Data processing method and device, storage medium and computer equipment

Abstract

The embodiment of the invention provides a data processing method and device, a storage medium and computer equipment, and the method comprises the steps: in a current reputation value updating period, if a first object contributes intelligence data, sending the intelligence data to other second objects except the first object in a shared object set, and carrying out the confidence evaluation through the objects. After a specified number of target objects are evaluated, the system obtains evaluation scores, evaluation confidence and historical reputation values of the target objects, further determines an evaluation reputation change value, and records a contribution reputation change value of the first object at the same time. And at the end of the cycle, determining a cycle active reputation change value according to the contribution and evaluation quantity of each object, and summing the historical reputation value, the contribution and evaluation reputation change value and the cycle active reputation change value to obtain a current reputation value. Through mutual evaluation of the intelligence data by the objects, the intelligence data evaluation accuracy is improved, the object reputation value is updated, the object participation activeness is effectively improved, and the whole intelligence sharing and evaluation system is optimized.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a data processing method, device, storage medium and computer equipment. Background Art

[0002] Threat intelligence data is information about cybersecurity threats and is an important data asset that assists in attack detection and analysis. Threat intelligence describes existing or upcoming threats or dangers to assets and can be used to notify entities to take certain responses to relevant threats or dangers. Threat intelligence can help organizations and institutions discover and analyze threats, and make decisions and conduct defenses accordingly. By exchanging and sharing threat intelligence, the cost of intelligence collection can be reduced, the problem of information islands can be alleviated, the value of threat intelligence can be maximized, and the threat detection and emergency response capabilities of all parties involved in sharing can be improved.

[0003] In the related technologies, the threat intelligence data sharing scheme of the blockchain generally includes the threat intelligence center, intelligence producers, consumers, verifiers and other roles. Although the production and consumption of intelligence are carried out on the "trusted" chain, a certain mechanism is needed to ensure the reliability of the chain process, so as to ensure the trustworthiness of the operations of all parties and the security of data transmission. However, in the data sharing process, it is impossible to quantify the contribution of producers and verifiers, resulting in the object only expecting to obtain threat intelligence data, rarely contributing to and evaluating threat intelligence data, and the object activity is low, resulting in seldom updating threat intelligence data, and it is difficult to determine whether the threat intelligence data is trustworthy due to the small number of evaluations. Therefore, the related technology urgently needs to propose a data processing method to solve the above technical problems. Summary of the invention

[0004] The main purpose of this application is to provide a data processing method, apparatus, storage medium and computer equipment, which can realize the verification of intelligence data by having contributing objects evaluate each other's intelligence data, improve the accuracy of the evaluation of intelligence data, update the reputation value of objects participating in the contribution and evaluation, and improve the activity of the objects.

[0005] In a first aspect, an embodiment of the present application provides a data processing method, including: When it is detected that the first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; When it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, obtain the data confidence evaluation score of each of the target objects, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each of the target objects in the previous reputation value update cycle; Determining an evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object; Recording the contribution reputation change value of the first object for contributing to the intelligence data and the evaluation reputation change value of each of the target objects; When it is detected that the current reputation value update cycle has ended, determining the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle; The historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle are summed to obtain the current reputation value of each object in the current reputation value update cycle.

[0006] In a second aspect, an embodiment of the present application provides a data processing device, including: A sending unit, configured to send the intelligence data to each second object when detecting that the first object contributes intelligence data in the current reputation value update cycle, so that each second object performs a confidence evaluation on the intelligence data, wherein the second object is an object other than the first object in the shared object set; an acquisition unit, configured to acquire, when detecting that a specified number of target objects in the second object perform data confidence evaluation on the intelligence data, a data confidence evaluation score of each of the target objects, an evaluation confidence of the data confidence evaluation score, and a historical reputation value of each of the target objects in a previous reputation value update cycle; A first determining unit, configured to determine an evaluation reputation change value of each target object based on a confidence evaluation score, an evaluation confidence, and a corresponding historical reputation value of each target object; A recording unit, configured to record a contribution reputation change value of the first object contributing to the intelligence data and an evaluation reputation change value of each of the target objects; A second determining unit is configured to determine, when detecting that the current reputation value update cycle has ended, a periodic active reputation change value of each object in the shared object set according to the number of contributions and the number of evaluations of each object in the current reputation value update cycle; The calculation unit is used to sum the historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle to obtain the current reputation value of each object in the current reputation value update cycle.

[0007] In a third aspect, an embodiment of the present application provides a storage medium, wherein the computer-readable storage medium stores a plurality of instructions, which are suitable for loading by a processor to execute any of the above data processing methods.

[0008] In a fourth aspect, an embodiment of the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above data processing methods when executing the computer program.

[0009] In an embodiment of the present application, when it is detected that a first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; when it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, the data confidence evaluation score of each target object, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle are obtained; based on the confidence evaluation score of each target object, the evaluation confidence The credibility and the corresponding historical credibility value are used to determine the evaluation credibility change value of each target object; the credit value of the first object contributing to the intelligence data and the evaluation credibility change value of each target object are recorded; when the end of the current credibility update cycle is detected, the cycle active credibility change value of each object is determined according to the number of contributions and the number of evaluations of each object in the shared object set in the current credibility update cycle; the historical credibility value, the credit value of the contribution, the credibility change value of the evaluation and the corresponding cycle active credibility change value of each object in the previous credibility update cycle are summed to obtain the current credibility value of each object in the current credibility update cycle. Compared with the related art, it is impossible to quantify the contribution of the producer and the verifier, resulting in the object only expecting to obtain threat intelligence data, rarely contributing and evaluating threat intelligence data, and the object activity is low, resulting in seldom updating threat intelligence data, and it is difficult to determine whether the threat intelligence data is credible due to the small number of evaluations. The intelligence data can be verified by evaluating each other's intelligence data between objects, improving the accuracy of the evaluation of intelligence data, and updating the credibility of the objects participating in the contribution and evaluation, thereby improving the object activity.

[0010] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or understood by practicing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 A flowchart of a blockchain-based threat intelligence sharing solution provided in an embodiment of the present application.

[0013] Figure 2 A schematic diagram of a data processing system according to an embodiment of the present invention.

[0014] Figure 3 A flowchart of a data processing method provided in an embodiment of the present application.

[0015] Figure 4 A schematic diagram of an intelligence data sharing platform provided in an embodiment of the present application.

[0016] Figure 5 A schematic diagram of querying intelligence data by points provided in an embodiment of the present application.

[0017] Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application.

[0018] Figure 7 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0020] It should be noted that in some processes described in the specification, claims and the above-mentioned drawings, multiple steps appearing in a specific order are included, but it should be clearly understood that these steps may not be executed in the order in which they appear in this document or may be executed in parallel. The step numbers are only used to distinguish different steps, and the numbers themselves do not represent any execution order. In addition, descriptions such as "first", "second" or "target" in this document are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0021] Before further describing the embodiments of the present disclosure in detail, the nouns and terms involved in the embodiments of the present disclosure are described. The nouns and terms involved in the embodiments of the present disclosure are subject to the following interpretations: Threat intelligence sharing solutions based on blockchain: Figure 1 As shown, Figure 1 A flowchart of a threat intelligence sharing solution based on blockchain provided in the embodiment of the present application. It includes the threat intelligence center (central organization), producers, consumers, verifiers and other roles of intelligence data. Producers, verifiers and consumers first register for sharing platform objects through the central organization. Producers report a piece of threat intelligence data (referred to as intelligence data), and verifiers verify this intelligence data. After verification, it is shared on the blockchain, and consumers obtain this intelligence data from the blockchain, thereby realizing the sharing of intelligence data.

[0022] However, although the production and consumption of intelligence are both carried out on a "trusted" chain, a certain mechanism is needed to ensure the reliability of the chain process, so as to ensure the trustworthiness of the operations of all parties and the security of data transmission. In addition, the establishment and operation of the central agency also need to reduce the high cost. The distributed sharing solution based on blockchain and federated learning can ensure the security of data transmission, but it cannot guarantee the trustworthiness of the data "chain" operation, cannot solve the fairness problem, and cannot verify the quality of intelligence at a low cost. At present, the sharing of threat intelligence still faces the problems of imperfect mechanism and asymmetric trust. To solve the fairness problem faced by threat intelligence, it is necessary to encourage all parties to contribute their own intelligence, design a reasonable incentive mechanism to reward contribution behavior, and at the same time ensure that the results of contributors will not be unconditionally obtained by participants with less contribution. To solve the trust problem, it is necessary to ensure that the contributed threat intelligence is credible, set up a verification and evaluation mechanism for the contributed intelligence data, and ensure the quality of the contributed intelligence data.

[0023] In order to solve the above problems, the embodiment of the present application sends the intelligence data to each second object when it is detected that the first object contributes an intelligence data in the current reputation value update cycle, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; when it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, the data confidence evaluation score of each target object, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle are obtained; based on the confidence evaluation score, Evaluate the confidence and the corresponding historical reputation value, and determine the evaluation reputation change value of each target object; record the contribution reputation change value of the first object to the intelligence data, and the evaluation reputation change value of each target object; when the current reputation update cycle is detected to be over, determine the periodic active reputation change value of each object according to the number of contributions and the number of evaluations of each object in the shared object set in the current reputation update cycle; sum the historical reputation value, contribution reputation change value, evaluation reputation change value and the corresponding periodic active reputation change value of each object in the previous reputation update cycle to obtain the current reputation value of each object in the current reputation update cycle. Compared with the related art, it is impossible to quantify the contribution of producers and verifiers, resulting in the object only expecting to obtain threat intelligence data, rarely contributing and evaluating threat intelligence data, and the object activity is low, resulting in seldom updating threat intelligence data, and it is difficult to determine whether the threat intelligence data is credible due to the small number of evaluations. The verification of intelligence data can be achieved by evaluating intelligence data between contributing objects, improving the accuracy of intelligence data evaluation, and updating the reputation value of objects participating in contribution and evaluation, and improving object activity. Please continue to refer to the following specific embodiments for details.

[0024] See also Figure 2 , Figure 2 A schematic diagram of a data processing system provided in an embodiment of the present application, which includes a terminal 140, the Internet 130, a gateway 120, a server 110, etc.

[0025] The terminal 140 includes but is not limited to a pre-configured laptop, tablet computer, desktop computer or other electronic device with data reporting capability. In addition, it can be a single device or a collection of multiple devices. The terminal 140 can communicate with the Internet 130 in a wired or wireless manner to exchange intelligence data.

[0026] The terminal 140 refers to a computer system that can report data to the server 110. Compared with ordinary terminals, the server 110 has higher requirements in terms of stability, security, performance, etc. The server 110 can be a high-performance computer in a network platform, a cluster of multiple high-performance computers, a part of a high-performance computer (such as a virtual machine), a combination of parts of multiple high-performance computers (such as virtual machines), etc.

[0027] The gateway 120 is also called an internetwork connector or a protocol converter. The gateway realizes network interconnection at the transport layer and is a computer system or device that acts as a converter. The gateway is a translator between two systems that use different communication protocols, data formats or languages, or even completely different architectures. At the same time, the gateway can also provide filtering and security functions. The message sent by the terminal 140 to the server 110 must be sent to the corresponding server 110 through the gateway 120. The intelligence data sent by the server 110 to the terminal 140 must also be sent to the corresponding terminal 140 through the gateway 120.

[0028] The data processing method of the embodiment of the present disclosure may be implemented on the server 110 .

[0029] It should be noted that Figure 2 The scenario diagram of the data processing system shown is merely an example. The data processing system and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of image processing technology and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is equally applicable to similar technical problems.

[0030] In this embodiment, the description will be made from the perspective of a data processing device, which can be specifically integrated into a computer device having a storage unit and a microprocessor installed therein and having computing capabilities.

[0031] See also Figure 3 , Figure 3 A flow chart of a data processing method provided in an embodiment of the present application. The data processing method comprises: In step 201, when it is detected that a first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object so that each second object performs a confidence evaluation on the intelligence data. The second object is other objects in the shared object set except the first object.

[0032] The reputation value update cycle is a pre-set cycle for updating the reputation value of each object, for example, once every 6 hours. The shared object set is an object set consisting of objects that have registered their identities in the intelligence data sharing platform, and the second object is other objects in the shared object set except the first object. For example, the objects that have registered their identities in the intelligence data sharing platform include object A, object B, and object C. Object A is the first object, and the second objects are object B and object C.

[0033] Specifically, if it is detected that a first object contributes intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object in the shared object set except the first object, so that each second object performs a confidence evaluation on the intelligence data sent by the first object, thereby determining the authenticity of the intelligence data by performing confidence evaluation on multiple second objects.

[0034] In step 202, when it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, the data confidence evaluation score of each target object, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle are obtained.

[0035] Among them, the confidence evaluation is the data confidence evaluation score given by the target object based on its own experience for the intelligence data, and at the same time, the confidence level of the data confidence evaluation score given by the target object needs to be given, that is, the evaluation confidence level, which can also be called the degree of certainty. The purpose of setting the specified number here is that only when there are no less than the specified number of target objects in the second object to evaluate the confidence of the intelligence data, the intelligence data is considered to be effectively evaluated, avoiding the influence of subjective factors on the confidence evaluation of a single object.

[0036] Specifically, in order to increase the activity of the object, the credibility of the second object that performs the confidence evaluation on the intelligence data and the first object that contributes to the intelligence data will be recalculated. For the second object that performs the confidence evaluation, it is necessary to obtain the confidence evaluation score evaluated by it, the evaluation confidence of the given confidence evaluation score, and its historical credibility value in the previous credibility update cycle.

[0037] Let’s take the scenario where object A contributes intelligence data and objects B / C / D evaluate it as follows: object A contributes a piece of intelligence data X, and objects B / C / D give a confidence evaluation score of x / y / z to the intelligence. The confidence of the evaluation, i.e. the degree of certainty, is m / n / k. At the same time, the historical reputation values ​​of objects B / C / D are R B / R C / R Dx / y / z represent the evaluation scores of object B / C / D on the intelligence data X contributed by object A; m / n / k represent the degree of confidence of each object (i.e. B / C / D) in the evaluation score (i.e. x / y / z) of intelligence data X, i.e. the evaluation confidence. The confidence is obtained from the judgment of object B / C / D based on the information they have.

[0038] Evaluation confidence can be explained by the following example: Subject A likes to eat apples. Subject B's confidence evaluation score for this event is 80 points, and the confidence of the evaluation score of 80 points is 90%. This means that based on the information it has, Subject B is relatively certain (80 points) that Subject A likes to eat apples, and Subject B is 90% sure of this judgment that Subject A likes apples.

[0039] In step 203, based on the confidence evaluation score, evaluation confidence and corresponding historical reputation value of each target object, the evaluation reputation change value of each target object is determined.

[0040] Among them, for each target object, the contribution of each target object to the confidence evaluation of intelligence data is determined through the confidence evaluation score and the evaluation confidence of the confidence evaluation score; and then combined with the historical reputation value of each target object, the final evaluation reputation change value of each target object for the evaluation event of the confidence evaluation of intelligence data is determined.

[0041] In some implementations, determining the evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object includes: (1) Obtaining the reputation value ratio of each target object; (2) determining the product of the confidence evaluation score of each target object and the corresponding evaluation confidence, and obtaining the evaluation contribution value of each target object with respect to the intelligence data; (3) performing a weighted summation of the evaluation contribution values ​​of the multiple target objects with respect to the intelligence data according to the reputation value ratio of each target object, so as to obtain the actual evaluation contribution value of the intelligence data; (4) Obtaining the absolute value of the difference between the evaluation contribution value of each target object and the actual evaluation contribution value, to obtain the evaluation contribution difference of each target object; Based on the evaluation contribution difference of each of the target objects, determining the evaluation contribution difference ratio of each of the target objects; (5) Determine the product of the evaluation contribution difference ratio of each target object and the total evaluation reputation value of the intelligence data to obtain the evaluation reputation change value of each target object.

[0042] The reputation value ratio of each target object is the ratio of the historical reputation value of the target object to the sum of the historical reputation values ​​of all target objects. The evaluation contribution value is the quantitative result of the contribution made by each target object to the confidence evaluation of the intelligence data, that is, the product of the confidence evaluation score of each target object and the corresponding evaluation confidence, to obtain the evaluation contribution value of each target object for the intelligence data.

[0043] Specifically, object A contributes a piece of intelligence data X, and objects B / C / D give confidence evaluation scores of x / y / z to the intelligence. The confidence of the evaluation, i.e., the degree of certainty, is m / n / k. At the same time, the historical reputation values ​​of objects B / C / D are R B / R C / R D As an example, the actual evaluation contribution value It can be calculated by the following formula: ; in, That is, the quantified evaluation contribution value of object B to the confidence evaluation of intelligence data X. That is, the quantified evaluation contribution value of object C to the confidence evaluation of intelligence data X. That is, the quantified evaluation contribution value of the object D to the confidence evaluation of the intelligence data X; is the credit value ratio of object B, is the credit value ratio of object C, By weighted summing the evaluation contribution values ​​of multiple target objects to intelligence data, the actual evaluation contribution value of intelligence data X is obtained. .

[0044] Specifically, after publishing the evaluation task of each intelligence, the total reward set for the intelligence evaluation task (i.e., the total evaluation reputation value E) is given at the same time. After the specified time after the evaluation is completed, the reward distribution is carried out according to the quality of the evaluation made by each object, that is, the difference between the evaluation result of each object and the final result as the measurement standard. After calculating the actual evaluation contribution value, the evaluation contribution difference of each target object can be determined. The evaluation contribution difference is the absolute value of the difference between the evaluation contribution value of each target object and the actual evaluation contribution value, which can be calculated according to the following formula: ; ; ; in, , as well as They represent the difference in evaluation contributions of objects B / C / D respectively. It can be understood that the total reward set for the intelligence evaluation task in this application is not limited to the total evaluation reputation value, but can also be other virtual rewards such as total evaluation points, which are not limited here.

[0045] According to the difference in evaluation contribution of each target object, the proportion of different target objects in the total evaluation contribution difference is determined, that is, the proportion of evaluation contribution difference of each target object Finally, the evaluation reputation change value of each target object is obtained based on the product of the evaluation contribution difference ratio of each target object and the total evaluation reputation value of the intelligence data. .

[0046] Specifically, object A contributes a piece of intelligence data X, and objects B / C / D give confidence evaluation scores of x / y / z to the intelligence. The confidence of the evaluation, i.e., the degree of certainty, is m / n / k. At the same time, the historical reputation values ​​of objects B / C / D are R B / R C / R D Taking the scenario as an example, the evaluation reputation change value of each target object can be calculated according to the following formula: ; ; ; in, , as well as The difference ratios of the evaluation contributions of objects B / C / D are calculated respectively. For object B, the total evaluation reputation value E and its corresponding contribution difference ratio are calculated. The product of the total evaluation reputation value E and its corresponding contribution difference is calculated for object C. The product of the total evaluation reputation value E and its corresponding contribution difference is calculated for object D. The product of is used to obtain the evaluation reputation change value of object D.

[0047] Specifically, in some implementations, after an object contributes an intelligence data, a confidence evaluation task for the intelligence data is issued, and the number of members participating in the evaluation task of the intelligence to be evaluated is updated in real time, and the evaluation task is pushed to the object. The object can selectively participate in the evaluation task and obtain a reputation value or a point reward. For newly contributed threat intelligence and unverified threat intelligence, the object is recommended to participate in the evaluation first. For verified intelligence, objects that have not participated in the evaluation of the intelligence data can still evaluate it. Objects that have participated in the verification cannot participate again, but can modify and update the submitted evaluation results. After a period of time after the evaluation of a certain intelligence data is completed, the evaluation task for the intelligence data is closed, and the reward distribution is executed. The object can choose to modify the confidence of the verified and closed evaluation intelligence data based on its own practical experience, and give the basis, which will be submitted to other objects for review. After the review is passed, the confidence calculation of the intelligence data is updated, and a reward is given to the object that filed the appeal.

[0048] In this way, the evaluation reputation change value is determined by comprehensively considering the confidence evaluation score, evaluation confidence, and historical reputation value of the target object. The confidence evaluation score reflects the judgment of the credibility of intelligence data, the evaluation confidence reflects the degree of certainty of the judgment, and the historical reputation value represents the past reputation situation. The combination of multiple factors avoids the one-sidedness of single-factor evaluation, making the measurement of the evaluation behavior of the target object more comprehensive and scientific, and more accurately reflecting its actual contribution and value to the evaluation of intelligence data. By calculating the proportion of reputation value, the evaluation contribution value of the target object is weighted and summed to obtain the actual evaluation contribution value. This makes the evaluation of objects with high reputation values ​​have a greater weight in the overall evaluation, because their past performance is more reliable and in line with common sense, and also makes the evaluation results more in line with the actual situation, enhancing the objectivity of the evaluation system.

[0049] In some implementations, obtaining the reputation value ratio of each target object includes: (1.1) Obtaining the sum of the historical reputation values ​​of each target object to obtain a total reputation value; (1.2) Calculate the ratio of the historical reputation value of each target object to the total reputation value to obtain the reputation value ratio of each target object.

[0050] The calculation method of the reputation value ratio of each target object is as follows: obtain the sum of the historical reputation values ​​of each target object to obtain the total reputation value of all target objects; then calculate the ratio of the historical reputation value of each target object to the total reputation value to obtain the reputation value ratio of each target object.

[0051] Specifically, object A contributes a piece of intelligence data X, and objects B / C / D evaluate the confidence of the intelligence data. The historical reputation values ​​of objects B / C / D are R B / R C / R D Taking the scenario as an example, the reputation value ratio of objects B / C / D can be determined by the following formula: ; ; ; in, That is, the total reputation value. For object B, by calculating its corresponding historical reputation value R B Total credit value The corresponding credit value ratio can be obtained by ; For object C, by calculating its corresponding historical reputation value R C Total credit value The corresponding credit value ratio can be obtained by ; For object D, by calculating its corresponding historical reputation value R D Total credit value The corresponding credit value ratio can be obtained by .

[0052] In this way, by calculating the reputation value ratio, the historical reputation value of the target object is incorporated into the current intelligence data evaluation system. Objects with high historical reputation values ​​will have higher weights in the evaluation, which means that their evaluation opinions can have a greater impact on the final actual evaluation contribution value. For example, in a scenario where object A contributes intelligence data X and objects B, C, and D make evaluations, if object B has a higher historical reputation value, its reputation value ratio will be If the value of object B is relatively large, the evaluation contribution of object B to intelligence data X will account for a larger proportion in the weighted sum. This can more accurately integrate the evaluation of each object, avoid excessive interference of the results by the arbitrary evaluation of individual objects, make the evaluation results more in line with the actual situation, and enhance the reliability of the evaluation results. When there are multiple target objects participating in the evaluation, the calculation of the reputation value ratio can comprehensively consider the historical reputation of all objects. The historical reputation value of each object participates in the calculation of the actual evaluation contribution value in the form of a ratio, so that the evaluation result does not only depend on the evaluation of a single object, but is a reasonable weighted synthesis of the evaluation of all objects. This method can effectively avoid the one-sidedness of the evaluation of a single object and make the evaluation results more comprehensive and objective.

[0053] In some implementations, determining the evaluation contribution difference ratio of each target object based on the evaluation contribution difference of each target object includes: (1.1) Obtaining the sum of the evaluation contribution differences of each target object to obtain a total evaluation contribution difference; (1.2) Calculate the ratio of the evaluation contribution difference of each target object to the total evaluation contribution difference to obtain the evaluation contribution difference ratio of each target object.

[0054] Among them, the calculation method of the evaluation contribution difference ratio of each target object is: obtain the sum of the evaluation contribution differences of each target object to obtain the total evaluation contribution difference of all target objects; then calculate the ratio of the evaluation contribution difference of each target object to the total evaluation contribution difference to obtain the evaluation contribution difference ratio of each target object.

[0055] Specifically, object A contributes a piece of intelligence data X, and objects B / C / D evaluate the confidence of the intelligence data. The differences in the evaluation contributions of objects B / C / D are , as well as Taking the scenario as an example, the following formula can be used to determine the difference in evaluation contributions of objects B / C / D: ; ; ; in, That is, the total evaluation contribution difference. For object B, by calculating its corresponding evaluation contribution difference Difference from total evaluation contribution The ratio of can be obtained by ; For object C, by calculating its corresponding evaluation contribution difference Difference from total evaluation contribution The ratio of can be obtained by ; For object D, by calculating its corresponding evaluation contribution difference Difference from total evaluation contribution The ratio of can be obtained by .

[0056] In this way, by calculating the evaluation contribution difference ratio, the evaluation contribution difference of each target object is considered in the overall difference of all objects, and the relative difference between individual evaluation quality and overall evaluation is quantified. For example, in the scenario where object A contributes intelligence data X, and objects B, C, and D participate in the evaluation, the evaluation contribution difference ratios of objects B, C, and D are obtained respectively. , as well as , it can clearly show the degree of deviation of the evaluation of each object compared with the overall evaluation, so as to more accurately measure the quality of the evaluation of each object. The size of the evaluation contribution difference ratio directly reflects the relative quality of the evaluation of the target object. A small ratio means that the evaluation of the object is closer to the final actual evaluation contribution value and the evaluation quality is higher; a large ratio means that the evaluation deviation is large and the evaluation quality is relatively low. This provides a clear and intuitive basis for quality judgment for subsequent reward distribution and reputation adjustment.

[0057] In step 204, the contribution reputation change value of the first object contributing to the intelligence data and the evaluation reputation change value of each of the target objects are recorded.

[0058] According to the specific implementation scenario, a contribution reputation change value that can be obtained by contributing a piece of intelligence data is set. Based on this, after the first object contributes intelligence data, the corresponding contribution reputation change value can be obtained by recording its contribution intelligence data, and the evaluation reputation change value of each target object that performs confidence evaluation on this intelligence data is recorded for subsequent reputation value calculation.

[0059] In step 205, when it is detected that the current reputation value update cycle ends, the periodic active reputation change value of each object in the shared object set is determined according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle.

[0060] If it is detected that the current reputation value update cycle has ended, the periodic active reputation change value of each object for the current reputation value update cycle will be calculated based on the contribution quantity and evaluation quantity of each object in the current reputation value update cycle.

[0061] In some implementations, determining the periodic active reputation change value of each object in the shared object set according to the number of contributions and the number of evaluations of each object in the current reputation value update period includes: (1) Determine the contribution activity according to the contribution amount of each object in the shared object set in the current reputation value update cycle; (2) determining the evaluation activity according to the number of evaluations of each object in the shared object set in the current reputation value update cycle; (3) Obtain contribution index and evaluation index; (4) Determine the weighted activity of each object in the shared object set based on the contribution activity, contribution index, evaluation activity, and evaluation index of each object in the shared object set; (5) Determine a periodic active reputation change value of each object in the shared object set according to the weighted activity of each object in the shared object set, a preset proportional coefficient, and an adjustment parameter.

[0062] Among them, the periodic active reputation change value is determined according to the activity of each object in the current reputation value update cycle. Each object can be a contributor to intelligence data or an evaluator of the confidence evaluation of intelligence data. Therefore, for an object, its activity in the current reputation value update cycle can be reflected in two dimensions: contribution activity and evaluation activity. For contribution activity, it is associated with the number of contributions of the object in the current reputation value update cycle. Specifically, the number of contributions can be directly determined as contribution activity, or the product of the number of contributions and the corresponding contribution coefficient can be used as contribution activity, which is not limited here; for evaluation activity, it is associated with the number of evaluations of the object in the current reputation value update cycle. Specifically, the number of evaluations can be directly determined as evaluation activity, or the product of the number of evaluations and the corresponding evaluation coefficient can be used as evaluation activity, which is not limited here.

[0063] Specifically, after obtaining the contribution activity and the evaluation activity, the weighted activity of each object in the shared object set is determined in combination with the pre-set contribution index and evaluation index, and finally the periodic active reputation change value of each object is determined based on the weighted activity of each object in the shared object set, the preset proportional coefficient and the adjustment parameter.

[0064] In this way, the activity of the object is measured from the two dimensions of contribution and evaluation, which comprehensively covers the main behaviors of the object in the intelligence sharing system. It not only pays attention to the contribution of the object as an intelligence data provider (contribution activity), but also attaches importance to its role as an evaluator in controlling data quality (evaluation activity), avoiding the one-sidedness of evaluating the contribution of the object based on a single behavior, so that the reputation change value can more comprehensively reflect the actual participation and value of the object in the system. The reputation change value is determined based on weighted activity, so that objects that actively participate in contributions and evaluations can obtain higher reputation enhancement. Active objects invest more time and energy in the system, and can get corresponding rewards through this mechanism. For example, a higher reputation value may bring more resource access rights or system rewards, thereby motivating objects to continue to be active, further enrich and improve intelligence data resources, and improve the overall value of the system.

[0065] In some implementations, determining the weighted activity of each object in the shared object set based on the contribution activity, contribution index, evaluation activity, and evaluation index of each object in the shared object set includes: (1.1) using the contribution index as an index and the contribution activity of each object in the shared object set as a base, calculating the contribution sub-weighted activity of each object in the shared object set; (1.2) using the evaluation index as an index and the evaluation activity of each object in the shared object set as a base, calculating the evaluation sub-weighted activity of each object in the shared object set; (1.3) Calculate the sum of the contribution sub-weighted activity and the corresponding evaluation sub-weighted activity of each object in the shared object set to obtain the weighted activity of each object in the shared object set.

[0066] Among them, for each object in the shared object set in the contribution dimension, the contribution sub-weighted activity is calculated with the contribution index as the index and the corresponding contribution activity as the base. For example, the contribution activity of object B is , the contribution index is , then the weighted activity of the contribution of object B is For each object in the shared object set, the evaluation sub-weighted activity in the evaluation dimension is calculated with the evaluation index as the index and the corresponding evaluation activity as the base. For example, the contribution activity of object B is , the evaluation index is , then the weighted activity of the contribution of object B is .

[0067] The weighted activity of each object in the shared object set is obtained by calculating the sum of the contribution sub-weighted activity and the corresponding evaluation sub-weighted activity. For example, the contribution sub-weighted activity of object B is , the weighted activity of the contributing child is , then the weighted activity of object B is .

[0068] Specifically, the contribution index and evaluation index are indices corresponding to contribution and evaluation, respectively, which determine the degree of "reward acceleration" or "penalty deceleration". >1 or >1, the object is considered to be highly active, which will lead to a faster increase in reputation score. <1 or <1, it will form a "penalty slowdown" effect.

[0069] In this way, the weighted activity is determined by comprehensively considering the activity of the two dimensions of contribution and evaluation, and the contribution of the object to the system is comprehensively and accurately measured. Different objects may have their own advantages in contribution and evaluation, and this method can reflect these advantages. For example, some objects are good at collecting intelligence and have high contribution activity; some objects are experienced in evaluation and have high evaluation activity. By calculating the contribution sub-weighted activity and the evaluation sub-weighted activity separately and summing them up, the contribution of each type of object can be reasonably reflected in the weighted activity, providing a reliable basis for the subsequent accurate evaluation of the object's reputation. By adjusting the contribution index and evaluation index , the system can flexibly control the degree of incentives for different behaviors of the object. >1, the weighted activity of the contribution sub-weighted of the object with higher contribution activity will grow rapidly, and the credit score will increase faster, which encourages the object to actively contribute more high-quality intelligence data; similarly, >1, subjects with high evaluation activity will receive more rewards, encouraging them to participate in the evaluation seriously. <1 or When <1, the credit score decay rate of low-activity objects slows down, avoiding excessive punishment of temporarily low-activity objects and maintaining the objects' enthusiasm for participation.

[0070] In some implementations, determining the periodic activity reputation change value of each object in the shared object set according to the weighted activity of each object, a preset proportionality coefficient, and an adjustment parameter includes: (1.1) determining the product of the weighted activity of each object in the shared object set and a preset proportional coefficient to obtain a third calculation result for each object in the shared object set; (1.2) Obtaining a difference between the third calculation result and the adjustment parameter for each object in the shared object set, and obtaining a periodic active reputation change value of each object.

[0071] After obtaining the weighted activity of each object in the shared object set, the third calculation result of each object in the shared object set is determined by calculating the product of the weighted activity and the preset proportional coefficient k. The preset proportional coefficient k is used to control the overall growth or decay rate; the periodic active reputation change value of each object is obtained by calculating the difference between the third calculation result of each object in the shared object set and the adjustment parameter l. l is a constant term that can be set to a positive or negative number as needed, and is used to adjust the baseline level or the set minimum growth / maximum decay limit.

[0072] Specifically, the periodic active reputation change value of each object can be calculated using the following formula: =k( )−l; in, It is the periodic active reputation change value, which is calculated by calculating the weighted activity of each object in the shared object set. The third calculation result k( ) is used to calculate the difference between the value of the shared object and the adjustment parameter l to determine the periodic active reputation change value of each object in the shared object set.

[0073] Specifically, to achieve the growth or decay of reputation points based on activity, you need to pay attention to the following: if you want to give greater rewards to high activity, you can set it larger. Conversely, if you want to reduce the negative impact of low activity, you can choose a smaller index. In addition, you need to regularly evaluate and adjust the parameters k, , Or l to ensure more flexible adjustment of the speed of rewards and punishments to better adapt to different business needs.

[0074] In this way, the preset proportional coefficient k and adjustment parameter l provide a flexible means for regulating the reputation change value. k controls the overall growth or decay rate. As k increases, the magnitude of the change in reputation with weighted activity increases, and the rewards for highly active users are more generous, which can effectively motivate users to actively participate; as k decreases, the magnitude of the change in reputation decreases, making the reputation system more stable. The adjustment parameter l can set the minimum growth or maximum decay limit. When it is a positive number, it can increase the baseline level of reputation growth and reduce the degree of reputation decay; as a negative number, it can limit the excessive growth of reputation, ensure the rationality of the reputation system, and adapt to the requirements of the speed and range of reputation changes in different business scenarios. The reputation change value is calculated based on the weighted activity, which closely links the actual contribution of users in the system with the change in reputation. The weighted activity comprehensively considers the activity of user contributions and evaluations, and accurately reflects the degree of user participation and contribution. The calculation result of the reputation change value can accurately reflect the impact of user contributions on reputation, and users who actively contribute will gain reputation improvement, which encourages users to continue to provide value to the system and achieve a reasonable match between user contributions and reputation returns.

[0075] In step 206, the historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle are summed to obtain the current reputation value of each object in the current reputation value update cycle.

[0076] Among them, after obtaining the contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value obtained by each contribution or evaluation of each object in the shared object set in the current reputation value update cycle, the historical reputation value of the previous reputation value update cycle is combined and summed up to finally obtain the current reputation value of each object in the current reputation value update cycle.

[0077] Specifically, object A contributed 3 intelligence data in the current reputation update cycle, and the contribution reputation change values ​​of each contribution record were e, f, and g respectively; it evaluated intelligence data twice, and the evaluation reputation change values ​​of each evaluation record were m and n respectively; the historical reputation value of the previous reputation update cycle was h, and the corresponding period active reputation change value was j. Then the current reputation value of object A in the current reputation update cycle is: e+f+g+m+n+h+j.

[0078] As can be seen from the above, the embodiment of the present application sends the intelligence data to each second object when it is detected that the first object contributes an intelligence data in the current reputation value update cycle, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; when it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, the data confidence evaluation score of each target object, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle are obtained; based on the confidence evaluation score, evaluation confidence of each target object The confidence and the corresponding historical reputation value are used to determine the evaluation reputation change value of each target object; the contribution reputation change value of the first object contributing to the intelligence data and the evaluation reputation change value of each target object are recorded; when the end of the current reputation update cycle is detected, the periodic active reputation change value of each object is determined according to the number of contributions and the number of evaluations of each object in the shared object set in the current reputation update cycle; the historical reputation value, contribution reputation change value, evaluation reputation change value and the corresponding periodic active reputation change value of each object in the previous reputation update cycle are summed to obtain the current reputation value of each object in the current reputation update cycle. Compared with the related art, it is impossible to quantify the contribution of producers and verifiers, resulting in the object only expecting to obtain threat intelligence data, rarely contributing and evaluating threat intelligence data, and the object activity is low, resulting in seldom updating threat intelligence data, and it is difficult to determine whether the threat intelligence data is credible due to the small number of evaluations. The intelligence data can be verified by evaluating each other's intelligence data between objects, improving the accuracy of the evaluation of intelligence data, and updating the reputation value of the objects participating in the contribution and evaluation, thereby improving the object activity.

[0079] For details, please refer to Figure 4 , Figure 4 The main function of the intelligence sharing platform is to provide a platform for each member to report intelligence. Its basic principle is as follows: Figure 4As shown. Each member (object) in the system contributes intelligence data to the intelligence public pool and obtains a certain reputation value. When an object submits intelligence data to the shared pool, it must provide the confidence of the intelligence data while providing the intelligence data expressed according to a fixed standard. The reported intelligence data is marked as "unverified" before being evaluated by other objects. When a certain number of members have evaluated the intelligence data, the intelligence data is considered "verified", thereby reducing the subjectivity of threat intelligence evaluation. The reputation score obtained by the intelligence data contributor by contributing the intelligence data is related to the final confidence of the intelligence after verification. Members who contribute high-confidence intelligence will receive a higher reputation score, and vice versa.

[0080] See also Figure 5 , Figure 5 A schematic diagram of querying intelligence data through points provided for an embodiment of the present application. The threat intelligence contributed by all members is stored in the public intelligence pool, and the intelligence source and verification status are marked, which is visible to each internal member of the threat intelligence alliance. After the module receives the intelligence data reported by the object, the intelligence data at this time is only reported. The intelligence duplication check module will be called to detect whether the intelligence already exists in the public pool. If it does, the submission of the intelligence will be rejected. Otherwise, the submission will be accepted and the evaluation process will be started. That is, the intelligence data at this time has been verified and is valid intelligence data, which belongs to the contributed intelligence data. The object can query the threat intelligence based on this module, but it needs to pay a certain amount of points as a cost. If the query is successful, the response points will be deducted, and if the query fails, no deduction will be made.

[0081] The specific implementation of the above steps can be found in the previous embodiments, which will not be described in detail here.

[0082] In order to better implement the data processing method provided in the embodiment of the present application, the embodiment of the present application also provides a device based on the above data processing method. The meanings of the terms are the same as those in the above data processing method, and the specific implementation details can refer to the description in the method embodiment.

[0083] See also Figure 6 , Figure 6 The data processing device provided in the embodiment of the present application is a schematic diagram of the structure of the data processing device, which is applied to a computer device. The data processing device may include a sending unit 601, an acquiring unit 602, a first determining unit 603, a recording unit 604, a second determining unit 605, and a calculating unit 606.

[0084] The sending unit 601 is used to send the intelligence data to each second object when it is detected that the first object contributes an intelligence data in the current reputation value update cycle, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; An acquisition unit 602 is used to acquire a data confidence evaluation score of each target object, an evaluation confidence of the data confidence evaluation score, and a historical reputation value of each target object in a previous reputation value update cycle when it is detected that a specified number of target objects in the second object perform data confidence evaluation on the intelligence data; A first determining unit 603 is used to determine the evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence and corresponding historical reputation value of each target object; A recording unit 604 is used to record the contribution reputation change value of the first object contributing to the intelligence data and the evaluation reputation change value of each of the target objects; The second determining unit 605 is configured to determine the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation update cycle when the current reputation update cycle is detected to have ended; The calculation unit 606 is used to sum the historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle to obtain the current reputation value of each object in the current reputation value update cycle.

[0085] In some embodiments, the first determining unit 603 includes: A first acquisition subunit is used to acquire a reputation value ratio of each target object; A first determination subunit is used to determine the product of the confidence evaluation score of each target object and the corresponding evaluation confidence, to obtain the evaluation contribution value of each target object with respect to the intelligence data; A calculation subunit, configured to perform a weighted summation of the evaluation contribution values ​​of the plurality of target objects with respect to the intelligence data according to a proportion of the reputation value of each target object, so as to obtain an actual evaluation contribution value of the intelligence data; A second acquisition subunit is used to acquire the absolute value of the difference between the evaluation contribution value of each target object and the actual evaluation contribution value, so as to obtain the evaluation contribution difference of each target object; A second determining subunit is used to determine the evaluation contribution difference ratio of each target object based on the evaluation contribution difference of each target object; The third determination subunit is used to determine the product of the evaluation contribution difference ratio of each target object and the total evaluation reputation value of the intelligence data to obtain the evaluation reputation change value of each target object.

[0086] In some embodiments, the first acquisition subunit is configured to: Obtaining the sum of the historical reputation values ​​of each target object to obtain a total reputation value; The ratio of the historical reputation value of each target object to the total reputation value is calculated to obtain the reputation value ratio of each target object.

[0087] In some embodiments, the second determining subunit is configured to: Obtaining the sum of the evaluation contribution differences of each target object to obtain a total evaluation contribution difference; The ratio of the evaluation contribution difference of each target object to the total evaluation contribution difference is calculated to obtain the evaluation contribution difference ratio of each target object.

[0088] In some embodiments, the second determining unit 605 includes: A fourth determining subunit, configured to determine the contribution activity according to the contribution quantity of each object in the shared object set in the current reputation value update cycle; A fifth determining subunit, configured to determine the evaluation activity according to the number of evaluations of each object in the shared object set in the current reputation value update cycle; The third acquisition subunit is used to acquire the contribution index and the evaluation index; a sixth determining subunit, configured to determine a weighted activity of each object in the shared object set based on the contribution activity, contribution index, evaluation activity, and evaluation index of each object in the shared object set; The seventh determining subunit is used to determine the periodic activity reputation change value of each object in the shared object set according to the weighted activity of each object, the preset proportional coefficient and the adjustment parameter.

[0089] In some embodiments, the sixth determining subunit is configured to: The contribution index is used as an index and the contribution activity of each object in the shared object set is used as a base to calculate the contribution sub-weighted activity of each object in the shared object set; Using the evaluation index as an index and the evaluation activity of each object in the shared object set as a base, calculating the evaluation sub-weighted activity of each object in the shared object set; The sum of the contribution sub-weighted activity and the corresponding evaluation sub-weighted activity of each object in the shared object set is calculated to obtain the weighted activity of each object in the shared object set.

[0090] In some embodiments, the seventh determining subunit is configured to: Determine the product of the weighted activity of each object in the shared object set and the preset proportional coefficient to obtain a third calculation result for each object in the shared object set; The difference between the third calculation result and the adjustment parameter of each object in the shared object set is obtained to obtain a periodic active reputation change value of each object.

[0091] The specific implementation of each of the above units can be found in the previous embodiments, which will not be described in detail here.

[0092] As can be seen from the above, in the embodiment of the present application, when the sending unit 601 detects that the first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; when the acquisition unit 602 detects that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, the data confidence evaluation score of each target object, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle are acquired; the first determination unit 603 determines the historical reputation value of each target object based on the confidence evaluation score, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each target object in the previous reputation value update cycle. The evaluation reputation change value of each target object is determined by the price confidence and the corresponding historical reputation value; the recording unit 604 records the contribution reputation change value of the first object contributing to the intelligence data and the evaluation reputation change value of each target object; the second determination unit 605 determines the periodic active reputation change value of each object according to the contribution number and evaluation number of each object in the shared object set in the current reputation update cycle when detecting the end of the current reputation update cycle; the calculation unit 606 sums the historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding periodic active reputation change value of each object in the previous reputation update cycle to obtain the current reputation value of each object in the current reputation update cycle. Compared with the related art, it is impossible to quantify the contribution of producers and verifiers, resulting in the object only expecting to obtain threat intelligence data, rarely contributing and evaluating threat intelligence data, and the object activity is low, resulting in seldom updating threat intelligence data, and it is difficult to determine whether the threat intelligence data is credible due to the small number of evaluations. The verification of intelligence data can be achieved by evaluating intelligence data between contributing objects, improving the accuracy of intelligence data evaluation, and updating the reputation value of objects participating in contribution and evaluation, and improving object activity.

[0093] The specific implementation of each of the above units can be found in the previous embodiments, which will not be described in detail here.

[0094] Reference Figure 7 , Figure 7 The block diagram of the structure of part of the computer device 1000 for implementing the embodiment of the present disclosure. The computer device 1000 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPUs) 622 (for example, one or more processors) and memory 632, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 642 or data 644. Among them, the memory 632 and the storage medium 630 can be temporary storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the server 600. Furthermore, the central processing unit 622 can be configured to communicate with the storage medium 630 and execute a series of instruction operations in the storage medium 630 on the server 600.

[0095] The computer device 1000 may also include one or more power supplies 626, one or more wired or wireless network interfaces 650, one or more input and output interfaces 658, and / or one or more operating systems 641, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc.

[0096] The central processor 622 in the computer device 1000 may be used to execute the data processing method of the embodiment of the present disclosure, for example: When it is detected that the first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; When it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, obtain the data confidence evaluation score of each of the target objects, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each of the target objects in the previous reputation value update cycle; Determining an evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object; Recording the contribution reputation change value of the first object for contributing to the intelligence data and the evaluation reputation change value of each of the target objects; When it is detected that the current reputation value update cycle has ended, determining the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle; The historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle are summed to obtain the current reputation value of each object in the current reputation value update cycle.

[0097] The embodiments of the present disclosure further provide a computer-readable storage medium, which is used to store program codes, and the program codes are used to execute the data processing methods of the aforementioned embodiments.

[0098] The present disclosure also provides a computer program product, which includes a computer program. The processor of a computer device reads and executes the computer program, so that the computer device executes the above-mentioned data processing method. For example: When it is detected that the first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; When it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, obtain the data confidence evaluation score of each of the target objects, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each of the target objects in the previous reputation value update cycle; Determining an evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object; Recording the contribution reputation change value of the first object for contributing to the intelligence data and the evaluation reputation change value of each of the target objects; When it is detected that the current reputation value update cycle has ended, determining the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle; The historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle are summed to obtain the current reputation value of each object in the current reputation value update cycle.

[0099] In addition, the terms "comprises" and "includes" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus that includes a series of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, product or apparatus.

[0100] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0101] It should be understood that in the description of the embodiments of the present application, the meaning of multiple (or multiple items) is more than two, greater than, less than, exceed, etc. are understood to not include the number, and above, below, within, etc. are understood to include the number.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0106] It should also be understood that the various implementations provided in the embodiments of the present application can be combined arbitrarily to achieve different technical effects.

[0107] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program that has a predetermined function and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories), or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0108] The above is a specific description of the implementation method of the present application, but the present application is not limited to the above-mentioned implementation method. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A data processing method, characterized in that: include: When it is detected that the first object contributes an intelligence data in the current reputation value update cycle, the intelligence data is sent to each second object, so that each second object performs a confidence evaluation on the intelligence data, and the second object is other objects in the shared object set except the first object; When it is detected that there are a specified number of target objects in the second object that perform data confidence evaluation on the intelligence data, obtain the data confidence evaluation score of each of the target objects, the evaluation confidence of the data confidence evaluation score, and the historical reputation value of each of the target objects in the previous reputation value update cycle; Determining an evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object; Recording the contribution reputation change value of the first object for contributing to the intelligence data and the evaluation reputation change value of each of the target objects; When it is detected that the current reputation value update cycle has ended, determining the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle; The historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle are summed to obtain the current reputation value of each object in the current reputation value update cycle.

2. The data processing method according to claim 1, characterized in that: The step of determining the evaluation reputation change value of each target object based on the confidence evaluation score, evaluation confidence, and corresponding historical reputation value of each target object includes: Obtaining a reputation value ratio of each target object; Determine the product of the confidence evaluation score of each target object and the corresponding evaluation confidence, and obtain the evaluation contribution value of each target object with respect to the intelligence data; Performing a weighted summation of the evaluation contribution values ​​of the multiple target objects with respect to the intelligence data according to the reputation value ratio of each target object, to obtain an actual evaluation contribution value of the intelligence data; Obtaining the absolute value of the difference between the evaluation contribution value of each target object and the actual evaluation contribution value, to obtain the evaluation contribution difference of each target object; Based on the evaluation contribution difference of each of the target objects, determining the evaluation contribution difference ratio of each of the target objects; The product of the evaluation contribution difference ratio of each target object and the total evaluation reputation value of the intelligence data is determined to obtain the evaluation reputation change value of each target object.

3. The data processing method according to claim 2, characterized in that: The obtaining of the reputation value ratio of each target object includes: Obtaining the sum of the historical reputation values ​​of each target object to obtain a total reputation value; The ratio of the historical reputation value of each target object to the total reputation value is calculated to obtain the reputation value ratio of each target object.

4. The data processing method according to claim 2, characterized in that: The determining, based on the evaluation contribution difference of each target object, the evaluation contribution difference ratio of each target object includes: Obtaining the sum of the evaluation contribution differences of each target object to obtain a total evaluation contribution difference; The ratio of the evaluation contribution difference of each target object to the total evaluation contribution difference is calculated to obtain the evaluation contribution difference ratio of each target object.

5. The data processing method according to claim 1, characterized in that: The step of determining the periodic active reputation change value of each object in the shared object set according to the contribution quantity and evaluation quantity of each object in the current reputation value update cycle includes: Determining contribution activity according to the contribution amount of each object in the shared object set in the current reputation value update cycle; Determining the evaluation activity according to the number of evaluations of each object in the shared object set in the current reputation value update cycle; Obtain contribution index and evaluation index; Determine the weighted activity of each object in the shared object set based on the contribution activity, contribution index, evaluation activity, and evaluation index of each object in the shared object set; According to the weighted activity of each object in the shared object set, a preset proportional coefficient and an adjustment parameter, a periodic activity reputation change value of each object is determined.

6. The data processing method according to claim 5, characterized in that: The step of determining the weighted activity of each object in the shared object set based on the contribution activity, contribution index, evaluation activity, and evaluation index of each object in the shared object set includes: The contribution index is used as an index and the contribution activity of each object in the shared object set is used as a base to calculate the contribution sub-weighted activity of each object in the shared object set; Using the evaluation index as an index and the evaluation activity of each object in the shared object set as a base, calculating the evaluation sub-weighted activity of each object in the shared object set; The sum of the contribution sub-weighted activity and the corresponding evaluation sub-weighted activity of each object in the shared object set is calculated to obtain the weighted activity of each object in the shared object set.

7. The data processing method according to claim 5, characterized in that: The step of determining the periodic active reputation change value of each object in the shared object set according to the weighted activity of each object, a preset proportional coefficient, and an adjustment parameter includes: Determine the product of the weighted activity of each object in the shared object set and the preset proportional coefficient to obtain a third calculation result for each object in the shared object set; The difference between the third calculation result and the adjustment parameter of each object in the shared object set is obtained to obtain a periodic active reputation change value of each object.

8. A data processing device, characterized in that: include: A sending unit, configured to send the intelligence data to each second object when detecting that the first object contributes intelligence data in the current reputation value update cycle, so that each second object performs a confidence evaluation on the intelligence data, wherein the second object is an object other than the first object in the shared object set; an acquisition unit, configured to acquire, when detecting that a specified number of target objects in the second object perform data confidence evaluation on the intelligence data, a data confidence evaluation score of each of the target objects, an evaluation confidence of the data confidence evaluation score, and a historical reputation value of each of the target objects in a previous reputation value update cycle; A first determining unit, configured to determine an evaluation reputation change value of each target object based on a confidence evaluation score, an evaluation confidence, and a corresponding historical reputation value of each target object; A recording unit, configured to record a contribution reputation change value of the first object contributing to the intelligence data and an evaluation reputation change value of each of the target objects; A second determining unit is configured to determine, when detecting that the current reputation value update cycle has ended, a periodic active reputation change value of each object in the shared object set according to the number of contributions and the number of evaluations of each object in the current reputation value update cycle; The calculation unit is used to sum the historical reputation value, contribution reputation change value, evaluation reputation change value and corresponding period active reputation change value of each object in the previous reputation value update cycle to obtain the current reputation value of each object in the current reputation value update cycle.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute the data processing method according to any one of claims 1 to 7.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the data processing method according to any one of claims 1 to 7 is implemented.

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