A unified omnichannel access system for marketing systems

Abstract

The present invention discloses an omni-channel unified access system for a marketing system, and specifically relates to the technical field of omni-channel unified access systems. By acquiring data flow speed information of a terminal device carried by the omni-channel unified access system and network disconnection response information of an area where the terminal device is located, a fluctuation degree evaluation model is constructed to evaluate the influence of the hardware performance of the terminal device and the network environment of the area where the terminal device is located on the abnormal fluctuation of the operation of the omni-channel unified access system, classify the influence of the abnormal fluctuation of the operation of the omni-channel unified access system, quantify the influence of the abnormal fluctuation of the terminal device end on the system, improve the monitoring accuracy, and control intervention on whether the omni-channel unified access system generates maintenance information according to the classification result, timely identify and respond to the influence of the abnormal fluctuation of the terminal device end on the operation of the omni-channel unified access system, provide enterprises with more reliable marketing services, and enhance the user experience.

Description

Technical Field

[0001] The present invention relates to the technical field of omni-channel unified access systems, and more specifically, to an omni-channel unified access system for a marketing system. Background Art

[0002] With the rapid development of the Internet and mobile communication technologies, the marketing channels of enterprises have gradually evolved from a single traditional model to a diversified, omni-channel marketing system. Omni-channel marketing includes not only online channels, such as e-commerce platforms, social media, search engine optimization (SEO) and content marketing, but also offline channels, such as physical stores, telephone sales and traditional media advertising. Faced with such diverse marketing channels, enterprises need a unified access system that can integrate all channel resources to improve marketing efficiency and customer experience. The omni-channel unified access system can not only provide multi-channel marketing activity management and effect monitoring functions. It can also enable enterprises to plan, execute and monitor marketing activities in a unified manner through the system, and adjust marketing strategies in real time. However, the hardware performance of the terminal devices equipped with the omni-channel unified access system and the network environment of the terminal devices in the area may be different, resulting in slight abnormal fluctuations in the operation of the omni-channel unified access system on different terminal devices. The omni-channel unified access system may generate misleading maintenance information based on the slight abnormal fluctuations in the operation, which may cause some inexperienced operation and maintenance personnel to mistakenly believe that the omni-channel unified access system itself has a fault, thereby taking unnecessary maintenance measures, wasting more time and resource costs, and affecting the marketing decisions of enterprises and the user experience.

[0003] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a marketing system omni-channel unified access system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A marketing system omni-channel unified access system, including:

[0007] The terminal hardware monitoring module obtains the data flow speed information of the terminal device equipped in the omni-channel unified access system, obtains the data flow speed fluctuation delay coefficient according to the data flow speed information, and transmits the data flow speed fluctuation delay coefficient to the fluctuation degree evaluation module;

[0008] The terminal environment monitoring module obtains the network disconnection response information of the area where the terminal equipment carried by the omni-channel unified access system is located, obtains the network disconnection response anomaly coefficient according to the network disconnection response information, and transmits the network disconnection response anomaly coefficient to the fluctuation degree assessment module;

[0009] The fluctuation degree assessment module builds a fluctuation degree assessment model based on the data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient, assesses the impact of the hardware performance of the terminal equipment and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system, and generates a fluctuation degree impact index;

[0010] The fluctuation classification module compares the fluctuation degree impact index with the preset fluctuation degree impact index threshold, and classifies the impact of the hardware performance of the terminal device and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system;

[0011] The control intervention module controls and intervenes whether the omni-channel unified access system generates maintenance information based on the classification results.

[0012] In a preferred embodiment, the logic for obtaining the data flow speed fluctuation delay coefficient is as follows:

[0013] Obtain the data flow speed data of the terminal device within the T time period, and establish a data flow speed data set based on the data flow speed data of the terminal device within the T time period, and mark it as LZ = {lz i}, where i = {1, 2, 3...n}, n is a positive integer, lz i Indicates the data flow speed data of the i-th data;

[0014] Calculate the standard deviation lzb of the data flow rate data in the data flow rate data set. The expression is as follows Where lzp represents the average value of the data flow rate data in the data flow rate data set, and the expression is as follows

[0015] Compare the data flow speed in the data flow speed data set with the expected data flow speed. When the data flow speed is less than the expected data flow speed, calculate the delay value ycz j , the expression is as follows ycz j =yq-lzy j , where yq represents the expected data flow rate, lzy j Indicates the jth data flow rate in the data flow rate data set that is less than the expected data flow rate;

[0016] Calculate the data flow speed fluctuation delay coefficient In the formula, Represents the average value of the delay value, the expression is as follows m represents the total number of data flow speeds in the data flow speed data set that are lower than the expected data flow speed.

[0017] In a preferred embodiment, the logic for obtaining the network disconnection response anomaly coefficient is as follows:

[0018] Obtain network disconnection events in the area where the terminal device is located within the T time period, and record the timestamp S1 of each time the terminal device loses connection with the network and the timestamp S2 of each time it reconnects to the network, calculate the network disconnection response time sc, the expression is as follows sc = S2-S1, take the multiple groups of network disconnection response time obtained within the T time period as feature values, use the LOF algorithm to calculate the local outlier factor of each feature value, and mark it as LOF (sc u ), where sc u It represents the u-th network disconnection event, u={1, 2, 3…v}, v is a positive integer, and the average value of the local outlier factor is calculated as follows: In the formula It represents the average value of the local outlier factor. The standard deviation of the local outlier factor is calculated based on the average value of the local outlier factor. The expression is as follows Where LOFB represents the standard deviation of the local outlier factor;

[0019] Calculate the network disconnection response anomaly coefficient, the expression is as follows Where wdl represents the network disconnection response anomaly coefficient.

[0020] In a preferred embodiment, the data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient are normalized, and a fluctuation degree evaluation model is constructed based on the normalized data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient to generate a fluctuation degree impact index bdy. The formula of the model is as follows: Wherein a1 and a2 represent preset proportional coefficients of data flow speed fluctuation delay coefficient and network disconnection response abnormality coefficient respectively, and both a1 and a2 are greater than 0.

[0021] In a preferred implementation, the fluctuation degree impact index is compared with a preset fluctuation degree impact index threshold, and the impact of the hardware performance of the terminal device and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system is classified as follows:

[0022] If the volatility impact index is greater than the volatility impact index threshold, an abnormal volatility impact signal is generated;

[0023] If the volatility impact index is less than or equal to the volatility impact index threshold, there is no need to generate an abnormal volatility impact signal.

[0024] In a preferred embodiment, the number of times abnormal fluctuation influence signals are generated within a time period of T is counted, and the proportion of abnormal fluctuation influence signals is calculated. The expression is as follows In the formula, yzb represents the proportion of abnormal fluctuation impact signals, cas represents the number of times abnormal fluctuation impact signals are generated within the time period T, and fam represents the number of terminal devices that users use to log in to the omni-channel unified access system within the time period T.

[0025] In a preferred embodiment, the abnormal fluctuation influence signal ratio calculated in the subsequent multiple T time periods is obtained, and a data set of abnormal fluctuation influence signal ratio is established, and the abnormal fluctuation influence signal ratio data set is marked as YCZ={yzb p}, where p = {1, 2, 3…q}, q is a positive integer;

[0026] Calculate the outlier value of the abnormal fluctuation impact signal ratio numbered q in the data set. The expression is as follows In the formula, yzb q It represents the proportion of abnormal fluctuations affecting signals numbered q, and wq represents the average value of the proportion of abnormal fluctuations affecting signals in the data set. The expression is as follows ql represents the standard deviation of the proportion of abnormal fluctuations affecting signals in the data set, and the expression is as follows

[0027] In a preferred implementation, the outlier value of the abnormal fluctuation influence signal ratio numbered q in the abnormal fluctuation influence signal ratio data set is compared with a preset control intervention threshold, and control intervention is performed on whether the omni-channel unified access system generates maintenance information. The specific situation is as follows:

[0028] If the outlier value of the abnormal fluctuation influence signal ratio numbered q in the abnormal fluctuation influence signal ratio data set is greater than the control intervention threshold, a control intervention signal is generated;

[0029] If the outlier value of the abnormal fluctuation influence signal ratio numbered q in the abnormal fluctuation influence signal ratio data set is less than or equal to the control intervention threshold, there is no need to generate a control intervention signal.

[0030] Technical effects and advantages of the present invention:

[0031] 1. The present invention obtains the data flow speed information of the terminal equipment carried by the omni-channel unified access system and the network disconnection response information of the area where the terminal equipment is located, and constructs a fluctuation degree evaluation model according to the data flow speed information and the network disconnection response information, evaluates the influence of the hardware performance of the terminal equipment and the network environment in the area where the terminal equipment is located on the abnormal fluctuation of the operation of the omni-channel unified access system, generates a fluctuation degree impact index, compares the fluctuation degree impact index with a preset fluctuation degree impact index threshold, and classifies the influence of the hardware performance of the terminal equipment and the network environment in the area where the terminal equipment is located on the abnormal fluctuation of the operation of the omni-channel unified access system, quantifies the influence of the abnormal fluctuation on the terminal equipment end on the system, and the quantified indicators facilitate the enterprise to monitor and manage the system performance and improve the accuracy of monitoring.

[0032] 2. The present invention calculates the proportion of abnormal fluctuation impact signals by counting the number of times abnormal fluctuation impact signals are generated within a time period of T, and simultaneously obtains the proportion of abnormal fluctuation impact signals calculated within multiple subsequent time periods of T, and establishes a data set of the proportion of abnormal fluctuation impact signals, and performs control intervention analysis on the proportion of abnormal fluctuation impact signals in the data set of the proportion of abnormal fluctuation impact signals, thereby reducing false alarms caused by problems on the terminal device side, preventing operation and maintenance personnel from mistaking the failure of the system itself, reducing unnecessary maintenance operations, saving time and resource costs, timely identifying and responding to the degree of influence of abnormal fluctuations on the operation of the omni-channel unified access system by the terminal device side, improving the overall stability of the omni-channel unified access system, providing enterprises with more reliable marketing services, and enhancing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0034] Figure 1 Schematic diagram of the structure of the system of the embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example

[0037] The present invention provides Figure 1 The illustrated marketing system omni-channel unified access system includes:

[0038] The terminal hardware monitoring module obtains the data flow speed information of the terminal device equipped in the omni-channel unified access system, obtains the data flow speed fluctuation delay coefficient according to the data flow speed information, and transmits the data flow speed fluctuation delay coefficient to the fluctuation degree evaluation module;

[0039] The hardware system of the terminal device carried by the omni-channel unified access system usually includes multiple components. For example, the hardware system of a modern computer includes memory, arithmetic unit, controller, input device, output device, etc. The hardware system of a smart phone includes central processing unit, memory, memory, etc. The data transmitted by the omni-channel unified access system usually flows between multiple components of the terminal device. Since the use time and maintenance frequency of the terminal devices carried by the omni-channel unified access system are inconsistent, their hardware performance is also inconsistent, so the delay of data flow speed on different terminal devices may also be different. When the delay is large, it may cause abnormal fluctuations in the operation of the omni-channel unified access system;

[0040] When data transmission speed is delayed on the terminal device, it may cause the following impacts on the omni-channel unified access system installed on the terminal device:

[0041] Performance degradation: Latency causes data to be transmitted more slowly between hardware components, which may cause overall system performance degradation, slow response speed, and affect user experience.

[0042] Data loss or corruption: If latency causes data transmission failure or corruption, it may result in data loss or misinterpretation, affecting the accuracy and stability of the system;

[0043] System crash or freeze: In extreme cases, excessive latency may cause system crash or freeze, making the omni-channel unified access system unable to work properly, affecting the company's marketing activities and user experience;

[0044] Misleading information: Delays may cause the system to generate misleading maintenance information, misleading operation and maintenance personnel to believe that the system is faulty and take unnecessary maintenance measures, increasing maintenance costs and reducing system stability;

[0045] Uneven user experience: Inconsistent latency on different terminal devices may result in inconsistent experience for different users. Some users may face longer waiting times or performance issues, which may affect their trust in the omni-channel unified access system.

[0046] Therefore, by obtaining the data flow speed information of the terminal devices equipped with the omni-channel unified access system, analyzing the delay of the data flow of the terminal devices, and obtaining the data flow speed fluctuation delay coefficient, the influence of the delay of the data flow of the terminal devices on the abnormal fluctuation of the operation of the omni-channel unified access system is measured;

[0047] The logic for obtaining the data flow speed fluctuation delay coefficient is as follows:

[0048] Obtain the data flow speed data of the terminal device within the T time period, and establish a data flow speed data set based on the data flow speed data of the terminal device within the T time period, and mark it as LZ = {lz i}, where i = {1, 2, 3...n}, n is a positive integer, lz i Indicates the data flow speed data of the i-th data;

[0049] It should be noted that the data flow speed data can be obtained by using professional performance monitoring tools such as Nagios, Zabbix, and Prometheus to monitor the sending and receiving time of the data transmitted by the omni-channel unified access system between the various hardware components of the terminal device, and calculate the difference between the sending and receiving time. The data flow speed is calculated by comparing the data packet size with the difference between the sending and receiving time.

[0050] Calculate the standard deviation lzb of the data flow rate data in the data flow rate data set. The expression is as follows Where lzp represents the average value of the data flow rate data in the data flow rate data set, and the expression is as follows

[0051] Compare the data flow speed in the data flow speed data set with the expected data flow speed. When the data flow speed is less than the expected data flow speed, calculate the delay value ycz j , the expression is as follows ycz j =yq-lzy j , where yq represents the expected data flow rate, lzy j Indicates the jth data flow rate in the data flow rate data set that is less than the expected data flow rate;

[0052] Calculate the data flow speed fluctuation delay coefficient In the formula, Represents the average value of the delay value, the expression is as follows m represents the total number of data flow speeds in the data flow speed data set that are less than the expected data flow speed;

[0053] From the above calculation, it can be seen that the larger the data flow speed fluctuation delay coefficient is, the greater the impact of the delay in the data flow of the terminal device on the abnormal fluctuation of the operation of the omni-channel unified access system is. On the contrary, the smaller the data flow speed fluctuation delay coefficient is, the smaller the impact of the delay in the data flow of the terminal device on the abnormal fluctuation of the operation of the omni-channel unified access system is.

[0054] The terminal environment monitoring module obtains the network disconnection response information of the area where the terminal equipment carried by the omni-channel unified access system is located, obtains the network disconnection response anomaly coefficient according to the network disconnection response information, and transmits the network disconnection response anomaly coefficient to the fluctuation degree assessment module;

[0055] Network disconnection response refers to the response status of a terminal device losing connection with the local area network and reconnecting within the range of the terminal device. Due to the differences in the network environments of the areas where different terminal devices equipped with the omni-channel unified access system are located, the network disconnection response of the terminal device may have different abnormalities. The higher the abnormality of the network disconnection response of the terminal device, the greater the impact on the abnormal fluctuation of the operation of the omni-channel unified access system.

[0056] When the terminal device responds abnormally to network disconnection in the range area, it may cause the following impacts on the omni-channel unified access system installed on the terminal device:

[0057] Data transmission interruption: Network disconnection may cause the terminal device to be unable to transmit data to the omni-channel unified access system normally, thereby affecting the data collection and processing of the omni-channel unified access system;

[0058] Data loss: In the event of a network disconnection, some data packets may be lost or unable to be transmitted to the omni-channel unified access system in a timely manner, resulting in data loss or incompleteness;

[0059] Data delay: Network disconnection may cause data transmission delay. Even if the network connection is restored later, the previously accumulated data may take a long time to transmit, causing data delay;

[0060] Data inconsistency: During network disconnection, some data may have been cached or temporarily stored by the terminal device, while other data may have been sent to the omni-channel unified access system, which may cause data inconsistency and affect the data accuracy and consistency of the omni-channel unified access system;

[0061] Degraded user experience: Network disconnection affects the user's application experience on the terminal device. For example, the user cannot obtain updated information or services in a timely manner, which reduces user satisfaction and trust.

[0062] Therefore, by obtaining the network disconnection response information of the area where the terminal devices equipped with the omni-channel unified access system are located, the abnormal degree of the network disconnection response of the terminal devices is analyzed, and the network disconnection response abnormality coefficient is obtained to measure the impact of the abnormal network disconnection response of the terminal devices on the abnormal fluctuation of the operation of the omni-channel unified access system;

[0063] The logic for obtaining the network disconnection response anomaly coefficient is as follows:

[0064] Obtain network disconnection events in the area where the terminal device is located within the T time period, and record the timestamp S1 of each time the terminal device loses connection with the network and the timestamp S2 of each time it reconnects to the network, calculate the network disconnection response time sc, the expression is as follows sc = S2-S1, take the multiple groups of network disconnection response time obtained within the T time period as feature values, use the LOF algorithm to calculate the local outlier factor of each feature value, and mark it as LOF (sc u ), where sc u It represents the u-th network disconnection event, u={1, 2, 3…v}, v is a positive integer, and the average value of the local outlier factor is calculated as follows: In the formula It represents the average value of the local outlier factor. The standard deviation of the local outlier factor is calculated based on the average value of the local outlier factor. The expression is as follows Where LOFB represents the standard deviation of the local outlier factor;

[0065] It should be noted that the use of the LOF algorithm to calculate the local outlier factor of each eigenvalue is an existing mature technology and will not be described in detail here. Since the frequency of network disconnection events in the area where the terminal device is located is difficult to capture, and the data set density of the obtained network disconnection response information is also uneven, the use of the LOF algorithm can effectively detect abnormal points of network disconnection responses when the data set density is uneven;

[0066] Calculate the network disconnection response anomaly coefficient, the expression is as follows Where wdl represents the network disconnection response anomaly coefficient;

[0067] From the above calculation, it can be seen that the larger the network disconnection response anomaly coefficient is, the greater the impact of the network disconnection response anomaly of the terminal device on the abnormal fluctuation of the operation of the omni-channel unified access system is; conversely, the smaller the network disconnection response anomaly coefficient is, the smaller the impact of the network disconnection response anomaly of the terminal device on the abnormal fluctuation of the operation of the omni-channel unified access system is;

[0068] The fluctuation degree assessment module builds a fluctuation degree assessment model based on the data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient, assesses the impact of the hardware performance of the terminal equipment and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system, and generates a fluctuation degree impact index;

[0069] The data flow speed fluctuation delay coefficient and the network disconnection response anomaly coefficient are normalized, and a fluctuation degree evaluation model is constructed based on the normalized data flow speed fluctuation delay coefficient and the network disconnection response anomaly coefficient to generate the fluctuation degree impact index bdy. The model is based on the following formula: Wherein a1 and a2 represent the preset proportional coefficients of the data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient, respectively, and a1 and a2 are both greater than 0;

[0070] It can be seen from the above calculation expression that the larger the data flow speed fluctuation delay coefficient and the larger the network disconnection response anomaly coefficient, the larger the fluctuation degree impact index, indicating that the delay of terminal device data flow and the network disconnection response anomaly in the area have a greater impact on the abnormal fluctuation of the operation of the omni-channel unified access system. On the contrary, the smaller the data flow speed fluctuation delay coefficient and the smaller the network disconnection response anomaly coefficient, the smaller the fluctuation degree impact index, indicating that the delay of terminal device data flow and the network disconnection response anomaly in the area have a smaller impact on the abnormal fluctuation of the operation of the omni-channel unified access system.

[0071] The fluctuation classification module compares the fluctuation degree impact index with the preset fluctuation degree impact index threshold, and classifies the impact of the hardware performance of the terminal device and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system, as follows:

[0072] If the fluctuation degree impact index is greater than the fluctuation degree impact index threshold, the delay in data transfer of the terminal device and the abnormal network disconnection response in the area where the terminal device is located have a greater impact on the abnormal fluctuation of the operation of the omni-channel unified access system, and an abnormal fluctuation impact signal is generated;

[0073] If the fluctuation degree impact index is less than or equal to the fluctuation degree impact index threshold, the delay in data transfer of the terminal device and the abnormal network disconnection response in the area have little impact on the abnormal fluctuation of the operation of the omni-channel unified access system, and there is no need to generate an abnormal fluctuation impact signal;

[0074] The control intervention module controls whether the omni-channel unified access system generates maintenance information based on the classification results;

[0075] Count the number of abnormal fluctuation impact signals generated within the T time period, and calculate the proportion of abnormal fluctuation impact signals. The expression is as follows In the formula, yzb represents the proportion of abnormal fluctuation impact signals, cas represents the number of abnormal fluctuation impact signals generated within the time period T, and fam represents the number of terminal devices used by users to log in to the omni-channel unified access system within the time period T;

[0076] It should be noted that since the generation of abnormal fluctuation impact signals is based on the evaluation of different terminal devices, the abnormal fluctuation impact signal ratio is used to measure how many terminal devices have data flow delays and abnormal network disconnection responses in the area within the T time period, which will affect the abnormal fluctuations in the operation of the omni-channel unified access system;

[0077] Obtain the proportion of abnormal fluctuation-affected signals calculated in subsequent multiple T time periods, and establish a data set of the proportion of abnormal fluctuation-affected signals, and mark the data set of the proportion of abnormal fluctuation-affected signals as YCZ={yzbp}, where p={1, 2, 3…q}, and q is a positive integer;

[0078] Calculate the outlier value of the abnormal fluctuation impact signal ratio numbered q in the data set. The expression is as follows In the formula, yzb q It represents the proportion of abnormal fluctuations affecting signals numbered q, and wq represents the average value of the proportion of abnormal fluctuations affecting signals in the data set. The expression is as follows ql represents the standard deviation of the proportion of abnormal fluctuations affecting signals in the data set, and the expression is as follows

[0079] The outlier value of the abnormal fluctuation impact signal ratio numbered q in the abnormal fluctuation impact signal ratio data set is compared with the preset control intervention threshold, and control intervention is performed on whether the omni-channel unified access system generates maintenance information. The specific situation is as follows:

[0080] If the outlier value of the abnormal fluctuation impact signal ratio numbered q in the abnormal fluctuation impact signal ratio data set is greater than the control intervention threshold, it means that the hardware performance of the terminal equipment and the regional network environment have a continuously increasing impact on the abnormal fluctuation of the operation of the omni-channel unified access system, and the slight abnormal fluctuations in the operation of the omni-channel unified access system are more likely to come from the terminal equipment side rather than from the omni-channel unified access system itself. A control intervention signal is generated, and control intervention measures are implemented on whether the omni-channel unified access system generates maintenance information. For example, the maintenance information generated by the omni-channel unified access system is marked as risk, or maintenance information may not be generated according to the actual operation of the omni-channel unified access system.

[0081] If the outlier value of the abnormal fluctuation impact signal ratio numbered q in the abnormal fluctuation impact signal ratio data set is less than or equal to the control intervention threshold, it means that the hardware performance of the terminal device and the regional network environment have no continuous increase in the impact of abnormal fluctuations in the operation of the omni-channel unified access system, and there is no need to generate a control intervention signal;

[0082] The present invention obtains data flow speed information of terminal devices carried by the omni-channel unified access system and network disconnection response information of the area where the terminal devices are located, and constructs a fluctuation degree evaluation model according to the data flow speed information and the network disconnection response information, evaluates the influence of the hardware performance of the terminal devices and the network environment of the area where they are located on the abnormal fluctuations in the operation of the omni-channel unified access system, generates a fluctuation degree impact index, compares the fluctuation degree impact index with a preset fluctuation degree impact index threshold, and classifies the influence of the hardware performance of the terminal devices and the network environment of the area where they are located on the abnormal fluctuations in the operation of the omni-channel unified access system, quantifies the influence of abnormal fluctuations on the system at the terminal device end, and the quantified indicators facilitate enterprises to monitor and manage system performance and improve the accuracy of monitoring.

[0083] The present invention calculates the proportion of abnormal fluctuation influence signals by counting the number of times abnormal fluctuation influence signals are generated within a time period of T, and simultaneously obtains the proportion of abnormal fluctuation influence signals calculated within multiple subsequent time periods of T, and establishes a data set of the proportion of abnormal fluctuation influence signals, and performs control intervention analysis on the proportion of abnormal fluctuation influence signals in the data set of the proportion of abnormal fluctuation influence signals, so as to reduce false alarms caused by problems on the terminal device side, avoid operation and maintenance personnel from mistaking the failure of the system itself, reduce unnecessary maintenance operations, save time and resource costs, timely identify and respond to the degree of influence of abnormal fluctuations on the operation of the omni-channel unified access system by the terminal device side, improve the overall stability of the omni-channel unified access system, provide enterprises with more reliable marketing services, and enhance user experience.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0089] 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.

[0090] 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 marketing system omni-channel unified access system, characterized by: include: The terminal hardware monitoring module obtains the data flow speed information of the terminal device equipped in the omni-channel unified access system, obtains the data flow speed fluctuation delay coefficient according to the data flow speed information, and transmits the data flow speed fluctuation delay coefficient to the fluctuation degree evaluation module; The terminal environment monitoring module obtains the network disconnection response information of the area where the terminal equipment carried by the omni-channel unified access system is located, obtains the network disconnection response anomaly coefficient according to the network disconnection response information, and transmits the network disconnection response anomaly coefficient to the fluctuation degree assessment module; The fluctuation degree assessment module builds a fluctuation degree assessment model based on the data flow speed fluctuation delay coefficient and the network disconnection response abnormality coefficient, assesses the impact of the hardware performance of the terminal equipment and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system, and generates a fluctuation degree impact index; The fluctuation classification module compares the fluctuation degree impact index with the preset fluctuation degree impact index threshold, and classifies the impact of the hardware performance of the terminal device and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system; The control intervention module controls whether the omni-channel unified access system generates maintenance information based on the classification results; The logic for obtaining the data flow speed fluctuation delay coefficient is as follows: Obtain the data flow speed data of the terminal device within the T time period, and establish a data flow speed data set based on the data flow speed data of the terminal device within the T time period, and mark it as LZ = {lz i }, where i = {1, 2, 3...n}, n is a positive integer, lz i Indicates the data flow speed data of the i-th data; Calculate the standard deviation lzb of the data flow rate data in the data flow rate data set. The expression is as follows Where lzp represents the average value of the data flow rate data in the data flow rate data set, and the expression is as follows Compare the data flow speed in the data flow speed data set with the expected data flow speed. When the data flow speed is less than the expected data flow speed, calculate the delay value ycz j , the expression is as follows ycz j =yq-lzy j , where yq represents the expected data flow rate, lzy j Indicates the jth data flow rate in the data flow rate data set that is less than the expected data flow rate; Calculate the data flow speed fluctuation delay coefficient In the formula, Represents the average value of the delay value, the expression is as follows m represents the total number of data flow speeds in the data flow speed data set that are less than the expected data flow speed; The logic for obtaining the network disconnection response anomaly coefficient is as follows: Obtain network disconnection events in the area where the terminal device is located within the T time period, and record the timestamp S1 of each time the terminal device loses connection with the network and the timestamp S2 of each time it reconnects to the network, calculate the network disconnection response time sc, the expression is as follows sc = S2-S1, take the multiple groups of network disconnection response time obtained within the T time period as feature values, use the LOF algorithm to calculate the local outlier factor of each feature value, and mark it as LOF (sc u ), where sc u It represents the u-th network disconnection event, u={1,2,3…v}, v is a positive integer, and the average value of the local outlier factor is calculated as follows: In the formula It represents the average value of the local outlier factor. The standard deviation of the local outlier factor is calculated based on the average value of the local outlier factor. The expression is as follows Where LOFB represents the standard deviation of the local outlier factor; Calculate the network disconnection response anomaly coefficient, the expression is as follows Where wdl represents the network disconnection response anomaly coefficient.

2. The marketing system omni-channel unified access system according to claim 1, characterized in that: The data flow speed fluctuation delay coefficient and the network disconnection response anomaly coefficient are normalized, and a fluctuation degree evaluation model is constructed based on the normalized data flow speed fluctuation delay coefficient and the network disconnection response anomaly coefficient to generate the fluctuation degree impact index bdy. The model is based on the following formula: Wherein a1 and a2 represent preset proportional coefficients of data flow speed fluctuation delay coefficient and network disconnection response abnormality coefficient respectively, and both a1 and a2 are greater than 0.

3. The marketing system omni-channel unified access system according to claim 2, characterized in that: The fluctuation degree impact index is compared with the preset fluctuation degree impact index threshold, and the impact of the hardware performance of the terminal device and the regional network environment on the abnormal fluctuation of the operation of the omni-channel unified access system is classified as follows: If the volatility impact index is greater than the volatility impact index threshold, an abnormal volatility impact signal is generated; If the volatility impact index is less than or equal to the volatility impact index threshold, there is no need to generate an abnormal volatility impact signal.

4. The marketing system omni-channel unified access system according to claim 3, characterized in that: Count the number of abnormal fluctuation impact signals generated within the T time period, and calculate the proportion of abnormal fluctuation impact signals. The expression is as follows In the formula, yzb represents the proportion of abnormal fluctuation impact signals, cas represents the number of times abnormal fluctuation impact signals are generated within the time period T, and fam represents the number of terminal devices that users use to log in to the omni-channel unified access system within the time period T.

5. The marketing system omni-channel unified access system according to claim 4, characterized in that: Obtain the proportion of abnormal fluctuations affecting signals calculated in subsequent multiple T time periods, and establish a data set of abnormal fluctuations affecting signals, and mark the data set of abnormal fluctuations affecting signals as YCZ = {yzb p }, where p = {1, 2, 3…q}, q is a positive integer; Calculate the outlier value of the abnormal fluctuation impact signal ratio numbered q in the data set. The expression is as follows In the formula, yzb q It represents the proportion of abnormal fluctuations affecting signals numbered q, and wq represents the average value of the proportion of abnormal fluctuations affecting signals in the data set. The expression is as follows ql represents the standard deviation of the proportion of abnormal fluctuations affecting signals in the data set, and the expression is as follows 6. The marketing system omni-channel unified access system according to claim 5, characterized in that: The outlier value of the abnormal fluctuation impact signal ratio numbered q in the abnormal fluctuation impact signal ratio data set is compared with the preset control intervention threshold, and control intervention is performed on whether the omni-channel unified access system generates maintenance information. The specific situation is as follows: If the outlier value of the abnormal fluctuation influence signal ratio numbered q in the abnormal fluctuation influence signal ratio data set is greater than the control intervention threshold, a control intervention signal is generated; If the outlier value of the abnormal fluctuation influence signal ratio numbered q in the abnormal fluctuation influence signal ratio data set is less than or equal to the control intervention threshold, there is no need to generate a control intervention signal.

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