Multi-sensor linkage monitoring method

Abstract

The invention discloses a multi-sensor linkage monitoring method, which relates to the technical field of logistics management, and comprises the following steps of: I, creating a sensor state monitoring timer, starting an independent communication thread, creating a file descriptor in a running process of the communication thread, and checking a sensor file; iI, a communication thread judges a specified type of each sensor file, and dynamically adjusts a data acquisition period and a processing sequence according to a sensor type, a data change frequency and shelf service importance; according to the method, misjudgment can be avoided under the fault-tolerant path, the monitoring robustness is enhanced, key task priority and efficient resource utilization are achieved, the accuracy of the fusion result is improved, fine-grained goods shelf state recognition is achieved, the real-time performance, stability and fault tolerance of a semantic map are guaranteed, and the controllability of service operation is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics management, and in particular to a multi-sensor linkage monitoring method. BACKGROUND

[0002] In modern warehousing and logistics systems, electronic shelves have gradually replaced traditional shelves and become an important carrier for intelligent management. With the rapid development of intelligent warehousing and intelligent logistics, the shelf monitoring system has gradually become a key link to improve the efficiency and safety of material management. Traditional single-sensor monitoring methods have limited sensing range, isolated data, and insufficient reliability, making it difficult to meet the needs of real-time, accuracy, and intelligence in complex warehouse environments. Multi-sensor linkage monitoring technology integrates weight sensors, infrared sensors, vision modules, and other multi-modal information to achieve fine identification and dynamic management of shelf status. However, the heterogeneity of multi-sensor data, the asynchronization of space and time, and the real-time processing pressure have brought new challenges to the design and implementation of the system. Existing multi-sensor linkage monitoring methods are prone to misjudgment, reducing the robustness of monitoring, and cannot guarantee key task priority and efficient resource utilization. The accuracy of the fusion result is low, and fine-grained shelf state identification cannot be achieved. The real-time, stability, and fault tolerance of the semantic map are poor, reducing the controllability of business operations. Therefore, we propose a multi-sensor linkage monitoring method. SUMMARY

[0003] The purpose of the present application is to solve the defects in the prior art and provide a multi-sensor linkage monitoring method.

[0004] The present application proposes a multi-sensor linkage monitoring method, which adopts the following technical solutions to solve the technical problems: I. Create a sensor state monitoring timer and start an independent communication thread. The communication thread creates a file descriptor during operation and checks the sensor file. II. The communication thread determines the specified type of each sensor file, and then dynamically adjusts the data acquisition period and processing order according to the sensor category, data change frequency, and shelf business importance. III. During continuous monitoring, real-time reading of various sensor data is performed, and data from different sources is aligned and fused to construct a unified shelf semantic map. IV. The edge node processes the fused sensor data in real time, and when the sensor data is confirmed to have changed, sends a signal containing the sensor position and state to the main thread. V. The main thread receives the signal, judges the shelf state and outputs the coordinate and state information, manages the shelf state according to the judgment result, and establishes the corresponding virtual shelf model. VI. According to the shelf state, execute the processing strategy, and update the shelf map data in the local JSON file, while adjusting the sampling frequency, communication cycle and processing strategy of the sensor.

[0005] The application confirms that the sensor file can be read, the original sensor meta-information is pre-processed, and is converted into structured features, while maintaining a target sensor template set in the offline stage. After real-time feature comparison with the template, device identification and log recording are completed according to the similarity score, and the determination result is returned to the upper scheduling module to determine whether to establish a monitoring timer. If it is determined to be a target sensor, the historical observation sequence is collected, and abnormal value elimination, frame loss processing and change frequency calculation are performed, and then combined with the importance weight of the shelf business to form a comprehensive priority score, and converted into a sampling period through logistic mapping. Finally, multi-thread scheduling and abnormal rollback control are performed according to the priority, and then the sensor time field is uniformly corrected and compensated in the time zone, the time offset is aligned using the cross-correlation method, and resampling is performed to the common time grid, and the confidence weight is generated combined with the uncertainty evaluation. Then, multi-modal features are extracted, converted into semantic probabilities using a classifier, and weighted and fused to form the posterior probability of the shelf unit. In spatial modeling, a three-dimensional grid and topological unit are generated based on the warehouse layout, the geometric center and adjacency relationship are recorded, the initial semantic map is constructed, and dynamic updating and conflict rollback are realized through exponential smoothing and neighborhood consistency constraints. If a "seating" event is detected, a scanning session is started, and code scanning or manual input verification is performed; if successful, the semantic map and database are updated, and if failed, manual intervention is prompted and logs are recorded. If a "leave" event is detected, the shelf is marked as empty and the resources are released, and the sampling utility value is calculated and mapped to the target sampling frequency through the sigmoid function, and based on the preset sampling frequency, the target sampling frequency is adjusted, and based on the adjusted target sampling frequency, real-time shelf information is collected, which can avoid misjudgment in the fault-tolerant path, enhance the robustness of monitoring, realize key task priority and efficient resource utilization, improve the accuracy of fusion results, and realize fine-grained shelf state recognition, ensure the real-time, stability and fault tolerance of the semantic map, and improve the controllability of business operations. BRIEF DESCRIPTION OF DRAWINGS

[0006] Figure 1 It is a multi-sensor linkage monitoring method framework. DETAILED DESCRIPTION

[0007] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0008] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative labor fall within the scope of the present application. Embodiment 1

[0009] The embodiment of the present application provides a multi-sensor linkage monitoring method. Referring to Figure 1 , Figure 1 The embodiment of the present application provides a multi-sensor linkage monitoring method. The method comprises the following steps: A sensor state monitoring timer is created, an independent communication thread is started, the communication thread creates a file descriptor during running, and sensor files are checked.

[0010] Specifically, the communication thread inputs the path of each sensor as a parameter into a system interface call function, returns a non-negative integer as a file descriptor if the call is successful, and returns -1 if the call fails. Then, the system checks the accessibility of each sensor file according to the returned file descriptor, and checks whether the read permission is available. If the sensor file is not accessible, the error processing flow is entered, and the communication thread converts the error code into a text form and outputs it after being spliced with the artificial prompt. Otherwise, the communication thread inputs the corresponding file descriptor as an input, reads the basic information of the corresponding sensor device from the sensor file according to the length limit, and stores the result in a structure or a buffer. The system calculates the similarity between the target sensor device information and the actual read information of the communication thread. If the similarity is higher than a preset recognition threshold, it is determined that the target sensor file is the target sensor file, and the subsequent monitoring process is entered. Otherwise, the communication thread exits.

[0011] It should be further pointed out that the specific calculation formula of the file descriptor is as follows: , wherein represents the file descriptor; represents the system function mapping of the file opening operation; represents the sensor device file path.

[0012] The specific determination formula of the file accessibility is as follows: , wherein represents the file accessibility result, if , it indicates that the file can be normally accessed; if , it indicates that the file is not accessible; represents the read permission flag, if the read permission is available , otherwise ; represents the file descriptor validity function, if , it returns 1, otherwise it returns 0; represents the permission validity function, when Return 1 if, otherwise return 0.

[0013] The communication thread determines the specified type of each sensor file, and dynamically adjusts the data collection period and processing order according to the sensor category, data change frequency, and shelf business importance.

[0014] Specifically, after confirming that the sensor file is readable, the communication thread reads the original meta-information from the sensor file in text or binary mode, removes the white space and invisible characters before and after each original meta-information, unifies the case of the text data of each original meta-information, and performs synonym normalization processing on the manufacturer name and model number, while parsing the version number. Then, using the successfully read same model field or default placeholder to fill in the missing field records in each original meta-information, record the missing flag, convert the preprocessed meta-information to corresponding structured features through structured mapping, then in the offline stage, according to the known target device sample statistics or manual definition of device specifications, obtain multiple groups of target sensor templates, and regularly maintain and update the target sensor template set in the configuration or database. The structured features generated in real time are compared with each group of templates in the target sensor template set, and the similarity score between the two is calculated. If the similarity score exceeds the preset strict threshold, it is directly determined that the corresponding sensor file is of the target sensor type and the template is recorded. If it is higher than the preset tolerance threshold but lower than the preset strict threshold, it enters the fault tolerance path, reads more fields, extends the reading length, and rechecks. If it is lower than the preset tolerance threshold, it is determined to be a non-target sensor and is recorded. If there are multiple template similarity scores higher than the preset strict threshold, the target sensor template with the highest similarity score is selected as the final determination result. The determination result of each sensor file is written into the local log and returned to the upper scheduling module. The upper scheduling module selects to establish a monitoring timer according to the determination result.

[0015] Specifically, after determining that it is a target sensor, the historical observation sequence of the sensor is collected, and based on the 3 The principle is to remove outliers from historical observation sequences, use interpolation or marking as unavailable to handle frame drops in sensor data collection, and calculate the absolute change of each pair of adjacent readings in the historical observation sequence. Simultaneously, normalize these changes according to the actual time difference to obtain the corresponding rate of change per unit time. Then, perform a moving average on each obtained rate of change per unit time to generate a frequency of change. Establish a shelf business importance weight table, and update the importance weight of each shelf business in the table regularly based on recent inbound / outbound frequencies, stockout alarm counts, and manual priority. Before prioritizing, query the corresponding importance weight in the shelf business importance weight table based on the current sensor's shelf location. If the weight data is outdated, trigger a weight refresh, and recalculate based on the inventory turnover rate of the most recent 24 hours. The corresponding weights are recalculated. Based on the sensor category, change frequency, and importance weight of the shelf business, the comprehensive priority score of each sensor is calculated. The obtained comprehensive priority scores are converted into actual sampling periods through logistic mapping. The sensors are then prioritized according to their comprehensive priority scores. Time slots for sensors with the same priority are allocated according to timestamps or round-robin strategies. After the update is completed, the sampling periods and comprehensive priority scores of each sensor are returned to the local scheduling configuration. In a multi-threaded environment, the communication thread acquires the configuration information lock of the corresponding sensor and releases the lock after the update. If an exception occurs during the update or application process, the previous stable period is retained and the event is recorded, and a rollback is performed. At the same time, after each round of updates, the current update operation is recorded in the audit log in a transactional manner.

[0016] It should be further explained that the original metadata specifically includes the manufacturer string, product ID, hardware version, interface type, device description text, etc. The specific formula for calculating the frequency of change is as follows: In the formula, Represents the frequency of change; Represents the total time; Representing the The first numerical observation read; Representing the The first numerical observation read; Represents absolute value operations.

[0017] The specific formula for calculating the actual sampling period is as follows: In the formula, This represents the time interval for the next time the sensor is read. This represents the shortest allowed sampling period; This represents the longest allowed sampling period; The lower limit of the range of values ​​for the sensor's overall priority score; an upper limit of a value range of the comprehensive priority score of the sensor; a comprehensive priority score of the sensor.

[0018] The specific calculation formula of the priority ranking is as follows: , wherein, a comprehensive priority score of the sensor a processing priority ranking in a current scheduling period; a comprehensive priority score of the sensor a priority score of other sensors in the system. a priority score of other sensors in the system. a priority score of other sensors in the system. a priority score of other sensors in the system.

[0019] Embodiment 2 The embodiment of the present application provides a multi-sensor linkage monitoring method. Referring to Figure 1 , Figure 1 A framework diagram of a multi-sensor linkage monitoring method provided by the embodiment of the present application. The method comprises the following steps: In the process of continuous monitoring, real-time reading of various sensor data is performed, and data of different sources are aligned and fused to construct a unified shelf semantic map.

[0020] Specifically, the time field of each sensor data is parsed and verified, and the time with time zone and daylight saving time deviation is converted to a unified time zone. Using a fixed reference time as a reference, all sensor data times are converted to the standard time unit of the reference. According to the calibration plate, the known reference point or manual annotation, a calibration matrix is established for each type of sensor, and the rigid transformation between the sensor coordinate system and the global coordinate system of the shelf is determined. Select one or more overlapping time windows of a pair of sensors, calculate the cross-correlation of the time shift, and select the time shift with the maximum correlation as the offset of the corresponding sensor. Then, through the sliding window calculation of the time sequence of the offset and the fitting, the corresponding compensation value is obtained. Based on each compensation value, the clocks of each sensor are synchronized. The discrete observations of different sensors at their respective time points are unified into a common time grid through resampling. The uncertainty of each resampled sensor data is evaluated, and the uncertainty of each resampled sensor is inversely mapped to generate the confidence weight of each sensor data. Then, the modal features corresponding to each sensor data are extracted, and then the modal features are converted to corresponding semantic probabilities through a lightweight classifier. The semantic probabilities or confidence weights from each sensor are weighted and fused based on the common time point, and the posterior probability of each bin unit in the shelf at the current time is output. Based on the warehouse plan and the shelf layout, each topological unit in the shelf is defined, and the global coordinate system of each shelf is fixed with the calibration point. A three-dimensional grid covering the entire area of interest is generated. The geometric center, adjacent unit list and belonging shelf ID of each topological unit are calculated, and the grid metadata is recorded to generate an initial semantic map. The posterior probability of a single bin at the current time is written into the semantic map, and the semantic map is iteratively updated between new observations and historical states through exponential smoothing. At the same time, consistency constraints are imposed on the adjacent units of the corresponding bin. Then, the probability distribution difference of the affected grid cells before and after updating is calculated. If the difference exceeds the preset threshold or triggers a violation of business rules, the current update is marked as a conflict and a rollback or degradation process is triggered. At the same time, the conflict event is recorded in the audit log.

[0021] The edge node processes the fused sensor data in real time, and sends a signal containing the sensor position and state to the main thread when the sensor data is confirmed to have changed.

[0022] The main thread receives the signal, judges the shelf state and outputs the coordinate and state information, manages the shelf state according to the judgment result, and establishes the corresponding virtual shelf model.

[0023] According to the shelf state, the processing strategy is executed, and the shelf map data in the local JSON file is updated, and the sampling frequency, communication period and processing strategy of the sensor are adjusted.

[0024] Specifically, when the main thread receives a "seating" event reported by the sensor, a scanning session is started for the corresponding shelf, and the communication thread locally establishes session information matching the shelf, including session ID, shelf ID, and user prompt state, records the session start time, and triggers a scanning prompt on the dot matrix screen, sets the maximum waiting time for this scan and the maximum number of retries allowed, and receives multiple attempts from the code scanner or manual input within the scanning window. The communication thread de-duplicates and time-sequences the received attempt results, and evaluates the scanning quality through statistical indicators. If the scanning quality is higher than the pre-set qualified threshold, it is determined as "successful shelf scanning"; if the maximum waiting time is exceeded and the scanning quality is lower than the pre-set qualified threshold, re-determination is performed; if the pre-set maximum number of retries is reached and still fails, the session information is marked as "scanning failure / pending review", and a timeout information and manual intervention guidance are prompted on the dot matrix screen, and an audit log is written. When the scanning is successful and the material ID and shelf location are parsed and verified, the communication thread reads the corresponding entry of the local semantic map based on the material ID and location in the session, and constructs update information containing the material ID, quantity, timestamp, and source trust level. Through weighted fusion, the update information is merged in memory in a transaction manner, and then written back to the disk or embedded database. If the writing fails, the memory state is rolled back and error update information is recorded. After the sensor detects a "disengagement" event, the communication thread immediately marks the shelf in the local map, displays a disengagement prompt on the dot matrix screen, marks the shelf as empty and releases the corresponding resources, calculates the sampling utility value of the shelf based on the business importance, risk of change, and resource cost of the shelf, performs a non-linear transformation on the sampling utility value of the shelf through a sigmoid function, converts it to the corresponding target sampling frequency, and detects whether the target sampling frequency is within the pre-set minimum and maximum sampling frequencies. If it exceeds the constraint range, the target sampling frequency is adjusted to the minimum or maximum sampling frequency, and data collection is performed again based on the latest target sampling frequency.

[0025] The above describes one embodiment of the present application in detail, but the above description is only a preferred embodiment of the present application and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made within the scope of the present application should still fall within the scope of the present application.

Claims

1. A multi-sensor linkage monitoring method, characterized by, The method comprises the following steps: I. Create a sensor state monitoring timer, start an independent communication thread, and create a file descriptor during the running process of the communication thread, and check the sensor file; II. The communication thread judges the specified type of each sensor file, and then dynamically adjusts the data acquisition period and processing order according to the sensor category, data change frequency and shelf business importance; III. In the process of continuous monitoring, real-time reading of various sensor data is performed, and different sources of data are aligned and fused to construct a unified shelf semantic map; IV. The edge node processes the fused sensor data in real time, and sends a signal containing the sensor position and state to the main thread when the sensor data is confirmed to have changed; V. The main thread receives the signal, judges the shelf state and outputs the coordinate and state information, manages the shelf state according to the judgment result, and establishes the corresponding virtual shelf model; VI. According to the shelf state, the processing strategy is executed, and the shelf map data in the local JSON file is updated, and the sampling frequency, communication period and processing strategy of the sensor are adjusted.

2. The multi-sensor linkage monitoring method of claim 1, wherein, The specific steps of creating a file descriptor during the running process of the communication thread in step I are as follows: S1.1: The communication thread inputs the path of each sensor as a parameter into the system interface call function, and returns a non-negative integer as a file descriptor if the call is successful, or returns -1 if the call fails. Then the system checks the accessibility of each sensor file according to the returned file descriptor, and checks whether it has read permission; S1.2: If the sensor file is not accessible, the error processing flow is entered, and the communication thread converts the error code into text form and concatenates it with the artificial prompt to output. Otherwise, the communication thread reads the basic information of the corresponding sensor device from the sensor file according to the length limit, and stores the result in the structure or buffer; S1.3: The system calculates the similarity between the target sensor device information and the actual read information of the communication thread. If the similarity is higher than the preset recognition threshold, it is determined that it is the target sensor file, and the subsequent monitoring process is entered. Otherwise, the communication thread exits.

3. The multi-sensor linkage monitoring method of claim 2, wherein, The specific calculation formula of the file descriptor described in S1.1 is as follows: , wherein, represents the file descriptor; represents the system function mapping of the file opening operation; represents the sensor device file path; The specific decision formula of the file accessibility in S1.1 is as follows: , wherein, represents the file accessibility result, if , it indicates that the file can be normally accessed; if , it indicates that the file cannot be accessed; represents the read permission flag, if the read permission is possessed, , otherwise ; represents the file descriptor validity function, if , 1 is returned, otherwise 0 is returned; represents the permission validity function, when , 1 is returned, otherwise 0 is returned.

4. The multi-sensor linkage monitoring method of claim 2, wherein, The specific steps of judging the specified type of each sensor file in step II are as follows: S2.1: After confirming that the sensor file is readable, the communication thread reads the original meta information from the sensor file in text or binary form, removes the white space and invisible characters before and after each original meta information, unifies the case of the text data of each original meta information, and performs synonym normalization processing on the manufacturer name and model number. Then parse the version number, and fill in the missing field records with the successfully read same model field or default placeholder, and record the missing flag; S2.2: Convert each preprocessed meta information into corresponding structured features through structured mapping. Then in the offline stage, obtain multiple groups of target sensor templates by statistical or manual definition of device specifications, and regularly maintain and update the target sensor template set in the configuration or database. S2.3: The real-time generated structured features are compared with each group of templates in the target sensor template set respectively, the similarity scores between them are calculated, if the similarity score exceeds the preset strict threshold, it is directly determined that the corresponding sensor file is of the target sensor type and the template is recorded, if it is higher than the preset tolerant threshold but lower than the preset strict threshold, it enters the fault-tolerant path, more fields are read, the reading length is extended, the verification is re-performed, if it is lower than the preset tolerant threshold, it is determined as a non-target sensor and recorded; S2.4: If there are multiple template similarity scores higher than the preset strict threshold, the target sensor template with the highest similarity score is selected as the final determination result, the determination results of each sensor file are written into the local log and returned to the upper scheduling module, and the upper scheduling module selects to establish a monitoring timer according to the determination result.

5. The multi-sensor linkage monitoring method of claim 4, wherein, The specific steps of dynamically adjusting each data acquisition period and processing order in step II are as follows: S3.1: When judging as the target sensor, collect the historical observation sequence of the sensor, and at the same time, based on 3 The principle, eliminate outliers in the historical observation sequence, use interpolation or mark the unavailable processing of each sensor acquisition existing frame loss situation, and calculate the absolute change of each pair of adjacent readings in the historical observation sequence, and normalize it according to the actual time difference to obtain the corresponding unit time change rate, and then perform sliding average processing on the obtained unit time change rate to generate the change frequency; S3.2: Establish a shelf business importance weight table, and adjust the importance weight of each shelf business in the shelf business weight table according to the recent in-out warehouse frequency, out-of-stock alarm times and manual priority, then before priority calculation, according to the current sensor belonging to the shelf position, the corresponding importance weight in the shelf business importance weight table is queried, if the weight data is too old, a weight refresh is triggered, and the corresponding weight is recalculated according to the inventory turnover rate in the last 24 hours; S3.3: According to the sensor category, change frequency and shelf business importance weight, the comprehensive priority score of each sensor is calculated, the obtained comprehensive priority score is converted into the actual sampling period through logistic mapping, and the sensors of the same priority are sorted according to the comprehensive priority score, and then the time stamp or polling strategy is used to allocate time slots for the sensors of the same priority; S3.4: After the update is completed, the sampling period and comprehensive priority score of each sensor are returned to the local scheduling configuration, when in a multi-thread environment, the communication thread acquires the configuration information lock of the corresponding sensor, and releases the lock after updating, if an exception occurs during updating or application, the previous stable period is retained and the event is recorded, and a rollback is performed, and after each update, the current update operation is recorded in the audit log in a transaction manner.

6. The multi-sensor linkage monitoring method of claim 5, wherein, The specific calculation formula of the change frequency S3.1 is as follows: , wherein, represents the change frequency; represents the total time; represents the number of observations of the numerical type read at the first time; represents the number of observations of the numerical type read at the i th time; represents the number of observations of the numerical type read at the i th time; represents the number of observations of the numerical type read at the i th time; represents the absolute value operation; The specific calculation formula of the actual sampling period described in S3.3 is as follows: , wherein, represents the time interval of the next timing reading of the sensor; represents the allowed minimum sampling period; represents the allowed maximum sampling period; represents the lower limit of the value range of the comprehensive priority score of the sensor; represents the upper limit of the value range of the comprehensive priority score of the sensor; represents the comprehensive priority score of the sensor; The specific calculation formula of the priority ranking described in S3.3 is as follows: , wherein, represents the processing priority ranking of the sensor in the current scheduling period; represents the comprehensive priority score of the sensor ; represents the priority score of other sensors in the system; represents an indication function, which is 1 if the condition is true, and 0 otherwise.

7. The multi-sensor linkage monitoring method of claim 5, wherein, The specific steps of constructing a unified shelf semantic map in step III are as follows: S4.1: Analyze and verify the time field of each sensor data, and convert the time with time zone and daylight saving time deviation to a unified time zone, use a fixed reference time as a reference, convert all sensor data time to the standard time unit of the reference, establish a calibration matrix for each type of sensor according to the calibration plate, known reference point or manual annotation, and determine the rigid transformation between the sensor coordinate system and the global coordinate system of the shelf; S4.2: Select one or more overlapping time windows of sensors, calculate the cross-correlation of time shift, and select the time shift with the largest correlation as the offset of the corresponding sensor, then calculate the time series of the offset through the sliding window and fit to obtain the corresponding compensation value, and then synchronize the clocks of each sensor based on the compensation values. S4.3: Discrete observations of different sensors at respective time points are unified into a common time grid by resampling, the uncertainty of each resampled sensor data is evaluated, and the resampled sensor uncertainty is inversely mapped to generate confidence weights for each sensor data. Then, modal features corresponding to each sensor data are extracted, and each modal feature is converted into a corresponding semantic probability by a lightweight classifier. The semantic probabilities or confidence weights from each sensor are weighted and fused in units of common time points, and the posterior probability of each bin unit in the shelf at the current time is output. S4.4: Based on the warehouse floor plan and shelf layout, each topological unit in the shelf is defined, and a global coordinate system for each shelf is fixed by using calibration points. A three-dimensional grid covering the entire area of interest is generated, and the geometric center, adjacent unit list, and shelf ID of each topological unit are calculated. The grid metadata is recorded to generate an initial semantic map. S4.5: The posterior probability of a single bin at the current time is written into the semantic map, and the semantic map is iteratively updated between new observations and historical states by exponential smoothing. The consistency of the corresponding bin adjacent units is constrained, and the probability distribution difference of the affected grid cells before and after updating is calculated. If the difference exceeds the preset threshold or triggers a violation of business rules, the current update is marked as a conflict and a rollback or degradation process is triggered. The conflict event is recorded in the audit log.

8. The multi-sensor linkage monitoring method of claim 1, wherein, The specific steps of updating the shelf map data in the local JSON file according to the shelf state in step VI are as follows: S5.1: When the main thread receives a "seating" event reported by the sensor, a scanning session for the corresponding shelf is started, and a communication thread is established locally to match the session information of the shelf, including session ID, shelf ID, and user prompt state. The session start time is recorded and a scanning prompt is triggered on the dot matrix screen. The maximum waiting time for this scan and the maximum number of allowed retries are set. S5.2: Within the scanning window, multiple attempt results from the code scanner or manual input are received. The communication thread de-duplicates and time-sequences the received attempt results, and evaluates the scanning quality using statistical indicators. If the scanning quality is higher than the preset qualified threshold, it is determined as a "successful shelving scan". S5.3: If the maximum waiting time is exceeded and the scanning quality is lower than the preset qualified threshold, a re-determination is made. If the maximum number of retries is reached and the scan still fails, the session information is marked as "scan failure / pending review", and a timeout information and manual intervention guidance are prompted on the dot matrix screen. The audit log is written. S5.4: When the scan is successful and the material ID and shelf location are parsed and verified, the communication thread reads the corresponding entry of the local semantic map based on the material ID and location in the session, and constructs update information containing the material ID, quantity, timestamp, and source trust level. The update information is merged in memory using a transactional approach through weighted fusion, and then written back to the disk or embedded database. If the writing fails, the memory state is rolled back and the error update information is recorded. S5.4: After the sensor detects the "unattended" event, the communication thread immediately marks the shelf as unattended in the local map, displays an unattended prompt on the dot matrix screen, marks the shelf as empty and releases the corresponding resources, and calculates the sampling utility value of the shelf according to the importance of the shelf business, the risk of change, and the cost of resources; S5.5: The sampling utility value of the shelf is nonlinearly transformed by a sigmoid function to convert to the corresponding target sampling frequency, and it is detected whether the target sampling frequency is between the preset minimum and maximum sampling frequencies. If it exceeds the constraint range, the target sampling frequency is adjusted to the minimum or maximum sampling frequency, and data collection is performed again based on the latest target sampling frequency.

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