Integrated scheduling optimization resource allocation method and system for multi-source data

Abstract

The invention discloses an integrated scheduling optimization resource allocation method and system for multi-source data, relates to the technical field of resource allocation, and aims to judge whether current equipment supports payment or not based on a health state and a network state, predict a jump response duration increase trend of a payment project and improve the payment efficiency. Dividing all payment items into a traffic priority set and a traffic transfer-in set based on a prediction result, comprehensively analyzing the payment amount of the payment items and the jump response duration of the payment items to generate priority indexes for the payment items, and generating a priority index for the payment items in the traffic priority set and the traffic transfer-in set, and performing priority ranking on all payment items based on the priority indexes, and performing flow distribution on a flow priority set and a flow transfer-in set according to the priority ranking. According to the invention, the distribution system can perform priority ranking on a plurality of payment items generated at the same time in combination with the overall payment state of the user, so that traffic resource scheduling is performed more reasonably, and the user retention rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to a method and system for integrated scheduling and optimization of resource allocation for multi-source data. Background Art

[0002] In order to improve the convenience of online shopping, shopping web pages usually support web payment, that is, after the user selects a product, he / she will be redirected to the payment page to make the payment. When facing the peak period of web payment, there may be multiple payment items in the same time period. In the prior art, a unified jump delay time is usually adopted for all payment items. However, when the user's payment device or the current network condition is abnormal, the existing distribution system has no priority sorting for the payment items in the same time period, that is, it is impossible to bias more traffic to users with a good overall payment status, which may cause users with a good overall payment status to wait too long when paying, thereby reducing the user experience and causing user loss.

[0003] Based on this, the present invention proposes an integrated scheduling and optimization resource allocation method and system for multi-source data, which can prioritize multiple payment items generated at the same time based on the user's overall payment status, thereby more reasonably scheduling traffic resources and improving user retention rate. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing resource allocation for integrated scheduling of multi-source data, so as to solve the deficiencies in the background technology.

[0005] In order to achieve the above object, the present invention provides the following technical solution: an integrated scheduling optimization resource allocation method for multi-source data, the allocation method comprising the following steps:

[0006] The allocation system regularly monitors the payment status of shopping web pages. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then obtain the health status of the device and the network status through the security management software;

[0007] Based on the health status and network status, determine whether the current device supports payment. If not, interrupt the payment. If supported, predict the increasing trend of the payment item jump response time. Based on the prediction results, divide all payment items into a traffic priority set and a traffic transfer set.

[0008] A priority index is generated for the payment item based on a comprehensive analysis of the payment amount of the payment item and the jump response time of the payment item. All payment items are prioritized in the traffic priority set and the traffic transfer set based on the priority index, and traffic is allocated to the traffic priority set and the traffic transfer set according to the priority ranking.

[0009] In a preferred embodiment, judging whether the current device supports payment based on the health status and the network status includes the following steps:

[0010] The hardware anomaly index, software anomaly index, network anomaly index and payment security index are calculated comprehensively to obtain the warning coefficient.

[0011] Compare the obtained warning coefficient with the preset warning threshold, which is used to determine whether the payment can be made;

[0012] If the warning coefficient is less than or equal to the warning threshold, it is determined that the payment can be carried out. If the warning coefficient is greater than the warning threshold, it is determined that the payment cannot be carried out.

[0013] In a preferred embodiment, the hardware anomaly index, software anomaly index, network anomaly index and payment security index are comprehensively calculated to obtain the warning coefficient, which is expressed as:

[0014] In the formula, js z is the warning coefficient, e (μ+ε) For health status data, is the network status data, α and β are the adjustment coefficients of health status data and network status data respectively, and α and β are both greater than 0, μ, ε, st, and δ are the hardware anomaly index, software anomaly index, network anomaly index, and payment security index respectively.

[0015] In a preferred embodiment, the hardware anomaly index, software anomaly index and network anomaly index that affect the payment item jump response time are comprehensively calculated to obtain the adjustment coefficient, and the expression is:

[0016] In the formula, TJ z is the adjustment coefficient, C is the correction factor, and C=5.236, μ, ε, st are the hardware anomaly index, software anomaly index, and network anomaly index respectively, γ1, γ2, γ3 are the weights of the hardware anomaly index, software anomaly index, and network anomaly index respectively, and γ1+γ2+γ3=1;

[0017] The adjustment coefficient is compared with a preset adjustment threshold, and the adjustment threshold is used to determine whether the jump response time needs to be dynamically adjusted;

[0018] If the adjustment coefficient is less than or equal to the adjustment threshold, it is determined that there is no need to dynamically adjust the jump response time. If the adjustment coefficient is greater than the adjustment threshold, it is determined that the jump response time needs to be dynamically adjusted. The adjustment algorithm is:

[0019] In the formula, xc newTo adjust the jump response time, xc old is the initial jump response time, TJ z is the adjustment coefficient, H is the adjustment threshold;

[0020] If the adjusted jump response time of the payment item is greater than the time threshold, it is predicted that the jump response time of the payment item will increase quickly. If the adjusted jump response time of the payment item is less than or equal to the time threshold, it is predicted that the jump response time of the payment item will increase slowly.

[0021] In a preferred embodiment, comprehensively analyzing the payment amount of the payment item and the jump response time of the payment item to generate a priority index for the payment item includes the following steps:

[0022] Get the payment amount of the payment item, normalize the payment amount, map the value range of the payment amount to [0,1], get the normalized value of the payment amount, and calculate the priority index by combining the normalized value of the payment amount with the adjusted jump response time. The expression is:

[0023] In the formula, yxz is the priority index, je gih is the normalized value of the payment amount, To adjust the jump response time.

[0024] In a preferred embodiment, traffic allocation is performed for the traffic priority set and the traffic transfer set according to the priority sorting, including the following steps:

[0025] In the traffic priority set, all payment items are prioritized from large to small according to the priority index. In the traffic transfer set, all payment items are prioritized from large to small according to the priority index. The traffic priority of the traffic priority set is greater than the priority of the traffic transfer set, and traffic allocation is performed based on the sorting results of the payment items in the traffic priority set and the traffic transfer set.

[0026] In a preferred embodiment, the distribution system regularly monitors the payment status of the shopping webpage. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered, including the following steps:

[0027] In the same time period, the number of payment items generated is obtained. When the number of payment items is greater than or equal to the quantity threshold, it is determined that there is a payment warning;

[0028] In the same time period, the current traffic usage is obtained. When the traffic usage is greater than or equal to the usage threshold, it is determined that there is a traffic warning;

[0029] When there is a payment warning or traffic warning, the priority sorting mechanism is triggered.

[0030] Integrated scheduling and optimization resource allocation system, including mechanism trigger module, payment judgment module, response time prediction module, priority sorting module, and traffic allocation module;

[0031] Mechanism trigger module: regularly monitors the payment status of shopping web pages. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then obtain the health status of the device and the network status through the security management software;

[0032] Payment judgment module: judge whether the current device supports payment based on health status and network status. If not, the payment is interrupted;

[0033] Response time prediction module: If supported, predict the increasing trend of payment item jump response time, and divide all payment items into a traffic priority set and a traffic transfer set based on the prediction results;

[0034] Priority sorting module: Comprehensively analyzes the payment amount of the payment item and the jump response time of the payment item to generate a priority index for the payment item, and prioritizes all payment items in the traffic priority set and traffic transfer set based on the priority index:

[0035] Traffic allocation module: allocates traffic to the traffic priority set and the traffic transfer set according to the priority sorting.

[0036] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0037] The present invention obtains the health status and network status of the device through security management software, first determines whether the current device supports payment based on the health status and network status, predicts the increasing trend of the jump response time of the payment item, divides all payment items into a flow priority set and a flow transfer set based on the prediction results, comprehensively analyzes the payment amount of the payment item and the jump response time of the payment item to generate a priority index for the payment item, and prioritizes all payment items in the flow priority set and the flow transfer set based on the priority index, and allocates traffic to the flow priority set and the flow transfer set according to the priority ranking. The allocation system can prioritize multiple payment items generated at the same time in combination with the overall payment status of the user, so as to more reasonably schedule traffic resources and thus improve user retention rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0039] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0041] Example 1: Please refer to Figure 1 As shown, the integrated scheduling optimization resource allocation method for multi-source data in this embodiment includes the following steps:

[0042] The allocation system regularly monitors the payment status of the shopping webpage. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then the health status and network status of the device are obtained through the security management software. First, based on the health status and network status, it is determined whether the current device supports payment. If not, the payment is interrupted. If supported, the increasing trend of the jump response time of the payment item is predicted. Based on the prediction results, all payment items are divided into a traffic priority set and a traffic transfer set. The payment amount of the payment item and the jump response time of the payment item are comprehensively analyzed to generate a priority index for the payment item. In the traffic priority set and the traffic transfer set, all payment items are prioritized based on the priority index, and traffic is allocated to the traffic priority set and the traffic transfer set according to the priority sorting.

[0043] This application obtains the health status and network status of the device through security management software, first determines whether the current device supports payment based on the health status and network status, predicts the increasing trend of the jump response time of the payment item, and divides all payment items into a traffic priority set and a traffic transfer set based on the prediction results. A comprehensive analysis of the payment amount of the payment item and the jump response time of the payment item generates a priority index for the payment item, and prioritizes all payment items in the traffic priority set and the traffic transfer set based on the priority index, and allocates traffic to the traffic priority set and the traffic transfer set according to the priority ranking. The allocation system can prioritize multiple payment items generated at the same time in combination with the overall payment status of the user, so as to more reasonably schedule traffic resources and thereby improve user retention.

[0044] During the Double 11 Shopping Festival, a certain e-commerce platform experienced a peak payment period. A large number of users submitted payment requests at the same time, and the server faced huge traffic pressure. In order to ensure the efficient operation of the payment system and the payment experience of high-priority users, the platform adopted a multi-source data integrated scheduling and optimization resource allocation method.

[0045] Monitor payment status in real time and trigger alerts

[0046] During a certain period of time during the payment peak period, the platform detected a rapid increase in the number of payment items and the traffic usage was close to the threshold. The system determined that there were payment warnings and traffic warnings, triggering the priority sorting mechanism.

[0047] Payment amount: The system collects the payment amount of each payment item. For example, user A paid 100 yuan, user B paid 500 yuan, and user C paid 50 yuan.

[0048] Device health status: The device hardware and software status is collected through security management software. User A's device is running well, user B's device has insufficient hardware resources, and user C's device has a delay in loading the payment software.

[0049] Network status: collects data such as network delay, jitter, and packet loss rate. User A's network status is stable, user B's network signal is weak, and user C's network connection is frequently interrupted.

[0050] User A: The user is in good health, has a stable network, and the device supports payment.

[0051] User B: The hardware is in average condition, but the network signal is barely available and the device supports payment.

[0052] User C: The payment software is loaded abnormally and the network connection is interrupted. The device does not support payment. The system interrupts the payment request and prompts the user to try again.

[0053] The system analyzes the health status and network status of users A and B and predicts their payment jump response time:

[0054] The jump response time for user A is expected to be 2 seconds.

[0055] The jump response time for user B is expected to be 6 seconds.

[0056] Divide traffic priority set and traffic transfer set

[0057] User A's device health and network status are both excellent, payment response speed is fast, and incoming traffic is prioritized.

[0058] The device status of user B is average, the jump response time is long, and the traffic is transferred to the collection.

[0059] Generate priority index and sort

[0060] Comprehensively analyze the payment amount and payment redirect response time to generate a priority index for the payment item:

[0061] User A has a high payment amount and a short jump response time, so his priority index is the highest.

[0062] User B’s payment amount is low and the response time is long, so his priority index is low.

[0063] The system sorts the payment items in the two sets according to the priority index.

[0064] Traffic priority aggregation: Allocate more bandwidth resources to user A to ensure that his payment request is completed quickly.

[0065] Traffic transfer collection: Some traffic resources are allocated to user B, but the priority is lower than that of user A, and payment may be slightly delayed.

[0066] After user A completes the payment, the released traffic resources are allocated to user B first.

[0067] If there is any remaining traffic resource after user B completes the payment, it will be reallocated to the new incoming payment request.

[0068] User A's payment request is completed quickly, ensuring a high-quality experience for users who make large payments.

[0069] Although user B's payment request was delayed, it was successfully completed and resources were used reasonably.

[0070] User C's payment was interrupted in advance due to abnormal device status, avoiding waste of system resources.

[0071] The overall payment efficiency is improved, the payment experience of high-priority users is guaranteed, and the system traffic is reasonably optimized and allocated.

[0072] This technical solution monitors the payment status in real time, combines the device health status, network status and payment amount, and dynamically optimizes resource allocation, effectively ensuring system operation efficiency and user experience during peak payment periods.

[0073] Embodiment 2: The distribution system regularly monitors the payment status of the shopping webpage. When there is a payment warning or traffic warning in a certain time period, the priority sorting mechanism is triggered, including the following steps:

[0074] In the same time period, the number of payment items generated is obtained. When the number of payment items is greater than or equal to the quantity threshold, it is determined that there is a payment warning;

[0075] In the same time period, the current traffic usage is obtained. When the traffic usage is greater than or equal to the usage threshold, it is determined that there is a traffic warning;

[0076] When there is a payment warning or traffic warning, the priority sorting mechanism is triggered.

[0077] For example:

[0078] A shopping website experienced a peak in payments during the Double 11 promotion period, and the payment system regularly monitored payment items and traffic usage;

[0079] Get the number of payment items:

[0080] The system detected that 3,000 payment items were submitted within the one-minute period from 10:00 to 10:01.

[0081] The system preset payment item quantity threshold is 2,000.

[0082] Check result: 3000≥2000, judged as payment warning.

[0083] Get traffic usage:

[0084] At the same time, the system detects that the current traffic usage is 80%.

[0085] The system preset traffic usage threshold is 75%.

[0086] Inspection result: 80% ≥ 75%, which is considered as a traffic warning.

[0087] Triggering the prioritization mechanism:

[0088] When there is a payment warning or traffic warning, the system starts the priority sorting mechanism.

[0089] Obtain the payment amount for each payment item, and then obtain the health status of the device and the network status through the security management software, including the following steps:

[0090] Obtain the health status of the device through security management software. The health status data includes hardware anomaly index and software anomaly index. The network status data includes network anomaly index and payment security index.

[0091] The calculation formula of the hardware anomaly index is: In the formula, 0~T is the monitoring period, H t It represents the hardware anomaly index value collected at time t. The hardware anomaly index value is obtained by adding the CPU usage rate, the memory usage rate and the remaining storage capacity.

[0092] The calculation formula of software anomaly index is: Where, is the frequency of occurrence of software error logs, is the abnormal frequency of payment module response time, and is the alarm frequency of security software.

[0093] The calculation formula of network anomaly index is: Where, L t is the network latency abnormal value, J t is the network jitter abnormal value, P t is the packet loss rate.

[0094] The calculation formula of payment security index is: In the formula, R t is the rate of security vulnerability discovery in the network, U t is the abnormal rate of network encryption protocol, W t Frequent disconnection rate.

[0095] 1. Relationship between hardware anomaly index and overall payment status of the device

[0096] The hardware anomaly index reflects the degree of abnormality in the hardware operation status of the device itself. The higher the index, the more serious the hardware problem of the device, and the greater the impact on payment.

[0097] Influencing factors: The device's CPU utilization, memory usage, insufficient storage space, battery power and other hardware resources directly determine the smoothness of the payment module's operation.

[0098] Positive relationship: The lower the hardware anomaly index, the better the payment response capability of the device and the higher the payment success rate; conversely, the higher the hardware anomaly index, the payment process may be stuck, timed out or even fail.

[0099] Overall payment status: A high hardware anomaly index will reduce the priority of payment requests, and the system needs to limit its traffic allocation.

[0100] 2. Relationship between software anomaly index and overall payment status of the device

[0101] The software anomaly index assesses the degree of anomalies in the operating system and payment-related software running on the device.

[0102] Influencing factors: whether the payment module is loaded normally, whether the payment software version is the latest, whether the security software is effective, and the frequency of error logs, etc.

[0103] Positive relationship: The lower the software anomaly index, the more stable the payment system operation, and the higher the payment security and response speed; conversely, a high anomaly index may lead to payment failure or security issues.

[0104] Overall payment status: When the software anomaly index is too high, the operating capacity of the payment device will be limited and it may be downgraded to a lower priority set for processing.

[0105] 3. Relationship between network anomaly index and overall payment status of the device

[0106] The network anomaly index reflects the quality and stability of the device's current network connection.

[0107] Influencing factors: including network delay, packet loss rate, network jitter, signal strength and stability, etc.

[0108] Positive relationship: The lower the network anomaly index, the more stable the network connection and the higher the success rate of payment requests; conversely, a high network anomaly index will cause payment requests to be delayed, failed or unable to be sent.

[0109] Overall payment status: When the network anomaly index is high, the payment request processing of the device may be delayed or even marked as a low-priority item that needs to be retried.

[0110] 4. Relationship between payment security index and overall payment status of the device

[0111] The Payment Security Index measures the security of the current payment network. The higher the index, the better the security and the smaller the negative impact on payment.

[0112] Influencing factors: whether the payment connection is encrypted, whether the network is trustworthy (such as public Wi-Fi), and whether there are vulnerabilities or suspicious behaviors (such as man-in-the-middle attacks).

[0113] Positive relationship: The higher the payment security index, the safer the payment environment and the more reliable the overall payment status; conversely, a low index may lead to payment failure, account leakage or malicious attack risks.

[0114] Overall payment status: Devices with a low payment security index will usually be downgraded by the system, and their payment requests may even be directly rejected to ensure the overall system security.

[0115] Based on the health status and network status, determine whether the current device supports payment. If not, interrupt the payment, including the following steps:

[0116] The hardware anomaly index, software anomaly index, network anomaly index and payment security index are calculated comprehensively to obtain the warning coefficient, which is expressed as follows: In the formula, js z is the warning coefficient, e (μ+ε) For health status data, is the network status data, α and β are the adjustment coefficients of health status data and network status data respectively, and α and β are both greater than 0, μ, ε, st, and δ are the hardware anomaly index, software anomaly index, network anomaly index, and payment security index respectively;

[0117] The larger the warning coefficient, the less likely the payment is to be made. The obtained warning coefficient is compared with the preset warning threshold. The warning threshold is used to determine whether the payment is supported. If the warning coefficient is less than or equal to the warning threshold, the payment is supported. If the warning coefficient is greater than the warning threshold, the payment is not supported.

[0118] If supported, the increasing trend of payment item jump response time is predicted, including the following steps:

[0119] The adjustment coefficient is obtained by comprehensively calculating the hardware anomaly index, software anomaly index and network anomaly index that affect the response time of the payment item jump. The expression is:

[0120] In the formula, TJ z is the adjustment coefficient, C is the correction factor, and C=5.236, μ, ε, st are the hardware anomaly index, software anomaly index, and network anomaly index respectively, γ1, γ2, γ3 are the weights of the hardware anomaly index, software anomaly index, and network anomaly index respectively, and γ1+γ2+γ3=1;

[0121] The adjustment coefficient reflects the overall payment status of the device. The larger the adjustment coefficient, the worse the hardware, software or network status of the device, and the longer the response time for payment item jump.

[0122] The adjustment coefficient is compared with the preset adjustment threshold. The adjustment threshold is used to determine whether the jump response time needs to be dynamically adjusted. If the adjustment coefficient is less than or equal to the adjustment threshold, it is determined that the jump response time does not need to be dynamically adjusted. If the adjustment coefficient is greater than the adjustment threshold, it is determined that the jump response time needs to be dynamically adjusted. The adjustment algorithm is:

[0123] In the formula, xc new To adjust the jump response time, xc old is the initial jump response time, TJ z is the adjustment coefficient, H is the adjustment threshold;

[0124] If the adjusted jump response time of the payment item is greater than the time threshold, it is predicted that the jump response time of the payment item will increase quickly. If the adjusted jump response time of the payment item is less than or equal to the time threshold, it is predicted that the jump response time of the payment item will increase slowly.

[0125] Based on the prediction results, all payment items are divided into a traffic priority set and a traffic transfer set, including the following steps:

[0126] When the predicted increase trend of the payment item jump response time is fast, the payment item is classified into the traffic priority set. When the predicted increase trend of the payment item jump response time is slow, the payment item is classified into the traffic transfer set. The traffic allocation priority is: the traffic priority set is greater than the traffic transfer set.

[0127] A priority index is generated for the payment item by comprehensively analyzing the payment amount of the payment item and the jump response time of the payment item, and all payment items are prioritized in the traffic priority set and the traffic transfer set based on the priority index, and traffic is allocated to the traffic priority set and the traffic transfer set respectively according to the priority ranking, including the following steps:

[0128] Get the payment amount of the payment item, normalize the payment amount, map the value range of the payment amount to [0,1], get the normalized value of the payment amount, and calculate the priority index by combining the normalized value of the payment amount with the adjusted jump response time. The expression is:

[0129] In the formula, yxz is the priority index, je gih is the normalized value of the payment amount, The jump response time after adjustment is the time taken for the payment item to be redirected, that is, the larger the payment amount of the payment item, the larger the priority index; the larger the jump response time after adjustment of the payment item, the smaller the priority index. Based on this comprehensive analysis, on the one hand, more traffic can be allocated to payment items with larger payment amounts; on the other hand, more traffic can be allocated to payment items with good overall device status to complete payment in advance (for example, when the overall status of the device is poor, even if more traffic is allocated, the payment efficiency will not be improved).

[0130] In the traffic priority set, all payment items are prioritized from large to small according to the priority index. In the traffic transfer set, all payment items are prioritized from large to small according to the priority index. The traffic priority of the traffic priority set is greater than the priority of the traffic transfer set, and traffic is allocated according to the ranking results of the payment items in the traffic priority set and the traffic transfer set.

[0132] Embodiment 3: The integrated scheduling optimization resource allocation system for multi-source data described in this embodiment includes a mechanism trigger module, a payment judgment module, a response time prediction module, a priority sorting module, and a traffic allocation module;

[0133] Mechanism trigger module: regularly monitors the payment status of shopping web pages. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then obtain the health status and network status of the device through the security management software. The health status and network status are sent to the payment judgment module;

[0134] Payment judgment module: judges whether the current device supports payment based on health status and network status. If not, the payment is interrupted and the judgment result is sent to the response time prediction module;

[0135] Response time prediction module: If supported, predict the increasing trend of payment item jump response time, divide all payment items into a traffic priority set and a traffic transfer set based on the prediction results, and send the traffic priority set and the traffic transfer set to the priority sorting module;

[0136] Priority sorting module: Comprehensively analyzes the payment amount of the payment item and the jump response time of the payment item to generate a priority index for the payment item, and prioritizes all payment items in the traffic priority set and the traffic transfer set based on the priority index. The priority sorting results are sent to the traffic allocation module:

[0137] Traffic allocation module: allocates traffic to the traffic priority set and the traffic transfer set according to the priority sorting.

[0138] 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.

[0139] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0140] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An integrated scheduling and optimization resource allocation method for multi-source data, characterized by: The allocation method comprises the following steps: The allocation system regularly monitors the payment status of shopping web pages. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then obtain the health status of the device and the network status through the security management software; Based on the health status and network status, determine whether the current device supports payment. If not, interrupt the payment. If supported, predict the increasing trend of the payment item jump response time, and divide all payment items into a traffic priority set and a traffic transfer set based on the prediction results; A priority index is generated for the payment item based on a comprehensive analysis of the payment amount of the payment item and the jump response time of the payment item. All payment items are prioritized in the traffic priority set and the traffic transfer set based on the priority index, and traffic is allocated to the traffic priority set and the traffic transfer set according to the priority ranking.

2. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 1 is characterized in that: Judging whether the current device supports payment based on the health status and network status includes the following steps: The hardware anomaly index, software anomaly index, network anomaly index and payment security index are calculated comprehensively to obtain the warning coefficient. Compare the obtained warning coefficient with the preset warning threshold, which is used to determine whether the payment can be made; If the warning coefficient is less than or equal to the warning threshold, it is determined that the payment can be carried out. If the warning coefficient is greater than the warning threshold, it is determined that the payment cannot be carried out.

3. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 2 is characterized in that: The hardware anomaly index, software anomaly index, network anomaly index and payment security index are calculated comprehensively to obtain the warning coefficient, which is expressed as follows: In the formula, js z is the warning coefficient, e (μ+ε) For health status data, is the network status data, α and β are the adjustment coefficients of health status data and network status data respectively, and α and β are both greater than 0, μ, ε, st, and δ are the hardware anomaly index, software anomaly index, network anomaly index, and payment security index respectively.

4. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 3 is characterized in that: The adjustment coefficient is obtained by comprehensively calculating the hardware anomaly index, software anomaly index and network anomaly index that affect the response time of the payment item jump. The expression is: In the formula, TJ z is the adjustment coefficient, C is the correction factor, and C=5.236, μ, ε, st are the hardware anomaly index, software anomaly index, and network anomaly index respectively, γ1, γ2, γ3 are the weights of the hardware anomaly index, software anomaly index, and network anomaly index respectively, and γ1+γ2+γ3=1; The adjustment coefficient is compared with a preset adjustment threshold, and the adjustment threshold is used to determine whether the jump response time needs to be dynamically adjusted; If the adjustment coefficient is less than or equal to the adjustment threshold, it is determined that there is no need to dynamically adjust the jump response time. If the adjustment coefficient is greater than the adjustment threshold, it is determined that the jump response time needs to be dynamically adjusted. The adjustment algorithm is: In the formula, xc new To adjust the jump response time, xc old is the initial jump response time, TJ z is the adjustment coefficient, H is the adjustment threshold; If the adjusted jump response time of the payment item is greater than the time threshold, it is predicted that the jump response time of the payment item will increase quickly. If the adjusted jump response time of the payment item is less than or equal to the time threshold, it is predicted that the jump response time of the payment item will increase slowly.

5. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 4 is characterized in that: The payment amount of the payment item and the jump response time of the payment item are comprehensively analyzed to generate a priority index for the payment item, including the following steps: Get the payment amount of the payment item, normalize the payment amount, map the value range of the payment amount to [0,1], get the normalized value of the payment amount, and calculate the priority index by combining the normalized value of the payment amount with the adjusted jump response time. The expression is: In the formula, yxz is the priority index, je gih is the normalized value of the payment amount, To adjust the jump response time.

6. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 5 is characterized in that: Traffic is allocated to the traffic priority set and the traffic transfer set according to the priority sorting, including the following steps: In the traffic priority set, all payment items are prioritized from large to small according to the priority index. In the traffic transfer set, all payment items are prioritized from large to small according to the priority index. The traffic priority of the traffic priority set is greater than the priority of the traffic transfer set, and traffic allocation is performed based on the sorting results of the payment items in the traffic priority set and the traffic transfer set.

7. The integrated scheduling and optimization resource allocation method for multi-source data according to claim 6 is characterized by: The allocation system regularly monitors the payment status of the shopping webpage. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered, including the following steps: In the same time period, the number of payment items generated is obtained. When the number of payment items is greater than or equal to the quantity threshold, it is determined that there is a payment warning; In the same time period, the current traffic usage is obtained. When the traffic usage is greater than or equal to the usage threshold, it is determined that there is a traffic warning; When there is a payment warning or traffic warning, the priority sorting mechanism is triggered.

8. An integrated scheduling and optimization resource allocation system, used to implement the allocation method according to any one of claims 1 to 7, characterized in that: It includes mechanism trigger module, payment judgment module, response time prediction module, priority sorting module and traffic allocation module; Mechanism trigger module: regularly monitors the payment status of shopping web pages. When there is a payment warning or traffic warning in a certain period of time, the priority sorting mechanism is triggered to obtain the payment amount of each payment item, and then obtain the health status of the device and the network status through the security management software; Payment judgment module: judge whether the current device supports payment based on health status and network status. If not, the payment is interrupted; Response time prediction module: If supported, predict the increasing trend of payment item jump response time, and divide all payment items into a traffic priority set and a traffic transfer set based on the prediction results; Priority sorting module: Comprehensively analyzes the payment amount of the payment item and the jump response time of the payment item to generate a priority index for the payment item, and prioritizes all payment items in the traffic priority set and traffic transfer set based on the priority index: Traffic allocation module: allocates traffic to the traffic priority set and the traffic transfer set according to the priority sorting.

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