Film and television full-process production management system and method based on industrial big data

Abstract

The invention relates to the technical field of project management, in particular to a film and television full-process production management system and method based on industrial big data, and the system comprises an index binding module, a chain of responsibility construction module, a resource scheduling module, a priority adjustment module and a cooperative relationship verification module. According to the method, the file timestamp, the user identifier, the format type and the equipment number combination field are embedded, the unique mapping structure of the material path is constructed, the whole-process tracking and calling accuracy of the material is enhanced, the time sorting based on the material operation behavior is realized, the task responsibility crawler visualization is realized, and the cross screening is carried out in combination with the scene number and the equipment use condition; a scheduling conflict contrast set is established, resource allocation efficiency and scheduling precision are improved, scheduling priority and execution strength are introduced, scenes needing intervention are identified, scheduling contradictions are accurately positioned, member scheduling and scene adjustment windows are matched, cooperation time periods are screened, manpower allocation efficiency and cross-group cooperation capability are improved, and a dynamic closed loop of the whole task process is constructed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of project management, in particular to a film and television whole-process production management system and method based on industrial big data. BACKGROUND

[0002] The technical field of project management involves systematic planning, organization, coordination and control of the whole process of a project. Its core matters include project goal setting, task decomposition, resource allocation, schedule arrangement and quality monitoring. This technical field usually achieves efficient management of the project life cycle through the development of project plans, the implementation of control strategies and feedback adjustment mechanisms. In terms of method implementation, it mainly relies on schedule management methods, resource scheduling strategies, critical path identification methods and network diagram techniques to ensure that the project is completed on time and with quality. Traditional film and television production management refers to the whole process management of coordinating and controlling various stages of a film and television project, such as planning, shooting, post-production and distribution. The technical matters it aims to address are how to achieve unified scheduling, information synchronization and optimal resource allocation in a complex process involving multiple work sequences and departments. The traditional way usually uses manual recording, paper process tables and tabular management or uses general office software such as spreadsheets to prepare shooting plans and manually coordinate and communicate to control progress and allocate tasks. Data statistics rely on manual compilation or simple database queries, making it difficult to achieve real-time processing of large-scale dynamic change data and full-process information tracking.

[0003] Traditional film and television project management relies on manual recording and paper processes for material path identification, which is prone to confusion due to repeated paths or inconsistent naming, and makes it difficult to trace history and assign responsibility. Task transfer records are scattered in different stage documents or communication channels, lacking centralized organization and time sequence arrangement capabilities, resulting in unclear responsibility division during task execution, affecting subsequent problem accountability and optimization review. Equipment resource scheduling mainly relies on manual coordination and experience estimation, failing to effectively calculate resource utilization and available time periods, leading to overlapping equipment allocation and task scheduling conflicts, resulting in idle equipment and stacked tasks coexisting. When scheduling conflicts occur, there is usually a lack of effective task priority consideration mechanism, which can cause disorderly and repeated changes in resource reallocation and scheduling, delaying the overall project schedule. In terms of personnel deployment, scheduling information is not logically linked to scene tasks, manual matching is inefficient and prone to missing suitable time periods, affecting multi-process collaboration efficiency. For example, during a shooting peak period, an adjustment to a scene requires re-coordination of multiple teams. If the scheduling time conflicts or is not fully covered, it will directly lead to task delays and cost increases. These problems reflect the significant shortcomings of traditional methods in terms of lagging information collection, fragmented data processing and extensive scheduling decisions. SUMMARY

[0004] To solve the technical problems existing in the prior art, the embodiments of the present application provide a film and television whole-process production management system and method based on industrial big data. The technical solution is as follows: On the one hand, it provides a film and television production management system based on industrial big data, which includes: The index binding module obtains the media creation timestamp, user identity, media format type and device network number, performs field order combination operation and writes it into the index field, establishes a mapping relationship with the media file path field, and generates a unique mapping structure for the media path; The responsibility chain construction module extracts the operator identifier, operation time and operation type of each round of operation based on the unique mapping structure of the material path, sorts the operation records according to the time field, writes them into the responsibility field and establishes a sequence structure to generate a material flow responsibility structure chain; The resource scheduling module extracts the scene usage time and device number based on the scene number in the material flow responsibility structure chain, and obtains the available time period of the device and the resource utilization rate. It cross-references the time field and screens the utilization rate and resource limit value to generate a set of scene and device scheduling conflict comparison relationships. The priority adjustment module extracts the scheduling priority and task execution intensity level based on the scenario number marked in the scenario and device scheduling conflict comparison set, calculates the scheduling reference value and compares it with the intervention standard, marks the scenarios that meet the conditions, and generates a scenario scheduling intervention identifier set.

[0005] As a further embodiment of the present invention, the unique mapping structure of the material path includes field combination results, material file path, and mapping relationship index; the material flow responsibility structure chain includes operation time sequence, operator identity chain, and operation type distribution; the scenario and equipment scheduling conflict comparison relationship set includes scenario usage time, equipment availability time, and resource limit comparison item; and the scenario scheduling intervention identifier set includes scheduling reference value, scheduling intervention standard, and priority identifier.

[0006] As a further aspect of the present invention, the index binding module includes: The time recognition submodule obtains the file creation timestamp, user identity, media format type and device network number from the material generation node, sorts the material records according to the file creation timestamp, filters the record content in the sorted results by user identity, media format type and device network number, and generates time indexing information. The field construction submodule calls the user identity identifier, media format type and device network number in the timestamp information, combines the contents of multiple fields according to the field order rules, compares the combined fields with the position information of the file creation timestamp, sets the index field content in the task data master table according to the combination structure, and generates the index structure position value of the master table. The path mapping submodule calls the positional value of the main table index structure and the material file path field in the timestamp information, compares the corresponding parts of the main table index field and the path field, sets the replacement rules for path characters according to the matching relationship, constructs the mapping method between the index field and the material path field, and generates a unique mapping structure for the material path.

[0007] As a further aspect of the present invention, the chain of responsibility construction module includes: The operation extraction submodule extracts the operator identifier, operation time, and operation type from the post-editing and processing task records based on the unique mapping structure of the material path. It then arranges the record content in order according to the operation time, sets the time node sequence structure of the task operation based on the arrangement order, and generates an operation sequence arrangement sequence. The sequential writing submodule calls the operator identifier and operation time content in the operation sequence arrangement, writes the operator information into the task responsibility field in chronological order, sets the responsibility attribution order according to the operation time and establishes a corresponding relationship, and generates the corresponding value of task responsibility. The structure generation submodule calls the operator time sequence in the numerical value corresponding to the task responsibility, sets the node connection method according to the time order, constructs a continuous structural relationship based on the operator record and time node, establishes a structural sequence after completing the structural node sorting, and generates a material flow responsibility structural chain.

[0008] As a further aspect of the present invention, the resource scheduling module includes: The scene extraction submodule extracts the usage time and equipment information in the scene scheduling plan based on the scene number identified in the material flow responsibility structure chain, associates the time and equipment content according to the scene number, sets the corresponding structure between the scene and the resource, and generates the scene resource mapping quantity. The time crossover submodule calls the usage time and device information in the scene resource mapping quantity, connects with the device available time period and resource utilization rate in the resource management data table, performs field crossover processing based on the time field content, determines the overlap status of device time period and usage time, and generates device usage time matching rate. The conflict screening submodule uses the time status identified in the device usage time matching rate, combined with the device usage rate recorded in the resource management data table, to set resource screening standards based on resource usage limits, and performs conditional screening on device status and scene number to generate a set of scene and device scheduling conflict comparison relationships.

[0009] As a further aspect of the present invention, the priority adjustment module includes: The scenario reading submodule extracts the scenario number from the scenario and device scheduling conflict comparison set, reads the scheduling priority and task execution intensity level of the corresponding scenario, associates the scheduling level and task intensity content, integrates the scheduling association data, and generates a set of scheduling feature parameters. The scheduling calculation submodule calls the scheduling level and task intensity content in the scheduling feature parameter set, sets judgment conditions according to the combination relationship between the two data, calculates the scheduling reference value for the scenario number, processes it in correspondence with the scheduling intervention standard, and generates a scheduling intervention comparison value. The intervention identification submodule sets an identification benchmark value based on the numerical results recorded in the scheduling intervention comparison value and the scheduling intervention standard, performs conditional judgment between the scene number and the benchmark value, extracts the scenes that need to be adjusted and imports them into the set, and generates a scene scheduling intervention identifier set.

[0010] As a further aspect of the present invention, the system further includes: The collaboration relationship verification module extracts the group identifier, member number and scheduling time period based on the scenario number in the scenario scheduling intervention identifier set, performs overlap judgment on the member's scheduling time and the adjustable time period, filters the member information that meets the conditions, and generates a set of collaborative member scheduling windows. The set of collaborative member scheduling windows includes member shift time, adjustable time period, and group member number; The set of collaborative member scheduling windows is a set of member numbers and corresponding time periods that meet the time overlap requirement.

[0011] As a further aspect of the present invention, the collaboration relationship verification module includes: The scene location submodule extracts the group identifier, member number and task time period from the personnel scheduling record based on the scene number in the scene scheduling intervention identifier set, associates the scene number with the scheduling content, organizes the member task time information, and generates a member scheduling time set. The time matching submodule calls the member ID and task time period in the member scheduling time set, compares it with the adjustable time period in the scenario scheduling intervention identifier set, identifies the intersection of time periods, filters members and time information that meet the conditions, and generates member collaboration time intervals. The scheduling construction submodule organizes the corresponding groups and time periods of members based on the member information and time content in the member collaboration time interval, according to the group identifier in the personnel scheduling record, summarizes and constructs the scheduling mapping structure, and generates a set of collaborative member scheduling windows.

[0012] As a further aspect of the present invention, the index field is the location of the field in the database where a primary key index or logical mapping is established; The unique mapping structure of the material path is a unique string pointer structure composed of timestamp, identity, format and device number; The operation type is a field that represents the task behavior, and can be an identifier for categories such as edit, export, or transfer. The task responsibility field is a data field in the task record that registers changes in the responsible party. The structure sequence is a set of operation nodes arranged in chronological order; The material flow responsibility structure chain is a chain-like responsibility tracking structure composed of multiple operation nodes; The available time period for the equipment is a continuous time interval during which the equipment is not scheduled to be used, and can be described based on calendar time. The resource utilization rate is the percentage of tasks allocated to the equipment per unit time, expressed as a decimal or percentage. The resource limits are set based on the maximum workload indicators, technical specifications, and recommended continuous usage time provided by the equipment manufacturer, combined with usage behavior, and can also be set based on the equipment maintenance frequency and actual attendance time in the production plan. The scheduling priority refers to the execution priority of shooting scenes or material tasks in the overall scheduling of film and television production projects, which is derived from the director's overall planning, the shooting order in the storyboard, the time limit for location rental, and the coordination of actors' schedules. The task execution intensity level is automatically assigned by the project management system based on the standard template set according to the task type; The scheduling intervention criteria are set by the project schedule, and are automatically calculated in combination with the overall project rhythm requirements, resource utilization model and scheduling success rate. They can also be manually fine-tuned based on the production experience of the executive producer.

[0013] On the other hand, the film and television production management method based on industrial big data, which is executed based on the aforementioned film and television production management system based on industrial big data, includes the following steps: S1: Obtain the file creation timestamp, user identity, media format type and device network number, combine the field values ​​in order to generate field values, establish a corresponding relationship with the media file path field, and generate a unique mapping structure for the media path; S2: Based on the unique mapping structure of the material path, call the post-task records, extract the operation time, operator identifier and operation type, arrange the records in chronological order, write the sorting structure into the task responsibility field, and establish a structure sequence according to the time node to generate the material flow responsibility structure chain; S3: Call the scene number in the material flow responsibility structure chain, extract the usage time and equipment information in the scene schedule, and obtain the available time period of the equipment and the resource utilization rate. Perform overlap judgment and utilization rate screening on the time field to generate a set of scene and equipment scheduling conflict comparison relationships. S4: Extract the scenario number based on the scenario and equipment scheduling conflict comparison set, read the corresponding scheduling priority and task execution intensity level, generate scheduling reference value, compare it with the scheduling intervention standard, filter the scenario numbers that meet the intervention conditions, and generate a scenario scheduling intervention identifier set. S5: Based on the scenario number in the scenario scheduling intervention identifier set, extract the member number, group identifier and task time period, determine the overlap between the member's shift time and the scenario's adjustable time period, filter the member time periods that meet the conditions, and generate a set of collaborative member scheduling windows.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a unique mapping structure for material paths is constructed by embedding a combination of file timestamps, user identifiers, format types, and device numbers, thereby enhancing the accuracy of material tracking and retrieval throughout the entire process. Based on the time-based sorting of material operation behaviors, task performance visualization is achieved. By combining scene numbers and device usage for cross-filtering, a scheduling conflict comparison set is established to improve resource allocation efficiency and scheduling accuracy. By introducing scheduling priority and execution intensity, scenarios requiring intervention are identified, scheduling conflicts are accurately located, member shifts and scene adjustment windows are matched, and collaborative time periods are filtered to improve manpower allocation efficiency and cross-group collaboration capabilities, thus constructing a dynamic closed loop for the entire task process. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the present invention; Figure 3 This is a flowchart of the index binding module of the present invention; Figure 4 This is a flowchart of the chain of responsibility construction module of the present invention; Figure 5 This is a flowchart of the resource scheduling module of the present invention; Figure 6 This is a flowchart of the priority adjustment module of the present invention; Figure 7 This is a flowchart of the collaborative relationship verification module of the present invention; Figure 8 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "examplely" and "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as an "example" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meanings expressed by "and / or "" may be both, or either one may be preferred.

[0019] In this embodiment of the invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the difference, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the difference, their intended meanings are consistent.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a film and television production management system based on industrial big data, such as... Figures 1-2 The diagram shown illustrates a film and television production management system based on industrial big data. The system includes: The index binding module obtains the file creation timestamp, user identity, media format type and device network number from the material generation node, performs field order combination operation and writes them into the index field of the task data main table, establishes a mapping relationship between the combined fields and the material file path field, and generates a unique mapping structure for the material path. The index field is the location of the field in the database used to create a primary key index or logical mapping; The unique mapping structure for the media path is a unique string pointer structure composed of timestamp, identity, format, and device number; The responsibility chain construction module calls the post-editing and processing task records based on the unique mapping structure of the material path, extracts the operator identifier, operation time and operation type of each round of operation, sorts the records according to the operation time, writes the sorting results into the task responsibility field, establishes a structural sequence according to the time node, and generates a material flow responsibility structure chain. Operation type is a field that indicates the task behavior, and can be an identifier for categories such as edit, export, or transfer; The task responsibility field is a data field in the task record that records changes in the responsible party. A structure sequence is a collection of operation nodes arranged in chronological order, and is a data structure with associative reference relationships. The material flow responsibility structure chain is a chain-like responsibility tracking structure composed of multiple operation nodes, which has temporal and irreversible characteristics; The resource scheduling module extracts the usage time and equipment information from the scene scheduling plan based on the scene number identified in the material flow responsibility chain. At the same time, it calls the available time period of equipment and resource utilization rate in the resource management data table, cross-references the time field, and screens according to the equipment usage and resource limits to generate a set of scene and equipment scheduling conflict comparison relationships. The equipment availability period is a continuous time interval during which the equipment is not scheduled to be used, and can be described based on calendar time. Resource utilization rate is the percentage of tasks assigned to equipment per unit of time, expressed as a decimal or percentage. Resource limits are set based on the maximum workload indicators, technical specifications, and recommended continuous usage time provided by the equipment manufacturer, combined with usage behavior, or can be set based on the equipment maintenance frequency and actual attendance time in the production plan. The priority adjustment module extracts the scenario number from the scenario and device scheduling conflict comparison set, reads the scheduling priority and task execution intensity level of the corresponding scenario, calculates the scheduling reference value and compares it with the scheduling intervention standard, identifies the scenarios that need scheduling intervention and marks them into the adjustment set, and generates a scenario scheduling intervention identifier set. Scheduling priority refers to the execution priority of shooting scenes or material tasks in the overall scheduling of a film and television production project. It is derived from factors such as the director's overall planning, the shooting order in the storyboard, location rental time limits, and actor scheduling coordination. The task execution intensity level is automatically assigned by the project management system based on the standard template set by the task type (extra scheduling, special effects shooting, night shooting); The scheduling intervention standard refers to the critical value preset by the system to determine whether the current scheduling status requires manual intervention or system self-matching adjustment. It is set by the project scheduling and automatically calculated in combination with the overall project rhythm requirements, resource utilization model and scheduling success rate. It can also be manually fine-tuned based on the production experience of the executive producer. The collaboration relationship verification module extracts the group identifier, member number and task time period from the personnel scheduling record based on the scenario number in the scenario scheduling intervention identifier set. It then performs overlap judgment on the member scheduling time and the adjustable time period of the scenario, filters the member time periods that meet the collaboration conditions, establishes a scheduling coordination table, and generates a set of collaborative member scheduling windows. The set of collaborative member scheduling windows is a set of member numbers and corresponding time periods that meet the time overlap requirements.

[0023] The unique mapping structure for material paths includes field combination results, material file paths, and mapping relationship indexes. The material flow responsibility structure chain includes operation time sequence, operator identity chain, and operation type distribution. The scenario and equipment scheduling conflict comparison set includes scenario usage time, equipment availability time, and resource limit comparison items. The scenario scheduling intervention identifier set includes scheduling reference value, scheduling intervention standard, and priority identifier. The collaborative member scheduling window set includes member shift time, adjustable time period, and team member number.

[0024] Specifically, such as Figure 2 , 3 As shown, the index binding module includes: The time recognition submodule obtains the file creation timestamp, user identity, media format type and device network number from the material generation node, sorts the material records according to the file creation timestamp, filters the record content in the sorted results by user identity, media format type and device network number, and generates time indexing information. The time recognition submodule reads the file creation timestamp, user identifier, media format type, and device network ID from the material generation node. It extracts the corresponding fields item by item by parsing the node metadata. For example, the timestamp field is usually a set of consecutive numbers, such as 1629273600, representing the number of seconds since 1970. The user identifier can be marked using UID encoding, the media format is recorded using the extension record such as mp4 or jpg, and the device network ID is represented as a device identification code or MAC address. Then, all records are sorted in ascending order by timestamp, using a stable sorting method to ensure consistent order of records with the same timestamp. This can be done using quicksort or a two-end merge strategy. After sorting, the system filters according to set conditions, including the specified user identifier. The filtering process uses specific media formats and target device IDs. For example, the filtering criteria are UID 3487, media format mp4, and device ID 01AC39. Logically, this means UID equals 3487, format is mp4, and device ID is 01AC39. The filtering process is implemented through logical judgment functions. Each record is selected and enters the result set after meeting all three conditions. Then, a time-segmented statistical operation is performed, and the selected records are counted hourly. For example, if 12 pieces of material meet the conditions are generated between 13:00 and 14:00, then the information content in the corresponding time period is 12. The statistical analysis is performed segment by segment for all time intervals through a sliding window mechanism. With a window step size of 1800 seconds, the statistical cycle is 30 minutes. The results are summarized to form the material generation distribution information, which is used for subsequent structural reference and scheduling process reference.

[0025] The field construction submodule calls the user identity, media format type and device network number in the timestamp information, combines the contents of multiple fields according to the field order rules, compares the combined fields with the file creation timestamp position information, sets the index field content in the task data master table according to the combination structure, and generates the master table index structure position value. The field construction submodule reads the core fields from the timestamp information, including user identification, media format type, and device network number. These fields are arranged in a predetermined order, such as user identification first, then media type, and finally device number. Combined fields are generated through concatenation; for example, UID 3487, media type mp4, and device number 01AC39 are concatenated to form UID_3487_MP4_DEV_01AC39. The system then compares these combined fields with the file creation timestamp of each record in the timestamp information to verify that the combined fields match the corresponding information in the record. If the records match, they are recorded as valid entries. This combined field is then concatenated with the timestamp to form the task master table index field, for example, the resulting field is UID_3487_MP4_DEV_01AC39_1629273600. This index field serves to locate the unique row of task data in the task master table. The index field is further processed into a fixed-length index code, which can be implemented using compressed dictionary mapping or an auto-incrementing sequence method, such as assigning a sequential code to form IDX_000458. Finally, this positional value is used as the main index structure input into the task data master table to ensure that the master table records have a stable index field, which is beneficial for subsequent batch retrieval and calling operations.

[0026] The path mapping submodule calls the position value of the index structure of the main table and the material file path field in the time index information, compares the corresponding parts of the index field and the path field in the main table, sets the replacement rules of the path characters according to the matching relationship, constructs the mapping method between the index field and the material path field, and generates a unique mapping structure for the material path. The specific calculation formula for comparing the corresponding parts of the index field and the path field in the main table is as follows: ; Calculate the matching deviation index value, construct the mapping method between the index field and the material path field, and generate a unique mapping structure for the material path; in, This represents the matching deviation index Δ value between the i-th item in the main table's index field and the j-th item in the path field. This represents the total character length of the i-th item in the index field of the main table. This represents the total character length of the j-th item in the path field. This represents the ASCII code value of the k-th character in the i-th item of the index field in the main table. This represents the ASCII code value of the k-th character in the j-th item of the path field. This represents the maximum number of characters involved in the comparison. This represents the sum of the ASCII values ​​of all characters in the i-th item of the index field in the main table. This represents the sum of the ASCII values ​​of all characters in the j-th item of the path field. Representing characters and normalized unit quantities, used to ensure and Dimensional consistency in difference calculation; This represents the total character length of the i-th item in the main table index field. It is obtained by reading the index field of the original data table and counting the characters of the string of the i-th item. For example, if the main index field content is "CMPT_FILE_2019", which contains 13 characters, the value obtained is 13. The total character length of the j-th item in the path field is obtained by reading the file path data and counting the characters in the path string of the j-th item. For example, if the path field content is "DATA / SEGMENT / CMPT_FILE_2019.MP4", which has a total of 32 characters, the value obtained is 32. This represents the ASCII code value of the k-th character in the i-th item of the main table index field. It uses the standard ASCII code table for bit-by-bit encoding. For example, the first to fourth characters of the field "CMPT_FILE_2019" are "C", "M", "P", and "T", and their corresponding ASCII codes are 67, 77, 80, and 84, respectively. This represents the ASCII code value of the k-th character in the j-th item of the path field. It is converted according to the corresponding position. For example, the ASCII codes of "D", "A", "T", and "A" are 68, 65, 84, and 65, respectively. The maximum number of characters to be used in the calculation is determined by the shortest of the two fields. In this example, the minimum length is 13, so we set n=13. The sum of the ASCII values ​​of all characters in the i-th item of the index field of the main table. In the example, the ASCII sequence corresponding to CMPTFILE2019 is [67, 77, 80, 84, 95, 70, 73, 76, 69, 95, 50, 48, 50, 53], and the sum is 916. This is the sum of the ASCII values ​​of all characters in the j-th item of the path field. For example, the sum of the ASCII values ​​for the path "DATA / SEGMENT / CMPT_FILE_2019.MP4" is 2268. For characters and normalized units, the setting is based on the average of the sum of ASCII values ​​in the most recent 100 path fields, which is calculated by the system to have an average value of 1920. Parameter value assignment results: ; ; ; ; ; ; ; Formula calculation derivation: The first step is to calculate the percentage of character length difference: ; The second step is to calculate the sum of squared Euclidean distances between ASCII code differences: ;

[0027] ; The third step is to calculate the difference in the sum of normalized characters: ; Combining the three terms and substituting them into the original expression: ; The results show that the matching deviation index Δ between the i-th item of the main table index field and the j-th item of the path field is 74.4044. This value reflects the comprehensive matching difference between the main field and the path field in terms of character length, character structure and overall character set composition. The higher the index, the greater the difference between the fields. This value will be used as the upper limit parameter of the error reference for the field replacement structure in the subsequent mapping rule construction, and will be included in the matching degree sorting stage in the path unique mapping structure generation process. This formula constructs a comprehensive expression of the matching deviation index Δ value through a three-part structure. The first term is the relative difference between the main table index field and the path field in terms of total character length, expressed as a ratio of the difference to the main table length, used to measure the relative skewness of the two in the structural dimension. The middle term is the square root of the sum of squares of the ASCII code differences between corresponding characters, forming a quantitative form of Euclidean distance, used to characterize the degree of difference between the index field and the path field in the character sequence arrangement. This term reflects the aggregation of the overall offset trend through the square root operation after summing the squares. The last term is the normalized result of the difference between the sums of the ASCII code values ​​of all characters in the two fields. The directionality is eliminated by taking the absolute value, and then divided by a unified unit to construct an equal-dimensional comparison. This term reveals the degree of offset of the overall character encoding energy level. The three terms describe the similarities and differences between the fields from the perspectives of structural length, character arrangement, and cumulative encoding value, respectively. They use a combination of addition and subtraction and place them under absolute value operation to form a composite evaluation index with a unified dimension, thereby realizing a quantitative expression of the comprehensive difference between the path and index fields. The matching deviation index is used to quantify the overall difference between the main table index field and the material path field at the character composition level. This index comprehensively considers the relative difference in field length, the difference in bit-by-bit encoding of the character sequence at corresponding positions, and the total offset of ASCII code values ​​contained in the overall character set. It reflects the matching tightness of the two fields in multiple dimensions such as structural consistency, semantic relevance, and encoding composition through numerical means. The smaller the value, the higher the matching degree between the two fields, and the stronger the field relevance and path mapping possibility. Conversely, it indicates that there is a large difference between the fields and they are not suitable as a basis for mapping correspondence. This index is a key judgment parameter for the formulation of sorting, filtering, and replacement rules in the process of establishing path mapping relationship.

[0028] Specifically, such as Figure 2 , 4 As shown, the chain of responsibility construction module includes: The operation extraction submodule extracts the operator identifier, operation time, and operation type from the post-editing and processing task records based on the unique mapping structure of the material path. It arranges the record content in order according to the operation time, sets the time node sequence structure of the task operation according to the arrangement order, and generates the operation sequence arrangement sequence. The operation extraction submodule extracts the operator identifier, operation time, and operation type from the post-editing and processing task records based on the unique mapping structure of the material path. The system first calls the constructed unique mapping structure of the material path, reads the task record data file corresponding to each mapping path one by one, and extracts three key elements related to the operator's behavior from the record according to the field parsing method: the operator identifier field (e.g., user IDA001), the operation time field (e.g., timestamp 1694553600, representing September 13, 2018, 12:00), and the operation type field (e.g., editing, compositing, filter processing, etc.). Each task record contains a set of structured data corresponding to these three fields. The system reads each record one by one according to the task log structure rules. The system retrieves and parses task behavior logs, generating a preliminary operation record table. Then, it standardizes the time field, converting it to a continuous and comparable format, such as converting all timestamps to year-month-day-hour-minute-second format. This time field is then used as the sorting key to sort the operation records. A stable sorting method, such as merge sort, ensures that the order of operations at the same time is fixed. For example, if record 1 is at 12:30, record 2 at 13:20, and record 3 at 12:50, the sorting result is record 1, record 3, and record 2. After sorting, the system generates an operation time node sequence structure based on the operation time, containing the operation events and time sequence chain for each operator. The final output is an operation sequence arrangement.

[0029] The operator identifier and operation time content in the sequential writing submodule call operation sequence are arranged. The operator information is written into the task responsibility field in chronological order. The responsibility attribution order is set according to the operation time and a corresponding relationship is established. The corresponding value of task responsibility is generated. The sequential writing submodule calls the operation sequence arrangement sequence of the operator identifier and operation time content. The system first reads the operation sequence arrangement sequence generated by the previous process, extracts the operator identifier and corresponding operation time of each record, arranges the records in chronological order, and processes them one by one starting from the earliest time. The operator identifier is written into the task responsibility field in the order of operation time. The corresponding structure adopts a hierarchical responsibility allocation method, with the first operator as the initial responsible person, the second operator as the successor responsible person, and so on. For example, if the sequence is user A operates at 12:00, user B operates at 12:30, and user C operates at 13:00, then the system will write the operator identifier into the task responsibility field in sequence. The fields are labeled A, B, and C. The responsibility field can be recorded in list form, such as [Responsibility 1: A, Responsibility 2: B, Responsibility 3: C]. The system then sets the order of responsibility based on the order of operation time, using time difference calculation to assist in determining the responsibility level. For example, if the interval between two adjacent operations is less than 20 minutes, they are classified into the same responsibility level. If the interval is greater than 30 minutes, a new level number is added. For example, if users A and B are 10 minutes apart, they are in the same level; if B and C are 40 minutes apart, a new level is set. The system records this responsibility relationship as a responsibility level sequence and assigns corresponding responsibility codes such as R1, R2, R3, etc. Finally, it generates task responsibility corresponding values ​​for responsibility identification and operation marking.

[0030] The structure generation submodule calls the operator time sequence in the numerical value corresponding to the task responsibility, sets the node connection method according to the time order, constructs a continuous structural relationship based on the operator record and time node, establishes a structural sequence after completing the structural node sorting, and generates a material flow responsibility structural chain. The structure generation submodule calls the operator time sequence from the numerical values ​​corresponding to task responsibilities. The system reads the operator operation time chain constructed from the numerical values ​​corresponding to responsibilities, extracts the temporal relationship between each node, and establishes the connection method between nodes. The connection method is constructed based on the sequential adjacency method. For example, a linear connection is established between user A's operation at 12:00 and user B's operation at 12:20. B is then connected to the node of user C at 13:10. The system uses a directed graph structure to describe the dependency relationship between nodes. Each node contains operator ID and time attribute, and connection edges are set to represent the temporal order relationship. If the time difference between two nodes is less than a set threshold, such as 30 minutes, they are directly connected as the same level structure. If the time difference exceeds the threshold, a new branch is created and assigned to the parent node. After establishing the connection graph, the node structure is sorted. The sorting principle is based on the continuity of the timeline and the order of responsibility hierarchy. An ordered structure chain is generated using a depth-first traversal method. For example, if the structure chain is A→B→C, the system marks this chain as structure sequence S_001. The final output structure chain represents the actual responsibility path of each operator in the material processing task, forming a material flow responsibility structure chain.

[0031] Specifically, such as Figure 2 , 5 As shown, the resource scheduling module includes: The scene extraction submodule extracts the usage time and equipment information from the scene scheduling plan based on the scene number identified in the material flow responsibility structure chain, associates the time and equipment content according to the scene number, sets the corresponding structure between scenes and resources, and generates scene resource mapping volume. The scene extraction submodule, based on the scene numbers marked in the material flow responsibility chain, reads each data entry within the chain, locates the corresponding material node using the scene number field, and then extracts data by associating it with the scene scheduling plan table. It matches the usage time and equipment information by the scene number. The time field is recorded in the form of a date plus a time period, such as 09:00 to 11:00 on August 20th. The equipment field contains multiple equipment numbers, such as CAM_A1, MIC_B2, etc. During the extraction process, a key-value pair structure is used to store the data, with the scene number as the key and the time and equipment as the value. For example, if the scene number SC003 corresponds to a time period of 14:00 to 15:30, the equipment would be LGT_C1 and DOLLY_C1. 1. The mapping content between scene and resource is set as SC003 corresponding to the time period and two devices. During the processing, the scene number in the entire structure chain needs to be traversed. For each number, the relevant fields are searched and read from the schedule plan. After completion, the scene is grouped according to the number and organized to form the mapping structure between scene and resource. If a scene involves multiple time periods or multiple devices, the mapping content is extended to a one-to-many structure. For example, SC004 contains two time periods and three types of device information. The mapping structure sets the number to correspond to multiple resource entries. The data structure output is uniformly the scene resource mapping quantity. The fields include scene number, usage time range, and associated device identifier. All information is stored in a structured manner and classified into the scene resource table.

[0032] The time crossover submodule calls the usage time and device information in the scene resource mapping quantity, connects with the device available time period and resource utilization rate in the resource management data table, performs field crossover processing based on the time field content, determines the overlap status of device time period and usage time, and generates device usage time matching rate. The time-intersection submodule reads the scene resource mapping data, extracts the usage time and device list involved in each scene, and then interfaces with the resource management data table to obtain the available time period and resource utilization rate for each device. Device time data is usually divided into time periods throughout the day, such as 08:00 to 20:00. Some devices are marked as available in segments, such as 09:00 to 12:00 and 13:30 to 17:00. The system compares the time period required by the scene with the available time period of the device. If there is an overlap, it is judged as time overlap. The matching degree is calculated based on the ratio of the overlap duration to the scene requirement duration. For example, if the scene requires... For a certain device, the available time is from 09:00 to 11:00, and the available time is from 09:30 to 12:00. The matching time is 90 minutes, accounting for 75% of the total demand. The system records the matching ratio of the device in this scenario in this way. The processing method is to traverse all scenario device mapping records, cross-process the usage time and available time of each record, and analyze whether there is any overlap in the time period and the degree of overlap. When the matching ratio is lower than a certain fixed ratio, such as 60%, it is considered insufficient matching. When the matching ratio is higher than 90%, it is classified as highly matched. The matching status of all devices is uniformly written into the matching ratio result set to support subsequent scheduling screening and conflict identification.

[0033] The conflict screening submodule uses the time status identified in the equipment usage time matching rate, combined with the equipment usage rate recorded in the resource management data table, and sets resource screening standards based on resource usage limits. It then performs conditional screening on equipment status and scene number to generate a set of scene and equipment scheduling conflict comparison relationships. The specific calculation formula based on the equipment utilization rate recorded in the resource management data table is as follows: ; Calculate resource usage assessment values, set resource screening standards based on resource usage limits, perform conditional screening on equipment status and scene numbers, and generate a set of scene and equipment scheduling conflict comparison relationships. in, Representative Resources In the equipment Resource usage assessment values ​​on the surface Representative Resources In the equipment In time Actual utilization rate Representative Resources In time The weighting of usage Representative Resources In the equipment Maximum utilization rate during the evaluation period Representative Resources In the equipment Average utilization rate during the evaluation period This represents the total number of time slices within the evaluation period. Indicates time From 1 to The range of summation, Indicates the resource number. Indicates the equipment number. Represents a time series index; The parameter values ​​are obtained from the resource usage logs in the equipment monitoring system. The resource utilization rate is calculated, and the usage weight is derived from the scheduling strategy settings. Fluctuations are generated through dynamic adjustments, and the quantification standards are as follows: The value comes from the monitoring records and represents the actual utilization rate of resource r in time slice t of device j. It is calculated by the monitoring device resource utilization rate acquisition system, for example, the cumulative average utilization rate of multiple usage records. The value is the highest recorded value of resource r on this device during the monitoring period, which is calculated by the utilization monitoring system. The value is the average utilization rate of resource r on the device over all time slices during the monitoring period, which is obtained by summing and dividing by the number of time slices by the monitoring system; The value is set by the resource scheduling strategy module. Its value is dynamically set according to the importance of the resource time period. The weight value is generated through the scheduling strategy formulation process. For example, the weight value of the idle time period at night is lower than the weight of the high load time period. The T value is the total number of time slices divided within the monitoring period, which is set by the scheduling system according to the time granularity and recorded in the system's time-sharing parameters. This represents the summation of all time slices t from 1 to T.

[0034] Substituting the specific values ​​of the above parameters into the process is as follows: The monitoring system records that the utilization rates of resource r on device j in three time slices are 0.45, 0.60, and 0.30, respectively; the maximum value is 0.60 as statistically determined by the system; the average value is (0.45+0.60+0.30) / 3=0.45 as calculated by the system; the scheduling strategy mechanism sets the weights of these three time slices to 0.5, 0.8, and 0.3, respectively; the total number of time slices T=3. The steps to substitute these values ​​into the formula are as follows: Calculate the normalized product sum: ; Calculate the square root of the sum of squares: ; ; ; Calculate the deviation term: ; Molecular overall calculation: ; Step 5: Calculate the denominator: ; ; Substitute the whole into the formula: ; The result indicates The value is approximately 0.0187. This value represents the resource usage assessment value after normalization and weighting, which is consistent with the resource usage assessment value in the above execution process steps and corresponds to the final output of the assessment stage. The calculation logic in the formula is based on the relationship between resource utilization and scheduling scenarios. First, by summing the product of normalized resource utilization and weight parameters, a weighted evaluation of the distribution of resources in each time slice is constructed, reflecting the superimposed effect of resource utilization intensity and scheduling priority. Then, the square root of the sum of the square of normalized utilization and the square of weight in each time slice is taken to reflect the overall volatility of resource utilization distribution and enhance the sensitivity to discrete fluctuations. Next, a deviation ratio structure between the maximum and average values ​​is constructed, and the denominator is taken as the square root of its geometric mean to enhance the ability to distinguish concentrated deviations and avoid the dominance of extreme values. In the denominator, the absolute values ​​of the number of time slices and the total fluctuation of normalized utilization are summed and added to construct a distribution amplitude correction factor to balance the amplitude of utilization changes. Through this overall structure of the numerator and denominator, the evaluation value considers not only the level of utilization but also the comprehensive influence of fluctuation trends, average stability, and scheduling importance, thus forming a resource status evaluation index with distinctive characteristics. The resource utilization assessment value is a comprehensive indicator that measures the degree of matching between the actual utilization of a specific resource by a device and the scheduling expectation within the scheduling cycle. This value integrates the intensity, distribution stability, temporal volatility, and importance of scheduling weights of resource utilization rate, reflecting the device's resource utilization efficiency and scheduling fit. When the assessment value is high, it indicates that the resource usage behavior in different time slices is closer to the scheduling strategy setting and the usage intensity is concentrated and stable, indicating high adaptability. When the assessment value is low or close to zero, it indicates that the resource usage behavior fluctuates greatly in time, the utilization deviates from the maximum available value or the key scheduling period, and there is a risk of unreasonable allocation or conflict. This assessment value is used to drive the subsequent screening of device status and scene number, determine whether the resource configuration meets the scheduling rules, and form a conflict comparison.

[0035] Specifically, such as Figure 2 ,6 As shown, the priority adjustment module includes: The scenario reading submodule extracts the scenario number based on the scenario and device scheduling conflict comparison relationship, reads the scheduling priority and task execution intensity level of the corresponding scenario, associates the scheduling level and task intensity content, integrates scheduling related data, and generates a set of scheduling feature parameters. The scene reading submodule processes each scene number extracted from the scene-device scheduling conflict comparison set. Each number is used to locate the scheduling priority and task execution intensity level of the corresponding scene in the resource management database. The scheduling priority is divided into five levels, with 1 being the highest and 5 the lowest. The task execution intensity level uses integer values ​​from 0 to 100 to reflect the resource consumption degree of the scene during execution; for example, on-site shooting might have an intensity of 80, while simple dubbing might have 30. The system extracts two types of fields from the database based on the scene number, forming a three-dimensional data structure composed of scene number, priority, and intensity level. Each data entry is stored in the structure set using key-value binding. During processing, the system... All scenarios are initially grouped according to priority, and then sorted within each group according to task intensity value. This method establishes a scenario priority scheduling reference structure. For example, if SC010 is priority 2 with intensity level 85 and SC011 is priority 3 with intensity level 50, these two data points are assigned to different priority groups and bound to the corresponding level content to form scheduling feature records. This process traverses all scenario numbers in the conflict relationship set to complete extraction, association, transformation, and merging operations, ultimately forming a scheduling feature parameter set with a unified structure and clear values. Each record contains two fields: scheduling level and task intensity level, and the numerical format is kept uniform for easy use by subsequent processing modules.

[0036] The scheduling calculation submodule calls the scheduling level and task intensity content in the scheduling feature parameter set, sets judgment conditions based on the combination relationship between the two data, calculates the scheduling reference value according to the scenario number, processes it in correspondence with the scheduling intervention standard, and generates a scheduling intervention comparison value. The scheduling calculation submodule calls the scheduling level and task intensity level information from the scheduling feature parameter set. Each record is processed independently using the scene number as an identifier. First, the system sets the correlation standard between the scheduling level and the task intensity level. The lower the scheduling level, the higher the urgency of the processing; the higher the task intensity level, the greater the resource pressure during scene execution. The system constructs a scheduling reference value based on these two parameters as an evaluation criterion. The scheduling reference value is obtained by mapping the scheduling level to a proportional coefficient and then multiplying it by the task intensity level. For example, a scheduling level of 1 is mapped to a coefficient of 95, a level of 2 is mapped to a coefficient of 90, and so on, decreasing until a task intensity level of 8 is reached. Scenario 5 can yield a relatively high scheduling reference value under Level 1 scheduling, such as 8075. During the processing, the system will execute the same logic for each scenario record, record all results in the scheduling comparison set, and set scheduling intervention standards as the basis for interval division. For example, a reference value greater than 6000 is high risk, 3001 to 6000 is medium risk, and less than 3000 is low risk. Each record is assigned to the corresponding risk interval based on the calculation results and is labeled. All records are merged into the scheduling intervention comparison value set, including the scenario number, scheduling reference value, and label field content. The set content is used to support the intervention identification module to perform screening operations.

[0037] The intervention identification submodule sets an identification benchmark value based on the numerical results recorded in the scheduling intervention comparison value and the scheduling intervention standard, performs conditional judgment between the scene number and the benchmark value, extracts the scenes that need to be adjusted and imports them into the set, and generates a scene scheduling intervention identifier set. The intervention identification submodule reads each record from the scheduled intervention comparison value set and compares it with the baseline value in the intervention standard. The identification baseline value is generally set to 6000. When the reference value corresponding to a certain scenario is greater than this value, it is marked as needing scheduling adjustment. The system compares all scenario numbers with the reference values ​​one-to-one. For example, the reference value corresponding to the number SC020 is 7200, the number SC021 is 2800, and the number SC022 is 6400. The former and the latter are both higher than the baseline value and are classified into the category to be intervened. When processing, the system judges whether each record meets the intervention conditions in sequence. Those that meet the conditions are included in the set and an intervention mark is added. Those that do not meet the conditions are not processed. Finally, a scheduled intervention mark set composed of scenario number and intervention status is formed. This mark set is organized in a structured format. For example, each record contains a number and a status mark "Intervention Required" or "Intervention Not Required". The set is used for subsequent scheduling optimization module to receive and process, supporting further priority adjustment, resource scheduling reallocation and other work processes.

[0038] Specifically, such as Figure 2 , 7 As shown, the collaboration relationship verification module includes: The scenario positioning submodule extracts the group identifier, member number and task time period from the personnel scheduling record based on the scenario number in the scenario scheduling intervention identifier set, associates the scenario number with the scheduling content, organizes the member task time information, and generates a member scheduling time set. The scenario location submodule reads the scenario numbers contained in the scenario scheduling intervention identifier set. Each number represents a target scenario to be scheduled for intervention. The system matches the corresponding scheduling entry from the personnel scheduling record based on the number. The extracted information fields include group identifier, member number, and task time period. The member number corresponds to the specific personnel identity, the group identifier distinguishes different collaboration units, and the task time period consists of the start and end times of the daily tasks. All extracted items are associated one-to-one with the scenario number, forming a binding relationship between each record. In the example, if the scenario number is SC305, the member M201 in the corresponding personnel scheduling record belongs to group A01. The task time period is from 9:00 to 13:00. The system records this mapping relationship. During the subsequent processing, the system integrates multiple task time periods of each member into a member's shift time set. Each set contains the member's number and all its shift time periods. For example, M201 has two shift time periods, 9:00–13:00 and 14:00–18:00. The system integrates them into the time set corresponding to M201, and then processes other members in turn. Finally, it outputs the member's shift time set with the member number as the key and the time period array as the value. The data structure completely saves all personnel and their shift content related to the scheduling intervention scenario.

[0039] The time matching submodule calls the member ID and task time period from the member scheduling time set, compares it with the adjustable time period in the scenario scheduling intervention identifier set, identifies the overlapping part of the time period, filters the members and time information that meet the conditions, and generates the member collaboration time interval. The time matching submodule reads member IDs and task time periods one by one from the member scheduling time set and compares them with the adjustable time periods in the scheduling intervention identifier set to identify whether there is an overlap between the two time periods. The system determines whether the start and end times of the member's scheduling time cover the adjustable time period or whether there is any overlap with the adjustment time period. During the execution process, the relationship between the member's scheduling time and the scenario adjustment time is determined one by one. If the intersection is not empty, it means that the match is successful. For example, if the adjustment time period is from 10:00 to 15:00, and member M202's scheduling time period is 12:00. If the intersection is between 12:00 and 15:00 by 17:00, record the content of this intersection and bind the member number. All member information that meets the intersection condition is summarized as candidate collaborative members. Continue to process the next member until all members have been processed. The system finally forms a set of member collaboration time intervals. Each entry contains the member number and the content of the collaborable time period. For example, the time period corresponding to member M202 is 12:00 to 15:00, and that of member M203 is 10:00 to 12:30. The collaboration interval set of scenario SC401 can contain these two member records, which are used in the subsequent scheduling structure construction stage.

[0040] The scheduling construction submodule organizes the corresponding groups and time periods of members based on the member information and time content in the member collaboration time interval, according to the group identifier in the personnel scheduling record, summarizes and constructs the scheduling mapping structure, and generates a set of collaborative member scheduling windows. The scheduling construction submodule reads the member IDs and corresponding time periods recorded in the member collaboration time interval set. It then uses the member ID to look up the group identifier in the scheduling record to build a binding relationship between members, groups, and time periods. Each binding entry includes the group ID, member ID, and collaboration time period. The processing flow is aggregated by group dimension. The time period information of members within each group is standardized, and it is further compared to see if there is any overlap in the collaboration time periods between two or more members within the group. If there is an overlap, it is identified as a valid collaboration window. For example, members M301 and M302 under group A02 have time periods of 9:00 to 12:00 and 11:00 to 14:00 respectively. Then the collaboration time period of this group is 11:00 to 12:00. This time period is recorded and associated with the corresponding group ID and member ID list. After processing all members, a set of collaborative member scheduling windows is formed. Each entry in the structure includes the group ID, the combination of member IDs, and the valid collaboration time period information. The data structure fully supports the subsequent workflow for group task arrangement or schedule modification.

[0041] Please see Figure 8 The film and television production management method based on industrial big data is executed based on the aforementioned film and television production management system based on industrial big data, and includes the following steps: S1: Obtain the file creation timestamp, user identity, media format type and device network number, combine the field values ​​in order to generate field values, establish a corresponding relationship with the media file path field, and generate a unique mapping structure for the media path; S2: Based on the unique mapping structure of the material path, call the post-task records, extract the operation time, operator identifier and operation type, arrange the records in chronological order, write the sorting structure into the task responsibility field, and establish a structure sequence according to the time node to generate the material flow responsibility structure chain; S3: Call the scene number in the material flow responsibility structure chain, extract the usage time and equipment information in the scene schedule, and obtain the available time period of the equipment and resource utilization rate. Perform overlap judgment and utilization rate screening on the time field to generate a set of scene and equipment scheduling conflict comparison relationship; S4: Extract the scenario number based on the scenario and device scheduling conflict comparison set, read the corresponding scheduling priority and task execution intensity level, generate scheduling reference value, compare it with the scheduling intervention standard, filter the scenario numbers that meet the intervention conditions, and generate a scenario scheduling intervention identifier set; S5: Based on the scenario number in the scenario scheduling intervention identifier set, extract the member number, group identifier and task time period, determine the overlap between the member's scheduled time and the scenario's adjustable time period, filter the member time periods that meet the conditions, and generate a set of collaborative member scheduling windows.

[0042] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included 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 film and television production management system based on industrial big data, characterized in that: The system includes: The index binding module obtains the media creation timestamp, user identity, media format type and device network number, performs field order combination operation and writes it into the index field, establishes a mapping relationship with the media file path field, and generates a unique mapping structure for the media path; The responsibility chain construction module extracts the operator identifier, operation time and operation type of each round of operation based on the unique mapping structure of the material path, sorts the operation records according to the time field, writes them into the responsibility field and establishes a sequence structure to generate a material flow responsibility structure chain; The resource scheduling module extracts the scene usage time and device number based on the scene number in the material flow responsibility structure chain, and obtains the available time period of the device and the resource utilization rate. It cross-references the time field and screens the utilization rate and resource limit value to generate a set of scene and device scheduling conflict comparison relationships. The priority adjustment module extracts the scheduling priority and task execution intensity level based on the scenario number marked in the scenario and device scheduling conflict comparison set, calculates the scheduling reference value and compares it with the intervention standard, marks the scenarios that meet the conditions, and generates a scenario scheduling intervention identifier set.

2. The film and television full-process production management system based on industrial big data according to claim 1, characterized in that: The unique mapping structure of the material path includes field combination results, material file path, and mapping relationship index. The material flow responsibility structure chain includes operation time sequence, operator identity chain, and operation type distribution. The scenario and equipment scheduling conflict comparison set includes scenario usage time, equipment availability time, and resource limit comparison item. The scenario scheduling intervention identifier set includes scheduling reference value, scheduling intervention standard, and priority identifier.

3. The film and television full-process production management system based on industrial big data according to claim 1, characterized in that: The index binding module includes: The time recognition submodule obtains the file creation timestamp, user identity, media format type and device network number from the material generation node, sorts the material records according to the file creation timestamp, filters the record content in the sorted results by user identity, media format type and device network number, and generates time indexing information. The field construction submodule calls the user identity identifier, media format type and device network number in the timestamp information, combines the contents of multiple fields according to the field order rules, compares the combined fields with the position information of the file creation timestamp, sets the index field content in the task data master table according to the combination structure, and generates the index structure position value of the master table. The path mapping submodule calls the positional value of the main table index structure and the material file path field in the timestamp information, compares the corresponding parts of the main table index field and the path field, sets the replacement rules for path characters according to the matching relationship, constructs the mapping method between the index field and the material path field, and generates a unique mapping structure for the material path.

4. The film and television full-process production management system based on industrial big data according to claim 3, characterized in that: The chain of responsibility construction module includes: The operation extraction submodule extracts the operator identifier, operation time, and operation type from the post-editing and processing task records based on the unique mapping structure of the material path. It then arranges the record content in order according to the operation time, sets the time node sequence structure of the task operation based on the arrangement order, and generates an operation sequence arrangement sequence. The sequential writing submodule calls the operator identifier and operation time content in the operation sequence arrangement, writes the operator information into the task responsibility field in chronological order, sets the responsibility attribution order according to the operation time and establishes a corresponding relationship, and generates the corresponding value of task responsibility. The structure generation submodule calls the operator time sequence in the numerical value corresponding to the task responsibility, sets the node connection method according to the time order, constructs a continuous structural relationship based on the operator record and time node, establishes a structural sequence after completing the structural node sorting, and generates a material flow responsibility structural chain.

5. The film and television full-process production management system based on industrial big data according to claim 4, characterized in that: The resource scheduling module includes: The scene extraction submodule extracts the usage time and equipment information in the scene scheduling plan based on the scene number identified in the material flow responsibility structure chain, associates the time and equipment content according to the scene number, sets the corresponding structure between the scene and the resource, and generates the scene resource mapping quantity. The time crossover submodule calls the usage time and device information in the scene resource mapping quantity, connects with the device available time period and resource utilization rate in the resource management data table, performs field crossover processing based on the time field content, determines the overlap status of device time period and usage time, and generates device usage time matching rate. The conflict screening submodule uses the time status identified in the device usage time matching rate, combined with the device usage rate recorded in the resource management data table, to set resource screening standards based on resource usage limits, and performs conditional screening on device status and scene number to generate a set of scene and device scheduling conflict comparison relationships.

6. The film and television full-process production management system based on industrial big data according to claim 5, characterized in that: The priority adjustment module includes: The scenario reading submodule extracts the scenario number from the scenario and device scheduling conflict comparison set, reads the scheduling priority and task execution intensity level of the corresponding scenario, associates the scheduling level and task intensity content, integrates the scheduling association data, and generates a set of scheduling feature parameters. The scheduling calculation submodule calls the scheduling level and task intensity content in the scheduling feature parameter set, sets judgment conditions according to the combination relationship between the two data, calculates the scheduling reference value for the scenario number, processes it in correspondence with the scheduling intervention standard, and generates a scheduling intervention comparison value. The intervention identification submodule sets an identification benchmark value based on the numerical results recorded in the scheduling intervention comparison value and the scheduling intervention standard, performs conditional judgment between the scene number and the benchmark value, extracts the scenes that need to be adjusted and imports them into the set, and generates a scene scheduling intervention identifier set.

7. The film and television full-process production management system based on industrial big data according to claim 1, characterized in that: The system also includes: The collaboration relationship verification module extracts the group identifier, member number and scheduling time period based on the scenario number in the scenario scheduling intervention identifier set, performs overlap judgment on the member's scheduling time and the adjustable time period, filters the member information that meets the conditions, and generates a set of collaborative member scheduling windows. The set of collaborative member scheduling windows includes member shift time, adjustable time period, and group member number; The set of collaborative member scheduling windows is a set of member numbers and corresponding time periods that meet the time overlap requirement.

8. The film and television full-process production management system based on industrial big data according to claim 7, characterized in that: The collaboration relationship verification module includes: The scene location submodule extracts the group identifier, member number and task time period from the personnel scheduling record based on the scene number in the scene scheduling intervention identifier set, associates the scene number with the scheduling content, organizes the member task time information, and generates a member scheduling time set. The time matching submodule calls the member ID and task time period in the member scheduling time set, compares it with the adjustable time period in the scenario scheduling intervention identifier set, identifies the intersection of time periods, filters members and time information that meet the conditions, and generates member collaboration time intervals. The scheduling construction submodule organizes the corresponding groups and time periods of members based on the member information and time content in the member collaboration time interval, according to the group identifier in the personnel scheduling record, summarizes and constructs the scheduling mapping structure, and generates a set of collaborative member scheduling windows.

9. The film and television full-process production management system based on industrial big data according to claim 1, characterized in that: The index field is the location of the field in the database where a primary key index or logical mapping is established; The unique mapping structure of the material path is a unique string pointer structure composed of timestamp, identity, format and device number; The operation type is a field that represents the task behavior, and can be an identifier for categories such as edit, export, or transfer. The task responsibility field is a data field in the task record that registers changes in the responsible party. The structure sequence is a set of operation nodes arranged in chronological order; The material flow responsibility structure chain is a chain-like responsibility tracking structure composed of multiple operation nodes; The available time period for the equipment is a continuous time interval during which the equipment is not scheduled to be used, and can be described based on calendar time. The resource utilization rate is the percentage of tasks allocated to the equipment per unit time, expressed as a decimal or percentage. The resource limits are set based on the maximum workload indicators, technical specifications, and recommended continuous usage time provided by the equipment manufacturer, combined with usage behavior, and can also be set based on the equipment maintenance frequency and actual attendance time in the production plan. The scheduling priority refers to the execution priority of shooting scenes or material tasks in the overall scheduling of film and television production projects, which is derived from the director's overall planning, the shooting order in the storyboard, the time limit for location rental, and the coordination of actors' schedules. The task execution intensity level is automatically assigned by the project management system based on the standard template set according to the task type; The scheduling intervention criteria are set by the project schedule, and are automatically calculated in combination with the overall project rhythm requirements, resource utilization model and scheduling success rate. They can also be manually fine-tuned based on the production experience of the executive producer.

10. A film and television production management method based on industrial big data, characterized in that: The execution of the film and television full-process production management system based on industrial big data according to any one of claims 1-9 includes the following steps: S1: Obtain the file creation timestamp, user identity, media format type and device network number, combine the field values ​​in order to generate field values, establish a corresponding relationship with the media file path field, and generate a unique mapping structure for the media path; S2: Based on the unique mapping structure of the material path, call the post-task records, extract the operation time, operator identifier and operation type, arrange the records in chronological order, write the sorting structure into the task responsibility field, and establish a structure sequence according to the time node to generate the material flow responsibility structure chain; S3: Call the scene number in the material flow responsibility structure chain, extract the usage time and equipment information in the scene schedule, and obtain the available time period of the equipment and the resource utilization rate. Perform overlap judgment and utilization rate screening on the time field to generate a set of scene and equipment scheduling conflict comparison relationships. S4: Extract the scenario number based on the scenario and equipment scheduling conflict comparison set, read the corresponding scheduling priority and task execution intensity level, generate scheduling reference value, compare it with the scheduling intervention standard, filter the scenario numbers that meet the intervention conditions, and generate a scenario scheduling intervention identifier set. S5: Based on the scenario number in the scenario scheduling intervention identifier set, extract the member number, group identifier and task time period, determine the overlap between the member's shift time and the scenario's adjustable time period, filter the member time periods that meet the conditions, and generate a set of collaborative member scheduling windows.

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