Dynamic load balanced distribution system for gear motor

Abstract

The invention relates to the technical field of gear motor control, and discloses a dynamic load balanced distribution system for a gear motor. The system comprises a load data acquisition module, a load rule verification module, an association network construction module and a balanced distribution and anomaly identification module. The load data acquisition module performs standardization processing on the original load data to generate a standardized load data unit including a load value, a timestamp, a motor identifier and other elements; the load rule verification module retrieves the rule database based on the standardized data and judges the load compliance state; the association network construction module calculates an association closeness index through a shared entity and constructs a multi-load association graph; and the balanced allocation and anomaly identification module generates a dynamic load balanced allocation suggestion in combination with the association map and the compliance state. According to the system, association analysis and dynamic balance of loads of the multiple gear motors are achieved, the accuracy and adaptability of load management are improved, and the system is suitable for an industrial scene of cooperative work of the multiple motors.

Description

Technical Field

[0001] The present invention relates to the technical field of reduction motor control, and in particular to a dynamic load balancing distribution system for a reduction motor. Background Art

[0002] In industrial production and automated equipment operation, geared motors, as core components for power transmission, face a significant impact on their load status, including operational stability, energy consumption, and service life. As industrial scenarios become increasingly complex, multiple geared motors are often required to work together in a single scenario. Therefore, ensuring the proper load distribution between these motors becomes a key factor limiting overall system performance.

[0003] Currently, most load management methods for geared motors still rely on the independent monitoring and control of a single motor, lacking effective consideration of load correlations in multi-motor collaborative scenarios. For example, in an assembly line transmission system, the load changes of adjacent geared motors exhibit a clear temporal correlation, but existing systems often only judge the load threshold of a single motor, making it difficult to identify chain reactions caused by unbalanced load distribution. Furthermore, load data collection and processing often uses non-standardized formats, and the sensor data structures of different motors vary significantly, making cross-motor load analysis difficult to conduct efficiently.

[0004] Existing load balancing strategies are mostly based on fixed, preset rules and lack the ability to adapt to dynamic operating conditions. When operating conditions change suddenly, these fixed rules often fail to respond promptly, leading to imbalanced conditions where some motors are overloaded while others are underloaded. This can lead to increased equipment wear, increased energy consumption, and even system downtime. Regarding load anomaly identification, the lack of in-depth analysis of the correlation characteristics of multiple motor loads makes many potential anomalies difficult to detect, increasing the risk of system operation. Summary of the Invention

[0005] The object of the present invention is to provide a dynamic load balancing distribution system for a reduction motor to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides a dynamic load balancing distribution system for a reduction motor, the system comprising: The load data acquisition module is used to collect raw load data from the reduction motor sensor, identify and extract element information, including load value, timestamp, and motor identifier element, map the element information to preset data fields and establish internal association identifiers to generate standardized load data units; The load rule verification module searches the rule database for rule condition clauses that match the load type and the allowable threshold based on the standardized load data unit, calculates the rule application complexity factor based on the number of matching clauses and the hierarchical depth, compares the load data with the clause constraint value, time window and location range item by item based on the rule application complexity factor, determines the attribute compliance status, and generates a load compliance status judgment; An association network construction module searches for motor identifiers, associated positions, and timestamp entities shared between different reduction motors based on the plurality of standardized load data units, obtains a cross-motor association closeness index based on the number and category of shared entities, and constructs a graph structure with load entities as nodes and shared relationships as edges based on the cross-motor association closeness index to establish a multi-load association graph; The balanced distribution and anomaly identification module calculates a balanced score based on the multi-load association map and the load compliance status of the associated loads, and generates a dynamic load balanced distribution suggestion.

[0007] Preferably, the step of obtaining the standardized payload data unit is: Based on the original load data of the reduction motor sensor, the sensor field content in the data is scanned and identified item by item, field category identification and matching are performed, and time series is split. The load value, timestamp and motor identifier are extracted one by one to generate a set of original load data elements. Based on the original payload data element set, mapping matching verification of the fields is performed one by one, and the numerical format conversion and field reconstruction are performed on the successfully matched field contents to generate a payload data standardized mapping field set; Based on the load data standardized mapping field set, internal association matching is performed between each standardized mapping field, and an internal association relationship identifier is established according to the matching result, and index construction and relationship binding of the standardized mapping fields are performed; Based on the index building and relationship binding results, all fields are integrated to generate a standardized payload data unit.

[0008] Preferably, the steps for obtaining the rule applicability complexity factor are: Based on the load type field and the allowable threshold field in the standardized load data unit, extract the original field value and perform field content normalization matching with the field definition library, fill in the missing fields and unify the field expression method to obtain a normalized load type field and allowable threshold field combination set; According to the normalized payload type field and the allowed threshold field combination set, the rule condition clauses matching the payload type are sequentially retrieved from the rule database, and the clauses matching the allowed threshold field and the combination set are screened to extract the nesting level, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable position overlap of each clause; Based on the extraction results, a complexity factor is applied to the calculation rules.

[0009] Preferably, the steps of obtaining the load compliance status determination are: Based on the rule application complexity factor, the payload type field value and clause constraint value in the standardized payload data unit are called one by one to perform threshold comparison, value range verification, and time matching judgment between the field value and the constraint value; Based on the comparison and verification results, the location field value in the standardized payload data unit is obtained, and the payload location field is mapped to the applicable location range of the rule clauses one by one, and the clauses with successful location mapping are selected; Based on the screening results, determine whether the attributes of each load data are consistent; Generate a load compliance status judgment based on the attribute compliance.

[0010] Preferably, the step of obtaining the cross-motor correlation closeness index is: Based on the plurality of standardized load data units, extracting the motor identifier field, the associated position field, and the timestamp field in each standardized load data unit, performing entity classification matching and deduplication processing on all load data according to field type, and generating a load sharing entity set; According to the load sharing entity set, performing entity occurrence statistics and entity category marking in different reduction motor loads one by one, and recording distribution information of each type of shared entity in each standardized load data unit; Based on the distribution information, the cross-motor correlation closeness index is calculated.

[0011] Preferably, the steps of obtaining the multi-load association map are: Extracting the motor identifier field, the associated position field, and the timestamp field from a plurality of standardized load data units based on the cross-motor association closeness index, and performing entity normalization identification coding and entity type labeling on each field content; Based on the annotation results, a set of load entity nodes is generated; Calculating the sharing relationship strength value between load entities according to the load entity node set; Based on the shared relationship strength value, a graph structure is constructed with the load entity as the node and the shared relationship strength value as the edge weight, and an entity edge connection relationship mapping table between the nodes is established; Based on the entity edge connection relationship mapping table, a multi-load association graph is generated.

[0012] Preferably, the steps for obtaining the dynamic load balancing suggestion are: Based on the multi-load association graph, the bidirectional paths of all load entity nodes are extracted from the graph structure, and the load compliance status judgment result, path length, out-degree of the path end node, number of path starting node fields, and number of intermediate nodes passed by the path corresponding to each path are retrieved; Generate multiple load path attribute sets based on the retrieval results; Calculating a balance score of a corresponding entity node based on the multiple load path attribute sets; Generate dynamic load balancing recommendations based on the balance score.

[0013] Preferably, the element information identification and extraction steps of the load data acquisition module are: Receive the original load data stream from the reduction motor sensor and perform data stream segmentation processing; Based on the segmentation processing result, identifying the load value, time stamp and motor identifier elements; Mapping identification elements to pre-set data fields; Based on the mapping results, an internal association identifier is established; Based on the internal correlation identifier, the standardized payload data unit is output.

[0014] Preferably, the balanced distribution and exception identification module further includes an exception handling step, specifically: Call the load compliance status judgment in the multi-load association map; detect abnormal load patterns based on the load compliance status judgment; Adjust dynamic load balancing recommendations based on abnormal load patterns; generate final load balancing output based on the adjustment results.

[0015] Preferably, the rule condition clauses for matching the load type and the allowable threshold in the search rule database include: Query the load rule set in the rule database and match the load type with the allowed threshold based on the query results; According to the matching results, the rule condition clauses are extracted, and based on the rule condition clauses, the rule applicable complexity factor calculation is executed.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By establishing a standardized load data processing mechanism, we achieve unified integration and correlation analysis of load information from different geared motors, breaking the limitations of traditional single-motor independent monitoring. The load data acquisition module maps raw data to preset fields and establishes internal correlation identifiers, making load data across motors comparable and correlated, laying the foundation for collaborative multi-motor analysis.

[0017] The load rule verification module introduces a rule application complexity factor, enabling refined application of load determination rules by calculating the number of matching clauses and the depth of the hierarchy. This approach avoids the mechanical application of fixed rules and dynamically adjusts the determination logic based on different load types and operating conditions, improving the accuracy and flexibility of load compliance determination.

[0018] The association network construction module calculates the cross-motor association density index based on shared entities such as motor identifiers, locations, and timestamps, and constructs a multi-load association map, visually presenting the load association characteristics between multiple motors. This graphical presentation makes previously hidden load associations explicit, providing an effective way to identify potential problems caused by imbalanced associations.

[0019] The load balancing and anomaly identification module combines correlation maps with load compliance status to generate dynamic load balancing recommendations, achieving a transition from static rules to dynamic adaptation. This module responds to changes in operating conditions in real time, adjusting balancing strategies based on the dynamic changes in the correlation characteristics of multiple motor loads. This avoids the lag in response to sudden changes in operating conditions associated with traditional fixed strategies. Furthermore, with the help of multi-load correlation maps, load imbalances caused by abnormal correlation relationships can be more accurately identified, expanding the scope of anomaly identification and reducing the risk of missing hidden anomalies. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a timing diagram of the dynamic load balancing distribution system for the reduction motor according to the present invention; Figure 2 A flow chart for obtaining a standardized load data unit; Figure 3 A flowchart for obtaining the rule application complexity factor; Figure 4 A flowchart for obtaining a multi-load correlation map; Figure 5 Flowchart for element information identification and extraction of load data acquisition module. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] See also Figure 1The present invention provides a dynamic load balancing distribution system for a reduction motor, the system comprising a load data acquisition module, a load rule verification module, an association network construction module, and a balancing distribution and abnormality identification module.

[0023] The load data acquisition module collects raw load data from the reduction motor sensor, identifies and extracts load value, timestamp, and motor identifier elements, maps them to preset data fields and establishes internal association identifiers to generate standardized load data units; the load rule verification module retrieves the rule database based on the standardized load data unit to match the load type and the rule condition clauses of the allowable threshold, calculates the rule applicability complexity factor, compares the load data with the clause constraint value, time window and position range item by item, and generates a load compliance status judgment; the association network construction module searches for motor identifiers, associated positions and timestamp entities shared between different reduction motors based on multiple standardized load data units, calculates the cross-motor association closeness index, constructs a graph structure with load entities as nodes and shared relationships as edges, and establishes a multi-load association map; the balanced distribution and anomaly identification module calculates the balanced score based on the multi-load association map combined with the load compliance status judgment, and generates a dynamic load balanced distribution recommendation.

[0024] Example 1: See Figure 2 As the initial step in the system, the load data acquisition module's core function is to extract and standardize useful information from dispersed raw sensor data, providing a unified data format for subsequent processing. This module receives the raw load data stream from the geared motor sensor. This data stream typically exists in a mixed format, potentially containing both binary sensor signals and text-based device status information. The data rate varies depending on the motor's operating state, ranging from approximately 500 to 1000 data points per second during stable operation. However, short, high-frequency pulses may occur during abnormal fluctuations.

[0025] The original data stream is segmented. Segmentation is based on the data's temporal and structural characteristics. For time-synchronized continuous data, fixed time intervals are used to ensure that each sub-data block corresponds to a complete sampling period. For non-time-synchronized discrete data, segmentation is performed based on the data length threshold to avoid processing delays caused by excessively long data. The segmentation process also addresses potential packet sticking. When data from two sampling periods is combined into a single data block due to transmission delays, checksums are used to identify and split the data block, ensuring the independence and integrity of each sub-data block.

[0026] The segmented sub-data blocks enter the feature recognition stage. This stage requires the precise extraction of three key elements: load value, timestamp, and motor identifier from the mixed binary and text data. Load values ​​typically exist in numerical form, expressed as a two's complement (e.g., a 16-bit signed integer, corresponding to a numerical range of -32768 to 32767) or a floating-point text format (e.g., "150.5"). Recognition uses a pattern matching algorithm: for binary data, the load value field is located by parsing the type identifier in the data header (e.g., 0x01 represents a load value), and then converted to a decimal value. For text data, a regular expression is used to match the numerical pattern (e.g., "[0-9]+(.[0-9]+)?"), and the location of the load value is confirmed by combining the context (e.g., the leading character "LOAD="). Timestamp identification relies on the device's time synchronization protocol. Common examples include the system time synchronized using the NTP (Network Time Protocol) (e.g., "2025-08-11T10:13:43.430Z") or the counter value of the device's local clock (e.g., the number of milliseconds since startup, "123456789"). Identification involves locating the timestamp field using keyword matching (e.g., "TS=", "Time:") and converting it to the standardized ISO8601 format (e.g., "2025-08-1110:13:43.430"). Motor identifiers, used to distinguish between motors, are typically strings (e.g., "MOTOR_001," "DRV-2025A") or numeric numbers (e.g., "001," "2025A"). Identification involves extracting a fixed-position character sequence (e.g., the string following "ID:" at the beginning of a data block) and verifying its format validity (e.g., a maximum of 20 characters and containing only letters, numbers, and underscores).

[0027] Identified elements must be mapped to predefined data fields to ensure standardization. These fields are defined during system initialization and include three core fields: load value (unit: N·m), timestamp (format: YYYY-MM-DDHH:MM:SS.sss), and motor identifier (format: string, length ≤ 20). The mapping process uses key-value pair matching rules: for the load value, the identified value (such as 150.5) is directly assigned to the "Load Value" field, and its physical rationality is checked (for example, whether it is within the rated load range of the motor. Assuming the rated load is 200N·m, 150.5N·m is a valid value, while -50N·m or 250N·m will be marked as abnormal); for the timestamp, the converted ISO format string is assigned to the "Timestamp" field, and its timeliness is verified (for example, the deviation from the current system time does not exceed 1 second, otherwise it is considered outdated data); for the motor identifier, the extracted string is assigned to the "Motor Identifier" field, and the device ledger is checked (for example, whether there is registration information for "MOTOR_001"). Unregistered identifiers will be marked as unknown devices.

[0028] After mapping is complete, an internal association identifier must be established to uniquely identify each standardized load data unit and enable fast retrieval. The association identifier is generated using a hash algorithm, specifically the SHA-256 algorithm, which hashes the string concatenation of "load value + timestamp + motor identifier." A standardized load data unit consists of a set of standardized mapping fields and an internal association identifier.

[0029] Throughout the entire processing process, it is necessary to consider the mechanism for handling abnormal situations. For example, when garbled characters or format errors appear in the sensor data stream, the data stream segmentation module will trigger the error detection program, mark the sub-data block as "invalid data", and record the error log to prevent invalid data from entering the subsequent processing links. For repeated motor identifiers, the system uses the timestamp field to distinguish them, ensuring that each data corresponds to a unique sampling time, avoiding data redundancy caused by repeated transmission of the device. In addition, to cope with high-concurrency data collection scenarios, the load data collection module adopts a multi-threaded processing mechanism, and each data stream is assigned an independent processing thread to ensure that the real-time performance and throughput of data processing meet system requirements.

[0030] Through the above process, the load data acquisition module converts the distributed and heterogeneous sensor raw data into standardized load data units with unified format and complete information, providing standardized data input for subsequent load rule verification, associated network construction and balanced distribution, which is the basic prerequisite for the operation of the entire dynamic load balancing distribution system.

[0031] Example 2: See Figure 3 After the standardized load data unit is generated, the system enters the processing phase of calculating the rule applicability complexity factor and determining the load compliance status. These two steps are closely related. The former is used to evaluate the complexity of the current load data matching the rule conditions, while the latter verifies the compliance of the load data based on the former's results.

[0032] The calculation of the rule applicability complexity factor begins with the extraction and normalization of key fields in the standardized load data unit. The standardized load data unit includes the load type field and the allowable threshold field, which serve as the core basis for rule matching. The system normalizes the raw value of the load type field. If the raw value is "variable torque load" or "variable torque load," the system uses a predefined synonym mapping table to standardize it to the standard expression "variable torque load." If the raw value is missing or ambiguous, the system automatically completes the load type based on the time series characteristics of the load value. For the allowable threshold field, the system checks its unit and dimension compliance. For example, if the load value is in "N·m" (Newton·meter) and the allowable threshold field is in "kgf·m" (kilogram-force·meter), the system converts it to a unified unit using the unit conversion formula (1kgf·m ≈ 9.8N·m). If the allowable threshold field lacks dimension information, the system references the standard dimension for that load type in the equipment manual to complete it. After normalization, a set of load type and allowable threshold combinations is formed, providing standardized input for subsequent rule retrieval.

[0033] The system searches the rule database based on the normalized combination set. The rule database stores multi-dimensional and multi-level load rule condition clauses. Each clause contains information such as load type matching conditions, allowed threshold constraints, logical judgment relationships, nesting levels, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable location overlap. The system performs a preliminary match between the load type field and the load type classification in the rule database, and selects all clauses containing the current load type as a set of candidate clauses. The allowed threshold field is further matched in the candidate clause set. If the unit and dimension of the current allowed threshold are exactly the same as the constraint value in the clause, or are compatible after unit conversion, the clause is included in the list of clauses to be evaluated.

[0034] For each clause in the list of clauses to be evaluated, the system extracts parameters such as its nesting level, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable position overlap. The nesting level is determined by parsing the internal structure of the clause: if the clause contains sub-clauses, the nesting level of the main clause is 2 (main clause + sub-clause); if the clause has no nested structure, the nesting level is 1. The number of logical judgments is obtained by counting the number of judgment conditions in the clause: the above clause containing time window and load type involves 2 judgments (load type matching, time window matching), so the number of logical judgments is 2. The clause reference frequency is obtained by querying historical matching records. For example, if a clause has been matched 150 times in the past 7 days, its reference frequency is 150 times / cycle. The coverage rate of the clause activation period is determined by calculating the overlap ratio between the clause's applicable time window and the current time window: if the clause's applicable time period is "2025-08-10 to 2025-08-20" and the current time window is "2025-08-11 10:00 to 2025-08-11 11:00," the coverage rate is 100%; if the current time window is "2025-08-09 09:00 to 2025-08-09 10:00," the coverage rate is 0%. The overlap rate of the clause's applicable location is calculated by comparing the spatial relationship between the clause's applicable location and the current motor location: if the clause's applicable location is "Workshop Area A" and the current motor location is "Workshop Area A, Production Line B," the overlap rate is 100%; if the current motor location is "Workshop Area B," the overlap rate is 0%.

[0035] Based on the extracted parameters, the system calculates the rule application complexity factor. This factor reflects the overall difficulty of matching the current load data with the rule conditions. Its calculation process comprehensively considers the relative importance of various parameters: higher nesting levels indicate a more complex clause structure, requiring more sub-conditions to be considered during matching; a greater number of logical decisions lead to more complex conditions to be verified; a higher frequency of reference indicates a more general clause, potentially requiring more careful verification; a lower activation period coverage indicates less applicable to the current time; and a lower position overlap indicates less targeted application of the clause to the current motor position. The rule application complexity factor is calculated by weighting these parameters according to preset weights (e.g., nesting levels account for 30%, number of logical decisions 20%, frequency of reference 20%, activation period coverage 20%, and position overlap 10%). This factor typically ranges from 0 to 1. Values ​​closer to 1 indicate a more complex matching process, requiring more computing resources for verification; values ​​closer to 0 indicate a simpler matching process, allowing for direct verification based on the basic conditions.

[0036] The load compliance status determination is based on the rule's applicable complexity factor and the specific values ​​of the standardized load data unit, aiming to determine whether the current load data meets the rule conditions. The system uses different matching strategies based on the complexity factor: a low complexity factor (≤0.3) corresponds to a loose match, which only verifies whether the load value is within the allowed threshold range; a medium complexity factor (0.3 < factor ≤0.7) corresponds to a standard match, which verifies the load value range, time window (whether the timestamp of the load data is within the validity period specified by the clause), and location range (whether the location corresponding to the motor identifier is within the geographic area specified by the clause); a high complexity factor (>0.7) corresponds to a strict match, which, in addition to the above verification, also checks the load value fluctuation trend (for example, whether it exceeds 80% of the threshold for three consecutive sampling periods) and the reference status of related clauses (for example, whether there is a logical conflict with other matched clauses).

[0037] In implementation, the system compares the load type field value in the standardized load data unit with the clause constraint value. If the load value is 190 N·m and the maximum allowable load value in the clause constraint is 180 N·m, the system is judged as "exceeding the upper limit." If the load value is 170 N·m and falls within the clause-specified time window (e.g., 8:00-18:00), and the motor location is in "Workshop Area A" as specified in the clause, the system preliminarily determines it as "compliant." The system then checks the timestamp field value in the load data to see if it falls within the clause activation period. For example, if the clause requires a time window of "2025-08-10 to 2025-08-20" and the current timestamp is "2025-08-11 10:13:43," the time match is successful. However, if the timestamp is "2025-08-09 09:00:00," the time match fails. Based on the motor identifier field value, the system checks whether it falls within the location range to which the clause applies through the location mapping table (such as "MOTOR_003" corresponds to "Workshop Area A"). If the applicable location of the clause is "Workshop Area A" and the current motor position is "Workshop Area A", the position matching is successful; if the motor position is "Workshop Area B", the position matching fails.

[0038] Based on the results of the threshold comparison, time window match, and location range check, the system generates a load compliance status determination. If all three verifications pass (load value compliance, time match, and location match), the determination is "fully compliant." If only partially pass (e.g., load value compliance but time match failure), the determination is "partially compliant," and the specific reason for the non-match is recorded (e.g., "time window mismatch"). If any one of the verifications fails (e.g., the load value exceeds the upper limit), the determination is "non-compliant," and the specific violation point is noted (e.g., "the load value exceeds the maximum allowable threshold of 180 N·m"). For strict matches corresponding to high complexity factors, the system further analyzes the load value fluctuation trend: if the current load value is 185 N·m (exceeding the threshold by 5 N·m), and the load values ​​in the previous two sampling cycles were 182 N·m and 178 N·m, respectively, this is considered "continuously exceeding the upper limit," and the compliance status is marked as "non-compliant." If the load value only briefly exceeds the threshold in a single sampling cycle (e.g., 185 N·m), while the values ​​in the preceding and subsequent cycles are below the threshold, it may be marked as "occasional exceeding the upper limit," requiring further verification in conjunction with other criteria. The load compliance status determination result includes the compliance level (full / partial / non-compliant), the reason for non-compliance (if any), and the corresponding rule clause reference information (such as "Rule Clause R003"), providing key compliance basis for subsequent associated network construction and balanced distribution.

[0039] Example 3: See Figure 4 The process of obtaining the cross-motor correlation closeness index begins with the extraction and classification of key entities from multiple standardized load data units. A standardized load data unit contains a motor identifier field, an associated location field, and a timestamp field. The system then performs entity classification matching and deduplication on these fields. For motor identifiers, a string fuzzy matching algorithm is used to standardize "Workshop A" and "Workshop A" into a standard name, generating a location entity set. For timestamps, continuous time is divided into discrete windows according to preset time windows (e.g., 5-minute intervals). "2025-08-11 10:13:43" and "2025-08-11 10:14:00" both fall within the "10:10-10:15" time window, generating a time entity set. After deduplication, a load-sharing entity set is obtained, which includes the unique identifiers of the motor entity, location entity, and time entity, as well as the number of occurrences in each standardized load data unit.

[0040] The system counts the number of occurrences of each type of shared entity in different geared motor loads. For example, the motor entity "MOTOR_001" appears in 10 standardized load data units, the location entity "Workshop Area A" appears in 15 units, and the time entity "10:10-10:15" appears in 8 units. The system also labels the entity categories (motor / location / time) and records the distribution of each type of shared entity in each standardized load data unit, namely, which motor entities, location entities, and time entities each unit contains. This distribution information is stored in a hash table, where the key is the unique identifier of the standardized load data unit and the value is the set of entities associated with that unit.

[0041] The calculation of the cross-motor correlation closeness index is based on the above distribution information and adopts the improved Jaccard similarity formula. For any two reduction motors (recorded as motor i and motor j), the set of load sharing entities associated with them is extracted. (i.e., entities that appear in both the load data units of motor i and motor j), and then calculate the ratio of the size of this set to the total number of entities associated with motor i and motor j. The specific formula is:

[0042] in: represents the cross-motor correlation closeness index between motor i and motor j; represents the number of shared entities associated with motor i and motor j; Indicates the total number of entities associated with motor i (including the union size of motor entities, position entities, and time entities); Represents the total number of entities associated with motor j. This formula balances the impact of the difference in the total number of entities between motor i and motor j on the result by using the square root of the denominator, avoiding underestimation of similarity due to too many entities associated with a motor. For example, if motor i is associated with 10 entities and motor j is associated with 16 entities, and both are associated with 6 entities, then For all motor pairs Normalization (ranging from 0 to 1) yields a cross-motor correlation index matrix, where each element represents the correlation between two motors. This index reflects the degree of coordination between the loads of different motors at the entity level. A higher index indicates a stronger correlation between the loads of the two motors in the motor, position, and time dimensions.

[0043] When constructing a multi-load association map based on the cross-motor association closeness index, a set of load entity nodes is generated. Each load entity node corresponds to a standardized load data unit, and the node attributes include motor identifier, load value, timestamp, compliance status determination result, etc. The unique identifier of the node is generated by combining the hash value of the motor identifier, timestamp and load value to ensure the global uniqueness of each node. Calculate the shared relationship strength value between load entities, which is a combination of the cross-motor association closeness index and the degree of overlap of shared entities. The specific calculation method is: for two load entity nodes (associated motor i) and (associated motor j), whose shared relationship strength value Equal to the cross-motor correlation density index Multiply by the ratio of the number of shared entities between the two to the total number of entities of each, that is:

[0044] in: Represents the load entity node (Load data unit associated with motor i) and load entity node The shared relationship strength value between (the load data unit associated with motor j) is used to quantify the closeness of the association between the two load entities; Indicates a reduction motor With reduction motor The cross-motor correlation closeness index between them; Represents a set of commonly related entities size; Indicates motor The total number of entities associated; Indicates motor The total number of entities associated; denominator is the union size of the total number of entities of motor i and motor j, ensuring that the proportional term reflects the proportion of shared entities in the union. For example, if , , , ,but The strength value quantifies the strength of the association between two load entities, with larger values ​​indicating a closer association.

[0045] A graph structure with load entities as nodes and sharing relationship strength values ​​as edge weights is stored in an adjacency table. Each edge connects two load entity nodes, and the edge weight is the corresponding sharing relationship strength value. A mapping table of entity-edge connection relationships between nodes is also established, recording each edge's source node, target node, edge weight, and associated shared entity type (such as motor entity, location entity, or time entity). For example, an edge connecting node U001 (motor M001, time T01) and node U002 (motor M002, time T01) has an edge weight of 0.142 and the associated shared entity types are "time entity T01" and "location entity A01." The resulting multi-load association graph intuitively reflects the associations between different geared motor loads, providing a structured analytical foundation for subsequent load balancing. This graph supports efficient path querying and relationship analysis, quickly identifying the synergistic impact between loads, and thus optimizing dynamic load balancing strategies.

[0046] Example 4: See Figure 5 Calculating balance scores and generating dynamic allocation recommendations based on multi-load association graphs are key steps in achieving precise load regulation. The following describes how this process is implemented, using specific application scenarios.

[0047] Consider a factory with three geared motors: MOTOR_001 (Workshop Area A), MOTOR_002 (Workshop Area A), and MOTOR_003 (Workshop Area B). Their operating hours range from 10:00 AM to 10:30 AM on August 11, 2025. The system has constructed a multi-load association map based on the previous three hours of operating data. This map contains 12 load entity nodes (U001 to U012). Each node records the motor identifier, load value, timestamp, compliance status (fully compliant, partially compliant, or non-compliant), and associated location information.

[0048] Bidirectional path extraction for multiple load entity nodes begins with a fully connected graph analysis. The system traverses all possible node pairs and identifies all connected paths between any two nodes. Path length is defined as the number of edges in the path (i.e., the number of intermediate nodes + 1). For example, nodes U001 (MOTOR_001, time T01, load 150 N·m) and U002 (MOTOR_002, time T01, load 160 N·m) are directly connected, with a path length of 1. Nodes U002 and U003 (MOTOR_003, time T02, load 170 N·m) are indirectly connected through U004 (MOTOR_002, time T02, load 165 N·m), with a path length of 2. The out-degree of a path endpoint node refers to the number of outgoing edges connected to that node in the graph (i.e., the number of connections with other nodes). For example, U002 connects to U001, U004, and U005, with an out-degree of 3. The number of fields at the path start node refers to the number of attribute fields contained in that node (e.g., motor identifier, load value, timestamp, compliance status, and associated position, a total of 5 fields). For example, U001 has 5 fields. The number of intermediate nodes refers to the number of nodes in the path excluding the start and end points. For example, the number of intermediate nodes in the path U002→U004→U003 is 1 (U004). The system organizes all extracted path attributes into a multi-load path attribute set. For partial data, see Table 1.

[0049] Table 1: The system organizes all extracted path attributes into a multi-load path attribute set. Some of the data is as follows.

[0050]

[0051] Based on a set of multiple load path attributes, the system calculates a balance score for each node. The scoring process comprehensively considers four dimensions: path length, out-degree of the endpoint node, number of fields at the starting node, and number of intermediate nodes. The shorter the path length, the less impact load adjustments will have on other nodes, resulting in a higher score weight. The higher the out-degree of the endpoint node, the closer the node's connections to other nodes, and the greater the impact of adjusting its load on the overall system, resulting in a higher score weight. The more fields a starting node has, the more comprehensive the load information for that node (including more dimensions of operational data), the more comprehensive the basis for adjustment decisions, and the higher the score weight. The greater the number of intermediate nodes, the more complex the path, the higher the risk of chain reactions that may be triggered during the adjustment, and the lower the score weight.

[0052] During the calculation, the system assigns a weight coefficient to each dimension (path length 0.4, out-degree of the end node 0.3, number of fields at the start node 0.2, and number of intermediate nodes 0.1). The original values ​​of each dimension are normalized (ranging from 0 to 1). For example, the path length of path P001 is 1 (maximum path length is 3), which is normalized to 1 / 3 (≈ 0.333); the out-degree of the end node is 3 (maximum out-degree is 4), which is normalized to 3 / 4 (0.75); the number of fields at the start node is 5 (fixed value), which is normalized to 1; and the number of intermediate nodes is 0 (maximum number of intermediate nodes is 2), which is normalized to 0. The balanced score of path P001 is 0.4 × 0.333 + 0.3 × 0.75 + 0.2 × 1 + 0.1 × 0 (≈ 0.133 + 0.225 + 0.2 + 0), which is 0.558. Similarly, the balanced scores of other paths are calculated; see Table 2.

[0053] Table 2: The equilibrium scores of other paths are calculated as follows.

[0054]

[0055] Based on the balance score, the system generates dynamic load balancing recommendations. Node pairs corresponding to paths with higher scores are considered to have lower adjustment costs and more manageable impacts, and are therefore prioritized for adjustment. For example, path P003 (score 0.712) connects U003 (MOTOR_003, load 170 N·m) and U004 (MOTOR_002, load 165 N·m). The compliance status of both nodes at both ends is "fully compliant" (load values ​​do not exceed their respective permitted thresholds of 180 N·m and 175 N·m). Furthermore, the path is short and has few intermediate nodes. Therefore, the system recommends maintaining the current load distribution without adjustment.

[0056] Path P004 (score 0.485) connects U004 (MOTOR_002, load 165 N·m) and U005 (MOTOR_003, load 190 N·m). The compliance status of U005 is "partially compliant" (the load value of 190 N·m is close to the allowed threshold of 195 N·m). The path length is long (3) and the number of intermediate nodes is large (2). The system recommends reducing the load value of U005 by 5% (from 190 N·m to 180.5 N·m) to reduce the risk of exceeding the threshold and reduce the impact on the associated nodes.

[0057] Path P002 (score 0.621) connects U002 (MOTOR_002, load 160 N·m) and U003 (MOTOR_003, load 170 N·m). U002's compliance status is "fully compliant," and U003's compliance status is "fully compliant." However, U003's load value (170 N·m) is far below its permitted threshold (200 N·m). The system detects low load utilization and recommends transferring part of U002's load (approximately 8 N·m) to U003 to improve overall load balance. After adjustment, U002's load value is adjusted to 152 N·m (still below the threshold of 175 N·m), and U003's load value is adjusted to 178 N·m (still below the threshold of 200 N·m). The balance score of path P002 is expected to improve to 0.685.

[0058] Exception handling steps are integrated throughout the entire process of generating balanced load recommendations. The system uses the load compliance status determination results from the multi-load association graph and detects abnormal load patterns using a pattern recognition algorithm. For example, the compliance status of node U001 (MOTOR_001, time T01 to T03) was "non-compliant" for three consecutive sampling periods. This was because the load values ​​(150 N·m, 155 N·m, 160 N·m) continuously exceeded the allowable threshold (140 N·m). Furthermore, the load values ​​(160 N·m, 165 N·m, 170 N·m) of the associated node U002 (MOTOR_002) also showed a synchronous upward trend. This resulted in the system identifying a "persistent association exceeding the threshold anomaly."

[0059] To address this abnormal pattern, the system adjusted its dynamic load balancing recommendations: The original recommendation, which maintained U001's load at 150 N·m, was reduced by 12% (to 132 N·m), and the monitoring frequency was increased (from every 100 milliseconds to every 50 milliseconds). Furthermore, the load adjustment range for U002 was increased from 5% to 8% (from 160 N·m to 147.2 N·m) to help offset U001's excessive load. After these adjustments, U001's load value (132 N·m) fell below the threshold (140 N·m), restoring its compliance status to "fully compliant." U002's load value (147.2 N·m) remained below the threshold (175 N·m), significantly reducing system operational risks.

[0060] Dynamic load balancing recommendations are output as structured data, including the target load value for each motor, adjustment priority (highest-scoring nodes take precedence), adjustment time window (e.g., "Complete adjustment before 10:30"), and exception handling instructions (e.g., "U001 requires enhanced temperature monitoring"). These recommendations are transmitted to the motor controller via Industrial Ethernet, guiding it to perform load adjustment operations to ensure safe and balanced operation of the geared motor system.

[0061] Example 5: Retrieving the rule database to match the load type and the rule condition clauses of the allowed threshold and calculating the rule applicability complexity factor is a key link connecting load data collection and compliance status determination, and directly affects the accuracy of the subsequent balanced distribution strategy.

[0062] Once a standardized load data unit is generated, the system extracts the load type field and the allowable threshold field from the unit as the core input for rule matching. The load type field reflects the motor's current operating mode, such as "constant torque load," "variable torque load," or "impact load." Its value may be derived from feature analysis of sensor data (such as the rate of change of load values ​​over time) or preset classifications in the equipment manual. The allowable threshold field contains safety or performance constraints related to the load type, such as "maximum allowable load value," "minimum allowable load value," and "load fluctuation threshold," which are typically specified by the equipment manufacturer or industry specifications.

[0063] To ensure accurate matching, the system normalizes the original value of the load type field. If the original value is "variable torque load" or "variable torque load," the system uses a predefined synonym mapping table to standardize it to the standard expression "variable torque load." If the original value is missing or ambiguous (for example, simply labeled "variable torque"), the system automatically completes the load type based on the time series characteristics of the load value (such as whether the load value changes periodically over time). After normalization, a set of combinations of load types and allowable thresholds is formed, providing standardized input for subsequent rule retrieval.

[0064] The system searches the rule database based on the normalized combination set. The rule database stores multi-dimensional and multi-level load rule condition clauses. Each clause contains the following key information: load type matching conditions, allowable threshold constraints, logical judgment relationships, nesting levels, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable location overlap.

[0065] The system performs a preliminary match between the load type field and the load type classifications in the rule database, selecting all clauses containing the current load type as the candidate clause set. For example, if the current load type is "variable torque load," the candidate clause set includes all clauses matching "variable torque load." The candidate clause set is further matched against the allowable threshold field. If the unit and dimension of the current allowable threshold are identical to the constraint value in the clause, or are compatible after unit conversion, the clause is included in the list of clauses to be evaluated.

[0066] For each clause in the list of clauses to be evaluated, the system extracts parameters such as its nesting level, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable position overlap. The nesting level is determined by parsing the internal structure of the clause: if the clause contains sub-clauses, the nesting level of the main clause is 2 (main clause + sub-clause); if the clause has no nested structure, the nesting level is 1. The number of logical judgments is obtained by counting the number of judgment conditions in the clause: the above clause containing time window and load type involves 2 judgments (load type matching, time window matching), so the number of logical judgments is 2. The clause reference frequency is obtained by querying historical matching records. For example, if a clause has been matched 150 times in the past 7 days, its reference frequency is 150 times / cycle. The coverage of the clause activation period is determined by calculating the overlap ratio between the clause's applicable time window and the current time window: if the clause's applicable time period is "2025-08-10 to 2025-08-20" and the current time window is "2025-08-11 10:00 to 2025-08-11 11:00," the coverage is 100% (complete overlap); if the current time window is "2025-08-09 09:00 to 2025-08-09 10:00," the coverage is 0% (no overlap). The overlap of the clause's applicable location is calculated by comparing the spatial relationship between the clause's applicable location and the current motor location: if the clause's applicable location is "Workshop Area A" and the current motor location is "Workshop Area A, Production Line B," the overlap is 100% (complete inclusion); if the current motor location is "Workshop Area B," the overlap is 0% (no correlation).

[0067] Based on the extracted parameters, the system calculates the rule application complexity factor. This factor reflects the overall difficulty of matching the current load data with the rule conditions. Its calculation process comprehensively considers the relative importance of each parameter: higher nesting levels, more complex clause structures, and more sub-conditions must be considered during matching; more logical judgments, more complex conditions to verify; higher reference frequencies, more universal clauses, and potentially requiring more careful verification; lower activation period coverage, less applicable clauses at the current time; and lower position overlap, less targeted clauses for the current motor position. The rule application complexity factor is ultimately calculated by weighting and summing these parameters according to preset weights. This factor typically ranges from 0 to 1. Values ​​closer to 1 indicate a more complex matching process, requiring more computing resources for verification; values ​​closer to 0 indicate a simpler matching process, allowing for direct verification based on the basic conditions.

[0068] The calculation result of the rule application complexity factor directly influences the strategy selected for subsequent load compliance status determination. For example, if the complexity factor is 0.2 (low complexity), the system only verifies whether the load value is within the allowable threshold range; if it is 0.5 (medium complexity), it also verifies the load value range, time window, and position range; if it is 0.8 (high complexity), it also checks the load value fluctuation trend and the reference status of related clauses. Through this differentiated verification strategy, the system can improve the efficiency of load balancing distribution while ensuring compliance, ensuring that the reduction motor system operates in a safe and stable state.

[0069] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0070] 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. A dynamic load balancing distribution system for a reduction motor, characterized in that: The system comprises: The load data acquisition module is used to collect raw load data from the reduction motor sensor, identify and extract element information, including load value, timestamp, and motor identifier element, map the element information to preset data fields and establish internal association identifiers to generate standardized load data units; The load rule verification module searches the rule database for rule condition clauses that match the load type and the allowable threshold based on the standardized load data unit, calculates the rule application complexity factor based on the number of matching clauses and the hierarchical depth, compares the load data with the clause constraint value, time window and location range item by item based on the rule application complexity factor, determines the attribute compliance status, and generates a load compliance status judgment; An association network construction module searches for motor identifiers, associated positions, and timestamp entities shared between different reduction motors based on the plurality of standardized load data units, obtains a cross-motor association closeness index based on the number and category of shared entities, and constructs a graph structure with load entities as nodes and shared relationships as edges based on the cross-motor association closeness index to establish a multi-load association graph; The balanced distribution and anomaly identification module calculates a balanced score based on the multi-load association map and the load compliance status of the associated loads, and generates a dynamic load balanced distribution suggestion.

2. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the standardized payload data unit are: Based on the original load data of the reduction motor sensor, the sensor field content in the data is scanned and identified item by item, field category identification and matching are performed, and time series is split. The load value, timestamp and motor identifier are extracted one by one to generate a set of original load data elements. Based on the original payload data element set, mapping matching verification of the fields is performed one by one, and the numerical format conversion and field reconstruction are performed on the successfully matched field contents to generate a payload data standardized mapping field set; Based on the load data standardized mapping field set, internal association matching is performed between each standardized mapping field, and an internal association relationship identifier is established according to the matching result, and index construction and relationship binding of the standardized mapping fields are performed; Based on the index building and relationship binding results, all fields are integrated to generate a standardized payload data unit.

3. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the rule applicable complexity factor are as follows: Based on the load type field and the allowable threshold field in the standardized load data unit, extract the original field value and perform field content normalization matching with the field definition library, fill in the missing fields and unify the field expression method to obtain a normalized load type field and allowable threshold field combination set; According to the normalized payload type field and the allowed threshold field combination set, the rule condition clauses matching the payload type are sequentially retrieved from the rule database, and the clauses matching the allowed threshold field and the combination set are screened to extract the nesting level, number of logical judgments, clause reference frequency, clause activation period coverage, and clause applicable position overlap of each clause; Based on the extraction results, a complexity factor is applied to the calculation rules.

4. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the load compliance status determination are as follows: Based on the rule application complexity factor, the payload type field value and clause constraint value in the standardized payload data unit are called one by one to perform threshold comparison, value range verification, and time matching judgment between the field value and the constraint value; Based on the comparison and verification results, the location field value in the standardized payload data unit is obtained, and the payload location field is mapped to the applicable location range of the rule clauses one by one, and the clauses with successful location mapping are selected; Based on the screening results, determine whether the attributes of each load data are consistent; Generate a load compliance status judgment based on the attribute compliance.

5. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the cross-motor correlation closeness index are as follows: Based on the plurality of standardized load data units, extracting the motor identifier field, the associated position field, and the timestamp field in each standardized load data unit, performing entity classification matching and deduplication processing on all load data according to field type, and generating a load sharing entity set; According to the load sharing entity set, performing entity occurrence statistics and entity category marking in different reduction motor loads one by one, and recording distribution information of each type of shared entity in each standardized load data unit; Based on the distribution information, the cross-motor correlation closeness index is calculated.

6. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the multi-load association map are: Extracting the motor identifier field, the associated position field, and the timestamp field from a plurality of standardized load data units based on the cross-motor association closeness index, and performing entity normalization identification coding and entity type labeling on each field content; Based on the annotation results, a set of load entity nodes is generated; Calculating the sharing relationship strength value between load entities according to the load entity node set; Based on the shared relationship strength value, a graph structure is constructed with the load entity as the node and the shared relationship strength value as the edge weight, and an entity edge connection relationship mapping table between the nodes is established; Based on the entity edge connection relationship mapping table, a multi-load association graph is generated.

7. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The steps for obtaining the dynamic load balancing suggestion are as follows: Based on the multi-load association graph, the bidirectional paths of all load entity nodes are extracted from the graph structure, and the load compliance status judgment result, path length, out-degree of the path end node, number of path starting node fields, and number of intermediate nodes passed by the path corresponding to each path are retrieved; Generate multiple load path attribute sets based on the search results; Calculating a balance score of a corresponding entity node based on the multiple load path attribute sets; Generate dynamic load balancing recommendations based on the balance score.

8. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The element information identification and extraction steps of the load data acquisition module are as follows: Receive the original load data stream from the reduction motor sensor and perform data stream segmentation processing; Based on the segmentation processing result, identifying the load value, time stamp and motor identifier elements; Mapping identification elements to pre-set data fields; Based on the mapping results, an internal association identifier is established; Based on the internal correlation identifier, the standardized payload data unit is output.

9. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The balanced distribution and exception identification module also includes an exception handling step, specifically: Call the load compliance status judgment in the multi-load association map; detect abnormal load patterns based on the load compliance status judgment; Adjust dynamic load balancing recommendations based on abnormal load patterns; Based on the adjustment results, the final load balancing distribution output is generated.

10. The dynamic load balancing distribution system for a reduction motor according to claim 1, characterized in that: The search rule database matches the load type and the allowed threshold, and the rule condition clauses include: Query the load rule set in the rule database and match the load type with the allowed threshold based on the query results; According to the matching results, the rule condition clauses are extracted, and based on the rule condition clauses, the rule applicable complexity factor calculation is executed.

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