utilization; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators. The first determining module 702 is used to determine the indicator evaluation score corresponding to the project evaluation indicator based on the evidence information. The score clustering module 703 is used to cluster the indicator evaluation scores corresponding to the project evaluation indicators to obtain multiple classes, and determine the first evaluation score corresponding to each class based on the indicator evaluation scores corresponding to each class. The second determining module 704 is used to determine the target score corresponding to the project evaluation index based on the number of indicator evaluation scores corresponding to the project evaluation index, the number of indicator evaluation scores corresponding to each category, and the first evaluation score corresponding to each category. The project evaluation module 705 is used to conduct a development and utilization value and / or development and utilization risk assessment on the government data development and utilization project to be evaluated based on the target scores corresponding to the project evaluation indicators, and to obtain the project evaluation results for the government data development and utilization project.

[0074] In this embodiment of the specification, the evidence determination module 701 is used for: Obtain preset data security requirements, and construct an evidence knowledge base based on the data security requirements and the government data development and utilization cases; Based on the evidence knowledge base, obtain the evidence information related to the project evaluation indicators.

[0075] In this embodiment of the specification, the evidence information related to the project evaluation indicators includes evidence corresponding to each project evaluation indicator, development and utilization methods, development and utilization scenarios, and a description of the association between the project evaluation indicators and the corresponding evidence. The first determining module 702 is used for: The evidence corresponding to the project evaluation indicators, the development and utilization methods, the development and utilization scenario descriptions, and the correlation descriptions between the project evaluation indicators and the corresponding evidence are sent to multiple preset evaluation parties. Based on the score returned by the multiple preset evaluation parties for each project evaluation indicator, the indicator evaluation score corresponding to the project evaluation indicator is determined.

[0076] In the embodiments described in this specification, the evidence determination module 701 is used for: Using a pre-defined large language model, knowledge extraction processing is performed on the data security requirements and the government data development and utilization cases to obtain the evidence corresponding to the project evaluation indicators, the development and utilization methods, the development and utilization scenario descriptions, and the correlation descriptions between the project evaluation indicators and the corresponding evidence.

[0077] In this embodiment of the specification, the second determining module 704 is used for: Substitute the number of evaluation scores corresponding to the project evaluation indicators, the number of evaluation scores corresponding to each category, and the first evaluation score corresponding to each category into the following formula.

[0078] The target score corresponding to the project evaluation index is obtained, where m(A) is the target score, H is the number of index evaluation scores corresponding to the project evaluation index, t is the number of classes, and NCI is the number of index evaluation scores corresponding to the i-th class. This is the first evaluation score corresponding to the i-th class.

[0079] In this embodiment of the specification, the project evaluation module 705 is used for: Obtain the project information corresponding to the government data development and utilization project to be evaluated, and the project information shall include at least the development and utilization method and the development and utilization scenario; Based on the project evaluation indicators and the project information, determine the evaluation result corresponding to each project evaluation indicator; Based on the evaluation results and target scores corresponding to each of the project evaluation indicators, the project evaluation results for the development and utilization of government data are determined.

[0080] This specification provides a project evaluation device that can determine evidence information related to project evaluation indicators based on government data development and utilization cases. The project evaluation indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the device determines the evaluation scores corresponding to the project evaluation indicators, performs clustering processing on the evaluation scores to obtain multiple classes, and determines a first evaluation score for each class based on the evaluation scores for each class. Based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class, the device determines the target score for each project evaluation indicator. Based on the target score for each project evaluation indicator, the device performs development and utilization value and / or development and utilization risk assessment processing on the government data development and utilization project to be evaluated, thereby obtaining the project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0081] The above are the project evaluation devices provided in the embodiments of this specification. Based on the same idea, the embodiments of this specification also provide an electronic device, such as... Figure 8 As shown.

[0082] The electronic device can provide a terminal device or server, etc., for the above embodiments.

[0083] Electronic devices can vary considerably due to differences in configuration or performance. They may include one or more processors 801 and memories 802, with the memory 802 storing one or more application programs or data. The memory 802 may be temporary or persistent storage. The application programs stored in the memory 802 may include one or more modules (not shown), each module including a series of computer-executable instructions for the electronic device. Furthermore, the processor 801 may be configured to communicate with the memory 802 and execute the series of computer-executable instructions stored in the memory 802 on the electronic device. The electronic device may also include one or more power supplies 803, one or more wired or wireless network interfaces 804, one or more input / output interfaces 805, and one or more keyboards 806.

[0084] Specifically, in this embodiment, the electronic device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for use in the electronic device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following: Based on cases of government data development and utilization, determine the evidentiary information related to project evaluation indicators; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators; Based on the evidence information, determine the evaluation scores of the corresponding project evaluation indicators; Cluster the evaluation scores of the project evaluation indicators to obtain multiple classes, and determine the first evaluation score for each class based on the evaluation scores of the indicators corresponding to each class. The target score corresponding to the project evaluation index is determined based on the number of evaluation scores for each project evaluation index, the number of evaluation scores for each category, and the first evaluation score for each category. Based on the target scores corresponding to the project evaluation indicators, the development and utilization value and / or development and utilization risk assessment of the government data development and utilization project to be evaluated is carried out to obtain the project evaluation results for the government data development and utilization project.

[0085] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the electronic device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0086] This specification provides an electronic device that can determine evidence information related to project evaluation indicators based on government data development and utilization cases. The project evaluation indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the device determines the evaluation scores corresponding to the project evaluation indicators, performs clustering processing on these scores to obtain multiple classes, and determines a first evaluation score for each class based on the evaluation scores for each class. Based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class, the device determines a target score for each project evaluation indicator. Based on the target score for each project evaluation indicator, the device performs development and utilization value and / or development and utilization risk assessment processing on the government data development and utilization project to be evaluated, thereby obtaining the project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0087] Furthermore, based on the above Figures 1 to 6 The method shown in this specification, along with one or more embodiments, also provides a storage medium for storing computer-executable instruction information. In one specific embodiment, the storage medium can be a USB flash drive, optical disc, hard disk, etc. When the computer-executable instruction information stored in the storage medium is executed by a processor, it can achieve the following process: Based on cases of government data development and utilization, determine the evidentiary information related to project evaluation indicators; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators; Based on the evidence information, determine the evaluation scores of the corresponding project evaluation indicators; Cluster the evaluation scores of the project evaluation indicators to obtain multiple classes, and determine the first evaluation score for each class based on the evaluation scores of the indicators corresponding to each class. The target score corresponding to the project evaluation index is determined based on the number of evaluation scores for each project evaluation index, the number of evaluation scores for each category, and the first evaluation score for each category. Based on the target scores corresponding to the project evaluation indicators, the development and utilization value and / or development and utilization risk assessment of the government data development and utilization project to be evaluated is carried out to obtain the project evaluation results for the government data development and utilization project.

[0088] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described storage medium embodiment is basically similar to the method embodiment, so the description is relatively simple; relevant parts can be referred to the description of the method embodiment.

[0089] This specification provides a storage medium that can determine evidence information related to project evaluation indicators based on government data development and utilization cases. The project evaluation indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the system determines the evaluation scores corresponding to the project evaluation indicators, performs clustering processing on these scores to obtain multiple classes, and determines a first evaluation score for each class based on the evaluation scores for each class. Based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class, the system determines the target score for each project evaluation indicator. Based on the target score for each project evaluation indicator, the system performs development and utilization value and / or development and utilization risk assessment processing on the government data development and utilization project to be evaluated, thereby obtaining the project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0090] Furthermore, based on the above Figures 1 to 6The method shown in this specification, along with one or more embodiments, also provides a computer program product including a computer program that, when executed by a processor, performs the following process: Based on cases of government data development and utilization, determine the evidentiary information related to project evaluation indicators; wherein, the project evaluation indicators include development and utilization risk indicators and / or development and utilization value indicators; Based on the evidence information, determine the evaluation scores of the corresponding project evaluation indicators; Cluster the evaluation scores of the project evaluation indicators to obtain multiple classes, and determine the first evaluation score for each class based on the evaluation scores of the indicators corresponding to each class. The target score corresponding to the project evaluation index is determined based on the number of evaluation scores for each project evaluation index, the number of evaluation scores for each category, and the first evaluation score for each category. Based on the target scores corresponding to the project evaluation indicators, the development and utilization value and / or development and utilization risk assessment of the government data development and utilization project to be evaluated is carried out to obtain the project evaluation results for the government data development and utilization project.

[0091] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described embodiment of a computer program product is relatively simple in description because it is fundamentally similar to the method embodiment; relevant parts can be referred to the description of the method embodiment.

[0092] This specification provides a computer program product that can determine evidence information related to project evaluation indicators based on government data development and utilization cases. The project evaluation indicators may include development and utilization risk indicators and / or development and utilization value indicators. Based on the evidence information, the product determines the evaluation scores corresponding to the project evaluation indicators, performs clustering processing on these scores to obtain multiple classes, and determines a first evaluation score for each class based on the evaluation scores for each class. Based on the number of evaluation scores for each project evaluation indicator, the number of evaluation scores for each class, and the first evaluation score for each class, the product determines a target score for each project evaluation indicator. Based on the target score for each project evaluation indicator, the product performs development and utilization value and / or development and utilization risk assessment processing on the government data development and utilization project to be evaluated, thereby obtaining the project evaluation result for the government data development and utilization project. In this way, by reviewing cases of government data development and utilization, we can accurately identify the evidence information related to project evaluation indicators. Based on this evidence information, we can effectively evaluate the project evaluation indicators and obtain the corresponding indicator scores. Then, based on the indicator evaluation scores, we can determine the target scores for the project evaluation indicators through clustering. Clustering can yield more effective evaluation results even when there are conflicting evidence or significant differences in indicator evaluation scores. Thus, by reviewing the target scores for the project evaluation indicators, we can accurately evaluate the government data development and utilization projects to be evaluated, thereby improving the development and utilization rate of government data.

[0093] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0094] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0095] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0096] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0097] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0098] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more 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 code.

[0099] Embodiments in this specification are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable parallel device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable parallel device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0100] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable fraud device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0101] These computer program instructions can also be loaded onto a computer or other programmable device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0102] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0103] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0104] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0105] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0106] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more 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 code.

[0107] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0108] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0109] The above description is merely an embodiment of this specification and is not intended to limit this document. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. A method of project evaluation, characterized by, The method comprises: According to the development and utilization case of government data, the evidence information related to the project evaluation index is determined; wherein, the project evaluation index comprises development and utilization risk index and / or development and utilization value index; Based on the evidence information, the index evaluation score corresponding to the project evaluation index is determined; The index evaluation score corresponding to the project evaluation index is clustered to obtain multiple classes, and the first evaluation score corresponding to each class is determined according to the index evaluation score corresponding to each class; According to the number of index evaluation scores corresponding to the project evaluation index, the number of index evaluation scores corresponding to each class, and the first evaluation score corresponding to each class, the target score corresponding to the project evaluation index is determined; According to the target score corresponding to the project evaluation index, the development and utilization value and / or development and utilization risk evaluation processing of the government data development and utilization project to be evaluated is carried out, and the project evaluation result of the government data development and utilization project is obtained.

2. The method of claim 1, wherein, According to the development and utilization case of government data, the evidence information related to the project evaluation index is determined, comprising: Obtain the preset data security requirement, and construct the evidence knowledge base according to the data security requirement and the government data development and utilization case; According to the evidence knowledge base, the evidence information related to the project evaluation index is obtained.

3. The method of claim 2, wherein, The evidence information related to the project evaluation index comprises evidence corresponding to each project evaluation index, development and utilization mode, development and utilization scene, and association description between the project evaluation index and the corresponding evidence, and the determination of the index evaluation score corresponding to the project evaluation index based on the evidence information comprises: The evidence corresponding to the project evaluation index, the development and utilization mode, the development and utilization scene description, and the association description between the project evaluation index and the corresponding evidence are respectively sent to multiple preset evaluation parties, and the index evaluation score corresponding to each project evaluation index is determined according to the score value returned by the multiple preset evaluation parties.

4. The method of claim 3, wherein, According to the data security requirement and the government data development and utilization case, the evidence knowledge base is constructed, comprising: Using a preset large language model, the data security requirement and the government data development and utilization case are subjected to knowledge extraction processing to obtain the evidence corresponding to the project evaluation index, the development and utilization mode, the development and utilization scene description, and the association description between the project evaluation index and the corresponding evidence.

5. The method of claim 1, wherein, According to the number of index evaluation scores corresponding to the project evaluation index, the number of index evaluation scores corresponding to each class, and the first evaluation score corresponding to each class, the target score corresponding to the project evaluation index is determined, comprising: The number of index evaluation scores corresponding to the project evaluation index, the number of index evaluation scores corresponding to each class, and the first evaluation score corresponding to each class are brought into the following formula obtaining a target score corresponding to the project evaluation index, wherein m(A) is the target score, H is the number of index evaluation scores corresponding to the project evaluation index, t is the number of classes, NCi is the number of index evaluation scores corresponding to the i-th class, is the first evaluation score corresponding to the i-th class.

6. The method of claim 1, wherein, The development and utilization value and / or development and utilization risk of the to-be-evaluated government data development and utilization project are evaluated according to the target score corresponding to the project evaluation index, and a project evaluation result of the government data development and utilization project is obtained, including: Obtaining project information corresponding to the to-be-evaluated government data development and utilization project, the project information at least including a development and utilization mode and a development and utilization scenario; According to the project evaluation index and the project information, determining the evaluation result corresponding to each project evaluation index; According to the evaluation result corresponding to each project evaluation index and the target score, determining the project evaluation result of the government data development and utilization project.

7. An item evaluation device characterized by comprising: The device comprises: An evidence determination module configured to determine evidence information related to a project evaluation index according to a government data development and utilization case, wherein the project evaluation index comprises a development and utilization risk index and / or a development and utilization value index; A first determination module configured to determine an index evaluation score corresponding to the project evaluation index based on the evidence information; A score clustering module configured to perform clustering processing on the index evaluation score corresponding to the project evaluation index to obtain multiple classes, and determine a first evaluation score corresponding to each class according to the index evaluation score corresponding to each class; A second determination module configured to determine a target score corresponding to the project evaluation index according to the number of index evaluation scores, the number of index evaluation scores corresponding to each class, and the first evaluation score corresponding to each class; A project evaluation module configured to evaluate the development and utilization value and / or development and utilization risk of a to-be-evaluated government data development and utilization project according to the target score corresponding to the project evaluation index, and obtain a project evaluation result of the government data development and utilization project.

8. An electronic device, comprising: A computer program is stored on the computer readable storage medium and is executed by the processor to implement the steps of the project evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer readable storage medium and is executed by the processor to implement the steps of the project evaluation method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, A computer program is stored on the computer readable storage medium and is executed by the processor to implement the steps of the project evaluation method according to any one of claims 1 to 6.

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