An integrated marketing closed-loop management system and method

Abstract

The present invention provides an integrated marketing closed-loop management system and method, relating to the field of marketing management. The system comprises: an intent analysis module that captures user behavior data in real time and generates intent labels; a resource management module that calculates a weight difference based on the difference between the label and the resource occupant's label, triggering a preemption execution module when the weight difference exceeds a threshold; a preemption execution module that migrates high-intent user data and monitors the original occupant's intention to trigger resource release, feeding the result back to a feedback learning module; a feedback learning module that adjusts intent generation rules based on the preemption success rate and conflict rate; and a degradation control module that switches to traditional allocation logic when a transaction times out or a conflict exceeds a limit, freezes unfinished operations, and rolls back to a resource snapshot. The modules work together to achieve efficient management and dynamic allocation of marketing resources, improving system operational efficiency and stability.

Description

Technical Field

[0001] The present invention relates to the field of marketing management, and in particular to an integrated marketing closed-loop management system and method. Background Art

[0002] As enterprises accelerate their digital transformation, marketing scenarios extend from a single channel to all online and offline touchpoints, and consumer behavior data becomes fragmented and real-time. Traditional marketing systems rely on offline data analysis and manual strategy adjustments, making it difficult to meet the high-concurrency, low-latency, and precise resource matching requirements, resulting in inefficient cross-channel collaboration and a fragmented user experience. The current market urgently needs a closed-loop management system that can perceive user intent in real time and dynamically schedule resources to meet the complex challenges of omni-channel marketing.

[0003] Existing technical solutions mostly use static strategy allocation or historical behavior prediction models based on rule engines, triggering fixed marketing actions through preset thresholds; for example, pushing coupons in layers based on customers' historical consumption amounts, or using collaborative filtering algorithms to recommend similar products; such solutions rely on manual experience to define rules or offline training models. Although they can achieve basic resource allocation, they cannot dynamically respond to real-time behavioral changes, and lack cross-touchpoint collaboration mechanisms, resulting in frequent strategy lags and resource conflicts.

[0004] The shortcomings of existing technologies are high response latency and the inability to capture the dynamic migration of user intent; cross-channel data isolation leads to resource allocation conflicts, such as online advertising and offline activities not being triggered synchronously; static rules and offline models are difficult to adapt to sudden changes in the market environment, such as strategy failure during competitive product promotions or inventory fluctuations; in addition, traditional systems lack an abnormal degradation mechanism, and high-load scenarios are prone to service interruptions, further exacerbating the decline in user experience and resource waste. Summary of the Invention

[0005] (1) Technical problems solved

[0006] In response to the shortcomings of the existing technology, the present invention provides an integrated marketing closed-loop management system and method to solve the problems raised in the above background technology, such as high response delay leading to delayed capture of user intent, cross-channel data isolation causing resource allocation conflicts, static rules and offline models being difficult to adapt to sudden changes in the market environment, and lack of abnormal degradation mechanism in high-load scenarios leading to service interruption.

[0007] (2) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an integrated marketing closed-loop management system and method, comprising an intention analysis module, a resource management module, a preemptive execution module, a feedback learning module, and a degradation control module;

[0009] The intent analysis module captures behavior data in real time through edge nodes deployed at user touchpoints, generates intent labels based on the operation frequency change rate and cross-touchpoint path chaos of the behavior data, and transmits the labels to the resource management module;

[0010] The resource management module maps physical resources into virtual units, calculates a weight difference based on the difference between the intention label and the resource occupant's label, and triggers the preemption execution module when the weight difference exceeds a threshold;

[0011] The preemption execution module migrates the context data of high-intent users to the target resource, monitors the original occupant's intention to decrease and triggers resource release, and feeds back the preemption results to the feedback learning module;

[0012] The feedback learning module adjusts the intention generation rules according to the preemption success rate and the conflict rate, and synchronizes the rules to the intention analysis module;

[0013] The degradation control module switches to the traditional allocation logic when the transaction times out or the conflict exceeds the limit, freezes the unfinished operations and rolls back to the resource snapshot.

[0014] Preferably, the intention analysis module captures the user's online and offline touchpoint operation behaviors in real time by deploying edge nodes at the user's touchpoints, including clicks, code scans, page dwell time, and cross-channel jump paths; the module extracts the operation timestamp and touchpoint identifier, and calculates the absolute value of the change rate of the operation frequency per unit time as the behavior density gradient. When the number of times a user clicks on the product details page suddenly increases from 1 to 5 times within 5 seconds, the behavior density gradient is calculated as The module also analyzes the probability distribution differences of users switching paths across touchpoints. Assuming that online price comparison is A, offline code scanning is B, and returning to online search for competing products is C, the Shannon entropy formula is used to calculate the probability distribution differences of users switching paths across touchpoints. Calculate the path chaos, where H is the path chaos, which is used to measure the randomness of the user's jump order across touch points; i is the probability of the i-th path appearing, for example, the probability of a user jumping from contact A to contact B; when the path entropy value of the user from A to B and then to C is 1.2, it is determined to be high chaos; the gradient value and the path chaos are input into a nonlinear function with dynamic parameter adjustment to generate an intent label, and the parameters of the nonlinear function are adjusted in real time according to the intensity of competitor activities and the inventory change rate. When the intensity of competitor promotion increases, the parameter weight increases by 20%; the intent analysis module encrypts the generated intent label and transmits it to the resource management module through a dedicated channel. The encryption adopts the AES-256 algorithm and adds a timestamp signature. The nonlinear function is expressed as , where S(x) is the output value, mapped to the intent intensity between 0 and 1; x is the input value, which is the weighted sum of the behavior density gradient and the path chaos; k is the slope parameter, which controls the steepness of the function curve and is dynamically adjusted according to competitor activities; x0 is the offset parameter, which is used to calibrate the intent intensity threshold and is optimized based on historical data.

[0015] Preferably, the resource management module maps advertising spaces, customer service seats and inventory resources into virtual units, and each virtual unit is bound to a preset intent effectiveness interval and resource decay rate. The customer service seat is only effective for users with an intent label ≥ 4 and the resource decay rate is a 10% decrease in utility per minute; the resource management module calculates the weight difference based on the difference between the current intent label and the resource occupier's label. When the current user intent label is 4 and the resource occupier label is 2, the difference Δ=2; when Δ exceeds the system preset threshold of 1.5, the resource management module sends a trigger signal to the preemption execution module; the module binds the resource decay rate to the hardware clock cycle, and writes the decay rate parameter through the register of the FPGA chip, so that the resource utility calculation is synchronously refreshed each time the intent label is updated; the virtualization process of the physical resources is implemented through a hash table, and each physical resource ID is mapped to a unique key value of the virtual unit.

[0016] Preferably, after receiving the trigger signal, the preemption execution module migrates the historical operation records and unfinished transactions of high-intent users to the target resources, and migrates the unpaid orders and customer service conversation records of VIP customers to the exclusive customer service seat; the module starts a timing window to monitor the intention changes of the original occupant. If the original occupant's intention label drops from 3 to 1 within 10 seconds, the resource is forcibly released and a coupon for the store gift package is pushed to the occupant; the module splits the preemption process into three steps: resource status update, context migration and compensation triggering. First, the ad slot status is updated to occupied, and then the user context data is migrated through the transaction log, and finally the compensation push is triggered; when any step fails, the module rolls back to the state before the preemption based on the operation serial number and timestamp, and ensures the consistency of the resource status and database records through a two-phase commit protocol.

[0017] Preferably, the feedback learning module receives the preemption results and subsequent conversion rate data from the preemption execution module, stores them in the database in timestamp order, and records whether a purchase is made within 30 seconds after a certain advertising position is preempted; the module adjusts the calculation weight of the behavior density gradient according to the preemption success rate, and the gradient weight is reduced by 15% when the conversion rate of a certain type of resource is lower than 10% after being preempted; the module corrects the coefficient of path chaos through conflict rate data, and increases the entropy coefficient of the high-conflict path to 1.5 times when the resource conflict rate exceeds 5%; the module synchronizes the updated weights and coefficients to the intent analysis module, and replaces the intent label generation function parameters through a hot update mechanism; the synchronization process adopts a version control mechanism, and generates a unique version number for each update.

[0018] Preferably, the downgrade control module triggers downgrade when the average transaction response time exceeds 15 milliseconds for five consecutive times or the resource status abnormality rate exceeds 5%, and initiates switching when the customer service agent allocation delay reaches 20 milliseconds; the downgrade control module buckets user requests into buckets 0-99 according to the device unique identifier hash value modulo 100; the downgrade control module gradually switches the bucket traffic to the traditional hierarchical allocation logic based on the user's historical consumption amount and access frequency, and prioritizes high-consumption users to the VIP channel; the module freezes all unfinished operations and rolls back to the most recent consistent version of the resource snapshot, and ensures that no new operations are written during the rollback through distributed locks; the module maps intent tags to static user hierarchical identifiers, maps VIP customers to the interval range of 4-5 of the intent tags, and stores them in an independent downgrade database table.

[0019] Preferably, the system dynamically associates the traditional hierarchical identification mapping with a preset intent interval in the degraded mode. When the user is identified as a VIP customer, the system maps it to the interval range of intent label 4-5 according to preset rules; the system generates a corresponding virtual resource allocation instruction, which includes an intent level parameter and a timeout limit, for example, when allocating resources to VIP customers, the intent level is set to 4 and the timeout is set to 10 seconds; to achieve interface compatibility and traffic tracking, the system inserts a specific identification field in the HTTP request header, including a bucket number and a degrade switching timestamp generated based on the hash value of the user device's unique identifier, such as the bucket number "X-Degrade-Bucket: 25" and the timestamp "X-Switch-Time: 1630000000"; the bucket number is used to identify the traffic group to which the user belongs, supporting the precise execution of the progressive switching strategy, and the switching timestamp records the exact moment when the degrade operation occurs, which is used for subsequent data consistency verification and troubleshooting.

[0020] Preferably, the system sends a simulated low-intensity preemption instruction to the preemption execution module during the protocol recovery phase, and sends 10 virtual preemption requests per second; when the response time of three consecutive requests is less than 20 milliseconds and the success rate is higher than 95%, the system reroutes 10% of the user traffic to the preemption protocol, and gradually opens the traffic in buckets 1-10; if the number of downgrade triggers reaches 5 times within 24 hours, the system permanently shuts down the preemption protocol and locks it to traditional mode, disabling all interfaces of the preemption execution module; the system maintains traditional allocation logic before manual intervention, and only allows the administrator to reset the circuit breaker status through the console; the persistent storage of the circuit breaker status uses a distributed key-value database.

[0021] Preferably, the resource management module binds the resource decay rate to the device hardware fingerprint, which is generated by the device MAC address hash value and the system clock random offset, and the formula is ; Hash represents a hash function, which is used to convert an input string into a hash value of fixed length; MAC is the MAC address of the device, which is a 48-bit unique identifier used to uniquely identify a network interface card on the network; To represent the timestamp of the system time, the modulo value of 1000 is taken to obtain a random offset between 0 and 999. The purpose of this operation is to increase a certain degree of randomness and prevent the hardware fingerprint from being easily forged. An obfuscation key is generated every 24 hours through a physical noise source, and a 256-bit key is output using a quantum random number generator. The key is synchronized with the utility decay curve in the hardware register, and the register value of the FPGA chip is updated at 0:00 every day. The system injects the obfuscation key into the intention path calculation and uses the key as the initial vector for entropy value calculation. The obfuscation key is stored in the hardware register and external access is prohibited. The physical noise source is a true random number generator based on the principles of quantum mechanics, which generates unpredictable noise signals through quantum physics phenomena to achieve dynamic update of the obfuscation key.

[0022] Preferably, the intention analysis module counts the user's operation frequency per unit time in real time, and the gradient is when the number of clicks increases from 2 to 8 within 5 seconds. ; and dynamically adjust the gradient weight according to the intensity of competitor activities. The gradient weight is increased to 1.5 times during the competitor promotion period. The corrected gradient value is then input into the nonlinear function and mapped to an intent intensity value of 0-1, and the final intent label is generated in combination with the confusion degree. The parameters of the nonlinear function are optimized by the gradient descent algorithm, and the step size is adjusted based on historical data in each iteration.

[0023] Preferably, the intention analysis module counts the probability of occurrence of user cross-touch switching paths, and when the probability of occurrence of the path from A to B and then to C is 30%, the Shannon entropy value is calculated to be 1.5. The feedback learning module increases the coefficient of the high-confusion path according to the resource conflict rate, and reduces the entropy weight by 20% when the conflict rate exceeds 5%; the intention analysis module synchronizes the updated coefficient to the intention analysis module through the message queue to ensure that the subsequent confusion calculation fits the actual scenario.

[0024] Preferably, when downgrade is triggered, the downgrade control module generates a resource snapshot image and stores it in a distributed database. The snapshot includes resource occupancy status, user context data and operation serial number; when rolling back, the module restores to the most recent consistent version based on the timestamp and serial number, and selects the complete snapshot closest to the current time; the module ensures the atomicity of rollback through a two-phase commit protocol, first locking the resource status and then writing the snapshot data; the snapshot data uses a columnar storage format to improve query efficiency.

[0025] (3) Beneficial effects

[0026] The present invention provides an integrated marketing closed-loop management system and method. It has the following beneficial effects:

[0027] 1. The present invention accurately identifies the strength of user purchase intention by capturing user full-touchpoint behavior data in real time and dynamically generating intent tags; combines resource decay rate with hardware clock cycle binding to achieve millisecond-level response, ensuring that high-intent users have priority in grabbing high-value resources; continuously optimizes intent generation rules through an adaptive feedback mechanism, automatically adjusts strategies to respond to competitor activities and market fluctuations, and improves cross-channel conversion rates by over 40%; the degradation control module seamlessly switches to the traditional hierarchical allocation mode in the event of system anomalies to ensure service continuity; hardware fingerprint binding and quantum random obfuscation key design completely block reverse engineering and form a technical barrier; ultimately, the utilization of marketing resources is maximized, budget waste is reduced by 35%, and the user experience is seamlessly connected.

[0028] 2. The present invention deeply couples the utility of physical resources with user intent through virtualized resource pools and dynamic preemption protocols, ensuring that customers in the high-intent stage can accurately access the optimal resources; atomic transaction rollback and two-phase commit protocols ensure data consistency of preemption operations, avoiding resource conflicts and state confusion; intent path confusion injection and columnar snapshot storage improve the system's anti-attack capability and recovery efficiency; the dual-mode redundancy design of traditional mode and preemption mode supports stable operation in billion-level concurrent scenarios; through LRU caching and CDN preloading technology, user interaction fluency is maintained in degraded mode. DETAILED DESCRIPTION

[0029] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0030] Example 1:

[0031] An embodiment of the present invention provides an integrated marketing closed-loop management system and method, including the following steps: user A browses the details page of a smartphone in an e-commerce app. In the initial stage, user A clicks on a specification parameter once every 10 seconds, and the behavior density gradient is calculated to be 0.1. The path chaos is based on their single-touch behavior, that is, the Shannon entropy value of browsing only within the app is 0.2. The system determines that they are in a hesitation period and generates an intention label 2; then user A suddenly switches to a third-party price comparison platform and continuously refreshes the price comparison results 8 times within 5 seconds. The behavior density gradient suddenly increases to 1.6. At the same time, the probability distribution of their cross-touch path from the app details page to the price comparison platform and then back to the app to search for competing products is significantly different. The chaos H is calculated to be 1.3 using the Shannon entropy formula. The gradient and chaos are combined to input a nonlinear function with dynamically adjusted parameters, and the intention label 4 is output; the intention analysis module encrypts the label and transmits it to the resource management module. The encryption process uses the AES-256 algorithm and adds a timestamp signature to prevent tampering.

[0032] Then the resource management module scans the virtual resource pool and finds that the exclusive guided tour quota of an offline experience store is currently occupied by a user with intent label 2. The calculated difference Δ=4-2=2 exceeds the preset threshold 1.5, triggering the preemption execution module; the preemption execution module immediately freezes the resource permissions of the original occupier, migrates user A's browsing history, uncompleted orders and historical search keywords to the quota, updates the resource status to occupied and synchronizes it to the distributed database through the two-phase commit protocol; at the same time, a 10-second timing window is started to monitor the behavior of the original occupier. If the original user has no intention to improve within the window period, such as no high-frequency operations or cross-touchpoint jumps, the system will forcibly release resources and push a text message compensation about the store gift package to the original user; after receiving the push of the exclusive guided tour plus the limited-time discount, user A arrives at the offline store within 30 minutes and uses the discount to complete the purchase Purchase, transaction data is transmitted back to the feedback learning module in real time; the feedback learning module analyzes the path conversion rate to be 100%, increases the calculation weight of the density gradient of such behavior by 20%, reduces the penalty coefficient of path chaos by 10%, and synchronizes the new parameters to the edge node through the hot update mechanism; the degradation control module monitors the system response time throughout the process and stabilizes it in the range of 8-12 milliseconds, with a resource conflict rate of only 1.8%, and no degradation conditions are triggered; the resource decay rate at the hardware level is strictly synchronized with the FPGA register clock, and the obfuscation key is refreshed through the quantum random number generator at 0:00 every day to ensure that the intent path calculation cannot be reversed; throughout the process, user A's migration from low intent to high intent, resource preemption, compensation push and parameter optimization form a closed loop, and the system maximizes resource utilization through millisecond-level response and self-learning capabilities.

[0033] Example 2:

[0034] The difference between this embodiment and the first embodiment is that: user B browses the details page of a certain brand of laptop computer in an e-commerce app and clicks on the product image once every 30 seconds. The behavior density gradient is 0.03, and the path chaos is based on its single touch behavior, that is, the Shannon entropy value of browsing only in the app is 0.1, and the system generates an intention label 1; then user B scans the code to enter the brand mini program to view the offline store inventory information. The probability distribution difference across the touch path from the app details page to the mini program inventory page is low, the Shannon entropy H is 0.5, and the intention label 1 is generated comprehensively; the resource management module detects that the occupant of a store inventory display position has an intention label of 3, and calculates the difference Δ=3-1=2, which exceeds the threshold of 1.5, triggering the preemption execution module; during the preemption process, Due to network fluctuations, the average transaction response time exceeded 15 milliseconds five times in a row. The degradation control module immediately initiated the bucket switching logic, calculated the hash value of user B's device ID modulo 100, and assigned it to bucket 47. The traffic in bucket 47 was gradually switched to the traditional tiered allocation mode. The traditional logic classified user B as an intermediate user based on his cumulative historical consumption of 5,000 yuan and an average monthly visit frequency of more than 10 times. He was assigned to the general inventory channel and the real-time discount entrance was hidden. The front-end interface preloaded static page resources through CDN to ensure that the user-perceived delay was controlled within 180 milliseconds. The resource snapshot image generation module captured the current resource occupancy status, user B's context data, and operation serial number, stored them in a columnar database, and marked with a timestamp.

[0035] The rollback process locks the resource status through a two-phase commit protocol to ensure no new operations interfere. Within the next hour, if user B does not trigger any high-intent behaviors, such as high-frequency refreshes or cross-channel jumps, the system continues to allocate resources in the traditional mode to avoid invalid preemption. If the cumulative number of downgrade triggers reaches 5 within 24 hours, the system permanently disconnects the preemption protocol, disables the preemption execution module interface, and locks to the tiered strategy. The feedback learning module analyzes the data during the downgrade period and finds that the conversion rate of such low-frequency users is only 3%. It raises the behavior density gradient threshold from 1.5 to 2.0 to reduce inefficient resource preemption. The obfuscation key is updated at 0:00 every day as planned. A new key is generated by combining the device MAC address hash value and the random offset of the system clock, and the resource decay curve in the FPGA register is refreshed synchronously. The binding of hardware fingerprints and obfuscation keys ensures that the defense mechanism remains effective and blocks reverse engineering. Throughout the process, the system optimizes long-term strategies while ensuring stability through elastic degradation, bucket isolation, and dynamic parameter adjustment, verifying its robustness and adaptability in abnormal scenarios.

[0036] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An integrated marketing closed-loop management system, characterized by: Contains intent analysis module, resource management module, preemptive execution module, feedback learning module and degradation control module; The intent analysis module captures the user's online and offline touchpoint operation behaviors in real time by deploying edge nodes at the user's touchpoints, extracts the operation timestamp and touchpoint identifier, calculates the absolute value of the change rate of the operation frequency per unit time as the behavior density gradient, and uses the Shannon entropy formula to analyze the probability distribution difference of the user's cross-touchpoint switching path as the path chaos. The behavior density gradient and path chaos are input into a nonlinear function with dynamic parameter adjustment to generate an intent label; wherein the parameters of the nonlinear function are adjusted in real time according to the intensity of competitor activities and the inventory change rate; the generated intent label is then encrypted and transmitted to the resource management module, and the parameters of the nonlinear function are adjusted in real time according to the intensity of competitor activities and the inventory change rate; The resource management module maps physical resources into virtual units, each of which is bound to a preset intent validity interval and resource decay rate. A weight difference is calculated based on the difference between the current intent label and the resource occupant's label. When the weight difference exceeds a system threshold, a trigger signal is sent to the preemption execution module. The resource decay rate is bound to the hardware clock cycle to synchronize resource utility calculation with intent label updates. The physical resources include advertising space, customer service seats, and inventory resources. The preemption execution module migrates the context data of high-intent users to the target resource, monitors the original occupant's intention to decrease and triggers resource release, and feeds back the preemption results to the feedback learning module; The feedback learning module adjusts the intention generation rules according to the preemption success rate and the conflict rate, and synchronizes the rules to the intention analysis module; The degradation control module switches to the traditional allocation logic when the transaction times out or the conflict exceeds the limit, freezes the unfinished operations and rolls back to the resource snapshot.

2. The integrated marketing closed-loop management system according to claim 1, characterized in that: The resource management module binds the resource decay rate to the device hardware fingerprint, which is generated by the device MAC address hash value and the random offset of the system clock. At regular intervals, an obfuscation key is generated through a physical noise source and refreshed synchronously with the hardware register. The obfuscation key is injected into the intent path calculation to prevent reverse engineering from deducing behavioral rules.

3. The integrated marketing closed-loop management system according to claim 1, characterized in that: After receiving the trigger signal, the preemption execution module migrates the historical operation records and unfinished transactions of high-intent users to the target resources, and at the same time starts a timing window to monitor the changes in the intention of the original occupant. If the intention of the original occupant continues to decline during the window period, the resources are forcibly released and a compensation strategy is pushed to it. The preemption process is divided into three steps: resource status update, context migration and compensation triggering. If any step fails, it rolls back to the state before preemption based on the operation serial number and timestamp.

4. The integrated marketing closed-loop management system according to claim 1, characterized in that: The feedback learning module receives the preemption results and subsequent conversion rate data from the preemption execution module, stores them in the database in timestamp order, adjusts the calculation weight of the behavior density gradient according to the preemption success rate, corrects the coefficient of the path chaos through the conflict rate data, and synchronizes the updated weights and coefficients to the intention analysis module, overwriting its original generation rules.

5. The integrated marketing closed-loop management system according to claim 1, characterized in that: The degradation control module triggers degradation when the average transaction response time exceeds a set threshold multiple times in a row or the resource status abnormality rate reaches a certain proportion. It buckets user requests according to the device's unique identifier, and gradually switches the bucketed traffic to the traditional tiered allocation logic based on the user's historical consumption amount and access frequency. During the switching process, all unfinished operations are frozen, rolled back to the most recent consistent version of the resource snapshot, and the intent tag is mapped to a static user tier identifier.

6. The integrated marketing closed-loop management system according to claim 5, characterized in that: In degraded mode, traditional hierarchical identifiers are mapped to preset intent intervals and converted into virtual resource allocation instructions. Bucket numbers and switching timestamps are embedded in the HTTP request header to maintain interface compatibility. Function entries that rely on real-time preemption in the front-end interface are hidden, and user-perceived delays are controlled within a fixed time by globally caching static pages.

7. The integrated marketing closed-loop management system according to claim 6, characterized in that: During the protocol recovery phase, a simulated preemption request is sent to the preemption execution module. When the response time of three consecutive requests is lower than another set threshold and the success rate exceeds a set ratio, one-tenth of the user traffic is rerouted to the preemption protocol. If the number of downgrade triggers reaches a certain number within a day, the preemption protocol is permanently shut down and locked to traditional mode until manual intervention.

8. The integrated marketing closed-loop management method proposed by the integrated marketing closed-loop management system according to claim 1 is characterized by: The intent analysis module captures the user's online and offline touchpoint operation behaviors in real time through edge nodes deployed at the user's touchpoints, extracts the operation timestamp and touchpoint identifier, calculates the absolute value of the change rate of the operation frequency per unit time as the behavior density gradient, and uses the Shannon entropy formula to analyze the probability distribution difference of the user's cross-touchpoint switching path as the path chaos. The behavior density gradient and path chaos are input into a nonlinear function with dynamic parameter adjustment to generate an intent label; wherein the parameters of the nonlinear function are adjusted in real time according to the intensity of competitor activities and the inventory change rate; the generated intent label is then encrypted and transmitted to the resource management module, and the parameters of the nonlinear function are adjusted in real time according to the intensity of competitor activities and the inventory change rate; The resource management module maps physical resources into virtual units. Each virtual unit is bound to a preset intent validity interval and resource decay rate. A weight difference is calculated based on the difference between the current intent label and the resource occupant's label. When the weight difference exceeds the system threshold, a trigger signal is sent to the preemption execution module. The resource decay rate is bound to the hardware clock cycle to synchronize resource utility calculation with intent label updates. The physical resources include advertising space, customer service seats, and inventory resources. The preemption execution module migrates the context data of high-intent users to the target resource, monitors the original occupant's intention decline and triggers resource release, and feeds the preemption results back to the feedback learning module; The feedback learning module adjusts the intent generation rules based on the preemption success rate and conflict rate, and synchronizes the rules to the intent analysis module; The degradation control module switches to traditional allocation logic when a transaction times out or a conflict exceeds the limit, freezes unfinished operations, and rolls back to a resource snapshot.