A method for optimizing resource allocation for public opinion control in a public opinion cloud platform

Abstract

The present invention discloses a method for optimizing the configuration of public opinion control resources in a public opinion cloud platform, relates to the technical field of public opinion control, and solves the problem that it is impossible to effectively optimize the control resources for false public opinions. The method comprises: obtaining the diffusion source subject of false information in the public opinion cloud platform, and analyzing the influence of the diffusion source subject, 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 through analysis, 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 based on the potential propagation factor of the false information. The present invention realizes the effective optimization of the control resources for false public opinions.

Description

Technical Field

[0001] The present invention belongs to the technical field of public opinion control, and specifically is a method for optimizing the configuration 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 exploded. 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.

[0003] However, existing public opinion cloud platforms have some problems with resource allocation. For example, traditional methods typically assess the influence of diffusion sources by simply accumulating dissemination interaction data, without analyzing the public opinion's dissemination coverage from multiple dimensions (such as the number of public opinion disseminations, the dissemination rate, and the public opinion fan diffusion index). At the same time, existing technologies do not achieve efficient analysis of the potential dissemination space of false public opinion, and cannot effectively utilize the control efficiency of the same type of public opinion to optimize the current public opinion control resources.

[0004] To this end, the present invention proposes a method for optimizing the allocation of public opinion management resources within a public opinion cloud platform. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method for optimizing the allocation of public opinion management resources in a public opinion cloud platform.

[0006] The technical problems to be solved by the present invention are:

[0007] How to effectively optimize the resources for controlling false public opinion.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform, the method comprising the following steps:

[0010] Step S1: Obtain the source of false information in the public opinion cloud platform and analyze its influence;

[0011] Step S2: Analyze the spread rate and target groups of false information.

[0012] Step S3: Analyze the potential propagation space of false information based on historical information diffusion or potential propagation data to obtain the potential propagation factor of false information;

[0013] Step S4, analyzing the control efficiency after applying control resource data to similar information in the public opinion cloud platform;

[0014] Step S5: Optimize the configuration of management resource data based on the potential propagation factors of false information.

[0015] Furthermore, the step S1 includes the following sub-steps:

[0016] Step S11, collecting the historical information diffusion amount of the diffusion source subject, dividing the historical information diffusion amount of the diffusion source subject by the total amount of information to obtain the historical average diffusion amount of the diffusion source subject;

[0017] Similarly, the historical information diffusion of all subjects in the public opinion cloud platform is collected, and the historical information diffusion of all subjects is added up and averaged to obtain the average diffusion of all subjects;

[0018] Step S12, comparing the historical average diffusion amount of the diffusion source subject with the average diffusion amount of all subjects;

[0019] If the historical average diffusion volume of the diffusion source is less than the average diffusion volume of all sources, the false information released by the corresponding diffusion source will be continuously monitored. If the diffusion volume of the false information released by the corresponding diffusion source is greater than or equal to the historical average diffusion volume of the corresponding diffusion source, the corresponding false information will be removed from the shelves;

[0020] If the historical average diffusion amount of the diffusion source subject is greater than or equal to the average diffusion amount of all subjects, proceed to the next step.

[0021] Furthermore, the diffusion volume of historical information is the total number of shares, likes, and reposts of the historical information released by the diffusion source;

[0022] The spread of false information refers to the total number of shares, likes, and reposts of the false information released by the corresponding diffusion source.

[0023] Furthermore, step S2 includes the following sub-steps:

[0024] Step S21: Construct a coordinate system with time as the horizontal axis and the number of disseminations as the vertical axis. The number of disseminations of false information at different time points is collected at fixed time intervals. The number of disseminations of false information at the current time point is subtracted from the number of disseminations of false information at the previous time point, and the result is divided by the time interval to obtain the dissemination rate of false information at the current time point.

[0025] Step S22: If the propagation rate of false information at the current time point is less than zero, continue to monitor the propagation rate of false information;

[0026] If the propagation rate of false information at the current time node is greater than or equal to zero, proceed to the next step.

[0027] Furthermore, the step S2 further includes the following sub-steps:

[0028] Step S23: Obtain the number of fans of the diffusion source, and divide the number of false information disseminations at the current time point by the number of fans of the diffusion source to obtain the fan diffusion index of the false information;

[0029] Step S24, comparing the fan diffusion index of the false information with the fan diffusion threshold;

[0030] If the fan diffusion index of false information is less than the fan diffusion threshold, the exposure of the corresponding diffusion source in the public opinion cloud platform will be reduced;

[0031] If the fan diffusion index of false information is greater than or equal to the fan diffusion threshold and less than one, the exposure of the false information in the public opinion cloud platform will be reduced;

[0032] If the fan diffusion index of the false information is greater than or equal to one, proceed to the next step.

[0033] Furthermore, step S3 includes the following sub-steps:

[0034] Step S31: traverse and compare the historical information diffusion amount of the diffusion source subject to obtain the maximum value of the historical information diffusion amount as the historical information diffusion peak value, and compare the historical information diffusion peak value of the diffusion source subject with the amount of false information dissemination at the current time node;

[0035] Step S32: If the historical information diffusion peak value of the diffusion source is less than the number of false information spread at the current time point, the corresponding false information is assigned a potential spread factor of 1 and the process proceeds to the next step;

[0036] Step S33: If the historical information diffusion peak value of the diffusion source entity is greater than or equal to the number of false information disseminations at the current time node, the potential propagation factor of the corresponding false information is analyzed and the next step is entered.

[0037] Furthermore, the step S33 includes the following sub-steps:

[0038] Step S331: Subtract the current time point from the historical information diffusion peak value of the diffusion source to obtain the potential spread of the false information. The potential spread of the false information is divided by the current time point to obtain the potential spread multiplication rate of the false information.

[0039] Step S332: When the potential propagation multiplication rate of false information is greater than or equal to the preset multiplication threshold, the false information is assigned a potential propagation factor of 1;

[0040] In step S333, when the potential propagation multiplication rate of the false information is less than the preset multiplication threshold, a potential propagation factor of 0 is assigned to the false information.

[0041] Furthermore, step S4 includes the following sub-steps:

[0042] Step S41: Collect the number of disseminations of all information in the public opinion cloud platform at the current time node, and divide the number of disseminations of false information at the current time node by the number of disseminations of all information in the public opinion cloud platform at the current time node to obtain the popularity ratio of false information at the current time node;

[0043] Step S42: Obtain information of the same type as the false information with the same popularity ratio in the public opinion cloud platform, and collect the control start time, control end time, control resource data, and dissemination data of the same type of information;

[0044] Among them, the dissemination data includes the dissemination start time and dissemination end time of the same type of information, and the management and control resource data includes the number of management and control personnel and the number of server nodes invested.

[0045] Furthermore, the step S4 further includes the following sub-steps:

[0046] Step S43: The amount of information disseminated at the start time of the control is recorded as the pre-control dissemination amount, and the amount of information disseminated at the end time of the control is recorded as the post-control dissemination amount. The control amount is obtained by subtracting the pre-control dissemination amount from the post-control dissemination amount. The control amount is then divided by the number of control personnel to obtain the per capita control amount.

[0047] Subtract the control start time from the control end time to get the control duration of the same type of information; divide the control amount by the control duration to get the control efficiency of the same type of information;

[0048] 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 used to control the false information;

[0049] If the control efficiency of the same type of information is less than the control efficiency threshold, proceed to the next step.

[0050] Furthermore, the optimization process of step S5 includes the following sub-steps:

[0051] In step S51, when the potential propagation factor of false information is 1, new server nodes are added to collect and restrict the propagation of false information, specifically:

[0052] The average propagation rate of false information is obtained by adding up the propagation rates of false information at different time points, and then dividing the potential propagation number by the average propagation rate to obtain the potential propagation duration of false information.

[0053] The potential propagation time of false information is divided by the duration of control of the same type of information, and then multiplied by the number of server nodes invested in the same type of information to obtain the number of new server nodes invested;

[0054] In step S52, when the potential spread factor of false information is 0, the number of new control personnel is added to control the false information, wherein the number of potential spreads is divided by the per capita control amount to obtain the number of new control personnel.

[0055] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0056] 1. The present invention first obtains the source of false information in the public opinion cloud platform and then analyzes the influence of the source of false information. At the same time, it analyzes the propagation rate and coverage group of false information, and analyzes the potential propagation space of false information based on the historical information diffusion volume or potential propagation data. The potential propagation factor of false information is obtained through analysis. The present invention realizes accurate analysis of the potential propagation space of false information.

[0057] 2. The present invention also analyzes the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform, analyzes the level of control efficiency, and optimizes the configuration of control resource data based on the potential propagation factors of false information. The present invention realizes the configuration optimization of control resources for false information. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0059] Figure 1 is a flow chart of the method of the present invention;

[0060] Figure 2 A schematic diagram of the spread trend of false information in the present invention;

[0061] Figure 3 This is a flow chart of the method of step S3 in the present invention;

[0062] Figure 4 is a flow chart of the method of step S33 in the present invention;

[0063] Figure 5 It is a structural schematic diagram of the electronic device in the present invention. DETAILED DESCRIPTION

[0064] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] Example 1: Please refer to Figure 1-Figure 4 As shown, the technical solution provided by the present invention is: a method for optimizing the configuration of public opinion control resources in a public opinion cloud platform. The method is suitable for optimizing the configuration of false information control resources. The method is specifically as follows:

[0066] Step S1: Obtain the source of false information in the public opinion cloud platform and analyze its influence;

[0067] The sources of dissemination of false information include but are not limited to personal accounts, official accounts, or marketing accounts that publish false information;

[0068] In this embodiment, the analysis process of step S1 includes the following sub-steps:

[0069] Step S11: Collect the historical information diffusion volume of the diffusion source subject, divide the historical information diffusion volume of the diffusion source subject by the total number of information to obtain the historical average diffusion volume of the diffusion source subject, wherein the historical information diffusion volume is the total number of shares, likes, and reposts of the historical information released by the diffusion source subject;

[0070] Similarly, the historical information diffusion volume of all subjects in the public opinion cloud platform is collected, and the historical information diffusion volume of all subjects is added up and averaged to obtain the average diffusion volume of all subjects. Among them, all subjects 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;

[0071] Step S12, comparing the historical average diffusion amount of the diffusion source subject with the average diffusion amount of all subjects;

[0072] If the historical average diffusion volume of the diffusion source entity is less than the average diffusion volume of all entities, it means that the influence of the corresponding diffusion source entity is relatively small. In this case, the false information released by the corresponding diffusion source entity will be continuously monitored. If 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, the corresponding false information will be removed from the shelves.

[0073] The amount of false information spread is the total number of shares, likes, and reposts of the false information released by the corresponding diffusion source.

[0074] If the historical average diffusion amount of the diffusion source subject is greater than or equal to the average diffusion amount of all subjects, it means that the influence of the corresponding diffusion source subject is greater, and then proceed to the next step.

[0075] Step S2: Analyze the spread rate and target groups of false information.

[0076] In this embodiment, the analysis process of step S2 includes the following sub-steps:

[0077] Step S21, please refer to Figure 2 As shown in the figure, a coordinate system is constructed with time as the horizontal axis and the number of spreads as the vertical axis. The number of spreads of false information at different time nodes is collected at fixed time intervals. The number of spreads of false information at the current time node is subtracted from the number of spreads of false information at the previous time node and divided by the time interval to obtain the spread rate of false information at the current time node.

[0078] Step S22: If the propagation rate of false information at the current time point is less than zero, indicating that the propagation trend of false information at the current time point is decreasing, the propagation rate of false information is continuously monitored;

[0079] If the propagation rate of false information at the current time point is greater than or equal to zero, it means that the propagation trend of false information at the current time point is on the rise, and then proceed to the next step;

[0080] Step S23: Obtain the number of followers of the diffusion source, and divide the number of false information disseminations at the current time point by the number of followers of the diffusion source to obtain the fan diffusion index of the false information. It should be explained that the fan diffusion index in this embodiment is used to reflect the degree of spread of false information among the fan group of the diffusion source. A larger fan diffusion index indicates a greater degree of spread of false information among the fan group of the diffusion source.

[0081] Step S24, comparing the fan diffusion index of the false information with the fan diffusion threshold;

[0082] If the fan diffusion index of false information is less than the fan diffusion threshold, it means that the false information has not reached all the fans of the diffusion source, and the exposure of the corresponding diffusion source in the public opinion cloud platform is reduced. In this embodiment, the exposure is the probability that the public opinion cloud platform recommends false information released by the diffusion source to fans, and the fan diffusion threshold is 0.2.

[0083] If the fan diffusion index of false information is greater than or equal to the fan diffusion threshold and less than one, the exposure of the false information in the public opinion cloud platform will be reduced;

[0084] If the fan diffusion index of the false information is greater than or equal to one, it means that the coverage group of the false information is not limited to the fans of the diffusion source, and then proceed to the next step.

[0085] Step S3: Analyze the potential propagation space of false information based on historical information diffusion or potential propagation data to obtain the potential propagation factor of false information;

[0086] Among them, the potential spread data is the potential spread amount and potential spread multiplication rate of false information;

[0087] In this embodiment, the analysis process of step S3 includes the following sub-steps:

[0088] Step S31: traverse and compare the historical information diffusion amount of the diffusion source subject to obtain the maximum value of the historical information diffusion amount as the historical information diffusion peak value, and compare the historical information diffusion peak value of the diffusion source subject with the amount of false information dissemination at the current time node;

[0089] Step S32: If the historical information diffusion peak value of the diffusion source is less than the number of false information spread at the current time point, a potential spread factor of 1 is assigned to the corresponding false information;

[0090] Step S33: If the historical information diffusion peak value of the diffusion source is greater than or equal to the number of false information spread at the current time point, the potential spread factor of the corresponding false information is analyzed, specifically:

[0091] Step S331: Subtract the current time point from the historical information diffusion peak value of the diffusion source to obtain the potential spread of the false information. The potential spread of the false information is divided by the current time point to obtain the potential spread multiplication rate of the false information.

[0092] In step S332, if the potential multiplication rate of false information is greater than or equal to the preset multiplication threshold, indicating that the potential spread of false information is greater than or equal to the spread of false information at the current time point, and that there is a large space for false information to spread, the false information is assigned a potential spread factor of 1;

[0093] In step S333, if the potential multiplication rate of false information is less than the preset multiplication threshold, indicating that the potential spread of false information is less than the spread of false information at the current time point, and the spread space of false information is limited, the false information is assigned a potential spread factor of 0;

[0094] Among them, the potential propagation factor is used to measure the potential propagation space of false information within the public opinion cloud platform. The potential propagation space of false information with a potential propagation factor of 1 is greater than the potential propagation space of false information with a potential propagation factor of 0.

[0095] Step S4, analyzing the control efficiency after applying control resource data to similar information in the public opinion cloud platform;

[0096] Among them, the management and control resource data includes the number of management and control personnel and the number of server nodes invested;

[0097] In this embodiment, the analysis process of step S4 includes the following sub-steps:

[0098] Step S41: Collect the number of disseminations of all information in the public opinion cloud platform at the current time node, and divide the number of disseminations of false information at the current time node by the number of disseminations of all information in the public opinion cloud platform at the current time node to obtain the popularity ratio of false information at the current time node;

[0099] Step S42: Obtain information of the same type as the false information with the same popularity ratio in the public opinion cloud platform, and collect the control start time, control end time, control resource data, and dissemination data of the same type of information;

[0100] Among them, the dissemination data includes the dissemination start time and dissemination end time of the same type of information;

[0101] Step S43: The amount of information disseminated at the start time of the control is recorded as the pre-control dissemination amount, and the amount of information disseminated at the end time of the control is recorded as the post-control dissemination amount. The control amount is obtained by subtracting the pre-control dissemination amount from the post-control dissemination amount. At the same time, the control amount is divided by the number of control personnel to obtain the per capita control amount.

[0102] Subtract the control start time from the control end time to get the control duration of the same type of information; divide the control amount by the control duration to get the control efficiency of the same type of information;

[0103] Step S44: If the control efficiency of the same type of information is greater than or equal to the control efficiency threshold, indicating that the control effect of applying the control resource data on the same type of information is significant, the same control resource data is applied to control the false information;

[0104] If the control efficiency of the same type of information is less than the control efficiency threshold, it means that the control effect of the application control resource data on the same type of information is not obvious, and then proceed to the next step.

[0105] Step S5: Optimize the configuration of control resource data based on the potential propagation factors of false information;

[0106] In this embodiment, the optimization process of step S5 includes the following sub-steps:

[0107] In step S51, when the potential propagation factor of false information is 1, new server nodes are added to collect and restrict the propagation of false information, specifically:

[0108] The average propagation rate of false information is obtained by adding up the propagation rates of false information at different time points, and then dividing the potential propagation number by the average propagation rate to obtain the potential propagation duration of false information.

[0109] The potential propagation time of false information is divided by the duration of control of the same type of information, and then multiplied by the number of server nodes invested in the same type of information to obtain the number of new server nodes invested;

[0110] Step S52: When the potential spread factor of false information is 0, the number of new control personnel is added to control false information. The number of new control personnel is obtained by dividing the potential spread factor by the average control factor per person.

[0111] It should be explained that when the potential spread factor of false information is 1, it means that there is a large potential spread space for false information. At this time, the control of false information mainly includes the control and restriction of the spread of false information; when the potential spread factor of false information is 0, it means that the potential spread space for 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 already been spread.

[0112] In this application, if a corresponding calculation formula appears, the above calculation formula is dimensionless and its numerical calculation is performed. The weight coefficient, proportional coefficient and other coefficients in the formula are set to a result value obtained by quantifying each parameter. Regarding the size of the weight coefficient and the proportional coefficient, as long as it does not affect the proportional relationship between the parameter and the result value, it is acceptable.

[0113] Example 2: Figure 5 The present invention is a structural diagram of an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus. The processor may call logic instructions in the memory to execute a method for optimizing the configuration of public opinion control resources within a public opinion cloud platform, the method comprising: obtaining the main source of dissemination of false information within the public opinion cloud platform and analyzing the influence of the main source of dissemination; analyzing the propagation rate and the group covered by the false information; analyzing the potential propagation space of the false information based on the historical information diffusion volume or potential propagation data, and analyzing the potential propagation factor of the false information; analyzing the control efficiency after applying control resource data to the same type of information within the public opinion cloud platform; and optimizing the configuration of the control resource data based on the potential propagation factor of the false information.

[0114] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0115] Example 3: The present application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a method for optimizing the configuration of public opinion control resources in a public opinion cloud platform provided by the above methods. The method includes: obtaining the diffusion source of false information in the public opinion cloud platform, and analyzing the influence of the diffusion source; analyzing the propagation rate and coverage group of false information; analyzing the potential propagation space of false information based on historical information diffusion volume or potential propagation data, and analyzing the potential propagation factor of false information; analyzing the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform; and optimizing the configuration of control resource data based on the potential propagation factor of false information.

[0116] Example 4: The present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute a method for optimizing the configuration of public opinion control resources in a public opinion cloud platform provided above, the method comprising: obtaining the diffusion source of false information in the public opinion cloud platform, and analyzing the influence of the diffusion source; 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 volume or potential propagation data, and obtaining the potential propagation factor of false information through analysis; analyzing the control efficiency after applying control resource data to the same type of information in the public opinion cloud platform; and optimizing the configuration of control resource data based on the potential propagation factor of false information.

[0117] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0118] Through the description of the above embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0119] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for optimizing the allocation of public opinion management resources in a public opinion cloud platform, characterized in that: The method comprises the following steps: Step S1: Obtain the source of false information in the public opinion cloud platform and analyze its influence; Step S2: Analyze the spread rate and target groups of false information. Wherein, the step S2 includes the following sub-steps: Step S21: Construct a coordinate system with time as the horizontal axis and the number of disseminations as the vertical axis. The number of disseminations of false information at different time points is collected at fixed time intervals. The number of disseminations of false information at the current time point is subtracted from the number of disseminations of false information at the previous time point, and the result is divided by the time interval to obtain the dissemination rate of false information at the current time point. Step S22: If the propagation rate of false information at the current time point is less than zero, continue to monitor the propagation rate of false information; If the propagation rate of false information at the current time point is greater than or equal to zero, proceed to the next step; Step S23: Obtain the number of fans of the diffusion source, and divide the number of false information disseminations at the current time point by the number of fans of the diffusion source to obtain the fan diffusion index of the false information; Step S24, comparing the fan diffusion index of the false information with the fan diffusion threshold; If the fan diffusion index of false information is less than the fan diffusion threshold, the exposure of the corresponding diffusion source in the public opinion cloud platform will be reduced; If the fan diffusion index of false information is greater than or equal to the fan diffusion threshold and less than one, the exposure of the false information in the public opinion cloud platform will be reduced; If the fan diffusion index of the false information is greater than or equal to one, proceed to the next step; Step S3: Analyze the potential propagation space of false information based on historical information diffusion or potential propagation data to obtain the potential propagation factor of false information; Wherein, the step S3 includes the following sub-steps: Step S31: traverse and compare the historical information diffusion amount of the diffusion source subject to obtain the maximum value of the historical information diffusion amount as the historical information diffusion peak value, and compare the historical information diffusion peak value of the diffusion source subject with the amount of false information dissemination at the current time node; Step S32: If the historical information diffusion peak value of the diffusion source is less than the number of false information spread at the current time point, the corresponding false information is assigned a potential spread factor of 1 and the process proceeds to the next step; Step S33: If the historical information diffusion peak value of the diffusion source is greater than or equal to the number of false information spread at the current time point, the potential spread factor of the corresponding false information is analyzed and the next step is entered; Step S4, analyzing the control efficiency after applying control resource data to similar information in the public opinion cloud platform; Step S5: Optimize the configuration of management resource data based on the potential propagation factors of false information.

2. A method for optimizing resource allocation for public opinion control in a public opinion cloud platform according to claim 1, characterized in that: The step S1 includes the following sub-steps: Step S11, collecting the historical information diffusion amount of the diffusion source subject, dividing the historical information diffusion amount of the diffusion source subject by the total amount of information to obtain the historical average diffusion amount of the diffusion source subject; Similarly, the historical information diffusion of all subjects in the public opinion cloud platform is collected, and the historical information diffusion of all subjects is added up and averaged to obtain the average diffusion of all subjects; Step S12, comparing the historical average diffusion amount of the diffusion source subject with the average diffusion amount of all subjects; If the historical average diffusion volume of the diffusion source is less than the average diffusion volume of all sources, the false information released by the corresponding diffusion source will be continuously monitored. If the diffusion volume of the false information released by the corresponding diffusion source is greater than or equal to the historical average diffusion volume of the corresponding diffusion source, the corresponding false information will be removed from the shelves; If the historical average diffusion amount of the diffusion source subject is greater than or equal to the average diffusion amount of all subjects, proceed to the next step.

3. A method for optimizing resource allocation for public opinion control in a public opinion cloud platform according to claim 2, characterized in that: The diffusion volume of historical information refers to the total number of shares, likes, and reposts of the historical information released by the diffusion source. The spread of false information refers to the total number of shares, likes, and reposts of the false information released by the corresponding diffusion source.

4. A method for optimizing resource allocation for public opinion control in a public opinion cloud platform according to claim 1, characterized in that: The step S33 includes the following sub-steps: Step S331: Subtract the current time point from the historical information diffusion peak value of the diffusion source to obtain the potential spread of the false information. The potential spread of the false information is divided by the current time point to obtain the potential spread multiplication rate of the false information. Step S332: When the potential propagation multiplication rate of false information is greater than or equal to the preset multiplication threshold, the false information is assigned a potential propagation factor of 1; In step S333, when the potential propagation multiplication rate of the false information is less than the preset multiplication threshold, a potential propagation factor of 0 is assigned to the false information.

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 step S4 includes the following sub-steps: Step S41: Collect the number of disseminations of all information in the public opinion cloud platform at the current time node, and divide the number of disseminations of false information at the current time node by the number of disseminations of all information in the public opinion cloud platform at the current time node to obtain the popularity ratio of false information at the current time node; Step S42: Obtain information of the same type as the false information with the same popularity ratio in the public opinion cloud platform, and collect the control start time, control end time, control resource data, and dissemination data of the same type of information; Among them, the dissemination data includes the dissemination start time and dissemination end time of the same type of information, and the management and control resource data includes the number of management and control personnel and the number of server nodes invested.

6. A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform according to claim 5, characterized in that: The step S4 further includes the following sub-steps: Step S43: The amount of information disseminated at the start time of the control is recorded as the pre-control dissemination amount, and the amount of information disseminated at the end time of the control is recorded as the post-control dissemination amount. The control amount is obtained by subtracting the pre-control dissemination amount from the post-control dissemination amount. The control amount is then divided by the number of control personnel to obtain the per capita control amount. Subtract the control start time from the control end time to get the control duration of the same type of information; Divide the amount of control by the duration of control to get 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, the same control resource data is used 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.

7. A method for optimizing the allocation of public opinion control resources in a public opinion cloud platform according to claim 6, characterized in that: The optimization process of step S5 includes the following sub-steps: In step S51, when the potential propagation factor of false information is 1, new server nodes are added to collect and restrict the propagation of false information, specifically: The average propagation rate of false information is obtained by adding up the propagation rates of false information at different time points, and then dividing the potential propagation number by the average propagation rate to obtain the potential propagation duration of false information. The potential propagation time of false information is divided by the duration of control of the same type of information, and then multiplied by the number of server nodes invested in the same type of information to obtain the number of new server nodes invested; In step S52, when the potential spread factor of false information is 0, the number of new control personnel is added to control the false information, wherein the number of potential spreads is divided by the per capita control amount to obtain the number of new control personnel.

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