A notarization method for property inventory

Abstract

The present application relates to the technical field of property management, in particular to a notarization method for property inventory, comprising the following steps: obtaining an inventory number timestamp area and an article index to generate a responsibility chain, sorting data to set a responsibility index, generating sequence data, generating a fingerprint by hashing and binding the number, marking an exception in case of timeout, obtaining log comparison consistency and completion rate score classification risk, backtracking to calculate the influence value of responsibility, and uploading the optimized responsibility record. In the present application, the responsibility chain is sorted to track the task sequence and timeliness of the inventory personnel, the data is recorded and the responsibility initialization index is set, the data fingerprint is generated by the hash algorithm to ensure the independence and integrity of the data, the timestamp comparison is used to mine the delay and mark the exception, the log comparison consistency and completion rate are obtained based on the abnormal data set, the personalized risk assessment and personnel classification are generated, the responsibility influence value is calculated by backtracking the responsibility chain sequence and timestamp, the responsibility attribution analysis and optimization are formed, the inventory compliance and transparency are improved, and the risk is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of property management, and in particular to a notarization method for property inventory. BACKGROUND

[0002] The technical field of property management includes various technologies related to the identification, storage, evaluation, inventory, and transfer of property. The core content is to achieve effective management of property through various information technology means, ensuring the accuracy, transparency, and security of property information. Property management technology is widely used in various fields such as individuals, enterprises, and governments, and is particularly important and challenging in the management, transfer, and verification of high-value assets. With the development of technology, especially the maturity of information technology and Internet of Things technology, property management has gradually developed towards intelligence, digitization, and automation. Research and practice in this field mainly focuses on the establishment of digital archives of property, asset information tracing, inventory and counting methods, etc.

[0003] The existing technology has certain operational limitations in the process of property inventory, mainly in the transparency of inventory data traceability and responsibility attribution. Although the notarization method ensures the legal effect of the inventory data, the traditional method relies on manual or periodic auditing, which is prone to data delays, personnel negligence, and other problems, resulting in lagging information updates and inability to real-time grasp the integrity and accuracy of the inventory data. In addition, the responsibility division of the inventory personnel is also relatively vague, lacking scientific indicators and data support, making it difficult to accurately determine the problem in a certain link or in the case of multiple personnel involvement, the responsibility is not clearly defined. More importantly, the existing technology often has difficulty in effectively processing large amounts of data and ensuring data consistency and timeliness when dealing with high-frequency and large-scale inventory tasks, greatly affecting the efficiency and accuracy of the inventory process. Including in the asset inventory process, if there is a delay or error in a certain link, the traditional method is difficult to discover and handle in a timely manner, which can easily lead to inconsistent information or record loss, causing unnecessary legal risks in asset transfer or dispute resolution. SUMMARY

[0004] The purpose of the present application is to solve the shortcomings in the prior art and to provide a notarization method for property inventory.

[0005] In order to achieve the above-mentioned purpose, the present application adopts the following technical solution: a notarization method for property inventory, comprising the following steps:

[0006] S1: Obtain the inventory personnel number, timestamp, and index of the inventory area and item code to generate a responsibility chain index table, sort the inventory data submitted by the inventory personnel according to the upload time and set the responsibility initialization index, and generate a responsibility chain sequence data;

[0007] S2: Based on the responsibility chain sequence data, generate data fingerprints by a hash algorithm and bind them with the inventory personnel number, and if the timestamp exceeds the time interval threshold, mark the delay verification state, and generate a time anomaly marking data set;

[0008] S3: Based on the time anomaly marking data set, obtain the data log of the corresponding inventory personnel and perform comparison to generate an operation consistency index, simultaneously obtain the task completion record to calculate the task completion rate, perform risk scoring by a fuzzy comprehensive evaluation model and classify the inventory personnel risk level to generate an inventory risk classification result;

[0009] S4: Based on the inventory risk classification result, obtain the items with inventory data changes, combine the responsibility chain sequence data and the timestamp for backtracking, calculate the responsibility impact value of the inventory personnel and perform responsibility attribution analysis to generate a responsibility impact total result;

[0010] S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, combine the responsibility impact total result to optimize the responsibility attribution of the inventory personnel, and upload the optimized responsibility record result.

[0011] The responsibility chain sequence data includes an inventory personnel number index, a timestamp order table, and a responsibility chain area mapping value, the time anomaly marking data set specifically includes a delay marking state, a data fingerprint hash value, and a time interval comparison result, the inventory risk classification result includes a risk score interval, a risk level label, and an inventory personnel number grouping, the responsibility impact total result specifically refers to a responsibility impact value mapping table, an inventory data change item number, and responsibility chain time sequence positioning information, and the responsibility record result includes a responsibility optimization number mapping, a responsibility adjustment amount, and an updated inventory personnel record table.

[0012] As a further scheme of the application, the specific steps of S1 include:

[0013] S101: Obtain the inventory personnel number, inventory timestamp, belonging inventory area code and item code index, arrange the inventory record in ascending order according to the timestamp field, call the sorted number index and map the inventory area code and item code to generate an inventory sequence index amount;

[0014] S102: Based on the inventory sequence index amount, identify the continuous submission interval of each inventory personnel in the specified area, perform interval calibration on the data according to the interval start time and end time, call the interval timestamp length and record quantity to calculate the inventory activity of the personnel in the area, and obtain the personnel inventory activity value;

[0015] S103: According to the personnel inventory activity value, the activity value is standardized by area to generate a responsibility value sequence, a personnel number, a region code and a responsibility value sequence are combined to generate an inventory responsibility chain data table, and the inventory responsibility chain data table is sorted in ascending order of an inventory timestamp to generate a responsibility chain sequence data.

[0016] As a further scheme of the present application, the specific steps of S2 include:

[0017] S201: Based on the responsibility chain sequence data, a hash algorithm is called to calculate a comprehensive feature value of the timestamp and the region code, a hash value of each record is obtained as a data fingerprint, and then the data fingerprint is bound with the corresponding personnel number to generate a responsibility chain data fingerprint;

[0018] S202: The responsibility chain data fingerprint is called, the timestamp difference value of the continuous records under the same number is sequentially calculated according to the inventory personnel number, it is judged whether the time interval exceeds a time interval threshold, the data records meeting the condition are marked, and a time abnormality candidate data amount is obtained;

[0019] S203: According to the time abnormality candidate data amount, the marking set is reorganized according to the inventory personnel number and the record position, a marking index table is constructed by calling the original number and the region field combination, the time delay marking and the original field sequence are merged to generate a time abnormality marking data set.

[0020] As a further scheme of the present application, the time interval threshold is set by statistical distribution analysis of the timestamp difference value of adjacent records in the inventory responsibility chain time sequence, first, the time difference value sequence of the continuous records under all inventory personnel numbers is collected, the mode and the standard deviation value are calculated, and then the 90% quantile point of the whole difference value sequence is compared and set.

[0021] As a further scheme of the present application, the specific steps of S3 include:

[0022] S301: Based on the time abnormality marking data set, the corresponding inventory personnel number and the time abnormality record index are extracted, the operation behavior field and the time field in the log are screened, the operation behavior sequence is constructed and compared with the abnormal record behavior sequence item by item, and the operation consistency index is calculated;

[0023] S302: The operation consistency index is called, the total amount of task completion records and the number of completed records of the corresponding inventory personnel are obtained, the task completion rate of the personnel is calculated, the operation consistency index value is normalized, the comprehensive risk score is calculated through a fuzzy comprehensive evaluation model, and the inventory risk score is generated;

[0024] S303: According to the inventory risk score, a set of risk level division standard value intervals is called, the position of the score value in the interval is judged, a corresponding level label table is established in combination with the personnel number, the risk level marking and grouping classification processing is completed, and the inventory risk classification result is generated.

[0025] The risk level division standard value interval is set by clustering analysis on the risk score of the inventory task data.

[0026] As a further scheme of the present application, the specific steps of S4 include:

[0027] S401: Based on the inventory risk classification result, all inventory item records and inventory time information corresponding to the personnel number are obtained, record entries with changes in the item state field are screened, corresponding item numbers and state change time position intervals are extracted, the item state change proportion is calculated, and the inventory item change rate is obtained.

[0028] S402: According to the inventory item change rate, the responsibility chain sequence data and time stamp corresponding to the item number are extracted, the behavior path and operation sequence of the inventory personnel number are combined, the responsibility score on a single responsibility chain is calculated through the appearance frequency of personnel in the responsibility chain and the corresponding operation interval, and the inventory responsibility score combination is obtained.

[0029] S403: According to the inventory responsibility score combination, the scores of the same inventory personnel in multiple responsibility chains are directly accumulated, a corresponding mapping table of the inventory personnel number and the cumulative score is established, and the mapping table is sorted according to the personnel number, and the responsibility impact total result is generated.

[0030] As a further scheme of the present application, the specific steps of S5 include:

[0031] S501: Based on the responsibility impact total result, the inventory personnel number, time stamp, data fingerprint, responsibility impact value and risk score are obtained and matched according to the inventory personnel number, all time stamp and data fingerprint information under the same number are extracted and sequence collection processing is performed, and the inventory data sequence set is generated.

[0032] S502: The inventory data sequence set is called, the data fingerprint at multiple time points in the sequence is combined with the corresponding responsibility impact value and risk score for difference judgment, records exceeding the responsibility attribution adjustment threshold are classified and labeled, and the attribution judgment adjustment amount is obtained according to the number of labels and the impact value sorting and mapping.

[0033] S503: According to the attribution judgment adjustment amount, the responsibility mapping relationship of all inventory personnel numbers in the original responsibility impact result is optimized and recorded, and the optimized responsibility record result is uploaded.

[0034] As a further scheme of the present application, the responsibility attribution adjustment threshold value is determined by collecting and standardizing the data of the inventory personnel, including the inventory time, the item variation record, the risk score and the responsibility chain information, by the risk influence, to determine the responsibility range and the risk fluctuation setting of each inventory personnel.

[0035] Compared with the prior art, the present application has the advantages and positive effects that:

[0036] In the present application, by indexing the responsibility chain of the inventory personnel number, time stamp, inventory area and item code, the task submission order and timeliness of each inventory personnel can be effectively tracked, and the inventory data is accurately recorded and the responsibility initialization index is set to ensure the clarity and accuracy of responsibility allocation. Combined with the hash algorithm to generate data fingerprints and binding, the independence and integrity of each inventory data can be ensured, and through the comparison of time stamps, the delay problem can be found in time and marked as abnormal, providing effective early warning for any sudden situation in the inventory process. In addition, based on the time anomaly marked data set, the data log of the inventory personnel is further obtained and compared for consistency to provide accurate quantitative indicators for the completion of the inventory task, so as to generate personalized risk assessment and personnel classification. This risk classification-based processing logic can identify potential problems at the earliest stage and avoid risk transmission caused by data bias. By backtracking the responsibility chain sequence data and time stamp, the responsibility influence value of the inventory personnel can be calculated in detail to form a comprehensive responsibility attribution analysis, providing data support for responsibility attribution optimization and avoiding the opacity in the traditional inventory process. Finally, through the optimization upload of the responsibility record, the compliance and transparency of the inventory process are improved, effectively avoiding the risks caused by human negligence or data errors. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The present application is a main step schematic diagram. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0039] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0040] Please refer to Figure 1 The present application provides a technical solution: a notarization method for property inventory, comprising the following steps:

[0041] S1: Obtain the index of the inventory personnel number, timestamp, inventory area and item code, generate a responsibility chain index table, sort the inventory data submitted by the inventory personnel according to the upload time and set the responsibility initialization index, and generate the responsibility chain sequence data;

[0042] S2: Based on the responsibility chain sequence data, generate data fingerprints through a hash algorithm and bind them with the inventory personnel number, and if the timestamp exceeds the time interval threshold, mark the delay verification state, generate the time anomaly marking data set;

[0043] S3: Based on the time anomaly marking data set, obtain the data log of the corresponding inventory personnel and compare to generate the operation consistency index, simultaneously obtain the task completion record to calculate the task completion rate, perform risk scoring through a fuzzy comprehensive evaluation model and classify the inventory personnel risk level, and generate the inventory risk classification result;

[0044] S4: Based on the inventory risk classification result, obtain the items with inventory data changes, combine the responsibility chain sequence data and the timestamp for backtracking, calculate the responsibility impact value of the inventory personnel and perform responsibility attribution analysis, and generate the responsibility impact total result;

[0045] S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, combine the responsibility impact total result to optimize the responsibility attribution of the inventory personnel, and upload the optimized responsibility record result.

[0046] The responsibility chain sequence data includes a check personnel number index, a timestamp order table, a responsibility chain area mapping value, a time anomaly marking data set, a check risk classification result, and a responsibility influence total result. The time anomaly marking data set is specifically a delay marking state, a data fingerprint hash value, and a time interval comparison result. The check risk classification result includes a risk score interval, a risk level label, and a check personnel number grouping. The responsibility influence total result is specifically a responsibility influence value mapping table, a check data change item number, and responsibility chain time sequence positioning information. The responsibility record uploading result includes a responsibility optimization number mapping, a responsibility adjustment amount, and an updated check personnel record table.

[0047] Referring to Figure 1 , the obtaining step of S1 is specifically as follows:

[0048] S101: Obtain a check personnel number, a check timestamp, a check area code, and an article code index. Arrange the check records in ascending order according to the timestamp field. Map the sorted number index, the check area code, and the article code to generate a check sequence index quantity.

[0049] Obtain a check personnel number, a check timestamp, a check area code, and an article code index. Query the check record table from the database, and extract the field values, including a personnel number P001, a timestamp 2023-01-01 09:00:00, an area code Z01, and an article code I001. Compare the timestamp values, exchange the record positions, and arrange them in ascending order. The timestamp values of the three records are 09:00:00, 09:05:00, and 08:55:00. After comparison, they are adjusted to 08:55:00, 09:00:00, and 09:05:00. Take the index positions of the sorted records, which start from 0 and increase by 1. Index 0 corresponds to the first record, index 1 corresponds to the second record, and index 2 corresponds to the third record. Map the index values, the area code, and the article code to generate a mapping sequence. Each entry includes an index value, a personnel number, an area code, and an article code. The index 0 is mapped to (P001, Z01, I001), the index 1 is mapped to (P001, Z01, I002), and the index 2 is mapped to (P002, Z01, I003). Form a check sequence index quantity.

[0050] Table 1: Check record example

[0051]

[0052] As shown in Table 1, the original inventory record data is listed, the timestamp unit is second, the number of records is 4, the sequence before sorting is record 1 timestamp 09:00:00, record 2 timestamp 09:05:00, record 3 timestamp 09:15:00, record 4 timestamp 08:55:00, compare timestamp values, record 4 timestamp 08:55:00 is less than record 1 timestamp 09:00:00, exchange record 4 and record 1 positions, record 1 timestamp 09:00:00 is less than record 2 timestamp 09:05:00, the position remains unchanged, record 2 timestamp 09:05:00 is less than record 3 timestamp 09:15:00, the position remains unchanged, the sequence after sorting is record 4 timestamp 08:55:00, record 1 timestamp 09:00:00, record 2 timestamp 09:05:00, record 3 timestamp 09:15:00, the index value is assigned index 0 corresponding to record 4, index 1 corresponding to record 1, index 2 corresponding to record 2, index 3 corresponding to record 3, the index value 0 is associated with the personnel number P001, the area code Z01, the item code I004, the index value 1 is associated with P001, Z01, I001, the index value 2 is associated with P001, Z01, I002, the index value 3 is associated with P002, Z01, I003, and the inventory sequence index amount is generated as list [(0, P001, Z01, I004), (1, P001, Z01, I001), (2, P001, Z01, I002), (3, P002, Z01, I003)].

[0053] S102: Based on the inventory sequence index amount, identify the continuous submission interval of each inventory personnel in the specified area, interval the data according to the start time and end time of the interval, call the length of the timestamp in the interval and the number of records to calculate the inventory activity of the personnel in the area, and obtain the personnel inventory activity value;

[0054] Based on the quantity of sequence indexes, take the timestamp, personnel number, area code field in the sequence index quantity, for each personnel number and area code combination, traverse the timestamp sequence, calculate the adjacent timestamp difference value, the difference value unit is second, set the continuous submission time threshold 30 seconds, the threshold setting is based on the current inventory operation average interval, including monitoring data average interval 20 seconds, set the threshold 30 seconds to tolerate delay, judge whether the difference value is less than or equal to 30 seconds, if the difference value is less than or equal to 30 seconds, merge into the same interval, otherwise end the current interval and start a new interval, record the interval start time, end time, record number, including personnel P001 area Z01 timestamp sequence 08:55:00, 09:00:00, 09:05:00, calculate the time difference 09:00:00 minus 08:55:00 300 seconds, judge 300 seconds greater than 30 seconds, end the first interval start 08:55:00 end 08:55:00 record number 1, start new interval start 09:00:00, calculate the next difference 09:05:00 minus 09:00:00 300 seconds, judge 300 seconds greater than 30 seconds, end the second interval start 09:00:00 end 09:00:00 record number 1, start the third interval start 09:05:00 end 09:05:00 record number 1, demarcate the interval list, call the interval start time and end time, calculate the timestamp length end time minus start time plus 1 second (avoid zero length), get the record number, calculate the inventory activity degree equal to the record number divided by the timestamp length, including interval start 09:05:00 end 09:10:00 timestamp length 1 second record number 1, activity degree 1 divided by 1 1.0 record per second, interval start 09:15:00 end 09:20:00 timestamp length 1 second record number 1, activity degree 1 divided by 1 1.0 record per second, define the high and low activity interval based on the average value of all interval activity 0.5 record per second, set low activity less than 0.4, medium activity 0.4 to 0.6, high activity greater than 0.6, including activity 1.0 greater than 0.6 is judged as high activity, get the personnel inventory activity value.

[0055] S103: According to the personnel inventory activity value, the activity value is standardized by area to generate the responsibility value sequence, and the inventory responsibility chain data table is generated combined with the personnel number, area code and responsibility value sequence, and is sorted in ascending order of inventory timestamp, to generate the responsibility chain sequence data;

[0056] According to the personnel inventory activity value, take the activity value list, for each area code, calculate the average activity of all personnel in the area, the average value is equal to the sum of the activity divided by the number of personnel, including area Z01 personnel P001 activity 1.0, P002 activity 0.5, the sum 1.5 divided by the number of personnel 2 to get the average value 0.75, the standardized responsibility value is equal to the individual activity divided by the regional average activity, including P001 responsibility value 1.0 divided by 0.75 is about 1.333, P002 responsibility value 0.5 divided by 0.75 is about 0.667, generate the responsibility value sequence, combine the personnel number, area code and responsibility value sequence, generate the data table fields personnel number, area code, responsibility value, including rows P001 Z01 1.333, P002 Z01 0.667, sort in ascending order of inventory timestamp, take the timestamp field in the inventory sequence index, sort the data table timestamps from small to large, including P001 timestamp 09:00:00 and P002 timestamp 09:15:00, the sorted sequence P001 responsibility value is 1.333, P002 responsibility value is 0.667, and the responsibility chain sequence data is generated.

[0057] See also Figure 1 , the specific steps for obtaining S2 are:

[0058] S201: Based on the responsibility chain sequence data, a hash algorithm is used to calculate a comprehensive feature value for the timestamp and region code, and the hash value of each record is obtained as a data fingerprint. The data fingerprint is then bound to the corresponding personnel number to generate a responsibility chain data fingerprint.

[0059] Based on the chain of responsibility sequence data, extract the timestamp field and area code field in the record, Represents the comprehensive feature value, which is used to generate the intermediate value of the data fingerprint. Represents the timestamp value (unit represents seconds), Unix timestamp format, Represents the time offset (unit represents seconds), the base time adjustment item, Represents the timestamp weight factor (dimensionless), which adjusts the contribution of the timestamp. represents the time component scaling factor (dimensionless), controlling the magnitude of the time square root term, Represents the regional coding adjustment parameters to balance the impact of regional coding. Represents the area code value (dimensionless), an integer mapped by a string, the acquisition process, : Get the record timestamp 2023-01-01, 09:00:00 by actually counting the records and convert it to Unix timestamp: Second, : Regional coding quantization, establishing coding dictionary, regional coding Z01 mapping is , : Based on data analysis, 1000 records are counted, and the timestamp variance accounts for 85% of the total feature variance, so take , : System reference time setting, system reference time 2023-01-01, 00:00:00, seconds, : Calculate by test data set, take 100 test records The average value is approximately 40900, the target range is 20000, so , : Regional influence weight analysis, regional code variance accounts for 8% of the total feature variance, so take Balanced influence, put into formula , : , , , , the result As input, SHA-256 hash operation generates data fingerprint (including e3b0c44298fc1c14), binds personnel number such as P001, generates data fingerprint entry, and repeats the process for each record to form the responsibility chain data fingerprint.

[0060] S202: Call the responsibility chain data fingerprint, calculate the timestamp difference of the continuous records under the same number in sequence according to the inventory personnel number, judge whether the time interval exceeds the time interval threshold, mark the data records that meet the conditions, and obtain the time abnormal candidate data amount;

[0061] Call the responsibility chain data fingerprint list, extract the personnel number field and the timestamp field corresponding to the data fingerprint, the timestamp unit is second, sort the records under the same personnel number in ascending order of timestamp, including personnel P001 record timestamp sequence 1672531200, 1672531300, 1672531600, calculate the adjacent timestamp difference, the difference is equal to the timestamp of the next record minus the timestamp of the previous record, including difference 1: 1672531300 minus 1672531200 is 100 seconds, difference 2: 1672531600 minus 1672531300 is 300 seconds, set the time interval threshold value to 300 seconds, the threshold value refers to the average interval of the current inventory operation, the average interval of the data is 250 seconds, set 300 seconds to tolerate reasonable delay, judge whether the difference is greater than 300 seconds, if the difference is greater than 300 seconds, mark the interval point as abnormal, including difference 100 seconds less than or equal to 300 seconds not marked, difference 300 seconds equal to 300 seconds not marked, but set the threshold value strictly greater than 300 seconds to mark, then difference 300 seconds not marked, if the record timestamp 1672531200, 1672531600 difference 400 seconds, judge 400 seconds greater than 300 seconds, mark the last record as abnormal, get the marking result, including the difference 400 seconds between record sequence index 1 and index 2 exceeds the threshold value, mark index 2 record as abnormal, repeat the process for all personnel numbers, generate a time abnormality candidate data volume list.

[0062] S203: According to the time abnormality candidate data volume, reorganize the identification set according to the inventory personnel number and the record position, call the original number and the area field combination to construct the marking index table, merge the delay identification and the original field sequence, and generate a time abnormality marking data set;

[0063] According to the time abnormality candidate data volume list, extract the marked abnormal record index, personnel number and area code field, group according to personnel number, sort the records in each group according to the original position, including personnel P001 abnormal record index 2, reorganize it into identification set entry (P001, index 2), call the personnel number and area code field in the original responsibility chain sequence data, combine to construct the marking index table, the fields include personnel number, area code, abnormal marking, including entry (P001, Z01, 1) indicating abnormality, combine the original timestamp field, merge the delay identification and the original field sequence, including the original record (P001, Z01, 1672531200), add the abnormal marking field 0 or 1, mark 1 represents abnormality, generate new sequence entries (P001, Z01, 1672531200, 0), (P001, Z01, 1672531600, 1), process all records to generate a time abnormality marking data set.

[0064] Please refer to Figure 1 , the obtaining step of S3 is specifically:

[0065] S301: Extract the corresponding inventory personnel number and time anomaly record index based on the time anomaly marked data set, and filter the operation behavior field and time field in the log to construct the operation behavior sequence and compare it with the abnormal record behavior sequence item by item, and calculate the operation consistency index;

[0066] Based on the time anomaly marked data set, the personnel number P001 and the time anomaly record index 2 are extracted, the operation behavior field (such as "scan", "input", "submit") and the time field (such as 1672531200) corresponding to the index in the operation log are filtered, the normal operation behavior sequence ["scan", "input", "submit"] is constructed, the abnormal record behavior sequence ["scan", "skip", "submit"] is constructed, the behaviors at the same index position are compared item by item, the behaviors at index 0 are the same (scan = scan), the behaviors at index 1 are different (input ≠ skip), the behaviors at index 2 are the same (submit = submit), the number of same items is 2, the total number of items is 3, and the consistency ratio is calculated , the operation consistency index value 0.6667 is obtained.

[0067] S302: Call the operation consistency index to obtain the total number of task completion records and the number of completed records of the corresponding inventory personnel, calculate the task completion rate of the personnel, normalize the operation consistency index value, calculate the comprehensive risk score through the fuzzy comprehensive evaluation model, and generate the inventory risk score;

[0068] The operation consistency index value 0.6667 is called, (the task completion rate), the process is: the number of completed records (8) and the total number of tasks (10) of the inventory personnel are counted, and the ratio , (normalized operation consistency index) comes from the behavior sequence comparison result, the normal sequence ["scan", "input", "submit"] is compared with the abnormal sequence ["scan", "skip", "submit"], and the same item 2 / 3 ≈ 0.6667, (environmental complexity factor), collect regional safety rating data (1-10 points, 10 safest), mapping formula: , the safety rating of region Z01 is 7 points → , (scaling factor), analyze 1000 risk scores, the maximum value is 95, and take the integer control the output range, and calculate the comprehensive risk score by substituting the formula, , , , , , , , , the result Less than the low-risk threshold 0.01, indicating that the current inventory risk is controllable, and directly output as the inventory risk score.

[0069] S303: According to the inventory risk score, call the set risk level division standard value interval, judge the position of the score value in the interval, and establish the corresponding level label table combined with the personnel number, complete the risk level marking and grouping classification processing, and generate the inventory risk classification result;

[0070] The risk level division standard value interval is set by clustering analysis on the risk score of the inventory task data;

[0071] According to the inventory risk score 0.008675, call the risk level division standard: low risk (0, 0.01), medium risk (0.01, 0.05), high risk [0.05, 1] (the standard is generated by 1000 risk scores K-means clustering, clustering centers 0.005, 0.03, 0.08), judge 0.008675∈(0, 0.01) belongs to low risk, mark the personnel number P001 risk level label low risk, and the other personnel (such as P002 score 0.032 mark medium risk) is processed in the same way, and the risk level label table as shown in table 2 is generated.

[0072] Table 2 Risk level label table

[0073]

[0074] According to the level grouping classification, the low-risk group [P001], the medium-risk group [P002], and the inventory risk classification result are generated.

[0075] Please refer to Figure 1 , the acquisition step of S4 is specifically:

[0076] S401: Based on the inventory risk classification result, acquire all the inventory item records and inventory time information corresponding to the personnel number, filter the record entries with changes in the item state field, extract the corresponding item number and state change time position interval, calculate the proportion of item state change, and acquire the inventory item change rate;

[0077] Based on the inventory risk classification result (such as low-risk personnel P001), extract all 10 inventory records of P001, each record containing item code, timestamp, and item status field (such as normal, damaged), filter state change records: record 2 (09:05 normal -> 09:10 damaged), record 5 (09:25 normal -> 09:30 missing), extract item codes I002, I005 and change time intervals [09:05, 09:10], [09:25, 09:30], calculate change ratio: 2 change records divided by total records 10, get inventory item change rate 20%, state change determination criteria: if adjacent timestamp state values are different, it is counted as a change, time interval is the start and end timestamp of the change record.

[0078] Table 3: Item state change record table

[0079]

[0080] As shown in Table 3, the state change record example is shown, the timestamp unit is accurate to seconds, and the change rate calculation uses 2 change records divided by total records 10.

[0081] S402: According to the inventory item change rate, extract the responsibility chain sequence data and timestamp corresponding to the item number, combine the behavior path and operation sequence of the inventory personnel number, calculate the responsibility score on a single responsibility chain through the appearance frequency of personnel in the responsibility chain and the corresponding operation interval, and get the inventory responsibility score combination;

[0082] According to the inventory item change rate 20%, extract the responsibility chain sequence data of item I002 (personnel P001, area Z01, responsibility value 1.333), timestamp 09:05, get P001's behavior path at 09:00-09:10: operation sequence [pick up, scan, place], operation interval: 3 seconds from pick up to scan, 2 seconds from scan to place, calculate single responsibility chain responsibility score: appearance frequency 1 time multiplied by average operation interval reciprocal (1 / 3+1 / 2) / 2≈0.4167, get responsibility score 0.4167, similarly calculate I005 responsibility score 0.5833 (operation interval, [pick up, scan] 1 second -> reciprocal 1), generate inventory responsibility score combination [I002: 0.4167, I005: 0.5833], wherein the operation interval calculation takes the time difference between the start and end of the action.

[0083] S403: According to the inventory responsibility score combination, directly accumulate the scores of the same inventory personnel in multiple responsibility chains, establish a corresponding mapping table of inventory personnel number and cumulative score, and sort and arrange the mapping table according to the personnel number, generate the responsibility impact total result;

[0084] Based on the inventory responsibility score combination P001: [I002: 0.4167, I005: 0.5833]; P002: [I003: 0.75, I007: 0.75]; P003: [I004: 1.2]; P004: [I006: 0.9, I008: 0.6, I009: 0.5], the multi-item score of the same person is accumulated: P001 total score = 0.4167 + 0.5833 = 1.0, P002 total score = 0.75 + 0.75 = 1.5, P003 total score = 1.2, P004 total score = 0.9 + 0.6 + 0.5 = 2.0, and a mapping table as shown in Table 4 is established:

[0085] Table 4 Mapping table:

[0086]

[0087] In ascending order of personnel number (P001→P002→P003→P004), a total responsibility impact result is generated, wherein the risk level comes from the previous generation, the number of responsible items is counted in the responsibility score combination, and the cumulative score is the direct addition of the multi-item responsibility score.

[0088] See Figure 1 , the acquisition step of S5 is specifically:

[0089] S501: Based on the total responsibility impact result, the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score are acquired, and according to the inventory personnel number, all timestamps and data fingerprint information under the same number are extracted and processed in sequence to generate an inventory data sequence set;

[0090] Based on the total responsibility impact result (personnel P001 cumulative score 1.0, P002 cumulative score 1.5), all records of P001 are extracted: timestamp sequence [1672531200, 1672531300], data fingerprint sequence ["e3b0c442", "a1b2c3d4"], responsibility impact value 1.0, risk score 0.008435, matched and collected according to the personnel number, to generate an inventory data sequence set: P001 group {timestamp sequence [1672531200, 1672531300], data fingerprint sequence ["e3b0c442", "a1b2c3d4"], responsibility impact value 1.0, risk score 0.008435}, wherein the data fingerprint comes from the SHA-256 hash value, the timestamp unit is second, and the sequence collection method is to combine the timestamp and data fingerprint into an array after grouping according to the number.

[0091] S502: Call the data sequence set, for the data fingerprint at multiple time points in the sequence, combine the corresponding responsibility impact value and risk score for difference judgment, and mark the records exceeding the responsibility attribution adjustment threshold, and map the marked number and impact value according to the order to obtain the attribution judgment adjustment amount;

[0092] Call P001 data sequence set, calculate the difference between adjacent data fingerprints: convert hexadecimal fingerprints to decimal integers, "e3b0c442"→3,814,749,762, "a1b2c3d4"→2,714,674,132, the absolute value of the difference is |3,814,749,762-2,714,674,132|=1,100,075,630, combined with the responsibility impact value 1.0 and the risk score 0.008435, the comprehensive difference value is calculated: 1,100,075,630×1.0×0.008435=9,279,137, set the responsibility attribution adjustment threshold 10,000,000 (difference value 95% quantile, through 1000 records statistics: maximum value 15,000,000, minimum value 500,000, 95% quantile value 10,000,000), determine 9,279,137<10,000,000 not marked, if P002 record difference value 12,000,000>10,000,000, mark, count the number of marks (such as P002 marked 1 time), sort by mark number × responsibility impact value: P002(1×1.5=1.5)>P001(0×1.0=0), establish the difference judgment parameter table as shown in Table 5.

[0093] Table 5 Difference judgment parameter table

[0094]

[0095] S503: According to the attribution judgment adjustment amount, optimize and record the responsibility mapping relationship of all inventory personnel in the original responsibility impact result, and upload the optimized responsibility record result;

[0096] According to the attribution judgment adjustment amount table (P002 adjustment amount 1.5, P001 adjustment amount 0), optimize the original responsibility impact result (P001 original value 1.0, P002 original value 1.5): P002 new value=original value+adjustment amount=1.5+1.5=3.0, P001 new value=1.0+0=1.0, establish an optimized mapping table, and pack the record format: {personnel number: "P001", optimized responsibility value: 1.0, timestamp: "2023-01-01"}, upload to the database responsibility record table.

[0097] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in other forms. Any skilled person in the art can modify or change the disclosed technical content into equivalent embodiments with equivalent changes, and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, without departing from the technical solution content of the present application, still falls within the protection scope of the present application.

Claims

1. A notarization method for property inventory, characterized in that: The following steps are involved: S1: Obtain the index of the inventory personnel number, timestamp, inventory area and item code to generate the responsibility chain index table, sort the inventory data submitted by the inventory personnel by upload time, set the responsibility initialization indicator, and generate the responsibility chain sequence data; S2: Based on the chain of responsibility sequence data, a data fingerprint is generated through a hash algorithm and bound to the inventory personnel number. If the timestamp exceeds the time interval threshold, the delayed verification status is marked to generate a time anomaly marking dataset; S3: Based on the time anomaly mark dataset, obtain the data logs of the corresponding inventory personnel and compare them to generate an operation consistency index. At the same time, obtain the task completion records to calculate the task completion rate. Use the fuzzy comprehensive evaluation model to perform risk scoring and classify the risk levels of the inventory personnel to generate an inventory risk classification result. S4: Based on the inventory risk classification results, items with inventory data changes are obtained, and backtracking is performed in combination with the responsibility chain sequence data and timestamps to calculate the responsibility impact value of the inventory personnel and perform responsibility attribution analysis to generate a total responsibility impact result; The specific steps of S1 include: S101: Obtain the counting personnel number, counting timestamp, counting area code and item code index, sort the counting records in ascending order by the timestamp field, and map the sorted number index with the counting area code and item code to generate a counting sequence index. S102: Based on the count sequence index, identify the continuous submission intervals of each count person in the specified area, calibrate the data according to the start and end time of the interval, and calculate the count activity of the person in the area by using the timestamp length and number of records in the interval to obtain the person's count activity value; S103: Based on the personnel inventory activity values, the activity values ​​are standardized by region to generate a responsibility value sequence, and a data table of inventory responsibility chain is generated by combining the personnel number, region code, and responsibility value sequence. The data table is sorted in ascending order by inventory timestamp to generate responsibility chain sequence data; The specific steps of S2 include: S201: Based on the responsibility chain sequence data, a hash algorithm is called to calculate a comprehensive feature value for the timestamp and the area code, and the hash value of each record is obtained as a data fingerprint. The data fingerprint is then bound to the corresponding personnel number to generate a responsibility chain data fingerprint; S202: Calling the responsibility chain data fingerprint, sequentially calculating the timestamp differences of consecutive records with the same number based on the inventory personnel number, determining whether the time interval exceeds the time interval threshold, marking the data records that meet the conditions, and obtaining the amount of candidate data for time anomaly; S203: Based on the amount of candidate time anomaly data, the identification set is reorganized according to the inventory personnel number and the record position, the original number and the regional field combination are called to construct a tag index table, the delayed identification and the original field sequence are merged, and the time anomaly tag data set is generated.

2. The notarization method for property inventory according to claim 1, characterized in that: The responsibility chain sequence data includes the inventory personnel number index, timestamp sequence table, and responsibility chain area mapping value. The time anomaly marking data set specifically includes the delay marking status, data fingerprint hash value, and time interval comparison result. The inventory risk classification result includes the risk score interval, risk level label, and inventory personnel number grouping. The total responsibility impact result specifically refers to the responsibility impact value mapping table, inventory data change item number, and responsibility chain timing positioning information.

3. The notarization method for property inventory according to claim 1, characterized in that: The time interval threshold is set by performing a statistical distribution analysis on the timestamp differences of adjacent records in the inventory responsibility chain time series. First, the time difference sequence of consecutive records under all inventory personnel numbers is collected, the mode and standard deviation are calculated, and then the 90% quantile of the entire difference sequence is used for comparison and setting.

4. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S3 include: S301: Extract the corresponding inventory personnel number and time anomaly record index based on the time anomaly mark data set, and filter the operation behavior field and time field in the log, construct the operation behavior sequence and compare it item by item with the abnormal record behavior sequence, and calculate the operation consistency index; S302: Call the operation consistency index to obtain the total number of task completion records and the number of completed records for the corresponding inventory personnel, calculate the personnel's task completion ratio, normalize it with the operation consistency index value, calculate the comprehensive risk score using the fuzzy comprehensive evaluation model, and generate the inventory risk score; S303: Based on the risk inventory score, the set risk level classification standard value interval is called to determine the level interval position of the score value. The corresponding level label table is established in combination with the personnel number, and the risk level labeling and grouping processing are completed to generate the risk inventory classification result; The risk level classification standard value interval is set by performing cluster analysis on the risk scores of the inventory task data.

5. The notarization method for property inventory according to claim 1, characterized in that: The specific steps of S4 include: S401: Based on the inventory risk classification result, all inventory item records and inventory time information corresponding to the personnel number are obtained, and record entries with changes in the item status field are screened. The corresponding item number and status change time and location interval are extracted, and the item status change ratio is calculated to obtain the inventory item change rate; S402: Based on the change rate of the counted items, the responsibility chain sequence data and timestamp of the corresponding item number are extracted. The behavior path and operation sequence of the inventory personnel number are combined, and the responsibility score of each responsibility chain is calculated based on the frequency of occurrence of the personnel in the responsibility chain and the corresponding operation interval to obtain the inventory responsibility score combination; S403: Based on the inventory responsibility score combination, the scores of the same inventory personnel in multiple responsibility chains are directly accumulated, a corresponding mapping table between the inventory personnel number and the accumulated score is established, and the mapping table is sorted and organized according to the personnel number to generate a total responsibility impact result.

6. The notarization method for property inventory according to claim 1, characterized in that: The method further comprises: S5: Obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score of the inventory personnel, optimize the responsibility attribution of the inventory personnel based on the total responsibility impact result, and upload the optimized responsibility record result; The responsibility record results include responsibility optimization number mapping, responsibility adjustment amount, and updated inventory personnel record table.

7. The notarization method for property inventory according to claim 6, characterized in that: The specific steps of S5 include: S501: Based on the total responsibility impact result, obtain the inventory personnel number, timestamp, data fingerprint, responsibility impact value and risk score and match them according to the inventory personnel number, extract all timestamp and data fingerprint information under the same number and perform sequence aggregation processing to generate an inventory data sequence set; S502: Calling the inventory data sequence set, performing difference determination on the data fingerprints at multiple time points in the sequence, combining the corresponding responsibility impact values ​​and risk scores, classifying and marking the records that exceed the responsibility attribution adjustment threshold, sorting and mapping them according to the number of markings and the impact value, and obtaining the attribution determination adjustment amount; S503: According to the attribution determination adjustment amount, the responsibility mapping relationship of all inventory personnel numbers in the original responsibility impact result is optimized and recorded and packaged, and the optimized responsibility record result is uploaded.

8. The notarization method for property inventory according to claim 7, characterized in that: The responsibility attribution adjustment threshold is determined by collecting and standardizing the data of inventory personnel, including inventory time, item change records, risk scores and responsibility chain information, and determining the responsibility scope and risk fluctuation setting of each inventory personnel through risk impact.

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