Multi-dimensional operation rule detection method and system, electronic equipment and medium

Abstract

The invention provides a multi-dimensional operation rule detection method and system, electronic equipment and a medium. The method comprises the steps that metadata configuration information and historical rule combinations of all rule dimensions are acquired; wherein the metadata configuration information comprises a value set of a non-interval dimension, an upper limit boundary, a lower limit boundary and a boundary specification of at least one interval dimension; generating a theoretical combination rule complete set based on the metadata configuration information; performing conflict and deletion detection on the theoretical combination rules in the theoretical combination rule set based on the historical rule combination to obtain a detection result; wherein the conflict and deletion detection at least comprises starting point coverage detection, adjacent connection fault detection, overlapping conflict detection, end point coverage detection and border crossing and zero length interval detection. According to the invention, the coverage degree of multi-dimensional operation rule detection can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing and operations management technology, and in particular to a method, system, electronic device, and medium for detecting multi-dimensional operations rules. Background Technology

[0002] Existing systems configure interval-based rules (such as distance / amount / time period) across multiple dimensions (city, vehicle type, number of people, date / holiday, weather, regional level, etc.). Due to the complexity of manual configuration, system upgrades, and data synchronization delays, issues such as missing entire groups, interval gaps, interval overlaps, coverage exceeding limits, and zero-length intervals frequently occur. Current verification methods mostly rely on single-dimensional scripts or sampling reviews, making it difficult to achieve full coverage verification of theoretically existing combinations. Furthermore, the lack of real-time alerts and unified boundary specifications leads to missed detections and delayed responses. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, system, electronic device and medium for detecting multi-dimensional operation rules, which can improve the coverage of multi-dimensional operation rule detection.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for detecting multi-dimensional operational rules, comprising: acquiring metadata configuration information and historical rule combinations for each rule dimension; wherein, the metadata configuration information includes: a set of values ​​for non-interval dimensions, an upper limit boundary and a lower limit boundary for at least one interval dimension, and boundary specifications; generating a complete set of theoretical combination rules based on the metadata configuration information; performing conflict and missing detection on the theoretical combination rules in the complete set of theoretical combination rules based on historical rule combinations, and obtaining detection results; wherein, conflict and missing detection includes at least: starting point coverage detection, adjacent connection fault detection, overlapping conflict detection, ending point coverage detection, boundary crossing and zero-length interval detection.

[0005] Optionally, theoretical combination rules are generated based on metadata configuration information, including: generating a complete set of theoretical combination rules based on a preset combination generator and a set of values ​​of non-interval dimensions; wherein, the combination generator generates the complete set of theoretical combination rules using one of the following methods: Cartesian product, incremental completion, and gap inference based on historical observations.

[0006] Optionally, conflict and missing detection is performed on the rule combinations in the complete set of theoretical combination rules based on historical rule combinations to obtain detection results, including: establishing a grouping index based on non-interval dimension combinations in historical rule combinations; performing missing detection on each theoretical combination rule in the complete set of theoretical combination rules based on the grouping index; if there is a dimension combination key of a theoretical combination rule in the grouping index, then the interval list of the interval dimension corresponding to the dimension combination key is obtained, and the interval list is detected to obtain detection results.

[0007] Optionally, the interval list is tested to obtain the test results, including: sorting the intervals in the interval list in ascending order according to the starting point of each interval, and performing a linear scan on each interval based on the half-open interval specification to obtain the test results.

[0008] Optionally, a linear scan is performed on each interval based on the half-open interval specification to obtain the detection results, including: determining whether the starting point of the first interval is equal to the lower boundary of the interval dimension corresponding to the interval; if not, outputting a missing starting point coverage; for two adjacent intervals, determining whether the ending point of the previous interval is equal to the starting point of the next interval; if not, outputting an adjacent fault conflict; for two adjacent intervals, determining whether the starting point of the next interval is less than the ending point of the previous interval; if so, outputting an interval overlap conflict; determining whether the ending point of the last interval is equal to the upper boundary of the interval dimension corresponding to the interval; if not, outputting a missing ending point coverage; for any interval, determining whether the starting point and ending point of the interval satisfy one or more preset conditions; if satisfied, outputting an out-of-bounds or zero-length anomaly; wherein, the preset conditions include: whether the starting point of the interval is less than the lower boundary of the interval dimension corresponding to the interval, whether the ending point of the interval is greater than the upper boundary of the interval dimension corresponding to the interval, and whether the starting point of the interval is greater than or equal to the ending point of the interval.

[0009] Optionally, the above method further includes: for two adjacent intervals, if the end point of the previous interval is not equal to the start point of the next interval, and the difference between the end point of the previous interval and the start point of the next interval is greater than a first threshold, then an alarm message is output.

[0010] Optionally, the above method further includes: performing priority conflict detection on intervals based on pre-set interval priorities; if a low-priority interval covers a high-priority interval, then outputting an alarm message.

[0011] Secondly, this invention provides a multi-dimensional operational rule detection system, comprising: a metadata management module for acquiring metadata configuration information and historical rule combinations for each rule dimension; wherein the metadata configuration information includes: a set of values ​​for non-interval dimensions, an upper limit boundary and a lower limit boundary for at least one interval dimension, and boundary specifications; a combination generation module for generating a complete set of theoretical combination rules based on the metadata configuration information; and a rule detection module for performing conflict and missing detection on the theoretical combination rules in the complete set of theoretical combination rules based on historical rule combinations, and obtaining detection results; wherein the conflict and missing detection includes at least: start point coverage detection, adjacent connection fault detection, overlapping conflict detection, end point coverage detection, and boundary crossing and zero-length interval detection.

[0012] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method provided in any of the first aspects above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, performs the steps of the method provided in any of the first aspects above.

[0014] This invention brings the following beneficial effects: The multi-dimensional operation rule detection method, system, electronic device, and medium provided by this invention first acquire metadata configuration information and historical rule combinations for each rule dimension. The metadata configuration information includes: the value set of non-interval dimensions, the upper and lower boundaries of at least one interval dimension, and boundary specifications. Then, a complete set of theoretical combination rules is generated based on the metadata configuration information. Finally, conflict and missing rule detection is performed on the theoretical combination rules in the complete set of theoretical combination rules based on historical rule combinations to obtain detection results. The conflict and missing rule detection includes at least: start point coverage detection, adjacent connection discontinuity detection, overlapping conflict detection, end point coverage detection, and boundary crossing and zero-length interval detection. In the above method, the business coverage of non-interval dimension value sets and interval dimensions is acquired based on metadata configuration information, and a complete set of theoretical combination rules is generated. The complete set of theoretical combination rules is then subjected to detections such as start point coverage detection, adjacent connection discontinuity detection, overlapping conflict detection, end point coverage detection, boundary crossing and zero-length interval detection, thereby improving the coverage of multi-dimensional operation rule detection.

[0015] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0016] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for detecting multi-dimensional operational rules provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of an interval continuity detection logic provided in an embodiment of the present invention; Figure 3 A schematic diagram of a multi-dimensional operation rule detection process provided for an embodiment of the present invention; Figure 4 A schematic diagram of the structure of a multi-dimensional operation rule detection system provided in an embodiment of the present invention; Figure 5 An architecture diagram of an automated detection system for conflicts and missing multi-dimensional operation rules provided in this embodiment of the invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Current inspection methods are mostly single-dimensional scripts or sampling reviews, which make it difficult to achieve full coverage verification of theoretically required combinations. They also lack real-time alerts and unified boundary specifications, leading to missed detections and delayed responses.

[0021] Based on this, the present invention provides a method, system, electronic device and medium for detecting multi-dimensional operation rules, which can improve the coverage of multi-dimensional operation rule detection.

[0022] To facilitate understanding of this embodiment, a method for detecting multi-dimensional operational rules disclosed in this invention will first be described in detail. This method can be applied to scenarios such as ride-hailing pricing, e-commerce promotional periods, and logistics billing periods, and can be executed by electronic devices such as smartphones, computers, and tablets. See also Figure 1 The flowchart shown illustrates a method for detecting multi-dimensional operational rules, indicating that the method mainly includes the following steps S101 to S103: Step S101: Obtain metadata configuration information and historical rule combinations for each rule dimension.

[0023] In one implementation, the metadata configuration information includes: a set of values ​​for non-range dimensions, and an upper and lower bound for at least one range dimension (i.e., the business coverage). , The system includes boundary specifications. Non-interval dimensions include one or more of the following: city, vehicle type, number of people, date / holiday, weather, and region level. Interval dimensions include at least one of the following: distance, amount, or time period. Interval dimensions use a unified half-open interval specification [start, end) to avoid ambiguity caused by overlapping endpoints.

[0024] Step S102: Generate a complete set of theoretical combination rules based on metadata configuration information.

[0025] In one implementation, generating theoretical combination rules based on metadata configuration information includes: generating a complete set G of theoretical combination rules based on a preset combination generator and a set of values ​​of non-interval dimensions; wherein the combination generator generates the complete set of theoretical combination rules using one of the following methods: Cartesian product, incremental completion, and gap inference based on historical observations.

[0026] Furthermore, duplicate or identical intervals are deduplicated and merged to reduce redundant alarms.

[0027] Step S103: Based on the historical rule combination, perform conflict and missing detection on the theoretical combination rules in the complete set of theoretical combination rules to obtain the detection results.

[0028] In one implementation, conflict and missing item detection includes at least: start point coverage detection, adjacent connection discontinuity detection, overlapping conflict detection, end point coverage detection, and boundary crossing and zero-length interval detection. The detection results are output as structured records and pushed to the alarm system via an interface. The structured record output includes at least: defect type, involved intervals or combinations, location key (a combination of non-interval dimension values), discovery time, suggested remediation actions, and priority. The interface can be at least one of log files, a database, a message queue, or an alarm platform API.

[0029] The multi-dimensional operation rule detection method provided in this embodiment of the invention obtains the non-interval dimension value set and the business coverage of the interval dimension based on metadata configuration information, and generates a complete set of theoretical combination rules. The complete set of theoretical combination rules is then subjected to detections such as start point coverage detection, adjacent connection discontinuity detection, overlap conflict detection, end point coverage detection, boundary crossing and zero-length interval detection, thereby improving the coverage of multi-dimensional operation rule detection.

[0030] In one implementation, for the aforementioned step S103, i.e., when performing conflict and missing detection on the rule combinations in the complete set of theoretical combination rules based on historical rule combinations to obtain the detection results, the following methods may be adopted, including but not limited to: First, establish a grouping index based on the non-interval dimension combinations in the historical rule combinations; based on the grouping index, perform missing detection on each theoretical combination rule in the complete set of theoretical combination rules; if there is a dimension combination key of the theoretical combination rule in the grouping index, then obtain the interval list of the interval dimension corresponding to the dimension combination key, and perform detection on the interval list to obtain the detection results.

[0031] In practice, a grouping index g is established for the existing rule data, with non-range dimension value combinations as keys. Then, for each theoretical combination rule in the complete set of theoretical combination rules, if the dimension combination key does not exist in the grouping index, the entire set of files is output as missing; if the dimension combination key exists in the grouping index, the interval list of the interval dimension corresponding to the dimension combination key is obtained, and the interval list is checked to obtain the detection result.

[0032] Further, see Figure 2 The diagram shows a logic diagram for interval continuity detection. When detecting the interval list, the intervals in the interval list are first sorted in ascending order according to the starting point of each interval. Then, each interval is linearly scanned based on the half-open interval specification to obtain the detection result.

[0033] In practice, performing a linear scan based on the half-open interval specification [start, end) includes: (1) Start point coverage detection: Determine whether the start point of the first interval is equal to the lower bound of the corresponding interval dimension. If not, output that the start point coverage is missing. Specifically, determine whether the start (interval start point) of the first interval is equal to... (Lower bound) If it is not equal to, it indicates that the starting point of the first interval cannot cover the lower bound of this dimension, and the output is missing starting point coverage.

[0034] (2) Adjacent connection or fault detection: For two adjacent intervals, determine whether the end point of the previous interval is equal to the start point of the next interval. If they are not equal, output the adjacent fault conflict. Specifically, determine whether the end point of the previous interval is equal to the start point of the next interval. If they are not equal, it indicates that the adjacent intervals cannot be connected, and output the adjacent fault conflict.

[0035] (3) For two adjacent intervals, determine whether the starting point of the latter interval is less than the ending point of the former interval. If so, output "interval overlap conflict". Specifically, determine whether the start of the latter interval is less than the end of the former interval; if so, it indicates that the two intervals overlap and output "interval overlap conflict".

[0036] (4) Endpoint Coverage Detection: Determine if the end point of the last interval is equal to the upper bound of the corresponding interval dimension. If not, output "Endpoint Coverage Missing". Specifically, determine if the end of the last interval is equal to... (Upper boundary); if it is not equal, it indicates that the end point of the last interval cannot cover the upper boundary of this dimension, and the output is "End point coverage missing".

[0037] (5) Boundary and Zero Length Detection: For any interval, determine whether the interval start point and interval end point meet one or more of the preset conditions. If they meet, output an out-of-boundary or zero length anomaly. The preset conditions include: whether the interval start point is less than the lower limit boundary of the interval dimension corresponding to the interval, whether the interval end point is greater than the upper limit boundary of the interval dimension corresponding to the interval, and whether the interval start point is greater than or equal to the interval end point.

[0038] Specifically, for any interval, determine whether start < end> Alternatively, start ≥ end; if the first two conditions are not met, it indicates that the interval length exceeds the boundary range, and an out-of-bounds exception is output. If the third condition is not met, it indicates that the interval length is zero, and a zero-length exception is output.

[0039] In this embodiment of the invention, linear scanning completes the detection of start point coverage, adjacent connection, overlapping conflict and end point coverage of m intervals in O(m) complexity after sorting.

[0040] It is important to note that when the endpoint specifications of an interval change (closed / opened), the existing intervals should be rewritten for consistency based on the endpoint conversion rules before detection is performed. The method provided in this embodiment supports both offline full and real-time incremental operation modes; in real-time incremental mode, detection is performed only on the affected dimension combinations.

[0041] In one implementation, a tolerance threshold is configured for faults and overlaps between adjacent intervals, and an alarm is output when the threshold is exceeded. Based on this, the method further includes: for two adjacent intervals, if the endpoint of the preceding interval is not equal to the starting point of the following interval, and the difference between the endpoint of the preceding interval and the starting point of the following interval is greater than a first threshold, then an alarm is output. Specifically, if the endpoint of the preceding interval is not equal to the starting point of the following interval (i.e., there is a fault or overlap between adjacent intervals), and if the difference between the endpoint of the preceding interval and the starting point of the following interval is greater than a first threshold (i.e., the fault or overlap between adjacent intervals exceeds the first threshold), then an alarm is output.

[0042] In one implementation, the method further includes: performing priority conflict detection on intervals based on pre-defined interval priorities; and outputting an alarm message if a lower-priority interval covers a higher-priority interval. In specific implementations, priority conflict detection is performed on rule entries with priorities, and an alarm message is output when a lower-priority interval covers a higher-priority interval.

[0043] For ease of understanding, this embodiment of the invention also provides a flowchart illustrating the detection of multi-dimensional operational rules, see [link / reference]. Figure 3 As shown, the method mainly includes: S1: Get metadata configuration.

[0044] S2: Generates the complete set of theoretical combinatorial sets G.

[0045] S3: Create a grouped index.

[0046] S4: Ergodic theory combinatorial G.

[0047] S41: If the key g does not exist in the grouping index, output the entire group is missing.

[0048] S42: If key g exists in the grouped index, obtain the corresponding interval list and sort it in ascending order by the starting point. Perform a linear scan based on the half-open interval specification, and perform starting point coverage checks, adjacent connection checks, overlapping conflict checks, ending point coverage checks, and out-of-bounds and zero-length checks, and output the corresponding check results. For specific detection methods, please refer to the aforementioned embodiments, which will not be repeated here.

[0049] S5: Output structured records.

[0050] The method provided by this invention expands the detection range to include the entire theoretical combination set; by using a sorting and linear scanning approach, the detection complexity of each combination interval is O(m log m); by using a unified half-open interval specification and out-of-bounds / zero-length check, it reduces missed detections caused by boundary ambiguity; by using structured alarms and real-time push notifications, it shortens the latency from problem discovery to notification; and by using metadata-driven and combination generators, it facilitates cross-industry migration and dimensional expansion.

[0051] In addition to the multi-dimensional operation rule detection method provided in the foregoing embodiments, this invention also provides a multi-dimensional operation rule detection system, see [link to relevant documentation]. Figure 4 The diagram shown illustrates the structure of a multi-dimensional operational rule detection system, indicating that the system mainly comprises the following components: The metadata management module 401 is used to obtain metadata configuration information and historical rule combinations for each rule dimension; wherein, the metadata configuration information includes: the value set of non-range dimensions, the upper limit boundary and lower limit boundary of at least one range dimension, and the boundary specification.

[0052] The combination generation module 402 is used to generate a complete set of theoretical combination rules based on metadata configuration information.

[0053] The rule detection module 403 is used to perform conflict and missing detection on the theoretical combination rules in the complete set of theoretical combination rules based on historical rule combinations, and obtain the detection results.

[0054] The multi-dimensional operation rule detection system provided in this embodiment of the invention obtains the set of non-interval dimension values ​​and the business coverage of interval dimension based on metadata configuration information, and generates a complete set of theoretical combination rules. It then performs detections such as start-point coverage detection, adjacent connection discontinuity detection, overlap conflict detection, end-point coverage detection, boundary crossing and zero-length interval detection on the complete set of theoretical combination rules, thereby improving the coverage of multi-dimensional operation rule detection.

[0055] In one implementation, the above-mentioned combination generation module 402 is specifically used to: generate a complete set of theoretical combination rules based on a preset combination generator and a set of values ​​of non-interval dimensions; wherein, the combination generator generates a complete set of theoretical combination rules using one of the following methods: Cartesian product, incremental completion, and gap inference based on historical observations.

[0056] In one implementation, the rule detection module 403 is specifically used to: establish a grouping index based on the non-interval dimension combination in the historical rule combination; perform missing detection on the non-interval dimension of each theoretical combination rule in the complete set of theoretical combination rules based on the grouping index; if a grouping index exists in the theoretical combination rule, obtain the interval list of the interval dimension corresponding to the grouping index, and perform detection on the interval list to obtain the detection result.

[0057] In one implementation, the rule detection module 403 is specifically used to: sort the intervals in the interval list in ascending order according to the starting point of each interval, and perform a linear scan on each interval based on the half-open interval specification to obtain the detection result.

[0058] In one implementation, the rule detection module 403 is specifically used to: determine whether the starting point of the first interval is equal to the lower boundary of the interval dimension corresponding to the interval; if not, output "starting point coverage missing"; for two adjacent intervals, determine whether the ending point of the previous interval is equal to the starting point of the next interval; if not, output "adjacent fault conflict"; for two adjacent intervals, determine whether the starting point of the next interval is less than the ending point of the previous interval; if so, output "interval overlap conflict"; determine whether the ending point of the last interval is equal to the upper boundary of the interval dimension corresponding to the interval; if not, output "ending point coverage missing"; for any interval, determine whether the starting point and ending point of the interval satisfy one or more preset conditions; if satisfied, output "out of bounds" or "zero length anomaly"; wherein, the preset conditions include: whether the starting point of the interval is less than the lower boundary of the interval dimension corresponding to the interval, whether the ending point of the interval is greater than the upper boundary of the interval dimension corresponding to the interval, and whether the starting point of the interval is greater than or equal to the ending point of the interval.

[0059] In one embodiment, the system further includes an alarm module, configured to: for two adjacent intervals, if the endpoint of the preceding interval is not equal to the starting point of the following interval, and the difference between the endpoint of the preceding interval and the starting point of the following interval is greater than a first threshold, then output alarm information.

[0060] In one embodiment, the system further includes a priority conflict detection module, used to: perform priority conflict detection on intervals based on pre-set interval priorities; and output alarm information if a low-priority interval covers a high-priority interval.

[0061] This invention also provides an architecture diagram of an automated detection system for conflicts and missing multi-dimensional operational rules, see [link / reference]. Figure 5 As shown, it mainly includes: The metadata management module is used to maintain the set of values ​​for non-range dimensions, the business coverage of range-type dimensions, and boundary specifications. The combinatorial generation module is used to generate a theoretical combinatorial complete set based on metadata; The rule indexing module is used to create grouped indexes for existing rules, using combinations of non-range dimension values ​​as keys; The interval continuity detection module is used to perform sorting and linear scanning on each dimension combination to detect start point coverage, adjacent connection discontinuities, overlapping conflicts, end point coverage, out-of-bounds and zero length. The results aggregation and alarm module is used to generate structured records and push them to the alarm system through an interface; The operation control module is used for scheduling between offline full and real-time incremental modes.

[0062] It should be noted that the system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment. The specific numerical values ​​provided in the implementation of this invention are merely exemplary and are not intended to limit the scope of the invention.

[0063] This invention also provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0064] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 100 includes: a processor 60, a memory 61, a bus 62, and a communication interface 63. The processor 60, the communication interface 63, and the memory 61 are connected through the bus 62. The processor 60 is used to execute executable modules, such as computer programs, stored in the memory 61.

[0065] The memory 61 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 63 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0066] Bus 62 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0067] The memory 61 is used to store programs. After receiving an execution instruction, the processor 60 executes the program. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0068] Processor 60 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 60 or by instructions in software form. Processor 60 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 61. Processor 60 reads the information in memory 61 and, in conjunction with its hardware, completes the steps of the above method.

[0069] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0070] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting multi-dimensional operational rules, characterized in that, include: Obtain metadata configuration information and historical rule combinations for each rule dimension; wherein, the metadata configuration information includes: the value set of non-range dimensions, the upper and lower bounds of at least one range dimension, and the boundary specifications; A complete set of theoretical combination rules is generated based on the aforementioned metadata configuration information; Based on the historical rule combination, conflict and missing rules in the complete set of theoretical combination rules are detected to obtain the detection results; wherein, the conflict and missing detection includes at least: starting point coverage detection, adjacent connection fault detection, overlapping conflict detection, ending point coverage detection, boundary crossing and zero length interval detection.

2. The method according to claim 1, characterized in that, Theoretical combination rules are generated based on the aforementioned metadata configuration information, including: A complete set of theoretical combination rules is generated based on a preset combination generator and the set of values ​​for the non-interval dimension; wherein, the combination generator generates the complete set of theoretical combination rules using one of the following methods: Cartesian product, incremental completion, or gap inference based on historical observations.

3. The method according to claim 1, characterized in that, Based on the historical rule combinations, conflict and missing rule combinations in the complete set of theoretical rule combinations are detected, and the detection results are obtained, including: A grouped index is built based on the non-range dimension combinations in the historical rule combinations; Based on the grouping index, a missing rule is detected for each theoretical combination rule in the complete set of theoretical combination rules; If the grouping index contains a dimension combination key of the theoretical combination rule, then obtain the interval list of the interval dimensions corresponding to the dimension combination key, and perform detection on the interval list to obtain the detection result.

4. The method according to claim 3, characterized in that, The list of intervals is tested to obtain the test results, including: The intervals in the interval list are sorted in ascending order according to the starting point of each interval, and each interval is linearly scanned based on the half-open interval specification to obtain the detection result.

5. The method according to claim 4, characterized in that, Based on the half-open interval specification, a linear scan is performed on each interval to obtain the detection results, including: Determine whether the starting point of the first interval is equal to the lower boundary of the interval dimension corresponding to the interval. If not, output that the starting point is missing. For two adjacent intervals, determine whether the end point of the previous interval is equal to the start point of the next interval. If they are not equal, output "adjacent fault conflict". For two adjacent intervals, determine whether the starting point of the latter interval is less than the ending point of the former interval. If so, output the interval overlap conflict. Determine whether the endpoint of the last interval is equal to the upper bound of the interval dimension corresponding to the interval. If not, output "Endpoint coverage missing". For any given interval, determine whether the start point and end point of the interval satisfy one or more preset conditions. If they are satisfied, output an out-of-bounds or zero-length exception. The preset conditions include: whether the start point of the interval is less than the lower boundary of the interval dimension corresponding to the interval, whether the end point of the interval is greater than the upper boundary of the interval dimension corresponding to the interval, and whether the start point of the interval is greater than or equal to the end point of the interval.

6. The method according to claim 5, characterized in that, Also includes: For two adjacent intervals, if the end point of the previous interval is not equal to the start point of the next interval, and the difference between the end point of the previous interval and the start point of the next interval is greater than a first threshold, then an alarm message is output.

7. The method according to claim 4, characterized in that, Also includes: Priority conflict detection is performed on the intervals based on pre-defined interval priorities; If a low-priority interval covers a high-priority interval, an alarm message will be output.

8. A multi-dimensional operational rule detection system, characterized in that, include: The metadata management module is used to obtain metadata configuration information and historical rule combinations for each rule dimension; wherein, the metadata configuration information includes: the value set of non-range dimensions, the upper limit boundary and lower limit boundary of at least one range dimension, and the boundary specification; The combination generation module is used to generate a complete set of theoretical combination rules based on the metadata configuration information; The rule detection module is used to perform conflict and missing detection on the theoretical combination rules in the complete set of theoretical combination rules based on the historical rule combination, and obtain the detection results; wherein, the conflict and missing detection includes at least: starting point coverage detection, adjacent connection fault detection, overlapping conflict detection, ending point coverage detection, boundary crossing and zero length interval detection.

9. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program thereon, characterized in that, The computer program is executed by the processor to perform the steps of the method described in any one of claims 1 to 7.

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