Public opinion management and control resource configuration optimization method in public opinion cloud platform

Abstract

The invention discloses a public opinion management and control resource configuration optimization method in a public opinion cloud platform, relates to the technical field of public opinion management and control, and solves the problem that management and control resources of false public opinions cannot be effectively optimized. The method comprises the following steps: acquiring a diffusion source main body of false information in the public opinion cloud platform, and analyzing the influence of the diffusion source main body; analyzing the propagation rate and the propagation coverage group of the false information, analyzing the potential propagation space of the false information according to the historical information diffusion amount or the potential propagation data, analyzing to obtain the potential propagation factor of the false information, and analyzing the management and control efficiency after the same type of information application management and control resource data in the public opinion cloud platform; and performing configuration optimization on the management and control resource data according to the potential propagation factors of the false information. According to the method, effective optimization of the management and control resources of the false public opinions is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of public opinion control, and specifically relates to a method for optimizing the allocation of public opinion control resources in a public opinion cloud platform. Background Art

[0002] With the rapid development of the Internet, online public opinion information has shown explosive growth. As an important tool for processing and controlling online public opinion, the public opinion cloud platform needs to collect, analyze, and process a large amount of public opinion data. In the process of public opinion control, the rationality of resource allocation directly affects the control effect and efficiency. However, there are some problems in the resource allocation of existing public opinion cloud platforms. For example, traditional methods usually only evaluate the influence of diffusion source entities by simply accumulating communication interaction data, without analyzing the communication coverage groups of public opinion from multiple dimensions (such as the communication quantity, communication rate of public opinion, and fan diffusion index of public opinion). At the same time, the existing technology has not realized the efficient analysis of the potential communication space of false public opinion, and cannot effectively use the control efficiency of the same type of public opinion to optimize the control resources of the current public opinion. Therefore, the present invention proposes a method for optimizing the allocation of public opinion control resources in a public opinion cloud platform. Summary of the Invention

[0003] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a method for optimizing the allocation of public opinion control resources in a public opinion cloud platform.

[0004] The technical problem to be solved by the present invention is: How to effectively optimize the control resources of false public opinion.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solutions: A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform, the method comprising the following steps: Step S1, obtaining the diffusion source entity of false information in the public opinion cloud platform and analyzing the influence of the diffusion source entity; Step S2, analyzing the communication rate and communication coverage group of the false information; Step S3, analyzing the potential communication space of the false information based on historical information diffusion amount or potential communication data, and obtaining the potential communication factor of the false information; Step S4, analyzing the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform; Step S5, configuring and optimizing the control resource data according to the potential communication factor of the false information.

[0006] Further, the step S1 includes the following sub-steps: Step S11: Collect the historical information diffusion volume of the diffusion source entity, and divide the historical information diffusion volume of the diffusion source entity by the total number of information to obtain the historical average diffusion volume of the diffusion source entity; Similarly, collect the historical information diffusion volumes of all entities in the public opinion cloud platform, add up the historical information diffusion volumes of all entities, sum them up, and take the average to obtain the average diffusion volume of all entities; Step S12: Compare the historical average diffusion volume of the diffusion source entity with the average diffusion volume of all entities; If the historical average diffusion volume of the diffusion source entity is less than the average diffusion volume of all entities, continuously monitor the false information released by the corresponding diffusion source entity. When the diffusion volume of the false information released by the corresponding diffusion source entity is greater than or equal to the historical average diffusion volume of the corresponding diffusion source entity, take down the corresponding false information; If the historical average diffusion volume of the diffusion source entity is greater than or equal to the average diffusion volume of all entities, proceed to the next step.

[0007] Furthermore, the historical information diffusion volume is the total number of shares, likes, and total number of forwards of the historical information released by the diffusion source entity; The false information diffusion volume is the total number of shares, likes, and total number of forwards of the false information released by the corresponding diffusion source entity.

[0008] Furthermore, the said Step S2 includes the following sub-steps: Step S21: Construct a coordinate system with time as the abscissa and the propagation quantity as the ordinate. Collect the propagation quantities of false information at different time nodes at a fixed time interval. Subtract the propagation quantity of false information at the previous time node from the propagation quantity of false information at the current time node and divide by the time interval to obtain the propagation rate of false information at the current time node; Step S22: If the propagation rate of false information at the current time node is less than zero, continuously monitor the propagation rate of false information; If the propagation rate of false information at the current time node is greater than or equal to zero, proceed to the next step.

[0009] Furthermore, the said Step S2 also includes the following sub-steps: Step S23: Obtain the number of fans of the diffusion source entity, and divide the propagation quantity of false information at the current time node by the number of fans of the diffusion source entity to obtain the fan diffusion index of the false information; Step S24: Compare the fan diffusion index of the false information with the fan diffusion threshold; If the fan diffusion index of the false information is less than the fan diffusion threshold, reduce the exposure of the corresponding diffusion source entity in the public opinion cloud platform; If the fan diffusion index of false information is greater than or equal to the fan diffusion threshold and less than one, reduce the exposure of false information in the public opinion cloud platform; If the fan diffusion index of false information is greater than or equal to one, proceed to the next step.

[0010] Further, the step S3 includes the following sub-steps: Step S31, traverse and compare the historical information diffusion volume of the diffusion source entity, and record the maximum value of the historical information diffusion volume as the historical information diffusion peak value. Compare the historical information diffusion peak value of the diffusion source entity with the propagation quantity of false information at the current time node; Step S32, if the historical information diffusion peak value of the diffusion source entity is less than the propagation quantity of false information at the current time node, assign a potential propagation factor of 1 to the corresponding false information and proceed to the next step; Step S33, if the historical information diffusion peak value of the diffusion source entity is greater than or equal to the propagation quantity of false information at the current time node, analyze the potential propagation factor of the corresponding false information and proceed to the next step.

[0011] Further, the step S33 includes the following sub-steps: Step S331, subtract the propagation quantity of false information at the current time node from the historical information diffusion peak value of the diffusion source entity to obtain the potential propagation quantity of false information. Divide the potential propagation quantity of false information by the propagation quantity of false information at the current time node to obtain the potential propagation multiplication rate of false information; Step S332, when the potential propagation multiplication rate of false information is greater than or equal to the preset multiplication threshold, assign a potential propagation factor of 1 to the false information; Step S333, when the potential propagation multiplication rate of false information is less than the preset multiplication threshold, assign a potential propagation factor of 0 to the false information.

[0012] Further, the step S4 includes the following sub-steps: Step S41, collect the propagation quantities of all information in the public opinion cloud platform at the current time node. Divide the propagation quantity of false information at the current time node by the propagation quantities of all information in the public opinion cloud platform at the current time node to obtain the heat ratio of false information at the current time node; Step S42, obtain the same-type information in the public opinion cloud platform with the same heat ratio as the false information, and collect the control start time, control end time, control resource data, and propagation data of the same-type information; Among them, the propagation data includes the propagation start time and propagation end time of the same-type information, and the control resource data includes the number of control personnel invested and the number of server nodes invested.

[0013] Further, the step S4 also includes the following sub-steps: In step S43, the dissemination volume of the same type of information at the control start time is recorded as the pre-control dissemination volume, and the dissemination volume of the same type of information at the control end time is recorded as the post-control dissemination volume. The control volume is obtained by subtracting the pre-control dissemination volume from the post-control dissemination volume, and the per capita control volume is obtained by dividing the control volume by the number of control personnel invested. The control duration of the same type of information is obtained by subtracting the control start time from the control end time; the control efficiency of the same type of information is obtained by dividing the control volume by the control duration. In step S44, if the control efficiency of the same type of information is greater than or equal to the control efficiency threshold, the same control resource data is applied to control the false information. If the control efficiency of the same type of information is less than the control efficiency threshold, the next step is entered.

[0014] Furthermore, the optimization process of step S5 includes the following sub-steps: In step S51, when the potential propagation factor of the false information is 1, the input quantity of new server nodes is added to collect and limit the propagation operation of the false information. Specifically: The average propagation rate of the false information is obtained by adding up and averaging the propagation rates of the false information at different time nodes, and the potential propagation duration of the false information is obtained by dividing the potential propagation quantity by the average propagation rate. The input quantity of new server nodes is obtained by dividing the potential propagation duration of the false information by the control duration of the same type of information and then multiplying by the input quantity of server nodes of the same type of information. In step S52, when the potential propagation factor of the false information is 0, the input quantity of new control personnel is added to control the false information, where the input quantity of new control personnel is obtained by dividing the potential propagation quantity by the per capita control volume.

[0015] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are: 1. The present invention first analyzes the diffusion source subject of the false information in the public opinion cloud platform and then analyzes the influence of the diffusion source subject. At the same time, the propagation rate and the propagation coverage group of the false information are analyzed, and the potential propagation space of the false information is analyzed based on the historical information diffusion volume or the potential propagation data, and the potential propagation factor of the false information is obtained, so that the present invention realizes the accurate analysis of the potential propagation space of the false information.

[0016] 2. The present invention also analyzes the control efficiency of the same type of information in the public opinion cloud platform after applying the control resource data, obtains the high or low of the control efficiency, and at the same time optimizes the configuration of the control resource data according to the potential propagation factor of the false information, so that the present invention realizes the optimization of the configuration of the control resources for the false information. Description of the Drawings

[0017] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 is the method flow chart of the present invention; Figure 2 is the schematic diagram of the dissemination trend of false information in the present invention; Figure 3 is the method flow chart of step S3 in the present invention; Figure 4 is the method flow chart of step S33 in the present invention; Figure 5 is the schematic diagram of the structure of the electronic device in the present invention. Specific embodiments

[0019] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work shall fall within the protection scope of the present invention.

[0020] Embodiment 1: Please refer to Figures 1-4 As shown, the technical solution provided by the present invention is: an optimization method for the configuration of public opinion control resources in a public opinion cloud platform, which is applicable to the configuration and optimization of false information control resources. The method is as follows: Step S1, obtain the diffusion source entities of false information in the public opinion cloud platform, and analyze the influence of the diffusion source entities; Among them, the diffusion source entities of false information include but are not limited to personal accounts, official accounts or marketing accounts that publish false information, etc.; In this embodiment, the analysis process of step S1 includes the following sub-steps: Step S11, collect the historical information diffusion volume of the diffusion source entity, divide the historical information diffusion volume of the diffusion source entity by the total number of information to obtain the historical average diffusion volume of the diffusion source entity. Among them, the historical information diffusion volume is the total sharing number, like number and total forwarding number of the historical information published by the diffusion source entity; Similarly, collect the historical information diffusion volume of all entities in the public opinion cloud platform, add up the historical information diffusion volume of all entities and take the average value to obtain the average diffusion volume of all entities. Among them, all entities include but are not limited to personal accounts, official accounts or marketing accounts that publish various types of information in the public opinion cloud platform, etc.; Step S12, compare the historical average diffusion volume of the diffusion source entity with the average diffusion volume of all entities; If the historical average diffusion volume of the diffusion source entity is less than the average diffusion volume of all entities, it indicates that the influence of the corresponding diffusion source entity is relatively small. Then, continuously monitor the false information released by the corresponding diffusion source entity. When the diffusion volume of the false information released by the corresponding diffusion source entity is greater than or equal to the historical average diffusion volume of the corresponding diffusion source entity, take down the corresponding false information; Among them, the diffusion volume of false information is the total number of shares, likes, and total number of forwards of the false information released by the corresponding diffusion source entity; If the historical average diffusion volume of the diffusion source entity is greater than or equal to the average diffusion volume of all entities, it indicates that the influence of the corresponding diffusion source entity is relatively large, and then proceed to the next step.

[0021] Step S2, analyze the propagation rate and the propagation coverage group of the false information; In this embodiment, the analysis process of the said step S2 includes the following sub-steps: Step S21, please refer to Figure 2 As shown, construct a coordinate system with time as the abscissa and the propagation quantity as the ordinate. Collect the propagation quantity of the false information at different time nodes at a fixed time interval. Subtract the propagation quantity of the false information at the previous time node from the propagation quantity of the false information at the current time node and divide it by the time interval to obtain the propagation rate of the false information at the current time node; Step S22, if the propagation rate of the false information at the current time node is less than zero, it indicates that the propagation trend of the false information at the current time node shows a downward trend. Then, continuously monitor the propagation rate of the false information; If the propagation rate of the false information at the current time node is greater than or equal to zero, it indicates that the propagation trend of the false information at the current time node shows an upward trend. Then, proceed to the next step; Step S23, obtain the number of fans of the diffusion source entity. Divide the propagation quantity of the false information at the current time node by the number of fans of the diffusion source entity to obtain the fan diffusion index of the false information. It should be explained that the fan diffusion index is used in this embodiment to reflect the diffusion degree of the false information in the fan group of the diffusion source entity. The larger the fan diffusion index, the greater the diffusion degree of the false information in the fan group of the diffusion source entity; Step S24, compare the fan diffusion index of the false information with the fan diffusion threshold; If the fan diffusion index of the false information is less than the fan diffusion threshold, it indicates that the false information does not cover all the fans of the diffusion source entity. Then, reduce the exposure of the corresponding diffusion source entity in the public opinion cloud platform. In this embodiment, the exposure is the probability that the public opinion cloud platform recommends the false information released by the diffusion source entity to the fans. The value of the fan diffusion threshold is 0.2; If the fan diffusion index of false information is greater than or equal to the fan diffusion threshold and less than one, reduce the exposure of false information in the public opinion cloud platform; If the fan diffusion index of false information is greater than or equal to one, it indicates that the coverage group of false information is not limited to the fans of the diffusion source subject, and then enter the next step.

[0022] Step S3: Analyze the potential propagation space of false information based on historical information diffusion volume or potential propagation data, and obtain the potential propagation factor of false information; Among them, the potential propagation data is the potential propagation quantity and potential propagation multiplication rate of false information; In this embodiment, the analysis process of step S3 includes the following sub-steps: Step S31: Traverse and compare the historical information diffusion volumes of the diffusion source subjects, and record the maximum value of the historical information diffusion volume as the historical information diffusion peak value. Compare the historical information diffusion peak value of the diffusion source subject with the propagation quantity of false information at the current time node; Step S32: If the historical information diffusion peak value of the diffusion source subject is less than the propagation quantity of false information at the current time node, assign a potential propagation factor of 1 to the corresponding false information; Step S33: If the historical information diffusion peak value of the diffusion source subject is greater than or equal to the propagation quantity of false information at the current time node, analyze the potential propagation factor of the corresponding false information. Specifically: Step S331: Subtract the propagation quantity of false information at the current time node from the historical information diffusion peak value of the diffusion source subject to obtain the potential propagation quantity of false information. Divide the potential propagation quantity of false information by the propagation quantity of false information at the current time node to obtain the potential propagation multiplication rate of false information; Step S332: When the potential propagation multiplication rate of false information is greater than or equal to the preset multiplication threshold, it indicates that the potential propagation quantity of false information is greater than or equal to the propagation quantity of false information at the current time node, and there is a large diffusion space for false information. Then assign a potential propagation factor of 1 to the false information; Step S333: When the potential propagation multiplication rate of false information is less than the preset multiplication threshold, it indicates that the potential propagation quantity of false information is less than the propagation quantity of false information at the current time node, and the diffusion space of false information is limited. Then assign a potential propagation factor of 0 to the false information; Among them, the potential propagation factor is used to measure the potential propagation space of false information in the public opinion cloud platform. The potential propagation space of false information with a potential propagation factor of 1 is greater than that of false information with a potential propagation factor of 0.

[0023] Step S4: Analyze the control efficiency of the same type of information in the public opinion cloud platform after applying control resource data; Among them, the controlled resource data includes the input quantity of controlled personnel and the input quantity of server nodes, etc.; In this embodiment, the analysis process of step S4 includes the following sub-steps: Step S41, collect the dissemination quantity of all information in the public opinion cloud platform at the current time node, and divide the dissemination quantity of false information at the current time node by the dissemination quantity of all information in the public opinion cloud platform at the current time node to obtain the heat proportion of false information at the current time node; Step S42, obtain the same-type information in the public opinion cloud platform with the same heat proportion as the false information, and collect the control start time, control end time, controlled resource data and dissemination data of the same-type information; Among them, the dissemination data includes the dissemination start time and dissemination end time of the same-type information; Step S43, obtain the dissemination quantity of the same-type information at the control start time and record it as the pre-control dissemination quantity, obtain the dissemination quantity of the same-type information at the control end time and record it as the post-control dissemination quantity, subtract the pre-control dissemination quantity from the post-control dissemination quantity to obtain the control quantity. At the same time, divide the control quantity by the input quantity of controlled personnel to obtain the per capita control quantity; Subtract the control start time from the control end time to obtain the control duration of the same-type information; divide the control quantity by the control duration to obtain the control efficiency of the same-type information; Step S44, if the control efficiency of the same-type information is greater than or equal to the control efficiency threshold, it means that the control effect of applying the controlled resource data to the same-type information is obvious, then apply the same controlled resource data to control the false information; If the control efficiency of the same-type information is less than the control efficiency threshold, it means that the control effect of applying the controlled resource data to the same-type information is not obvious, then enter the next step.

[0024] Step S5, optimize the configuration of the controlled resource data according to the potential dissemination factor of the false information; In this embodiment, the optimization process of step S5 includes the following sub-steps: Step S51, when the potential dissemination factor of the false information is 1, then increase the input quantity of server nodes to collect and limit the dissemination operation of the false information, specifically: Add up the dissemination rates of the false information at different time nodes and take the average value to obtain the average dissemination rate of the false information, and divide the potential dissemination quantity by the average dissemination rate to obtain the potential dissemination duration of the false information; Divide the potential dissemination duration of the false information by the control duration of the same-type information and then multiply by the input quantity of server nodes of the same-type information to obtain the increased input quantity of server nodes; Step S52: When the potential propagation factor of false information is 0, the number of newly added control personnel is invested to control the false information. Among them, the number of newly added control personnel is obtained by dividing the potential propagation quantity by the per capita control quantity. It should be explained that when the potential propagation factor of false information is 1, it indicates that there is a large potential propagation space for false information. At this time, the control of false information mainly includes the control and restriction of the dissemination operation of false information; when the potential propagation factor of false information is 0, it indicates that the potential propagation space of false information is limited. At this time, the control of false information mainly includes the deletion of false information and the deletion of false information that has been disseminated.

[0025] In this application, if there are corresponding calculation formulas, the above calculation formulas are all dimensionless and take their numerical values for calculation. The weight coefficients, proportionality coefficients, and other coefficients in the formulas are set to obtain a result value by quantifying each parameter. Regarding the magnitudes of the weight coefficients and proportionality coefficients, as long as the proportional relationship between the parameters and the result value is not affected.

[0026] Embodiment 2: Figure 5 It is a schematic structural diagram of an electronic device. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The processor can call the logical instructions in the memory to execute an optimization method for the configuration of public opinion control resources in a public opinion cloud platform. The method includes: obtaining the diffusion source entity of false information in the public opinion cloud platform and analyzing the influence of the diffusion source entity; analyzing the propagation rate and the propagation coverage group of false information; analyzing the potential propagation space of false information based on the historical information diffusion quantity or potential propagation data, and obtaining the potential propagation factor of false information; analyzing the control efficiency after applying the control resource data to the same type of information in the public opinion cloud platform; and optimizing the configuration of the control resource data according to the potential propagation factor of false information.

[0027] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0028] Embodiment 3: This application also provides a computer program product. The computer program product includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute an optimization method for the configuration of public opinion control resources in a public opinion cloud platform provided by the above-mentioned various methods. The method includes: obtaining the diffusion source entity of false information in the public opinion cloud platform and analyzing the influence of the diffusion source entity; analyzing the propagation rate and the propagation coverage group of the false information; analyzing the potential propagation space of the false information based on the historical information diffusion amount or potential propagation data, and obtaining the potential propagation factor of the false information; analyzing the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform; and configuring and optimizing the control resource data according to the potential propagation factor of the false information.

[0029] Embodiment 4: This application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute an optimization method for the configuration of public opinion control resources in a public opinion cloud platform provided by the above-mentioned various methods. The method includes: obtaining the diffusion source entity of false information in the public opinion cloud platform and analyzing the influence of the diffusion source entity; analyzing the propagation rate and the propagation coverage group of the false information; analyzing the potential propagation space of the false information based on the historical information diffusion amount or potential propagation data, and obtaining the potential propagation factor of the false information; analyzing the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform; and configuring and optimizing the control resource data according to the potential propagation factor of the false information.

[0030] The device embodiments described above are merely illustrative. 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 to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

[0031] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0032] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An optimization method for the allocation of public opinion control resources within a public opinion cloud platform, characterized in that, The method includes the following steps: Step S1: Obtain the diffusion source entities of false information in the public opinion cloud platform, and analyze the influence of the diffusion source entities. Step S2: Analyze the propagation rate and the propagated coverage group of the false information. Step S3: Analyze the potential propagation space of the false information based on the historical information diffusion volume or potential propagation data, and obtain the potential propagation factor of the false information. Step S4: Analyze the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform. Step S5: Optimize the configuration of the control resource data according to the potential propagation factor of the false information.

2. The optimization method for the configuration of public opinion control resources in a public opinion cloud platform according to claim 1, characterized in that, The said Step S1 includes the following sub-steps: Step S11: Collect the historical information diffusion volume of the diffusion source entity, and divide the historical information diffusion volume of the diffusion source entity by the total number of information to obtain the historical average diffusion volume of the diffusion source entity. Similarly, collect the historical information diffusion volume of all entities in the public opinion cloud platform, add up the historical information diffusion volumes of all entities, and take the average value to obtain the average diffusion volume of all entities. Step S12: Compare the historical average diffusion volume of the diffusion source entity with the average diffusion volume of all entities. If the historical average diffusion volume of the diffusion source entity is less than the average diffusion volume of all entities, continuously monitor the false information released by the corresponding diffusion source entity. When the diffusion volume of the false information released by the corresponding diffusion source entity is greater than or equal to the historical average diffusion volume of the corresponding diffusion source entity, take down the corresponding false information. If the historical average diffusion volume of the diffusion source entity is greater than or equal to the average diffusion volume of all entities, proceed to the next step.

3. An optimization method for the allocation of public opinion control resources in a public opinion cloud platform according to claim 2, characterized in that, The historical information diffusion volume is the total number of shares, likes, and total number of forwards of the historical information released by the diffusion source entity. The false information diffusion volume is the total number of shares, likes, and total number of forwards of the false information released by the corresponding diffusion source entity.

4. A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform according to claim 3, characterized in that, The said Step S2 includes the following sub-steps: Step S21: Construct a coordinate system with time as the abscissa and the propagation quantity as the ordinate. Collect the propagation quantity of the false information at different time nodes at fixed time intervals. Subtract the propagation quantity of the false information at the previous time node from the propagation quantity of the false information at the current time node and divide by the time interval to obtain the propagation rate of the false information at the current time node. Step S22: If the propagation rate of the false information at the current time node is less than zero, continuously monitor the propagation rate of the false information. If the propagation rate of the false information at the current time node is greater than or equal to zero, proceed to the next step.

5. A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform according to claim 4, characterized in that, The said Step S2 further includes the following sub-steps: Step S23: Obtain the number of fans of the diffusion source entity, and divide the propagation quantity of the false information at the current time node by the number of fans of the diffusion source entity to obtain the fan diffusion index of the false information. Step S24: Compare the fan diffusion index of the false information with the fan diffusion threshold. If the fan diffusion index of the false information is less than the fan diffusion threshold, reduce the exposure of the corresponding diffusion source entity in the public opinion cloud platform. If the fan diffusion index of the false information is greater than or equal to the fan diffusion threshold and less than one, reduce the exposure of the false information in the public opinion cloud platform. If the fan diffusion index of false information is greater than or equal to one, proceed to the next step.

6. The optimization method for the configuration of public opinion control resources in a public opinion cloud platform according to claim 5, wherein, The step S3 includes the following sub-steps: Step S31, traverse and compare the historical information diffusion volume of the diffusion source entity, obtain the maximum value of the historical information diffusion volume, denoted as the historical information diffusion peak value, and compare the historical information diffusion peak value of the diffusion source entity with the propagation quantity of false information at the current time node; Step S32, if the historical information diffusion peak value of the diffusion source entity is less than the propagation quantity of false information at the current time node, assign a potential propagation factor of 1 to the corresponding false information and proceed to the next step; Step S33, if the historical information diffusion peak value of the diffusion source entity is greater than or equal to the propagation quantity of false information at the current time node, analyze the potential propagation factor of the corresponding false information and proceed to the next step.

7. An optimization method for the configuration of public opinion control resources in a public opinion cloud platform according to claim 6, characterized in that, The step S33 includes the following sub-steps: Step S331, subtract the propagation quantity of false information at the current time node from the historical information diffusion peak value of the diffusion source entity to obtain the potential propagation quantity of false information, and divide the potential propagation quantity of false information by the propagation quantity of false information at the current time node to obtain the potential propagation multiplication rate of false information; Step S332, when the potential propagation multiplication rate of false information is greater than or equal to the preset multiplication threshold, assign a potential propagation factor of 1 to the false information; Step S333, when the potential propagation multiplication rate of false information is less than the preset multiplication threshold, assign a potential propagation factor of 0 to the false information.

8. An optimization method for the configuration of public opinion control resources in a public opinion cloud platform according to claim 7, characterized in that, The step S4 includes the following sub-steps: Step S41, collect the propagation quantities of all information in the public opinion cloud platform at the current time node, and divide the propagation quantity of false information at the current time node by the propagation quantities of all information in the public opinion cloud platform at the current time node to obtain the heat ratio of false information at the current time node; Step S42, obtain the same type of information in the public opinion cloud platform with the same heat ratio as the false information, and collect the control start time, control end time, control resource data, and propagation data of the same type of information; Among them, the propagation data includes the propagation start time and propagation end time of the same type of information, and the control resource data includes the number of control personnel invested and the number of server nodes invested.

9. A method for optimizing the configuration of public opinion control resources in a public opinion cloud platform according to claim 8, characterized in that, The step S4 also includes the following sub-steps: Step S43, obtain the propagation quantity of the same type of information at the control start time, denoted as the propagation quantity before control, obtain the propagation quantity of the same type of information at the control end time, denoted as the propagation quantity after control, subtract the propagation quantity before control from the propagation quantity after control to obtain the control quantity, and divide the control quantity by the number of control personnel invested to obtain the per capita control quantity; Subtract the control start time from the control end time to obtain the control duration of the same type of information; Divide the control quantity by the control duration to obtain the control efficiency of the same type of information; Step S44, if the control efficiency of the same type of information is greater than or equal to the control efficiency threshold, apply the same control resource data to control the false information; If the control efficiency of the same type of information is less than the control efficiency threshold, proceed to the next step.

10. A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform according to claim 9, characterized in that, The optimization process of the step S5 includes the following sub-steps: Step S51: When the potential propagation factor of false information is 1, new server node input is added to collect and restrict the propagation operation of false information. Specifically: Sum up the propagation rates of false information at different time nodes and take the average to obtain the average propagation rate of false information. Divide the potential propagation quantity by the average propagation rate to obtain the potential propagation duration of false information. Divide the potential propagation duration of false information by the control duration of the same type of information, and then multiply by the server node input of the same type of information to obtain the new server node input. Step S52: When the potential propagation factor of false information is 0, new control personnel input quantity is added to control false information. Among them, divide the potential propagation quantity by the per capita control quantity to obtain the new control personnel input quantity.

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