Internet marketing strategy making system based on big data

Abstract

The invention discloses an internet marketing strategy making system based on big data, relates to the technical field of strategy making, and discloses a marketing strategy making module, a strategy remaking check module, a marketing strategy remaking module and a marketing strategy making setting module. A reasonable formulating sequence can be set for internet marketing strategies in different directions according to product types, the method is suitable for formulating the internet marketing strategies of different product types, reasonable and efficient formulating of the internet marketing strategies is guaranteed, a strategy remaking checking module and a marketing strategy remaking module are arranged, and the system is convenient to use. According to the method, whether the Internet marketing strategies in all directions need to be reformulated or not can be periodically checked, the marketing effect in the Internet marketing strategy directions is fully and three-dimensionally checked, and after the Internet marketing strategy directions needing to be reformulated are checked out, the marketing strategies in the Internet marketing strategy directions are reformulated in time.

Description

Technical Field

[0001] The present invention relates to the technical field of strategy formulation, and more specifically, to an Internet marketing strategy formulation system based on big data. Background Art

[0002] Internet marketing strategy is a series of strategies and methods for enterprises to carry out market promotion and sales activities in the Internet environment. These strategies aim to achieve the marketing goals of enterprises through the Internet, using digital information and the interactivity of network media. In the process of formulating Internet marketing strategies, it is necessary to consider multi-directional strategy formulation, such as web page strategy, product strategy, price strategy, promotion strategy, channel strategy, etc. Formulating multi-directional Internet marketing strategies at the same time will lead to unclear focus of the marketing strategy of enterprise products, and it is also easy to cause the direction of Internet marketing strategy to be unmatched and unreasonable. How to formulate Internet marketing strategies reasonably and efficiently is an urgent problem to be solved. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an Internet marketing strategy formulation system based on big data.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An Internet marketing strategy formulation system based on big data, including a marketing strategy formulation module, a strategy re-formulation checking module, and a marketing strategy re-formulation module;

[0006] The marketing strategy formulation module is used to obtain the conventional formulation order of each marketing strategy direction in the Internet marketing strategy according to the product type, and formulate the marketing strategy of each marketing strategy direction in turn according to the conventional formulation order;

[0007] The strategy re-check module performs a strategy re-check on each marketing strategy direction of the product in a regular order based on a fixed time interval, and generates a strategy check log for the marketing strategy direction after the strategy re-check is performed on the marketing strategy direction;

[0008] The marketing strategy re-making module is used to determine whether to re-formulate the strategy of the re-making strategy direction and to readjust the re-making inspection order of the marketing strategy direction.

[0009] Furthermore, the conventional order of formulating each marketing strategy direction in the Internet marketing strategy is obtained according to the product type, which is as follows:

[0010] Get all policy check logs of the product type before the current system time, get the marketing policy direction of the policy check log, mark the policy check logs with the same marketing policy direction as the same-direction policy check logs, sort all the same-direction policy check logs in the order of inspection time, calculate the difference between the policy check values ​​of the two adjacent same-direction policy check logs after sorting and take the absolute value to get the policy check difference, sum up all the policy check differences and take the average to get the average policy check difference C tr , sum up the policy check values ​​of all the same-direction policy check logs and take the average value to get the average policy check value P ly , using the formula The formulation order value of the marketing strategy direction in the product type is obtained, and all the marketing strategy directions in the product type are sorted in descending order according to the formulation order value, so as to generate a conventional formulation order for each marketing strategy direction.

[0011] Furthermore, the strategy check log includes product type, marketing strategy direction, strategy check value, and check time.

[0012] Furthermore, the policy check value of the policy check log is obtained in the following way:

[0013] Get various marketing data for this marketing strategy direction and mark the total amount of marketing data as K og , get the marketing detection mean R cy , obtain the marketing detection threshold values ​​of various marketing data, compare the marketing detection value of the same marketing data with the marketing detection threshold value, when the marketing detection value of the same marketing data is greater than or equal to the marketing detection threshold value, no processing is performed, when the marketing detection value of the same marketing data is less than the marketing detection threshold value, mark this type of marketing data as indicator abnormal data, obtain the information graph of various marketing data in the product type, analyze all indicator abnormal data in pairs in the information graph, when two indicator abnormal data are related to each other in the information graph, increase the number of marketing dependencies by one, when two indicator abnormal data are not related in the information graph, no corresponding processing is performed, sum up all marketing dependency numbers, and obtain the total number of marketing dependencies C nt , using the formula The strategy check value Bs is obtained, where a1 is the marketing dependence ratio coefficient and a2 is the marketing detection coefficient.

[0014] Furthermore, the marketing detection mean R cy It is obtained by the following method: obtaining the marketing detection model of various marketing data in the product type, inputting various marketing data into the corresponding marketing detection model, obtaining the marketing detection values ​​of various marketing data, summing up the marketing detection values ​​of all marketing data and taking the average, obtaining the marketing detection mean, and marking it as R cy .

[0015] Further, it is determined whether to re-formulate a strategy for the re-policy direction, specifically: a strategy check threshold is set, and when the strategy check value of the strategy check log is greater than the strategy check threshold, the marketing strategy direction is marked as a re-policy direction, and the strategy for the re-policy direction is re-formulated;

[0016] When the policy check value of the policy check log is less than or equal to the policy check threshold, a policy re-check is performed on the next product marketing policy direction according to the regular formulation order.

[0017] Furthermore, the re-inspection order of the marketing strategy directions is readjusted, specifically: the remaining strategy directions after the re-inspected strategy direction in the regular formulation order are simultaneously marked as strategy directions to be inspected, the strategy inspection priority value of each strategy direction to be inspected is obtained, all strategy directions to be inspected are sorted in descending order according to the strategy inspection priority value, and the subsequent strategy directions to be inspected are subjected to strategy re-inspection in the sorted order.

[0018] Furthermore, the inspection priority value of the strategy direction to be inspected is obtained by obtaining the information graph of each marketing strategy direction in the product type, and when the re-made strategy direction and the strategy direction to be inspected are related to each other in the information graph, adding the synchronization influence coefficient F gv When the re-made strategy direction and the to-be-checked strategy direction are not associated in the information graph, no corresponding processing is performed. The strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction are obtained, and the difference between the strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction is calculated to obtain the strategy inspection gap value S zb , using the formula Get the inspection priority value of the policy direction to be inspected.

[0019] Furthermore, the policy check value is obtained in the following manner: obtain m policy check logs of the marketing strategy direction in the product type before the current system time, sum up and average the policy check values ​​of all policy check logs, and obtain the policy check value.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. Set up a marketing strategy formulation module, which can set a reasonable formulation order for Internet marketing strategies in different directions according to product types, adapt to the formulation of Internet marketing strategies for different product types, and ensure the reasonable and efficient formulation of Internet marketing strategies;

[0022] 2. Set up a strategy re-formulation inspection module and a marketing strategy re-formulation module to periodically check whether the Internet marketing strategies in various directions need to be reformulated, and fully and three-dimensionally check the marketing effects of the Internet marketing strategy directions. After checking the Internet marketing strategy directions that need to be reformulated, timely reformulate the marketing strategies of the Internet marketing strategy directions, and simultaneously adjust the inspection order of subsequent Internet marketing strategy directions to increase the inspection priority of Internet marketing strategy directions that have risks of reformulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of the present invention;

[0024] Figure 2 A flowchart of a strategy for determining whether to re-formulate the re-policy direction. DETAILED DESCRIPTION

[0025] Reference Figure 1-Figure 2 , an Internet marketing strategy formulation system based on big data, including a marketing strategy formulation module, a strategy re-formulation checking module, and a marketing strategy re-formulation module.

[0026] Marketing strategy formulation module: obtain the product types targeted by the company's Internet marketing (that is, the product types that the company needs to target when formulating the Internet marketing strategy this time), obtain the regular formulation order of each marketing strategy direction in the Internet marketing strategy based on the product type, and formulate marketing strategies for each marketing strategy direction in turn according to the regular formulation order.

[0027] The general order of formulating each marketing strategy direction in the Internet marketing strategy is obtained according to the product type, specifically:

[0028] Get all policy check logs of the product type before the current system time, get the marketing policy direction of the policy check log, mark the policy check logs with the same marketing policy direction as the same-direction policy check logs, sort all the same-direction policy check logs in the order of inspection time, calculate the difference between the policy check values ​​of the two adjacent same-direction policy check logs after sorting and take the absolute value to get the policy check difference, sum up all the policy check differences and take the average to get the average policy check difference C tr , sum up the policy check values ​​of all the same-direction policy check logs and take the average value to get the average policy check value P ly , using the formula The formulation order value of the marketing strategy direction in the product type is obtained, and all the marketing strategy directions in the product type are sorted in descending order according to the formulation order value, so as to generate a conventional formulation order for each marketing strategy direction.

[0029] By setting up a marketing strategy formulation module, you can set a reasonable formulation order for Internet marketing strategies in different directions according to product types, adapt to the formulation of Internet marketing strategies for different product types, and ensure the reasonable and efficient formulation of Internet marketing strategies.

[0030] Strategy re-check module: Based on fixed time intervals, at each interval point, the product's marketing strategy directions are re-checked in the regular order (for example, the regular order is web strategy direction, price strategy direction, channel strategy direction, product strategy direction. At the interval point, the web strategy direction is re-checked first).

[0031] When the marketing strategy direction performs a strategy re-check, a strategy check log for the marketing strategy direction is generated. The strategy check log includes product type, marketing strategy direction, strategy check value, and check time.

[0032] The policy check value of the policy check log is obtained in the following way:

[0033] Get various marketing data of the marketing strategy direction (each marketing strategy direction contains multiple marketing data. Taking the web strategy direction as an example, the web strategy direction includes but is not limited to website access data, website user behavior data, web influence data, and web interaction data), and mark the total amount of marketing data as K og , obtain the marketing detection model of various marketing data in the product type (each marketing data in the marketing strategy direction of the product type corresponds to a marketing detection model, and the same marketing data in the same marketing strategy direction of different product types corresponds to different marketing detection models. For example, when the product type is a smartphone, taking the web strategy direction as an example, the website access data of the smartphone corresponds to a marketing detection model, the website user behavior data of the smartphone corresponds to a marketing detection model, the web influence data of the smartphone corresponds to a marketing detection model, and the web interaction data of the smartphone corresponds to a marketing detection model), input various marketing data into the corresponding marketing detection model, obtain the marketing detection values ​​of various marketing data, sum up the marketing detection values ​​of all marketing data and take the average, obtain the marketing detection mean, and mark it as R cy, obtain the marketing detection threshold values ​​of various marketing data (the marketing data of the marketing strategy direction of each product type corresponds to a marketing detection threshold value, and the marketing detection threshold value can be modified and adjusted according to actual needs), compare the marketing detection value of the same marketing data with the marketing detection threshold value, when the marketing detection value of the same marketing data is greater than or equal to the marketing detection threshold value, no processing is performed, when the marketing detection value of the same marketing data is less than the marketing detection threshold value, mark this type of marketing data as indicator abnormal data, obtain the information map of various marketing data in the product type, analyze all indicator abnormal data in pairs in the information map, when two indicator abnormal data are related to each other in the information map, increase the number of marketing dependencies by one, when two indicator abnormal data are not related in the information map, no corresponding processing is performed, sum up all marketing dependency numbers, and obtain the total number of marketing dependencies C nt , using the formula The strategy check value Bs is obtained, where a1 is the marketing dependence ratio coefficient, a2 is the marketing detection coefficient, the value of a1 is 0.62, and the value of a1 is 3.56.

[0034] Different product types correspond to different marketing data information maps. In this embodiment, a smartphone is taken as an example. The association of various marketing data in the marketing data information map of the smartphone indicates that there is a dependency and influence relationship between the marketing data. For example, the website visit data and the web page interaction data of the smartphone are mutually dependent, so the website visit data and the web page interaction data are associated with each other in the marketing data information map of the smartphone.

[0035] Example: Each type of marketing data in the marketing strategy direction of each product type corresponds to a marketing detection model. Taking the website access data of the web strategy direction of smartphones as an example, the marketing detection model of the website access data of the web strategy direction of smartphones is trained in the following way: collect n groups of website access data of the web strategy direction of smartphones, wherein the above n groups of website access data of smartphones can be website access data from the same smartphone or from multiple smartphones, perform feature extraction on each website access data to obtain multiple website access feature data, construct a neural network model, use the website access feature data as training data of the neural network model, assign marketing detection values ​​to the training data, divide the training data into a training set and a validation set according to a preset ratio, perform neural network iterative training on the training set and the validation set, and obtain the marketing detection model of the website access data of the web strategy direction of smartphones.

[0036] Among them, the marketing detection value ranges from (0 to 3). The larger the marketing detection value, the more the website visit data in the marketing strategy direction of the smartphone meets the marketing indicators. Conversely, the smaller the marketing detection value, the less the website visit data in the marketing strategy direction of the smartphone meets the marketing indicators.

[0037] The above is just a marketing detection model trained with website access data in the direction of smartphone marketing strategy. The training method of other marketing detection models is similar. You only need to change the corresponding product type, marketing data and marketing strategy direction. However, the value range of all marketing detection models is the same, which is (0 to 3).

[0038] Marketing strategy re-making module: Set the policy check threshold. The policy check threshold is a system preset threshold and can be modified and adjusted according to actual needs. When the policy check value of the policy check log is greater than the policy check threshold, the marketing policy direction is marked as the re-policy direction, and the policy for the re-policy direction is re-formulated. At the same time, the remaining policy directions after the re-policy direction in the regular formulation order are marked as policy directions to be inspected, and the policy inspection priority value of each policy direction to be inspected is obtained. All policy directions to be inspected are sorted in descending order according to the policy inspection priority value, and subsequent policy directions to be inspected are subjected to policy re-inspection in the sorted order.

[0039] The inspection priority value of the strategy direction to be inspected is obtained by obtaining the information graph of each marketing strategy direction in the product type. When the re-made strategy direction and the strategy direction to be inspected are related to each other in the information graph, the synchronization influence coefficient F is added. gv When the re-made strategy direction and the to-be-checked strategy direction are not associated in the information graph, no corresponding processing is performed. The strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction are obtained, and the difference between the strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction is calculated to obtain the strategy inspection gap value S zb , using the formula Get the inspection priority value of the policy direction to be inspected.

[0040] The policy check value is obtained in the following manner: obtain m policy check logs of the marketing strategy direction in the product type before the current system time, sum up and average the policy check values ​​of all policy check logs, and obtain the policy check value.

[0041] When the policy check value of the policy check log is less than or equal to the policy check threshold, a policy re-check is performed on the next product marketing policy direction according to the regular formulation order.

[0042] Different product types correspond to different marketing strategy direction information maps. In this embodiment, taking smartphones as an example, the information map is used to indicate that there is a correlation between the marketing strategy directions of smartphones. When two marketing strategy directions are correlated in the information map, it indicates that there is an influence relationship between the two marketing strategy directions. For example, if the web strategy direction and product strategy direction of smartphones have a mutual influence relationship, then the web strategy direction and the product strategy direction are correlated with each other in the marketing strategy direction map of smartphones.

[0043] By setting up a strategy re-formulation inspection module and a marketing strategy re-formulation module, it is possible to periodically check whether the Internet marketing strategies in various directions need to be reformulated, and fully and three-dimensionally check the marketing effects of the Internet marketing strategy directions. After finding out that the Internet marketing strategy directions need to be reformulated, the marketing strategies of the Internet marketing strategy directions can be reformulated in a timely manner, and the inspection order of subsequent Internet marketing strategy directions can be adjusted synchronously to increase the inspection priority of Internet marketing strategy directions that have risks of reformulation.

[0044] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0045] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0046] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0047] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0050] If the functions 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 this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0051] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A big data-based Internet marketing strategy formulation system, characterized in that: It includes a marketing strategy formulation module, a strategy re-formulation checking module, and a marketing strategy re-formulation module; The marketing strategy formulation module is used to obtain the conventional formulation order of each marketing strategy direction in the Internet marketing strategy according to the product type, and formulate the marketing strategy of each marketing strategy direction in turn according to the conventional formulation order; The strategy re-check module performs a strategy re-check on each marketing strategy direction of the product in a regular order based on a fixed time interval, and generates a strategy check log for the marketing strategy direction after the strategy re-check is performed on the marketing strategy direction; The marketing strategy re-making module is used to determine whether to re-formulate the strategy of the re-making strategy direction and to readjust the re-making inspection order of the marketing strategy direction.

2. The Internet marketing strategy formulation system based on big data according to claim 1 is characterized in that: The general order of formulating each marketing strategy direction in the Internet marketing strategy is obtained according to the product type, specifically: Get all policy check logs of the product type before the current system time, get the marketing policy direction of the policy check log, mark the policy check logs with the same marketing policy direction as the same-direction policy check logs, sort all the same-direction policy check logs in the order of inspection time, calculate the difference between the policy check values ​​of the two adjacent same-direction policy check logs after sorting and take the absolute value to get the policy check difference, sum up all the policy check differences and take the average to get the average policy check difference C tr , sum up the policy check values ​​of all the same-direction policy check logs and take the average value to get the average policy check value P ly , using the formula The formulation order value of the marketing strategy direction in the product type is obtained, and all the marketing strategy directions in the product type are sorted in descending order according to the formulation order value, so as to generate a conventional formulation order for each marketing strategy direction.

3. The Internet marketing strategy formulation system based on big data according to claim 1 is characterized in that: The strategy check log includes product type, marketing strategy direction, strategy check value, and check time.

4. The Internet marketing strategy formulation system based on big data according to claim 3 is characterized in that: The policy check value of the policy check log is obtained in the following way: Get various marketing data for this marketing strategy direction and mark the total amount of marketing data as K og , get the marketing detection mean R cy , obtain the marketing detection threshold values ​​of various marketing data, compare the marketing detection value of the same marketing data with the marketing detection threshold value, when the marketing detection value of the same marketing data is greater than or equal to the marketing detection threshold value, no processing is performed, when the marketing detection value of the same marketing data is less than the marketing detection threshold value, mark this type of marketing data as indicator abnormal data, obtain the information graph of various marketing data in the product type, analyze all indicator abnormal data in pairs in the information graph, when two indicator abnormal data are related to each other in the information graph, increase the number of marketing dependencies by one, when two indicator abnormal data are not related in the information graph, no corresponding processing is performed, sum up all marketing dependency numbers, and obtain the total number of marketing dependencies C nt , using the formula The strategy check value Bs is obtained, where a1 is the marketing dependence ratio coefficient and a2 is the marketing detection coefficient.

5. The Internet marketing strategy formulation system based on big data according to claim 4 is characterized in that: Marketing detection mean R cy It is obtained by the following method: obtaining the marketing detection model of various marketing data in the product type, inputting various marketing data into the corresponding marketing detection model, obtaining the marketing detection values ​​of various marketing data, summing up the marketing detection values ​​of all marketing data and taking the average, obtaining the marketing detection mean, and marking it as R cy .

6. The Internet marketing strategy formulation system based on big data according to claim 1 is characterized in that: Determine whether to re-formulate a strategy for the re-policy direction, specifically: set a strategy check threshold, when the strategy check value of the strategy check log is greater than the strategy check threshold, mark the marketing strategy direction as the re-policy direction, and start to re-formulate a strategy for the re-policy direction; When the policy check value of the policy check log is less than or equal to the policy check threshold, a policy re-check is performed on the next product marketing policy direction according to the regular formulation order.

7. The Internet marketing strategy formulation system based on big data according to claim 1 is characterized in that: Re-adjust the re-inspection order of the marketing strategy direction, specifically: synchronously mark the remaining strategy directions after the re-inspected strategy direction in the regular formulation order as strategy directions to be inspected, obtain the strategy inspection priority value of each strategy direction to be inspected, sort all strategy directions to be inspected in descending order according to the strategy inspection priority value, and perform strategy re-inspection on subsequent strategy directions to be inspected in the sorted order.

8. The Internet marketing strategy formulation system based on big data according to claim 7 is characterized in that: The inspection priority value of the strategy direction to be inspected is obtained by obtaining the information graph of each marketing strategy direction in the product type. When the re-made strategy direction and the strategy direction to be inspected are related to each other in the information graph, the synchronization influence coefficient F is added. gv When the re-made strategy direction and the to-be-checked strategy direction are not associated in the information graph, no corresponding processing is performed. The strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction are obtained, and the difference between the strategy inspection value of the re-made strategy direction and the strategy inspection value of the to-be-checked strategy direction is calculated to obtain the strategy inspection gap value S zb , using the formula Get the inspection priority value of the policy direction to be inspected.

9. The Internet marketing strategy formulation system based on big data according to claim 8 is characterized in that: The policy check value is obtained in the following manner: obtain m policy check logs of the marketing strategy direction in the product type before the current system time, sum up and average the policy check values ​​of all policy check logs, and obtain the policy check value.

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